Monthly Archives: November 2023

Cranberries are a staple in U.S. households at Thanksgiving but how did this bog dweller end up on holiday tables?

Compared to many valuable plant species that were domesticated over thousands of years, cultivated cranberry (Vaccinium macrocarpon) is a young agricultural crop, just as the U.S. is a young country and Thanksgiving is a relatively new holiday. But as a plant scientist, Ive learned much about cranberries ancestry from their botany and genomics.

New on the plant breeding scene

Humans have cultivated sorghum for some 5,500 years, corn for around 8,700 years and cotton for about 5,000 years. In contrast, cranberries were domesticated around 200 years ago but people were eating the berries before that.

Wild cranberries are native to North America. They were an important food source for Native Americans, who used them in puddings, sauces, breads and a high-protein portable food called pemmican a carnivores version of an energy bar, made from a mixture of dried meat and rendered animal fat and sometimes studded with dried fruits. Some tribes still make pemmican today, and even market a commercial version.

Cranberry cultivation began in 1816 in Massachusetts, where Revolutionary War veteran Henry Hall found that covering cranberry bogs with sand fertilized the vines and retained water around their roots. From there, the fruit spread throughout the U.S. Northeast and Upper Midwest.

Today, Wisconsin produces roughly 60% of the U.S. cranberry harvest, followed by Massachusetts, Oregon and New Jersey. Cranberries also are grown in Canada, where they are a major fruit crop.

A flexible and adaptable plant

Cranberries have many interesting botanical features. Like roses, lilies and daffodils, cranberry flowers are hermaphroditic, which means they contain both male and female parts. This allows them to self-pollinate instead of relying on birds, insects or other pollinators.

A cranberry blossom has four petals that peel back when the flower blooms. This exposes the anthers, which contain the plants pollen. The flowers resemblance to the beak of a bird earned the cranberry its original name, the craneberry.

When cranberries dont self-pollinate, they rely on bumblebees and honeybees to transport their pollen from flower to flower. They can also be propagated sexually, by planting seeds, or asexually, through rooting vine cuttings. This is important for growers because seed-based propagation allows for higher genetic diversity, which can translate to things like increased disease resistance or more pest tolerance.

Asexual reproduction is equally important, however. This method allows growers to create clones of varieties that perform very well in their bogs and grow even more of those high-performing types.

Every cranberry contains four air pockets, which is why they float when farmers flood bogs to harvest them. The air pockets also make raw cranberries bounce when they are dropped on a hard surface a good indicator of whether they are fresh.

These pockets serve a biological role: They enable the berries to float down rivers and streams to disperse their seeds. Many other plants disperse their seeds via animals and birds that eat their fruits and excrete the seeds as they move around. But as anyone who has tasted them raw knows, cranberries are ultra-tart, so they have limited appeal for wildlife.

Reading cranberry DNA

For cranberries being such a young crop, scientists already know a lot about their genetics. The cranberry is a diploid, which means that each cell contains one set of chromosomes from the maternal parent and one set from the paternal parent. It has 24 chromosomes, and its genome size is less than one-tenth that of the human genome.

Insights like these help scientists better understand where potentially valuable genes might be located in the cranberry genome. And diploid crops tend to have fewer genes associated with a single trait, which makes breeding them to emphasize that trait much simpler.

Researchers have also described the genetics of the cultivated cranberrys wild relative, which is known as the small cranberry (Vaccinium oxycoccos). Comparing the two can help scientists determine where the cultivated cranberrys agronomically valuable traits reside in its genome, and where some of the small cranberrys cold hardiness might come from.

Researchers are developing molecular markers tools to determine where certain genes or sequences of interest reside within a genome to help determine the best combinations of genes from different varieties of cranberry that can enhance desired traits. For example, a breeder might want to make the fruits larger, more firm or redder in color.

While cranberries have only been grown by humans for a short period of time, they have been evolving for much longer. They entered agriculture with a long genetic history, including things like whole genome duplication events and genetic bottlenecks, which collectively change which genes are gained or lost over time in a population.

Whole genome duplication events occur when two species genomes collide to form a new, larger genome, encompassing all the traits of the two parental species. Genetic bottlenecks occur when a population is greatly reduced in size, which limits the amount of genetic diversity in that species. These events are extremely common in the plant world and can lead to both gains and losses of different genes.

Analyzing the cranberrys genome can indicate when it diverged evolutionarily from some of its relatives, such as the blueberry, lingonberry and huckleberry. Understanding how modern species evolved can teach plant scientists about how different traits are inherited, and how to effectively breed for them in the future.

Ripe at the right time

Cranberries close association with Thanksgiving was simply a practical matter at first. Fresh cranberries are ready to harvest from mid-September through mid-November, so Thanksgiving falls within that perfect window for eating them.

Cranberry sauce was first loosely described in accounts from the American colonies in the 1600s, and appeared in a cookbook for the first time in 1796. The berries tart flavor, which comes from high levels of several types of acids, makes them more than twice as acidic as most other edible fruits, so they add a welcome zing to a meal full of blander foods like turkey and potatoes.

In recent decades, the cranberry industry has branched out into juices, snacks and other products in pursuit of year-round markets. But for many people, Thanksgiving is still the time when theyre most likely to see cranberries in some form on the menu.

SerinaDeSalviois a doctorate candidate at the College of Agriculture and Life Sciences, Texas A&M University.

The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts.

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TUESDAY, Nov. 14, 2023 (HealthDay News) -- Receipt of government housing assistance is associated with increased rates of breast cancer (BC) and colorectal cancer (CRC) screening, according to a study published online Nov. 8 in theAmerican Journal of Preventive Medicine.

Jordan Baeker Bispo, Ph.D., from the American Cancer Society in Atlanta, and colleagues used data from the 2019 and 2021 National Health Interview Survey to examine the association between cancer screening and receipt of government housing assistance among low-income adults. Analyses included BC, cervical cancer, and CRC screening (2,258; 3,132; and 3,233 respondents, respectively).

The researchers observed no difference in cervical screening by housing assistance status, but screening for BC and CRC was higher among those who received assistance versus those who did not (BC: 59.7 versus 50.8 percent; CRC: 57.1 versus 44.1 percent). However, when adjusting for sociodemographic factors, health status, and insurance, these differences in BC and CRC were not statistically significant. Housing assistance was significantly associated with increased BC screening in urban areas (adjusted odds ratio [aOR], 1.35; 95 percent confidence interval [CI], 1.00 to 1.82), among Hispanic women (aOR, 2.20; 95 percent CI, 1.01 to 4.78), and among women 45 to 54 years of age (aOR, 2.10; 95 percent CI, 1.17 to 3.75).

