CryoEM of CBD Tau Suggests Another Unique Protofibril – Alzforum

14 Feb 2020

Evidence continues to pile up that filaments of aggregated tau form unique strains in different tauopathies. Why is that? A paper in the February 20 Cell suggests that post-translational modifications help decide a filaments ultimate shape. Investigators led by Anthony Fitzpatrick, Columbia University, New York, paired cryo-electron microscopy images of tau filaments from people with corticobasal degeneration (CBD) and Alzheimers disease with mass spectrometry to identify amino acid adducts. They report unique modifications in the protofibril of each structure.

The powerful combination of cryo-EM with mass spectrometry gives a more complete representation of the aggregated tau protein as it actually exists in the diseased brain, said Lary Walker, Emory University, Atlanta.

Cryo-EM alone yields important insights into the core structure of tau tangles, but precisely localizing post-translational modifications adds flesh to the bones, he said.

Meanwhile, scientists led by Sjors Scheres and Michel Goedert at the MRC Laboratory of Molecular Biology in Cambridge, England, U.K., also used cryo-EM to resolve the structure of CBD fibrils. In the February 12 Nature, they report a mysterious molecule hiding in a fold in the protofibril, much like they found in tau protofibrils from a person with chronic traumatic encephalopathy. Fitzpatricks group found a similar molecule. Scheres does not believe this is a post-translational modification because it does not seem covalently attached. Together, the two papers offer the first high-resolution view of tau fibrils in CBD.

Tau Doublet. CBD tau protofibrils comprise two monomers joined back-to-back. Each C-shaped monomer comprises 107 amino acids (circles), that form 11 -sheets (solid black lines). Non-tau moieties (pink) lie trapped within the fold or are covalently connected to the outside. [Courtesy of Arakhamia et al., 2020.]

In recent years, Scheres, Goedert, and colleagues have been methodically resolving the structures of tau filaments found in various tauopathies. They found that paired helical filaments and straight fibrils of tau from AD brain contained the same C-shaped protofibril structure (Jul 2017 news). Protofibrils in tau filaments from Picks disease and chronic traumatic encephalopathy assumed different J- and C-shaped structures (Aug 2018 news; Mar 2019). No one had yet examined filaments from CBD, which form from a tau isoform containing all four microtubule-binding domains. Tau can be alternatively spliced to have either three (3R) or four (4R) of these repeats. Fibrils in Picks disease incorporate only 3R tau, those in both AD and CTE have 3R and 4R forms.

Previous structural analyses of tau fibrils with cryo-EM focused on its fibril-forming core. To isolate that, researchers used the enzyme pronase to remove the fuzzy outer coat of the fibril, revealing the more stable interior of the filament. However, pronase can strip away post-translational modifications as well, and tau accrues a whole host of them, some being disease-dependent (Jul 2015 news; Sep 2015 news). The group wondered if these alterations explain the unique structures of fibrils found in different tauopathies.

To find out, co-first authors Tamta Arakhamia, Christina Lee, and Yari Carlomagno used cryo-EM to examine the undigested tau fibrils taken postmortem from the brain of a person with CBD. As has been reported previously, the sarkosyl-insoluble material was made up of both twisted and straight filaments (Ksiezak-Reding et al., 1996). The former was twice as wide and abundant as the latter.

The two fibrils were made of the same conformer of misfolded tau. While straight fibrils comprised just one column of monomers, each rung of the twisted fibril consisted of a linked pair (see image above). For both, the core-forming protofibril spanned amino acids 274 to 380. It included the last residue of R1, all of R2, R3, and R4, and 12 residues after R4. These regions formed 11 -sheetsthree from R2, three from R3, four from R4, and one formed by the last 13 amino acids. The sheets folded into four layers, forming a C-shaped loop (see image above).

Scheres and Goedert also analyzed undigested CBD tau fibrils using cryo-EM. First author Wenjuan Zhang and colleagues found essentially the same -sheet configuration and fold as did Fitzpatrick and colleagues. Zhang also found a molecule inside the fold of the protofibril. It was not covalently attached to any amino acid. Based on the positively charged amino acids that surround it, Zhang predicted this molecule to have a net negative charge of -3, and be 4 x 9 ngstroms in size. Scheres had reported a similarly mysterious molecule inside the fold of CTE protofibrils, but that one was hydrophobic.

Arakhamia also found a large density inside the molecule, deep within a hydrophilic cavity formed by amino acids 281296 and 358374. It was not covalently bound, and so does not appear to be an amino acid modification. However, they found other large, non-tau densities adorning the outside of the fibrils. On the straight fibrils, these were attached to lysines 321, 343, 353, and 369, and to one histidine, H362. On the twisted form, they linked to K321, K353, and H362.

To identify these non-tau densities, Arakhamia and co-authors analyzed fibrils from several people with CBD by mass spectrometry, then mapped the identified PTMs onto the cryo-EM structure (see image below). The authors found numerous phospho, trimethyl, acetyl, and ubiquitin additions. Some amino acids were either acetylated or ubiquitinated. Strikingly, while a few phospho groups attached to the superficial fuzzy outer coat, acetyl and ubiquitin groups predominated in the fibril-forming core.

PTM Map. Mass spectrometry identified modifications on amino acid sidechains of tau monomers from CBD (left) and AD (right). For the most part, acetylation (blue), ubiquitination (orange), and trimethylation (red) modified the fibril-forming cores, while phosphorylation (green) took place outside. [Courtesy of Arakhamiaet al., 2020.]

I found that to be surprising, said Li Gan, Weill Cornell Medicine, New York. I would have assumed that the tau fibrils in the diseased brain would be hyperphosphorylated.

Do these modifications affect folding of tau fibrils? That acetyl and ubiquitin groups bound to the core suggested to the authors that these were present as tau fibrils formed and played a hand in their aggregation. Acetylation may make tau protein less soluble, as it neutralizes positive charges on side chains and reduces their repulsion, predicted the authors. Ubiquitin may stabilize stacks of -sheets by providing more surfaces for hydrogen bonding. Likewise, Zhang and colleagues think the mysterious hydrophilic molecule inside the fold might also be important in formation of the filament. That it is buried inside each monomer suggests that it is continuously incorporated during fibrillization and may stabilize the CBD fold during filament assembly, they wrote.

Could modifications of tau dictate which type of fold, and therefore which strain, accumulates in the brains of different diseases? Arakhamia and colleagues compared CBD tau PTMs with those on tau fibrils from AD. Again, they mapped mass spectrometry data from many fibrils onto the cryoEM structure. As in the CBD fibril, phosphoryl groups attached mainly beyond the protofibril core of AD tau, while acetyl and ubiquitin groups bound to the core. However, the amino acids modified were different in the different protofibrils and in the filaments they formed. In CBD, ubiquitinated K353 and acetylated K343 were found in twisted fibrils. The reverse, acetylated K353 and ubiquitinated K343, modified straight filaments. Similarly, acetyl groups bound K311 and K317/K321 in AD paired helical filaments, but ubiquitin occupies each of those sidechains in straight filaments. The results hint that PTMs influence the shape of aggregating tau fibrils.

This finding implies that ubiquitin ligases and acetyltransferases modulate the behavior of tau, potentially tuning the ratio of fibril subtypes in tau inclusions, Fitzpatrick wrote to Alzforum. It will be informative to use our approach of combining cryo-EM with PTM mapping by mass spectrometry to determine the additional structural role played by PTMs in tau oligomer formation and template-based seeding.

This paper illustrates, on a single-molecule level, that the interplay between acetylation and ubiquitination could play a role in tau fibrillization and strain properties, Gan told Alzforum. If PTMs play such an important role in fibril formation, the recombinant seeds people have been using may not be as biologically relevant, she added. Goedert emphasized this at the Tau2020 meeting held in Washington, D.C., February 1213. He noted that tau structures formed from recombinant protein using heparin are different from those isolated from brain tissue, particularly with respect to the fourth repeat and the 12 amino acids that come after it. Cryo-EM findings cast a lot of doubt on work using recombinant tau structures, he said.

Gan noted that the physiological consequences of the different tau strainsor whether they are even toxicis unclear. Before we develop strain-specific approaches, we need to understand what the strains do. On that note, Marc Diamond, UT Southwestern Medical Center, Dallas, wondered whether PTMs were causal or incidental. He suggested that researchers find out by removing PTMs from fibrils before seeding. If that does not change the strain output, it would imply that they were not required for strain identity.Gwyneth Dickey Zakaib

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CryoEM of CBD Tau Suggests Another Unique Protofibril - Alzforum

High Focus on Product Innovation & Development to Assist the Growth of the Folding Cartons Market between and . 2017 2025 Dagoretti News -…

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Global Ethanol Market: Dynamics

The world ethanol market is prophesied to be strong against tough conditions that could paralyze its growth. Despite challenging production economics due to low oil prices, ramping up of opponents against the RFS, and uncertainty because of regulatory indecision, the market survived pretty well in 2015. The year showcased the resolve and strength of the ethanol sector. Producers were able to ride out the storm with the help of the indispensable value of ethanol as a low-cost, clean octane booster and thriving export demand. In the U.S., millions of metric tons of high-protein animal feed and billions of gallons of high-octane renewable fuel were produced in ethanol bio-refineries of several states.

Global Ethanol Market: Segmentation

The international ethanol market is forecasted to be classified according to two classes, viz. type of feedstock and end use. As per the classification by feedstock, the market could see a segmentation into coarse grain-based, sugarcane-based, and wheat-based ethanol. Although there could be different markets for ethanol in terms of feedstock type, one is expected to garner a larger share in the coming years. The analysts foresee the market to be dominated by coarse grain-based ethanol, which represented a 53.0% share in 2017.

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Global Ethanol Market: Competition

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High Focus on Product Innovation & Development to Assist the Growth of the Folding Cartons Market between and . 2017 2025 Dagoretti News -...

Folded, frozen, and faster: JUST Egg is now more convenient, and cheaper, to enjoy – FoodNavigator-USA.com

The folded version of the JUST Egg comes frozen and can be prepared in a few ways: toaster oven, skillet, or for the time-strapped, the standard toaster where the company suggests selecting the bagel setting for about 6.5 minutes.

"The genesis of it really was, how do we figure out a little bit of a faster way for people to enjoy it?We got really excited about this idea of folding and what that means from a texture perspective. It feels more like as if you made an omelette and put it in a biscuit," Tetrick told FoodNavigator-USA.

2019 was the first full year JUST Egg was in retail and the results surpassed both the company's and Tetrick's internal goals for the product.

"Last year we managed to get into most of the major retailers, most of them in the egg set and have managed to become the No. 1 liquid egg," said Tetrick who added that the company has sold the equivalent of 20 million eggs in its first year.

What was even more encouraging, and unexpected, was that the majority of consumers buying JUST Egg are not vegan or vegetarian, and 20% to 22% are buying the product not just as an egg replacement, but as their main source of protein, said Tetrick.

"We didnt expect that was going to happen.Even saying it out loud, you have a hard time wrapping your brain around it."

Tetrick noted that the new folded egg is a different, fluffier version than the patty plant-based egg product JUST Egg supplies to the foodservice channel.

The product will begin rolling out in April in the freezer section of Whole Foods Markets; select Albertsons Safeway stores; Gelsons Markets in Southern California; Stop & Shop in the Northeast, Kings Food Markets in the New York metro area and Giant Martin's in the Mid-Atlantic, with more to come.In all, it will be sold in approximately 5,000 stores at launch and will be available for restaurants and other foodservice destinations from major distributors.

"There are millions of shoppers going to the frozen set who have probably never heard of us, probably never heard of JUST Egg, and now theyll be able to see it," said Tetrick.

Retail frozen food alone is a $57bn business annually, with the category growing in both dollar and units in 2018, according to the American Frozen Food Institute and Food Marketing Institute.

The folded egg, which was developed with breakfast sandwiches in mind, has potentially much broader, all-day appeal with consumers, says the firm. According to arecent survey, conducted by the company of nearly 1,000 consumers, 50% would use the fold-over egg replacement as a general sandwich ingredient and 40% would add it to other dishes such as salads or fried rice, underscoring its appeal as a convenient protein source.

As a frozen product, the JUST Egg contains the same base ingredients (i.e. mung bean protein) but is free-from the preservatives of the liquid egg product, noted Tetrick. At retail, the products will be placed either next to traditional frozen breakfast sandwiches or in the plant-based protein alternatives section.

"It will next It will be good for us, because you can hypothesize about which set works best," Tetrick said. With the company's liquid JUST Egg product, the products perform better when placed next to conventional eggs than when put next to chilled alternative proteins.

"People are not just looking at this as a way to replace an egg, thats an element of it, but theres a big percentage of people that are looking at this as clean, healthy, sustainable protein,"he noted.

Opening up its own dedicatedproduction facility in Western Minnesota at the end of last year, means that JUST Egg can start reducing costs and make the products accessible to all, not just high-income shoppers.

"Our objective ultimately is to be the most cost effective protein source human beings consume, not just the most cost efficient egg source," Tetrick said. The average cost of an egg, globally, is about USD$0.08. Through efficient production and ingredient sourcing of the mung bean, JUST Egg has been able to reduce its cost to just over USD$0.20/egg, while still more than double the cost, it is much more cost effective than when the company first launched seven and a half years ago, said Tetrick.

According to the company, it has cut its cost of the final JUST Egg product to $4.99 SRP a bottle, down from $7.99. Its folded egg product is also $4.99 SRP, with four folded eggs to a box.

"Its not just the taste and texture, we need to hammer on this cost piece so that the folks that I grew up with in Birmingham, Alabama, can not just afford, but easily afford," added Tetrick.

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Folded, frozen, and faster: JUST Egg is now more convenient, and cheaper, to enjoy - FoodNavigator-USA.com

Gocycle to partner with nutrition brand Fuel10k to promote benefits of e-bikes – Bike Biz

Gocycle is set to partner with protein breakfast brand Fuel10k to increase awareness of how e-bikes can help more people to lead an active lifestyle.

Fuel10k will give away five fast-folding Gocycle GX electric bikes as part of its biggest-ever on-pack promotion between January and April.

The GX will feature on three million of the brands high-protein breakfast drinks and porridge pots in outlets nationwide.

Richard Thorpe, Gocycle designer and founder, said: We are really excited about the opportunity to spread the message of the enormous health benefits of e-bikes to millions of people across the UK. E-bikes are the perfect travel solution for people who want to lead a more active and sustainable lifestyle and above all they are fun!

This partnership is all about fuelling more people to lead a more active lifestyle in the long-term. E-bikes are a great way to get back out onto two wheels. Having the electrical assistance on tap removes many of the daunting elements of cycling and encourages more people to cycle more of the time which can only be a good thing.

Individuals can enter the competition by purchasing a Fuel10k breakfast drink or porridge pot that features a Gocycle on the packaging. They will be presented with a unique code which they can enter on Fuel10ks competition site to be in with a chance of winning a fast-folding Gocycle GX and other prizes such as sports T-shirts, water bottles or discount codes.

Scott Chassels, Fuel10k managing director, added: We are an increasingly time-poor society and everyone seems to be busier than ever, but that shouldnt be at the detriment of our health. Fuel10k exists to give people a better for you, protein-based, breakfast on-the-go, which helps them to maximise the precious little time they have in the morning and fuel their active day ahead.

