Rest and relaxation – ASBMB Today

For many of us, we are not quite getting the sleep we need. Whether that is because of obligations at work, school, or with your family, the end result is often the same: waking up not feeling quite rested.

As a graduate student, I know this too well. I find the poem below by Edna St. Vincent Millay to represent the life I am currently living.

My candle burns at both ends; It will not last the night;But ah, my foes, and oh, my friends It gives a lovely light!

First Fig by Edna St. Vincent Millay

However, despite the seemingly attractiveness of a lovely light, the reality is that one cannot continue indefinitely or in a healthy manner without quality sleep. In fact, short sleep duration over a continual basis may lead one to a higher risk of developing cardiovascular disease, diabetes, depression, and dementia among other chronic conditions.

So how exactly can we maximize on this daily event to lead healthier lives? While the number of hours will vary depending on your age, health experts recommend that adults get at least 7 hours of sleep every night. But is 7 hours with your eyes closed quality sleep? In a given sleep cycle, your brain will move through different stages of electrical activity that lasts on average 90 minutes. Supplemental and more important than the number of hours you sleep is the quality of that sleep and the time spent in restorative stages.

Stage 1 (N1) is that initial stage of drifting off, and will only last a short period of time. The subsequent Stage 2 (N2) is what follows if undisturbed, and allows the body to slow down and relax. This stage can last between 10 minutes to a half hour, and most of your sleep time is spent here. Arguably the most import sleep stage, Stage 3 (N3), comes next and is know as deep sleep. During this stage, your body is able to not only recover from the day but boost your immune system and clear toxins from your brain! Finally, the last stage of the sleep cycle is the rapid eye movement (REM) stage, which has almost wake-like levels of brain activity. In REM sleep, which typically gets longer as the night goes on, we boost our cognitive functions like memory and creative thinking.

The Sleep Foundation provides an extensive list of ways to produce a healthy nights sleep, which include the creation of a sleep-inducing bedroom and being mindful of pro-sleep habits when we are awake during the day. I encourage you to take a look at them, and consider how you might be able to incorporate some/all of these items to improve your own sleep.

For me, reading a scientific manuscript before bed (and sometimes during the day) generally has a sleep-inducing effect. If you too can relate, or simply would like to learn about the awesome work being published in ASBMB journals related to sleep, take a look at the articles below!

Effects of sleep restriction on post-dinner metabolism: Researchers from Penn State and Harvard examined the effects of only 5 hours of sleep per night on after dinner metabolism following a high fat meal. Fifteen healthy men were evaluated for post-prandial lipemia, glycemia, and enteric hormonal and inflammatory responses following lunches and dinners with high calories from fat. The team concluded that sleep restriction impaired post-meal blood lipid levels and decreased satiety. If you need more information to feel satisfied, read on their findings here.

Bridging DAT sleep gap: Researchers from Vienna, Austria attempted to explore the molecular mechanism underlying juvenile dystonia and parkinsonism with regard to mutations in the human dopamine transporter (hDAT) by examining 13 mutants of DAT that are known to cause such disease and their ability to be pharmacologically rescued. The Drosophila models used have evolutionary conservation in respect to dopaminergic neurotransmission, and DAT deficiency results in reduced sleep for Drosophila. Their results identified protein folding deficits from specific DAT mutations that can be rescued with a chaperone compound to potentially prevent disease manifestation in affected children. Take a look for yourself here.

Regulation of molecular clockmakers: A team of researchers from Turkey used computational and biochemical techniques to screen and evaluate small molecules that modulate the regulatory proteins involved in circadian rhythm to improve management of sleep disorders. In many of these sleep disorders and other chronic medical conditions, a decline in the amplitude of the circadian rhythm is observed. The team found a molecule, CDK8, stabilized part of a transcriptional complex that allows for enhanced amplitude of the circadian rhythm. They believe this compound can serve as a tool for a variety of therapeutic areas. Read more about their work here.

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Rest and relaxation - ASBMB Today

Deep generative models could offer the most promising developments in AI – VentureBeat

Did you miss a session at the Data Summit? Watch On-Demand Here.

This article is contributed by Rick Hao, lead deep tech partner at pan-European VCSpeedinvest.

With an annual growth rateof 44%, the market for AI and machine learning is drawing continued interest from business leaders across every industry. Withsome projectionsestimating that AI will boost the GDP of some local economies by 26% by 2030, its easy to see the rationale for the investment and hype.

Among AI researchers and data scientists, one of the major steps in ensuring AI delivers on the promise of enhanced growth and productivity is through expanding the range and capabilities of models available for organizations to use. And top of the agenda is the development, training and deployment of Deep Generative Models (DGMs) which I consider to be some of the most exciting models set for use in industry. But why?

Youve likely already seen the results of a DGM in action theyre actually the same type of AI models that produce deepfakes orimpressionistic art.DGMs have long excited academics and researchers in computer labs, owing to the fact that they bring together two very important techniques that represent the confluence of deep learning and probabilistic modeling: the generative model paradigm and neural networks.

A generative model is one of two major categories of AI models and, as its name suggests, it is a model that can take a dataset and generate new data points based on the input its received so far. This contrasts with the more commonly used and far easier to develop discriminative models, which look at a data point in a dataset and then label or classify it.

The D in DGM refers to the fact that, alongside being generative models, they leverage deep neural networks. Neural networks are computing architectures that give programs the ability to learn new patterns over time what makes a neural network deep is an increased level of complexity offered by multiple hidden layers of inferences between a models input and a models output. This depth gives deep neural networks the ability to operate with extremely complex datasets with many variables at play.

Put together, this means that DGMs are models that can generate new data points based on data fed into them, and that can handle particularly complex datasets and subjects.

As mentioned above, DGMs already have some notable creative and imaginative uses, such as deepfakes or art generation. However, the potential full range of commercial and industrial applications for DGMs is vast and promises to up-end a variety of sectors.

For example, consider the issue of protein folding. Protein folding discovering the 3D structure of proteins allows us to find out which medicines and compounds interact with various types of human tissue, and how. This is essential to drug discovery and medical innovation, but discovering how proteins fold is very difficult, requiring scientists to dissolve and crystallize proteins before analyzing them, which means the whole process for a single protein can last weeks or months. Traditional deep learning models are also insufficient to help tackle the protein folding problem, as their focus is primarily on classifying existing data sets rather than being able to generate outputs of their own.

By contrast, last year the DeepMind teamsAlphaFoldmodel succeeded in reliably being able to anticipate how proteins would fold based solely on data regarding their chemical composition. By being able to generate results in hours or minutes, AlphaFold has the potential to save months of lab work and vastly accelerate research in just about every field of biology.

Were also seeing DGMs emerge in other domains. Last month,DeepMind released AlphaCode, a code-generating AI model thats successfully outperformed the average developer in trials. And the applicability of DGMs can be seen in fields as far-flung as physics, financial modelling, or logistics: through being able to tacitly learn subtle and complex patterns that humans and other deep learning networks are unable to spot, DGMs promise to be able to generate surprising and insightful results in just about every field.

DGMs face some notable technical challenges, such as the difficulty intraining them optimally(especially with limited data sets) and ensuring that they can yieldconsistently accurate outputsin real applications. This is a major driver of the need for further investment to ensure DGMs can be widely deployed in production environments and thus deliver on their economic and social promises.

Beyond the technical hurdles, however, a big challenge for DGMs is in ethics and compliance. Owing to their complexity, the decision-making process for DGMs is very difficult to understand or explain, especially by those who dont understand their architecture or operations. This lack of explainability can create a risk of an AI model developing unjustified or unethical biases without the knowledge of its operators, in turn generating outputs that are inaccurate or discriminatory.

In addition, the fact that DGMs operate on such a layer of high complexity means that theres a risk of it being difficult to reproduce their results. This difficulty with reproducibility can make it hard for researchers, regulators, or the general public to have confidence in the results provided by a model.

Ultimately, to mitigate risks around explainability and reproducibility, devops teams and data scientists looking to leverage DGMs need to ensure theyre using best practices in formatting their models and that they employrecognized explainability toolsin their deployments.

While only just beginning to enter production environments at scale, DGMs represent some of the most promising developments in the AI world. Ultimately, through being able to look at some of the most subtle and fundamental patterns in society and nature, these models will prove transformative in just about every industry. And despite the challenges of ensuring compliance and transparency, theres every reason to be optimistic and excited about the future DGMs promise for technology, our economy and society as a whole.

Rick Hao is lead deep tech partner at pan-European VCSpeedinvest.

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Deep generative models could offer the most promising developments in AI - VentureBeat

How healthcare has been transformed post covid by technology – Medium

Covid although causing considerable distress but has also allowed innovation in healthcare to be greatly accelerated. The regulatory framework has helped fast track new ideas and solutions. Telecare and total triage have helped patients access their doctors remotely and at their convenience improving access. Telemedicine consultations have increased by over 5000% and changed the doctor-patient interaction. Vaccine development had taken 10 -15years prior to the pandemic and has been shortened to less than a year with rapid design and delivery of clinical trials. Covid supplies were delivered to remote areas using drones. Accurate real-time vital sign measurements, including heart rate and oxygen saturation could be measured via an app using AI-powered light signal processing technology to convert light reflected from blood vessels in the face. Smart stethoscopes were developed that both listen to patients hearts and transmit images of the lungs. Virtual reality was introduced to support training. Robots were introduced to sterilise rooms and offer support to patients. The key to this has been collaboration with industry and healthcare providers coming together at the time of need to share different skill sets. The pandemic is a global problem and the world has responded by sharing global solutions and hopefully this allows the new normal to help springboard health into the future.

Exponential technologies and the digital transformation of health

The world is at an inflection point with exponential technologies converging to help reimagine and reinvent healthcare. Technologies like AI, Robotics, 3D Printing, Gene sequencing/editing, immersive technologies, 5G connectivity, remote sensors, nanobiotechnology offer interlocking building blocks to help prevent illness, monitor physiology, improve diagnosis and offer new treatment modalities. There have been 3 windows to humanity. Copernicus discovered the telescope as a window into the universe. The microscope has helped us understand the intricacies of the human body. Data, the third window is now helping understand and build a personalised approach to medicine. Patients with chronic diseases will be managed more remotely with access to their data and therefore empowering them to be involved in their health. Rare diseases are now being sequences and whole genomic data become available on a national scale which will help understand population health. Drones are delivering medical equipment across cities and 3d printing is allowing the planning of treatment and implant of devices. Eventually this may lead to the production of a bioengineered organs. 5G with its low latency and fast bandwidth will help with remote surgery across continents and smart ambulances bringing valuable support to the roadside. The development of chatbots, digital twins and digital humans will ask important questions on what the future interaction between a patient and doctor may look like.

The Future of medical education

Medical education is going through a paradigm shift as technology enhanced learning are enabling hybrid models of delivering quality education. We have moved from papyrus to books to eLearning/online platforms. More recently tele platforms have become the normal. The pandemic has forced new ways of delivering clinical medicine. The future will encompass more traditional models converted to using augmented, virtual and mixed reality. Students will be trained more remotely with classrooms replaced by virtual rooms. Anatomy dissection will be enhanced digitally, clinical ward rounds can be supported by mixed reality and surgical operations will be viewed in virtual reality with more interactivity allowing teachers to be hundreds of miles away or even on the other side of the world. This will allow the democratisation of education that will allow every student to access world class education breaking down the barriers of cost and location. The adage of see one do one, teach one will be superseded by simulation using CGI or real time images thereby supporting training of practical procedures. Digital Health, entrepreneurship and innovation needs to be at the heart of any new curriculum to produce the digital doctor of tomorrow.

Education 4.0 will allow clinical teaching to use all of the available platforms for a richer experience.

The Metaverse

In October 2017, I entered and embraced the metaverse for the first time from my operating theatre from the Royal London Hospital. I had created my avatar and joined surgeons from London, India and the US. We were able to interact in the virtual world across three continents and three time zones simultaneously. We shared assets like CT scans, X-rays and 3d models to discuss the patient during live surgery. Imagine a world where a surgeon could call on help when required by simply transitioning from the real to the virtual world, allowing the true democratisation of healthcare.

In his 1992 book Snow Crash, American author Neal Stephenson introduced the term metaverse, referring to the 3D visual space occupied by lifelike human avatars. People in this dystopian future wear realistic mirrors to connect with the digital world! In Silicon Valley, it is a well-known novel. Metaverse is an all-encompassing 3D visual space shared by everyone. The metaverse can be thought of as a cluster of connected earths, just as the visible universe is a group of planets connected to space. Can the metaverse become a part of the healthcare system of the future?

Digital Surgery

Surgery is moving rapidly from analogue to a digital interface. The new 5 pillars of digital surgery are connected care, robotics, data analytics/AI, surgical navigation and remote collaboration. Over the last 20 years we have seen enhanced visualisation and improved fine dexterity allowing surgeons to manipulate a robot with the potential improvement in outcomes for the patients. The robot wars in 2022 are allowing more access and flexibility in offering minimal access surgery. By having large amounts of real time data during an operation by using analytics will also aid surgeons to assess their performance and move away from subjective methods of performance to a more robust and transparent process. For decades surgical acumen has been only facilitated by personal judgement. The use of artificial intelligence and computer vision will allow surgical navigation during a procedure allowing surgeons an intraoperative map to help avoid damage to important structures and ensuring cancers can be removed in their entirety to allow a potential cure. Preoperative planning will be supported by mixed reality headsets to overlay images on the patient to improve accuracy and decision making. Remote telemonitoring will be possible due to the power of connectivity with 5G allowing surgeons across the world to support global training and improve standards.

AI in healthcare

Artificial intelligence and deep machine learning is changing the face of medicine and may be the most important technology to be integrated into healthcare. AI has promised hope which has been followed by much hype. We are now witnessing the reality of real time applications of AI. AI can enhance clinical productivity due to its ability to handle a large capacity of tasks that are well suited for automation. AI can reduce the burden of clerical work of doctors thus improving the quality of care and allow them to spend more time with patients and the healthcare team

The collection of good quality data is a pre requisite for an algorithm to produce meaningful conclusions and outputs. This data includes patient biometrics, natural language and operational data. The outcomes have been to improve early detection/triage, diagnosis, disease management and monitoring diseases and wellbeing. Analysis of images has been well researched with AI algorithms diagnosing abnormalities on chest xrays and CT scans with over 90% accuracy. Dermatology is being supported by smart AI to help diagnose skin cancers. Drug development has been accelerated by molecular identification and targeting. Protein folding has been unravelled using powerful AI. Chatbots and intelligent conversation engines are helping with triage and stratifying risk.

However, the ethics need to be considered as there also inherent risks of AI with false prediction and the inappropriate use of patient data. This needs careful debate and patient engagement to ensure that AI is safely and responsible implemented.

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How healthcare has been transformed post covid by technology - Medium

Molecular Modelling Market Share 2022 Competitive Analysis of Size, Industry Challenges, Top Manufacturers, Types, Applications and Forecast to 2025 -…

Molecular Modelling Market research report 2022 offers driving factors, competitive landscape, revenue share analysis, and challenges of the industry has been analysed in the report.

Final Report will add the analysis of the impact of COVID-19 on this industry

Molecular Modelling Market report provides a detailed analysis of global market size, segmentation market growth, industry share, competitive landscape, sales analysis, value chain optimization. Also, the Molecular Modelling market includes market size growth rate analysis by type, regional and country-level market size, impact of domestic, global market key players, trade regulations, strategic market growth analysis, and technological innovations.

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About Molecular Modelling Market:

Molecular modelling encompasses all methods, theoretical and computational, used to model or mimic the behaviour of molecules. The methods are used in the fields of computational chemistry, drug design, computational biology and materials science to study molecular systems ranging from small chemical systems to large biological molecules and material assemblies.Molecular modelling methods are now used routinely to investigate the structure, dynamics, surface properties, and thermodynamics of inorganic, biological, and polymeric systems. The types of biological activity that have been investigated using molecular modelling include protein folding, enzyme catalysis, protein stability, conformational changes associated with biomolecular function, and molecular recognition of proteins, DNA, and membrane complexes.In 2018, the global Molecular Modelling market size was million USD and it is expected to reach million USD by the end of 2025, with a CAGR during 2019-2025.

List of Top Key Players in Molecular Modelling Market Report Are:

This market study covers the global and regional market with an in-depth analysis of the overall growth prospects in the market. Furthermore, it sheds light on the comprehensive competitive landscape of the global market.

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Global Molecular Modelling Market: Segment Analysis

The research report includes specific segments by region (country), by company, by Type and by Application. This study provides information about the sales and revenue during the historic and forecasted period of 2019 to 2025. Understanding the segments helps in identifying the importance of different factors that aid the market growth.

Molecular Modelling Market by Applications:

Molecular Modelling Market by Types:

Molecular Modelling Market report provides comprehensive analysis of

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Some of the Key Questions Answered in this Report:

Geographical Regions covered in Molecular Modelling market report are:

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Molecular Modelling Market TOC Covers the Following Points:

1 Molecular Modelling Market Overview

1.1 Product Overview and Scope of Molecular Modelling

1.2 Segment by Type

1.2.1 Global Sales Growth Rate Comparison by Type (2021-2025)

1.3 Segment by Application

1.4 Global Market Size Estimates and Forecasts

1.4.1 Global Revenue 2015-2025

1.4.2 Global Sales 2015-2025

1.4.3 Market Size by Region: 2020 Versus 2025

1.5 Industry

1.6 Market Trends

2 Global Molecular Modelling Market Competition by Manufacturers

2.1 Global Sales Market Share by Manufacturers (2015-2020)

2.2 Global Revenue Share by Manufacturers (2015-2020)

2.3 Global Average Price by Manufacturers (2015-2020)

2.4 Manufacturers Manufacturing Sites, Area Served, Product Type

2.5 Market Competitive Situation and Trends

2.5.1 Market Concentration Rate

2.5.2 Global Top 5 and Top 10 Players Market Share by Revenue

2.5.3 Market Share by Company Type (Tier 1, Tier 2 and Tier 3)

2.6 Manufacturers Mergers and Acquisitions, Expansion Plans

2.7 Primary Interviews with Key Players (Opinion Leaders)

3 Molecular Modelling Retrospective Market Scenario by Region

3.1 Global Retrospective Market Scenario in Sales by Region: 2015-2020

3.2 Global Retrospective Market Scenario in Revenue by Region: 2015-2020

3.3 North America Market Facts and Figures by Country

3.3.1 North America Sales by Country

3.3.2 North America Sales by Country

3.3.3 U.S.

3.3.4 Canada

3.4 Europe Market Facts and Figures by Country

3.4.1 Europe Sales by Country

3.4.2 Europe Sales by Country

3.4.3 Germany

3.4.4 France

3.4.5 U.K.

3.4.6 Italy

3.4.7 Russia

3.5 Asia Pacific Market Facts and Figures by Region

3.5.1 Asia Pacific Sales by Region

3.5.2 Asia Pacific Sales by Region

3.5.3 China

3.5.4 Japan

3.5.5 South Korea

3.5.6 India

3.5.7 Australia

3.5.8 Taiwan

3.5.9 Indonesia

3.5.10 Thailand

3.5.11 Malaysia

3.5.12 Philippines

3.5.13 Vietnam

3.6 Latin America Market Facts and Figures by Country

3.6.1 Latin America Sales by Country

3.6.2 Latin America Sales by Country

3.6.3 Mexico

3.6.3 Brazil

3.6.3 Argentina

3.7 Middle East and Africa Market Facts and Figures by Country

3.7.1 Middle East and Africa Sales by Country

3.7.2 Middle East and Africa Sales by Country

3.7.3 Turkey

3.7.4 Saudi Arabia

3.7.5 UAE

4 Global Historic Market Analysis by Type

5 Global Historic Market Analysis by Application

6 Company Profiles and Key Figures in Molecular Modelling Business

7 Manufacturing Cost Analysis

7.1 Key Raw Materials Analysis

7.1.1 Key Raw Materials

7.1.2 Key Raw Materials Price Trend

7.1.3 Key Suppliers of Raw Materials

7.2 Proportion of Manufacturing Cost Structure

7.3 Manufacturing Process Analysis of Molecular Modelling

7.4 Molecular Modelling Industrial Chain Analysis

8 Marketing Channel, Distributors and Customers

8.1 Marketing Channel

8.2 Distributors List

8.3 Customers

9 Market Dynamics

9.1 Market Trends

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Molecular Modelling Market Share 2022 Competitive Analysis of Size, Industry Challenges, Top Manufacturers, Types, Applications and Forecast to 2025 -...

Heat shock and cold sensitivity – The Hindu

DNA is a linear chain of nucleotides, portions of which are faithfully transcribed into linear messenger RNA. The message in this RNA is translated into strings of amino acids - proteins. Proteins need to take a precise three-dimensional shape to become functional entities. This protein folding does not happen all by itself, at least most of the time. A special bunch of proteins called molecular chaperones assist in correctly folding the protein.

The idea of chaperones may sound quaint and Victorian, but in biological systems they play crucial roles. After the new protein chain has been shaped correctly, chaperones move on. Or else the new chain is eliminated. Without chaperones, newly synthesised proteins would soon become a tangled mess of insoluble aggregates, hindering cellular processes.

Many molecular chaperones belong to the class of heat shock proteins (or stress-response proteins). This is because whenever an organism is subjected to elevated temperatures a heat shock proteins in the system begin to lose their native shapes, and chaperones are produced in large quantities to restore order.

Chaperones are needed under physiological conditions too, for normal cellular function.

Misfolding of proteins can cause a number of diseases. Alpha-synuclein protein, present in neurons, is wrongly folded in Parkinson's disease. Brains of Alzheimer's patients have plaques formed from aggregates of amyloid beta-peptide. This accumulation of amyloid fibrils is toxic, leading to widespread destruction of neurons a 'neurodegenerative disorder. Aberrant folding of crystallins of the eye lens leads to cataract. In the eye lens, an abundant subset of proteins called alpha-crystallins themselves serve as chaperones a single R116G mutation in human alpha crystallin is responsible for autosomal dominant congenital cataract.

Major chaperones in humans include HSP70, HSC70 and HSP90: the numbers express the size of the proteins in kilodaltons. In normal cells 1%2% of all proteins present are heat shock proteins. This number rises threefold during stressful conditions.

HSP70 is induced by heat, whereas HSC70 is always present at high levels in normal cells. HSC70 appears to be more like a molecular thermometer, with an ability to sense cold temperatures. This knowledge comes from the study of an intriguing set of disorders, exemplified by Familial Cold Autoinflammatory Syndrome (FCAS). Symptoms of these disorders include rashes on the skin, pain in joints and fever. Periodic episodes may last from a few hours to a few weeks. These episodes begin early in life, the trigger being exposure to cold, or a stress such as fatigue. The confusing set of symptoms shown in this rare disorder make diagnosis difficult it often takes ten years from first clinical presentation to a confirmed diagnosis.

The first family with confirmed FCAS in India was reported only in August this year. Sagar Bhattad and colleagues, at the Aster CMI Hospital in Bengaluru, traced the genetic underpinnings of FCAS in a four-year old boy who frequently suffered from winter rashes. It turns out that several family members, including his paternal great-grandfather, had similar symptoms. This was published in Indian Journal of Pediatrics, August 2021, 88(8):834.

Addressing the role of HSC70 in sensing low temperatures, the group of Ghanshyam Swarup at the Centre for Cellular and Molecular Biology has worked out a framework for the triggering of autoinflammatory conditions This was published in The FEBS Journal, 2021; doi:10.1111/febs.16203. Disorders related to cold sensitivity are caused by mutations in proteins that regulate inflammation. At normal body temperatures, HSC70 is able to coax these mutated proteins to fold correctly and thus function normally. In cold conditions, however, the HSC70 molecule is itself slightly altered in its shape and is not able to unerringly interact with the mutated regulators of inflammation. This leads to a pathological state with symptoms such as chills, joint pains and rich red skin rashes setting in within two hours.

Cancer cells divide at break-neck pace, and heat shock proteins are very important in maintaining the stressful cancerous state. An overabundance of heat shock proteins in cancer cells is an indicator of a poor prognosis. Cancerous cells accumulate mutations in proteins that would normally suppress tumours. HSP70 and HSP90 play the roles of villains, as they continue to fold the mutated proteins, thus allowing tumour progression. In the laboratory, inhibitors of HSP90 have shown much promise as anti-cancer agents. However, no inhibitor has yet been approved for human use, as the levels required for these to be effective are too toxic for your body.

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Heat shock and cold sensitivity - The Hindu

Nakasone Prize Won By Arthur Horwich, MD < Yale School of Medicine – Yale School of Medicine

Arthur Horwich, MD, Sterling Professor of Genetics and professor of pediatrics, has received the 2022 Human Frontier Science Program (HFSP) Nakasone Award in recognition of his work uncovering a molecular machine and its mechanism of mediating protein folding in the cell. He is sharing the prize with his colleague Franz-Ulrich Hartl, MD, director of the Max Planck Institute of Biochemistry in Germany.

The HFSP Nakasone Award is designated for scientists whose work has led to significant breakthroughs in the life sciences. It was named after Japans former Prime Minister Yasuhiro Nakasone, who believed in the importance of international collaboration for the advancement of science. Since the establishment of the program in 1989, more than 4,500 awards have been given to over 7,500 researchers worldwide.

The prize represents a recognition that our work is a major step towards understanding the process of how information is transferred in the cell, says Horwich. The recipients of this award are all distinguished investigators, and it is humbling to join that group.

Although Horwich trained as a physician at Brown University, he also always found himself drawn to the lab. As a postdoctoral fellow at Yale School of Medicine, he grew curious about the biology behind how proteins within a cell move into membrane-bound organelles like mitochondria. This became the subject of his research after he established his own lab at Yale in the 1980s.

In the mid 1980s, two scientists had shown that proteins needed to be unfolded to pass through the two mitochondrial membranes into the innermost matrix compartment. Once a protein reached the matrix space, they believed it would then spontaneously refold on its own to its active form. Horwich, however, hypothesized that there may be machinery that assists the folding of imported proteins.

We proposed that under normal conditions, the machine would mediate the folding of the newly-imported proteins like pieces of spaghetti into its characteristic three dimensional structure, Horwich says. That was considered to be a heretical idea back then.

Horwichs work led to the discovery of a double-ringed macromolecule in the mitochondria, now called the chaperonin, required for protein folding. In mutant cells that lacked this structure, proteins would misfold inside the mitochondria and aggregate into clumps, similar to a process occurring in many neurodegenerative diseases. Over the next two decades, Horwich studied the newly discovered machineryfound also in the cytosol of all cells where it assists folding of newly made proteinsto better understand its mechanism.

The chaperonin, he found, has a hydrophobic [water-fearing] greasy surface at the inside of its rings. A protein, on the other hand, has a hydrophilic [water-loving] exterior and hydrophobic core. Yet when a protein is unfolded, its hydrophobic core is exposed. The chaperonin, he says, works like fly paper to capture an unfolded protein inside a ring. After this binding step, the machine attaches a lid-like structure (co-chaperonin) to the ring, housing the unfolded protein. In doing so, it releases the protein from the wall of the ring into the now-encapsulated chamber, where it can fold in solitary confinement without any chance of aggregating with other proteins, reaching its functional form. A final step of hydrolysis of ATP by the machine releases the lid and the folded protein from the machine like a jack-in-the-box.

I had bumped into something that was against the dogma of the field, and I had only been an independent investigator for a year or two, says Horwich. It was scary to make that proposition that protein folding was assisted rather than spontaneous.

Horwichs research was made possible through close collaboration with Hartl and his extensive expertise at the biochemistry level. The two have won numerous awards for their work, including the prestigious $3 million Breakthrough Prize 2020 and the Dr. Paul Janssen Award for Biomedical Research in 2019. Their joint efforts, says Horwich, embody the spirit of their most recent award. Prime Minister Nakasone felt that science could be significantly promoted by having investigators from different countries getting together to fulfill collaborative experiments, he says.

Studying protein folding, he continues, has significant implications. Neuroscientists have shown that protein misfolding can lead to neuronal injury and cell death, and it is closely associated with neurological diseases including Alzheimers, Parkinsons and amyotrophic lateral sclerosis (ALS). Horwichs lab has been studying protein misfolding in mouse models of ALS to better understand how to change the course of the disease.

Mother nature put together a beautiful, 7-fold symmetric folding machine, says Horwich. The biologist in me loves the notionreally, what was a privilegeof getting to learn more about this unbelievable structure.

Link:
Nakasone Prize Won By Arthur Horwich, MD < Yale School of Medicine - Yale School of Medicine

Sun is teaching COVID-19 researchers how to use high-performance AMD computers – William & Mary News

by Joseph McClain | December 8, 2021

Large segments of the worlds research community refocused in early 2020 in response to the growing COVID-19 pandemic.

Biochemists, epidemiologists, molecular biologists, geneticists and other specialists began working on various ways to model, track and attack the novel coronavirus using the most sophisticated scientific techniques and methods. Those techniques have become increasingly computationally intensive in nature, requiring high-performance computing not always available to the COVID research community.

The multinational semiconductor company Advanced Micro Devices AMD stepped into the fight against COVID-19 by supporting 23 organizations in addressing issues such as vaccine development, genetic sequencing and modeling of the outbreak. AMD provides researchers with high-performance computing devices, especially graphics processing units, supported by the AMD Radeon Open Compute (ROCm) platform.