"Receiving housing assistance has been associated with several positive health outcomes and health behaviors in past research, and our findings suggest it can also support cancer screening in some medically underserved groups," Bispo said in a statement.

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TUESDAY, Nov. 14, 2023 (HealthDay News) -- The rate of new lung cancer cases has decreased and survival has improved over the last five years, according to the State of Lung Cancer 2023 report published Nov. 14 by the American Lung Association.

In the report, researchers present the latest national and state-by-state lung cancer data, including new cases, survival, early diagnosis, and screening rates.

According to the report, almost 238,000 people will be diagnosed with lung cancer in 2023, with the lowest rate in Utah and highest rate in Kentucky; nationally, the rate of new cases decreased 8 percent during the last five years. The national average of people alive five years after a lung cancer diagnosis is 26.6 percent, which marks an improvement of 22 percent over the last five years. Only 26.6 percent of lung cancer cases are diagnosed at an early stage, while 44 percent are identified at a late stage. During the last five years, early diagnosis rates increased 9 percent nationally; the five-year survival rate was 63 and 8 percent for diagnosis at an early and late stage, respectively. Nationally, 20.6 percent of cases did not receive any treatment, with a 2 percent improvement noted over the last five years. Nationally, only 4.5 percent of those at high risk for lung cancer were screened, with rates varying from 11.9 to 0.7 percent in Massachusetts and California, respectively.

"While we have seen an improvement in lung cancer survival rates for people of color, more work needs to be done to address persistent health disparities," Harold Wimmer, president and chief executive officer of the American Lung Association, said in a statement. "Overall, people of color who are diagnosed with lung cancer are less likely to be diagnosed early, less likely to receive surgical treatment, and more likely to receive no treatment."

State of Lung Cancer 2023 Report

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TUESDAY, Nov. 14, 2023 (HealthDay News) -- Individuals racialized as Black and newly diagnosed with colorectal cancer (CRC) receive worse and less-timely guideline-concordant care, according to a study published online Nov. 8 in the Journal of Clinical Oncology.

Leticia M. Nogueira, Ph.D., M.P.H., from the American Cancer Society in Atlanta, and colleagues selected individuals aged 18 to 49 years racialized as non-Hispanic Black and White (self-identified) and newly diagnosed with CRC during 2004 to 2019. Individuals who received recommended care, which included staging, surgery, lymph node evaluation, chemotherapy, and radiotherapy, were considered to have received guideline-concordant care.

Overall, 20.8 and 14.5 percent of the 84,882 patients with colon cancer and 62,573 with rectal cancer, respectively, were racialized as Black. The researchers found that the likelihood of not receiving guideline-concordant care for colon and rectal cancers was increased for individuals racialized as Black (adjusted hazard ratios, 1.18 and 1.27, respectively). Among patients with colon and rectal cancer, 28.2 and 21.6 percent of the disparity, respectively, was explained by health insurance. Compared with individuals racialized as White, those racialized as Black had increased time to adjuvant chemotherapy for colon cancer and neoadjuvant chemoradiation for rectal cancer.

"With health insurance being the largest modifiable factor contributing to racial disparities in this study, it's critical to eliminate this barrier," Nogueira said in a statement. "Expanding access to health insurance coverage could help improve colorectal care and outcomes from individuals of all racialized groups."

Two authors disclosed ties to industry.

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Portrait of Prof. Heather Harrington

Credit: Z Goriely

Heather Harrington will join the team of directors at the Max Planck Institute (MPI-CBG) in Dresden. In her position, she will also lead the interinstitutional Center for Systems Biology Dresden (CSBD) together with partners from the Technical University Dresden and the Max Planck Institute for the Physics of Complex Systems. She was also appointed as honorary professor at the Faculty of Mathematics at the TU Dresden. Her vision is to create new mathematical approaches to glean additional information about living systems and understand how they self-organize across scales. She is also a Professor of Mathematics at the Mathematical Institute at the University of Oxford, UK, and a Fellow of St Johns College Oxford.

We are thrilled that Heather Harrington decided to join our community as a new director, says Anne Grapin-Botton, the Managing Director of the MPI-CBG. We are able to collect a lot of measurements and information on biological systems, and Heathers mathematical approach will be crucial to extracting structure and meaning from this information. Heather is an extremely talented mathematician who will undoubtedly find new ways to solve current and future challenges in biology, hence our enthusiasm.

I am delighted to join the Max Planck Society and to be a director at the MPI-CBG and the CSBD, says Heather Harrington. We will be creating new mathematical frameworks to model and analyze the detailed and multi-dimensional data we generated in modern biology. We will develop and apply techniques from nonlinear algebra to analyze complex spatio-temporal systems and from computational topology to study the shape and structure of high-dimensional data. Im excited to explore new opportunities to collaborate with researchers at the institute and the wider TU Dresden and Saxony research landscape. Heathers group will develop mathematical approaches to understand biological systems on multiple scales, from genes to tissues. Given the abstract nature of mathematics, the methods Heather and her team will develop can be applied to many different systems and contexts. There is huge scope for understanding disease in a new light.

Heather always enjoyed the application of new mathematics to biological and medical questions. She says, I have combined mathematical models with biological data throughout my career. And it is clear now that there is enormous untapped potential in understanding the shape and structure of biological data. By more formally characterizing the multi-scale and multi-dimensional relationships between different types of data, we can look towards a deeper understanding of organisms across multiple scales. Heather A. Harrington received her Ph.D. in 2010 from the Department of Mathematics at Imperial College London. After postdoctoral years at the Imperial College London and the Mathematical Institute at Oxford, she became an associate Professor and Royal Society University Research Fellow at Oxford in 2017, where she was promoted to Professor of Mathematics in 2020. She holds affiliations with St Johns College as a Research Fellow in Mathematics and the Sciences and the Wellcome Centre for Human Genetics as an Associate Group Leader. Heather became a director at the MPI-CBG and the Center for Systems Biology Dresden in October 2023. Her research interests are applied algebra, dynamical systems, networks, topological data analysis, and systems biology. Her research group develops mathematical approaches to study problems in the natural and medical sciences. She has received several prestigious awards, such as the Whitehead Prize of the London Mathematical Society in 2018 or the Philip Leverhulme Prize in 2020, for advances in the analysis of noisy data. She was a co-winner of the 2019 Adams Prize from the University of Cambridge.