We are really excited by this partnership as e-bikes can really enhance the lifestyles of busy people by helping them to have a healthier, more sustainable and speedier commute.

The fast-folding Gocycle GX is available to order now online and through select resellers throughout US, Canada, UK, and EU.

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Gocycle to partner with nutrition brand Fuel10k to promote benefits of e-bikes - Bike Biz

How To Grow (Almost) Anything – Hackaday

An off-shoot of the infamous How to Make (Almost) Anything course at the Massachusetts Institute of Technology, How to Grow (Almost) Anything tackles the core concepts behind designing with biology prototyping biomolecules, engineering biological computers, and 3D printing biomaterials. The material touches elements of synthetic biology, ethics of biotechnology, protein design, microfluidic fabrication, microbiome sequencing, CRISPR, and gene cloning.

In a similar fashion to the original HTMAA course, HTGAA works by introducing a new concept each week that builds up to a final project. Students learn about designing DNA experiments, using synthesized oligonucleotide primers to amplify a PCR product, testing the impact of genes on the production of lycopene in E coli., protein analysis and folding, isolating a microbiome colony from human skin and confining bacteria to image, printing 3D structures that contain living engineered bacteria, and using expansion microscopy (ExM) to visualize a mouse brain slice. The final projects run the gamut from creating a biocomputer in a cream to isolating yeast from bees.

Growing out from an initiative to create large communities around biotechnology research, the course requires minimal prior exposure to biology. By working directly with hands-on applications to biodesign concepts, students are able to direct apply their knowledge of theoretical biology concepts to real-world applications, making it an ideal springboard for bio-inspired DIY projects. Even though the syllabus isnt fully available online, theres a treasure trove of past projects to browse through for your next big inspiration.

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How To Grow (Almost) Anything - Hackaday

A conserved ATP- and Scc2/4-dependent activity for cohesin in tethering DNA molecules – Science Advances

INTRODUCTION

The establishment of sister chromatid cohesion is essential for accurate chromosome segregation during the mitotic cell cycle. Cohesin is a complex of the SMC (structural maintenance of chromosomes) family originally identified for its role in tethering sister chromatids from S phase until anaphase (1, 2). In addition to its function in sister chromatid cohesion, cohesin modulates the organization of interphase nuclei and mitotic chromosomes (1, 3, 4). Studies in vertebrates have shown that cohesin complexes maintain contacts between different loci in cis and in this way contribute to the folding of individual chromatids into distinct loops that provide an integral level of genome architecture (1, 3, 4). The current model for how SMC complexes, including cohesin, might form DNA loops involves the capture and bending of DNA segments followed by progressive enlargement of these to form loops (5, 6); this activity has been termed loop extrusion. Evidence for this model has been obtained from in vitro analysis of purified yeast condensin (7). Cohesins most prominent function is the tethering of sister chromatids, which is expected to involve an ability to bridge two DNA molecules in trans. Unlike condensin, cohesin has not yet been demonstrated to extrude loops in vitro. A potential activity in loop extrusion has been suggested for cohesin because of its involvement in the maintenance of cis looping and as a potential linear tracking mechanism that could explain the preferential use of convergent CTCF DNA motifs at TAD borders during genome folding (8, 9). However, it is currently not clear how loop extrusion could explain the well-established role of cohesin in sister chromatid cohesion.

Mechanistically, we only have a vague idea of how cohesin might generate intermolecular tethers while mediating sister chromatid cohesion. Two main models have been proposed to explain cohesin function in sister chromatid cohesion: the ring or embrace model (10, 11), in which a single cohesin ring entraps both sister DNA molecules (10), and the handcuff model, where sister chromatid cohesion is mediated by the entrapment of sister DNAs in different cohesin complexes and a subsequent cohesin-cohesin interaction (1, 12, 13). The capture of the pair of double-stranded DNA (dsDNA) molecules during the establishment of sister chromatid cohesion by a single cohesin molecule in the embrace model has been proposed to occur by either (i) passage of the replisomes through the ring lumen of a DNA-bound cohesin or (ii) when a DNA-bound cohesin captures a single-stranded DNA (ssDNA) at the fork, which is then converted into dsDNA by DNA synthesis (14). Although cohesin complexes have been purified from fission yeast (15), frogs (16), and human cells (17), single-molecule analyses of DNA bridging activities have not been reported. Purified cohesin complexes have been shown to exhibit DNA binding activity in a salt-resistant manner (18) and to rapidly diffuse on DNA; however, these were shown to be independent of adenosine triphosphate (ATP) (1517), suggesting that they are not at the core of its ATP-dependent activity.

Single-molecule studies of purified yeast condensin have shown that this SMC complex compacts DNA molecules on magnetic tweezers (19), translocates along linear DNA molecules in an ATP-dependent manner (20), and forms DNA looplike structures on surface-tethered, flow-stretched DNA (7). Furthermore, while purified condensin exhibits robust ATPase activity in the presence of DNA (19), purified yeast cohesin is a poor ATPase on its own (21, 22). Recent work has shown that the Scc2-Scc4 loader complex greatly stimulates cohesins ATPase activity (21, 22). On the basis of these findings, we sought to investigate activities of budding yeast cohesin in the presence of the Scc2-Scc4 loader complex using the following two complementary single-molecule approaches: DNA curtains and optical tweezers.

To investigate activities of yeast cohesin using single-molecule assays, we first purified budding yeast cohesin tetramers, containing Smc1, Smc3, Scc1/Mcd1 (thereafter referred to as Scc1), and Scc3, from exponentially growing yeast cultures (Fig. 1A). Cohesin subunits were overexpressed in high-copy plasmids using galactose (GAL)inducible promoters. Purified material was obtained via affinity chromatography, using a triple-StrepII tag fused to the Smc1 subunit, followed by passage through a HiTrap Heparin HP column (Fig. 1A and table S1). Analysis of purified complexes by negative-stain electron microscopy confirmed the presence of rod-shaped cohesin holocomplexes, the majority in a folded conformation (Fig. 1B) (23). The Scc2-Scc4 complex was also purified from budding yeast (Fig. 1C) using a similar strategy and showed DNA binding activity as expected (fig. S1A) (21, 22). Purified cohesin also bound plasmid DNA in a salt-resistant manner (fig. S1B), and the bound plasmid was released by DNA cleavage with restriction enzymes (fig. S1C). This is consistent with the topological binding mode proposed for this complex (18, 22). However, in our hands, this activity was not strictly dependent on ATP and was not stimulated by Scc2-Scc4 (fig. S1B), in contrast to what has been reported recently (18, 22). Last, we confirmed that our purified Scc2-Scc4 complex was able to stimulate cohesin ATPase activity (Fig. 1D) (21, 22).

(A) Purified cohesin tetramer containing Smc1, Smc3, Scc1, and Scc3 was analyzed by SDSpolyacrylamide gel electrophoresis (PAGE) followed by Coomassie blue staining. Western blot analysis shows the mobility of Smc1 and Scc1. (B) Top panel: Representative micrograph of a BS3-crosslinked cohesin sample observed in negative stain EM. Scale bar, 50 nm. Bottom panel: Class averages obtained with RELION. A set of the best ~5000 particles was used for this classification. The size of the circular mask is 450 . (C) Coomassie blue staining of the purified Scc2-Scc4 complex. (D) ATP hydrolysis by yeast cohesin and cohesin ATPase mutant Smc3-K38I with or without the Scc2-Scc4 complex.

Next, we sought to test whether budding yeast cohesin exhibited the behavior described for cohesin from other organisms on DNA curtains (1517). -DNA molecules (48.5 kb) were anchored to a lipid bilayer in a flow cell surface and aligned into double-tethered DNA curtains using nanofabricated barriers (Fig. 2A) (15). Quantum dots (Qdots) conjugated to antibodies against the hemagglutinin tag (HA3) fused to the C-terminal region of the Scc1 kleisin subunit were used to visualize the complexes (Fig. 2B). On flowing the labeled cohesin complex over the DNA curtains, binding was observed at low ionic strength (Fig. 2A). The chamber was flushed with a high ionic strength buffer to remove nontopologically bound complexes (Fig. 2A). While a large fraction of cohesin complexes dissociated, we observed diffusion along the DNA (Fig. 2B). The binding preference of cohesin to more A/T-rich regions reported earlier (15) was also observed (Fig. 2, C to E). The diffusion coefficients correlated with the ionic strength of the buffer (fig. S2F). The survival probabilities of cohesin were not affected by the addition of ATP, or the ATP analogs adenosine 5-diphosphate (ADP) and ATPS (Fig. 2C). We found that the presence of Scc2-Scc4 enhanced the ability of cohesin to stay bound on the DNA (Fig. 2D); however, the presence of nucleotides did not alter cohesin stabilities (Fig. 2D). Therefore, these results are consistent with the activities observed for cohesin from other organisms (15, 17) and show that budding yeast cohesin undergoes rapid diffusion on DNA curtains in an ATP-independent manner.

(A) Schematic representation of double-tethered DNA curtains used in the study. (B) Image of cohesin tagged with quantum dots (magenta) bound to -DNA stained with YOYO-1 (green). Scale bar, 10 m. (C) Survival probability plots of cohesin in the presence of ATP, ADP, ATPS, or no nucleotide. (D) Lifetimes of cohesin (fast phase and slow phase) in the presence or absence of Scc2-Scc4 and different ATP analogs. Error bars are 68% confidence intervals from bootstrapping. (E) Image of a pair of double-tethered DNA curtains bound by cohesin. DNA molecules are in green, and cohesin is in magenta. Diagrammatic representation is shown (left). (F) Time-lapse images of a pair of double-tethered DNA curtains bound by cohesin as they are tethered. DNA molecules are in green, and cohesin is in magenta. Diagrammatic representation is shown (top). Pairing events were observed frequently in the DNA curtains. An average of 5 to 10 events per DNA curtain was detected.

In our DNA curtain experiments, we made an observation not reported in earlier studies (15, 17). Cohesin signals were often observed bound between what appeared to be two fused DNAs (Fig. 2E). The pairing events formed under low-salt conditions in the presence of ATP (Fig. 2F and movies S1 and S2), but they persisted when the chamber was flushed with a high ionic strength buffer, raising the possibility that topologically bound complexes mediated these events.

To further explore our observation that cohesin tetramers paired -DNA molecules on the DNA curtains, we decided to use a dual-trap optical tweezer with confocal fluorescence microscopy capabilities. A similar approach has been previously used in the study of protein-DNA interactions (24). Briefly, we tether a -DNA molecule with biotinylated ends to two optically trapped streptavidin-coated polystyrene beads, enabling us to accurately apply and measure forces on the captured DNA molecule. We performed our experiments in multichannel laminar flow cells where we had the possibility to move the tethered DNA to different flow lanes containing different protein complexes and buffers. In addition, we were able to image the tethered DNAs using confocal fluorescence microscopy. Overall, the approach allows increased experimental control over DNA curtains. Proteins can be added, removed, or incubated under different salt conditions sequentially, and the physical effect of their activities can be measured accurately on a single DNA molecule.

To test for the formation of intramolecular cohesin bridges in cis, we adapted a previously published protocol that measures protein-mediated DNA bridging (Fig. 3A) (25, 26). First, we captured a single -DNA molecule and generated a force-extension (FE) curve in the absence of protein by extending the molecule slightly beyond its contour length (~16 m). We then moved the DNA to a channel containing 1 nM cohesin, 2.5 nM Scc2-Scc4 complex, and 1 mM ATP in 50 mM NaCl and incubated for 30 s in a relaxed conformation (~3 m between beads). Following incubation, the relaxed DNA was then moved to a channel without protein but containing 1 mM ATP in 125 mM NaCl. Reextending the DNA in the buffer-only channel yielded FE curves with sawtooth features at extensions shorter than the contour length (Fig. 3B, Cohesin + Scc2/4). This is characteristic of intramolecular bridge rupture events (25, 26) (Fig. 3A, right) and shows that cohesin can tether the DNA in cis forming a protein-mediated bridge between different segments of the molecule, thus creating an intramolecular loop. When we repeated this protocol in the presence of 1 nM cohesin and no Scc2-Scc4 (Fig. 3B, Cohesin), or 2.5 nM Scc2-Scc4 and no cohesin (Fig. 3B, Scc2/4), FE curves identical to those of the initial naked DNA were observed, demonstrating that no protein-mediated bridges were formed (Fig. 3A, left). Similarly, incubating 1 nM cohesin and 2.5 nM Scc2-Scc4 complex in the absence of ATP, or with the ATP analogs ADP or ATPS, yielded FE curves identical to those of naked DNA (fig. S3A). To confirm the requirement of ATP, we repeated the protocol in the presence of 1 nM cohesin ATPase mutant (K38I) (fig. S4) and 2.5 nM Scc2-Scc4 (Fig. 3B, CohesinK38I + Scc2/4). FE curves identical to those of the naked DNA were observed (Fig. 3B, CohesinK38I + Scc2/4). Therefore, the DNA bridging activity requires ATP and depends on the Scc2-Scc4 loader complex.

(A) Schematic representation of FE curve for -DNA exhibiting the presence (right diagram and graph) and absence (left diagram and graph) of protein DNA bridges. Dotted line is fit to worm-like chain for naked DNA. (B) FE curves for -DNAs preincubated with 1 nM cohesin and 2.5 nM Scc2-Scc4 complex and 1 mM ATP (Cohesin + Scc2/4), 1 nM cohesin and 1 mM ATP (Cohesin), 2.5 nM Scc2-Scc4 complex and 1 mM ATP (Scc2/4), or 1 nM cohesin ATPase mutant and 2.5 nM Scc2-Scc4 complex and 1 mM ATP (CohesinK38I + Scc2/4). Schematic diagram of the experimental design. After capturing a single DNA molecule between two optically trapped beads, DNA was incubated in the presence of protein in a relaxed conformation (3-m bead distance) for 30 s in 50 mM NaCl and then moved to a buffer channel with 125 mM NaCl for extension and measurements. Only incubation with 1 nM cohesin and 2.5 nM Scc2-Scc4 complex and 1 mM ATP (Cohesin + Scc2/4) showed DNA bridging rupture events. (C) FE curves in the presence of increasing ionic strength. High salt favors topologically constrained and permanent DNA bridges. (D) Schematic representation of the experimental design to test cohesin second DNA capture. After capture of -DNA between the two optically trapped beads, DNA is extended and incubated for 30 s in the protein channel. DNA is moved to a buffer channel and then relaxed (3-m bead distance) and incubated for 30 s before reextension to test for DNA bridges (E). The extended DNA is then incubated in a relaxed position in the protein channel and then moved to buffer channel and extended to confirm that bridges can be formed when protein is loaded while DNA is relaxed (F). (E) -DNA incubated with 1 nM cohesin, 2.5 nM Scc2-Scc4 complex, and 1 mM ATP in an extended conformation and then moved to a buffer channel (125 mM NaCl) in the presence of 1 mM ATP (buffer only, dark blue) or 2.5 nM Scc2-Scc4 complex and 1 mM ATP (+Scc2/4, light blue). DNAs were reextended, and the FE curves were recorded. (F) The -DNA molecules in (E) were incubated in a relaxed position (3-m bead distance) in the presence of 1 nM cohesin, 2.5 nM Scc2-Scc4 complex, and 1 mM ATP DNAs. DNAs were moved to a buffer-only channel (125 mM NaCl containing 1 mM ATP) and reextended. FE curves show the presence of DNA bridging rupture events.