AMD brought in a set of instructors, including William & Marys Yifan Sun, to make sure the researchers get the maximum benefit of the combined 12 petaflops of computing capacity. Sun and his fellow instructors are holding a series of online lectures and office hours to get the COVID researchers up to speed on graphics processing unit (GPU) computing and, in particular, the computing language needed.

Even before COVID, we were writing a book designed for the HIP program language, which is what you need to work with AMD GPUs, said Sun, an assistant professor in William & Marys Department of Computer Science. The book is not done yet, but almost there. So, the AMD people put together experts in component computing with experts in research for a 10-week session.

Sun said he has 33 people taking his classes. Its an international group, with representatives from research institutions in the U.S., Italy, Germany, the U.K., India, France and Canada.

Theyre assistant professors, researchers, Ph.D. students, he added. Five of them are from AMD; I think theyre want to take the course to get a better understanding about how to program AMD GPUs and how to improve GPU program performance.

The researchers are not novices to high performance computing, so Sun says he and the other instructors were able to hit the ground running, introducing the HIP language in the early weeks.

Then, later on, we dive into performance tuning, invoking libraries and other tools, he explained. This is a hyper-focused community. They care about performance. They care about how fast they can get a result.

The researchers develop their algorithms themselves. Sun said many of the attendees are interested in applying high-performance computing to tasks such as protein folding modeling, as well as gene sequencing and alignment. GPU-based computing shines in such computation-heavy chores, he added.

Sun explained that traditional computer architecture is based on the CPU the central processing unit. A CPU can have numerous cores, or brains, and CPU cores can number into the tens, or hundreds. To link all of those cores, CPU servers communicate from rack-to-rack of servers.

So the communications between CPU cores are intrinsically very slow, Sun explained. But in this initiative, were using GPU devices. GPUs are highly parallel, and there are thousands of cores in a single GPU.

The GPU architecture is much faster, he said, because of the parallel nature and assembly of more cores on a single device. No need for slow fabrics such as an Ethernet cable to communicate computer to computer. Sun said the result is much quicker computation for researchers working to combat the virus causing a global pandemic.

For common algorithms, GPUs can be hundreds to thousand times faster than CPUs. For more specific algorithms, like gene alignment, I would say at least tens of times faster, Sun said. You could think about it reducing a computation assignment from 10 days to one day. Thats a lot of speed-up.

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Sun is teaching COVID-19 researchers how to use high-performance AMD computers - William & Mary News

Funding news: Cyrus lands $18M and buys startup developing COVID-19 therapeutic; Spare Labs snags $18M for mobility software – GeekWire

The news: Seattle-based protein engineering company Cyrus Biotechnology has raised $18 million and acquired Orthogonal Biologics, a spinout from the University of Illinois at Urbana Champaign, which is developing a COVID-19 therapeutic.

Combining forces: Cyrus has built up a software-and-screening platform to re-design natural proteins, leveraging tech spun out of the University of Washingtons Institute for Protein Design. IPDs tools to predict protein folding have been a boon to Cyrus, which was co-founded in 2015 by former IPD postdoc Lucas Nivon. The company recently inked a deal with immune biotech Selecta Biosciences worth up to $1.5 billion and has worked with more than 90 other industry partners, including pharma giant Janssen.

The new acquisition brings on board Orthogonals platform for deep mutational scanning, a method that can assess up to one million mutant versions of a protein in a single experiment. Orthogonal also adds two new protein-based therapeutics to Cyrus pipeline, including a potential COVID-19 drug.

Counteracting COVID-19: Orthogonals COVID-19 agents are built to resemble ACE2, the human protein that the COVID-19 virus uses to enter human cells. The agents are designed to act as decoys, binding to the virus and disarming it.

Why it matters: Drug companies are fast leveraging IPDs recently-released RoseTTAfold and another powerful tool to predict protein folding developed by DeepMind, AlphaFold. Plugging in different tech and drug pipelines, such as those developed by Orthogonal, promises to accelerate the development of new therapeutics. Cyrus recently brought on RoseTTAfold, building on its use of an earlier IPD tool, called Rosetta.

Cyrus has proven the power of its Rosetta-based platform as a software and services company. We are very excited to now apply those software and laboratory tools directly for Cyruss partners and in house drug discovery, said Geeta Vemuri, founder and managing partner at Agent Capital, in a statement.

The field is growing rapidly. Alphabet, for instance, in November launchedIsomorphic Labs to build off of DeepMinds protein folding research.

The backers: Investors in the new deal include OrbiMed Advisors, Trinity Ventures, Agent Capital, Yard Ventures, Washington Research Foundation (WRF), iSelect Fund, W Fund, family offices, and individual investors. Selecta Bioscience is a strategic investor in the Series B round, which brings total funding to date to $28.9 million, including $8 million in venture funding raised in 2017. Terms of the acquisition were not disclosed.

Whats next: The cash will be used to move Cyrus labs from a temporary space atAlexandria LaunchLabstoa buildingnear the Seattle waterfront that houses Universal Cells and other biotechs. Cyrus will also partner with contract research organizations for preclinical testing of the the COVID-19 agent and other therapies.

The small Orthogonal team has moved to Seattle, including COOKui K. Chanand CEOErik Procko, a University of Illinois professor of biophysics and quantitative biology, now on leave. Both are former senior fellows in the lab of David Baker, IPD head. Cyrus will continue relationships with key University of Illinois researchers, including professorsJalees RehmanandAsrar B. Malik, who are performing studies in animals. Cyrus is hiring protein biochemists and senior leadership in drug discovery, aiming to grow from 25 to 30 employees in the next six months.

By merging our company with Cyrus we can create a unified biologics discovery platform, said Procko.

More deals:

Koch Investments Group invests $100 million in Vancouver, B.C.-based Standard Lithium.Standard Lithium is testing the commercial viability of extracting lithium, a key component of electric batteries, at a 150,000-acre location in Arkansas. The company has commissioned a demonstration plant to extract the metal. It also has 45,000 acres of mineral leases in the Mojave Desert in San Bernardino County, Calif.

Barn2Door raises $6 million to advance software that connects farmers to customers. Seattle-based Barn2Door serves thousands of farmers across the U.S., helping them sell food directly to consumers with e-commerce software that manages sales, inventory, logistics, and more. The new funding brings total funding to $17.6 million to date, building on a $6 million round in August, 2020. The latest funding was led by Quiet Capital, with participation from existing major investors Bullpen Capital, lead Edge Capital, RAINE Ventures, Sugar Mountain Capital, as well as new investors Serra Ventures and Navigate Ventures.

Vancouver, B.C.-based Spare Labs raises $18M for mobility software. Spare Labs provides software for public transit, ride-sharing and other shared transportation. It will use the funding to enable better cooperation between different transportation providers. The Series A round was led by Inovia Capital with participation from Kensington Capital, Link VC, Ramen VC, Ridge Ventures, TransLink Capital and Japan Airlines (as JAL Innovation Fund) and Nicola Wealth, amongst others.

Editors note: This story has been updated to include Cyrus future plans.

Original post:
Funding news: Cyrus lands $18M and buys startup developing COVID-19 therapeutic; Spare Labs snags $18M for mobility software - GeekWire

Structural dynamics in the evolution of a bilobed protein scaffold – pnas.org

Significance

Proteins conduct numerous complex biological functions by use of tailored structural dynamics. The molecular details of how these emerged from ancestral peptides remains mysterious. How does nature utilize the same repertoire of folds to diversify function? To shed light on this, we analyzed bilobed proteins with a common structural core, which is spread throughout the tree of life and is involved in diverse biological functions such as transcription, enzymatic catalysis, membrane transport, and signaling. We show here that the structural dynamics of the structural core differentiate predominantly via terminal additions during a long-period evolution. This diversifies substrate specificity and, ultimately, biological function.

Novel biophysical tools allow the structural dynamics of proteins and the regulation of such dynamics by binding partners to be explored in unprecedented detail. Although this has provided critical insights into protein function, the means by which structural dynamics direct protein evolution remain poorly understood. Here, we investigated how proteins with a bilobed structure, composed of two related domains from the periplasmic-binding proteinlike II domain family, have undergone divergent evolution, leading to adaptation of their structural dynamics. We performed a structural analysis on 600 bilobed proteins with a common primordial structural core, which we complemented with biophysical studies to explore the structural dynamics of selected examples by single-molecule Frster resonance energy transfer and HydrogenDeuterium exchange mass spectrometry. We show that evolutionary modifications of the structural core, largely at its termini, enable distinct structural dynamics, allowing the diversification of these proteins into transcription factors, enzymes, and extracytoplasmic transport-related proteins. Structural embellishments of the core created interdomain interactions that stabilized structural states, reshaping the active site geometry, and ultimately altered substrate specificity. Our findings reveal an as-yet-unrecognized mechanism for the emergence of functional promiscuity during long periods of evolution and are applicable to a large number of domain architectures.

Proteins drive and maintain all fundamental cellular processes (1) by interactions with small molecules and/or other biopolymers. Important mechanistic information on proteins are accessible via structural analysis of their functional cycle (2). While classical approaches rely on the interpretation of static structure snapshots, the visualization of structural dynamics (i.e., to follow the interconversion of distinct structural states at high spatial and temporal resolution) (37) has been recognized as an essential complement. The folding funnel model (8), rooted in the free-energy landscape theory (911), has by now become a widely accepted way to describe the ensemble of such states (1214).

Distinct structural states can originate from local flexibility (i.e., bond vibrations, and side-chain rotations [Fig. 1A, Tier-2 dynamics]), changes in secondary structure (Fig. 1A, Tier-1 dynamics) or large-scale domain motions (Fig. 1A, Tier-0 dynamics). The free-energy landscape of a protein defines the lifetime of its structural states, ranging from nanoseconds (local flexibility) to seconds (large-scale motions). Transitions between the states are referred to as structural changes and are induced by interactions with ligands, posttranslational modifications (e.g., phosphorylation), or chemical events such as nucleotide hydrolysis. The coupling of the latter to structural changes enables proteins to perform a diverse range of functions.

Energetic funnel, structure, and evolution of the cherry-core. (A) One-dimensional cross-section of a hypothetical protein energy landscape adapted from Kern and coworkers (12) according to the Tier description and definitions introduced by Ansari and coworkers (15). A structural state is defined as the lowest point of a well on the energy surface. The populations of the Tier-0 states, closed and open, are defined as Boltzmann distributions, and their relative probabilities (pC, pO) are determined in this paper by smFRET, which follows large-scale domain motions. Tier-1 states describe local and fast structural fluctuations (e.g., changes in secondary structure elements like loop motions or loss of secondary structure). Tier-1 dynamics were probed in this study by HDX-MS. Rapid and localized Tier-2 dynamics (e.g., side-chain rotations) were not considered here but can be monitored via molecular dynamics (MD) simulations. Changes in the chemical environment (i.e., absence or presence of a ligand) modify the energy landscape via a bias for one of the two states. Typically, thermodynamic parameters such as the free energy difference of Tier-0 wells (GOC) can be determined by ITC. (B) Structure of a representative bilobed protein, the substrate binding domain 2 (SBD2) of the ATP-binding cassette (ABC) amino acid transporter GlnPQ from Lactococcus lactis (PDB: 4KR5). (C) Summary of the structure-based phylogenetic tree with schematic representations of the different structural classes (class A through G) highlighting their termini. Complete sequence and structure-based phylogenetic trees are provided in SI Appendix, Fig. S1 and Datasets S1 and S2. We used the following notation: Secondary structure elements that are common between the different classes were assigned a number identifier (e.g., helix H4 in classes F and G), whereas the unique elements have a letter followed by a number identifier (e.g., HD1, first unique helix of class D proteins). An asterisk (*) marks a structural subclass (shown in detail in Dataset S3). Schematics are based on the crystal structures of the selected proteins. (D) Topology of bilobed proteins depicting the consensus of secondary structure elements: strands (s) or helices (H) belonging to the Domains (D1, D2), hinge-forming -strands (H1, H2), and C-tails of all seven classes (A through G) are shown. Revised alignments of bilobed proteins are shown in Dataset S3. The secondary structure elements forming the consensus cherry-core structure are depicted on the Top row.

Tier-0 dynamics were observed and characterized in various settings (e.g., in motor proteins, in which they are used in propelling movement along filaments) (16), in the transport of molecules or biopolymers across biological membranes (1721), or in the activity of proteins that perform mechanical work (22). Tier-1 dynamics drive the actions of various signaling proteins for transmission of signals (2325). Structural and biochemical data indicate that enzymes also show varying degrees of structural dynamics (26), although it is not well understood what precise role this plays for catalytic activity. The current belief is that extensive structural dynamics in enzymes are not necessarily required for catalysis (27) but rather enact in regulation. For instance, many protein kinases exploit Tier-0 dynamics to generate active or inactive structural states (28). Tier-2 dynamics have been shown to be important for the evolution of enzymatic function (29). In addition to domain motions occurring within a structure, protein oligomerization and the possible quaternary dynamics can also be relevant for function (30). A well-characterized example is the allostery of hemoglobin that occurs on the transition between two distinct quaternary states (relaxed and tense) (31, 32). All this highlights that multi-Tier dynamics of proteins occur on various time- and length-scales (Fig. 1A) and are often the basis for function. It is, however, not yet well understood how structural dynamics are optimized during evolution to tailor protein function.

Analysis of protein sequences and structures has provided important insights into the evolution of protein function (33). A powerful approach is to assign the domain components of proteins to families and superfamilies on the basis of sequence alone (Pfam database for the protein family) (34) or in combination with structural information (class architecture topology homologous superfamily, CATH and structural classification of proteins, SCOP databases) (35, 36). The CATH and SCOP databases combined have identified 3,000 domain superfamilies comprising >50 million domains that account for 70% of the domains in completed genomes (37). More recently, the database ECOD (evolutionary classification of protein domains), groups domains by considering their evolutionary relationships (38). In ECOD, 760,000 domains have been assigned to 3,700 homologous groups (39). The most highly populated domain superfamilies/homologous groups are universal to all kingdoms of life (40, 41). The prevalence of proteins with multidomain architectures and the recurrent appearance of the same domain in nonhomologous proteins suggests that functional domains are reused when creating new proteins and functions (42, 43). Analyses of selected domain groups (44, 45), and more-recent large-scale investigations (4648) have shown that domains in such groups generally share a common structural core (40 to 50% of the domain) that is highly conserved, even for relatives separated by billions of years. To achieve functional promiscuity of such a structural core, it has been proposed that larger structural embellishments (secondary structure elements or even entire domains) need to be added to the core for altered biochemical function (49). Such fusions occur frequently at the N or C termini (40), which has led to the belief that structural elements act as Lego bricks (47). Those are recombined in various ways for new functions to emerge during evolution (42, 49).

Much less is known on the role played by structural dynamics in the evolution of protein function (5053). A recent model suggests that the native state of an evolved protein is the most abundant state of all possible structural states, which was selected for a specific function (54, 55). The avant-garde evolvability theory proposes the existence of a highly promiscuous primordial protein structure. It is assumed that for the emergence of the native state, the ability to evolve (evolvability) was traded for ligand-functional specificity (56, 57). These observations were experimentally verified (58) and agree with the proposal that changes in structural dynamics serve as a mechanism for the evolution of specialist enzymes from promiscuous generalists (59), though discordant examples are also observed in evolution (60). Furthermore, recent studies indicate that this evolvability theory can explain the short-period evolution (e.g., variation of enzyme local flexibility [variation of Tier-1/2 dynamics] to acquire new functions) particularly well (5456, 61). Remarkably, this short-period evolution can be faithfully reproduced invitro using directed evolutionary approaches based on consecutive rounds of single point mutations (54, 61). Ancestral protein reconstruction has also been useful in elucidating the role of structural dynamics in the emergence of specialized amino acid binding proteins from a promiscuous ancestor (62). However, it still remains unclear how, during longer periods of evolution, a primordial core structure evolves to modulate or diverge Tier-0 and/or quaternary dynamics, generating completely new functionalities.

Here, we test this aspect of the evolvability theory for proteins separated by long evolutionary periods during which Tier-0 or quaternary dynamics were introduced to an existing protein core structure. For this, we identified proteins with a bilobed domain structure with a high degree of plasticity and neutrality (29, 55), having 1) a conserved core structure, 2) large sequence diversity and related functional divergence, and 3) occurrence in all kingdoms of life. The selected structural core is composed of two Rossmann-like domains, which are believed to be among the most ancient architectures (63) and that are connected by a single -sheet (Fig. 1B). We analyzed 600 proteins with this structural core that contain two related domains from the periplasmic-binding protein (PBP)like II domain homologs (ECOD: X-, H- and T-groups PBP-like II). We show here that different members of these homologous proteins diverged with respect to domains or secondary structure elements, predominantly at their termini (Fig. 1 C and D). Using a combination of structural analysis and biophysical investigations, we demonstrate that such structural embellishments confer multi-Tier structural dynamics that diversify the function of the core structure to yield transcription factors, enzymes, or extracytoplasmic transport-related proteins. To understand both the mechanistic distinction of these proteins and the evolutionary trajectories, we used single-molecule Frster resonance energy transfer (smFRET) (64) and HydrogenDeuterium Exchange Mass Spectrometry (HDX-MS) (65). smFRET allows to monitor Tier-0 dynamics with a temporal resolution down to microseconds and subnanometer spatial resolution at the single-molecule level, even for highly heterogeneous structural ensembles (12, 6668). HDX-MS complements smFRET, as it can probe Tier-1 dynamics throughout the structure that occur within the structural states identified by smFRET (Fig. 1A) (69). Using this combination of techniques, we show how the C-terminal extensions modulate Tier-0 dynamics in the structural core in a manner specific to their three-dimensional orientation. This arrangement also dictates specific geometrical criteria, crucial for establishing specific ligand interactions, all of which we detail in this work. On the other hand, we reveal the means by which N-terminal domain additions enable oligomerization to provide distinct quaternary dynamics in LysR-type transcriptional regulators (LTTRs). The remarkable modularity of these proteins permits us to confirm and expand the evolvability theory for the primordial core structure during a long-period evolution, which is largely facilitated, seemingly, by genetic recombination events.

In this study, our focus was on proteins composed of two globular lobes (bilobed), each of three layers (//), articulated around a central -sheet hinge (Fig. 1B). This focus meant that not all multidomain architectures harboring the PBP-like II domains were included. To investigate the structural dynamics of the selected proteins, we constructed phylogenetic trees based on both sequence and structural information (Fig. 1C). This analysis indicated that the proteins evolved from a common ancestor that diversified into seven distinct structural classes A to G (Fig. 1 C and D), members of which are found throughout all kingdoms of life, in which some are even present in viruses (SI Appendix, Fig. S1A and Datasets S1 and S2). They have a consensus structure (Fig. 1B, Fig. 1 D, Top, and Dataset S3), which we have dubbed the cherry-core (hereafter CC, and proteins harboring this core, cherry-core proteins, CCPs) because of the bilobed structures resemblance to a cherry (SI Appendix, Fig. S2A), that contains two PBP-like II domains (70). According to ECOD database, the two domains of CCPs belong to the same X-, H-, and T-groups (PBP-like II; Dataset S3), supporting their common ancestry (38). In the consensus structure (Fig. 1 C and D), domains D1 and D2 adopt a face-to-face mirror-fashioned geometry with the active site, which is typically a ligand-binding site, located at their interface (SI Appendix, Fig. S2B).

Most of the selected proteins have distinct segments linked N-terminally to the CC (Fig. 1 C and D). Class A proteins have the addition of a LysR winged helix-turn-helix (HTH)type DNA-binding domain (ECOD: X-, H-group: HTH, T-group: Winged; Fig. 1C). This element has been shown to be responsible for oligomerization and binding to promoter DNA (7173). Most proteins of classes B through D, and F and G contain N-terminal localization signals for export via the general secretion (Sec) or the twin-arginine translocation (Tat) pathway (7477). The presence of these signal peptides was obtained from UniProtKB (78) and verified manually by inspecting all 600 protein sequences with PRED-TAT, prediction of twin-arginine and secretory signal peptides (79). Class E proteins are predominantly cytosolic and lack an N-terminal signal peptide.

In addition, classes B through G have distinct C-terminal structural embellishments, hereafter termed C-tails (Fig. 1D). For example, Helical-tail 1 (H1) is common to all classes except C, and importantly, it has a similar placement in the three-dimensional architecture of the proteins. Other C-tails are unique to a specific class, such as the Helical-tails G (HG1 and 2), present only in class G proteins.

The ligand specificity and function of the CCPs, as documented in UniProtKB (78), were found to correlate with the assigned structural class (SI Appendix, Fig. S1A). Class A proteins are bacterial transcription factors of the LTTR family. Class E proteins are predominantly eukaryotic single-turnover enzymes. The majority of the remainder (class B through D, F, and G) are found in prokaryotes and associate with the translocator domains of ABC transporters or with the membrane-embedded domains of chemoreceptors, in which they mediate unidirectional solute transport and signal transduction, respectively.

To investigate what role the distinct C-tails of these proteins might have played in structural dynamics and the evolution of new functions, we examined CCPs for which high-resolution structures of unliganded (apo) and liganded (holo) states were available (Fig. 2 and SI Appendix, Table S1). Interestingly, class A and E proteins, in most cases, exhibit nearly identical apo and holo structures. They also display the widest variety of substrates with little chemical structure similarity (SI Appendix, Fig. S3). For most members of the other classes, D1 and D2 of the CC undergo a rigid body rotation of varying degrees (Fig. 2 and SI Appendix, Table S1). For the solute-binding proteins, this mode of substrate binding has been termed the Venus-Fly Trap mechanism (80). These proteins recognize ligands with a specific pharmacophore (81): amino acids, ethanolamines, phosphonates, ironphosphate complexes, and carbohydrates are recognized by classes B, C, D, F, and G, respectively (SI Appendix, Fig. S3). Another striking difference is that proteins in classes B through G are monomeric, whereas those in class A are oligomeric (see Fig. 4C).

Structures of CCPs highlighting their C-tails. Structures (Top) and schematics (Bottom) of the identified structural classes (A through G). The apo and holo structures of the indicated CCPs are superimposed to highlight the role of the C-tail. The domain to which the two structures were superimposed is represented in gray (D1 in A through E and D2 in F and G), whereas the one that is displaced relative to the C-tail in the open state is represented in green. Arrows and schematics indicate domain displacement (SI Appendix, Table S1). Interaction contact maps are presented in SI Appendix, Table S2. The most prominent contacts between the indicated secondary structure elements of the C-tail and D1 (F, G) or D2 (B through E) that stabilize the open state are indicated by a yellow dashed line. The PDB codes and protein names are indicated.

To investigate structural dynamics of the CCPs, we first used smFRET (12, 6668) to probe Tier-0 dynamics at near-physiological conditions in aqueous buffer at room temperature. One representative protein from each class was investigated: the effector binding domain (EBD) of CynR, representing the CC of full-length CynR (72) (class A), SBD2 (82) (class B), OpuAC (83) (class C), PhnD (84) (class D), CmpA (85) (class E), FbpA (86) (class F), and MalE (87) (class G). For these experiments, D1 and D2 were stochastically labeled with donor and acceptor fluorophores via cysteines that were substituted for nonconserved and surface-exposed residues, one in each domain (Fig. 3A). Fluorophore labeling was performed by maleimidethiol conjugation (22, 88). Labeling positions were selected based on the crystal structures to show large changes in separation between the apo and holo states. smFRET was performed by confocal microscopy with alternating laser excitation (ALEX) (66).

Monitoring structural changes and ligand binding in CCPs using smFRET and ITC. (A) Schematic of the experimental strategy to monitor structural states by FRET efficiency via stochastic labeling of D1 and D2 with donor and acceptor fluorophores. (BH) Solution-based apparent FRET efficiency histograms in the absence (Top) or presence (Bottom) of saturating ligand concentrations for the indicated proteins. Gray bars are experimental data, and solid line is the fit. Centre position of Gaussian fits are given in SI Appendix, Table S5. (I and J) Binding isotherms of the calorimetric titration of azide (I) and calcium carbonate (J) to CynR (I) or CmpA (J), respectively, with the indicated thermodynamic parameters. For the apo condition of FbpA (E), the sample was treated extensively with citrate, as Iron (III) and the synergistic anion carbonate required for high-affinity binding to FbpA are removed efficiently by citrate treatment at low pH (92). According to high-resolution structural data (93), both Ca2+ and CO3 are present in the CmpA binding cleft. Indeed, we observed heat release upon titration of Ca2+ to a CO3 bound CmpA. Data points represent the heat of reaction per injection, and the line is the fit.

As predicted by our structural analysis (Fig. 2 and SI Appendix, Table S1) and in line with our previous observations (89), the FRET efficiency histograms and fitted distributions (Fig. 3 BF) shifted toward higher FRET efficiency (E) values upon addition of saturating concentrations of ligand, for SBD2, OpuAC, PhnD, FbpA, and MalE. This indicates that in the apo state, the donor and acceptor dyes are further apart (Fig. 3 BF, Upper, low FRET) compared to the holo state (Fig. 3 BF, Lower, high FRET). Thus, our data suggest that ligand binding drives Tier-0 dynamics in these CCPs.

In contrast, the distributions of class A and E proteins [i.e., CynR (90) and CmpA (85), respectively] (Fig. 3 G and H), were virtually identical in the absence or presence of saturating ligand concentrations. Ligand binding was confirmed via isothermal titration calorimetry (ITC) showing binding affinities of both proteins in the micromolar range (Fig. 3 I and J). Thus, we conclude that the CC of class A and E proteins lack Tier-0 dynamics on the probed reaction coordinates for the selected FRETdistance pairs in contrast to the other structural classes.

Interestingly, this observation is in line with the known biological function. Class E proteins are predominantly single-turnover enzymes (SI Appendix, Fig. S1A), for which the rigidity of their active site is a prerequisite for catalysis (SI Appendix, Fig. S4A) (91). As previously suggested by us and others, Tier-0 dynamics in periplasmic-binding proteins (classes B, C, D, F, and G) are utilized in the regulation of transport in ABC importers (Discussion). A remaining question is, however, how ligand binding in class A proteins triggers transcriptional processes without major structural changes related to ligand binding.

The DNA-binding domain of class A proteins typically comprises a 58-aa HTH motif. This is followed by a 20-aa-long helix that provides a dimerization interface and a connecting loop that links the DNA-binding domain to the CC (Fig. 4A). The CC acts as the tetramerization interface within the full-length CCP or the dimerization interface within the EBD (Fig. 4B and SI Appendix, Fig. S4 BE). Indeed, by using size exclusion chromatography with multi angle light scattering (SEC-MALS) we observed that (full-length) CynR is tetrameric, whereas its CC is dimeric (Fig. 4C).

CynR tertiary and quaternary assemblies probed by MALS and HDX-MS. (A) Crystal structure of the CynR CC (PDB: 2HXR) with colored secondary structure elements discussed in the text that are critical for its quaternary dynamics (Top). A homology model of full-length CynR obtained from the SWISS-MODEL server with CbnR (PDB: 1IZ1) as a template (Bottom). The HTH domain is colored green, and the loop connecting it to the CC splitpea. W64 at the tip of the dimerization helix is indicated with an arrow. (B, Top) Schematic representation of the CC of CynR using the same color-coding as in A. (Middle) Schematic representation of the CynR homology model, with one of the protomers in the compact and the other in the extended configuration. The CC of the CynR protomers self-associate to form dimers or, for the full-length protein, tetramers. For clarity, two of the protomers have been omitted. (Bottom) The tetrameric assembly formed by the self-association of the dimerization helices and the CC. Of the two interacting protomers, one is present in the compact configuration, whereas the other is extended. The two protomers with the compact configuration are shown at the top of the plane, whereas those that are extended are at the bottom. (C) SEC-MALS analysis of full-length and CC of CynR (3 M). Ultraviolet (UV) traces of the chromatograms were superimposed on the measured mass (black cycles). (D) Structural dynamics of full-length CynR in the absence of ligand and DNA by HDX-MS. Deuterium uptake values are reported for the incubation times in deuterated buffer and expressed relative to the fully deuterated control. These values are mapped onto the CynR homology model (as in A), using the indicated color gradient. Proline residues as well as the first residue of each peptide were excluded from mapping, as they do not contribute to the observed D-uptake. (E) Scatter plot visualization of the statistical analysis of D-uptake differences between the apo and holo states of full-length CynR (Upper) and the CC of CynR (Bottom). Three statistical criteria were used (SI Appendix, Material and Method), as described previously (98). Statistically significant differences would appear as black spheres (indicating that D-uptake > 2SD for a specific peptide), lying outside the 99% confidence threshold (1-P 0.99; indicated on y-axis) and outside the 4 pooled average SD cut-off (indicated on x-axis; value given on the right).

To determine whether ligand binding to CynR resulted in structural changes that were not detectable along our selected reaction coordinate or that were too small or fast for smFRET (Fig. 3G), we performed HDX-MS. In contrast to smFRET, which reports on a single distance along a single reaction coordinate, HDX-MS probes structural dynamics at nearresidue level resolution, providing global insights into Tier-1 dynamics. HDX-MS detects the exchange of hydrogens with deuterium at solvent-accessible and nonhydrogen-bonded backbone amides (94, 95). Hydrogens involved in stabilizing the secondary, tertiary, or quaternary structure of a protein via hydrogen bonds are exchanged more slowly through structural transitions that disrupt these bonds. Deuterium incorporation into the protein can then be determined, following proteolysis, by MS. The mass difference between hydrogen (1H) and deuterium (2H) results in a mass shift between nondeuterated and deuterated peptides that is a measure of the number of exchanged hydrogens (65, 96, 97).