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Familial hypercholesteremia (FH) is an inherited condition that affects about 1 in 250 people, and often shows no signs until they have a heart attack. For individuals with FH the lowering of bad cholesterol levels cant be done by dietary or behavioral changes, the problem is in their genes, and targeted medications therapy is needed. Now, new research from the Intermountain Health in Salt Lake City has found that genetic screening can identify these patients and make them candidates for treatment that could prevent heart attack, stroke, and death.

Credit: Intermountain Health

Familial hypercholesteremia (FH) is an inherited condition that affects about 1 in 250 people, and often shows no signs until they have a heart attack. For individuals with FH the lowering of bad cholesterol levels cant be done by dietary or behavioral changes, the problem is in their genes, and targeted medications therapy is needed.

Now, new research from the Intermountain Health in Salt Lake City has found that genetic screening can identify these patients and make them candidates for treatment that could prevent heart attack, stroke, and death.

Most of these patients already had tests that showed they had high cholesterol, said Stacey Knight, PhD, cardiovascular and genetic epidemiologist at Intermountain Health. Our findings show that we should be genetic testing people who have unexplained high cholesterol, so we can aggressively treat it and cut down their risk of having a major heart event.

Findings were presented at the American Heart Associations Scientific Sessions 2023 in Philadelphia on Sunday, Nov 12.

Findings from the study come from the HerediGene: Population Study, one of the worlds largest DNA mapping initiatives, which is a partnership between Intermountain Health and Icelandic company deCODE.

The goal of the project is to discover new connections between genetics and human disease. When appropriate, its also providing genetic screenings for participants so they know about their risks of disease development, and what actions they can take to protect their health.

Familial hypercholesteremia is one of the diseases tested for, via identification of the LDLR gene variant.

In the study, researchers looked at the first 32,159 sequenced patients, and found 157 with a pathogenic/likely pathogenic variant in LDLR.

These participants were then divided into three groups: those with no prior FH diagnosis (47); patients with an FH diagnosis after a major cardiovascular event like heart attack, heart failure hospitalization, stroke periphery artery disease and carotid artery disease (41); and patients with a FH diagnosis before any cardiac event (69).

Researchers found that compared to patients without an prior FH diagnosis, those with a diagnosis before a major heart event had significantly more tests of their LDL cholesterol levels, increased statin and other lipid-lowering medications and a large change in LDL cholesterol. They were also slightly less like to have subsequent major heart events.

Researchers also found that LDL cholesterol measurements and statin use were similar for patients with an FH diagnosis either before or after a cardiac event. However, patients with FH diagnosis after an event had higher death rates.

These findings show the importance of more widespread genetic testing for familial hypercholesteremia.

Referring these patients for genetic counseling could lead to intervention through medication, and lead to better quality of life, and save their lives as well as could result in additional testing and early intervention for their family members, said Dr. Knight.

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STK11 loss alters tumor-intrinsic cytokine expression

We knocked-out STK11 in three genetically independent human KRAS-driven LUAD cell lines that normally harbor intact STK11 alleles: NCI-H2009, NCI-H441 and NCI-H1792. STK11 loss was validated by Western Blot analysis (Fig. 1A and Supplemental Data). Based on studies reporting a correlation between Stk11 loss and Il-6 upregulation in mouse models of Kras-driven lung cancers in vivo [12], we compared IL-6 expression between STK11 WT (aka Parent) and matched STK11-KO human LUAD cells using qRT-PCR. Unexpectedly, under standard culture conditions no difference in IL-6 expression was detected between the Parent and STK11-KO cells (Fig. 1B, +Glutamine). However, significant STK11-loss-dependent IL-6 upregulation was observed when cells were cultured under conditions of nutrient stress, achieved via glutamine depletion (Fig. 1B, Glutamine). The rationale for evaluating nutrient stress as a variable was based on evidence that STK11 functions as a nutrient sensor to regulate metabolic homeostasis [19,20,21]. We reasoned STK11 loss might be irrelevant when cells are grown in standard media as nutrients are in excess. Given that tumor microenvironments in vivo are characterized by nutrient stress [22,23,24], we used glutamine depletion to simulate nutrient-deprivation in vitro.

A Western blot analysis confirming knock-out of STK11 (S) in NCI-H2009 and NCI-H441 parent (P) cell lines. B IL-6 mRNA expression in parent versus STK11-KO cell lines grown in standard media (+Glutamine) or glutamine depleted media (-Glutamine). Gene expression normalized to PSMB4. Data presented as meanSD (N=3). C MA plots generated from RNA-seq analysis demonstrate few differentially expressed genes (DEGs) between parent (WT) and STK11-KO cell lines when grown in standard media (+Glutamine; 1100 DEGs for H2009, 928 DEGs for H441). In contrast, the same cells grown in glutamine depleted media exhibit massive increases in DEGs in both cell lines (Glutamine; 7453 DEGs for H2009, 5202 DEGs for H441). D GSEA performed on DEGs from each cell line pair grown in the absence of glutamine identified Cytokine Activity (GO: 0005125) as significantly enriched and positively correlated with STK11 loss. Upregulated genes from the Cytokine Activity list shared across H2009 and H441 STK11 KO cell lines are listed. E, F KEGG Pathway Enrichment Analysis performed on DEGs from H2009 and H441 cell lines comparing parent and STK11-KO cells following glutamine depletion. As expected, pathways related to cytokine signaling were identified. Notably, the Hippo signaling pathway (red box) was significantly enriched in both cell lines. G GSEA performed on DEGs using a curated YAP1 transcriptional signature demonstrates a strong positive correlation with STK11 loss in both cell lines suggesting YAP1 transcriptional activation occurs when cells experience glutamine depletion in the absence of STK11. Upregulated genes from the curated YAP1 signature shared across H2009 and H441 STK11 KO cell lines upon glutamine depletion are listed. ****p<0.0001 was calculated by two-way ANOVA and the Tukey test in (B).