Next, we tested the effect of ionic strength on cohesin bridging (Fig. 3C). Cohesin bridges were observed at all salt concentrations tested (Fig. 3C). The length of DNA extension released during the rupture of a DNA bridge can be directly related to the loop size encompassed by the bridge. We analyzed the sizes of the DNA bridges from the FE curves at 125 mM salt (fig. S3B) and found that the distribution of loop sizes is exponential with a characteristic size of ~900 base pairs (bp), consistent with a model of random bridge formation (5, 6). Most of the small sawtooth peaks observed at low forces and extensions disappear under high-salt conditions, while the overall contour length of the DNA remained reduced (Fig. 3C). We also recorded FE curves when we relaxed tethers (Fig. 3, B and C, reverse arrows) after the extensions are done in the buffer channel, therefore in the absence of protein (Fig. 3, B and C, forward arrows). These showed that compaction due to DNA bridges formed at low-salt concentrations was lost after extension (Fig. 3C, reverse arrows; 50 mM NaCl) with force. However, relaxation of tethers with DNA bridges formed at high-salt concentrations showed compaction events that had resisted after extension (Fig. 3C, reverse arrows; 300 and 500 mM NaCl). These results show two distinct types of cohesin bridging events: (i) one predominantly occurring at low salt that is characterized by frequent interactions that are reversible and can be disrupted by moderate force (5 to 40 pN) and (ii) a second permanent bridge class that resists higher ionic strength conditions and full physical stretching of the DNA molecule. Both classes of DNA bridges were not observed when an ATPase mutant cohesin complex (SMC3-K38I) was used (Fig. 3B, CohesinK38I + Scc2/4), confirming that the ATPase activity of the complex is a requirement for both types of bridges. Next, we tested whether permanent bridges could resist repeated extensions. We performed two cycles of bead extension and relaxation and confirmed the persistence of the permanent cohesin bridge using FE curves (fig. S5). We conclude that permanent cohesin bridges resist high stretching forces and that the complexes mediating the tethers cannot be displaced from the DNA molecules. This explains the repeated detection of the same bridge on FE curves during the cycle of bead extension and relaxation (fig. S5).

Recent studies using purified cohesin from Schizosaccharomyces pombe have shown that cohesin can capture a second DNA, but only if single stranded (14). This led the authors to conclude that cohesin is not capable of trapping to dsDNAs in vitro (14). Moreover, it was suggested that this activity is likely to occur at replication forks, where cohesin bound to a dsDNA molecule is exposed to nascent ssDNA (14). The second capture of the single-stranded molecule was dependent on the presence of cohesin loader and ATP (14). Our results seem to contradict this because we show that cohesin purified from Saccharomyces cerevisiae is fully able to trap two dsDNA molecules (Fig. 3, B and C). Next, we decided to investigate whether capture of the two molecules is sequential or simultaneous. In our original tethering assay, we could not differentiate whether the two dsDNAs are captured sequentially or in a single step, as we had incubated the DNA in a relaxed position (with the two DNA segments in proximity). To distinguish whether one or two events were involved in the formation of the cohesin tethers observed, we sought to test whether cohesin could capture a second DNA after initial loading. To this aim, we captured a single -DNA molecule and generated an FE curve. We maintained the DNA in an extended position (~15 m between beads) using a pulling force of 5 pN (Fig. 3D) and loaded cohesin by moving the DNA to a channel containing 1 nM cohesin, 2.5 nM Scc2-Scc4 complex, and 1 mM ATP in 50 mM NaCl. We incubated the DNA for 30 s (Fig. 3D) before moving it to a different channel containing 1 mM ATP in 125 mM NaCl. We then relaxed the DNA conformation (~3 m between beads) to allow DNA segments to come into proximity (Fig. 3D) and incubated in the relaxed conformation for an additional 30 s. The FE curve obtained after reextension of the DNA was identical to the initial naked DNA profile (Fig. 3E, Only buffer, and fig. S6). We obtained a similar result when we included 2.5 nM Scc2-Scc4 complex and 1 mM ATP in the channel where we relaxed the DNA (Fig. 3E, +Scc2/4, and fig. S6). These results show that loaded cohesin is unable to capture a second DNA segment. To confirm that DNA bridges could be formed in the same DNA in one step, we relaxed the molecules used in the experiments and incubated them for 30 s in a channel containing 1 nM cohesin, 2.5 nM Scc2-Scc4 complex, and 1 mM ATP. When molecules were reextended, the resulting FE curves confirmed the formation of DNA bridges (Fig. 3F and fig. S6). In addition, we confirmed that cohesin complexes can bind to extended DNAs using a published DNA friction protocol (fig. S7) (27). Therefore, our results are consistent with a previous report (14), showing that cohesin bound to DNA cannot undergo a second capture event involving a dsDNA molecule, but demonstrate that cohesin is able to capture two dsDNAs simultaneously. A previous study could not evaluate the possibility that cohesin could capture two dsDNAs simultaneously, thus reaching an erroneous conclusion (14). We conclude that cohesin establishes bridges between two dsDNA in a single step, or two kinetically very close steps, which requires physical proximity of the DNA segments.

Next, we investigated whether cohesin can form intermolecular bridges. We developed an intermolecular bridging assay, where two dsDNA molecules are tethered in parallel between the pair of beads, and tested the ability of cohesin to form bridges between these two molecules (Fig. 4A). DNA molecules were visualized with SYTOX Orange. After confirming the presence of two DNA molecules tethered in parallel between the beads (Fig. 4B, Naked), the DNA was incubated in a relaxed state to bring the DNAs into proximity (~3-m bead distance) in the presence of 1 nM cohesin and 2.5 nM Scc2-Scc4 and 1 mM ATP for 30 s. The DNAs were moved to a buffer-only channel (300 mM NaCl plus 1 mM ATP). Strikingly, clear bridging was observed between the two molecules on reextension (Fig. 4B, Cohesin + Scc2/4). DNA bridges did not form in the absence of ATP (Fig. 3B, no ATP) or when we used cohesin ATPase mutant complex (Fig. 4B, K38I + Scc2/4), confirming that cohesins ATPase activity is required. Bridge formation in this assay was very efficient; of 10 molecules tested, 8 showed intermolecular bridges (Fig. 4B, Cohesin + Scc2/4) and 2 showed intramolecular bridging on the two individual DNAs. Intermolecular bridges always appeared to be near the midpoint of the DNA (Fig. 4B, Cohesin + Scc2/4). Potential reasons to explain this include the fact that the central region of -DNA molecules is rich in A/T content where cohesin might bind preferentially. Alternatively, cohesin might be able to slide on the DNA while maintaining tethers and therefore likely to move to the center regions as the molecules are extended. To further characterize this, we used a quadruple-trap optical tweezer setup, which allows the independent manipulation of the two DNA molecules (27).

(A) Schematic representation of the experimental design for the dual-trap optical tweezer to generate permanent intermolecular cohesin bridges. Two -DNA molecules are tethered between the two beads and incubated in a relaxed position (3-m bead distance) in the presence or absence of protein in buffer containing 50 mM NaCl. The relaxed molecules are then moved to a different channel containing 300 mM NaCl and reextended. Imaging is done before incubations and after reextension in a buffer containing 300 mM NaCl and 50 nM SYTOX Orange to visualize DNA. (B) Two -DNA molecules were tethered and treated as described in (A) and incubated with either (i) 1 nM cohesin, 2.5 nM Scc2-Scc4, and no ATP (Cohesin + Scc2/4, left); (ii) 1 nM cohesin, 2.5 nM Scc2-Scc4, and 1 mM ATP (Cohesin + Scc2/4, middle); or (iii) 1 nM cohesin ATPase mutant K38I, 2.5 nM Scc2-Scc4, and 1 mM ATP (K38I + Scc2/4, right). Imaging was performed before incubation and after DNA reextension in a buffer containing 300 mM NaCl to minimize DNA entanglement and 50 nM SYTOX Orange to visualize DNA. Images from three independent experiments are shown. Three independent experiments are shown for each category. (C) Schematic representation of the experimental design to test for sliding of permanent cohesin bridges (top diagram). Following the formation of an intermolecular cohesin bridge (see fig. S8 for details in bridge formation protocol), beads 3 and 4 were moved together in the x axis to slide the bridge along DNA1. Images showing two representative sliding experiments are shown. Experiments were performed in a buffer containing 300 mM NaCl and 50 nM SYTOX Orange. Movies of the experiments are shown in movies S4 and S5. The experiment was performed three times, and sliding was observed in all cases. (D) Schematic representation of the experimental design to disrupt intermolecular cohesin bridges. Following the formation of an intermolecular cohesin bridge, bead 3 is moved down in the y axis until one of the DNA ends loses contact with the bead. Imaging was performed before and after the pull in a buffer containing 300 mM NaCl and 50 nM SYTOX Orange. Representative experiment is shown. A movie of the experiment is shown in movie S6.

We first captured two single -DNA molecules using a pair of traps for each (DNA1 between traps 1 and 2 and DNA2 between traps 3 and 4) in a parallel conformation (fig. S8). Both DNA molecules were stretched close to their contour lengths (~16 m). We then manipulated DNA2 using beads 3 and 4 and moved it upward (in the z direction) before rotating it 90 and moving it into a crossed conformation directly above DNA1 (fig. S8). We then lowered DNA2 to its original z position and relaxed it to ensure physical contact between the two DNA molecules at the junction point (fig. S8). We then moved the crossed DNAs into a different channel containing 1 nM cohesin, 2.5 nM Scc2-Scc4, and 1 mM ATP (60 s, 50 mM NaCl) before returning the DNAs to a channel containing 1 mM ATP in 300 mM NaCl. We reversed the manipulation of DNA2, first moving bead 3 upward and over DNA1 before manipulating beads 3 and 4 so that DNA2 was rotated 90 and lowered back to the original position where DNA1 and DNA2 were parallel to each other. When we moved the beads to a channel containing SYTOX Orange to visualize DNA, we observed that DNA1 and DNA2 were bridged (fig. S8), as expected from our analysis of parallel DNA bridging in the dual-trap optical tweezers setup (Fig. 4B, Cohesin + Scc2/4 + 1 mM ATP). We then tested whether simultaneously moving DNA2 using beads 3 and 4 in the x axis would cause the sliding of the bridge along DNA1 (Fig. 4C). We observed that the bridge could be moved, showing that cohesin can slide on DNAs while tethering two DNA molecules in trans (Fig. 4C and movies S4 and S5). When we applied force to disrupt the bridge [moving bead 3 down in the y axis (away from beads 1 and 2); Fig. 4D], we were not able to break apart the cohesin tether. At high forces, the interaction between the ends of the DNAs and the beads often snapped (Fig. 4D and movie S6). Amazingly, cohesin bridges resisted this, and half of DNA2 could be observed hanging from the bridge (Fig. 4D and movie S6). We conclude that permanent intermolecular cohesin bridges can slide on DNA and resist high force. We predict that the forces exerted to disrupt the interaction between the DNAs and the bead exceed 80 pN. At these high forces, the prediction is that all the protein interfaces on cohesin rings should be disrupted. Therefore, cohesin association with DNA in permanent tethers is likely to occur in a manner that resists opening of the interfaces.

Previous studies using purified cohesin from different organisms did not report DNA bridging activities (1517); however, the studies did not use budding yeast cohesin. We therefore decided to test whether the bridging activity observed is specific for S. cerevisiae cohesin tetramers or it has been conserved in cohesin from other organisms. To this aim, we purified the human cohesin (hCohesin) tetramer complex, containing hSmc1, hSmc3, hRad21, and Stag1, as described previously (fig. S9A) (28). We then tested whether hCohesin could bridge DNA intramolecularly. We captured a single -DNA molecule and generated an FE curve in the absence of protein to confirm the presence of naked DNA. We then moved the DNA to a channel containing 1 nM hCohesin and 1 mM ATP in 50 mM NaCl and incubated it for 30 s in a relaxed conformation (~3 m between beads). We then moved the relaxed DNA to a channel without protein in the presence of 1 mM ATP in 125 mM NaCl. Reextending the DNA resulted in FE curves with a naked DNA profile (Fig. 5A, hCohesin), demonstrating that hCohesin on its own cannot promote DNA bridges. Although we could not obtain hScc2-Scc4, we decided to test whether the budding yeast loader complex Scc2-Scc4 (scScc2-Scc4) had any effect on hCohesin activity. To this aim, we repeated the intramolecular DNA bridging assays with hCohesin and included the Scc2-Scc4 loader complex in the incubations. Relaxed DNA was incubated in the presence of 1 nM hCohesin tetramer, 2.5 nM scScc2-Scc4 complex, and 1 mM ATP in 50 mM NaCl. The relaxed DNA was then moved to a channel with 1 mM ATP in 125 mM NaCl. Reextension yielded the sawtooth features characteristic of intramolecular bridge rupture events (Fig. 5B, hCohesin + Scc2/4) detected with yeast cohesin tetramers (Fig. 3C, 125 mM). Therefore, hCohesin tetramers containing Stag1 have conserved the ability to bridge DNA. hCohesin was able to form both reversible and permanent bridges (Fig. 4B, hCohesin + Scc2/4).

(A) FE curve for -DNA preincubated with 1 nM human cohesin and 1 mM ATP in 125 mM NaCl buffer (hCohesin). Dotted line is fit to worm-like chain model. After capturing a single DNA molecule between two optically trapped beads, DNA was incubated in the presence of protein in 50 mM NaCl buffer in a relaxed conformation (3-m bead distance) for 30 s and then moved to the 125 mM NaCl buffer channel for extension and measurements. No evidence of DNA bridges was observed under this condition. (B) FE curve for -DNA preincubated with 1 nM human cohesin, 2.5 nM yeast Scc2-Scc4, and 1 mM ATP in 125 mM NaCl buffer (hCohesin + Scc2/4). Experimental procedure as in (A). FE curves exhibited multiple rupture events indicating the presence of reversible and permanent DNA bridges. (C) DNA compaction trace for -DNA molecule extended using a force of 1 pN (top). The DNA was tethered between two beads. One bead was clamped (fixed), while a 1-pN force was applied to the second bead to maintain the molecule extended. The DNA was then incubated in the presence of 1 nM condensin (1 mM ATP in 50 mM NaCl) (left, magenta trace). The FE curve for the -DNA full extension after incubation is shown (bottom). Additional examples can be found in fig. S10. (D) DNA compaction trace for -DNA molecule extended using a force of 1 pN (top) in the presence of 1 nM cohesin and 2.5 nM Scc2-Scc4 complex (1 mM ATP in 50 mM NaCl) (right, yellow trace). The distance between the beads was recorded over time. The FE curve for the -DNA full extension after incubation is shown (bottom). Additional examples can be found in fig. S10. (E) Kymograms of single-tethered -DNA stained with (YOYO-1) during the incubation with yeast cohesin and Scc2-Scc4 in the presence of ATP in 50 mM NaCl buffer at a flow rate of 20 l/min. HF, high flow. The free end of DNA is marked with orange arrowheads. No compaction of single-tethered -DNAs was observed. (F) Kymograms of single-tethered -DNA stained with (YOYO-1) during the incubation with yeast cohesin and Scc2-Scc4 in the presence of ATP in 50 mM NaCl buffer at a flow rate of 10 l/min. The conditions are as in (E) except for the reduced flow rate. Slow compaction of single-tethered -DNAs was observed over time (orange arrowheads mark the free end of DNA). (G) Kymograms of single-tethered -DNA stained with (YOYO-1) during the incubation with yeast cohesin and Scc2-Scc4 in the presence of ATP in 50 mM NaCl buffer at stopped flow. The free end of DNA is marked with orange arrowheads. The HF phase at the end of the experiment shows that the DNA was compacted during the stopped flow phase. Note that under stopped flow conditions, DNA molecules that diffuse laterally on the flow chip can transiently cross the field of view and also appear in a kymogram representation. Examples are marked with asterisks (*). These events bear no relevance for the interpretations of the assay.