For such investigations, the CC of CynR or full-length CynR were isotopically labeled (pD, 7.4; 25C) for different time periods (10 to 105 sec) either in free or DNA-bound states, and the peptic fragments were identified by MS (Fig. 4D and Dataset S4). For each peptide, the fraction of deuterium uptake relative to the maximum determined deuterium incorporation was calculated (Dataset S4). The data reveal a rigid character of the CC of CynR, whereas the dimerization helix and the rest of the DNA-binding domain turned out to be more flexible (Fig. 4D). To identify the regions in which pronounced structural changes were induced by azide binding, we performed comparative HDX-MS. For this, we determined the difference in deuterium uptake (D) for each peptide between different conditions (e.g., CynR apo versus holo states). Observed differences would indicate a decrease (protection) or an increase (deprotection) of deuterium uptake upon azide binding (Dataset S4). No statistically significant change in the deuterium uptake was observed (Fig. 4E). From this, we can conclude that no detectable change in structural dynamics is induced by the ligand in the CC of CynR in its free or DNA-bound form (Fig. 4E and Dataset S4). This includes Tier-0 dynamics (detected by smFRET and HDX-MS) and Tier-1 or quaternary dynamics (both detected by HDX-MS).

From our results on the selected CCPs, only those with an asymmetric C-tail display Tier-0 dynamics (Figs. 2 and 3). To investigate how the C-tails introduced Tier-0 dynamics to the CC, we compared crystal structures of the apo and holo states of the CCPs to identify interactions between their C-tails and the CC. Fig. 2 summarizes the results of these comparisons. Interestingly, we found that the holo structures are similar for all CCPs, but the apo states are class specific. Contact mapping of the interactions between the CC and C-tail using the protein interaction calculator web-server (99) showed that the number and characteristics of the interactions depends on the structural class (Fig. 2 and SI Appendix, Table S2). Strikingly, these interactions stabilize predominantly the open state of the CC, the only exception being for classes A (C-tail-less) and E. In the former cases, stabilization is associated with an asymmetrical placement of the C-tail with respect to D1 and D2. In contrast, the C-tail of class E (predominantly H3 and HE1; Fig. 2) is placed symmetrically around D1 and D2 and thus cannot provide the required structural asymmetry needed to create a stable open state. The interactions of the C-tail to stabilize the open state involve the consensus CC-helices of D1 and D2 (Fig. 2 and SI Appendix, Table S2). In classes B, C, and D, such interactions involve D2, whereas D1 is contacted in classes F and G. These asymmetrical interactions create active sites with distinct geometries (Fig. 2 and SI Appendix, Fig. S2).

In alternative phylogenetic trees, based on the protein sequence using either D1/D2-domains or the C-tail, the clustering remains similar (SI Appendix, Fig. S1 BD and Datasets S1 and S2), indicating that D1/D2 (CC domains) and the C-tail of a specific class coevolved to be part of the same polypeptide. This is in line with the role of the C-tail to interact with one specific domain, D1 or D2, to stabilize the open structural state.

To confirm the role of the C-tail interactions with D1 or D2 for stabilization of the open state, we manipulated the relevant ones (Fig. 2 and SI Appendix, Table S2) in SBD2, SBD1, and MalE (Fig. 5 and SI Appendix, Figs. S5 and S6). The impact was tested via assessing structural states and ligand-binding affinities and monitored in smFRET experiments. Test cases were selected from class B and G, as these CCP classes are only remotely related by the first major clade in the evolutionary trees (Fig. 1C), and their open state is stabilized by distinct helices within the C-tails (Fig. 2), contacting either D2 (class B) or D1 (class G).

Experimental verification of the role of C-tail interactions in stabilizing the open conformation of SBD2 and MalE by smFRET and HDX-MS. (A) Dotted rectangles (Left) on the SBD2 (in A, PDB: 4KR5) structure highlights the critical contact region between the C-tail and the CC that stabilize the open state. Zoom in of rectangle regions (Middle and Right) depicting interactions between the C-tail helix H1 and D2 in the indicated apo (open) or holo (closed) states. Distances () between L480 and P419 are shown as black dotted lines. (B and C) Solution-based apparent FRET efficiency histograms of SBD2 (B) and SBD2 (L480A) (C) at different conditions as indicated. (D) Fraction of the closed state (high-FRET state) of SBD2 and the indicated derivative as a function of glutamine concentration. (E) As in panel (A) for the MalE (PDB: 1OMP) structure with the indicated secondary structure elements and critical contacts. (F and G) Solution-based apparent FRET efficiency histograms of MalE (F) and MalE (M321K) (G) at different conditions as indicated. (H) Fraction of the closed state (high-FRET state) of MalE and its derivatives as a function of maltose concentration. (I) Maltose release from MalE (M321K) over time determined by solution-based smFRET (reference detailed values in SI Appendix, Fig. S6). Data points (panels D and H) and gray bars (panels B, C, F, G, and I) are the experimental data, and the solid line is the fit. (J) Map of regions in MalE structure that show statistically significant increase in deuterium uptake caused by M321K (numerical values and complete statistical analysis presented in Dataset S5). n = 3. (K and L) Deuterium uptake for the indicated MalE C-tail helices. SDs (SD) are shown in the deuterium uptake plots.

In SBD2, a hydrophobic interaction between L480 in the C-tail and P419 in D2 was weakened by substitution of L480 with alanine (L480A; Fig. 5A and SI Appendix, Table S2). The mutation resulted in the appearance of a subpopulation of molecules (20%) that were in the closed state in the absence of glutamine (Fig. 5 C versus B). To rule out the possibility that this might have been due to an artifact introduced by the choice of fluorophores, a second pair was tested, which showed a comparable result (SI Appendix, Fig. S5 A and B). We also examined whether residual endogenous ligand might account for this subpopulation by performing smFRET measurements on diluted samples, but these experiments displayed subpopulations of a similar size (SI Appendix, Fig. S5C). A closed-unliganded conformation was also observed for SBD2 previously but with a much lower abundance (1%). Detection of this small subpopulation required the use of confocal scanning microscopy (89), as populations <5% cannot be detected reliably with ALEX spectroscopy. We also observed small differences in the mean E values for apo and holo states of SBD2 (L480A) as compared to SBD2, suggesting that the structural landscape had been altered (SI Appendix, Fig. S5B). Destabilizing the open state is in line with the 10-fold increase in glutamine-binding affinity of SBD2 (L480A) as compared to SBD2 (Kd of 209 64 nM and 1,990 130 nM, respectively; Fig. 5D).

Determination of the dissociation constant (Kd) by smFRET measurements reports on the stability of the open state via Kd(1+exp()), where =1/kbT, kb is the Boltzmann constant, T is the absolute temperature, and =GC-GO is the conformational (free) energy difference, where GC and GO are the (free) energies of the closed and open structural states without ligand bound, respectively (100). Thus, destabilizing the open state will decrease Kd and vice versa. From the percentages of open and closed in the absence of ligand, we obtain =1.4 and =4.4kBT for SBD2 (89) and SBD2 (L480A) (Fig. 5 BD and SI Appendix, Fig. S5 AC), respectively. Based on these results, the Kd value would be expected to decrease by 16 folds due to the L480A mutation, in close agreement with the estimated 10-fold difference. Similar trials on SBD1 did not show this trend (SI Appendix, Fig. S5 DF), as the mutation had no impact on the structural dynamics of SBD1 (I249A). The absence of a subpopulation of molecules in the closed state at apoprotein conditions (alike SBD2) is based on the inability to evaluate all stabilizing open state interactions (SI Appendix, Table S2, compare SBD1 versus SBD2) so as to abolish the relevant ones. This is likely due to the fact that H1 residues of SBD1 (SI Appendix, Fig. S5G) participating in interprotomer contacts arose from crystallographic conditions (SI Appendix, Fig. S5H).

For MalE, we constructed the derivatives MalE (M321A) (101) and MalE (M321K), with weakened interactions between the C-tail HG1 and D1, by disrupting the hydrophobic interaction of M321 with Y90 and F92 and the aromatic sulfur interactions with Y90 (Fig. 5E and SI Appendix, Table S2). For MalE (M321A), the stabilizing interactions of M321 are partially abolished. This resulted in an eightfold increase of maltose affinity (Kd from 2,400 400 nM in MalE to 300 50 nM in MalE [M321A] [Fig. 5H and SI Appendix, Fig. S6]). An even-stronger effect was observed for MalE (M321K) with an affinity enhanced by 3,000-fold (Kd of 0.81 0.15 nM; Fig. 5 G versus F and Fig. 5H). Shilton and coworkers have also proposed the hydrophobic interactions of M321 as being important structural determinants of the open state (101), affecting the affinity of MalE for maltose, in agreement with our results.

We next compared the lifetime of the closed, maltose-bound conformation of MalE (M321K) with that of MalE. Addition of 10 nM maltose allowed MalE (M321K) to occupy the closed state exclusively (SI Appendix, Fig. S6H). A total 20 M unlabeled MalE (M321K) protein was subsequently added to scavenge maltose, which is stochastically released from the labeled protein. In a time-course experiment, the decrease in the population of closed, maltose-bound MalE (M321K) was then followed as a function of time (102) (Fig. 5I and SI Appendix, Fig. S6H). From these experiments, we established that the lifetime of the closed, maltose-bound conformation of MalE (M321K) was 122 12 s. This value was 2,500-fold higher than the value determined for MalE (0.048 0.010 s; SI Appendix, Fig. S6E). This result is consistent with the observed increase affinity of the derivatives for maltose.

In the above-described MalE derivatives, the structural basis for the destabilization of the open state could not be addressed mechanistically. Our smFRET experiments report on Tier-0 dynamics that affect the proteins tertiary structure (Fig. 1A). Considering that we observed differences in the Tier-0 dynamics, as a consequence of C-tail-D1/D2 destabilization, only for SBD2, we performed comparative HDX-MS (Dataset S5) to monitor changes in the secondary structure of MalE in comparison to MalE (M321K). For this, we determined the difference in deuterium uptake (D) for each peptide between MalE and MalE (M321K) in their apo states (Fig. 5J). Remarkably, comparative HDX-MS indicates that the differences between MalE and MalE (M321K) are localized almost exclusively at the C-tail and specifically in regions interacting with D1 (Dataset S5 and Fig. 5J). The D-uptake of the C-tail helices H2/HG1 that are critical for stabilizing the open state (Fig. 5E and SI Appendix, Table S2) denotes that their rigidity was significantly reduced in the MalE derivative (Fig. 5 K and L). As might be expected, reduced rigidity occurred also in a region within the CC containing Y90 and F92. Notably, the same regions become allosterically destabilized in MalE upon maltose binding (Dataset S5). These results support the idea that the mutation leads to a weaker interaction between the C-tail and the CC resulting in a destabilization of the open state, because these C-tail elements and the region containing contact residues were found to be more flexible and solvent exposed.

Taken together, we postulate that ligands attenuate the stability of the open state in the CCPs, adopting multiple allosteric models for signal propagation (103): either via Tier-0 (SBD2 [Fig. 5 C and D]) or Tier1/2 (MalE [Fig. 5 G, H, K, and L]) dynamics.

Common (structural) origin represents the hallmark of Darwinian evolution. Homology or descent from a common ancestor is often deduced from similarities in protein sequences or better from structures, as the latter are more conserved during evolution (104). However, similar structures can originate from divergent, convergent or parallel evolution (105). The most common tricks nature uses to vary a protein domain are the following: -strand invasion/withdrawal, insertions/deletions/substitutions of secondary structure elements, domain flip/swaps, and circular permutations (106, 107). The currently established evolvability theory relies on investigations involving a fixed-length polypeptide chain by observing the effects of sequence variations, accomplished primarily by directed evolutionary approaches or by investigating closely related functional homologs. The functional promiscuity originating from structural variability is altered by the few amino acid modifications that can yield alterations of local structural fluctuations. For this reason, this theory can well explain protein evolution during short time periods.

In this study, we focused on the analysis of structures that have diverged over longer evolutionary periods. We analyzed a group of 600 proteins that share a core structure with the same topology of secondary structure elements giving rise to identical three-dimensional structures. The structure was predominantly varied by terminal embellishments and exhibits detectable sequence identity, used for constructing the sequence-based phylogenetic trees (SI Appendix, Fig. S1). The identified proteins likely emerged from divergent long-term evolution from a common ancestor, which spreads throughout the tree of life. This common ancestor is seemingly represented by the consensus core structure (CC; SI Appendix, Fig. S2) and encountered within the type-II class of PBPs (70, 108). As proposed previously (70), the CC derived possibly from a gene duplication of a PBP-like II domain (ECOD; Dataset S3) connected by a -sheet (Fig. 6A).

Model for the evolution of the cherry-core and hypothetical energetic funnels. (A) A gene duplication of a PBP-like II domain gave rise to the CC, being composed of a unique closed structural state, represented by a single well in the energetic funnel. The function of the CC was uniquely binding. Extant proteins acquired different extensions that altered their localization and dynamics and by that their function and specificity. (B) An N-terminal signal peptide and an asymmetrical C-terminal tail generated extracytoplasmic proteins evolving an additional open state. The different flavors of open states conferred distinct substrate specificities. The two states of CCPs, predominantly, signal substrate transport by their association to the membrane-embedded translocator domains of (ABC) transporters. (C) A symmetrical C-tail rigidified the closed state and yielded primarily single-turnover enzymes. (D) The N-terminal domain addition of a flexible dimerization helix and a DNA-binding motif (HTH-type) conferred distinct oligomeric assemblies with different quaternary dynamics, yielding transcription factors. (E) The different flavors of open states, conferred distinct substrate specificities. For details, refer to SI Appendix, Fig. S8.

When the CC is fused N-terminally to a signal peptide and C-terminally to an asymmetric C-tail, the CC acts as an extracytoplasmic monomeric protein that associates with the translocator domains of ABC transporters or with chemoreceptors (SI Appendix, Fig. S1 and Fig. 6B). The two structural states are apo-open versus holo-closed and originate from Tier-0 dynamics, which are critical determinants of their biological function: the membrane-embedded partners can discriminate between open versus closed states to activate or inactivate a biological process (88, 109112), such as solute transport. These structural transitions rely on the generation of an open state of the CC, accomplished by the C-tail, through its asymmetric interactions with either of the domains of the CC (D1 or D2; Fig. 2). Evidently, the open state is stabilized by such enthalpic contributions and destabilized by mutations or ligand binding that increase the flexibility of the interacting regions (Fig. 5J) (the C-tail elements with either D2 [classes B, C, and D] or D1 [classes F and G] [Fig. 2]). Seemingly, entropic contributions (protein conformational entropy) bias the structural equilibrium toward the closed state. Given the fact that the holo is the closed state driven by ligand binding (89), we anticipate that the interactions of the ligand with the CC cleft allosterically induce order-to-disorder transitions to alter the structural equilibrium. Depending on the placement of the asymmetric C-tails in the three-dimensional space, we identified five different flavors of open states, establishing distinct geometries of active sites; all resembling a triangle distinctly oriented in space (Figs. 2 and 6E). We verified that each active site geometry can recognize a specific chemical structure (SI Appendix, Fig. S3), in full agreement with ligand binding triggering closing.

In contrast to the C-tail, the presence of the N-terminal signal peptide does not affect the Tier-0 states of MalE (MalE versus proMalE; SI Appendix, Table S5). However, to render protein trafficking to extracytoplasmic locations possible, the presence of the signal peptide is known to delay MalE folding (113, 114). By that, preproteins are allowed to be secreted by the protein translocase (115).

The evolvability theory of Tawfik and coworkers (29, 57) explains the functional promiscuity encountered within the different structural classes. We selected two closely related proteins in our phylogenetic tree in class B (Fig. 1C) to illustrate this (Dataset S2, marked red asterisk). SBD1 (Protein Data Bank [PDB]: 4LA9) mediates the unidirectional transport of two substrates (glutamine and asparagine), whereas SBD2 (PDB: 4KR5) transports one of them (glutamine), though it captures it with higher affinity (88). SBD1 can be transformed to bind glutamine with higher affinity like SBD2 by mutating three amino acids (82). Clearly, functional promiscuity is traded seemingly with ligand specificity (62). However, only the modularity notion introduced in this study can explain a greater ligand-functional promiscuity. Class B proteins recognizing and mediating amino acid transport (SI Appendix, Fig. S1) would gain tertiary structural variability by acquiring four more helical elements at their C termini (H2, HG1, H4, and HG2; Fig. 1 C and D) (Fig. 6). This would potentially allow to switch them to class G to bind and mediate transport of carbohydrates (SI Appendix, Fig. S1A). According to another evolutionary trajectory, class B proteins, by acquiring helices H3, HE1, and HE2, could divert to class E and switch from transport-related proteins to enzymes (Fig. 6) by restricting their tertiary structural variability to a unique closed state. An extreme case of functional divergence has been experimentally verified in a recent study that restored the evolutionary history of the enzyme cyclohexadienyl dehydratase (116), a class B member according to our classification. The reconstructed ancestor of this enzyme was a highly promiscuous, transport-related protein possessing the open-unliganded and the closed-liganded structural states and was able to bind four different cationic amino acids. On the other side, cyclohexadienyl dehydratase binds a single substrate and forms a unique closed state for its catalytic function, since it is believed that structural sampling represents a constrain for catalytic activities (117, 118). Our study can now explain the means by which the addition of modular elements to a conserved core structure diversify its function (i.e., by modulating its structural landscape).

When the CC is combined C-terminally to a symmetric C-tail during evolution (Fig. 6C), the CC either operates as an enzyme or is associated with ABC transporters (SI Appendix, Fig. S1). Those proteins are present in a unique (apo and holo) closed state (Fig. 2), as asymmetrical interactions are an essential prerequisite for open state formation. Interestingly, these ABC transporterassociated proteins are both interacting with the actual translocator from the extracytoplasmic side (like the classes B, C, D, F, and G; described in the previous paragraph and in such a case also possess an N-terminal signal peptide) but are also tethered covalently to the translocator ATPase motor domains (93). As the switching (open to closed) behaviorto activate the transport cycleis missing (88, 89, 109), the noncanonical arrangement of these ABC transporters could only trigger transport activation using a yet-uncharacterized mechanism. On the other hand, the lack of Tier-0 dynamics observed in the single-turnover enzymes is conforming to their enzymatic mechanism demanding an extremely rigid active site (SI Appendix, Fig. S4A) (91). Evidently, the rigidity required for the chemistry is so dramatic that the flexibility of a noncovalently (to the polypeptide chain) bound histidine to the cleft would render the reaction unproductive.

Lastly, the CC bearing an N-terminal domain harboring the HTH-type DNA-binding domain but no C-tail (Fig. 6D) yields transcription factors of the LTTR family. Apparently, in such a case, Tier-0 dynamics of the CC are not required for function (Fig. 3G). The rigidity of the CC (Fig. 4D) is evidently required for the stability of the quaternary assemblies, as those have large cavities and holes (SI Appendix, Fig. S7) (119124).

Our data and analysis gave insights into the steps taken during evolution to shape the CC into functional ligand receptorassociated proteins (classes B, C, D, F, and G) but also one-turn enzymes (class E). Yet, how the ligand-driven signal propagates within the LTTR family, such as CynR, remains largely elusive.

The CC of CynR constitutes only one part of its structure (i.e., the sensory EBD) of a full-length transcription factor of the LTTR family with an additional winged HTH-type DNA-binding domain (Fig. 4 A and B). Although only little direct experimental evidence is available, it has been proposed that changes in the tetrameric assembly of LysR-type transcription factors can be induced by ligand binding (72, 125). These quaternary dynamics were suggested to activate transcription by a transition from a bent DNA (transcription OFF) to an unbent state (transcription ON) (72, 125). Our smFRET and HDX results (Figs. 3G and 4E), however, did not reveal any detectable changes of the CC upon azide binding. This suggests that ligand-driven structural changes within the CC protomers are minor and might thus be very hard to detect.

To gain insights into potential quaternary dynamics, we analyzed the available high-resolution structures of the oligomeric assemblies belonging to the LysR transcription factors (119124) to identify the evolutionary relevant ones by Evolutionary ProteinProtein Interface Classifier (EPIC) (126, 127). Subsequently, we modeled the CynR sequence after these structures in the presence or absence of DNA (SI Appendix, Fig. S7). The structural basis of the distinct tetrameric assemblies is the positioning of the dimerization helix with respect to the CC, dictated by the connecting loop (Fig. 4 A and B). In the extreme case that an additional helix (RD1-CH5; Dataset S3) is linked to and displaces the connecting loop, an octameric assembly is obtained (SI Appendix, Fig. S7H). To shed light on this, we monitored intrinsic Trp fluorescence during thermal melting (SI Appendix, Table S3). From the two tryptophans of CynR, one present in the dimerization helix (64 aa) and the other on D1 (274 aa) of the CC (Fig. 4 A and B), only the former one contributes to a fluorescent signal (SI Appendix, Table S3). Under DNA-free apo conditions, CynR displays a Tm(app) 55C that is significantly destabilized (6C) after binding to DNA. Addition of azide at free or DNA-bound CynR causes a ligand-characteristic signature throughout the Trp-temperature spectrums, giving rise to a secondary Tm(app) at 27C in both cases (SI Appendix, Table S3).

To understand the signal propagation originating from ligand binding, we inspected the available structural information (SI Appendix, Fig. S7). In agreement with the experimental findings (Figs. 3G and 4E), our structural analysis indicated that D1 and D2 motions are not required (SI Appendix, Table S4) for the oligomeric assemblies of the same transcription factor (OxyR) to differ dramatically in order to trigger structural changes on DNA (SI Appendix, Fig. S7 EG). What varies between those assemblies is the orderdisorder of specific secondary structure elements within D2 of the CC (S4 till 5; Dataset S3). We anticipate that such changes in the flexibility of secondary structure elements are induced by ligand binding, like in the case of MalE (Fig. 5 JL).

We conclude that signal propagation in CCPs comprising large D1/D2 rearrangements (Fig. 2; classes B, C, D, F, and G) driven by the C-tail/D1 and D2 interactions involves bending/unbending of the spring-like hinge (108). On the other side, propagation in class A is initiated by small/localized rearrangements of secondary structure element within the CC somehow transmitted to the N-terminal HTH domain leading to global quaternary structural changes (SI Appendix, Figs. S7 and S8). Additional analysis regarding the molecular mechanisms of LTTR-transcriptional regulators will be the subject of future studies.

Fig. 6 summarizes our proposed evolutionary path taken by the CC proteins over long time periods. The terminal modules impact and finetune the multi-Tier structural dynamics as can be described by the folding funnel model (8). The CCPs with asymmetric C-tails displaying Tier-0 dynamics (i.e., open and closed states) have the two characteristic wells in the funnel separated by a large energetic barrier (Fig. 6B and SI Appendix, Fig. S8C). For that reason, such proteins are found predominantly in the lowestenergetic level open state (apo energetic funnel; SI Appendix, Fig. S8C), with infrequent transitions to the closed one (Fig. 5B). Only in the SBD1/2 proteins (class B), 1% occurrence of a closed state has been experimentally observed (89). By destabilizing the open state of SBD2, we obtained a 20% occurrence of a closed state (Fig. 5C). As in the holo state energetic funnel, the lowest energy state is the closed one (SI Appendix, Fig. S8C); addition of the ligand shifts the equilibrium toward the closed state. Since the dissociation constant (KD) derives from the difference between the lowest energetic levels of the apo and holo funnels (SI Appendix, Fig. S8), destabilization of the C-tail (Fig. 5 D and H) that directs the open state of the apo funnel at a higher energetic level leads to an increased affinity.

Alternatively, CCPs with a symmetric C-tail (Fig. 6C) have a single main energetic well, defining the unique closed structural state both in the apo or holo funnels (SI Appendix, Fig. S8D).

The CCPs with N-terminal domain additions (Fig. 6D) have multiple energetic funnels corresponding to oligomerization states (SI Appendix, Fig. S8E). The energetic funnel of such CCPs is having multiple minima corresponding to the many different arrangements of the flexible N-terminal domain with respect to the CC (SI Appendix, Fig. S8E). Self-association (oligomer formation) and/or binding to their partners/ligands deepens specific wells that are required for function.

We anticipate the energetic funnel of the primordial consensus CC to be extremely rugged, with small energetic barriers between the wells, with a single well somewhat deeper (corresponding to the closed state), thus allowing sampling of multiple structural states (SI Appendix, Fig. S8A). Such a structural variability would lead to increased substrate promiscuity, however, exploiting extremely weak interactions (i.e., low binding affinities; SI Appendix, Fig. S8B).

Clearly, the structural promiscuity achieved by the modularity introduced in this study, expands the protein evolvability theory and establishes the notion that in order to comprehend protein evolution, it is essential to decode the energetic funnel of structural homologs. Structural elements or even domains added alike Lego bricks to a structural core, being the Lego Board, trigger distinct evolutionary trajectories.

Detailed materials and methods are included in SI Appendix, Material and Method. All proteins were expressed in Escherichia coli cells (BL21 DE3 or BL21 pLysS DE3) and grew in Luria Bertani or Terrific Broth media. Purification was based on affinity [Ni-NTA (Qiagen)], anion exchange [Q Sepharose (GE Healthcare)], and/or size-exclusion [Hi Load 26/60 Superdex 200 (GE Healthcare)] chromatography. Samples used for analysis (ITC, smFRET, HDX-MS, etc.) were single monodisperse peaks. Phylogenetic, structural analysis, alignments, and protein visualization were accomplished by widely accepted procedures via freely available software or servers: Structure similarity search-PDB webserver (128, 129), PDBeFold (130), Dali server (131), multidimensional QR factorization of multiple sequence and structure alignments (132)/visual molecular dynamics 1.9.2 software package (133), Protein blast (134), DynDom domain motion server (135, 136), Protein Interaction calculator webserver (99), EPIC server (126, 127), ECOD database (38), ConSurf-DB server (137, 138), SWISS-MODEL server (139), and PyMOL (The PyMOL Molecular Graphics System, Version 2.0 Schrdinger, LLC). smFRET experiments were performed with a custom-made confocal ALEX microscope that has been previously described in the literature (22, 89) and HDX-MS by a nanoACQUITY Ultra Performance Liquid Chromatography System with HDX technology (Waters, United Kingdom). Data and statistical analyses were performed by established procedures described in detail in the literature.

This work was financed by an Netherlands Organization for Scientific Research (NWO Veni grant 722.012.012 to G.G.), an ERC Starting Grant (European Research Council StG No. 638536, SM-IMPORT to T.C.), Deutsche Forschungsgemeinschaft within GRK2062 (project C03 to T.C.), SFB863 (project A10 to M.Z. and A13 to T.C.), Research Foundation Flanders (FWO CARBS #G0C6814N to G.G. and A.E.). G.G. also acknowledges a fellowship from the European Molecular Biology Organization (EMBO long-term fellowship ALF 47-2012 to G.G.) and financial support by the Zernike Institute for Advanced Materials and the Rega foundation. Y.A.M. was supported by the Indonesia Endowment Fund for Education (Lembaga Pengelola Dana Pendidikan, Republik Indonesia, LPDP RI PhD scholarship). G.G. and Y.A.M. acknowledge financial support from the IMBB-FORTH (start-up grant to G.G.). N.Z. acknowledges an Alexander von Humboldt postdoctoral fellowship. N.E. acknowledges a fellowship from the Marie Sklodowska Curie Action (MSCA SoE FWO (195872). R.X. was supported by the Chinese Scholarship Council grant. T.C. was supported by the German Academic Exchange Service, Center of Nanoscience Munich, LMU excellent and the Center for Integrated Protein Science Munich. We thank Eitan Lerner for useful comments and critical reading of the manuscript, Monique Wiertsema for help with smFRET experiments, and Florence Husada for support with ITC experiments.

Author contributions: G.G., Y.A.M., M.d.B., and T.C. designed research; G.G., Y.A.M., M.d.B., D.A.G., A.T., K.T., N.Z., R.X., Y.S., and S.K. performed research; G.G., Y.A.M., M.d.B., D.A.G., K.T., R.X., Y.S., M.Z., A.D., C.P., and A.E. contributed new reagents/analytic tools; G.G., Y.A.M., M.d.B., D.A.G., N.Z., A.T., N.E., and T.C. analyzed data; and G.G., Y.A.M., D.A.G., and T.C. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

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The roadmap to an effective AI assurance ecosystem – extended version – GOV.UK

Introduction

This extended Roadmap is designed to complement the CDEIs Roadmap to an effective AI assurance ecosystem, which sets out the key steps, and the roles and responsibilities required to develop an effective, mature AI assurance ecosystem.

Where the short version of the roadmap is designed to be accessible as a quick-read for decision makers, this extended version incorporates further research and examples to provide a more detailed picture of the ecosystem and necessary steps forward.

Additionally, chapter 1 of this extended roadmap offers further context on the AI assurance process, delivering AI assurance, the role of AI assurance in broader AI governance and the roles and responsibilities for AI assurance. Chapter 3 discusses some of the ongoing tensions and challenges in a mature AI assurance ecosystem.