Next, to comprehensively characterize STK11-loss-dependent transcriptional changes, we expanded our analyses and performed whole transcriptome sequencing comparing standard media to glutamine depletion. In standard media, relatively few genes differed between parent and STK11-KO cells (Fig. 1C, +Glutamine; H2009: 1100 DEGs, H441: 928 DEGs). In contrast, when comparing both H2009 and H441 parent lines with their paired STK11-KO lines following glutamine depletion we identified 7453 and 5202 differentially expressed genes (DEGs) respectively (Fig. 1C; Glutamine). This marked STK11-loss-dependent transcriptional impact indicates STK11 plays a critical and generalizable role in regulating transcription in response to nutrient stress. We then performed Gene Set Enrichment Analysis (GSEA) [25] on the DEGs for both H2009 and H441 cell lines and found significant associations between STK11 loss and altered tumor-intrinsic cytokine signaling, specifically upregulation of genes within the Gene Ontology (GO) term Cytokine Activity (GO: 0005125) (Fig. 1D). Of the upregulated genes in this curated list, 9 were shared between the H2009 and H441 cell lines, suggesting overlapping regulatory pathways. Intriguingly, these overlapping genes consist of effectors previously associated with cancer progression, immune evasion, and therapy resistance [26,27,28]. For example, both IL-6 and CXCL8 are reported to be elevated in KRAS-driven STK11-null LUADs and proposed to promote tumor immune evasion [29,30,31]. Similarly, CXCL2 is known to drive neutrophil recruitment, a phenotype associated with cold tumor immune microenvironments [27]. Finally, BMP2 expression is correlated with metastatic burden and STK11 loss in lung cancer and mediates activation of SMAD transcription factors [32, 33], which are known YAP1 binding partners [34].

In addition to GSEA, we also performed pathway enrichment using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [35]. This approach revealed several significantly enriched networks in STK11-KO cells relative to matched parental lines (Fig. 1E, F). Consistent with prior published reports, both focal adhesion and HIF-1 pathways were over-represented in cells lacking STK11 [36, 37]. In addition, NF-kappa B signaling, TNF signaling, chemokine signaling and HIPPO signaling were significantly enriched in STK11-KO cells. We chose to focus on the HIPPO pathway as STK11 has previously been implicated in HIPPO regulation via direct activation of MARK family kinases and subsequent modulation of YAP1 activity [13]. YAP1-mediated transcriptional activation is controlled in part via cytosolic sequestration; a kinase-dependent process regulated by activation of the HIPPO cascade [16]. Utilizing a curated list of YAP1 transcriptional target genes [13] we repeated GSEA and found a significant positive correlation between STK11 loss and enhanced expression of YAP1 target genes in both the H2009 and H441 cell lines (Fig. 1G).

STK11 has previously been proposed to indirectly modulate the HIPPO/YAP1 axis via MARK activation, ultimately promoting YAP1 sequestration and degradation [13]. We therefore hypothesized that STK11 loss would result in increased YAP1 protein due to enhanced protein stabilization (Fig. 2A). Western blot analysis comparing whole cell extracts from parent and STK11-KO LUAD cell lines support this assertion, showing a ~2-fold increase in relative YAP1 abundance (Fig. 2B), an observation supported by prior studies in mice [13]. Interestingly, this difference occurs only at the protein level, as YAP1 transcript levels remain unchanged, supporting our hypothesis that STK11 loss results in YAP1 protein stabilization (Fig. 2C). Nuclear and cytosolic fractionation analyses further demonstrate that increased YAP1 protein levels are not isolated to either compartment but increased throughout cells lacking STK11. Upon glutamine deprivation, we observed increased YAP1 nuclear translocation in both parent and STK11-null cells, though the increase was more pronounced in the STK11-null cells (Fig. 2D). This data supports an STK11-dependent impact on global YAP1 protein abundance, including nuclear localization, which we posit drives changes in YAP1-mediated gene expression (Figs. 1G and 2A).

A We posit STK11, either directly or indirectly, contributes to YAP1 cytoplasmic sequestration and degradation. If true, STK11 loss should lead to enhanced YAP1 protein accumulation and potentially increased transcriptional activity. B Western blot analysis targeting YAP1 in whole cell extracts (WCE) from H2009 parent (P) versus H2009 STK11 KO (S) cells results in a ~2-fold increase in YAP1 protein. Data presented as meanSD (N=4). C YAP1 qRT-PCR analysis argues the difference in YAP1 protein abundance is not due to enhanced YAP1 gene expression. Data presented as meanSD (N=3). D Western blot analysis performed on nuclear and cytoplasmic fractions isolated from H2009 parent (P) or STK11 KO (S) cells support the whole cell extract data showing enhanced YAP1 protein abundance in the absence of STK11. Nuclear fraction data presented as meanSD (N=4). Cytoplasmic fraction data presented as meanSD (N=5). *p<0.0332, **p<0.0021, ***p<0.0002 was calculated by Students t Test (B, C) or two-way ANOVA and Tukey test in (D).

To validate our pathway analyses we reasoned we could inhibit STK11-loss-dependent cytokine induction following glutamine depletion by blocking the downstream signaling networks responsible. To examine the role of YAP1 in driving this phenotype, we engineered STK11/YAP1 double knockouts in both H2009 and H441 LUAD cell lines (Fig. 3A). Our data demonstrate significantly less IL-6, CXCL8 and CXCL2 expression in the STK11/YAP1 double KO lines compared with STK11-KO lines following glutamine depletion (Fig. 3B). Importantly, these changes were mirrored by levels of secreted IL-6 and CXCL8 protein levels measured by ELISA (Fig. 3C). YAP1-KO alone had no impact on expression of these cytokines, regardless of glutamine availability, demonstrating the necessity of STK11 loss in producing this phenotype (Fig. 3B).

A Western blot analysis confirming knockout of YAP1 (Y) in NCI-H2009 and NCI-H441 parent (P) and STK11-KO (S) cell lines. The STK11/YAP1 double knockout lines are abbreviated as SY. B IL-6, CXCL8, and CXCL2 qRT-PCR analysis demonstrates that upon glutamine depletion, the STK11-loss-dependent induction is blunted by the absence of YAP1. Expression normalized to PSMB4, and data presented as meanSD (N=3). C IL6 and CXCL8 ELISAs performed on conditioned media from H2009 cell lines. Data presented as meanSD (N=3). D qRT-PCR analysis of IL-6, CXCL8, and CXCL2 on cells treated with 1.5mM verteporfin (VP) vs vehicle. Expression normalized to PSMB4, and data presented as meanSD (N=3). *p<0.0332, **p<0.0021, ***p<0.0002, ****p<0.0001 was calculated by two-way ANOVA and Tukey test in (B, C) or three-way ANOVA and Tukey test in (D).