Besides mediating sister chromatid cohesion (1, 2), cohesin holds individual chromatids in cis, thus forming loops (4, 29, 30). Recently, yeast condensin was the first SMC complex shown to exhibit an activity compatible with loop extrusion (7). It is unclear whether this activity is also present in the other eukaryotic SMC complexes cohesin and Smc5/6. Condensin can compact linear DNA against forces of up to 2 pN (19). However, condensin loop extrusion activity is only observed when DNA is stretched under significantly lower forces (below 0.5 pN) (7). We purified yeast condensin (fig. S9, B and C) using an established protocol (20, 31) and tested whether, as predicted from studies using magnetic tweezers (19), it could also compact -DNA molecules extended in the optical tweezers against a force of 1 pN. A single -DNA molecule was first captured between the beads. We then immobilized one of the beads and applied a constant force of 1 pN to the other bead in the opposite direction. This maintains the DNA extended with ~14 m between beads. We then moved the DNA to a channel containing 1 nM condensin in 50 mM NaCl buffer supplemented with 1 mM ATP. We incubated the extended DNA recording the distance between the two beads over time (Fig. 5C, Condensin). We observed progressive decrease of the distance between the beads (Fig. 5C, Condensin, and fig. S10), consistent with the activity of condensin as a motor that compacts DNA (19). Some condensation events occurred in short bursts and caused the molecule to shorten ~1 to 2 m in a few seconds (Fig. 5C, Condensin, and fig. S10). After incubation, we generated an FE curve, which showed the presence of sawtooth peaks characteristic of protein-mediated DNA bridging (Fig. 5C, bottom) (25, 26). Condensin bridges were fully reversible and disappeared when the DNA was extended (Fig. 5C, bottom). It is unclear whether the compaction observed is due to loop extrusion because this activity was reported to occur at forces below 1 pN (7). Next, we sought to text whether yeast cohesin tetramers could also compact extended -DNA molecules in this assay. We incubated the DNA extended using 1 pN of force with 1 nM cohesin, 2.5 nM Scc2-Scc4 complex, and 1 mM ATP in 50 mM NaCl buffer and incubated the extended DNA recording the distance between the two beads (Fig. 5D, Cohesin). The distance between the beads did not change over time (Fig. 5D, Cohesin, and fig. S10); therefore, we conclude that cohesin tetramers do not exhibit DNA compaction activity in this assay. As expected, the FE curve generated after incubation showed no evidence of protein-mediated DNA bridging (Fig. 5D, bottom), and similar results were obtained when we used a stretching force of 0.5 pN.

Since loop extrusion activity of condensin occurs at forces below 0.5 pN (7), we considered the possibility that yeast cohesin might also be able to extrude loops (and hence condense DNA) at extremely low forces. Below 0.5 pN, our optical tweezer did not reliably maintain the distance between the beads (data not shown). We therefore used single-tethered DNA curtains and different flow rates to extend DNA at very low tensions. Initially, we incubated cohesin in the presence of Scc2/Scc4 and ATP using a 125 mM NaCl buffer and a flow rate of 30 l/min; however, we did not observe compaction of single-tethered DNAs (data not shown). We then decided to reduce the ionic strength of the buffer to 50 mM NaCl and the flow rate to 20 l/min (Fig. 5E). We did not observe compaction in the course of the experiment (Fig. 5E). However, when we further reduced the flow rate to 10 l/min we observed slow compaction of the majority of the molecules (Fig. 5F). Last, we performed the same experiment but stopped the flow after protein injection (Fig. 5G). We observed rapid compaction of the single-tethered DNAs (Fig. 5G). From these data, we conclude that DNA compaction in single-tethered DNA curtains at such low flow is likely to be formed as a consequence of compaction that might involve loop extrusion since this activity only occurs at low ionic strength conditions and when DNA is extended by very low force.

The original role attributed to cohesin was the maintenance of sister chromatid cohesion from S phase until the anaphase onset (1, 2). Here, we have developed powerful single-molecule assays to probe the mechanisms by which cohesin holds DNAs together. Using them, we have shown that cohesin complexes can form different types of bridges between dsDNAs and that this requires Scc2-Scc4 and ATP. The two classes of cohesin tethers exhibited different physical properties, particularly the sensitivity to being broken by force. The reversible bridges were disrupted when moderate forces (5 to 40 pN) were applied (Fig. 3C). In contrast, permanent bridges could withstand extreme forces without being disrupted (Figs. 3C and 4D). They are also more predominant in high ionic strength conditions (Fig. 3C). On the basis of these physical properties, we propose that permanent bridges represent cohesin complexes that maintain sister chromatid cohesion. However, further characterization of their genesis, architecture, and biochemistry will be important to confirm such proposal. Reversible bridges were more predominant at low-salt concentrations (Fig. 3C), which suggest that they are likely formed by protein-protein interactions. In low salt, cohesin is likely to be saturated on DNA and being relatively sticky could easily engage in nonspecific interactions. Therefore, some reversible bridging events could potentially represent nonspecific protein aggregation. In particular, this might be the case for intramolecular DNA bridging at 50 mM NaCl salt (Fig. 3C). However, even under these conditions, reversible bridges were ATP and Scc2/4 dependent (fig. S3A). At 125 mM NaCl salt, which is in the physiological range, reversible bridges were also significant and resisted forces of up to 40 pN (Fig. 3C), strongly arguing that reversible bridges are biologically relevant. Previous studies have demonstrated that cohesin can use nontopological mechanisms (32); in addition, interallelic complementation between different cohesin alleles has also been reported (33). It is therefore possible that reversible DNA bridges reflect functional cohesin-cohesin interactions.

Recent studies have interrogated cohesin mechanisms using biochemical reconstitution of topological loading onto plasmids (14, 18, 34, 35). We believe that the single-molecule assay presented in this study is more informative for the study of cohesin bridging. In our hands, cohesin loading in the gel-based assay was not strictly ATP dependent and was not stimulated by Scc2/4, as observed for S. pombe cohesin (14, 18, 34, 35). Topological loading efficiency can be dependent on multiple factors, but critically on the amount of protein used, the times of incubation, and the number and stringency of the washes. We followed the original protocol described for S. pombe cohesin (18), and despite attempting different conditions, we never observed ATP-dependent loading. It is therefore likely that S. pombe and S. cerevisiae cohesins behave differently. The observation that S. pombe cohesin does not show bridging activity in double-tethered DNA curtains (15), while S. cerevisiae cohesin does (Fig. 2, E and F), supports this possibility.

Our results using two DNA molecules demonstrate that permanent cohesin tethers can slide when force is applied (Fig. 4C); however, when the permanent bridges occur in cis, cohesin complexes cannot slide off the DNA molecules (Fig. 3C and fig. S5). The simplest explanation is that the two DNA molecules tethered are not located in the same physical space within the protein (Fig. 6A). The two main models proposed to explain how cohesin holds sister chromatids are the ring and handcuff models. The basic difference between these two models is the fact that in the ring model, the two DNAs occupy the same physical space within cohesin, i.e., they are co-entrapped in one compartment of the cohesin structure (10, 11), while in the handcuff model (and all its variations), the two DNAs are located in different physical compartments (1, 12, 13), generally argued to be two separate (but interacting) complexes. On the basis of the single-ring model, it would be expected that cohesin slides off DNA molecules when bridging them intramolecularly (Fig. 6A). In contrast, our observations suggest that this is not the case (Fig. 3C). Using in vivo cysteine cross-linking of trimer cohesin complexes, it has been recently shown that cohesin has different subcompartments (36). Sister DNAs occupied the K (kleisin) compartment formed between the SMC ATPase heads and the Scc1 subunit (36). Scc3 and Scc1 form a module that binds DNA and is necessary for cohesin association to chromosomes (37), but Scc3 was not crosslinked in the subcompartment study (36). We propose that DNAs in permanent cohesin bridges might be held in two chambers of the K (kleisin) compartment (Fig. 6B, K1 and K2), physically separated by Scc3 (Fig. 6B), and the architecture would resemble a pretzel-like structure (Fig. 6B). The DNAs might be separated (one in K1 and the other in K2), or might travel through the two K compartments together. Alternatively, different compartments of two cohesin complexes might be involved (Fig. 6C).

(A) Schematic representation of expected behavior of intramolecular cohesin tethers from the previously proposed ring model. The model proposes that cohesin co-entraps two DNAs within its ring structure, i.e., both DNAs occupy one physical space within cohesin. From this model, it is expected that cohesin should be fully displaced from -DNA molecules when tethering in cis as force is applied to separate the beads. This is not what it was observed experimentally (Fig. 3C and fig. S5). (B) Schematic representation of expected behavior of intramolecular cohesin tethers from the subcompartment model. The subcompartment model is based on the assumption that DNAs are located in different physical compartments. The prediction from the model is that cohesin cannot be fully displaced from -DNA molecules when tethering them in cis. This is what we observed experimentally (Fig. 3C and fig. S5). (C) Proposed model for a single cohesin complex with at least three subcompartments (cohesin pretzel). In this model, sister DNAs occupy two different chambers (K1 and K2) of the K (kleisin) compartment formed between the SMC ATPase heads and the Scc1 subunit (36). Two possible conformations of SMC hinges are shown. Note that the experimental data are also compatible with the possibility that both DNAs jointly travel through the two chambers (K1 and K2) of the K compartment. (D) Schematic representation of previously proposed cohesin handcuffs models holding sister DNAs in different compartments of two separate complexes, which also fits with our experimental observations.

Kimura et al. (5) first proposed that the SMC complex condensin might generate DNA loops (5). This was conceived as one of two models that could explain how condensin specifically produced (+) trefoil knots in the presence of a type II topoisomerase (5). The proposal was based on an earlier model of loop expansion that was put forward for bacterial MutS action (38). MutS loop expansion was shown to occur as a consequence of ATP-dependent bidirectional movement of the MutS dimer from the initial loading site (38). The proposal of Kimura et al. (5) has been recently demonstrated directly through the observation of condensin-dependent DNA looplike structures on surface-tethered, flow-stretched DNA (7). The loop extrusion activity of cohesin was also conceived as a model that could explain the role of cohesin in genome folding through cis looping and the preferential use of convergent CTCF DNA motifs at TAD borders (8, 9). We detected DNA compaction by yeast cohesin tetramers at very low flow rates (Fig. 5, F and G), as would be predicted from a loop extrusion activity similar to the one shown for condensin (7). HiC data show that removal of cohesin leads to loss of contacts at TAD boundaries (6, 8, 39), demonstrating that the complex is involved in the formation or maintenance of loops. It is likely that cohesin extrudes DNA loops in a similar manner to condensin (7). However, our data, although consistent with cohesin function as a loop extruder, do not demonstrate it. We would like to note that our data showing intramolecular tethering by cohesin do not imply that cohesin generates loops in vivo through random DNA bridging. We feel that this would be highly unlikely. The intramolecular tethers observed might reflect an in vitro activity (as cohesin is unlikely to differentiate between cis and trans tethering when loaded onto DNA in these assays). Further experiments will be required to test whether intramolecular tethering is of any relevance in vivo. The activities described here are fully consistent with the original role attributed to cohesin in maintaining sister chromatid cohesion (1, 2). Our work provides a new critical tool for future investigations to further decipher how cohesin executes one of the critical functions required for genome inheritance, i.e., maintaining sister chromatids in close proximity from the time they are born in S phase until they are separated in anaphase.

The different subunits of the S. cerevisiae Scc2-Scc4 and cohesin complexes were synthesized under the control of galactose-inducible promoters and cloned into multicopy episomal vectors (URA3-SCC4-GAL1-10promoter-SCC2-3xmyc-3xStrepII;TRP1-SMC1-3xStrepII-GAL1-10promoter-SMC3 GAL7promoter-MCD1-8xHis-3xHA; URA3-GAL1-10promoter-SCC3). Yeast W303-1a strains carrying the different constructs (CCG14800 for the Scc2-Scc4 complex, CCG14801 for cohesin tetramer, and CCG14815 for cohesin smc3-K38I tetramer) were grown at 30C in selective dropout medium containing 2% raffinose and 0.1% glucose to an OD600 (optical density at 600 nm) of 1. Protein expression was induced by addition of 2% galactose, and cells were grown for further 16 hours at 20C. Cells were then harvested by centrifugation at 4C, resuspended in two volumes of buffer A [25 mM Hepes (pH 7.5), 200 mM NaCl, 5% glycerol, 5 mM -mercaptoethanol] containing 1 cOmplete EDTA-free protease inhibitor mix (Roche), frozen in liquid nitrogen, and lysed in Freezer-Mill (SPEX CertiPrep 6870). Cell powder was thawed at 4C for 2 hours before mixing it with one volume of buffer A containing benzonase (Millipore) and incubated at 4C for an extra hour. Cell lysates were clarified by centrifugation at 45,000g for 1 hour followed by filtration using 0.22-m syringe filters. Clarified lysates were loaded onto 5-ml StrepTrap-HP columns (GE Healthcare) preequilibrated with buffer A. The resin was washed with five column volumes of buffer A and eluted with buffer B (buffer A containing 5 mM desthiobiotin). The peak fractions containing the overexpressed proteins were pooled together, and salt concentration was adjusted to 150 mM NaCl using 100 mM NaCl buffer A. Samples were then filtered as described above to remove residual aggregates and loaded onto 5-ml HiTrap Heparin HP (GE Healthcare) columns preequilibrated with 150 mM NaCl buffer A. Elution was carried out using a linear gradient from 150 mM to 1 M NaCl in buffer A. Peak fractions were pooled and concentrated by centrifugal ultrafiltration (100 kDa Amicon Ultra, Millipore). Salt concentration was adjusted to 300 mM NaCl during the concentration step. Gel filtration was carried out using a Superose 6 Increase 100/300 GL column (GE Healthcare) in 300 mM NaCl buffer A. Fractions corresponding to monomeric complexes were pooled and concentrated as described above. Purified proteins were analyzed by SDS-PAGE (NuPAGE 4 to 12% bis-tris protein gels, Thermo Fisher Scientific) and Coomassie staining (InstantBlue, Expedeon). Protein identification was carried out by mass spectrometry analysis and Western blot. S. cerevisiae condensin complex was expressed and purified, as previously described (20, 31).