This extended roadmap will be valuable for readers interested in finding out more information about how to build an effective, mature assurance ecosystem for AI.

Data-driven technologies, such as artificial intelligence (AI), have the potential to bring about significant benefits for our economy and society. AI systems offer the opportunity to make existing processes faster and more effective, and in some sectors offer new tools for decision-making, analysis and operations.

AI is being harnessed across the economy, helping businesses to improve their day-to-day operations, such as achieving more efficient and adaptable supply chain management. AI has also enabled researchers to make a huge leap forward in solving one of biologys greatest challenges, the protein folding problem. This breakthrough could vastly accelerate efforts to understand the building blocks of cells, and could improve and speed up drug discovery. AI presents game changing opportunities in other sectors too, through the potential for operating an efficient and resilient green energy grid, as well as helping tackle misinformation on social media platforms.

However, AI systems also introduce risks that need to be managed. The autonomous, complex and scalable nature of AI systems (in particular, machine learning) pose risks beyond that of regular software. These features pose fundamental challenges to our existing methods for assessing and mitigating the risks of using digital technologies.

The autonomous nature of AI systems makes it difficult to assign accountability to individuals if harms occur; the complexity of AI systems often prevents users or affected individuals from explaining or understanding the link between a systems output or decision and its causes, providing further challenges to assigning accountability; and the scalability of AI makes it particularly difficult to define legitimate values and governance frameworks for a systems operation e.g. across social contexts or national jurisdictions.

As these technologies are more widely adopted, there is an increasing need for a range of actors to check that these tools are functioning as expected and demonstrate this to others. Without being able to assess the trustworthiness of an AI system against agreed criteria, buyers or users of AI systems will struggle to trust them to operate effectively, as intended. Furthermore they will have limited means of preventing or mitigating potential harms if a system is not in fact trustworthy.

Assurance as a service draws originally from the accounting profession, but has since been adapted to cover many areas such as cyber security and quality management. In these areas, mature ecosystems of assurance products and services enable people to understand whether systems are trustworthy. These products and services include: process and technical standards; repeatable audits; certification schemes; advisory and training services. For example, in financial accounting, auditing services provided by independent accountancy firms enable an assurance user to have confidence in the trustworthiness of the financial information presented by a company.

AI assurance services have the potential to play a distinctive and important role within AI governance. Its not enough to set out standards and rules about how we expect AI systems to be used. It is also important that we have trustworthy information about whether they are following those rules.

Assurance is important for assessing efficacy, for example through performance testing; addressing compliance with rules and regulations, for example performing an impact assessment to comply with data protection regulation; and also for assessing more open ended risks. In the latter category, rules and regulations cannot be relied upon to ensure that a system is trustworthy, more individual judgement is required. For example, assessing whether an individual decision made by an AI system is fair in a specific context.

By ensuring both trust in and the trustworthiness of AI systems, AI assurance will play an important enabling role in the development and deployment of AI, unlocking both the economic and social benefits of AI systems. Consumer trust in AI systems is crucial to widespread adoption, and trustworthiness is essential if systems are going to perform as expected and therefore bring the benefits we want without causing unexpected harm.

An effective AI assurance ecosystem is needed to coordinate appropriate responsibilities, assurance services, standards and regulations to ensure that those who need to trust AI have the sort of evidence they need to justify that trust. In other industries, we have seen healthy assurance ecosystems develop alongside professional services to support businesses from traditional accounting, to cybersecurity services. Encouraging a similar ecosystem to develop around AI in the UK would be a crucial boon to the economy.

For example, the UKs cyber security industry employed 43,000 full-time workers, and contributed nearly 4bn to the UK economy in 2019. More recently, research commissioned by the Open Data Institute (ODI) on the nascent but buoyant data assurance market found that 890 data assurance firms are now working in the UK with 30,000 staff. The research carried out by Frontier Economics and glass.ai noted that 58% of these firms incorporated in the last 10 years. Following this trend, AI assurance is likely to become a significant economic activity in its own right. AI assurance is an area in which the UK, with particular strengths in legal and professional services, has the potential to excel.

The roadmap provides a vision of what a mature ecosystem for AI assurance might look like in the UK and how the UK can achieve this vision. It builds on the CDEIs analysis of the current state of the AI assurance ecosystem and examples of other mature assurance ecosystems.

The first section of the roadmap looks at the role of AI assurance in ensuring trusted and trustworthy AI. We set out how assurance engagements can build justified trust in AI systems, drawing on insights from more mature assurance ecosystems, from product safety through to quality management and cyber security. We illustrate the structure of assurance engagements and highlight assurance tools relevant to AI and their applications for ensuring trusted and trustworthy AI systems. In the latter half of this section we zoom out to consider the role of assurance within the broader AI governance landscape and highlight the responsibilities of different actors for demonstrating trustworthiness and their needs for building trust in AI.

The second section sets out how an AI assurance ecosystem needs to develop to support responsible innovation and identifies six priority areas:

We set out the current state of the ecosystem and highlight the actions needed in each of these areas to achieve a vision for an effective, mature AI assurance ecosystem. Following this, we discuss the ongoing tensions that will need to be managed, as well as the promise and limits of assurance. We conclude by outlining the role that the CDEI will play in helping deliver this mature AI assurance ecosystem.

We have combined multiple research methods to build the evidence and analysis presented in this roadmap. We carried out literature and landscape reviews of the AI assurance ecosystem to ground our initial thinking and performed further desk research on a comparative analysis of mature assurance ecosystems. Based on this evidence, we drew on multidisciplinary research methods to build our analysis of AI assurance tools and the broader ecosystem.

Our desk-based research is supported by expert engagement, through workshops, interviews and discussions with a diverse range of expert researchers and practitioners. We have also drawn on practical experience from assurance pilot projects with organisations adopting or considering deploying AI systems, across both private sector organisations (in partnership with researchers from University College London), along with the CDEIs work with public sector organisations across recruitment, policing and defence.

Building and maintaining trust is crucial to realising the benefits of AI systems. If organisations dont trust AI systems, they will be less willing to adopt these technologies because they dont have the confidence that an AI system will actually work or benefit them. They might not adopt for fear of facing reputational damage and public backlash. Without trust, consumers will also be cautious about using data-driven technologies, as well as sharing the data that is needed to build them.

The difficulty is, however, that these stakeholders often have limited information, or lack the appropriate specialist knowledge to check and verify others claims to understand whether AI systems are actually deserving of their trust.

This is where assurance is important. Being assured is about having confidence or trust in something, for example a system or process, documentation, a product or an organisation. Assurance engagements require providing evidence - often via a trusted independent third party - to show that the AI system being assured is reliable and trustworthy.

The distinction between trust and trustworthiness is important here: when we talk about trustworthiness, we mean whether something is deserving of peoples trust. On the other hand, when we talk about trust, we mean whether something is actually trusted by someone. Someone might trust something, even if it is not in fact trustworthy.

A successful relationship built on justified trust requires both trust and trustworthiness:

Trust without trustworthiness = misplaced trust. If we trust technology or the organisations deploying a technology when they are not in fact trustworthy, we incur potential risks by misplacing our trust.

Trustworthy but not trusted = (unjustified) mistrust. If we fail to trust a technology or organisation which is in fact trustworthy, we incur the opportunity costs of not using good technology.

Fulfilling both of these requirements produces justified trust.

There are two key problems which organisations must overcome to build justified trust:

An information problem: Organisations need to reliably and consistently evaluate whether an AI system is trustworthy to provide the evidence base for whether or not people should trust it.

A communication problem: Organisations need to communicate their evidence to other assurance users and translate this evidence at the appropriate level of complexity so that they can direct their trust or distrust accordingly.

The value of assurance is overcoming both of these problems to enable justified trust.

Assurance requires measuring and evaluating a variety of information to show that the AI system being assured is reliable and trustworthy. This includes how these systems perform, how they are governed and managed, whether they are compliant with standards and regulations, and whether they will reliably operate as intended. Assurance provides the evidence required to demonstrate that a system is trustworthy.

Assurance engagements rely on clear metrics and standards against which organisations can communicate that their systems are effective, reliable and ethical. Assurance engagements therefore provide a process for (1) making and assessing verifiable claims to which organisations can be held accountable and (2) for communicating these claims to the relevant actors so that they can build justified trust, where a system is deserving of their trust.

This challenge of assessing the trustworthiness of systems, processes and organisations to build justified trust is not unique to AI. Across different mature assurance ecosystems, we can see how different assurance models have been developed and deployed to respond to different types of risks that arise in different environments. For example: from risks around professional integrity, qualifications and expertise in legal practice; to assuring operational safety and performance risks in safety critical industries, such as aviation or medicine.

The requirements for a robust assurance process are most clearly laid out in the accounting profession, although we see very similar characteristics in a range of mature assurance ecosystems.

In the accounting profession, the 5 elements of assurance are specified as:

The accounting model is helpful for thinking about the structure that AI assurance engagements need to take. The five elements help to ensure that information about the trustworthiness of different aspects of AI systems is reliably evaluated and communicated.

While this roadmap draws on the formal definitions developed by the accounting profession, similar roles, responsibilities and institutions for standard setting, assessment and verification are present across the range of assurance ecosystems - from cybersecurity to product safety - providing transferable assurance approaches.

Within these common elements, there is also variation in the use of different assurance models across mature ecosystems. Some rely on direct performance testing, while others rely on reviewing processes or ensuring that accountable people have thought about the right issues at the right time. In each case, the need to assure different subject matters has led to variation in the development and use of specific assurance models, to achieve the same ends.

In this section, we will build on our analysis of AI assurance and how it can help to build justified trust in AI systems, by briefly explaining some of the mechanisms that can be used to deliver AI assurance. We will explore where they are useful for assuring different types of subject matter that are relevant to the trustworthiness of AI systems.

A more detailed exploration of AI assurance mechanisms and how they apply to different subject matter in AI assurance is included in our AI assurance guide.

There are multiple approaches to delivering assurance. The spectrum of assurance techniques offer different processes for providing assurance, enabling assurance users to have justified trust in a range of subject matters relevant to the trustworthiness of AI systems.

On one end of this spectrum, impact assessments are designed to account for uncertainty, ambiguity and the unobservability of potential future harms. Impact assessments require expertise and subjective judgement to account for these factors, but they enable standardised processes for qualitatively assessing potential impacts. Assurance can be provided against these processes and the mitigation strategies put in place to deal with potential adverse impacts.

At the other end of this spectrum, formal verification is appropriate for assessing trustworthiness for subject matters which can be measured objectively and with a high degree of certainty. It is ineffective if the subject matter is ambiguous, subjective or uncertain. For example, formal guarantees of fairness cannot be provided for an AI systems outputs.

AI assurance services are a distinctive and important aspect of broader AI governance. AI governance covers all the means by which the development, use, outputs and impacts of AI can be shaped, influenced and controlled, whether by government or by those who design, develop, deploy, buy or use these technologies. AI governance includes regulation, but also tools like assurance, standards and statements of principles and practice.

Regulation, standards and other statements of principles and practice set out criteria for how AI systems should be developed and used. Alongside this, AI assurance provides the infrastructure for checking, assessment and verification, to provide reliable information about whether organisations are following these criteria.

An AI assurance ecosystem can offer an agile regulatory market of assurance services, consisting of both for-profit and not-for-profit services. This regulatory market can support regulators as well as standards development bodies and other responsible AI authorities to ensure trustworthy AI development and deployment while enabling industry to innovate at pace and manage risk.

AI assurance services will play a crucial role in a regulatory environment by providing a toolbox of mechanisms and processes to monitor regulatory compliance, as well as the development of common practice beyond statutory requirements to which organisations can be held accountable.

Compliance with regulation

AI assurance mechanisms facilitate the implementation of regulation and the monitoring of regulatory compliance in the following ways: implementing and elaborating rules for the use of AI systems in specific circumstances; translating rules into practical forms useful for end users and evaluating alternative models of implementation; and providing technical expertise and capacity to assess regulatory compliance across the system lifecycle.

Assurance mechanisms are also important in the international regulatory context. Assurance mechanisms can be used to facilitate assessment against designated technical standards that can provide a presumption of conformity with essential legal requirements. The presumption of conformity can enable interoperability between different regulatory regimes, to facilitate trade. For example, the EUs AI act states that compliance with standardsshould be a means for providers to demonstrate conformity with the requirements of this Regulation.

Managing risk and building trust

Assurance services also enable stakeholders to manage risk and build trust by ensuring compliance with standards, norms and principles of responsible innovation, alongside or as an alternative to more formal regulatory compliance. Assurance tools can be effective as post compliance tools where they can draw on alternative, commonly recognised sources of authority. These might include industry codes of conduct, standards, impact assessment frameworks, ethical guidelines, public values, organisational values or preferences stated by the end users.

Post-compliance assurance is particularly useful in the AI context where the complexity of AI systems can make it very challenging to craft meaningful regulation for them. Assurance services can offer means to assess, evaluate and assign responsibility for AI systems impacts, risks and performance without the need to encode explicit, scientific understandings in law.

Effective AI assurance will rely on a variety of actors with different roles and responsibilities for evaluating and communicating the trustworthiness of AI systems. In the diagram below we have categorised four important groups of actors who will need to play a role in the AI assurance ecosystem: the AI supply chain, AI assurance service providers, independent research and oversight, and supporting structures for AI assurance. The efforts of different actors in this space are both interdependent and complimentary. Building a mature assurance ecosystem will therefore require an active and coordinated effort.

The actors specified in the diagram are not meant to be exhaustive, but represent the key roles in the emerging AI assurance ecosystem. For example, Business to Business to Consumer (B2B2C) deployment models can greatly increase the complexity of assurance relationships in the real-world, where the chain of deployment between AI developers and end consumers can go through multiple client layers.

Similarly, it is important to note that while the primary role of the supporting structures in developing an AI assurance ecosystem is to set out the requirements for trustworthy AI through regulation, standards or guidance, these actors can also play other roles in the assurance ecosystem. For example, regulators also provide assurance services via advisory, audit and certification functions e.g. the Information Commissioners Offices (ICO) investigation and assurance teams assess the compliance of organisations using AI. Government and other public sector organisations also play the executive role when procuring and deploying AI systems.

These actors play a number of interdependent roles within an assurance ecosystem. The table below illustrates each actors role in demonstrating the trustworthiness of AI systems and their own requirements for building trust in AI systems.

This section sets out a vision for a mature AI assurance ecosystem, and the practical steps that can be taken to make this vision a reality. We have based this vision on our assessment of the current state of the AI assurance ecosystem, as well as comparison with more mature ecosystems in other domains.

An effective AI assurance ecosystem matters for the development of AI. Without it, we risk either trust without trustworthiness, where risky, unsafe or inappropriately used AI systems are deployed, leading to real world harm to people, property, and society. Alternatively, the prospect of these harms could lead to unjustified mistrust in AI systems, where organisations hesitate to deploy AI systems even where they could deliver significant benefit. Worse still, we risk both of these happening simultaneously.

An effective AI assurance ecosystem will rely on accommodating the perspectives of multiple stakeholders who have different concerns about AI systems and their use, different incentives to respond to those concerns, and different skills, tools and expertise for assurance. This coordination task is particularly challenging for AI, as it is a general purpose group of technologies that can be applied in many domains. Delivering meaningful assurance requires understanding not only the technical details of AI systems, but also relies on subject matter expertise and knowledge of the context in which these systems are used.

The current ecosystem contains the right ingredients for success, but is highly fragmented and needs to mature in a number of different ways to become fully effective. Responsibilities for assurance need to be distributed appropriately between actors, the right standards need to be developed and the right skills are needed throughout the ecosystem.

The market for AI assurance is already starting to grow, but action is needed to shape this ecosystem into an effective one that can respond to the full spectrum of risks and compliance issues presented by AI systems. To distribute responsibilities effectively and develop the skills and supporting structures needed for assurance, we have identified six key areas for development. These are:

In the following sections, we will outline the current state of the AI assurance ecosystem with regard to these six areas and compare this with our vision for a mature future ecosystem. We highlight the roles of different actors and outline important next steps for building towards a mature AI assurance ecosystem.

Early demand for AI assurance has been driven primarily by the reputational concerns of actors in the AI supply chain, along with proactive efforts by AI developers to build AI responsibly. However, pressure on organisations to take accountability for their use of AI is now coming from a number of directions. Public awareness of issues related to AI assurance (especially bias) is growing in response to high profile failures. We are also seeing increasing interest from regulators, higher customer expectations, and concerns about where liability for harms will sit. The development community is being proactive in this space, managing risks as part of the responsible AI movement, however we need others in the ecosystem to better recognise assurance needs.

Increased interest and higher consumer expectations mean that organisations will need to demand more evidence from their suppliers and their internal teams to demonstrate that the systems they use are safe and otherwise trustworthy.

Organisations developing and deploying AI systems already have to respond to existing regulations including data protection law, equality law and sector specific regulations. As existing motivations to regulate AI appear, organisations will need to anticipate future regulation to remain competitive. This will include both UK sector-based regulation and for organisations exporting products, non-UK developments such as the EU AI regulations and the Canadian AIA.

Regulators are starting to demand evidence that AI systems being deployed are safe and otherwise trustworthy, with some regulators starting to set out assurable recommendations and guidelines for the use of AI systems.

In a mature assurance ecosystem, assurance demand is driven by:

Organisations desire to know that their systems or processes are effective and functioning as intended.

The need for organisations to earn and keep the trust of their customers and staff, by demonstrating the trustworthiness of the AI systems they deploy. This will partly need to happen proactively but will also be driven by commercial pressures.

An awareness of and a duty to address real material risks to the organisation and wider society.

The need to comply with, and demonstrate compliance with regulations and legal obligations.

Demonstrating trustworthiness to the wider public, competing on the basis of public trust.

The importance of these drivers will vary by sector. For example, in safety-critical industries the duty to address material risks and build consumer trust will be stronger in driving assurance demand, compared to low risk industries. In most industries where AI is being adopted, the primary driver for assurance services will be gaining the confidence that their systems will actually work effectively, as they intend them to.

To start to respond to these demands, organisations building or deploying AI systems should be developing a clear understanding of the concrete risks and concerns that arise. Regulators and professional bodies have an important supporting role here in setting out guidance to inform industry about key concerns and drive effective demand for assurance. When these concerns have been identified, organisations need to think about the sorts of evidence that is needed to understand, manage and mitigate these risks, to provide assurance to other actors in the ecosystem.

In a mature AI assurance ecosystem, those accountable for the use of AI systems will demand and receive evidence that these systems are fit-for-purpose. Organisations developing, procuring or using AI systems should be aware of the risks, governance requirements and performance outcomes that they are accountable for, and provide assurance accordingly. Organisations that are aware of their accountabilities for risks will be better placed to demand the right sort of assurance services to address these risks. As well as setting accountabilities, regulation and standards will play an important role in structuring incentives for assurance i.e. setting assurance requirements and criteria to incentivise effective demand.

The drivers discussed above will inevitably increase the demand for AI assurance, but we still need to take care to ensure that the demand is focused on services that add real value. Many current AI assurance services are focused primarily on aspects of risk and performance that are most salient to an organisations reputation. This creates risks of deception and ethics washing, where actors in the supply chain can selectively commision or perform assurance services primarily to benefit their reputation, rather than address the underlying drivers of trustworthiness.

This risk of ethics washing relates to an incentive problem. The economic incentives of actors within the AI supply chain come into conflict with incentives to provide reliable, trustworthy assurance. Incentive problems in the AI supply chain currently prevent demand for AI assurance from satisfactorily ensuring AI systems are trustworthy, and misalign demand for AI assurance with broader societal benefit. Avoiding this risk will require a combination of ensuring that assurance services are valuable and attractive for organisations, but also that assurance requirements whether regulatory or non-regulatory are clearly defined across the spectrum of relevant risks, and organisations are held to account on this basis.

Demand is also constrained by challenges with skills and accountability within the AI supply chain. In many organisations there is a lack of awareness about: the types of risks and different aspects of systems and development processes that need to be assured for AI systems to be trustworthy, and appropriate assurance approaches for assessing trustworthiness across these different areas. There is also a lack of knowledge and coordination across the supply chain around who is accountable for assurance across different areas. Clearer understanding of accountabilities is required to drive demand for assurance.

As demand for AI assurance grows, a market for assurance services needs to develop in response to limitations in skills and competing incentives that actors in the AI supply chain, government and regulators are not well placed to overcome.

Organisations in the AI supply chain will increasingly demand evidence that systems are trustworthy and compliant as they become aware of their own accountabilities for developing, procuring and deploying AI systems.

However, actors in the AI supply chain wont have the expertise required to provide assurance in all of these areas. In some cases building specialist in-house capacity to serve these needs will make sense. For example, in the finance industry, model risk management is a crucial in-house function. In other areas, building specialist in-house capacity will be difficult and will likely not be an efficient way to distribute skills and resources for providing assurance services.

The business interests of actors in the AI supply chain means that without independent verification, in many cases first and second party assurance will be insufficient to build justified trust. Assurance users will be unable to have confidence that the assurance provided by the first party faithfully reflects the trustworthiness of the AI system.

Therefore, as demand for AI assurance grows, a market for external assurance providers will need to grow to meet this demand. This market for independent assurance services should include a dynamic mix of small and large providers offering a variety of services to suit a variety of needs.

A market of AI-specific assurance services has started to emerge, with a range of companies including established professional services firms, research institutions and specialised start-ups beginning to offer assurance services. There is a more established market of services addressing data protection issues, with a relatively new but growing sector of services addressing the fairness and robustness of AI systems. More novel services are also emerging to enable effective assurance, such as testbeds to promote the responsible development of autonomous vehicles. Similarly, the Maritime Autonomy Surface Testbed enables the testing of autonomous maritime systems for verification and proof of concept.

Not all AI assurance will be new though. In some use-cases and sectors, existing assurance mechanisms will need to evolve to adapt to AI. For example, routes such as conformity assessment, audit and certification used in safety assurance mechanisms will inevitably need to be updated to consider AI issues. Regulators in safety critical industries are leading the way here. The Medicines and Healthcare Products Regulatory Agency (MHRA) is committed to developing the worlds leading regulatory system for the regulation of Software as a Medical Device (SaMD) including AI.

The ICO has also begun to develop a number of initiatives to ensure that AI systems are developed and used in a trustworthy manner. The ICO has produced an AI Auditing Framework alongside Draft Guidance, which is designed to complement their guidance on Explaining decisions made with AI, produced in collaboration with the Alan Turing Institute. In September 2021, Healthily, the creator of an AI-based smart symptom checker submitted the first AI explainability statement to the ICO. ForHumanity, a US led non-profit, has submitted a draft UK GDPR Certification scheme for accreditation by the ICO and UK Accreditation Body (UKAS).

There are a range of toolkits and techniques for assuring AI emerging. However, the AI assurance market is currently fragmented and at a nascent stage. We are now in a window of opportunity to shape how this market emerges. This will involve a concerted effort across the ecosystem, in both the public and private sectors, to ensure that AI assurance services can meet the UKs objectives for ethical innovation.

Assuring AI systems requires a mix of different skills. Data scientists will be needed to provide formal verification and performance testing, audit professionals will be required to assess organisations compliance with regulations, and risk management experts will be required to assess risks and develop mitigation processes.

Given the range of skills, it is perhaps unlikely that demand will be met entirely by multi-skilled individuals; multi-disciplinary teams bringing a diverse range of expertise will be needed. A diverse market of assurance providers needs to be supported to ensure the right specialist skills are available. The UKs National AI Strategy has begun to set out initiatives to help develop, validate and deploy trustworthy AI, including building AI and data science skills through skills bootcamps. It will be important for the UK to develop both general AI skills and the specialist skills in assurance to do this well.

In addition to independent assurance providers, there needs to be a balance of skills for assurance across different roles in the ecosystem. For example, actors within the AI supply chain will require a baseline level of skills for assurance to be able to identify risks to provide or procure assurance services effectively. This balance of skills should reflect the complexity of different assurance processes, the need for independence, and the role of expert judgement in building justified trust.

Supporting an effective balance of skills for assurance, and more broadly enabling a trustworthy market of independent assurance providers, will rely on the development of two key supporting structures. Standards (both regulatory and technical) are needed to set shared reference points for assurance engagements enabling agreement between assurance users and independent providers. Secondly, professionalisation will be important in developing the skills and best-practice for AI assurance across the ecosystem. Professionalisation could involve a range of complementary options, from university or vocational courses to more formal accreditation services.

The next section will outline the role of standards in enabling independent assurance services to succeed as part of a mature assurance ecosystem. After exploring the role of standards, the following section will expand on the possible options for developing an AI assurance profession.

Standards are crucial enablers for AI assurance. Across a whole host of industries, the purpose of a standard is to provide a reliable basis for people to share the same expectations about a product, process, system or service.

Without commonly accepted standards to set a shared reference point, a disconnect between the values and opinions of different actors can prevent assurance from building justified trust. For example, an assurance user might disagree with the views of an assurance provider about the appropriate scope of an impact assessment, or how to measure the level of accuracy of a system. As well as enabling independent assurance, commonly understood standards will also support the scalability and viability of self-assessment and assurance more generally across the ecosystem.

There are a range of different types of standards that can be used to support AI assurance, including technical, regulatory and professional standards. The rest of this section will specifically focus on the importance of Global technical standards for AI assurance. Global technical standards set out good practice that can be consistently applied to ensure that products, processes and services perform as intended safely and efficiently. They are generally voluntary and developed through an industry-led process in global standards developing organisations, based on the principles of consensus, openness, and transparency, and benefiting from global technical expertise and best practice. As a priority, independent assurance requires the development of commonly understood technical standards which are built on consensus.

Read more here:
The roadmap to an effective AI assurance ecosystem - extended version - GOV.UK

The Best Science and Tech Breakthroughs of 2021 – Nerdist

Scientists and engineers explored new frontiers in every technological category in 2021. Advances in everything from spaceflight to microrobotics to artificial intelligence abounded, offering a glimpse of a world in which humanity is a multiplanet species. As well as one physiologically connected to intelligent machines. Below are the best science and tech breakthroughs of 2021, in our humble opinion, which may change when we get our Neuralink brain implants.

Although SpaceX had several spectacular failures trying to fly and land its prototype Starship rocket, that just made the first successful attempt (below) all the sweeter. According to SpaceX, the company plans to use Starships to send people to the Moon and Mars. The complete Starship system, once it comes online, will be an astounding 394 feet tall.

While seeing rovers roll around on Mars can feel commonplace, mobility breakthroughs on the Red Planet are beginning to happen. Below is video of the first-ever (mini) helicopter flight on Mars, which occurred on April 19. The flight, while short, was exceptional thanks not only to the helicopters long journey to Mars in the Perseverance rover, but also the planets super-thin atmosphere.

In July of this year, Googles DeepMind subsidiary announced it had solved a grand challenge in biology known as the protein folding problem. Using its cutting-edge AI, AlphaFold, DeepMind released the structures of 350,000 proteins. And noted that the tech will eventually be able to help identify and cure diseases.

Engineers the world over have been working on ways to shrink robots. Emblematic of the efforts from this year are microflier robots that can float on the wind. While the microfliers themselves will reportedly record things like changes in climate and the spread of disease, we cant help but experience foreboding Black Mirror vibes.

As their name implies, brain organoids, or cerebral organoids, are very much like tiny human brains; a fact that makes scientists giving them eye balls in August of this year all the wilder. The eyed organoids, while somewhat disturbing, will hopefully help to cure congenital retinal disorders and even personalize drug testing. And help to raise some important issues for bioethics as well, we imagine.

Smart clothes that can sense and record all of your movements, as well as give you posture suggestions, are now here thanks to MIT. While not wholly new, MITs smart clothes are unique because they consist of simple, knitted conductive yarn, and are amenable to mass production. As well as collecting large amounts of data from their users for robot training.

Finally on the list is Neuralinks breakthrough demonstration of a monkey telepathically playing Pong. Or, in this context, MindPong. Neuralink was able to pull off the feat by plunging 1,024 ultra-thin electrodes into a Macaques brain. (Banana smoothies were essential as well.) The company says that, in the near-term, the tech could help paralyzed people surf the net and express themselves artistically. Merging with superintelligent AI is also apparently not off the table for this rapidly moving decade.

Feature image: Neuralink/Cell Stem Cell/NASA

Read the original here:
The Best Science and Tech Breakthroughs of 2021 - Nerdist

Rumbleverse, a pro wrestling battle royale, announced at TGAs 2021 – Polygon

The next take on the battle royale genre is Rumbleverse, a 40-player brawler royale from Extinction maker Iron Galaxy. Revealed Thursday night during the Game Awards, Rumbleverse is a city full of zany-costumed pro rasslers, elbow-dropping and pile-driving one another until the last is standing.

And yes, the characters are rasslers, not wrestlers. The day that we were first jamming on the concept, Chelsea [Blasko] our co-CEO, she just goes, We should do rasslin!, said Adam Boyes, Iron Galaxys other co-CEO since 2016. Just like that, right? And then we just started, like, What would happen in a world where a wrestling match could break out anywhere on planet Earth?