After establishing YAP1 functions downstream of STK11 and is at least in part responsible for the increased cytokine expression occurring in STK11-KO cells following glutamine depletion, we next sought to phenocopy YAP1 KO via pharmacologic antagonism of YAP1 with verteporfin (VP) [38]. One mechanism by which VP is known to alter YAP1 activity occurs via physically disrupting the interaction between YAP1 and members of the TEAD transcription factor family [38]. Our data clearly show that the STK11-loss-dependent upregulation of IL-6 and CXCL8 upon glutamine depletion is blunted by VP treatment (Fig. 3D). Interestingly, this affect does not extend to CXCL2 (Fig. 3D). Together these results support CXCL8 and IL-6 expression are likely regulated, at least in part, by YAP1/TEAD interactions. The fact that CXCL2 expression is reduced upon YAP1 genetic ablation, but not VP treatment, was unexpected and suggests YAP1s impact on CXCL2 expression may be independent of TEAD. YAP1 is known to interact with many transcription factors, including SMAD family members and the b-catenin/TBX5 complex [34]. We think it likely that YAP1s impact on CXCL2 expression relies on a transcription factor other than a TEAD family member, which is why genetic ablation of YAP1 results in altered expression, whereas TEAD dissociation with VP does not. Whether this definitively explains the discrepancy in our CXCL2 data awaits further investigation but remains a favored hypothesis.

To define the transcriptome-wide impact of YAP1 KO in STK11 deficient cells, we performed RNA-seq on H2009 cells following 24h in either standard or glutamine depleted media. In standard media, few genes differed between STK11-KO and STK11/YAP1 double KO cells (Fig. 4A, +Glutamine; 733 DEGs). Compared with the H2009 parent line, similar numbers of DEGs were detected in the STK11/YAP1 double KO as were seen in the STK11 KO when grown in the absence of glutamine (Fig. 4A, Glutamine; 7698 DEGs vs Fig. 1C, Glutamine; 7453 DEGs).

A MA-Plots generated from RNA-seq data contrast the number of differentially expressed genes in H2009 cell lines upon glutamine depletion. As expected, few DEGs are identified between STK11 KO and STK11/YAP1 double KO cells when cultured with glutamine (+Glutamine; 733 DEGs). A similar number of DEGs were detected in the STK11/YAP1 double KO compared with the parent line when grown in glutamine depleted media (Glutamine, 7698 DEGs) as were seen in the STK11 KO (Fig. 1C, Glutamine; 7453 DEGs). When the STK11/YAP1 double KO cells are compared directly with STK11 KO cells in the absence of glutamine, 4167 DEGs are detected. B K-means clustering of all mapped transcripts highlights genes that are induced upon glutamine depletion in STK11 KO cells, but whose induction is blunted in STK11/YAP1 double KO cells (Cluster 2, Red vs Orange). This group represents candidate YAP1-transcriptional targets. C GSEA performed on DEGs identified between STK11 KO and STK11/YAP1 double KO cells using the curated YAP1 signature gene list results in a strong negative correlation indicating reduced expression in the absence of YAP1. K-means clustering of the YAP1 gene signature supports this assertion (Cluster 1, Red vs Blue). Dot plot visualization of the 17 genes shared between H2009 and H441 cells (Fig. 1G) indicates the magnitude of expression blunting that occurs in the absence of YAP1. D GSEA performed on DEGs identified between STK11 KO and STK11/YAP1 double KO cells using the gene ontology cytokine activity list demonstrates no significant correlation, in line with a blunted response due to YAP1 loss. K-means clustering of the cytokine activity signature supports this assertion (Cluster 1, Red vs Blue). Dot plot visualization of the 9 genes shared between H2009 and H441 cells (Fig. 1D) indicates the magnitude of expression blunting that occurs in the absence of YAP1. E Proposed model linking the tumor-intrinsic role of an STK11/YAP1 axis with altered transcriptional profiles in KRAS-driven, STK11-null LUADs that promote a cold tumor immune microenvironment, potentiating anti-PD-1 therapy resistance. Our data support targeting YAP1 as a strategy to foster a hot tumor immune microenvironment, thereby sensitizing patients to anti-PD-1 therapy. ***p<0.0001 reflects the padj values attained by the Wald test and corrected for multiple testing using the Benjamini and Hochberg method within DESeq2.

However, when the STK11/YAP1 double KO cells are compared directly with STK11 KO cells in the absence of glutamine, 4167 DEGs were detected (Fig. 4A, Glutamine; 4167 DEGs). If YAP1 loss had no impact, we would predict no DEGs identified between these two conditions. The DEGs detected represent genes that still change upon glutamine depletion, but the magnitude of that change is significantly reduced in the absence of YAP1 indicating these genes are candidates for YAP1-mediated regulation. K-means clustering of genes differentially expressed between STK11-KO and STK11/YAP1 double KO cells revealed a large group of genes that, while still induced by glutamine depletion, were repressed relative to the induction observed in STK11-null/YAP1-competent cells (Fig. 4B; cluster 2, Red v Orange). GSEA performed on DEGs identified between H2009 STK11-KO and STK11/YAP1 double KO cells using the previously described curated YAP1 gene signature demonstrated a significant negative correlation, indicating gene repression in STK11/YAP1 double KO cells relative to STK11-KO/YAP1-intact cells (Fig. 4C). Specifically, 102 genes within the curated YAP1 signature exhibited reduced expression upon YAP1 ablation in STK11-KO cells, highlighted by dot plot analysis of the 17 genes identified in Fig. 1G, which show overlap in gene induction between H2009 and H441 cells upon STK11 ablation (Fig. 4C). We posit those genes demonstrating significant reduction in expression are regulated in part by YAP1. We also performed GSEA using the cytokine activity signature (GO: 0005125) previously described (Fig. 1D) and observed repression of 35 member genes upon YAP1 ablation in STK11-KO cells (Fig. 4D). Again, dot plot analysis highlights repression of a subset of these genes following YAP1 deletion in H2009 cells lacking STK11 (Fig. 4D). Taken together, these data support YAP1 antagonism as a strategy to curb expression of key genes, including immunomodulatory cytokines, in KRAS-driven STK11-null LUADs. We speculate a similar response in vivo would aid in transitioning immunologically cold tumor immune microenvironments to hot, potentiating the effectiveness of checkpoint inhibitor therapies such as anti-PD-1 monoclonal antibodies (Fig. 4E).