Human cohesin tetramer was purified, as described before (28). Human cohesin subunits (RAD21, SMC1A, SMC3-FLAG, 10xHis-SA1) were coexpressed in High Five insect (BTI-Tn-5B1-4) cells. Cells were disrupted by short sonication. Afterwards, the lysate was clarified by high-speed centrifugation. The complex was then purified via HisTrap [washing buffer: 25 mM tris (pH 7.5), 500 mM NaCl, 5% glycerol, 2 mM MgCl2, 20 mM imidazole, 0.01% Tween-20, 20 mM -mercaptoethanol; elution buffer: 25 mM tris (pH 7.5), 150 mM NaCl, 5% glycerol, 2 mM MgCl2, 150 mM imidazole, 0.01% Tween-20]. Fractions were pooled and dialyzed [25 mM tris (pH 7.5), 150 mM NaCl, 5% glycerol, 2 mM MgCl2]. The protein was further purified by tandem ion exchange chromatography by using an anion-exchange column connected to a cation exchange column. The complex was then eluted from the cation-exchange column [25 mM tris (pH 7.5), 1 M NaCl, 5% glycerol, 2 mM MgCl2]. Subsequently, the peak fractions were pooled and dialyzed into storage buffer [25 mM tris (pH 7.5), 150 mM NaCl, 5% glycerol, 2 mM MgCl2]. Purity was confirmed by gel electrophoresis and mass spectrometry.

Increasing concentrations of the Scc2-Scc4 complex ranging from 100 to 800 nM were incubated for 45 min with 50 ng of pUC19 at 30C in 25 mM tris-HCl (pH 7.0), 50 mM NaCl, 8% glycerol, bovine serum albumin (BSA; 0.1 mg/ml), and 0.5 mM dithiothreitol (DTT) in a final volume of 15 l. The reactions were resolved by electrophoresis for 1 hour at 80 V on 0.8% (w/v) tris-acetate-EDTA (TAE) agarose gels at 4C. DNA was detected on a fluorescent image analyzer (FLA-5000, Fujifilm) after SYBR Green I (Invitrogen, Thermo Fisher Scientific) gel staining. Condensin assays were carried out as previously described (20).

For cross-linking of cohesin complex, protein samples were incubated with BS3 at a 1:3000 molar ratio in buffer XL [25 mM Hepes, 125 mM NaCl, 5% glycerol, 1 mM DTT (pH 8)] for 2 hours on ice before quenching with 10 mM tris-HCl (pH 8) for 30 min on ice.

Negative-stain grids were prepared as follows: 3.5 l of suspended sample (final concentration of 0.02 mg/ml in buffer XL) was deposited on glow-discharged grids coated with a continuous carbon film. The sample was left on the grid for 1 min before blotting the excess liquid. A 3.5-l drop of 2% uranyl acetate solution was added for 1 min, the stain was blotted away, and the grids were left to dry.

A set of 250 micrographs was collected on a Philips CM200 TWIN FEG electron microscope operated at 160 kV. Images were recorded on a Tietz 2k charge-coupled device camera at a nominal magnification of 38,000 and a final pixel size of 3.58 , contrast transfer function (CTF) parameters were estimated using Gctf (40). A total of ~9000 particles were automatically picked using Gautomatch software using class averages obtained from a manually picked subset of 1500 particles as references. The following two-dimensional classifications were performed with RELION v3.0 beta (41).

Cohesin loading assays were done as described in (14) using the pUC19 plasmid. Topologically bound DNA-cohesin complexes were immunoprecipitated using a MACS HA Isolation kit (Miltenyi Biotec). Following incubation with Pst I and/or protein digestion, the recovered DNA was analyzed by electrophoresis on a 0.8% (w/v) TAE agarose gel in 1 TAE and visualized as described above.

For the ATPase assays, 30 nM cohesin was incubated at 29C with 60 nM Scc2/4 and 0.2 nM -DNA (New England Biolabs) in ATPase buffer [35 mM tris-HCl (pH 7.0), 20 mM NaCl, 0.5 mM MgCl2, 13.3% glycerol, 0.003% Tween-20, 1 mM tris(2-carboxyethyl)phosphine (TCEP), BSA (0.2 mg/ml)]. The reaction was started by adding 400 M ATP spiked with [-32P]ATP. One microliter of samples was taken after 1, 15, 30, and 60 min. The reaction was immediately stopped by adding 1 l of 50 mM EDTA before spotting the samples on polyethyleneimine cellulose F sheets. The free phosphate was separated from ATP using thin-layer chromatography with 0.5 M LiCl, 1 M formic acid as the mobile phase. The spots were detected on a phosphor imager and analyzed using ImageJ. Data points were corrected for spontaneous ATP hydrolysis. Each reaction was performed in triplicate. Data were fitted to Michaelis-Menten kinetics.

DNA curtain experiments were performed as described previously (42). Briefly, flow cells were produced by deposition of chromium features onto fused silica microscope slides by e-beam lithography. Flow cells were connected to a microfluidics system based on a syringe pump (Landgraf GmbH) and two injection valves (Idex) and illuminated by 488- or 561-nm lasers (Coherent) in a prism-type total internal reflection fluorescence (TIRF) configuration on an inverted microscope (Nikon Ti2e). Imaging was performed using an electron multiplying charge-coupled device (EMCCD) camera (Andor iXon life) with illumination times of 100 ms. -DNA (NEB) was end-modified by hybridizing biotinylated or digoxigeninated oligos complementary to the cos site and purified by size exclusion chromatography. Modified -DNA was anchored to the surface of a lipid bilayer in flow cells by biotin-streptavidin-biotin interactions, stretched by flow across chromium barriers, and anchored to downstream chromium pedestals by the digoxigenin-binding protein DIG10.3 (43). Experiments were performed in buffer M [40 mM tris-HCl (pH 7.8), 1 mM MgCl2, 1 mM DTT, BSA (1 mg/ml), 0.16 nM YOYO-1]. Cohesin complexes were labeled by incubating them at a concentration of 3 nM in a small volume of buffer M supplemented with 50 mM NaCl with 3 molar excess Qdots (SiteClick 705 kit, Invitrogen) fused to anti-HA antibodies (3F10, Roche) for 30 min at 4C. The mixture was then supplemented with 8 nM Scc2/Scc4, 100 m biotin, and 0.5 mM nucleotide (ATP, ADP, or ATPS), if required, before injection. For diffusion measurements, the flow cell was flushed after the completion of loading with buffer M supplemented with KCl at the indicated concentrations and the flow was stopped. Illuminations were performed either continuously (diffusion and lifetime measurements) or with lower frame rates (intermolecular bridging videos). To minimize photodamage, 488-nm pulses to illuminate the DNA, if required, were only used at every 10th illumination.

Videos were recorded in NIS Elements (Nikon) and analyzed using custom-written software in Igor Pro (WaveMetrics). Lifetime measurements and initial binding distributions of cohesin complexes on DNA were generated by manually analyzing kymograms. Survival curves were generated by a Kaplan-Meier estimator, bootstrapped, and fitted to a double-exponential model.

For the determination of diffusion coefficients, labeled cohesin complexes were tracked using custom-written software, and the diffusion coefficients were extracted using a maximum-likelihood estimator (44), as described previously (15).

Optical tweezers experiments were carried out on C-trap and Q-trap systems integrating optical tweezers, confocal fluorescence microscopy, and microfluidics and recorded using BlueLake software (LUMICKS). The laminar flow cell was passivated using 0.50% pluronic and BSA (2 mg/ml). Biotin-labeled double-stranded -DNA molecules were tethered between two streptavidin-coated polyesterene beads (4.42 m in diameter, Spherotech). Depending on the experiment, one or two individual double-stranded -DNA molecules were attached between two beads. The beads were previously passivated with BSA (1 mg/ml). After DNA capture, beads were incubated inside the protein channel either in a relaxed (~3 m apart) or extended position (force clamp at 5 pN, ~14 m apart) for 30 s and then returned to the buffer channel for FE, force clamp, and fluorescence analysis. Cohesin and Scc2-Scc4 complex were used at 1 nM and 2.5 nM concentrations, respectively. Beads and DNA catching and protein loading were performed in a buffer containing 50 mM tris-HCl (pH 7.5), 50 mM NaCl, 2.5 mM MgCl2, BSA (0.5 mg/ml), 40 M biotin, and 1 mM DTT. When indicated, ADP, ATPS, and ATP were added to both protein and buffer channels at a final concentration of 1 mM. Salt concentration was modified from 50 mM to 125, 300, or 500 mM NaCl in the buffer channel as specified in the text and figures. FE curves were performed at a speed of 1 m/s. Compaction experiments were carried out at a constant force of 1 pN. For friction experiments, beads were moved 6 m, back and forth, at a speed of 0.2 m/s. SYTOX Orange (Invitrogen, Thermo Fisher Scientific) was used at a final concentration of 50 mM for DNA imaging, using a 532-nm wavelength laser. Force data were processed using Igor Pro 7 software (WaveMetrics), and images were processed using Adobe Photoshop CC.

For Western blot, 2 g of purified complexes was run on NuPAGE 4 to 12% bis-tris gels (Thermo Fisher Scientific), transferred to Immobilon-P membranes (Millipore), and probed with anti-Strep (ab180957, Abcam, 1:5000) and anti-HA (3F10, Roche, 1:5000) antibodies in 5% milkphosphate-buffered saline (PBS)/0.01% Tween overnight at 4C. Membranes were then washed and incubated with horseradish peroxidase anti-rabbit (Santa Cruz Biotechnology, 1:40,000) and anti-rat (Jackson ImmunoResearch, 1:10,000) secondary antibodies, respectively, for 1 hour at room temperature. Immunoblots were developed using the Luminata Forte detection reagent (Millipore) and Hyperfilms ECL (GE Healthcare).

Samples were processed by in-Stage Tip digestion (PreOmics GmbH, Planegg/Martinsried) following the manufacturers recommendation. Protein digests were solubilized in 30 l of reconstitution buffer and transferred to autosampler vials for liquid chromatographymass spectrometry analysis. Peptides were separated using an Ultimate 3000 RSLC nanoliquid chromatography system (Thermo Fisher Scientific) coupled to an LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific) via an EASY-Spray source. Sample volumes were loaded onto a trap column (Acclaim PepMap 100 C18, 100 m 2 cm) at 8 l/min in 2% acetonitrile and 0.1% trifluoroacetic acid. Peptides were eluted online to an analytical column (EASY-Spray PepMap C18, 75 m 50 cm). Peptides were separated using a ramped 120-min gradient from 1 to 42% buffer B [buffer A: 5% dimethyl sulfoxide (DMSO), 0.1% formic acid; buffer B: 75% acetonitrile, 0.1% formic acid, 5% DMSO]. Eluted peptides were analyzed operating in positive polarity using a data-dependent acquisition mode. Ions for fragmentation were determined from an initial MS1 survey scan at 30,000 resolution [at mass/charge ratio (m/z) of 200] in the Orbitrap followed by CID (collision-induced dissociation) of the top 10 most abundant ions in the Ion Trap. MS1 and MS2 scan AGC targets were set to 1 106 and 1 105 for a maximum injection time of 50 and 110 ms, respectively. A survey scan m/z range of 350 to 1500 m/z was used, with CID parameters of isolation width 1.0 m/z, normalized collision energy of 35%, activation Q of 0.25, and activation time of 10 ms.

Data were processed using the MaxQuant software platform (v1.6.2.3) with database searches carried out by the in-built Andromeda search engine against the UniProt S. cerevisiae database (6729 entries, v.20180305). A reverse decoy database was created, and results were displayed at a 1% false discovery rate for peptide spectrum matches and protein identification. Search parameters included the following: trypsin, two missed cleavages, fixed modification of cysteine carbamidomethylation and variable modifications of methionine oxidation, asparagine deamidation, and protein N-terminal acetylation. Label-free quantification (LFQ) was enabled with an LFQ minimum ratio count of 2. Match between runs function was used with match and alignment time limits of 0.7 and 20 min, respectively. Protein and peptide identification and relative quantification outputs from MaxQuant were further processed in Microsoft Excel, with hits to the reverse database, potential contaminants (peptide list only), and only identified by site fields removed.

Acknowledgments: We thank J. C. Danes and J. Andrecka (LUMICKS) for technical help. We thank our laboratory members for discussion and critical reading of the manuscript. We thank D. DAmours, C. Haering, and J. Peters for sharing plasmids for the expression of yeast condensin and human cohesin. Funding: The work in the L.A. laboratory was supported by Wellcome Trust Senior Investigator award to L.A. (100955, Functional dissection of mitotic chromatin) and the London Institute of Medical Research (LMS), which receives its core funding (intramural program) from the UK Medical Research Council. J.S. acknowledges support by the Center of Nanoscience (CeNS) of Ludwig-Maximilians-Universitt as well as funding from the Deutsche Forschungsgemeinschaft (DFG) under grant STI673-2-1 and from the European Research Council under ERC grant agreement 758124. The Single Molecule Imaging Group is funded by a core grant of the MRCLondon Institute of Medical Sciences (UKRI MC-A658-5TY10), a Wellcome Trust Collaborative Grant (P67153), and a BBSRC CASE-studentship (to M.D.N.). Author contributions: P.G.-E. expressed and purified yeast cohesin and Scc2-Scc4 proteins and performed biochemical assays. J.D. expressed and purified yeast condensin. I.A. expressed and purified human cohesin. P.G.-E., M.D.N., and A.L. collected optical tweezers datasets. P.G.-E., M.D.N., and J.S. processed optical tweezers data. J.H. and L.T. performed ATPase assays. R.A. prepared electron microscopy grids and collected and processed electron microscopy images. H.K. and A.M. performed mass spectrometry analysis. J.H. and J.S. performed, collected, and analyzed DNA curtain datasets. P.G.-E. and L.A. conceived the project. L.A. wrote the manuscript. L.A., D.S.R., and J.S revised the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. The plasmid for the expression of human cohesin was a gift by the laboratory of J. M. Peters. Requests should be submitted to IMP Vienna.

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A conserved ATP- and Scc2/4-dependent activity for cohesin in tethering DNA molecules - Science Advances

Early detection of brain degeneration on the horizon with innovative sensor – UNM Newsroom

UNM research builds on findings involving protein aggregation in brain cells and neurodegenerative diseases

Neurodegenerative diseases such as Alzheimers and Parkinsons can be devastating to patients and their families. These diseases are difficult to diagnose before symptoms show, meaning its often already too late to reverse the damage to the central nervous system. Early detection is key for management of symptoms and attempts to stall progression of the disease, but current knowledge is limited when it comes to tools that aid in early detection. That knowledge gap is being addressed through cutting-edge research by a team at The University of New Mexicoled by Professor Eva Chi of the Department of Biomedical Engineering.

Dr. Eva Chi

In order to understand complex diseases of the brain, one has to understand the complexity of human biology and the brain itself. Of particular importance is proteinsmolecular structures inside a cell that can number into the tens-of-thousandsand their ability to dictate how cells function. Proteins start off with the same basic building blocks, called amino acids. The amino acids organize into a chain, and the unique function of the protein depends on how the amino acids are ordered in the chain. Once the amino acid ordering is complete, the protein chains fold themselves in various ways in order to bind to other molecules to perform certain tasks.