Rumbleverse, to be published by Epic Games for PlayStation 4, PlayStation 5, Windows PC, Xbox One, and Xbox Series X, borrows the battle royale conventions of parachuting into a map, scarfing up loot and power-ups, and moving to stay within a steadily closing area. But because there are no guns except the ones attached to your arms, says lead designer Adam Hart Iron Galaxys developers hope the fighting will more engaging and more entertaining than the fast-twitch, shoot-or-be-shot immediacy of Fortnite or PUBG Battlegrounds.

When you see somebody in this game, theyre not a threat to you just because youve seen them, Hart said. You can kind of watch them fight or have a, you know, emote conversation with them across rooftops. Of course, at some point theyll start throwing down. While sneak attacks are possible, normally this is mutually initiated combat.

Iron Galaxy figures most events will take between 12 and 15 minutes to crown a winner. The ring shrinks tighter and more quickly to compensate for inactivity, Hart said, to force combat on anyone who is avoiding a fight. The games map, Grapital City, is quite large but more importantly, it has a lot of verticality. It packs a lot of visual appeal into the fights, and of course, supplies a lot more force to moves landed from way higher than the top rope.

I saw Hart, whose fighter was kitted out in a tuxedo under a karate gi, with a full-head cat mask (customization, of course, is very important here) pull out an old-school belly-to-back suplex on a clown (another devs costume), landing it from what looked like the observation deck of the Empire State Building.

How often do you guys imitate Jim Ross, by the way? I asked.

Every day! Boyes laughed. For the past couple of years! Hart added.

Eliminations are a simple case of draining another players health bar, which can be replenished or buffed by the power-ups strewn about Grapital City. Hart guzzled down protein powder, for example, and picked up weightlifting magazines to get himself in shape. Theres also plenty of roasted poultry, the international sign of video game health since Castlevania and Gauntlet. Its available from a drive-thru window for Squatch Chicken: The Home of Slow-Squatted Chicken.

[The cook] has all the chicken on a squat rack, and he just dips it into the fire, Hart explained.

Other pick-ups supply perks or modify one of three core attributes arms (power), core (health), and legs (stamina). Players can work up a match-to-match character build that emphasizes certain areas of their wrestling prowess. Harts character, in the playthrough, went 3-5-2, for example, with his core being the top attribute. But a max-stamina fighter could do comparable damage per second with a flurry of lesser attacks and I saw plenty of chained, juggling strikes that fighting game fans will recognize.

One thing we found out is that a lot of people that are crazy-good platform players, like Mario, Crash, Spyro fans, became the best players, very quickly, Boyes said. So its as much, I think, about how you move around the world as it is your offensive integrity.

Melee weapons are also available, like baseball bats and the de-rigueur folding chairs. But as Hart pointed out, anything a player can hold can also be knocked from their hands (and used) weapon or power-up. That means you can also improvise throwing attacks with the can of whey.

Grapital Citys environment also presents tactical possibilities for players, too. No one can swim, so if the circle is closing around an area with water, players can get rung out quickly even if theyre at maximum health. Being outside the circle doesnt inflict damage, as it does in Fortnite, but it does start a 10-second countdown, akin to disqualification matches in real pro rasslin. (Not that pro rasslin is real. Just, you know, real-life.)

Other wrestling tropes include a perk that revives a player after theyve been counted out (that is, when their health has been fully drained), much like the scripted reversals and comebacks in epic-length wrestling matches.

What I love about this is, I see someone, and I see a series of choices for me, Boyes summarized. Do I have enough stuff that Ive picked up? Do I feel like my stats are good enough? Do I have that one move I need? Can I sneak up behind him? Can I sort of stalk them? Or can I just run away, get health, and come back.

I dont want to say its casual, but because theres so much depth to the combat, it makes it less pressure, Boyes said, but more, you know, just more fun to experiment and try new things.

Rumbleverse kicks off a First Look gameplay event on Friday, Dec. 10, available to a limited number of players (the games official website has registration information and more details). Iron Galaxy expects to launch Rumbleverse in early access on the Epic Games Store on Feb. 8, 2022, as well as on PlayStation 4, PlayStation 5, Xbox One, and Xbox Series X through those consoles marketplaces. Rumbleverse will support cross-platform play and progression, Iron Galaxy said.

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Rumbleverse, a pro wrestling battle royale, announced at TGAs 2021 - Polygon

Stress increases in exopher-mediated neuronal extrusion require lipid biosynthesis, FGF, and EGF RAS/MAPK signaling – pnas.org

Proteostasis is critical for the function, maintenance, and long-term survival of all cells (1). Successful maintenance of the proteome entails efficacious balancing of protein synthesis and degradation, with coordinated actions of regulated gene expression and translation, the chaperone-folding network, the ubiquitin proteasome system, and autophagy all serving critical functions in overall protein quality control. Neurons are particularly susceptible to proteostasis disruption, and aggregates are a striking and common feature of neuropathology in most human neurodegenerative diseases (2). Recent studies have shown that in diseases characterized by distinctive aggregates, including Alzheimers disease, Parkinsons disease, and Huntingtons disease, aggregates can spread from neurons to neighboring cells, inducing deleterious consequences (3). The mechanisms operative for aggregate spread are unclear and remain a matter of considerable research attention.

We discovered that adult Caenorhabditis elegans neurons can select and then extrude aggregated proteins, such as expanded polyglutamine HTTQ128-CFP (related to neurotoxic Huntingtons disease protein) and an aggregation-prone, high-expression mCherry reporter (4) (see Fig. 1A). Aggregates are sent out of the neuron in strikingly large, membrane-surrounded vesicles [4-m average diameter; for comparison, the neuron soma is 6 m; this vesicle is two orders of magnitude larger than exosomes that form by a distinct mechanism (5)]. We refer to these massive cell extrusions as exophers (exo = outside and pher = carry away). In the case of the C. elegans touch receptor neurons that mediate sensitivity to gentle touch stimuli (6), exophers are passed into the surrounding hypodermis, which can partially degrade their contents. Baseline production of exophers under nonstress growth conditions is low, detected in 5 to 20% of ALMR touch neurons, and occurs at lower rates for other touch neurons. However, we found that exacerbated proteostress (for example, expressing the human A142 fragment implicated in Alzheimers disease pathology, disrupting specific autophagy genes via RNA interference [RNAi] or introducing the MG132 proteasome inhibitor) can significantly increase exopher production (4). Animals expressing toxic HTTQ128-CFP in touch neurons that extruded aggregates in exophers retained better touch sensitivity than transgenic HTTQ128-CFP animals in which the neurons did not produce exophers (4), suggesting that exopher production that helps clear the neuron of toxic aggregates is neuroprotective, at least in the short term.

A key mystery in the biology of exophers is the precise nature of the cellular conditions and stresses that induce or elevate exophergenesis. To address this question, we systematically tested external, physiological stresses for the capacity to influence the expulsion of cellular trash. Our data 1) show a clear link between specific, environmental stresses and neuronal exopher production (namely food withdrawal, osmotic stress, and oxidative stress); 2) emphasize that there exists a permissive window for exopher production in early adult life during which external stresses can elevate exopher levels; and 3) identify a previously unrecognized limit to the level of stress that can induce exophers, such that, beyond this upper stress limit, exophergenesis is not observed. We linked the fasting-induced increase in exophers (as much as 10-fold elevation in exophers) to activities of the intestinal peptide transporter PEPT-1, the transcription factors MDT-15 and SBP-1/SREBF2, and their target FASN-1/fatty acid synthase, as well as to epidermal growth factor (EGF) and fibroblast growth factor (FGF)/RAS/MAPK signaling pathways. Our data reveal how stress conditions might promote the spread of aggregates from neurons to their neighbors and suggest pathways that might be targeted to regulate analogous processes, with implications for addressing human neurodegenerative disease.

A common stress that C. elegans encounter in their natural environment is variable food type and/or food abundance, which can have a significant impact on C. elegans gene expression, development, metabolism, and longevity (79). In the laboratory, C. elegans eat a diet of Escherichia coli spread on agar plates, and food sources can be easily manipulated. We first asked whether neuronal exophergenesis levels are sensitive to food source, quantitating ALMR exophers produced by neurons expressing an mCherry protein (strain ZB4065 bzIs166[Pmec-4mCherry1]), which is avidly expelled as exopher cargo (10) (see Fig. 1 for an example). For claritys sake we refer to assay strain ZB4065 as mCherryAg2 in the text hereafter. Exophers are typically produced by young adult animals, peaking at days 2 to 3 of adulthood and returning close to baseline by adult day 4 (Ad4) (4, 11). We therefore measured exophers produced each day, Ad1 to Ad4, to compare both daily relative levels and temporal profiles.

We quantitated exopher production when mCherryAg2 animals were fed four different E. coli strains: E. coli OP50, which is the standard C. elegans food source (12); HT115, a strain that lacks RNAaseIII and is used in RNAi studies (13); HB101, a food source that promotes larger body size and faster development (14); and NA22, a strain that fosters enhanced growth in liquid culture (15). We noted no major differences among the different E. coli strains in common laboratory use, all of which support the basic pattern of peak exopher production around Ad2 that returns to the baseline around Ad4 (SI Appendix, Fig. S1A).

Complete food withdrawal markedly induces exophers. We reared mCherryAg2 animals at 20 C and at Ad1 (onset of egg laying) moved animals to unseeded plates at 20 C for either 3 or 6 h, counting ALMR exophers shortly thereafter. Continuously fed control mCherryAg2 animals generate exophers at levels that range from 5 to 20% of animals, and in this study, baseline was close to 5% on Ad1. Subsequent to a 3-h fast, we found exophers in more than 20% ALMRs; a 6-h fast increased average exopher numbers 5- to 10-fold to nearly 50% of ALMRs examined (Fig. 1B). Fasting also causes an increase in multiple-exopher events, in which more than one exopher is generated (SI Appendix, Fig. S1B). We conclude that food withdrawal can have a rapid and dramatic impact on the extrusion of exophers in animals expressing a noxious mCherry reporter.

At 20 C, in the presence of food, the young adult wave of exopher production falls narrowly within the first 4 d of adulthood (SI Appendix, Fig. S1A). We were therefore curious as to whether the fasting-induced elevation of exophers was restricted to the Ad1 to Ad4 timeframe or, alternatively, whether food withdrawal experienced at any time point could be effective for inducing exopher production. We subjected animals from a single, synchronized population to 6-h fasting regimens as L4 larvae as well as on Ad1 to Ad10; we measured exopher levels immediately after the 6-h fast on the experimental fasting day (Fig. 1C). Our analysis of mCherryAg2 ALMR exophers in this study revealed the following: 1) a 6-h fasting protocol does not induce exopher production during the L4 stage; 2) the 6-h fasting protocol elevates exopher production to peak levels at Ad1 when administered on that day; 3) fasting efficacy is slightly lower when delivered at Ad2 and lower still when delivered at Ad3; and 4) by Ad4, a fasting-induced exopher increase is no longer observed. We conclude that the impact of food withdrawal on enhancing exopher production is limited to a window of time that covers Ad1 to Ad3. The flanking of the permissive phase by exopher-recalcitrant periods suggests the existence of a licensed period, in which the expulsion of significant amounts of neuronal material is biologically feasible and during which exophergenesis can be modulated by environmental stress signals. We observed the 6-h fastinginduced elevation of exophers in strains that expressed GFP (Fig. 1D) or expanded polyglutamine (Fig. 1F) mNG:HttQ74 in touch neurons or expressed GFP in dopaminergic neurons (Fig. 1E), indicating that the fasting induction of exophers was not reporter- or cell typespecific.

Temperature is an environmental factor that dramatically influences the C. elegans reproductive life cycle time, lifespan (16), and proteostress (17). We therefore asked whether culture temperature might influence the timing or levels of adult exopher production. We reared animals continuously at 15, 20, and 25 C from the egg stage and measured exopher levels over the first 4 d of adult life (SI Appendix, Fig. S2A). Somewhat unexpectedly, exopher levels did not vary substantially with culture temperature. In an alternative experimental design, we reared synchronized populations of animals at 20 C and then split cultures at the L4 adolescent stage into three parallel cultures that were thereafter maintained at 15, 20, and 25 C (SI Appendix, Fig. S2B). We found that, in animals shifted to 25 C, the peak of exopher production occurred earlier (Ad1) than 20 C animals, but exopher levels were not significantly elevated, relative to 20 C. Our data suggest that the experience of a temperature shift, rather than continuous noxious temperature exposure, might exert the strongest impact on levels of exopher production (SI Appendix, Fig. S2C). That said, the approximate doubling of exopher scores after an L4 shift from 20 to 25 C is modest in comparison to the 5 to 10 exopher elevation measured for food withdrawal. We conclude that, within the normal confines of C. elegans laboratory culture, the temperature at which animals are grown exerts a relatively minor influence on levels of exopher production.

Hypoxia can induce protein aggregation in C. elegans models (18), raising the possibility that hypoxia might enhance exopher production. We tested the exposure to 0.1% oxygen using an adjustable hypoxia C-174 chamber (Biospherix) to generate exposures to a controlled, hypoxic environment. We subjected Ad1 to Ad5 mCherryAg2 animals to 0.1% oxygen for 6 h and monitored ALMR exophers after the removal of animals from the chamber.

We failed to find differences in exophers produced under these hypoxic conditions on any day (SI Appendix, Fig. S2D). We also tested anoxic conditions induced by replacing oxygen with nitrogen in a sealed anoxia chamber (19), measuring ALMR exophers after 6-h exposure to anoxia (SI Appendix, Fig. S2D, dark purple line). Exposure to anoxia failed to markedly increase exopher production on any exposure day. While it is possible that longer exposures or different recovery times might alter outcomes, we conclude that hypoxia/anoxia treatments, as delivered, do not elevate exopher levels.

Osmotic stress introduces proteostasis challenges (20), and thus, we were curious as to how exopher production might respond to osmotic stress conditions. We first made agar plates designed to introduce osmotic stresses based on standard C. elegans conditions, testing 250 mM concentrations of sucrose, glucose, sorbitol, and NaCl. At Ad1, we introduced mCherryAg2 animals to these osmotic stresses for 6 h and scored ALMR exophers thereafter (Fig. 2A). We found that a 6-h exposure to osmotic stress increased exopher production 4 above baseline. Since each solute-induced stress resulted in a similar elevation of exophers, the exopher response is likely grounded in osmotic stress itself, rather than the chemical nature of the specific osmolytes.

Osmotic stress and oxidative stress can increase exopher production, but extreme stress levels can decrease exophergenesis. For all panels, bars are SEM, ***P < 0.001, **P < 0.01, *P < 0.05, and CMH statistics. (A) Transient osmotic stress significantly elevates ALMR exophers on Ad1. We exposed mCherryAg2 animals to 250 mM sucrose, glucose, sorbitol, or NaCl on Ad1 and scored for exophers 6 h later: 5 trials and 50 animals per trial. (B) Animals chronically exposed to osmotic stress from Ad1 into adulthood produce elevated exophers with early onset peak production. We grew mCherryAg2 animals to the Ad1 stage under standard growth conditions and shifted to 250 mM sucrose, glucose, sorbitol, or NaCl, measuring ALMR exophers in these populations on Ad1 to Ad4: 4 trials and 50 animals per time point. CMH compares osmotic stress media to normal growth media. (C) Osmotic stress at 250 mM enhances exopher production more than at 500 mM. We exposed strain mCherryAg2 to 250 and 500 mM sucrose, glucose, sorbitol, and NaCl for 6 h on Ad1 and measured exophers shortly thereafter: 5 trials and 50 animals per trial. (DF) Increasing oxidative stress enhances exopher production to a limit, after which exopher production is suppressed. We grew mCherryAg2 animals to Ad1 at 20 C, and then, transferred animals to plates housing increasing concentrations of juglone, rotenone (rot), or paraquat (PQ) for 6 h, as indicated, measuring ALMR exophers shortly thereafter (black asterisks compare to control): 7 trials and 50 animals per trial. (G) Combined food withdrawal and osmotic stress can suppress exopher induction under conditions that individually induce high-exopher levels. We fasted Ad1 mCherryAg2 animals for 6 h on plates containing the solutes indicated (20 C) and scored for ALMR exophers shortly thereafter: 3 trials and 50 animals per trial (asterisks compare two indicated conditions at bar ends). (H) Combined food withdrawal with oxidative stress can suppress exopher induction under conditions that individually induce high-exopher levels. We fasted Ad1 mCherryAg2 animals for 6 h on juglone, rotenone, and paraquat at the concentrations indicated, scoring for ALMR exophers shortly thereafter: 5 trials and 50 animals per trial (asterisks compare two indicated conditions). (I) While anoxia does not increase exophers, combining food withdrawal and anoxic stress can suppress fasting-associated exopher induction. We fasted Ad1 mCherryAg2 animals for 6 h under anoxic conditions, scoring for ALMR exophers shortly thereafter: 4 trials and 50 animals per trial. Note that our anoxia protocol is not effective in limiting fasting-induced exopher elevation (SI Appendix, Fig. S2 D and H).

We also quantitated exopher levels under conditions of long-term, hyperosmotic stress during adult life (Fig. 2B). We raised animals under standard growth conditions until the L4 stage and then moved animals to 250 mM sucrose, glucose, sorbitol, or NaCl plates for adult life. We first measured exopher levels 24 h after initiating osmotic stress and tested the same population on Ad1 to Ad4 thereafter. For all solutes tested, osmotic stress resulted in an 5 increase in exophers over the baseline at peak and shifted the measured peak of exopher onset forward 1 d. Notably, exopher levels returned to baseline at Ad4, even in the presence of osmotic stressors. We conclude that both transient and extended exposure to hyperosmotic stress can elevate exopher production substantially in early adult life and infer that hyperosmotic conditions rapidly generate a trigger that elevates exophers.

Oxidative stress is a central player in aging biology and proteostasis (21). We tested whether well-characterized chemical inducers of mitochondrial oxidative stress can influence exopher production in ALMR neurons. We exposed Ad1 mCherryAg2 animals to 6 h of increasing concentrations of juglone (juglone is a plant-derived compound that can induce superoxide production when incorporated into nematode growth medium [NGM]); rotenone, which interacts with mitochondrial electron transport complex I to elevate reactive oxygen species (ROS); and paraquat, which increases mitochondrial superoxide production (Fig. 2 DF). For all mitochondrial ROS generators, we observed a dose-dependent increase in exophers at Ad1 (to a point, discussed in Excessive Stress Decreases Exopher Production) that ranged from 4 to 6 over untreated controls. We conclude that exposure to mitochondrial ROS induction can enhance exopher production.

We also tested conditions of continuous ROS exposure during adult life. We raised animals under standard growth conditions until the L4 stage and shifted cultures to paraquat plates, measuring ALMR exophers on Ad1 to Ad8 (SI Appendix, Fig. S2E). We find that continuous paraquat exposure increases exopher levels. Interestingly, exopher levels in the 2-mM paraquat treatment do not begin to fall at Ad3, as is typical for other stresses, but instead remain higher than control levels through Ad6, returning to baseline at Ad7. Thus, paraquat-mediated oxidative stress can extend the time period of permissive exophergenesis by 2 d, raising the possibility that paraquat might induce signals that normally promote exopher production. Overall, data from three mitochondrial ROS-generating compounds indicate that chemically-induced oxidative stress can increase exopher formation.

While initially testing the ability of stressors to modulate exopher production, we often utilized a doseresponse approach. Our studies revealed a striking commonality regardless of stressor type: excessive stress suppresses exophergenesis. For example, 6-h exposure of 240 M juglone (Fig. 2D), 25 M rotenone (Fig. 2E), or 25 mM paraquat (Fig. 2F) reduces exopher levels, even though lower levels of these ROS stressors enhance exopher production. The same pattern emerges under osmotic stress conditions; 6-h exposure to 500 mM concentrations of sucrose, glucose, sorbitol, and NaCl suppresses exopher levels, compared to 250 mM concentrations of each of these solutes (Fig. 2C). Although at Ad1 ALMR exophers modestly increase with 6-h exposures to increasing temperatures up to 30 C, 6 h at 37 C causes a collapse in exophergenesis (SI Appendix, Fig. S2F).

We generated additional evidence in support of the idea that excessive stress can inhibit exopher formation by exposing Ad1 mCherryAg2 animals to combined two-stress conditions that, by themselves, individually enhance exopher production. For example, whereas a 6-h fast elevates exophers (Figs. 1 BF and 2G), cointroducing osmotic stress with fasting (which also normally also elevates exophers; Fig. 2 A and B) suppresses exopher levels (Fig. 2G). We also found a combined inhibitory effect for fasting + oxidative stress (Fig. 2H).

Furthermore, temperature (SI Appendix, Fig. S2G) and anoxia (Fig. 2I and SI Appendix, Fig. S2H), stresses that did not affect exophergenesis on their own (SI Appendix, Fig. S2 A, B, and D), could suppress the effects of fasting on exopher production. Data suggest that under extreme stress neurons either cannot meet molecular requirements for exopher production or might enact mechanisms that actively suppress exophergenesis (see Discussion).

In summary, food withdrawal, osmotic stress, and oxidative stress can enhance exopher production, although above specific threshold levels of these stresses (including when two individual, exopher-promoting stresses are combined), exopher production can be suppressed. Thus, in addition to a temporal constraint on when environmental stresses can elevate exopher levels, we demonstrate that there is a limit to the severity of environmental stress capable of inducing the exopher production response.

With a goal of defining molecular mechanisms by which stresses elevate neuronal exopher production, we sought to define genetic requirements for the fasting-dependent induction response. We elected to focus on the fasting response because of the robust and highly reproducible level of induction associated with 6 h food withdrawal (Fig. 1 B and C), combining genetic mutant and RNAi strategies. To probe the mechanism by which fasting elevates neuronal exopher production, we first tested mutants for well-characterized, stress-activated transcription factors: heat shock factor 1 hsf-1/HSF1, required for transcription of heat shock chaperones and proteostasis (22); hypoxia inducible factor hif-1/HIF1, required for hypoxia stress responses (23); hlh-30/TFEB, required for starvation resistance and lysosomal integration with metabolism (24); skn-1/NRF2, which promotes response to oxidative stress and xenobiotic challenge (25, 26); and daf-16/FOXO, which is activated by low insulin pathway signaling and functions in a range of stress-protective responses, including proteostasis (27).

We constructed mCherryAg2 strains with viable mutant alleles of each transcription factor and subjected mutants to 6 h food deprivation on Ad1, measuring ALMR exophers thereafter (Fig. 3A). We observed the significant elevation of exophergenesis in hsf-1, hif-1, hlh-30, and skn-1 backgrounds in response to food withdrawal, indicating that these transcription factors are not critical for the induction of exophers in response to fasting. In contrast, the daf-16 null mutant exhibited a partial defect in the food withdrawal response, with exopher levels clearly increasing over baseline in the absence of food (P < 0.001) but never reaching the levels observed in wild-type (WT) animals (P < 0.001) (Fig. 3A). The partial effect in the daf-16 null mutant background is consistent with a model in which a daf-16dependent process mediates one component of the fasting response but that a daf-16independent process works in parallel to elevate exophers when food is withdrawn.

Lipid synthesis and FGF-activated RAS/ERK signaling are required for the induction of neuronal exophers in response to fasting. For all panels, bars are SEM, ***P < 0.001, **P < 0.01, *P < 0.05, and CMH statistics. Feeding RNAi was initiated at the L4 larval stage and continued until Ad2. (A) The daf-16 null mutant has diminished exopher production in response to fasting. We tested mutants defective in major stress-responsive transcription factors (daf-16/FOXO, hsf-1/HSF-1, hif-1/HIF1, hlh-30/TFEB and skn-1/NRF2) in the mCherryA2 background, Ad2: exopher counts 6 trials, 50 animals per trial, and CMH difference between WT control and daf-16 deletion mutant ***P < 0.001. (B) Lipid synthesis is implicated in the fasting-induced boost in exophergenesis. RNAi knockdown of pept-1, mdt-15, sbp-1, and fasn-1 beginning at L4 in the mCherryAg2 strain, 6 h fast followed by exopher assay on Ad2, control is empty vector RNAi. We compared WT fed versus fasted P < 0.001 CMH, the others are not significant: 3 trials and 50 animals per trial. (C) Schematic of C. elegans MAPK signaling pathways targeted in genetic tests for fasting-induced exopher elevation: p38 MAPK signaling (purple), JUN/FOS MAP kinase signaling (orange), EGF-mediated MAPK signaling (green), and FGF-mediated MAPK signaling (blue). Black boxes highlight a common function in both EGF and FGF MAPK signaling. (D) RNAi knockdown of most components of the p38 and JUN/FOS signaling cascades does not impair fasting-induced exopher increases. The strain was mCherryAg2; RNAi was initiated at the L4 stage; and 6 h fast was followed by exopher counts at Ad2: 3 trials and 50 animals per trial. mek-1(RNAi) stood out as exceptional in failing to induce exophers. (E) Loss-of-function mutants for JUN/FOS signaling do not suppress exopher production. kgb-1; mCherryAg2 and jun-1; mCherryAg2 mutants were fasted for 6 h at Ad2 before exopher counts: 5 trials and 50 animals per trial. (F) An egl-17/FGFmediated MAPK signaling cascade is necessary for a fasting-induced increase in exopher production. RNAi for the indicated genes was initiated at the L4 stage on strain mCherryAg2, and at Ad2, animals were fasted 6 h and then scored for exophers. RNAi knockdown of let-23, ksr-2, and FGF ligand let-756 did not disrupt the fasting exopher response 3 trials and 50 animals per trial.

We also tested whether autophagy, a pathway activated by food limitation, might be critical for the fasting-induced increase in exophers. We used RNAi approaches to knockdown autophagy genes lgg-1, atg-7, and bec-1 in mCherryAg2 animals and scored for exophers on Ad2 (SI Appendix, Fig. S3A). Since all three disruptions in the autophagy pathway failed to suppress fasting-induced exopher elevation, we infer that engagement of autophagy functions is not required for fasting-induced exopher increase. Pharmacological inhibition of autophagy with 1 mM spautin or of the proteasome with 10 mM MG132 (SI Appendix, Fig. S3B) did not block the fasting-induced elevation of exophers, in further support that autophagy and proteasome contributions are not critical for fasting-induced exopher increase.

To better characterize the pathways involved in fasting-induced exopher production, we compiled a list of known genes implicated in starvation and feeding in the C. elegans literature and screened for fasting-induced exopher elevation when the candidate genes were knocked down using RNAi (28) (see SI Appendix, Table S1 for a list of candidates tested). Note that the strain we targeted with feeding RNAi, mCherryAg2, should permit efficient RNAi knockdown in all tissues except neurons, because neurons do not express a double-stranded RNAi transporter, sid-1, required for the efficacious knockdown in feeding RNAi (29). Utilizing this experimental design, we expected to identify genes operative in the nonautonomous initial events in the sensation and signaling of fasting stress to the touch neurons, rather than genes involved in neuron-intrinsic exophergenesis.

We tested positive clones from the first round of the RNAi screen in triplicate to identify intestinal peptide transporter pept-1, lipid synthesis-implicated Mediator complex factor mdt-15, MDT-15 binding partner sbp-1/SREBF2, and fatty acid synthase fasn-1, as required for robust exopher elevation in response to fasting (Fig. 3B).

pept-1 encodes a conserved intestinal di-/triamino acid transporter implicated in C. elegans nutrient sensing (30). Loss of the pept-1 function increases the intestinal absorption of free fatty acids from ingested bacteria, such that short- and medium-chain fatty acids are highly increased in the mutant, and de novo synthesis of long-chain and polyunsaturated fatty acids is greatly decreased (31). Our data suggest that sudden withdrawal of food causes a metabolic reconfiguration that signals for enhanced exopher production and that the sensing of food limitation requires PEPT-1 transporter activity.

MDT-15 has been shown to promote health and longevity by orchestrating many of the metabolic changes that occur in response to short-term fasting (32). MDT-15 encodes a subunit of the transcriptional coregulator Mediator complex that is required to express fatty acid metabolism genes and fasting-induced transcripts (3235), heavy metal and xenobiotic detoxification genes (32, 36), and oxidative stress genes (37). SBP-1, the homolog of the mammalian sterol regulatory element-binding protein (SREBF2) transcription activator that regulates fatty acid homeostasis, is a known partner of MDT-15, and together, MDT-15 and SBP-1 promote the expression of lipid synthesis genes (32). FASN-1 encodes the sole C. elegans fatty acid synthase, and its expression is regulated by MDT-15/SBP-1 (38, 39). Together, MDT-15, SBP-1, and FASN-1 may act to promote the synthesis of a lipid-based factor that signals for, or is otherwise required for, neuronal exopher production under fasting stress.