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CRISPR-broad framework

We developed a procedural pipeline for detecting gRNAs and implemented this in Python as a standalone application (Fig.1a). For speeding up gRNA selection, we employed multithreading and used big data Python module Pandas. This allowed splitting millions of short sequences for mapping and processing large numbers of uncompressed alignments. The different steps and options of CRISPR-broad are implemented in seven different modules (in Python with Pandas and PyRanges packages) to avoid re-performing steps that are computationally demanding. Multiple options for user input are available (Fig.1b).

Modules and features of CRISPR-broad. (a) Working scheme of the CRISPR-broad tool. Several steps in this pipeline are multithreaded. The input is a multiFASTA genome file and each step can be individually executed. Indexing and mapping steps are time limiting and can be performed separately. The output of this pipeline is a ranked list of gRNAs in text format. (b) The different modules in execution of CRISPR-broad, their features and applicability as well as the respective options for user input are shown. The different options for running the individual modules are described in detail at https://github.com/AlagurajVeluchamy/CRISPR-broad.

Running CRISPR-broad on the C. elegans genome (target window size 50kb), we obtained 5,734,064 candidate gRNAs with the Cas9 PAM pattern NGG at the 3-end and flanked by 20 nt at the 5-end. We allowed a range of mismatches from 0 to 3 to map to the C. elegans genome assembly Ce235 using the end-to-end all alignment option in bowtie2. The large pairwise alignment was parsed for indels and matches to calculate a ranking score. About 18% of these candidate gRNAs were mapped to multiple sites. We further filtered entries that were aligned to less than five genomic loci. Our analysis resulted in 27,858 gRNAs (five hits in the selected window) that could target 6421 unique 50kb regions (Supplementary Fig.2a).

Next, we scanned the human genome (target window size 500kb) and filtered candidate gRNAs with a cutoff of 50% GC. This resulted in around 120 million gRNAs. We mapped these sequences with a range of mismatches from zero to three and maximum hits of 10,000. The multi-mapped positions were verified for PAM sequences at their 3-end and pooled. We processed candidate gRNAs further that had at least five hits in the genome. This combined filtering resulted in 2,413,602 (0.6%) gRNAs that target 1,678,629 windows (Supplementary Fig.2b). The targetable windows with minimally five loci for a unique gRNA of the C. elegans and H. sapiens genomes were spread throughout the different chromosomes (Supplementary Fig.2b). The aggregate gRNA score pattern distribution for both sample genomes showed that although off-targets are high (negative score), a significant number of high scoring regions in these genomes are available for gRNA targeting (Fig.2a). Irrespective of genome size or sequence content, the aggregate score decreased with the number of off-targets thereby validating the score-based selection of gRNAs (Fig.2b,c).

Aggregate gRNA score distribution for two model organisms. (a) The aggregate gRNA score ranging from 1 to +1 for two datasets is shown in a density plot. Aggregate gRNA score with up to 10k off-target (OT) settings in C. elegans (b) and H. sapiens (c).

Inter-bin distance defines the gap between two target regions and hence illustrates the density of target windows. Analysis of this parameter between different gRNA candidates with or without off-targets revealed that gRNA distribution is not biased over different chromosomes (Fig.3a). Finding potential gRNAs was further supported by increase in window size and by selecting gRNA that are multi-targeting (Fig.3b).

Distribution of gRNAs along the chromosomes of C. elegans and H. sapiens. (a) gRNA sequences clustered in small intervals are evident from this analysis on distribution of gRNA hits. Inter-bin distances of multi-hit gRNA sequences with and without off-target. The distances of gRNA hits are shown in bp (in equal bin size). Note the difference in the distribution of gRNAs with or without off-targets for C. elegans and H. sapiens due to the different repetitiveness of the two genomes. (b) Boxplot showing the relationship between size and number of target bins in the genome of C. elegans. Off-target hits represent the sum of gRNA hits that fall outside all the multiple target windows. W, window size; N, number of target windows.

Typical unique sgRNA selection involves reducing off-target hits on multiple genomic regions and finding a unique target sequence. Tandem duplications in the genome are one cause of off-target effects. CRISPR-broad uses these duplication events in detecting gRNAs in bins (a large genomic region). Larger window sizes could reduce the potential off- target effect of gRNAs in our tool. This was evident from the number of on- and off-target hits (Fig.3b).

Each sgRNA has N total hits in the genome, T hits in the target window and O hits in the off-targets (region outside/different from the 50/500kb target window). When analyzing the C. elegans and H. sapiens genomes, there was no correlation between N and O (Fig.4a,b). The 50kb and 500kb windows showed a vast number of on-targets compared to off-targets, revealing a wide range of selectable regions. Indeed, on-target regions could be identified that showed a high number of gRNA loci with zero off-targets. This included a pericentromeric region of human chromosome 1, which has 272 gRNAs loci with no apparent off-targets (Supplementary Fig.3a). Similarly, in C. elegans analysis with a window size of 10kb revealed a region on the X chromosome (chrX:73517361kb) where at least 1000 loci could be found for one gRNA (Supplementary Fig.3b). The candidate target regions identified in both, C. elegans and H. sapiens were not limited to functionally annotated repetitive regions (e.g. telomeres, satellites) that could be directly targeted by classical gRNA design tools such as CHOPCHOP (Supplementary Fig.3c,d).

Relationship of on-target and off-target sites for each gRNA. Multi-hit alignment with short read aligner was performed for each gRNA. Number of hits within the selected window and off-target windows were enumerated from the alignment. (a) Off-target distribution in comparison to the number of on-target hits in C. elegans (50kb window). (b) off-target distribution in comparison to the number of on-target hits in H. sapiens (500kb window). (c) Off-targets predicted by CasOFFinder compared to the CRISPR-broad scoring system in C. elegans. (d) CRISPR-broad score for gRNAs in H. sapiens compared to off-targets predicted by CasOFFinder. The number of off-targets predicted for individual gRNAs is anticorrelated to our CRISPR-broad scoring system.