All proteins are made of the same building blocks; the folding of the protein into distinct shapes dictates its unique purpose inside the body. For example, digestive enzyme proteins break down our food into nutrients, and transport proteins such as hemoglobin carry substances throughout our body. Of particular interest to those who study brain degeneration are tau proteins inside neurons (brain cells) that help with cellular and nerve communication in the brain.

Protein folding is an intricate process, and as such, a lot can go wrong inside the cell. Protein folds can fail altogether, or an error in the protein chain could cause a misfold. Some of these misfolds have been linked by research scientists to numerous diseases in humans, especially when the misfolded proteins stick together. The resulting sticky clumps of proteins are called protein aggregates.

Proteins have such important functions in the body, and once they do something else such as aggregate, it can have devasting consequences in the body, creating the potential for systemic and neurodegenerative diseases, says Chi.

Illustration demonstrating how OPEs bond to and illuminate both toxic protein aggregates and proteins with normal folds.

Previous research over the past decade has shown a link between degenerative brain diseases and aggregation of tau proteins inside neurons (tauopathy), as well as plaque-forming clumps of protein fragments called amyloid beta that disrupt the pathways between the cells. Scientists hypothesize that these protein aggregates form in the brain long before symptoms appear, and Chis research is focused on detection of these aggregates using a type of biosensor. Through past research, Chi and her team have developed a highly responsive biosensor called Oligo(p-phenylene ethynylene) electrolytes, or OPEs. OPEs are as a molecular structure created in a lab that can regulate electrical signals between neurons, as well as light up under a microscope when interacting with certain types of proteins.

Aggregates form inside one cell at the start, and as a disease such as Alzheimers progresses into the next stages, the aggregates recruit more healthy proteins inside the cell before spreading to multiple cells in the brain. Since Alzheimers, Parkinsons, and similar diseases are not infectious, is unclear how the aggregation spreads from cell to cell. Mice models can track functionality through cognitive tests, but researchers cannot yet track biochemical changes inside a living human brain. Chi hopes the OPE sensors will also shine some light on this process.

These diseases have a stage based on what the brain looks like, and the disease spreads throughout the brain, but we dont know how it spreads. With other types of problems in the body, there are testsX-rays, MRIsbut there is nothing for aggregates in the brain, and its something the field has been working towards, says Chi. The goal is to discover the next generation of sensors that can detect the protein aggregates that are more relevant to causing these diseases. In the long run, these sensors, if effective, will work along the lines of brain imaging that can detect the size, location, and cell-to-cell spread of the aggregates.

Using mouse models, rat models, and donated human brain tissue in her lab, Chi takes proteins from these models in test tubes and treats them chemically to form aggregates. Her OPE sensors are added, and once the sensors find the aggregates, they bond to them and light up. Chi and her students then look at the results under a powerful microscope to see the features of the proteins and their sensors.

Fundamental interactions between the sensor and the aggregate is the main focus, Chi explains. The sensor can seek out and find these aggregates and could potentially work to repair the damage. This knowledge can be applied for other purposes, such as sensors for antimicrobial applications, or used as therapies.

Chi began this research at UNM in 2013 through a private grant from the Huning family, and her current grant from the National Institutes of Health builds on the knowledge she has gained since starting on this journey. She has published three papers and filed two patent applications for the sensors.Much like the aggregates themselves, understanding diseases of the brain is a tangled web of complexity, but Chis sensors are a huge step forward in the quest for successful treatment and reversal of degenerative diseases, giving hope to the millions of people and their families affected by these devastating illnesses.

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Early detection of brain degeneration on the horizon with innovative sensor - UNM Newsroom

Covid19 Folding@home

Coronavirus What were doing and how you can help

Proteins are molecular machines that perform many functions we associate with life. They sense the environment (e.g. in taste and smell), perform work (e.g. muscle contraction and breaking down food), and play structural roles (e.g. your hair). They are made of a linear chain of chemicals called amino acids that, in many cases, spontaneously fold into compact, functional structures. Much like any other machine, its how a proteins components are arranged and move that determine the proteins function. In this case, the components are atoms.

Viruses also have proteins that they use to suppress our immune systems and reproduce themselves.

To help tackle coronavirus, we want to understand how these viral proteins work and how we can design therapeutics to stop them.

There are many experimental methods for determining protein structures. While extremely powerful, they only reveal a single snapshot of a proteins usual shape. But proteins have lots of moving parts, so we really want to see the protein in action. The structures we cant see experimentally may be the key to discovering a new therapeutic.

Using football as an analogy for the experimental situation, its as if you could only see the players lined up for the snap (the single arrangement the players spend the most time in) and were blind to the rest of the game.

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Covid19 Folding@home

New Study Reveals US Airlines With the Healthiest Food Options – TravelPulse

Alaska Airlines offers the healthiest food choices among the 10 major U.S. carriers and tied with Air Canada for the best when folding in all major North American airlines, according to a study conducted by the Hunter College NYC Food Policy Center and DietDetective.com.

The Airline Food Study ranked the airlines on the nutrients and calorie levels of meals, snack boxes, and individual snacks.

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Airlines were also scored on several other categories ranging from sodium levels in food, water quality, the availability of meals on flights that are under three hours long, level of transparency in terms of displaying nutritional information, and posting their menus and ingredients online.

Alaska Airlines scored a 4.0 on a five-point scale and was the highest-rated U.S. carrier in the study for the second straight year. Lead author Charles Platkin wrote he was pleased with many of the options Alaska offers, including Mediterranean Tapas snack box and its Fresh Start Protein Platter breakfast.

Rounding out the top five American airlines in the study were Delta and JetBlue, tied with a 2.9 score, and United and American at 2.7.

Hawaiian Airlines scored the lowest rating among carriers with full food offerings, and Southwest came in last with a score of 1.7 based mostly on the fact that the budget carrier offers only individual snacks.

"If the airline really does have a heart (as it does on its logo), it would care about the food thats being served. Southwest needs to add some healthy snacks," the authors wrote.

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New Study Reveals US Airlines With the Healthiest Food Options - TravelPulse

Petrobras directing supercomputer capacity to Folding@home Project effort on coronavirus – Green Car Congress

Brazil-based oil company Petrobras will direct part of the processing capacity of its high-performance computers (HPC) to contribute to the Folding@home Project effort on studying the coronavirus behavior in the human body and how the disease progresses, from the interaction of viral proteins, making way for for the development of medication and vaccines.

Launched in 2000, the Folding@home project is a distributed computing project for simulating protein dynamics, including the process of protein folding and the movements of proteins implicated in a variety of diseases. It brings together citizen scientists who volunteer to run simulations of protein dynamics on their personal computers.

Insights from this data are helping scientists to better understand biology, and providing new opportunities for developing therapeutics. Among other advancements, this project has already helped in identifying the protein which links the SARS-CoV-2 betacoronavirus (the virus that causes COVID-19) to human cells.

Up to two supercomputers in Petrobras service may have their processing capacity redirected to this research: the Santos Dumont, Latin Americas largest supercomputer, located in the National Scientific Computing Lab (Laboratrio Nacional de Computao Cientfica - LNCC), in Petrpolis (RJ), which recently had its capacity enhanced by collaboration with another lab, the company and its partners in the Libra Consortium; and OBGON, result of the partnership with Senai-Cimatec, installed in Salvador (BA).

For the initiative, the company will mobilize 60% of Santos Dumonts capacity2 petaflops (equivalent to the computational capacity of 2 million laptops)in addition to 50% of Senai-Cimatec capacity, corresponding to one petaflop (1 million laptops).

The use of these supercomputers allows for accelerating the simulation time in order for researchers to achieve results faster in their research.

In addition to this initiative, Petrobras will mobilize its high performance computational resources for research projects of Brazilian universities in fighting coronavirus. One of the potential projects, in a partnership with both PUC-Rio and Senai-Cimatec, is the use of artificial intelligence techniques (deep learning) in order to help differentiate the X-ray exam of a regular flu patient and the X-ray exam of a coronavirus patient.

The algorithms create repetition patterns and, by comparing the data, it is possible to arrive at a diagnosis. It is a test cheaper and faster than, for example, tomography and PCR blood exams.

These initiatives integrate a broad front led by Petrobras, which is mobilizing its professionals from various fields of knowledge that may contribute in fighting the coronavirus, in partnership with universities, companies, social organizations, Brazilian and foreign institutions. Its goal is to propose solutions that may use the companys technological structure, equipment and technical consulting in order to aid the effort in fighting the pandemic, in the prevention, treatment and hospital support fronts.

In the same way, Petrobras is also dedicated to initiatives such as donation supply to institutionsincluding, for example, safety and hygiene items to the UFRJ hospitaland mobilizing its structures for storage, among others.

On the Folding@home Project. Viruses have proteins that they use to suppress our immune systems and reproduce themselves. To help tackle coronavirus, researchers want to understand how these viral proteins work and how to design therapeutics to stop them.

Folding@homes specialty is in using computer simulations to understand proteins moving parts. Watching how the atoms in a protein move relative to one another is important because it captures valuable information that is inaccessible by any other means.

Taking the experimental structures as starting points, Folding@home can simulate how all the atoms in the protein move, effectively filling in the rest that experiments miss. Doing so can reveal new therapeutic opportunities.

In a recent paper, Folding@home simulated a protein from Ebola virus that is typically considered undruggable because the snapshots from experiments dont have obvious druggable sites. But the simulations uncovered an alternative structure that does have a druggable site. Experiments confirmed the computational prediction, and now there is a search for drugs that bind this newly discovered binding site.

Folding@home seeks to do the same thing with SARS-CoV-2. On 10 March, after initial quality control and limited testing phases, the Folding@home team released an initial wave of projects simulating potentially druggable protein targets from SARS-CoV-2 and the related SARS-CoV virus (for which more structural data is available) into full production on Folding@home.

SARS-CoV-2 RBD domain in complex with human ACE2 receptor (PDBID: 6vsb, 6acg) [10.1126/science.abb2507, 10.1371/journal.ppat.1007236]

This initial wave of projects focuses on better understanding how these coronaviruses interact with the human ACE2 receptor required for viral entry into human host cells, and how researchers might be able to interfere with them through the design of new therapeutic antibodies or small molecules that might disrupt their interaction.

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Petrobras directing supercomputer capacity to Folding@home Project effort on coronavirus - Green Car Congress

What is Folding@home and how can we use it to fight the Coronavirus? – Pocket-lint

In modern times, the advent of more intelligent computing technology means that processing power can be used to help with scientific research.

That research involves using simulations to analyse the make-up of proteins in the human body and how they "fold".

Misfolding proteins are often the cause for diseases likes Alzheimers, Parkinson's, various types of cancer, ALS and more.

Using technology to research these proteins allows scientists to more efficiently and more quickly develop drugs to help combat the issues.

This is particularly relevant at the moment with the Coronavirus pandemic.

The good news is, you can help with this and doing so is really simple too. All you need to do is get involved with Folding@home.

Folding@home is a distributed computing project run by Stanford University. The aim of the project is to examine how proteins fold and it does this using spare computing power.

We first wrote about folding@home in 2007, but with rising concern about coronavirus - and confirmation of the project's involvement in researching COVID-19 - now is a great time to revisit this project and lend some support.

The idea behind the project is around shared computing power.

Lots of people have computers and a lot of the time those computers aren't doing anything - they're just sitting around with spare computational power. Folding@home takes advantage of that spare power to put it to a good cause - researching various diseases.

It's a very technical thing - both in terms of how a distributed computing project works and investigating folding proteins, but fortunately, you don't have to understand either of those things to lend your support, because it all happens in the background.

The idea is that when there are millions of computers doing a little bit of work in the background, the project will have greater computational power at its disposal, which is a great benefit to researchers.

To help, you just need to download the software to your computer and set it to run. The program then downloads "work units" and processes them to send the data back.

Generally, you'll still find you're able to use your computer as you normally would without any hassle, but while you work, play, stream or browse, you'll be helping fight disease.

All you have to do is head over to the folding@home website and you can download the software for whatever platform you're on. You'll install a small programme that will connect to the back to the project and then start churning data.

You can download Folding@home for Windows, Mac or Linux machines, so whatever you're using it's easy to get started.

It's also free to download, so it'll cost you nothing to do your part.

There are detailed guides on how to install the Folding@homesoftware for Windows, Mac and Linuxon the site too.

The installation process is really simple though. Download the software, install it, set up an identity and start folding.

You can open Folding@homein a browser to see how you're doing. You also have the option to adjust how much processing power the software is using. If you're not using your computer you could set it to "full" to do the most work or "light" if you're doing something more intensive and need to dial back the folding for a bit.

The benefits of Folding@homeare fairly straightforward. With very little technical knowledge you can set up your computer to help find cures for disease.

The more people that get involved, the more processing power there is to simulate the protein folding and the faster the results will be achieved.

This system also means that the organisation doesn't need to pay for supercomputers as everyone around the world is lending a hand.

When running Folding@home,it is possible to choose a project. This means you can dedicate your processing power to support fighting a particular disease. You can choose from Alzheimer's, Cancer, Huntington's, Parkinson's or any disease.

There is no current way to select Coronavirus as a disease to fight, but the team has said selecting any disease will still help with the research into the pandemic.

When you starting using the software you'll see you'll slowly accumulate points. These points are designed to encourage friendly competition between you, your friends and other people online.

Points are calculated based on the work units you complete and the points vary depending on the complexity of those work units. Some of the work involves studying small proteins, others are on more complex proteins and so the points awarded will varying depending on that.

You can also join a team in order to help climb a stats ladder to compete for the position of the best team. The stats of the teams are viewable here. Though you don't need to join a team and can fold anonymously if you'd prefer.

If you'd rather be part of a group effort, you can join a team easily from the web control interface that opens in a browser.

Under "I'm folding as" you'll find a link to "change identity". If you click that you'll see a pop-up that lets you choose a name and a team.

To join a team you need to know the team's number. You can find the team numbers from the stats page.

Alternatively, you can create your own team by filling out this simple form. Once you've set your team up, make a note of the number and get your friends to join in too to help do their part.

Folding@home is designedto be safe. It's been carefully tested and the servers for it are behind high-security firewalls to keep everything safe and secure. You won't have any problems running this software on your computer.

The folding@home team has confirmed that it is supporting researchers at Memorial Sloan Kettering in New York City to develop treatments for COVID-19. As part of an open science approach, findings are shared with other researchers, with the global goal of developing drugs or therapies to combat the coronavirus.

This video shows the Folding@homesimulations of the COVID-19 protein. It's this sort of simulation that helps researchers understand what's happening with the proteins and how they're infecting human cells.

That data could then be used to develop ways to block the virus in the first place.

The worldwide issues with COVID-19 has lead to more and more people using Folding@home. That, in turn, has lead to a massive increase in processing power for the project. The project has now broken theexaFLOP barrier meaning it's more powerful than even the most powerful supercomputer. This also means it's carrying out over1,000,000,000,000,000,000 operations per second.

Dr Greg Bowman has recently revealed that the number of people folding has reached almost five times the amount as the number before the pandemic outbreak.

What are you waiting for? Download the software and do your bit too.

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What is Folding@home and how can we use it to fight the Coronavirus? - Pocket-lint

How NVIDIA Is Using Its GPU Technolgy To Fight Against COVID-19 Virus – Forbes

As Silicon Valley is gearing up to fight against the novel coronavirus, NVIDIA is putting its GPU technology to use by enabling researchers and gamers to join the on-going efforts.