Expression of lipid synthesis genes in multiple tissues may contribute to fasting-induced exopher elevation. To address where the lipid synthesis gene group required for fasting-induced exopher elevation acts, we took a tissue-specific RNAi knockdown approach. We worked with strains that expressed mCherryAg2 in touch neurons but were defective in either the double-stranded RNA (dsRNA) transporter sid-1 (neurons, muscle, pharynx, intestine, and hypodermis) or RISC complex factor rde-1 (germline and vulva), and we reintroduced sid-1 or rde-1 using tissue-specific promoters to drive expression and restore RNAi knockdown capability only in the rescued tissue. The promoters we used to restore expression (and therefore RNAi targeting) in specific tissues were the following: pan-neuronal rgef-1, muscle myo-3, pharynx myo-2, intestine vha-6, hypodermis hyp7 semo-1, vulva lin-31, and germline sun-1. This test set of seven tissue-specific RNAi lines enabled us to target most cells of the animal. We fasted animals to ask whether RNAi disruption in individual tissues is sufficient to disrupt the exopher induction response, which would indicate that expression in that targeted tissue is necessary in the response. We found that although whole body knockdown of pept-1, mdt-15, sbp-1, and fasn-1 could disrupt the fasting-induced exopher elevation (Fig. 3B), no tissue-specific knockdown was effective in blocking this response (SI Appendix, Fig. S4A). Data are consistent with a model in which multiple tissues can contribute the required lipid biosynthesis, although we cannot rule out that RNAi targeting was ineffective or missed necessary cells.

Food limitation stresses engage multiple signaling pathways that can activate animal defenses, including RAS/MAPK pathways that transduce developmental and stress responses to activate specific transcription programs (40, 41). We tested members of three canonical, well-characterized C. elegans MAPK signaling pathwaysthe PMK-1/p38 pathway that functions in some innate immunity and oxidative stress responses; the JNK pathway, which, among other activities, functions in intermittent fasting programs (42); and the RAS/ERK pathway, which, among other things, affects vulval precursor fate and vulval development response to starvation stress (43) (pathways summarized in Fig. 3C).

The core MAP kinases in the p38 pathway are nsy-1/MAPKKK and pmk-1/MAPK (40). RNAi directed against nsy-1 and pmk-1 genes was ineffective in blocking the fasting induction of neuronal exophers (Fig. 3D), and thus, we do not find evidence supporting the engagement of the pmk-1/p38 stress pathway in fasting-induced exopher elevation.

In C. elegans, an adult intermittent fasting protocol of 2 d without food followed by 2 d of food extends lifespan via an MLK-1, MEK-1, and KGB-1 JNK pathway that converges on AP-1 (JUN-1, FOS-1)mediated transcription (42). We find that kgb-1(RNAi), kgb-1, and jun-1 genetic mutations do not block the fasting-induced elevation of exopher production (Fig. 3 D and E). This result, coupled with published data examining a transcriptional time course following food withdrawal that revealed a distinct transcription pattern for 3 to 6 h after fasting, as compared to the more chronic starvation of 9 h and longer (44), suggests that the response to short-term food withdrawal that we characterize here does not operate via the characterized, intermittent fasting pathway. Still, the positive mek-1/MAPKK and partial mlk-1/MAPK outcome (mlk-1(RNAi) fasted versus WT fasted, not significant; mlk-1(RNAi) fasted versus mlk-1(RNAi) fed, P < 0.01) (Fig. 3D), suggest possible pathway involvement or cross-talk involving these kinases. As evidence for more definitive engagement of the RAS/ERK pathway was evident in our studies (Fig. 3F), we focused on examining pathway members in more detail.

RAS/ERK signaling in C. elegans (reviewed in refs. 40 and 41; see Fig. 3C) can involve the EGF or FGF activation of receptor tyrosine kinases that interact with adaptor proteins such as SEM-5/GRB2 or SOC-1/GAB1, which recruit guanine nucleotide exchange factor SOS-1 to activate small GTPase LET-60/RAS. LET-60/RAS-GTP activates MAPKKK LIN-45/RAF. Scaffold proteins KSR-1 and/or KSR-2 collect downstream members of the MAPK cascade, so LIN-45/MAPKKK activates MEK-2/MAPKK, which in turn phosphorylates and activates MPK-1/ERK. MPK-1 can then enter the nucleus to phosphorylate transcription factors that execute a transcriptional response. We found RNAi knockdown of multiple members of the conserved core RAS/ERK signaling, namely let-60/RAS, mek-2/MAPKK, mpk-1/ERK, sem-5, and ksr-1, disrupts the fasting-induced increase of touch neuron exophers (Fig. 3F). Given the implication of five core MAPK components in fasting-induced exopher elevation, we conclude that the RAS/ERK pathway plays a critical role in the mechanism by which neurons increase mCherry expulsion (and other cell contents) under food withdrawal stress.

The characterized C. elegans EGF and FGF signaling pathways share the aforementioned signaling components in the conserved RAS/ERK pathways but differ in ligands, receptors, KSR-type (EGFR uses both KSR-1 and KSR-2; FGFR exclusively uses KSR-1), and FGF pathway requirement for the adaptor SOC-1/Gab1 (41). We tested MAPK components in an effort to distinguish whether FGF, EGF, or both, contribute to exopher elevation in response to fasting.

We used RNAi approaches to test for a requirement of FGF ligands egl-17 and let-756 as well as FGF receptor egl-15 in fasting-induced exopher elevation. Interventions with ligand egl-17/FGF and receptor egl-15/FGFR, but not FGF ligand let-756, disrupted the capacity to increase exophers in response to 6-h fasting, implicating a specific FGF in exopher regulation biology (Fig. 3F). Moreover, we find that FGF pathway-specific soc-1 (RNAi) diminishes fasting-induced exopher production. Our data, which implicate eight FGF pathway genes (FGF ligand egl-17, FGF receptor egl-15, FGF pathway-specific soc-1 and sem-5, and core pathway components let-60, mek-2, ksr-1, and mpk-1) in the fasting induction of exophers, identify an FGF-activated ERK/MAPK pathway that acts in response to 6-h food withdrawal, as an essential mechanistic step in neuronal exopher increase.

To address the potential source of egl-17/FGF and identify the tissue via which FGFR acts to promote fasting-elevated exopher production, we used tissue-specific RNAi approaches to disrupt all identified components in all neurons, muscle, pharynx, intestine, hypodermis, vulva, or germline (SI Appendix, Fig. S4A).

We were unable to identify single-tissue sources that were required for FGF ligand EGL-17 or FGF receptor EGL-15 activity in fasting-induced exopher elevation, suggesting that either multiple tissues can execute effective FGF signaling, that cells that we were unable to target are responsible, or that unknown technical issues might apply. We did, however, find that knockdown of the most downstream pathway targets, mek-2 and mpk-1, only in the hypodermis or only in the germline (SI Appendix, Fig. S4B) could disrupt fasting induction. Our data thus suggest that mek-2 and mpk-1 are required in both the hypodermis and in the germline for fasting-induced exopher induction.

Importantly, mek-2 and mpk-1 are downstream kinases for both the FGF and the EGF MAPK pathways. Indeed, our RNAi perturbation screens of MAPK pathway members indicated that lin-3/EGF also acts in fasting-induced exopher elevation (Fig. 3F). Tissue-specific RNAi studies reveal that germline-specific knockdown of lin-3/EGF disrupted fasting-induced exopher elevation (while hypodermal knockdown did not) (SI Appendix, Fig. S4C), implicating the germline production of LIN-3/EGF in a transgenerational influence on neuronal exophergenesis. Knockdown of mek-2 and mpk-1 are also needed in the germline for fasting-induced exophergenesis (SI Appendix, Fig. S4B).

RNAi disruption of EGF receptor let-23 and EGF pathway-specific ksr-2 did not eliminate the exopher elevation in response to fasting in our original screen (Fig. 3F). This observation raised the possibility that the EGF-responsive pathway might function in neurons (which are generally not as susceptible to RNAi as other tissues). Because let-23(gf) alleles caused extensive reproductive system development consequences that complicated the interpretation of exopher production (not shown), we tested the available strains that expressed the EGF pathway-activating EGFR/let-23(gf) in specific tissues for elevated exopher levels in the presence of food. We found that the transgenic introduction of EGFR/let23(gf) in neurons (but not muscle or intestine) elevates exophers in the presence of abundant food (Fig. 4A), suggesting a neuronal-based EGF pathway in promoting fasting-induced exopher elevation.

Molecular pathways that influence fasting-induced exopher elevation. For all panels, bars are SEM, ***P < 0.001, **P < 0.01, *P < 0.05, and CMH statistics. (A) let-23 (gain of function) increases exopher formation when expressed specifically in neurons. let23(sa62) is a gain-of-function allele that activates MAPK signaling. We measured the exopher levels in strains that expressed let-23(sa62gf) from neuronal (unc-119), intestinal (vha-6), or muscle (myo-3) promoters in the mCherryAg2 background at Ad2 in the presence of food: 3 replicates and 50 animals per trial. (B) let-60 (gain of function) increases exopher formation under abundant food conditions. let-60(n1046) is a gain-of-function RAS allele that activates MAPK signaling. We measured exopher levels in control mCherryAg2 and in let-60(n1046);mCherryAg2 strains at Ad2 in the presence of food: 3 replicates and 50 animals per trial. (C) Schematic of the logic of epistasis within the MAPK signaling context. The phenotype of a gain-of-function allele that continuously signals will not be affected by the knockdown of any effector upstream of it within the cascade. On the other hand, any actors downstream of a constitutively active component will affect the phenotype. FGF signaling is used as an example here. (D) Lipid synthesis genes act upstream of let-60(gf)/RAS to elevate neuronal exopher production. RNAi interventions were initiated at the L4 stage on gain-of-function let-60;mCherryAg2 animals. Exophers were scored at Ad2: 3 replicates and 50 animals per trial. Although mek-2 and mpk-1, known to act downstream of let-60(gf), are critical for exopher elevation, the known upstream genes egl-15, egl-17, and soc-1 are not, consistent with data that previously ordered the FGF pathway. pept-1, mdt-15, sbp-1, and fasn-1 do not suppress let-60(gf)-induced exophergenesis, indicating that the lipid synthesis genes likely act upstream of let-60/RAS. (E) Lipid synthesis genes and lin-3 act upstream of let-23(gf) activity when it is expressed in neurons. RNAi knockdown was initiated at the L4 developmental stage on gain-of-function let-23(sa62);mCherryAg2 animals. Exophers were scored at Ad2: 3 replicates and 50 animals per trial. Cascade components let-60, mek-2, and mpk-1 are known to act downstream of let-23 and effectively abrogate the MAPK signaling leading to exophers, while lin-3 works upstream of let-23 and therefore cannot suppress a downstream gain-of-function mutation. RNAi knockdown of pept-1, mdt-15, sbp-1, and fasn-1 do not suppress neuronal let-23(gf)-induced exophergenesis, indicating that the lipid synthesis genes likely act upstream of neuronal let-23(gf). (F) Model for signaling that elevates neuronal exopher formation in response to fasting. In the sudden absence of food or when dipeptide transporter PEPT-1 activity is low, a lipid-based signal is produced/released that depends upon MDT-15/SBP-1 transcriptional activity and fatty acid synthase FASN-1. The lipid signal may act on the neurons directly or may contribute to the activation of a required FGF pathway in a relay tissue (unlikely to be neurons) to promote transcription of an essential signal that acts on touch neurons to elevate exopher production. EGF pathway activation in neurons can elevate exopher production; it remains to be determined whether EGF signaling is required in touch neurons per se. In sum, transtissue stress signaling influences remote neuronal trash management through lipid, FGF, and EGF RAS/MAPK signaling. Note that we cannot distinguish between an inducible lipid signal and a static, essential lipid product required for exopher elevation.

let-60(n1046) is a well-characterized, gain-of-function RAS allele that constitutively activates MAPK signaling (45). Our RNAi data, identifying the FGF and EGF/RAS/MAPK pathways as required for fasting-induced exopher induction, predict that the let-60(gf) allele should induce higher exopher levels, even in the absence of starvation. To test this model, we constructed a let-60(n1046gf);mCherryAg2 strain and measured exopher levels at Ad2. We find that exophers are indeed elevated in the let-60(n1046gf) background in the absence of fasting, confirming that RAS activation enhances exopher production (Fig. 4B).

let-60(n1046gf) has been used extensively in epistasis pathway ordering (45), and thus we pursued an epistasis approach to clarify the relationship between the lipid synthesis gene group and the FGF/RAS/MAPK pathways (Fig. 4C). We reasoned that if the genes involved in lipid synthesis act downstream or in parallel to the RAS/MAPK pathway that elevates exophers, RNAi knockdown of the lipid synthesis genes in the let-60(gf) background should suppress constitutive exopher production. Alternatively, if the lipid synthesis branch acts upstream of LET-60/RAS activation, disruption of the upstream genes should not change the exopher elevation associated with let-60(gf) (Fig. 4C). We therefore repeated RNAi knockdowns of genes in the lipid synthesis and FGF/RAS/MAPK pathways in the let-60(gf);mCherryAg2 background, quantitating the impact on exopher formation. As expected for known downstream kinases in the RAS pathway, the RNAi disruption of mek-2/MAPKK and mpk-1/MAPK suppressed the let-60(gf) phenotype (Fig. 4D). In contrast, genes encoding egl-17/FGF, egl-15/FGFR, and soc-1 that act upstream of let-60/RAS did not suppress the let-60(gf)/RAS phenotype, consistent with the known action of these genes upstream of RAS in the signaling pathway.

Importantly, RNAi directed against pept-1, and the lipid biosynthesis genes mdt-15, sbp-1, and fasn-1 did not suppress elevated exopher levels in the let-60(gf) background (Fig. 4D). We conclude that the lipid biosynthesis branch is likely to act upstream of the FGF/RAS/MAPK to promote exopher elevation.

Lipid and FGF signaling may act upstream of EGF signaling. We also used an epistasis strategy to begin to address how lipid biosynthesis and FGF pathways might relate to the EGF pathway in fasting-induced exopher elevation. We performed the RNAi knockdown of required lipid synthesis genes and of upstream FGF pathway-specific genes egl-17/FGF, egl-15/FGFR, and soc-1 on the neuronal let-23(gf) strain in which exopher levels are elevated, reasoning that if a critical lipid or FGF pathway is normally activated downstream of neuronal EGFR activation, perturbation of lipid or FGF pathway-specific genes would block the let-23(gf) exopher elevation under well-fed conditions. Our RNAi-dependent disruptions of either pathway, however, did not suppress neuronal let-23(gf) high-exopher levels (Fig. 4E), suggesting that the essential lipid and FGF signaling required for fasting-induced exopher induction normally occurs upstream of neuronal EGF signaling. Although these studies constitute only the first rudimentary tests required to establish pathway details (caveats discussed in more detail in SI Appendix, Fig. S4), data suggest a basic framework for mechanistic evaluation (Fig. 4F).

Overall, we identify three pathways that are required for fasting-induced exopher elevation in stressed touch receptor neurons: lipid synthesis, an FGF/MAPK pathway, and an EGF/MAPK pathway that can act in neurons. Our data suggest a model for signaling that influences a dramatic expulsion of neuronal contents upon the introduction of fasting stress. Upon food withdrawal, di-/tripeptide transporter PEPT-1 plays a role in nutrient sensing and a lipid-based stress signal (the generation of which depends on MDT-15/SBP-1 transcriptional activity and fatty acid synthase FASN-1) is produced. The lipid-dependent process could act directly to nonautonomously influence exopher production in the touch neurons or could trigger/activate the required FGF/RAS/MAPK and/or EGF MAPK signaling and the likely consequent downstream transcription. Downstream, or in parallel, RAS/MAPK activity in hypodermis and germline contributes to the stress-sensing tissue network, establishing that that nonautonomous signaling directs exopher production in the touch neurons. FGFR signaling pathway genes are known to be highly expressed in the hypodermis (46). Notably, RNAi evidence supports the hypothesis that the same group of genes that mediate fasting-induced exopher increase is required for exopher elevation in response to osmotic stress (SI Appendix, Fig. S3C), indicating that general, rather than fasting-specific, mechanisms are engaged.

Although many details of this complex lipid-FGF-EGF signaling network remain to be further elucidated, our data provide documentation that aging and proteostasis-relevant stresses engage multiple pathways that can act over multiple tissues to influence a dramatic expulsion of neuronal contents. Conserved signaling molecules can modulate a process of fundamental interest in neuronal proteostasis, relevant to the understanding of neuronal degeneration.

Maintaining neuronal proteostasis is a critical goal for healthy brain aging and a fundamental challenge for diseased neurons in a range of neurodegenerative diseases (47). A recently identified facet of Alzheimers disease, Parkinsons disease, and other proteopathies is the transfer of aggregates to neighboring cells, which can seed aggregate spread and promote pathology (48, 49). In vivo dissection of the biology of protein aggregate spread is challenging to investigate in mammalian brain, but it is clear that the understanding of mechanisms that regulate autonomous and nonautonomous aggregate expulsion in relation to other neuroprotective strategies is of considerable importance in addressing potential treatment.

C. elegans touch neuron exopher production, which increases with high proteostress (4), models several aspects of aggregate/organelle transfer biology. We find that the production of neuronal exophers can be dramatically responsive to specific stress conditions, being enhanced by food withdrawal, oxidative stress, and osmotic stress but influenced relatively little by temperature or hypoxia. We also demonstrate the temporal restriction of stress-induced exopher production to the first 3 d of adult life, and we document a stress ceiling phenomenon, in which the highest levels of individual stress, or a combination of two distinct noninhibitory stresses, suspend exopher production. Finally, we show that fasting-induced exophergenesis is dependent on nonautonomous lipid biosynthesis, FGF-activated RAS/MAPK, and EGF-activated RAS/MAPK signaling pathways. Although details remain to be filled in regarding the complex interactions of the signaling steps, a major point is that environmental and genetic factors can be manipulated nonautonomously to regulate the expulsion of offensive aggregates from neurons. Given the importance of aggregate management in aging and neurodegenerative disease and the poorly understood biology of in vivo aggregate transfer, exopher-related mechanisms may suggest new strategies toward the manipulation of the analogous process in higher organisms.

Acute food withdrawal, oxidative stress, and hyperosmotic stress elevate exopher production, but temperature elevation and hypoxia/anoxia are relatively ineffective at provoking similar responses. Starvation (8, 50), oxidative stress (51), and osmotic stress (52) perturb proteostasis and share ROS elevation (53, 54). The future dissection of the intersection of the genetic and physiological conditions common to these three stresses should provide insight into molecular mechanisms that promote exophergenesis. Likewise, physiological differences in stress responses to temperature and hypoxia, which are not potent inducers of exophers, may help distinguish particular conditions that are specifically correlated with exopher induction.

Exopher production, comprising the release of a large, membrane-surrounded vesicle filled with cellular contents, has the appearance of an energetically costly incident that involves the dynamic loss of organelles and aggregates. Our working model posits that exophergenesis is invoked when the levels of damaged organelles and proteins surpass the neuronal capacity for internal degradation. Consistent with this idea, increasing oxidative challenge and increasing hyperosmotic exposure both increase exophergenesis. Interestingly, conditions of extreme osmotic and oxidative stress markedly suppress the formation of exophers. Moreover, combining two stresses, either of which is sufficient to promote exopher production when introduced alone, can result in exopher suppression. This combinatorial effect can also occur with stress stimuli that themselves do not significantly induce exophers, such as anoxia and modestly elevated temperature. Together, these observations reveal a molecular summing of stress signals that appear to flip the off switch for exophergenesis. The suppression of exopher production under conditions of extreme stress may be caused by energy exhaustion, a molecular repression mechanism, or grievous loss of homeostasis, leading to physiological dysregulation.

Exopher production follows a distinctive and reproducible temporal profile in early adult life (4, 11). In the Ag2mCherry strain, exophers are not produced in larval development but begin to be detected after animals reach reproductive maturity, typically peaking in numbers around Ad2 and returning to low-baseline detection by Ad4. Data included here underscore that the temporal pattern is generally maintained, despite the continued or introduced presence of stresses. In other words, stresses definitively elevate exopher production, but for the most part, these stresses do not extend the period of exopher production later into adult life. Our findings thus define a limited temporal window in which exopher production can be modulated by stresses and suggest the existence of physiological states permissive (or restrictive) for exopher production. A link to reproduction likely defines this permissive period. For some stimuli (paraquat, rotenone, osmotic stress, and a shift to 25 C), we do report a capacity to move the peak day of exopher production ahead to Ad1 or at least to markedly enhance Ad1 levels above nonstressed controls. We infer that these stimuli reach the molecular threshold for exopher triggering faster than other conditions.

The fasting-induced elevation of exopher levels does not require stress transcription factors HSF-1, HIF-1, HLH-30/TFEB, or SKN-1/NRF2 but does depend in part on DAF-16/FOXO, a conserved stress-responsive transcription factor that drives the expression of food-sensitive, oxidative stress resistance and proteostasis genes (27, 44, 55) and is known to exert autonomous and nonautonomous impacts on stress resistance and longevity (56). Our data implicate a FOXO family member in regulation of neuronal aggregate expulsion. Our data do not rule out whether HSF-1, HIF-1, HLH-30/TFEB, or SKN-1/NRF2 might function redundantly in the exopher response to fasting.

How DAF-16 interacts with fat biosynthesis pathways and RAS/ERK signaling in exopher induction remains to be clarified. DAF-16 can control the expression of mdt-15 and has been previously implicated in transtissue benefits by interactions with MDT-15 (57). DAF-16 also intersects with FGF, ERK, and lipid biogenesis pathways and vice versa (for example, refs. 5861). The future definition of how DAF-16 integrates with these signaling pathways and the identification of the transcription factor that mediates the DAF-16independent component of the fasting-induced exopher response will add molecular understanding to what appears to be a complex regulatory network.

Mediator complex subunit MDT-15 is a transcriptional coregulator involved in lipid metabolism (34), response to fasting (32, 62), and oxidative, stress-induced expression of detoxification genes associated with the exposure to reagents like paraquat (63). The requirement for mdt-15 in fasting-induced, neuronal exophergenesis adds a new facet to the MDT-15 integration of multiple, transcriptional regulatory pathways (32), expanding the known roles of MDT-15/SBP-1 to include the activation of extrusion of remote neuronal aggregates in exophers. SBP-1/SREBF2 acts with MDT-15 to promote the expression of lipid metabolism genes (33, 34), and fatty acid synthase fasn-1 can be regulated by these (38, 39, 62, 64, 65). The requirement for multiple genes involved in lipid synthesis in fasting-induced exopher increase suggests that a lipid-based signal may be issued to ultimately direct or modulate neuronal trash expulsion. An equally plausible model is that lipid-dependent machinery is required for upstream signaling that promotes exophergenesis.

Our findings identify FGF ligand egl-17 (but not FGF ligand let-756), FGF receptor egl-15, FGF pathway specific soc-1, and pathway components let-60, mek-2, ksr-1, and mpk-1 as required for the fasting induction of exophers. These data reveal a specific FGF/RAS/ERK signaling pathway that enhances neuronal exopher production when food is withdrawn. Since the candidate RNAi screen for the factors required for fasting-induced exopher elevation was conducted in a strain background that is not readily permissive for neuronal RNAi effects and since RNAi knockdown of pathway components (specifically in neurons) does not block fasting-induced exopher increase, FGF/ERK signaling likely takes place outside of the touch receptor neurons to exert a regulatory role on neuronal exopher production. An interesting potential site of FGFR action is the hypodermis, which is necessary for some MEK-2/MPK-1 MAPK signaling. Such signaling is permissive for fasting-induced exopher production and the hypodermis is a known site of expression of FGFR pathway components (SI Appendix, Fig. S4B). Detailed, cell-specific expression studies will be required to test this model.

The conserved FGF pathway executes numerous roles in mammalian development and homeostasis (66). Interestingly, mammalian FGF21 acts as a global starvation signal that, among other things, impacts lipid metabolism. Although most FGF21 studies feature extended starvation and mousehuman differences have been noted (67), FGF21 is one of the most up-regulated rat liver genes under the conditions of an 8-h fast (68). FGF21 can cross the bloodbrain barrier to change hypothalamic neuron gene expression (69). Overall, the implication of FGF/ERK pathways in the response of neurons to food limitation across diverse metazoans suggests a mechanism that may be ancient and raises the possibility that the FGF branch of these pathways might activate extracellular trash expulsion mechanisms within mammalian neurons. If so, FGF signaling might be considered as a target for the therapeutic elimination of stored neuronal aggregates.

EGF RAS/MAPK signaling is also engaged in fasting-induced exopher elevation. The knockdown of the sole C. elegans EGF ligand lin-3 in whole animal, or only in germline, impaired fasting-induced exopher elevation.

Although RNAi knockdown of the EGFR let-23 and EGF pathway-specific ksr-2 in whole animal or only neurons was not effective, expressing activated, gain-of-function EGFR receptor allele let-23(sa62) in neurons resulted in elevated exopher levels in the absence of fasting. Epistasis studies suggest that EGFR activation in neurons could occur as a downstream target of lipid biosynthesis and FGF signaling. Although definitive establishment of fasting-induced EGFR activation in neurons remains (experimental caveats in interpretation of data are discussed in detail in SI Appendix, Fig. S4C), data are consistent with a role for EGF signaling originating in the germline as an inducer of EGFR-activated responses in neurons that promote exopher production. Future studies will need to confirm neuron requirements and to address whether EGFR directly activates exophergenesis in touch neurons or engages additional neurons as intermediatory signaling centers.

It is important that the germline serves as an EGF source needed for fasting-induced exopher elevation. Indeed, in studies of exopher production under standard growth conditions, we have defined a role for germline in the production of young adult exophers. A key point here is that food-sensing, nonautonomous growth factor signaling across generations can influence seemingly extreme neuronal proteostasis activity.

EGF- and FGF-dependent processes cooperate in development. For example, EGF signaling activates FGF production and a downstream FGF pathway required in vulval epithelial fate specification (70). Starvation conditions can influence the signaling level for the EGF/RAS/ERK pathway that specifies C. elegans vulval cell fateseither starvation or pept-1(RNAi)can enhance RAS/MAPK signaling during vulval fate specification (43). Our documentation of FGF signaling in food limitation responses that elevate neuronal proteostasis outcomes identifies a second C. elegans EGF- and FGF-regulated signaling pathway that responds to food limitation. Why food limitation might induce neuronal trash elimination is unclear. One possibility is that exophergenesis [which we track in single neurons in our study but is likely to also occur in other neurons and cells (4)] might serve as a mechanism to discard superfluous, neuronal proteins and organelles for degradative recycling in neighboring cells as resources become limited.

Our study defines the basic framework by which metabolic stresses engage a distributed network that influences a significant neuronal expulsion phenomenon. Evidence is accumulating that exopher-like extrusion capabilities are not limited to stressed C. elegans neurons [mammalian examples in refs. 71 and 72)]. For example, a recent comprehensive study of mitochondrial expulsion by mouse cardiomyocytes revealed numerous analogies between C. elegans exophers and mouse mitochondrial expulsion models (72). Although elaborating details of molecular homologies remain for the future, that related biology is likely to be conserved holds significant implications of interest with regard to mammalian aging and neurodegeneration. 1) It is interesting that stresses, particularly associated with aging (i.e., oxidative stress) or proteostasis impairment (i.e., osmotic stress), are especially potent in inducing C. elegans exopher elevation; the disruption of exopher-related biology may contribute generally to the decline/dysfunction in aging neurons across phyla. 2) Likewise, the direct demonstration that extreme stress levels, or the summation of distinct, nonconsequential stresses, can effectively shut down the exopher response suggests a type of potential excessive stress impairment relevant to pathological mechanisms. 3) Our data establish that exopher production can be responsive to specific, conserved biochemical signaling, such that chemical strategies for inducing, or limiting, the expulsion of neurotoxic material by exploiting exopher-related mechanisms in mammals might be considered targets for therapeutic manipulation.

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Glycomics Market | By Solution Type, By Application Type, By Industry Type, By Brand,By Region and Forecast 2021-2027 UNLV The Rebel Yell – UNLV The…

Glycomics Market research report delivers a comprehensive study on production capacity, consumption, import and export for all major regions across the world. Report provides is a professional inclusive study on the current state for the market. Analysis and discussion of important industry like market trends, size, share, growth estimates are mentioned in the report.

Glycomics is an emerging field which aims to focus on the structure and function of the glycans in a cell, tissue or in an organism. Glycans are the chain like structures of the carbohydrates that are free or conjugated to macromolecules such as lipids or proteins. They contribute in a diverse selection of biological processes such as protein folding, cell signaling, and immune recognition. These are implicated in a number of diseases such as oncological, autoimmune, and others.

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MARKET SCOPEThe Global Glycomics Market Analysis to 2027 is a specialized and in-depth study of the biotechnology industry with a special focus on the global market trend analysis. The report aims to provide an overview of enteral feeding formulas market with detailed market segmentation by product, application, end user and geography. The global glycomics market is expected to witness high growth during the forecast period. The report provides key statistics on the market status of the leading enteral feeding formulas market players and offers key trends and opportunities in the market.

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Glycomics Market | By Solution Type, By Application Type, By Industry Type, By Brand,By Region and Forecast 2021-2027 UNLV The Rebel Yell - UNLV The...

ASC Therapeutics Receives IND Clearance From the US Food and Drug Administration for ASC618 Second-Generation Gene Therapy for Hemophilia A | DNA RNA…

DetailsCategory: DNA RNA and CellsPublished on Thursday, 08 July 2021 09:39Hits: 355

MILPITAS, CA, USA I July 7, 2021 I ASC Therapeutics, a privately-held biopharmaceutical company pioneering the development of transformative in-vivo gene replacement, gene editing and allogeneic cell therapies for hematologic and other rare disorders today announced that the U.S. Food & Drug Administration (FDA) has cleared an Investigational New Drug (IND) application for ASC618, an investigational second-generation gene therapy for patients with severe and moderately severe hemophilia A. The transformational Adeno-Associated Virus (AAV) construct contains a proprietary B-domain deleted codon-optimized bioengineered chimeric Factor VIII (FVIII) gene and a minimal-length liver-specific promoter, shown in pre-clinical studies to produce therapeutic levels of FVIII protein at doses that are significantly lower than other constructs expressing the native human FVIII currently in clinical trials.