Global comparison of the CRISPR-broad scores derived from analyzing the C. elegans and H. sapiens genomes to the results of an independent, state-of-the-art off-target scanning tool for individual gRNAs (CasOffinder), indicated that these are higher for gRNAs that were identified to have a lower number of predicted off-targets (Fig.4c,d). This supported the notion that our scoring method is relevant for selection of multi-targeting gRNAs.

We calculated cumulative scores for the gRNAs matching to selected loci and including a penalty score in case off-targets were found. These scores range from 1 to +1. In both genomes analyzed, C. elegans and H. sapiens we observed a bias towards the extreme values on both sides of the aggregate gRNA score, i.e. many gRNAs are either good candidates for multi-targeting with many hits and no off-targeting (aggregate gRNA score close to +1) or are showing many off-target hits and mismatching (aggregate gRNA score of close to 1) (Fig.2a). The very high negative aggregate gRNA scores observed are reflection of repetitive elements such as Alu sequences, LINE-1 retrotransposons, MIR, and human endogenous retroviruses (HERVs), which represent 55% of the human genome, occurring in multiple copies27. Similarly, in the C. elegans genome MITE sequence repeats might elevate the number of off-targets28. These off-targets are correlated to the aggregate gRNA score (Fig.2b,c).

sgRNA efficiency has been correlated with the GC content of the nucleotide sequence29. We explored whether the GC content feature impacted the number of available gRNAs (with significant number of on-target hits and lower off-target hits). The aggregate gRNA scores (gRNA scores of each window) varied highly from the GC-contents of the sequences (Fig.5). This indicated that CRISPR-broad scans a wide range of gRNAs that may have different levels of repetitive nucleotide sequences. The repetitive elements may be AT-rich and gRNA selection based on gRNA score is not limited by GC content.

gRNA score correlation to GC composition of the 23 nucleotides gRNA sequence. (a, b) Sequence composition as dinucleotide frequencies were calculated. The gRNA score (range from 2 to +1) and the GC content are depicted in the density plot. Aggregate gRNA score and repetition of sequence (off-target) are independent of the sequence composition. Many candidate gRNAs with high aggregate gRNA score that corresponds to candidate target windows are available for varied GC content.

To elucidate the effects of user-defined bin size and number of distinct gRNA combinations, we scanned the C. elegans genome with two window sizes of 1kb and 200kb and targeting window numbers of 3 and 10. As expected, the number of off-targets decreased with increasing target window sizes and the number of target regions (Fig.3b). Our analysis showed that with different bin sizes and using multiple gRNA, a wide range of regions can be selected for targeting with singular gRNAs.

The dispersion of a gRNA within a bin is depending on the number of hits and this increases with the number of mismatches (03). Nevertheless, most hits for gRNAs were unique with no mismatches. This is revealed from sgRNA mismatch analysis of the whole genome of C elegans and a random selection of 10,000 sgRNA in H. sapiens (Fig.6a and Supplementary Fig.5a). Also, these mismatches were independent of the position within a bin (Supplementary Fig.4). Further, the dispersion of individual gRNAs did not correlate with the aggregate gRNA score in both C. elegans and H. sapiens. In C. elegans most gRNAs with higher standard deviation from the mid position of the bin showed lower aggregate gRNA scores (Fig.6b). Also, in H. sapiens, the standard deviation was not correlated to the gRNA score but was associated with a varied range of gRNA scores (Supplementary Fig.5b). This difference is because the H. sapiens genome is large and has more multi-targetable regions compared to the C. elegans genome. In both cases, a substantial number of gRNAs of varied standard deviation and with no off-targets could be selected.

Assessment of displacement of gRNAs within on-target windows. (a) CRISPR-broad was used to scan for potential gRNAs with different levels of mismatches, since earlier reports have shown that the efficiency of gRNAs are limited by the number of mismatches. Mismatch levels and number of on-target hits for gRNAs of individual 50kb windows in C. elegans are shown. Mismatch levels are set in the range from 0 to 3. Many selectable gRNAs and their corresponding target windows are available even at a mismatch level of 0. (b) Hexbin plot showing the relationship between aggregate gRNA score and dispersion. Standard deviation (dispersion) was calculated from the position of the gRNA hits within a target window. The aggregate gRNA score ranges from negative to positive values. Higher values of standard deviation correspond to higher distribution of gRNA within a target window. Standard deviation and gRNA score were calculated using 500kb windows in H. sapiens.

Using PyRanges, we created intervals of user-defined size that are overlapping with gRNA candidates containing the Cas9 PAM pattern (3-NGG-5). Since this step is computationally intensive, we have implemented options to narrow down the search with minimum and maximum number of hits for a target window.

Analysis of the annotation of regions of the C. elegans and H. sapiens genomes that can be targeted by multi-targeting gRNAs indicated that a broad range of features including genes and gene regulatory elements are available for selection. The range of annotated, targetable regions for each genome could be further significantly increased when combining gRNA searches for different genome-targeting systems that use different PAM sequences (Supplementary Fig.6).

To test CRISPR-broad we resorted to a previously described method of painting genome regions by targeting dCas9 fused to green fluorescent protein (GFP). Singular gRNAs targeting more than 100 directly repeated sequences within telomeres or pericentromeres identified by classical gRNA design tools has enabled mapping of these functional chromosome elements in cellular context4,5,6. Using CRISPR-broad we identified a singular gRNA targeting a 317kb region on human chromosome 19 at 19p13.2 with 86 hits (Fig.7a). Human U2OS transfected with a plasmid expressing dCas9-3XGFP together with a plasmid expressing the identified sgRNA showed two or 4 dots of accumulated green fluorescence in the nucleus in agreement with a 2n (G1- and S-phase) or 4n (G2-phase) chromosome content. In contrast and as described before4,5,6, dCas9-3XGFP in the absence of specific gRNA-mediated targeting displayed nucleolar background staining in the cell nucleus (Fig.7b). The results indicated that CRISPR-broad can identify large genomic regions for efficient targeting of dCas9 apart from simple and obvious repetitive elements of the genome.

Targeting of a broad region of the genome using a singular gRNA designed by CRIPSR-broad. (a) Scheme depicting a 317kb region on human chromosome 19 that can be targeted by a sgRNA at 86 locations. (b) Fluorescence imaging of U2OS cells transfected with a plasmid expressing dCas9-3XGFP together with a plasmid expressing the sgRNA targeting the region depicted in (A) (top) or the corresponding empty vector (bottom). Focal enrichment of GFP inside the nucleus is marked by arrows. Note that due to the different cell cycle stages two (2n chromosome content, G1- , S-phases) or four (4n chromosome content, G2-phase) labeled spots are expected. Scale bar represents 20m. Details on the selection of the presented cells and images can be found in Supplementary Fig.8.