Covid-19

GPUs are not only meant to enhance the gaming experience through fast graphics or accelerating the training and inference of machine learning models. They also play a crucial role in assisting the scientific community involved in researching genome analysis and sequencing.

To fight the growing threat of novel coronavirus, NVIDIA is making its platform, Parabricks, free for 90 days to any researcher working on sequencing the novel coronavirus and the genomes of people afflicted with COVID-19.

Genome analysis is a computationally intensive effort that needs a high performance computing environment powered by CPUs and GPUs. Sequencing platforms such as DNBSEQ-T7 from MGI generate as much as 6 TerraBytes of data every day, which is analyzed by scientists performing whole genome sequencing. According to NVIDIA, these systems will generate about 20 ExaBytes of data by 2025 more than Twitter, YouTube and astronomy combined. Interestingly, it would take all the CPUs in every cloud and more than 200 days to run genome analysis.

Parabricks, an Ann Arbor, Michigan-based startup, built a platform based on GPU to speed up the process of analyzing whole genomes all 3 billion base pairs in human chromosomes from days to under an hour.

As platforms like DNBSEQ-T7 generate more data, analysis has becomes a major bottleneck in both time and cost perspectives. Parabricks solution addresses both of these barriers to accelerate the genomic analysis.

Parabricks platform is powered by NVIDIA CUDA-X and benefits from CUDA, cuDNN and TensorRT inference software and runs on NVIDIA entire computing platform from NVIDIA T4 to DGX to cloud GPU instances.

Earlier this year, NVIDIA acquired Parabricks with a goal to release the companion technology that accelerates single-cell and RNA analysis.

The Parabricks acquisition helped NVIDIA to officially offer genome sequencing and analysis on its HPC platform.

By making Parabricks accessible to the research community, NVIDIA aims to dramatically reduce the time for variant calling on a whole human genome from days to less than an hour on a single server.

Since Parabricks is available as a part of NVIDIA GPU Cloud (NGC), it is expected to run on major cloud platforms and NVIDIAs own appliances including DGX-1. Researchers with access to NVIDIA GPUs can fill out a form to request access to Parabricks.

Apart from offering Parabricks free for 90 days, NVIDIA is also encouraging gamers to participate in the Folding@Home project, a distributed computing project for disease research that simulates protein folding, computational drug design and other types of molecular dynamics.

Folding@home is a collaborative project focused on disease research. The problems they deal with rely on many calculations that can be effectively offloaded to idle PCs running in homes and offices for globally distributed processing. The project is managed by Washington University in St. Louis School of Medicine.

NVIDIA is joining Intel and AMD in an effort to utilize unused GPU computing power on PCs and gaming machines to fight against COVID-19.

NVIDIA is putting its best technology to use in fighting COVID-19 through the 90 day free trial of Parabricks and by participating in the Folding@Home project.

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How NVIDIA Is Using Its GPU Technolgy To Fight Against COVID-19 Virus - Forbes

Organisms grow in wave pattern, similar to ocean circulation – Big Think

When an egg cell of almost any sexually reproducing species is fertilized, it sets off a series of waves that ripple across the egg's surface.

These waves are produced by billions of activated proteins that surge through the egg's membrane like streams of tiny burrowing sentinels, signaling the egg to start dividing, folding, and dividing again, to form the first cellular seeds of an organism.

Now MIT scientists have taken a detailed look at the pattern of these waves, produced on the surface of starfish eggs. These eggs are large and therefore easy to observe, and scientists consider starfish eggs to be representative of the eggs of many other animal species.

In each egg, the team introduced a protein to mimic the onset of fertilization, and recorded the pattern of waves that rippled across their surfaces in response. They observed that each wave emerged in a spiral pattern, and that multiple spirals whirled across an egg's surface at a time. Some spirals spontaneously appeared and swirled away in opposite directions, while others collided head-on and immediately disappeared.

The behavior of these swirling waves, the researchers realized, is similar to the waves generated in other, seemingly unrelated systems, such as the vortices in quantum fluids, the circulations in the atmosphere and oceans, and the electrical signals that propagate through the heart and brain.

"Not much was known about the dynamics of these surface waves in eggs, and after we started analyzing and modeling these waves, we found these same patterns show up in all these other systems," says physicist Nikta Fakhri, the Thomas D. and Virginia W. Cabot Assistant Professor at MIT. "It's a manifestation of this very universal wave pattern."

"It opens a completely new perspective," adds Jrn Dunkel, associate professor of mathematics at MIT. "You can borrow a lot of techniques people have developed to study similar patterns in other systems, to learn something about biology."

Fakhri and Dunkel have published their results today in the journal Nature Physics. Their co-authors are Tzer Han Tan, Jinghui Liu, Pearson Miller, and Melis Tekant of MIT.

Previous studies have shown that the fertilization of an egg immediately activates Rho-GTP, a protein within the egg which normally floats around in the cell's cytoplasm in an inactive state. Once activated, billions of the protein rise up out of the cytoplasm's morass to attach to the egg's membrane, snaking along the wall in waves.

"Imagine if you have a very dirty aquarium, and once a fish swims close to the glass, you can see it," Dunkel explains. "In a similar way, the proteins are somewhere inside the cell, and when they become activated, they attach to the membrane, and you start to see them move."

Fakhri says the waves of proteins moving across the egg's membrane serve, in part, to organize cell division around the cell's core.

"The egg is a huge cell, and these proteins have to work together to find its center, so that the cell knows where to divide and fold, many times over, to form an organism," Fakhri says. "Without these proteins making waves, there would be no cell division."

MIT researchers observe ripples across a newly fertilized egg that are similar to other systems, from ocean and atmospheric circulations to quantum fluids. Courtesy of the researchers.

In their study, the team focused on the active form of Rho-GTP and the pattern of waves produced on an egg's surface when they altered the protein's concentration.

For their experiments, they obtained about 10 eggs from the ovaries of starfish through a minimally invasive surgical procedure. They introduced a hormone to stimulate maturation, and also injected fluorescent markers to attach to any active forms of Rho-GTP that rose up in response. They then observed each egg through a confocal microscope and watched as billions of the proteins activated and rippled across the egg's surface in response to varying concentrations of the artificial hormonal protein.

"In this way, we created a kaleidoscope of different patterns and looked at their resulting dynamics," Fakhri says.

The researchers first assembled black-and-white videos of each egg, showing the bright waves that traveled over its surface. The brighter a region in a wave, the higher the concentration of Rho-GTP in that particular region. For each video, they compared the brightness, or concentration of protein from pixel to pixel, and used these comparisons to generate an animation of the same wave patterns.

From their videos, the team observed that waves seemed to oscillate outward as tiny, hurricane-like spirals. The researchers traced the origin of each wave to the core of each spiral, which they refer to as a "topological defect." Out of curiosity, they tracked the movement of these defects themselves. They did some statistical analysis to determine how fast certain defects moved across an egg's surface, and how often, and in what configurations the spirals popped up, collided, and disappeared.

In a surprising twist, they found that their statistical results, and the behavior of waves in an egg's surface, were the same as the behavior of waves in other larger and seemingly unrelated systems.

"When you look at the statistics of these defects, it's essentially the same as vortices in a fluid, or waves in the brain, or systems on a larger scale," Dunkel says. "It's the same universal phenomenon, just scaled down to the level of a cell."

The researchers are particularly interested in the waves' similarity to ideas in quantum computing. Just as the pattern of waves in an egg convey specific signals, in this case of cell division, quantum computing is a field that aims to manipulate atoms in a fluid, in precise patterns, in order to translate information and perform calculations.

"Perhaps now we can borrow ideas from quantum fluids, to build minicomputers from biological cells," Fakhri says. "We expect some differences, but we will try to explore [biological signaling waves] further as a tool for computation."

This research was supported, in part, by the James S. McDonnell Foundation, the Alfred P. Sloan Foundation, and the National Science Foundation.

Reprinted with permission of MIT News. Read the original article.

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Organisms grow in wave pattern, similar to ocean circulation - Big Think

Join Team Hackaday To Crunch COVID-19 Through Folding@Home – Hackaday

Donate your extra computer cycles to combat COVID-19. The Folding@Home project uses computers from all over the world connected through the Internet to simulate protein folding. The point is to generate the data necessary to discover treatments that can have an impact on how this virus affects humanity. The software models protein folding in a search for pharmaceutical treatments that will weaken the virus ability to attack the human immune system. Think of this like mining for bitcoin but instead were mining for a treatment to Coronavirus.

Initially developed at Standford University and released in the year 2000, this isnt the first time Hackaday has advocated for Folding@Home. The Team Hackaday folding group was started by readers back in 2005 and that team number is still active, so lets pile on and work our way up the rankings. At the time of writing, were ranked 267 in the world, can we get back up to number 30 like we were in 2008? To use the comparison to bitcoin once again, this is like a mining pool except what we end up with is a show of goodwill, something I think we can all use right about now.

You can get set up in five minutes. The software package is just a few megabytes and configuration is minimal:

Thats about it, just open FAHControl and the software will connect to the Folding at Home servers and request a Work Unit (WU) part of the protein folding math puzzle currently being solved. Once it has a WU the software will solve that unit and upload the result. Rinse and repeat and youre a worker bee in a super-computer thats distributed throughout the world.

The F@H project is seeing a surge of new computers on the network. Because of this you may run into a situation where no new WUs are getting downloaded. I experienced this on Wednesday morning and believe its simply caused by the buffer of work running out and needing to be replenished. The nice thing is you dont need to do anything, so just let your instance run and itll get to work when more is available.

The software does allow you to use your GPU for much more efficient calculations, but that setup may be non-trivial and beyond the scope of this article. I suggest you just get the client up and running and then look to configure GPU as a later step.

Are you making a difference? Yes! But of course metrics tell this message the best. You can see the team summary above. This statistics page includes a user summary showing 21 active users right now, including the hackaday_wrencher instance I added when working on this article which is just beginning to score points.

This group has over 1600 members right now but most are inactive. Can we reactivate those? Can we double that number? Grab those gaming rigs and let the electrons flow. Folding@Home has made a huge impact on research over the last twenty years and now more than ever we can build on that groundwork by joining in to fight this global pandemic.

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Join Team Hackaday To Crunch COVID-19 Through Folding@Home - Hackaday

How Ethereum Mining Rigs Can Help Battle the Coronavirus – Live Bitcoin News

Several cryptocurrency mining projects particularly those devoted to extracting new Ethereum tokens have been pulled away from their mining duties and been made to turn their attention towards coronavirus research.

The coronavirus was recently declared a global pandemic by the World Health Organization (WHO). As many as 245,000 people have been infected with the virus at the time of writing, while more than 11,000 deaths across the globe have been recorded.

Recently, world leaders such as President Donald Trump in the United States have declared a national emergency, while the governors of both California and New York have issued stay at home orders, asking that residents stay within their domiciles and limit their outdoor activities with others to stop the virus spread.

At this time, it seems like people need all the help they can get, and research regarding how to combat the virus is at an all-time high, but how, exactly, can crypto mining rigs help to get this done?

Its not so much that they help with the research aspect, but what they do have is high computational power enough so that the computers and devices conducting or holding present research can stay operational and functional during these stressing times, and its here where the mining rigs can serve great purpose.

Among the major companies working to better understand the problems and symptoms associated with the growing respiratory virus is Stanford Universitys Folding @home, which helps to develop therapeutic drugs. As recently as last month, the company was devoting much of its time, energy and resources towards establishing drugs and products designed to combat HIV, but now, it has shifted focus to work on coronavirus research.

One of the main things that Folding @home does is sort through protein structures of products approved by the Food and Drug Administration (FDA). Proteins, depending on how theyre built, can lessen a disease or even fully treat it, and the venture is looking to see which proteins are available that could potentially bring the virus to its knees.

In a statement, the company explains:

Proteins have lots of moving parts, so we really want to see the protein in action. The structures we cant see experimentally may be the key to discovering a new therapeutic.

Right now, Folding @home and several other drug-related companies are getting their power from sources such as Core Weave, which is one of the largest Ethereum mining projects in the rural United States. At press time, Core Weave is dedicating mountains of computational power to these companies to assist in their time spend performing appropriate research.

The mining venture stated:

Core Weave is proud to support this effort with over 6,000 of our high-end GPUs.

As many as 20 separate companies are presently working on a coronavirus vaccine.

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How Ethereum Mining Rigs Can Help Battle the Coronavirus - Live Bitcoin News

Thousands of These Computers Were Mining Cryptocurrency. Now They’re Working on Coronavirus Research – CoinDesk – Coindesk

CoreWeave, the largest U.S. miner on the Ethereum blockchain, is redirecting the processing power of 6,000 specialized computer chips toward research to find a therapy for the coronavirus.

These graphics processing units (GPUs) will be pointed toward Stanford University's Folding@home, a long-standing research effort that unveiled a project on Feb. 27 specifically to boost coronavirus research by way of a unique approach to developing pharmaceutical drugs: connecting thousands of computers from around the world to form a distributed supercomputer for disease research.

CoreWeave co-founder and Chief Technology Officer (CTO) Brian Venturo said the project has at least a shot at finding a drug for the virus. As such, CoreWeave has responded by doubling the power of the entire network with its GPUs, which are designed to handle repetitive calculations.

According to Venturo, those 6,000 GPUs made up about 0.2 percent of Ethereum's total hashrate, earning roughly 28 ETH per day, worth about $3,600 at press time.

There is no cure for the coronavirus just yet (though various groups are working on vaccines and research to combat the disease, including IBM's supercomputer). Venturo noted that Folding@home has been used to contribute to breakthroughs in the creation of other important drugs.

"Their research had profound impacts on the development of front-line HIV defense drugs, and we are hoping our [computing power] will aid in the fight against coronavirus," Venturo said.

The coronavirus is taking a toll across the world. Italy and Spain are on lockdown. Conferences, stores and restaurants are closing to stem the spread of the disease; by stoking fears, it's slamming the financial markets in the process.

World computer

When the idea of using GPUs for coronavirus research was mentioned to CoreWeave, the team didn't think twice.

They had a test system up and running "within minutes," Venturo said. Since then, the project quickly snowballed. CoreWeave has been contributing over half of the overall computing power going into the coronavirus wing of Folding@home.

"The idea of 'should we do this?' was never really brought up, it kind of just happened. We were all enthusiastic that we might be able to help," Venturo added.

Folding@home is a decentralized project in the same vein as Bitcoin. Instead of one research firm alone using a massive computer to do research, Folding@home uses the computing power of anyone who wants to participate from around the world even if it's just a single laptop with a little unused computing power to spare.

In this case, the computing power is used to find helpful information relating to the coronavirus. Much like in bitcoin mining, one user might detect a "solution" to the problem at hand, distributing this information to the rest of the group.

"Their protein simulations attempt to find potential 'pockets' where existing [U.S. federal agency Food and Drug Administration (FDA)] approved drugs or other known compounds could help inhibit or treat the virus," Venturo said.

Viruses have proteins "that they use to suppress our immune systems and reproduce themselves. To help tackle coronavirus, we want to understand how these viral proteins work and how we can design therapeutics to stop them," a Folding@home blog post explains.

Simulating these proteins and then looking at them from different angles helps scientists to understand them better, with the potential of finding an antidote. Computers accelerate this process by shuffling through the variations very quickly.