Ruhong Jiang, PhD, ASC Therapeutics CEO said, FDAs IND clearance of ASC618 is a significant endorsement of the discovery, pre-clinical, analytical, clinical, regulatory, quality, manufacturing, and overall capabilities of our organization. This milestone culminates our transformation into a clinical-stage gene and cell therapy biopharmaceutical company.

Steven Pipe, MD, PhD, professor of pediatrics and pathology and pediatric medical director of the hemophilia and coagulation disorders program at the University of Michigan, and the principal investigator of the ASC618 phase 1/2 clinical trial, stated, ASC618 design provides a novel perspective to the field of hemophilia gene therapy. For the first time we will be able to investigate in a clinical setting the relevance of a novel bioengineered construct that has been proven in pre-clinical studies to significantly improve the biosynthesis, protein folding and secretion of factor VIII.

About Hemophilia A

Hemophilia A accounts for most cases of hemophilia (~80%), affecting approximately 1 of every 5000 live-born males. Over a million people around the world are estimated to have hemophilia, including more than 30,000 in the United States (US)1.

Currently, patients with hemophilia A are managed with prophylactic or on-demand replacement therapy with recombinant FVIII or bypassing agents. The major challenges of current treatment regimens, such the short half-life of hemophilia therapeutics with need for frequent intravenous injections, justify ongoing focus on gene replacement therapies.

About ASC618

ASC618 is an AAV8-based gene therapy product incorporating a novel liver-specific promoter and a bioengineered, codon-optimized B domain-deleted FVIII variant; in preclinical studies, ASC618 exhibits at least a 10-fold increase in the biosynthesis and secretion of FVIII compared with native human FVIII bioengineered constructs. ASC618 has the potential to increase durability of clotting factor biosynthesis and secretion by minimizing cellular stress and induction of the unfolded protein response, which may lead to diminished FVIII production from liver cells. ASC618 was developed based on extensive work on a second-generation gene therapy for hemophilia A from an academic team at Emory University2. ASC Therapeutics has obtained exclusive global rights to ASC618 from Expression Therapeutics and has conducted IND-enabling studies in multiple animal models3.

ASC Therapeutics will conduct a phase 1/2 clinical trial to evaluate the safety, tolerability, and preliminary efficacy of ASC618. The program was granted Orphan Drug Designation by the FDA in 2020. The study design is available at http://www.clinicaltrials.gov4.

About ASC Therapeutics

ASC Therapeutics is a biopharmaceutical company pioneering the development of gene replacement therapies, in-vivo gene editing and allogeneic cell therapies for hematological and other rare diseases. Led by a management team of industry veterans with significant global experience in gene and cell therapy, ASC Therapeutics is developing multiple therapeutic programs based on three technology platforms: 1) In-vivo gene therapies that use hepatocytes as a protein biofactory, initially focusing on ASC618 for hemophilia A; 2) In-vivo gene editing, initially focusing on ASC518 for hemophilia A; and 3) Allogeneic cell therapy, initially focusing on Decidua Stromal Cell-based therapy for steroid-refractory acute Graft-versus-Host Disease in patients receiving bone marrow transplantation, mostly to treat hematological malignancies. To learn more please visit http://www.asctherapeutics.com.

References

1 Peyvandi F, Garagiola I, Young G. The past and future of haemophilia: diagnosis, treatments, and its complications. Lancet. 2016 Jul 9;388(10040):187-97 2 Brown HC, Wright JF, Zhou S, et al. Bioengineered coagulation factor VIII enables long-term correction of murine hemophilia A following liver-directed adeno-associated viral vector delivery. Mol Ther Methods Clin Dev. 2014;1:14036 3 Veselinovic M, Gilam A, Ross A, et al. Preclinical Development of ASC618, an Advanced Human Factor VIII Gene Therapy Vector for the Treatment of Hemophilia A: Results from FRG KO Humanized Liver Mice, C57Bl/6 Mice and Cynomolgus Monkeys. Molecular Therapy 2020;28/4:167-8 4 ASC618 Gene Therapy in Hemophilia A Patients. https://www.clinicaltrials.gov/ct2/show/NCT04676048

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ASC Therapeutics Receives IND Clearance From the US Food and Drug Administration for ASC618 Second-Generation Gene Therapy for Hemophilia A | DNA RNA...

From the journals: MCP – American Society for Biochemistry and Molecular Biology

Discovering drugs in deadly venom. Improving transplant outcomes with better storage. Linking mannose glycosylation to eye development. Read about papers on these topics recently published in the journal Molecular & Cellular Proteomics.

The purple cone snail, Conus purpurascens, hunts fish and uses venom to immobilize its prey. Cone snail venom contains diverse toxic peptides, or conopeptides, that are biologically active, target-specific and valuable for drug discovery. One of the most powerful known painkillers, ziconotide, marketed as Prialt, was derived from a conopeptide.

Alex Holt/NIST

A purple cone snail uses its harpoon to pierce through a latex-covered tube,allowing a researcher to collect its venom.

Conopeptides vary among the more than 800 cone snail species and among members of the same species. Even a single cone snail specimen can produce unique conopeptide cocktails, called cabals, specialized for predation or defense. Cone snails also can hypermodify conopeptides at the post-translational step, increasing the diversity of the toxins and extending their range of biological targets. The extreme diversity of conopeptides provides a rich source of biologically active molecules for drug discovery.

In a recent study in the journal Molecular & Cellular Proteomics, Meghan Grandal and colleagues at the National Institute of Standards and Technology collected and analyzed the injected venom from 27 specimens of the purple cone snail. Using high-resolution mass spectrometry techniques, they discovered 543 unique conopeptides derived from 33 base peptide sequences 21 of which were newly identified base peptides. An abundant and newly discovered conopeptide named PVIIIA illustrates the complexity and diversity of modifications to the base conopeptide that occur among various purple cone snail specimens. Building on previous studies, the researchers showed that the different snail specimens produce one of two unique venom cocktails. These two cocktails correspond to what are known as the lightning strike cabal that rapidly induces paralysis of the snails prey and the motor cabal that acts more slowly to induce irreversible paralysis.

Knowing which conopeptides are co-expressed within a specific cocktail will give the researchers important clues as to the possible neural targets of newly identified conopeptides. This will be a critical step in developing new conopeptides into neural probes or therapeutics.

The demand for kidney transplants exceeds the supply of available kidneys. Some donated kidneys go unused, however, due to the prolonged time between circulatory arrest and the start of cold storage. These kidney graftsoften fail or are slow to function. Repairing such kidneys before transplant could greatly increase the available supply.

An unusual type of protein glycosylation, C-mannosylation, involves attaching a single mannose sugar to the amino acid tryptophan by a carboncarbon bond. C-mannosylation, which regulates protein secretion, folding and function, occurs at a specific sequence of four amino acids that begins with the modified tryptophan. Even though about 18% of secreted or transmembrane proteins have this sequence, few studies have looked for the modification. Consequently, researchers know of few proteins that are C-mannosylated.

In a new study in the journal Molecular & Cellular Proteomics, Karsten Cirksena of the Institute of Clinical Biochemistry and a team of researchers in Germany found numerous proteins with altered secretion levels in cells lacking the C-mannosylation machinery. One of these potentially C-mannosylated proteins, a disintegrin and metalloprotease with thrombospondin motifs, or ADAMTS16, is essential during eye development and optic fissure closure. In Chinese hamster ovary cells and Japanese rice fish, the researchers demonstrated that ADAMTS16 can be C-mannosylated, that its secretion depends on C-mannosylation and that loss of a C-mannosylation enzyme causes a developmental eye defect known as a partial coloboma a gap in the eye tissue. Their findings suggest that C-mannosylation, an understudied protein modification, plays a critical role in eye development by regulating secretion of ADAMTS16.

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From the journals: MCP - American Society for Biochemistry and Molecular Biology

Joey Chestnut is commemorated with bobblehead as S.I. competitive eaters can gear up for contests – SILive.com

STATEN ISLAND, N.Y. Nathans Famous competitive eating champ Joey Chestnut now can claim fame on another level. The National Bobblehead Hall of Fame and Museum presented a new bobblehead this week to commemorate the 14-time winner of Coney Islands annual hot dog downing contest.

Chestnut helped design the limited edition bobblehead, each numbered to 2,020. They are available for purchase through the Milwaukee, Wisconsin-based, National Bobblehead Hall of Fame and Museums online store. Each sells for $30 plus a flat-rate shipping charge of $8 per order.

The Joey Chestnuts bobblehead (Courtesy of the National Bobblehead Museum)

The figurine features a slender Chestnut holding a tray of franks while standing on a hot dog-shaped base with a built-in counter. The chomping champ nicknamed Jaws wears a t-shirt that says, Hot Dog Eating Champion.

This year on the Fourth of July, the 37-year-old Chestnut had consumed 76 hot dogs and buns in the annual 10-minute contest. He beat his own record established over a six-year tenure as the reigning Mustard Belt holder. He took the top title in Nathans contest six years ago from another world famous food scoffer, Takeru Tsunami Kobayashi.

According to the HOF and Museum, Chestnut holds over 50 world eating records that include speed consumption of foods that range from lobsters to tamales to Twinkies. The Museum said that he prepares for gorges by expanding the stomach with milk, water and protein supplements, then fasts a few days prior to competition. Last year, Chestnut told CNBC he trains for three months prior to the Coney contest with weekend practice runs and recovery days to follow.

Museum co-founder and CEO Phil Sklar said, Were excited to unveil this unique bobblehead of the greatest eater of all time. By claiming top honors in the Nathans Hot Dog Eating Contest year after year, Joeys victories have become a Fourth-of-July tradition and his legacy as the worlds greatest eater will be hard to top.

Bill LaCurtis of Eltingville celebrates his hot dog eating contest win with a hot dog at the Richmond County Fair Sunday, September 4, 2016 at Historic Richmond Town. (Staten Island Advance File Photo)Staff-Shot

EATING CONTESTS ON STATEN ISLAND

Staten Islands competitive eating contests will happen at the Richmond County Fair this September. Details are forthcoming from the Historical Society that runs the showdowns but, generally speaking, the edibles will involve pies and hot dogs. Former champions have included Eltingville resident Bill LaCurtis, a perennial competitor who tackles the bun and dog separately. He first soaks the bread in water which eases it down the esophagus.

In 2019, Empire Outlets sponsored the boroughs first cannoli-eating competition. Nine competitors from around the country came to the St. George shopping complex to gobble down the goods for gift cards and a gym membership. Eaters bellied up to folding tables for a three-minute session. The winner that year was Gentleman Joe Menchetti of Cheshire, Ct. who shoveled down 22 red-white-and-blue sprinkled cannoli crafted by Marks Bake Shoppe in Richmond.

The annual San Gennaro Feast also holds a contest with cannoli as the centerpiece. Staten Islanders have been in the spotlight at this presentation in Little Italy in years past. Former winners have included Joe Gavone Rose and Krazy Kevin Lipsitz. Lipsitz announced his retirement from competitive eating at the 2008 Feast.

Pamela Silvestri is Advance Food Editor. She can be reached at silvestri@siadvance.com.

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Joey Chestnut is commemorated with bobblehead as S.I. competitive eaters can gear up for contests - SILive.com

Doing This in the Bathroom Can Reduce Your Dementia Risk Best Life – Best Life

As you age, your dementia risk increases rapidly. In fact, according to the Alzheimer's Association, your risk of Alzheimer's doubles every five years after the age of 65. However, there are some things you can do to lower your risk of developing cognitive impairmentincluding one you may be able to do in your very own bathroom. Experts say that by doing this one thing roughly three times per week, you can slash your odds of dementia in half over a 20 year period. Read on to find out which habit may stave off dementia, and how to do it safely.

RELATED:This Heartburn Medication Raises Your Dementia Risk 44 Percent, Study Says.

According to a 2020 study published in the journal Preventive Medicine Reports, "repeated heat exposure like sauna bathing" seems to be beneficial in preventing dementia development. The cohort study, which was conducted in Finland, utilized surveys and existing medical records from 13,994 middle-aged men and women who had not previously been diagnosed with dementia.

When the team compared the health data from those who took a sauna between nine and 12 times per month with that of those who did so four or fewer times per month, they found that those who regularly took a sauna had lower dementia risk. "During the first 20 years of follow-up, the dementia risk of those reporting 912 sauna baths per month (i.e., approximately three per week) was less than a half of the risk of those who had sauna baths only 04 times per month," the team wrote. "The reduction in the dementia risk was attenuated during the follow-up, but the decrease of the risk was still evident after nearly 40 years. Accordingly, a sauna bathing frequency of three times per week may be associated with a reduced risk of dementia," they added, noting that further research is required to verify the benefits.

RELATED:This Could Be Your First Sign of Dementia Years Before Diagnosis, Study Says.

Because sauna bathing is considered commonplace in Finland, nearly all of the study participants practiced the habit, and did so on average 6.03 times per month. These sessions typically lasted under 15 minutes at a temperature below 100 degrees Celsius (212 degrees Fahrenheit), the researchers wrote.

They found that "a straight stay in heat for five to 14 minutes per heat session vs. less than 5 minutes was suggestively related to a reduced risk [of dementia]. The most favorable sauna temperature for dementia protection was 8099 degrees Celsius [176-210 degrees Fahrenheit]," they wrote.

Wondering why a sauna would lower your dementia risk? The Finnish team said that while more research is needed on the matter, there are several possible answers related to "physiological, metabolic, and cellular changes which may affect brain function."

In particular, they say that a sudden elevation in temperature causes heat shock, which leads to the creation of something called "heat shock proteins." The researchers explained that these "are important regulators in normal cell functions and have an essential role in guarding and controlling protein formation. Because disturbances of protein construction and folding are central to the development of neurological diseases, heat shock proteins may be important in maintaining protein homeostasis in the brain."

Additionally, saunas may improve vascular and cardiovascular function, which increases cerebral blood flow and lowers inflammation. "It is possible that some of the effects of sauna in the brain are conveyed via reduced inflammation," the team wrote.

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Though the researchers found a lower risk of dementia in those who took a sauna on average three times per week, they also observed that those who did so in extremely hot temperatures were at significantly elevated risk of the brain disease. "Sauna heat which is too high may not be good for the brain. The dementia risk of those bathing in sauna temperatures higher than 100 degrees Celsius [212 degrees Fahrenheit] doubled compared to those bathing at temperatures lower than 80 degrees Celsius [176 degrees Fahrenheit] during the first twenty years of follow-up," the team warned.

However, most saunas in the U.S.like the one you may have at your gym, or, if you're lucky, in your homeare heated to temperatures between 150 and 195 degrees Fahrenheit. They typically include a thermometer and temperature controls. Be sure to check the sauna's settings before entering, and limit your time inside to no more than 15 minutes.

RELATED:If You Do This in Your Sleep, Get Checked for Dementia, Says Mayo Clinic.

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Doing This in the Bathroom Can Reduce Your Dementia Risk Best Life - Best Life

Scientists Pinpoint the Spots of Early Prion Protein Deposition in the Retina – Gilmore Health News

What is prion disease?

Prion diseases are a type of neurodegenerative disorder that is produced by the accumulation of abnormal proteins in the brain. Prion disease predominantly affects the brain, but it can also attack the eyes, especially the light-sensitive photoreceptors called cones and rods which are present in the retina, and other organs. These are steadily deteriorating and typically deadly diseases of the brain and can occur in people as well as some other mammals. Examples: mad cow disease in cattle, Creutzfeldt-Jakob disease in people, chronic wasting disease in deer, elk, and moose, and bovine spongiform encephalopathy in cattle.

Prion Infected Retina. Image Courtesy of NIH

Read Also: Creutzfeldt-Jakob Disease: A Lab Technician Gets Disease 7 Years After Accidental Cut

A recent study done by scientists at the National Institutes of Health states that the initial eye injury from prion disease occurs in the cone photoreceptor cells, especially in the cilia and the ribbon junctions. The researchers say, their discovery may provide understanding on human retinitis pigmentosa, an inherited disorder with closely related photoreceptor degradation advancing into blindness. The understanding of how prion diseases develop in the eyes can aid scientists to look for strategies to steady the growth of prion diseases.

In their study, the researchers, from NIHs National Institute of Allergy and Infectious Diseases at Rocky Mountain Laboratories in Hamilton, Montana, used research mice diseased with scrapie, a prion disease routine to sheep and goats. Scrapie is nearly associated with human prion diseases, Creutzfeldt-Jakob disease (CJD).

Read Also: Alzheimers: What If It Is Similar to Mad Cow Disease?

The scientists discovered the accumulation of a lump of prion protein was seen first in cone photoreceptors next to the cilia, pipe-like formation needed for transferring molecules between cellular sections with help of the confocal microscope. The study suggests that by obstructing the movement through cilia, these clumps may layout a key early process by which prion infection particularly smashes photoreceptors. Relatable findings were seen in rods as well.Exactly before the destruction of ribbon synapses (specialized neutron links present in the eye and ear neural pathways) and end of photoreceptors, there was an accumulation of prion protein in these structures.

The findings from this study were unique and were never observed before. The association between prion protein and retinal injury is probably present in all prion-vulnerable species, as well as humans.

There are other kinds of declining disorders that are also distinguished by abnormal folding of self-proteins, such as Alzheimers and Parkinsons diseases. The scientists are looking to investigate if related findings take place in the retinas of these people.

Read Also: An Artificial Retina to Restore Sight Could Soon Become a Reality

Prion-induced photoreceptor degeneration begins with misfolded prion protein accumulation in cones at two distinct sites: cilia and ribbon synapses

Prion Seeds Distribute throughout the Eyes of Sporadic Creutzfeldt-Jakob Disease Patients

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Scientists Pinpoint the Spots of Early Prion Protein Deposition in the Retina - Gilmore Health News

Perception of structurally distinct effectors by the integrated WRKY domain of a plant immune receptor – pnas.org

Significance

This study reveals a mechanism for effector perception by a plant NLR immune receptor that contains an integrated domain (ID) that mimics an authentic effector target. The Arabidopsis immune receptors RRS1 and RPS4 detect the Pseudomonas syringae pv. pisi secreted effector AvrRps4 via a WRKY ID in RRS1. We used structural biology to reveal the mechanisms of AvrRps4CWRKY interaction and demonstrated that this binding is essential for effector recognition in planta. Our analysis revealed features of the WRKY ID that mediate perception of structurally distinct effectors from different bacterial pathogens. These insights could enable engineering NLRs with novel recognition specificities, and enhance our understanding of how effectors interact with host proteins to promote virulence.

Plants use intracellular nucleotide-binding domain (NBD) and leucine-rich repeat (LRR)containing immune receptors (NLRs) to detect pathogen-derived effector proteins. The Arabidopsis NLR pair RRS1-R/RPS4 confers disease resistance to different bacterial pathogens by perceiving the structurally distinct effectors AvrRps4 from Pseudomonas syringae pv. pisi and PopP2 from Ralstonia solanacearum via an integrated WRKY domain in RRS1-R. How the WRKY domain of RRS1 (RRS1WRKY) perceives distinct classes of effector to initiate an immune response is unknown. Here, we report the crystal structure of the in planta processed C-terminal domain of AvrRps4 (AvrRps4C) in complex with RRS1WRKY. Perception of AvrRps4C by RRS1WRKY is mediated by the 2-3 segment of RRS1WRKY that binds an electronegative patch on the surface of AvrRps4C. Structure-based mutations that disrupt AvrRps4CRRS1WRKY interactions invitro compromise RRS1/RPS4-dependent immune responses. We also show that AvrRps4C can associate with the WRKY domain of the related but distinct RRS1B/RPS4B NLR pair, and the DNA-binding domain of AtWRKY41, with similar binding affinities and how effector binding interferes with WRKYW-box DNA interactions. This work demonstrates how integrated domains in plant NLRs can directly bind structurally distinct effectors to initiate immunity.

Plants coevolve with their pathogens, resulting in extensive genetic variation in host immune receptor and pathogen virulence factor (effector) repertoires (1). To enable host colonization, pathogenic microbes deliver effector proteins into host cells that suppress host immune responses and elevate host susceptibility by manipulating host physiology (2, 3). Plants have evolved surveillance mechanisms to detect and then activate defenses that combat pathogens, and detect host-translocated effectors via nucleotide-binding domain (NBD) and leucine-rich repeat (LRR)containing receptors (NLRs) (4). NLR genes are highly diverse, showing both copy-number and presence/absence of polymorphisms, and different alleles can exhibit distinct effector recognition specificities (5, 6). As described by the gene-for-gene model, plant NLRs usually recognize a single effector (7). However, NLRs capable of responding to multiple effectors are known (5, 8, 9).

NLRs typically contain an N-terminal Toll/interleukin-1 receptor (TIR) or coiled-coil (CC) domain, a central NBD (NB-ARC [NBD shared with APAF-1, various R proteins, and CED-4]), and a C-terminal LRR domain (6). In addition to these canonical domains, some NLRs have evolved to carry integrated domains that mimic effector virulence targets and facilitate immune activation by directly binding effectors (1015). Interestingly, integrated domain-containing NLRs (NLR-IDs) usually function with a paired helper NLR, which is required for immune signaling (10, 16).

The Arabidopsis NLR pair RRS1-R/RPS4 is a particularly interesting NLR-ID/NLR pair that confers resistance to bacterial pathogens Pseudomonas syringae and Ralstonia solanacearum, and also to a fungal pathogen (Colletotrichum higginsianum) where the effector is unknown (1720). RRS1-R contains an integrated WRKY domain near its C terminus (RRS1WRKY), which interacts with two structurally distinct type III secreted bacterial effectors, AvrRps4 from P. syringae pv. pisi and PopP2 from R. solanacearum (13, 14, 21, 22). The RRS1WRKY domain may mimic the DNA-binding domain of WRKY transcription factors (TFs), the putative virulence targets of AvrRps4 and PopP2, to enable immune perception of these effectors (13). Two alleles of RRS1 have been identified that differ in the length of the C-terminal extension after the WRKY domain (SI Appendix, Fig. S1). RRS1-R, from the accession Ws-2, has a 101amino acid C-terminal extension beyond the end of the WRKY domain, and can perceive AvrRps4 and PopP2, while RRS1-S from Col-0, which perceives AvrRps4 but not PopP2, is likely a derived allele with a premature stop codon, and has only an 18amino acid C-terminal extension (23). Most Arabidopsis ecotypes also carry a paralogous and genetically linked RRS1B/RPS4B NLR pair, which only perceives AvrRps4 (24). RRS1B/RPS4B share a similar domain architecture with RRS1/RPS4, including 60% sequence identity in the integrated WRKY domain.

AvrRps4 is proteolytically processed in planta to produce a 133amino acid N-terminal fragment (AvrRps4N) and an 88amino acid C-terminal fragment (AvrRps4C) (25, 26). Previous studies have highlighted the role of AvrRps4C in triggering RRS1/RPS4-dependent immune responses (25, 26). AvrRps4N has been reported to potentiate immune signaling from AvrRps4C (27, 28). PopP2 is sequence and structurally distinct from AvrRps4 and has an acetyltransferase activity that is likely related to its role in virulence. The structural basis of PopP2 perception by RRS1WRKY has been determined (29), but how RRS1WRKY binds AvrRps4C and whether this is via a shared or different interface to PopP2 is unknown.

Here, we determined the structural basis of AvrRps4C recognition by the integrated WRKY ID of RRS1. The recognition of AvrRps4C is mediated by the 2-3 segment of RRS1WRKY, the same region used to bind PopP2. This segment interacts with surface-exposed acidic residues of AvrRps4C. Structure-informed mutagenesis at the AvrRps4CRRS1WRKY interface identifies AvrRps4 residues required for proteinprotein interactions invitro and in planta and AvrRps4 perception and immune responses. Residues mediating the interaction of AvrRps4C and RRS1WRKY are conserved in both the RRS1BWRKY and the DNA-binding domain of WRKY TFs, and AvrRps4C mutants that prevent interaction with RRS1WRKY also disrupt binding to AtWRKY41. This supports the hypothesis that the RRS1WRKY mimics host WRKY TFs through a shared effector-binding mechanism. We also show that AvrRps4C prevents the interaction of RRS1WRKY and AtWRKY41 with W-box DNA, most likely via steric blocking, at the same WRKY domain site acetylated by PopP2.

To investigate how AvrRps4C interacts with the RRS1WRKY domain, constructs comprising residues 134 to 221 of AvrRps4C (the in planta processed C-terminal fragment) and residues 1194 to 1273 of RRS1-R (corresponding to the RRS1WRKY domain) were separately expressed in Escherichia coli and proteins were purified via a combination of immobilized metal-affinity chromatography (IMAC) via 6His tags and gel filtration (Superdex 75 26/60 and Superdex S75 16/60) (see SI Appendix, Materials and Methods for full details). We qualitatively assessed the interaction of purified AvrRps4C with RRS1WRKY using analytical gel filtration chromatography. Individually, the proteins displayed well-separated elution profiles. RRS1WRKY eluted at a volume (Ve) of 14.9 mL and AvrRps4C eluted at a Ve of 12.1 mL (Fig. 1A). Following incubation of a 1:1 molar ratio of the proteins, we observed a new elution peak with an earlier Ve of 11.8 mL, and a lack of absorption peaks for the separate proteins (Fig. 1A). This demonstrates complex formation invitro and suggests a 1:1 stoichiometry of the AvrRps4CRRS1WRKY complex.

AvrRps4C interacts with the WRKY domain of RRS1 invitro. (A) Analytical gel filtration traces (using a Superdex 75 10/300 column) for AvrRps4C alone (gold), RRS1WRKY alone (green), and AvrRps4C with RRS1WRKY (blue) with sodium dodecyl sulfatepolyacrylamide gels of relevant fractions. An equimolar ratio of AvrRps4C and RRS1WRKY was used for the analysis. AvrRps4C runs as a dimer invitro. Poor absorbance for AvrRps4C at 280 nm is due to its low molar extinction coefficient. (B) ITC titrations of AvrRps4C with RRS1WRKY. (B, Upper) Raw processed thermogram after baseline correction and noise removal. (B, Lower) The experimental binding isotherm obtained for the interaction of AvrRps4C and RRS1WRKY together with the global fitted curves (displayed in red) were obtained from three independent experiments using AFFINImeter software (61). Kd and binding stoichiometry (N) were derived from fitting to a 1:1 binding model.

We then determined the binding affinities of the interaction using isothermal titration calorimetry (ITC). Titration of AvrRps4C into a solution of RRS1WRKY resulted in an exothermic binding isotherm with a fitted dissociation equilibrium constant (Kd) of 0.103 M (Fig. 1B) and stoichiometry of 1:1. The thermodynamic parameters of the interaction are given in SI Appendix, Table S1. As RRS1WRKY may be a mimic of WRKY TFs, we explored the binding kinetics of AvrRps4C with AtWRKY41 and AtWRKY70 by ITC [previous reports have shown that AvrRps4 interacts with these proteins in yeast two-hybrid assay and by in planta coimmunoprecipitation (13, 30)]. We chose AtWRKY41 for further study as this protein expressed and purified stably from E. coli. AvrRps4C interacted with AtWRKY41 with a Kd of 0.02 M, and with similar thermodynamic parameters as RRS1WRKY (SI Appendix, Fig. S2 and Table S1).

To reveal the molecular basis of the AvrRps4C and RRS1WRKY interaction, we coexpressed the proteins in E. coli, purified the complex, and obtained crystals that diffracted with 2.65- resolution at the Diamond Light Source (SI Appendix, Materials and Methods). The crystal structure of the AvrRps4CRRS1WRKY complex was solved by molecular replacement using the structure of RRS1WRKY (from the PopP2RRS1WRKY complex, Protein Data Bank [PDB] ID code 5W3X) and AvrRps4C (PDB ID code 4B6X) as models (SI Appendix, Materials and Methods). X-ray data collection, refinement, and validation statistics are shown in SI Appendix, Table S2.

The structure comprises a 1:1 complex of AvrRps4C and RRS1WRKY (Fig. 2A), which supports the 1:1 binding model in ITC. Overall, AvrRps4C adopts the same antiparallel -helical CC structure in both free [PDB ID code 4B6X (26)] and complexed forms, with an rmsd of 0.66 over 59 C atoms (SI Appendix, Fig. S3A). Also, RRS1WRKY adopts a conventional WRKY domain fold [rmsd of 2.03 over 61 C atoms compared with AtWRKY1, PDB ID code 2AYD (31)] comprising a four-stranded antiparallel -sheet (2 to 5) stabilized by a zinc ion (C2H2 type). Comparison of RRS1WRKY in the AvrRps4CRRS1WRKY and PopP2RRS1WRKY complex (PDB ID code 5W3X) structures reveals high conformational similarity, with an rmsd of 1.81 over 64 C atoms. The characteristic WRKY sequence signature motif WRKYGQK maps to the 2-strand of RRS1WRKY and is directly involved in contacting AvrRps4C (Fig. 2B and SI Appendix, Figs. S3A and S4). The same surface, including the 2-3 strands of RRS1WRKY, forms contacts with PopP2 in the PopP2RRS1WRKY complex (29) (SI Appendix, Fig. S4), and mutants at this surface showed it to be essential for PopP2 recognition.