To assess the wider application and potential of CRISPR-broad, we compared the results of the test runs on the C. elegans and H. sapiens genomes using the single Cas9 PAM with annotated (epi-)genetic features using the ENCODE and modENCODE datasets. We found the multi-targetable windows defined by CRISPR-broad overlapping with the features transcription factor binding sites (ChIP-seq peak regions), histone modification region (ChIP-seq peaks), annotated transposable elements in the genome and sites of DNA methylation (WGBS: methylated CpG sites). The fact that the fraction of each of these sites that could be targeted by multi-targeting gRNAs (number of features overlapped to a gRNA window of 5kb/total number of features) is substantial (Supplementary Fig.7) indicated that CRISPR-broad could be useful in various strategies of epigenome editing.

CRISPR-broad was developed in Python and the source code is available at https://github.com/AlagurajVeluchamy/CRISPR-broad. CRISPR-broad runs in seven independent modules with multiple options for user input (Fig.1b). The limiting steps are mapping the gRNAs to the genome and obtaining all hits. We tested the performance of the tool on a Linux workstation with 3040 threads computed for genome sizes of 103Mb (C. elegans) and 3.2Gb (H. sapiens) (Table 1). With an increase in genome size and in the allowed number of mismatches, the run time increased. The gRNA sequences, aggregate gRNA scores, GC content, number of on- and off-target hits, optimal on-target window of pre- selected size, and co-ordinates of each hit are compiled and exported in a tab-delimited text (Supplementary Table 2).

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Researchers at Duke University have adapted CRISPR technologies for high-throughput screening of gene function in human immune cells and discovered that a single master regulator of the genome can be used to reprogram a network of thousands of genes in T cells and greatly enhance cancer cell killing.

The master regulator is called BATF3 and is one of several genes that the researchers identified and tested for improving T-cell therapies. These targets, and the methods developed to identify, test and manipulate them, could make any of the T cell cancer therapies currently in use and under development more potent. Combined with other advances, the platform could also enable generalized, off-the-shelf versions of the therapy and expansion into other disease areas such as autoimmune disorders.

The results appear online November 9 in the journal Nature Genetics.

T-cell therapy is a decade-old approach to treating cancer. More recent versions involve reprogramming the immune system's primary soldiers to seek and destroy cancerous cells that they might otherwise overlook. Many companies are working to enhance the technology, mostly through the use of genetic engineering techniques that instruct the T cells how to identify cancerous cells and make them more effective at destroying them.

There are currently six FDA-approved T-cell therapies for specific leukemias, lymphomas and multiple myeloma. Their approaches, however, do not currently fare well when applied to solid tumors, although there are hints of success in certain studies. Solid tumors often present large physical barriers for the T cells to overcome, and the sheer number and density of cancer cells presenting targets can lead to "T-cell exhaustion," wearing the attackers out to the point that they are not able to mount an antitumor response.

"In some cases, T-cell therapy works like a miracle drug, but in most others, it hardly works at all," said Charles Gersbach, the John W. Strohbehn Distinguished Professor of Biomedical Engineering at Duke. "We are looking for generic solutions that can make these cells better across the board by reprogramming their gene regulation software, rather than rewriting or damaging their genetic hardware. This demonstration is a crucial step toward overcoming a major hurdle to getting T-cell therapy to work in more patients across a greater range of cancer types."

Gersbach and his laboratory have spent the past several years developing a method that uses a version of the gene-editing technology CRISPR-Cas9 to explore and modulate genes without cutting them. Instead, it makes changes to the structures that package and store the DNA, affecting the activity level of the accompanying genes.

Sean McCutcheon, a PhD candidate working in Gersbach's lab and lead author of the study, focused on regions of this 'dark genome' that change as T cells transition between states, such as functional versus exhausted. He identified 120 genes that encode "master regulators," which are responsible for the activity levels of many other genes. Using the CRISPR platform, he dialed the activity levels of these targets both up and down to see how they affected other known markers of T cell function.

While several promising candidates emerged, one of the most promising was a gene called BATF3. When McCutcheon subsequently delivered BATF3 directly to the T cells, there were thousands of tweaks to the packaging structure of the T cells' DNA, and this correlated with increased potency and resistance to exhaustion.

"A known barrier to using T cells to fight cancer is that they tend to get 'tired' over time and lose their ability to kill cancer cells," McCutcheon said. "We're identifying manipulations that make T cells stronger and more resilient by mimicking naturally occurring cell states that work well in clinical products."

The researchers put BATF3 through a battery of tests. The most interesting results came when they overexpressed BATF3 in T cells programmed to attack human breast cancer tumors in a mouse model. While the standard-of-care T-cell therapy struggled to slow tumor growth, the exact same dose of T cells engineered with BATF3 completely eradicated the tumors.

While the results with BATF3 are exciting to Gersbach, McCutcheon and the rest of the group, they are even more enthusiastic about the general success of the methodology to identify and modulate master regulators to improve therapeutic performance, which they have been developing for the better part of a decade. They can now readily profile master regulators of T cell fitness using any T cell source or cancer model and under various experimental conditions that mimic the clinical setting.

For example, in the last part of this study, McCutcheon screened T cells, with or without BATF3, while using CRISPR to remove every other master regulator of gene expression -- more than 1,600 regulators in total. This led to the discovery of a whole new set of factors that could be targeted alone or in combination with BATF3 to increase the potency of T-cell therapy.

"This study focused in depth on one particular target identified by these CRISPR screens, but now that Sean and the team have the whole discovery engine up and running, we can do this over and over again for different models and tumor types," Gersbach said. "This study suggests many strategies for applying this approach to enhance T-cell therapy, from using a patient's own T cells to having a bank of generalized T cells for a wide variety of cancers. We hope that these technologies can be generally applicable across all strategies."

This research was supported by the National Institutes of Health (U01AI146356, UM1HG012053, UM1HG009428, RM1HG011123), the National Science Foundation (EFMA-1830957), the Paul G. Allen Frontiers, the Open Philanthropy Project, and the Duke-Coulter Translational Partnership.

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Master regulator of the dark genome greatly improves cancer T-cell ... - Science Daily