"Our specialty is in using computer simulations to understand proteins moving parts. Watching how the atoms in a protein move relative to one another is important because it captures valuable information that is inaccessible by any other means," the post reads.

Long shot

Folding@home could use even more power. Venturo urges other GPU miners to join the cause.

Even without these calls for participation, though, miners of other cryptocurrencies are already independently taking action. Tulip.tools founder Johann Tanzer put out a call to action to Tezos bakers (that blockchains equivalent of miners) last week, promising to send the leading contributor to Folding@home a modest 15 XTZ, worth roughly $20 at press time.

The initiative blew up, to Tanzer's surprise. Though they might not be contributing as much power as CoreWeave, 20 groups of Tezos miners are now contributing a slice of their hashing power to the cause. Tanzer's pot has swelled to roughly $600 as Tezos users caught wind of the effort and donated.

But that's not to say all miners can participate. While GPUs are flexible, application-specific integrated circuits (ASICs), a type of chip designed specifically for mining, aren't, according to Venturo. Though ASICs are more powerful than GPUs, they're really only made for one thing: To mine cryptocurrency. This is one advantage Venturo thinks Ethereum has over Bitcoin, since GPU mining still works on the former, whereas the latter is now dominated by ASICs.

"This is one of the great things about the Ethereum mining ecosystem, it's basically the largest GPU compute resource on the planet. We were able to redeploy our hardware to help fight a global pandemic in minutes," Venturo said. (However, it's worth noting that Ethereum has seen ASICs enter the fray. Not to mention, ether miners might soon go extinct when a pivotal upgrade makes its way into the network.)

ASICs are useless for the Folding@Home effort, but if bitcoin miners have old GPUs lying around from the early days that they could contribute, too.

Even if other miners join up, though, it's still a long shot that the effort will lead to a helpful drug.

"After discussing with some industry experts [...] we believe the chance of success in utilizing the work done on Folding@Home to deliver a drug to market to be in the 2-5% range," Venturo said.

The leader in blockchain news, CoinDesk is a media outlet that strives for the highest journalistic standards and abides by a strict set of editorial policies. CoinDesk is an independent operating subsidiary of Digital Currency Group, which invests in cryptocurrencies and blockchain startups.

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Thousands of These Computers Were Mining Cryptocurrency. Now They're Working on Coronavirus Research - CoinDesk - Coindesk

PC gamers and researchers asked to donate GPU and CPU time to help fight coronavirus – www.computing.co.uk

PC gamers and researchers asked to donate GPU and CPU time to help fight Coronavirus

A distributed computing project is urging researchers and PC gamers worldwide to donate some of their CPU and GPU computing power to help in the fight against coronavirus, also known as COVID-19.

Folding@home (FAH) is an international project based in the Pande Lab at Stanford University. Led by Dr Greg Bowman, the project utilises the idle computing power of hundreds of thousands of PCs owned by volunteers across the world to simulate the molecular dynamics of protein folding and misfolding in various diseases.

According to FAH, those simulations will help scientists in discovering new drug opportunities against diseases.

Please be patient with us! There is a lot of valuable science to be done

The FAH team is currently aiming to investigate how specific proteins in the novel coronavirus (COVID-19) operate and how those proteins can be destroyed to prevent the virus from multiplying within the human body.

Last month, the FAH team announced that it was taking up the fight again coronavirus, with the aim to help develop a therapeutic antibody, similar to that previously developed for SARS-Cov in 2003.

"To help tackle coronavirus, we want to understand how these viral proteins work and how we can design therapeutics to stop them," FAH said.

The deadly coronavirus has already killed more over 6,300 people worldwide, while hundreds of thousands still remain infected with the virus. Governments across the world are responding to coronavirus outbreak by curbing the movements of citizens and tightening borders.

FAH has so far added 23 coronavirus projects to use donated GPU or CPU power to study the coronavirus.

Contributing to the FAH project is easy, as users just need to download and install the client for their operating system from the FAH website. Once installed, the client will be configured to 'lightly' use system's GPU and CPU processing power to perform protein simulations.

Users can also use 'Medium' or 'Full' options to increase the amount of CPU and GPU utilisation.

"Usually, your computer will never be idle, but we've had such an enthusiastic response to our COVID-19 work that you will see some intermittent downtime as we sprint to setup more simulations," FIH said.

"Please be patient with us! There is a lot of valuable science to be done, and we're getting it running as quickly as we can."

Continued here:
PC gamers and researchers asked to donate GPU and CPU time to help fight coronavirus - http://www.computing.co.uk

Thousands of These Computers Were Mining Cryptocurrency. Now Theyre Working on Coronavirus Research – Yahoo Money

CoreWeave, the largest U.S. miner on the Ethereum blockchain, is redirecting the processing power of 6,000 specialized computer chips toward research to find a therapy for the coronavirus.

These graphics processing units (GPUs) will be pointed toward Stanford Universitys Folding@home, a long-standing research effort that unveiled a project on Feb. 27 specifically to boost coronavirus research by way of a unique approach to developing pharmaceutical drugs: connecting thousands of computers from around the world to form a distributed supercomputer for disease research.

CoreWeave co-founder and Chief Technology Officer (CTO) Brian Venturo said the project has at least a shot at finding a drug for the virus. As such, CoreWeave has responded by doubling the power of the entire network with its GPUs, which are designed to handle repetitive calculations.

Related: State Power After Coronavirus, Feat. Peter McCormack

See also: Bitcoiners Are Biohacking a DIY Coronavirus Vaccine

According to Venturo, those 6,000 GPUs made up about 0.2 percent of Ethereums total hashrate, earning roughly 28 ETH per day, worth about $3,600 at press time.

There is no cure for the coronavirus just yet (though various groups are working on vaccines and research to combat the disease, including IBMs supercomputer). Venturo noted that Folding@home has been used to contribute to breakthroughs in the creation of other important drugs.

Their research had profound impacts on the development of front-line HIV defense drugs, and we are hoping our [computing power] will aid in the fight against coronavirus, Venturo said.

Related: SkyWeaver Didnt Plan for a Captive Audience of Millions but It Sure Helps

The coronavirus is taking a toll across the world. Italy and Spain are on lockdown. Conferences, stores and restaurants are closing to stem the spread of the disease; by stoking fears, its slamming the financial markets in the process.

When the idea of using GPUs for coronavirus research was mentioned to CoreWeave, the team didnt think twice.

They had a test system up and running within minutes, Venturo said. Since then, the project quickly snowballed. CoreWeave has been contributing over half of the overall computing power going into the coronavirus wing of Folding@home.

The idea of should we do this? was never really brought up, it kind of just happened. We were all enthusiastic that we might be able to help, Venturo added.

Folding@home is a decentralized project in the same vein as Bitcoin. Instead of one research firm alone using a massive computer to do research, Folding@home uses the computing power of anyone who wants to participate from around the world even if its just a single laptop with a little unused computing power to spare.

See also: Bitcoiners in Europe Reflect on Economic Shocks as Coronavirus Spreads

In this case, the computing power is used to find helpful information relating to the coronavirus. Much like in bitcoin mining, one user might detect a solution to the problem at hand, distributing this information to the rest of the group.

Their protein simulations attempt to find potential pockets where existing [U.S. federal agency Food and Drug Administration (FDA)] approved drugs or other known compounds could help inhibit or treat the virus, Venturo said.

Viruses have proteins that they use to suppress our immune systems and reproduce themselves. To help tackle coronavirus, we want to understand how these viral proteins work and how we can design therapeutics to stop them, a Folding@home blog post explains.

Simulating these proteins and then looking at them from different angles helps scientists to understand them better, with the potential of finding an antidote. Computers accelerate this process by shuffling through the variations very quickly.

Our specialty is in using computer simulations to understand proteins moving parts. Watching how the atoms in a protein move relative to one another is important because it captures valuable information that is inaccessible by any other means, the post reads.

Folding@home could use even more power. Venturo urges other GPU miners to join the cause.

Even without these calls for participation, though, miners of other cryptocurrencies are already independently taking action. Tulip.tools founder Johann Tanzer put out a call to action to Tezos bakers (that blockchains equivalent of miners) last week, promising to send the leading contributor to Folding@home a modest 15 XTZ, worth roughly $20 at press time.

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The initiative blew up, to Tanzers surprise. Though they might not be contributing as much power as CoreWeave, 20 groups of Tezos miners are now contributing a slice of their hashing power to the cause. Tanzers pot has swelled to roughly $600 as Tezos users caught wind of the effort and donated.

But thats not to say all miners can participate. While GPUs are flexible, application-specific integrated circuits (ASICs), a type of chip designed specifically for mining, arent, according to Venturo. Though ASICs are more powerful than GPUs, theyre really only made for one thing: To mine cryptocurrency. This is one advantage Venturo thinks Ethereum has over Bitcoin, since GPU mining still works on the former, whereas the latter is now dominated by ASICs.

See also: Israeli Bitcoiners See Surveillance as Unavoidable During Coronavirus Crisis

This is one of the great things about the Ethereum mining ecosystem, its basically the largest GPU compute resource on the planet. We were able to redeploy our hardware to help fight a global pandemic in minutes, Venturo said. (However, its worth noting that Ethereum has seen ASICs enter the fray. Not to mention, ether miners might soon go extinct when a pivotal upgrade makes its way into the network.)

ASICs are useless for the Folding@Home effort, but if bitcoin miners have old GPUs lying around from the early days that they could contribute, too.

Even if other miners join up, though, its still a long shot that the effort will lead to a helpful drug.

After discussing with some industry experts [] we believe the chance of success in utilizing the work done on Folding@Home to deliver a drug to market to be in the 2-5% range, Venturo said.

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Thousands of These Computers Were Mining Cryptocurrency. Now Theyre Working on Coronavirus Research - Yahoo Money

How Every PC Gamer In The World Can Join The Fight Against Coronavirus – Forbes

Graphics cards are obviously a useful tool for gaming, video editing and even cryptocurrency mining. But did you know you can donate your AMD Radeon or Nvidia GPUs spare compute cycles to researching and potentially fighting against the ongoing coronavirus pandemic?

The Folding@Home "Web Control" page shows what projects your PC is dedicating resources to and gives ... [+] you control over when it's active.

Folding@Home is a distributed computing project that was founded in October 2000 at Stanford University, and it was specifically designed for disease research. It has historically been targeted at researching cancer, ALS, Parkinsons, Huntingtons and more.

The software uses the spare CPU and GPU cycles of thousands of computers globally to simulate protein folding and computational drug design.

Put another way, Folding@Home uses computer simulations to understand a proteins moving parts. Once scientists have a firm grasp on how the atoms move and interact within a protein, they can venture closer to discovering therapeutics to treat it.

But these simulations require massive computational power. And the Folding@Home team now wants to direct as much global compute power as possible at fighting SARS-CoV-2.

Viruses also have proteins that they use to suppress our immune systems and reproduce themselves. To help tackle coronavirus, we want to understand how these viral proteins work and how we can design therapeutics to stop them.

The video above demonstrates part of a simulation facilitated through Folding@Home of a protein where the atoms (shown as spheres) move aside, exposing a site where a drug can bind. The Folding@Home team has used similar simulations to expose a druggable site in the lethal Ebola virus.

The science behind what the team is doing is simultaneously fascinating and confusing (at least for the layman), and beyond the scope of this article. But I do encourage you to digest it on your own time via Greg Bowmans detailed writeup here.

The TL;DR

If you own a Windows, macOS or Linux PC that has a graphics card, you can join thousands of others around the world by donating your spare GPU cycles. This helps power the advanced simulations that could unlock a key to more deeply understanding the novel coronavirus (SARS-CoV-2) and its resulting disease COVID-19.

A list of Folding@Home installers

To get started, all you have to do is download the Folding@Home client for the OS youre currently using. The linked page should automatically detect your OS and present the right installer.

After that, simply install Folding@Home. Should you need help, heres a link to detailed installation guides for Windows, macOS and Linux.

Tips For Folding@Home

While this is meaningful software for a terrific cause, it isnt the most elegant. So Ill include a few tips for getting it up and running without grinding your PC to a halt.

1) Once the software is installed and launched, it should automatically open a web page that acts as a simple controller and monitor. If it doesnt, point your browser at https://client.foldingathome.org.

If you use Folding@Home with a GPU, choose "Any disease" will tell the software to direct its ... [+] computational power to various coronavirus-related research

2) If you have a GPU and you want to dedicate those compute cycles to various coranavirus research, choose Any disease from the dropdown box labeled I support research fighting...

3) You may need to manually add your GPU to the FAHControl app, but thats pretty straightforward. Just open FAHControl, select the Configure button on the top left, navigate to the Slots tab, and check the box designated as GPU (image below). Leave all the other options alone, as the software can handle that for you.

Configure a GPU in Folding@Home

4) When Folding@Home is active, it will put a heavy load on your system. This is normal. If youd rather the software run only when youre not using your PC, select only when idle. Ive also found that a Medium power setting strikes a good balance between the apps performance and system usability.

5) If it looks like your PC isnt getting any work, thats because theres been an unusually heavy influx of new users for the software, due in part to Nvidias call to action. You may experience some downtime, but the Folding@Home team says its working diligently to add new simulations to meet the increased demand.

No GPU? Your CPU Is Still Useful

If you dont have a dedicated graphics card, you can still make a difference. CPU-only workloads contribute to researching Alzheimers, Parkinsons, Huntingtons and cancer. The Folding@Home team is also working on adding COVID-19 simulations to CPU workloads as well, but no timeline was given for that.

View Forbes complete coronavirus coverage.

Read more:
How Every PC Gamer In The World Can Join The Fight Against Coronavirus - Forbes

Microsoft, Zuckerberg and Allen team up to use AI in the fight against coronavirus and are challenging other – Business Insider India

The initiative includes Microsoft Research, the Allen Institute for AI founded by Microsoft co-founder Paul Allen and the Chan Zuckerberg Initiative, set up by Facebook founder Mark Zuckerberg and his wife Priscilla Chan.

The entire database will be updated on a weekly basis, adding new research from peer-reviewed journals and other archival services. In order to motivate researchers to take the challenge head-on, Kaggle is hosting the Covid-19 Open Research Dataset Challenge (CORD-19).

All hands on deckThe actual papers are provided by the National Institute of Healths National Library of Medicine. Its also linked to the World Health Organisations (WHO) database of publications on coronavirus. The project is being coordinated by Georgetown Universitys Center for Security and Emerging Technology (CSET).

Even though the database was requested by the White Houses Office of Science and Technology Policy anyone from around the world harness the information to make their own deductions.

With this step, weve made available full-text, machine-readable resources to help speed response to this global crisis, said Dewey Murdick, CSETs director of data science.

Another tech company called Fold@Home is distributing a computing project online that helps users and contributors conduct research on Covid-19 by simulating molecular dynamics. Researchers can simulate processes like protein folding and drug design to understand how the coronavirus would react.

As of today, there are over 165,000 confirmed cases of the coronavirus worldwide across 146 countries with 126 people infected in India.

See also:Coronavirus recovery rate at 54% as doctors race to find a cure

Coronavirus pandemic: Bill Gates warned us that this day would come five years ago

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Microsoft, Zuckerberg and Allen team up to use AI in the fight against coronavirus and are challenging other - Business Insider India