Structure of the AvrRps4CRRS1WRKY complex. (A) Electrostatic surface representation of AvrRps4C in the AvrRps4CRRS1WRKY crystal structure displaying a prominent negative patch in AvrRps4 at the interacting interface. (B) Schematic representation of AvrRps4CRRS1WRKY, highlighting interfacing residues. AvrRps4C is shown in gold cartoon and RRS1WRKY is shown in green with surface-exposed side chains as sticks. (C) Close-up view of the interactions of AvrRps4C with the 2-3 segment of RRS1WRKY. Hydrogen bonds are shown as dashed lines, and water molecules are depicted as red spheres. The Zn2+ ion is also displayed.

The total interface area buried in the AvrRps4CRRS1WRKY complex is 591.8 2, encompassing 12.3% (589.7 2) and 11.9% (593.9 2) of the total accessible surface areas of the effector and integrated domain, respectively [as calculated by PDBePISA (32); full details are given in SI Appendix, Table S3]. The binding interface between AvrRps4C and RRS1WRKY is largely formed by residues from the 2-3 strand of RRS1WRKY, which present a positive surface patch that interacts with acidic residues on the surface of AvrRps4C (Fig. 2A, SI Appendix, Fig. S3B, and Movie S1). The interaction between the 2-segment of RRS1WRKY, which harbors the WRKYGQK motif, and AvrRps4C includes hydrogen bonds and/or salt-bridge interactions involving Tyr1218 and Lys1221 of RRS1WRKY and AvrRps4 Glu175, Glu187, and Asn171. Notably, the side chain of RRS1WRKY Lys1221 protrudes into an acidic cleft on the surface of AvrRps4C to contact the side chains of both AvrRps4 Glu175 and Glu187 (Fig. 2 B and C and Movie S1). The OH atom of RRS1WRKY Tyr1218 forms a hydrogen bond with the ND2 atom of AvrRps4 Asn171 (Fig. 2 B and C). Additional intermolecular contacts are formed by the 2-3 loop of RRS1WRKY involving the backbone carbonyl oxygen and nitrogen of Asp1222, which form hydrogen bonds with the side chains of AvrRps4 Asn190 and Gln194. The complex between AvrRps4C and RRS1WRKY is further stabilized by the 3-strand of RRS1WRKY that forms hydrogen bonds and salt-bridge interactions via side chains of RRS1WRKY Arg1230, Tyr1232, and Arg1234 to AvrRps4 Glu175 and Asp164 (Fig. 2 B and C). A detailed interaction summary is provided in SI Appendix, Table S4.

To evaluate the contribution of residues at the AvrRps4CRRS1WRKY interface to complex formation invitro, we generated six structure-guided mutants in AvrRps4C (native amino acid to Ala) and tested the effect on protein interactions by ITC. Each AvrRps4C mutant was purified from E. coli under the same conditions as for the wild-type protein, and proper folding was evaluated by circular dichroism (CD) spectroscopy (SI Appendix, Fig. S5). ITC titrations were carried out as for the wild-type interactions. Individual ITC isotherms are shown in Fig. 3, and the thermodynamic parameters of the interactions are shown in SI Appendix, Table S1. We found that mutating AvrRps4 residues Asp164 (D164A), Glu175 (E175A), Glu187 (E187A), and double mutant Glu175/Glu187 (EE/AA) essentially abolished complex formation invitro (Fig. 3). Mutations in residues Asn171 (N171A) and Gln194 (Q194A) retained binding to RRS1WRKY, with N171A displaying wild-type levels and Q194A showing an approximately sevenfold reduction in affinity. Besides structure-guided mutants, we also tested binding of an AvrRps4 quadruple mutant, carrying mutations in the N-terminal KRVY motif (KRVY/AAAA) [previously identified to be essential for the virulence activity and perception of AvrRps4 (25)], with RRS1WRKY. Unlike most interface mutants, the AvrRps4C KRVY/AAAA mutant retained wild typelike binding affinity with RRS1WRKY (Fig. 3).

Structure-guided mutants of AvrRps4C at the AvrRps4CRRS1WRKY interface disrupt interaction with RRS1WRKY invitro. ITC titrations of wild-type AvrRps4C and mutants with RRS1WRKY. (Upper) Raw processed thermograms after baseline correction and noise removal. (Lower) Experimental binding isotherms obtained for the interaction of AvrRps4C wild type and mutants with RRS1WRKY together with the global fitted curves (displayed in red) obtained from three independent experiments using AFFINImeter software (61). Kd was derived from fitting to a 1:1 binding model. N.B., nonbinding.

Since AvrRps4C binds RRS1WRKY and AtWRKY41 with similar affinity (SI Appendix, Fig. S2), we tested the impact of the AvrRps4C EE/AA double mutant on the binding to AtWRKY41. We found that this mutant also abolishes interaction with AtWRKY41, suggesting the same AvrRps4-binding interface is shared with different WRKY proteins (SI Appendix, Fig. S2).

To validate the biological relevance of the AvrRps4CRRS1WRKY interface observed in the crystal structure, we tested the effect of the AvrRps4C mutants above on RRS1-R/RPS4mediated immunity by monitoring the cell-death response in N. tabacum. Agrobacterium-mediated transient expression of wild-type AvrRps4 triggers a hypersensitive cell-death response (HR) 5 d post infiltration when coexpressed with RRS1-R/RPS4 (Fig. 4A). The previously characterized inactive AvrRps4 KRVY/AAAA mutant (25, 26) was used as a negative control. We found that AvrRps4 mutations at positions D164, E175, and E187 and the double mutant E175/E187 prevented RRS1-R/RPS4dependent cell-death responses, consistent with their loss of binding to RRS1WRKY invitro (Fig. 4A). Interestingly, the N171A mutation, which retained its binding to RRS1WRKY invitro, displayed wild typelike cell deathinducing activity, and Q194A with an approximately sevenfold reduction in RRS1WRKY affinity consistently exhibited a weaker cell-death response. Expression of all mutants was confirmed by immunoblotting (Fig. 4B). In addition to RRS1-R/RPS4, we also explored the effect of AvrRps4 structure-based mutations on RRS1-S/RPS4dependent cell death in N. tabacum (SI Appendix, Figs. S1 and S6A). We found that AvrRps4 variants elicited similar immune responses when transiently coexpressed with RRS1-S/RPS4 or RRS1-R/RPS4.

Structure-guided mutants of AvrRps4 at the AvrRps4CRRS1WRKY interface compromise RRS1-R/RPS4mediated cell-death responses and invivo binding in Nicotiana. (A) Representative leaf images showing RRS1-R/RPS4mediated cell-death response to wild-type structure-guided mutants of AvrRps4. Agroinfiltration assays were performed in 4- to 5-wk-old N. tabacum leaves, and cell death was assessed at 4 d post infiltration. The experiment was repeated three times with similar results. (B) Coimmunoprecipitation (co-IP) of RRS1-RWRKY+83 (6His/3FLAG-tagged) with AvrRps4 and variants (4myc-tagged) in N. benthamiana. Blots show protein accumulations in total protein extracts (input) and immunoprecipitates obtained with anti-FLAG magnetic beads when probed with appropriate antisera. Empty vector was used as a control. The experiment was repeated at least three times, with similar results.

To determine whether loss of RRS1-R/RPS4mediated HR in transient assays correlates with the loss of AvrRps4 binding to RRS1WRKY invivo as well as invitro, we performed coimmunoprecipitation assays using full-length C-terminal 4myc-tagged AvrRps4 constructs and C-terminal 6His/3FLAG-tagged constructs of RRS1-RWRKY+83 (equivalent to RRS1-D5/6R as defined in ref. 23). Wild-type AvrRps4 associates with RRS1-RWRKY+83 in its in planta processed form (Fig. 4B). Consistent with the cell-death phenotype and invitro binding data, no association between AvrRps4 mutants D164A, E175A, E187A, or EE/AA and RRS1WRKY+83 was detected (Fig. 4B). Further, we observed wild-type levels of association of AvrRps4 N171A with RRS1WRKY+83, while AvrRps4 Q194A appeared to coimmunoprecipitate weakly. The AvrRps4 KRVY/AAAA mutant displayed wild typelike binding affinity toward RRS1WRKY+83, as observed previously (26).

Next, we investigated the impact of AvrRps4 structure-guided mutations on the activation of RRS1-R/RPS4dependent immune responses using HR assays in Arabidopsis. Constructs carrying full-length AvrRps4 wild type and mutants, flanked by a 126-bp native AvrRps4 promoter, were delivered into plant cells by infiltration using the Pf0-EtHAn (Pseudomonas fluorescens effector-to-host analyzer, hence Pf0) system (33). HR assays used Arabidopsis ecotype Ws-2 (encoding RRS1-R/RPS4 and RPS4B/RRS1B) and Ws-2 rrs1-1/rps4-21/rps4b-1 (RRS1-R/RPS4/RPS4B triple-knockout) lines and were scored at 20 h post infiltration. Pf0 carrying wild-type AvrRps4 triggered HR in Ws-2, but not in Ws-2 rrs1-1/rps4-21/rps4b-1, as previously reported (13, 26). AvrRps4 KRVY/AAAA, an HR inactive mutant, was used as a negative control (26). The structure-guided mutants AvrRps4 D164A, E175A, E187A, and EE/AA all showed a complete loss of HR in Ws-2, with AvrRps4 Q194A showing a weaker HR and N171A showing a wild typelike phenotype (Fig. 5A). None of the AvrRps4 variants triggered HR in Ws-2 rrs1-1/rps4-21/rps4b-1 (Fig. 5A).

Structure-guided mutants of AvrRps4 compromise RRS1-R/RPS4dependent recognition specificities and restriction of bacterial growth in Arabidopsis. (A) HR assay in different Arabidopsis accessions using P. fluorescens Pf0-1 secreting AvrRps4 wild type and structure-guided mutants. Constructs were delivered to the Arabidopsis Ws-2 and rrs1-1/rps4-21/rps4b-1 knockout background and HR was recorded 20 h post infiltration. Fraction refers to the number of leaves showing HR of 12 randomly inoculated leaves. This experiment was repeated at least three times with similar results. (B) In planta bacterial growth assays of Pto DC3000 secreting AvrRps4 wild type and mutant constructs. Bacterial suspensions with OD600 = 0.001 were pressure-infiltrated into the leaves of 4- to 5-wk-old Arabidopsis plants. Values are plotted from three independent experiments (denoted in different colors). Statistical significance of the values was calculated by one-way ANOVA followed by post hoc Tukey honestly significant difference analysis. Letters above the data points denote significant differences (P < 0.05). A detailed statistical summary can be found in SI Appendix, Table S5. CFU, colony forming unit.

In addition to Ws-2, we also performed a parallel set of experiments in Arabidopsis ecotype Col-0 (which encodes the RRS1-S allele) and the Col-0 rrs1-3/rrs1b-1 (RRS1-S/RRS1B double-knockout) line. Overall, we observed a weaker HR toward AvrRps4 wild type and mutants in Col-0 in comparison with Ws-2. Nevertheless, a similar pattern of HR phenotypes was observed in Col-0 compared with Ws-2, and none of the AvrRps4 variants triggered HR in the Col-0 rrs1-3/rrs1b-1 line (SI Appendix, Fig. S6B). The pattern of HR phenotypes conferred by the AvrRps4 interface mutants further validates the AvrRps4CRRS1WRKY structure and the role of these residues in recognition of AvrRps4 by the RRS1/RPS4 receptor pair.

Having demonstrated the role of AvrRps4 interface residues in effector-triggered HR in Arabidopsis, we next investigated their effects on bacterial growth. We performed bacterial growth assays on Arabidopsis ecotypes Ws-2, Col-0, Ws-2 rrs1-1/rps4-21/rps4b-1, and Col-0 rrs1-3/rrs1b-1 using the P. syringae pv. tomato (Pto) DC3000 strain carrying AvrRps4 wild type or structure-based mutants. Since both the single mutants AvrRps4 E175A and E187A displayed the same impaired HR as the double AvrRps4 EE/AA mutant in previous assays, we focused on AvrRps4 EE/AA only for this assay. Bacterial growth was scored at 3 d post infection. Pto DC3000 carrying wild-type AvrRps4 displayed reduced growth on Ws-2 when compared with the mutant background (Ws-2 rrs1-1/rps4-21/rps4b-1), presumably due to the activation of RRS1-R/RPS4dependent immunity (Fig. 5B). The effector mutants AvrRps4 D164A, EE/AA, and KRVY/AAAA, which displayed a complete loss of HR in Ws-2, showed a severe or complete lack of restriction of bacterial growth in Ws-2 (Fig. 5B). Pto DC3000:AvrRps4 Q194A and Pto DC3000:AvrRps4 N171A showed reduced bacterial growth (but not full restriction) when compared with wild-type AvrRps4, even though they displayed a similar cell-death phenotype in N. tabacum (albeit weaker for AvrRps4 Q194A) and HR in Arabidopsis (Figs. 4A and 5A). All the Pto DC3000:AvrRps4 variants tested displayed indistinguishable bacterial growth in the RRS1-R/RPS4 loss-of-function line (Fig. 5B). Finally, all the Pto DC3000:AvrRps4 variants displayed similar bacterial growth profiles in the Col-0 and Col-0 rrs1-3/rrs1b-1 line when compared with Ws-2 and Ws-2 rrs1-1/rps4-21/rps4b-1 (SI Appendix, Fig. S6C).

In addition to RRS1/RPS4, the RRS1B/RPS4B pair can confer recognition of AvrRps4 in Arabidopsis (24). Sequence alignment revealed an overall 60% amino acid identity of the integrated WRKY domains from RRS1 and RRS1B, with the WRKYGQK motif and all residues interfacing with AvrRps4C conserved (SI Appendix, Fig. S7). To explore AvrRps4 recognition by RRS1B/RPS4B, we performed ITC titrations of RRS1BWRKY with wild-type AvrRps4C invitro. We found that RRS1BWRKY binds to AvrRps4C three times more weakly than RRS1WRKY (SI Appendix, Fig. S7), possibly due to subtle changes imposed by residues outside the direct binding interface. When comparing the binding kinetics with the strength of immune responses in planta, we observed a weaker RRS1B/RPS4B-dependent HR to AvrRps4 compared with RRS1/RPS4. Nonetheless, both NLR pairs displayed a similar profile of immune responses toward the AvrRps4 structure-guided mutants in transient cell-death assays and in Arabidopsis HR assays (SI Appendix, Fig. S7).

To regulate gene expression, WRKY TFs bind to specific W-box DNA motifs in the promoters of their target genes (3436). Intriguingly, the majority of the AvrRps4-interacting residues are conserved within the DNA-binding domain of WRKY TFs (SI Appendix, Fig. S4 and Movie S2) and are indispensable for DNA binding (34). To test if AvrRps4 interferes with the W-box DNA-binding activity of RRS1WRKY and AtWRKY41, we preincubated increasing concentrations of AvrRps4C and the AvrRps4C EE/AA mutant (as a negative control) and studied their effect on the DNA-binding capacity of RRS1WRKY and AtWRKY41 using both electrophoretic mobility-shift assay (EMSA) and surface plasmon resonance (SPR)based assays. We found that the interaction of RRS1WRKY and AtWRKY41 with W-box DNA was reduced after preincubation with increasing concentrations of AvrRps4C but not the AvrRps4C EE/AA mutant (Fig. 6 and SI Appendix, Figs. S8S10), revealing that AvrRps4C interferes with WRKY binding to W-box DNA.

AvrRps4 interferes with W-box DNA binding by the RRS1WRKY domain. (A) EMSA of DNA binding by RRS1WRKY following preincubation of increasing concentrations of AvrRps4C or AvrRps4C EE/AA mutant. Bovine serum albumin (BSA) was used as a negative control for W-box DNA binding. Scrambled DNA was used as a negative control to test the specificity of RRS1WRKY to W-box DNA. The experiment was repeated three times with similar results. (B) An SPR ReDCaT assay was performed using W-box and scrambled DNA (as a negative control). Percentage of normalized response (% Rmax) of RRS1WRKY binding to W-box DNA and scrambled DNA (denoted by an asterisk) immobilized on a ReDCaT SPR chip. Titrations were performed following preincubation of 2 M RRS1WRKY with increasing concentrations of AvrRps4C wild type and AvrRps4C EE/AA mutant. The experiment was performed in eight replicates (each dot represents one replicate).

Despite recent advances, structural knowledge of how diverse integrated domains in plant NLRs perceive pathogen effectors is limited. Here, we investigated how the integrated WRKY domain of the Arabidopsis NLR RRS1 binds to the Pseudomonas effector AvrRps4, and how this underpins RRS1/RPS4-dependent immunity in planta. Further, through this work, we gained insights into interfaces in the RRS1WRKY domain that are crucial for perception of two structurally unrelated effectors from distinct bacterial pathogens, which may have implications for NLR integrated domain engineering.

Transcriptional reprogramming upon NLR activation is well-established as an early immune response in plants (3739), and direct interactions between NLRs and TFs have been reported (4044). WRKY TFs are important molecular players in the regulation of plant growth and development and abiotic and biotic stresses (35, 36, 45). Typically, WRKY TFs target genes by binding W-box DNA in promoters, via a signature amino acid motif, WRKYGQK, to either promote or repress transcription (34, 4648). As WRKY TFs play an important role in plant immunity, it is unsurprising that they are often found as integrated domains in NLR immune receptors (49), supporting the hypothesis that pathogen effectors enhance virulence by targeting WRKY TFs. Therefore, understanding how effectors bind to WRKY integrated domains may inform how effector/WRKY binding promotes disease. The structure of the AvrRps4CRRS1WRKY complex reveals that the effector directly interacts with the DNA-binding WRKYGQK motif, likely rendering it unavailable for binding to DNA (SI Appendix, Fig. S4 and Movie S2). AvrRps4C binds to AtWRKY41 with similar thermodynamic parameters to RRS1WRKY, and interface mutants that prevent AvrRps4C interaction with RRS1WRKY prevent interaction with AtWRKY41, supporting the hypothesis that AvrRps4C binds different WRKY proteins via a similar interface. WRKY TFs bind W-box DNA sequences in the promoters of their target genes. We used EMSAs and SPR assays to observe how the interaction of AvrRps4C with RRS1WRKY or AtWRKY41 affects the binding of these proteins to a generic W-box DNA sequence. Preincubation of RRS1WRKY and AtWRKY41 with increasing amounts of AvrRps4C reduced DNA-binding activity, whereas preincubation with AvrRps4 EE/AA showed no significant difference (Fig. 6 and SI Appendix, Figs. S8S10). WRKY domain residues interacting with AvrRps4C are well-conserved in these TFs (SI Appendix, Fig. S11), suggesting that AvrRps4 could interfere with and sterically block DNA binding of multiple WRKY TFs, thus promoting virulence. In addition to WRKY TFs, a recent publication suggests AvrRps4 can interact with BTS (nucleus-located Fe sensor BRUTUS) domains to affect pathogen colonization (50). Understanding whether these functions are related requires further investigation.

Comparing the AvrRps4CRRS1WRKY structure with that of PopP2RRS1WRKY (29) reveals an overlapping binding site for the effectors, primarily mediated by the 2-3 segment of the WRKY domain (SI Appendix, Fig. S4 and Movie S2). The second lysine of the WRKYGQK motif, Lys1221, is acetylated by PopP2, abolishing the affinity of the WRKY domain for W-box DNA (13, 14, 29). Intriguingly, this acetylation event also abolished the association of AvrRps4C with RRS1WRKY (13), highlighting the important role of this interface in mediating the association of RRS1WRKY with both effectors. It also highlights the likely shared role of these effectors in preventing interaction of WRKY domains with DNA as their virulence activity, either via enzymatic modification or steric blocking.

Studies with the NLR pair Pik from rice have shown that the strength of effector binding to integrated domains invitro can correlate with immune responses in planta (5153). Of the AvrRps4 mutants we tested to validate the RRS1WRKY interface, all except N171A and Q194A prevented binding invitro (by ITC) and in planta (by coimmunoprecipitation), and these did not give cell death in Nicotiana species when coexpressed with either RRS1-R/RPS4 or RRS1-S/RPS4. Further, they did not give HR or restrict bacterial growth in Arabidopsis Ws-2 or Col-0 ecotypes (except for a partial restriction of bacterial growth for the D164A mutation in the Col-0 background). The N171A mutant retained the same level of binding as wild type invitro, and displayed the same in planta phenotypes, although restriction of bacterial growth in Arabidopsis was reduced compared with wild type in both Ws-2 and Col-0 ecotypes. Finally, the Q194A mutant showed a reduced binding invitro (approximately sevenfold compared with wild type) but maintained an HR in Arabidopsis as well as displaying a restriction of bacterial growth in Arabidopsis, albeit reduced compared with wild type. Interestingly, this mutant consistently showed a qualitative reduction in the intensity of cell death in Nicotiana. Taken together, these AvrRps4 mutations validate the complex with RRS1WRKY in that they prevent interaction invitro and in planta, but they are not sufficient to determine whether strength of binding invitro can directly correlate with in planta phenotypes. Further studies, including additional mutants, will be required to study this in the RRS1/RPS4 system.

Structural studies of singleton NLRs have shown that interactions between effectors and multiple domains within an NLR can be essential for activation (5457). It is yet to be established whether this is also the case for effector perception involving paired NLRs with integrated domains, although the rice blast pathogen effector AVR-Pia immunoprecipitates with its sensor NLR Pia-2 (RGA5) when the integrated HMA domain has been deleted. However, this interaction does not promote immune responses in planta (58). Although unresolved in the structure of AvrRps4C alone, or in complex with RRS1WRKY, the N-terminal KRVY motif is known to be required for both the virulence activity of the effector and its perception by RRS1/RPS4 (25, 26). Here, we verified that the quadruple mutant AvrRps4 KRVY/AAAA retains interaction with RRS1WRKY at wild-type levels invitro and invivo, but did not trigger RRS1/RPS4-dependent responses in our in planta assays. This suggests that while binding of AvrRps4 to the RRS1WRKY domain is essential for immune activation, an additional interaction mediated by the N-terminal region of the effector to a region of RRS1 and/or RPS4 outside this domain is also required for initiation of defense. Further studies are required to determine how additional receptor domains outside of integrated domains in NLR-IDs contribute to receptor function.

The Arabidopsis NLR pair RRS1B/RPS4B perceives AvrRps4, but not PopP2 (24). Phylogenetically, the RRS1 WRKY belongs to group III of the WRKY superfamily, whereas RRS1BWRKY is grouped into group IIe (14, 24, 48). Here we found that AvrRps4C binds RRS1BWRKY with threefold lower affinity and RRS1B/RPS4B shows a similar pattern of recognition specificity in planta but with reduced phenotypes compared with RRS1/RPS4. A full investigation addressing why AvrRps4 shows differential interaction strength and phenotypes between RRS1 and RRS1B is beyond the scope of this work, but will be a direction for future research.

The unique ability of RRS1/RPS4 to perceive two effectors that differ both in sequence and structure, via the same integrated domain, highlights the potential for engineering of sensor NLRs to recognize diverse effectors. Recently, the range of rice blast pathogen effectors recognized by the integrated HMA domain of Pia-2 (RGA5) has been expanded by molecular engineering (58). However, this expanded recognition was toward structurally related effectors and may not be via a shared interface. Further, although cell-death responses were observed in Nicotiana benthamiana, the engineered NLR was not able to deliver an expanded disease resistance profile in transgenic rice. This suggests we still require a better understanding of how NLR-IDs interact with effectors, and their partner helper NLRs, to enable bespoke engineering of disease resistance.

For invitro studies, the gene fragments of AvrRps4C (Gly134 to Gln221), RRS1WRKY (Ser1194 to Thr1273), RRS1BWRKY (Asn1164 to Thr1241), and AtWRKY41 (Thr125 to Ile204) were cloned in various pOPIN expression vectors using an in-fusion cloning strategy as described in SI Appendix, Materials and Methods.

For transient assays in N. tabacum and N. benthamiana, domesticated genomic fragments encoding RRS1-R, RRS1-S, RRS1B, RPS4, and RPS4B were cloned into binary vector pICSL86977 under a 35S (CaMV) promoter with a C-terminal 6His/3FLAG tag using the Golden Gate assembly method as described (23). Similar cloning techniques were used to generate constructs expressing RRS1WRKY+83. Full-length AvrRps4 (P. syringae pv. pisi) was PCR-amplified from published constructs (13, 23, 26) and assembled with a C-terminal 4myc tag in binary vector pICSL86977 under the control of the 35S (CaMV) promoter using the Golden Gate assembly method. DNA encoding each mutation was synthesized and cloned into pICSL86977 as described above.

For HR and bacterial growth assays in Arabidopsis, full-length AvrRps4 and variants were cloned into a Golden Gatecompatible pEDV3 vector with a C-terminal 4myc tag.

Plasmids expressing the in planta processed C-terminal fragment of AvrRps4 (AvrRps4C) and integrated WRKY domain of RRS1 (RRS1WRKY) were expressed in E. coli SHuffle cells. The proteins were purified via IMAC followed by size-exclusion chromatography. Purified fractions were pooled and concentrated to 15 mg/mL and used for further studies. Detailed procedures are provided in SI Appendix, Materials and Methods.

Crystals of the AvrRps4CRRS1WRKY complex were obtained from a 1:1 solution of 15 mg/mL protein with 0.8 M potassium sodium tartrate tetrahydrate, 0.1 M sodium Hepes (pH 7.5). Diffraction data were collected at the Diamond Light Source on the i03 beamline and processed in the P61/522 space group. The structure was determined by molecular replacement using the model of a monomer of AvrRps4C (PDB ID code 4B6X) and the RRS1WRKY from the PopP2RRS1WRKY complex (PDB ID code 5W3X) as the search model. Further details are provided in SI Appendix, Materials and Methods. X-ray data collection and refinement statistics are summarized in SI Appendix, Table S2.

AvrRps4CRRS1WRKY complex formation invitro was studied using analytical gel filtration chromatography and ITC. The effect of structure-guided mutations on the AvrRps4CRRS1WRKY interaction invitro was investigated using ITC as described in SI Appendix, Materials and Methods.

Agrobacterium-mediated transient cell-death assays were performed in N. tabacum and coimmunoprecipitation assays were performed in N. benthamiana. Detailed information concerning plant materials, growth conditions, plasmid construction, and immunoblotting are provided in SI Appendix, Materials and Methods.

Bacterial strains P. fluorescens Pf0-EtHAn and Pto DC3000 were used for HR or in planta bacterial growth assays, respectively. The Arabidopsis accessions Ws-2 and Col-0 were used as wild type for all the assays in this study. Further details about plant materials, growth conditions, plasmid construction and mobilization, pathogen infection assays, and bacterial growth assays are provided in SI Appendix, Materials and Methods.

Complementary single-stranded DNA fragments encoding the W-box DNA sequence (forward strand: 5-CGCCTTTGACCAGCGC-3) were synthesized by IDT. EMSAs were performed using a Cy3-labeled W-box DNA probe in a reaction buffer containing 10 mM TrisCl (pH 7.5), 50 mM KCl, 1 mM dithiothreitol, and 5% glycerol as described in SI Appendix, Materials and Methods.

Complementary single-stranded DNA fragments encoding the W-box DNA sequence were synthesized by IDT. For SPR assays, the forward strand encoded the W-box DNA sequence (5-CGCCTTTGACCAGCGC-3) while the complementary reverse strand added an extra 20-bp ReDCaT sequence (5-CCTACCCTACGTCCTCCTGC-3) to complement the linker DNA added to the SA chip. The double-stranded DNA was then diluted to a working concentration of 1 M. SPR measurements were performed at 25C using the reusable DNA capture technique (ReDCaT) as described (59, 60) and using a Biacore 8K System (Cytiva). Further details are provided in SI Appendix, Materials and Methods.

All study data are included in the article and/or supporting information.

This work was supported by the European Research Council (Proposal 669926, ImmunityByPairDesign); the UK Research and Innovation (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) Norwich Research Park Biosciences Doctoral Training Partnership, UK (Grant BB/M011216/1); the UKRI BBSRC, UK (Grants BB/P012574 and BBS/E/J/000PR9795); and the BBSRC Future Leader Fellowship (Grant BB/R012172/1). We thank Julia Mundy and Professor David Lawson from the John Innes Centre (JIC) Biophysical Analysis and X-Ray Crystallography platform for their support with CD spectroscopy, protein crystallization, and X-ray data collection; Andrew Davies and Phil Robinson from JIC Scientific Photography for their help with leaf imaging; and Dr. Tung Lee for advice on EMSAs. We also thank Dr. Kee Hoon Sohn for helpful suggestions for triparental mating and other members of the M.J.B. and J.D.G.J. laboratories for discussions.

Author contributions: N.M., H.B., P.D., J.D.G.J., and M.J.B. designed research; N.M. and D.G. performed research; H.B., P.D., and C.E.M.S. contributed new reagents/analytic tools; N.M., A.R.B., C.E.M.S., and M.J.B. analyzed data; and N.M., J.D.G.J., and M.J.B. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2113996118/-/DCSupplemental.

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