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1. The single-cell opioid responses in the context of HIV (SCORCH) consortium

Author

Ament, Seth A, Campbell, Rianne R, Lobo, Mary Kay, Receveur, Joseph P, Agrawal, Kriti, Borjabad, Alejandra, Byrareddy, Siddappa N, Chang, Linda, Clarke, Declan, Emani, Prashant, Gabuzda, Dana, Gaulton, Kyle J, Giglio, Michelle, Giorgi, Federico M, Gok, Busra, Guda, Chittibabu, Hadas, Eran, Herb, Brian R, Hu, Wen, Huttner, Anita, Ishmam, Mohammad R, Jacobs, Michelle M, Kelschenbach, Jennifer, Kim, Dong-Wook, Lee, Cheyu, Liu, Shuhui, Liu, Xiaokun, Madras, Bertha K, Mahurkar, Anup A, Mash, Deborah C, Mukamel, Eran A, Niu, Meng, O’Connor, Richard M, Pagan, Chelsea M, Pang, Alina PS, Pillai, Piya, Repunte-Canonigo, Vez, Ruzicka, W Brad, Stanley, Jay, Tickle, Timothy, Tsai, Shang-Yi A, Wang, Allen, Wills, Lauren, Wilson, Alyssa M, Wright, Susan N, Xu, Siwei, Yang, Junchen, Zand, Maryam, Zhang, Le, Zhang, Jing, Akbarian, Schahram, Buch, Shilpa, Cheng, Christine S, Corley, Michael J, Fox, Howard S, Gerstein, Mark, Gummuluru, Suryaram, Heiman, Myriam, Ho, Ya-Chi, Kellis, Manolis, Kenny, Paul J, Kluger, Yuval, Milner, Teresa A, Moore, David J, Morgello, Susan, Ndhlovu, Lishomwa C, Rana, Tariq M, Sanna, Pietro Paolo, Satterlee, John S, Sestan, Nenad, Spector, Stephen A, Spudich, Serena, Tilgner, Hagen U, Volsky, David J, White, Owen R, Williams, Dionne W, and Zeng, Hongkui

Subjects
Pharmacology and Pharmaceutical Sciences, Medical Microbiology, Biomedical and Clinical Sciences, HIV/AIDS, Sexually Transmitted Infections, Substance Misuse, Opioids, Opioid Misuse and Addiction, Brain Disorders, Drug Abuse (NIDA only), Neurosciences, Infectious Diseases, Behavioral and Social Science, 2.1 Biological and endogenous factors, 2.6 Resources and infrastructure (aetiology), Good Health and Well Being, Animals, Humans, Analgesics, Opioid, Brain, HIV Infections, Opioid-Related Disorders, Single-Cell Analysis, Comorbidity, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

Published
2024

2. ProteinRPN: Towards Accurate Protein Function Prediction with Graph-Based Region Proposals

Author

Mitra, Shania, Huang, Lei, and Kellis, Manolis

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

Protein function prediction is a crucial task in bioinformatics, with significant implications for understanding biological processes and disease mechanisms. While the relationship between sequence and function has been extensively explored, translating protein structure to function continues to present substantial challenges. Various models, particularly, CNN and graph-based deep learning approaches that integrate structural and functional data, have been proposed to address these challenges. However, these methods often fall short in elucidating the functional significance of key residues essential for protein functionality, as they predominantly adopt a retrospective perspective, leading to suboptimal performance. Inspired by region proposal networks in computer vision, we introduce the Protein Region Proposal Network (ProteinRPN) for accurate protein function prediction. Specifically, the region proposal module component of ProteinRPN identifies potential functional regions (anchors) which are refined through the hierarchy-aware node drop pooling layer favoring nodes with defined secondary structures and spatial proximity. The representations of the predicted functional nodes are enriched using attention mechanisms and subsequently fed into a Graph Multiset Transformer, which is trained with supervised contrastive (SupCon) and InfoNCE losses on perturbed protein structures. Our model demonstrates significant improvements in predicting Gene Ontology (GO) terms, effectively localizing functional residues within protein structures. The proposed framework provides a robust, scalable solution for protein function annotation, advancing the understanding of protein structure-function relationships in computational biology.

Published
2024

3. A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis

Author

Huang, Lei, Xiong, Lei, Sun, Na, Liu, Zunpeng, Wong, Ka-Chun, and Kellis, Manolis

Subjects
Quantitative Biology - Genomics, Quantitative Biology - Biomolecules
Abstract

The rapid advancement of single-cell ATAC sequencing (scATAC-seq) technologies holds great promise for investigating the heterogeneity of epigenetic landscapes at the cellular level. The amplification process in scATAC-seq experiments often introduces noise due to dropout events, which results in extreme sparsity that hinders accurate analysis. Consequently, there is a significant demand for the generation of high-quality scATAC-seq data in silico. Furthermore, current methodologies are typically task-specific, lacking a versatile framework capable of handling multiple tasks within a single model. In this work, we propose ATAC-Diff, a versatile framework, which is based on a latent diffusion model conditioned on the latent auxiliary variables to adapt for various tasks. ATAC-Diff is the first diffusion model for the scATAC-seq data generation and analysis, composed of auxiliary modules encoding the latent high-level variables to enable the model to learn the semantic information to sample high-quality data. Gaussian Mixture Model (GMM) as the latent prior and auxiliary decoder, the yield variables reserve the refined genomic information beneficial for downstream analyses. Another innovation is the incorporation of mutual information between observed and hidden variables as a regularization term to prevent the model from decoupling from latent variables. Through extensive experiments, we demonstrate that ATAC-Diff achieves high performance in both generation and analysis tasks, outperforming state-of-the-art models.

Published
2024

4. Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

Author

Bonev, Boyan, Castelo-Branco, Gonçalo, Chen, Fei, Codeluppi, Simone, Corces, M. Ryan, Fan, Jean, Heiman, Myriam, Harris, Kenneth, Inoue, Fumitaka, Kellis, Manolis, Levine, Ariel, Lotfollahi, Mo, Luo, Chongyuan, Maynard, Kristen R., Nitzan, Mor, Ramani, Vijay, Satijia, Rahul, Schirmer, Lucas, Shen, Yin, Sun, Na, Green, Gilad S., Theis, Fabian, Wang, Xiao, Welch, Joshua D., Gokce, Ozgun, Konopka, Genevieve, Liddelow, Shane, Macosko, Evan, Ali Bayraktar, Omer, Habib, Naomi, and Nowakowski, Tomasz J.

Published
2024
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6. TrustLLM: Trustworthiness in Large Language Models

Author

Huang, Yue, Sun, Lichao, Wang, Haoran, Wu, Siyuan, Zhang, Qihui, Li, Yuan, Gao, Chujie, Huang, Yixin, Lyu, Wenhan, Zhang, Yixuan, Li, Xiner, Liu, Zhengliang, Liu, Yixin, Wang, Yijue, Zhang, Zhikun, Vidgen, Bertie, Kailkhura, Bhavya, Xiong, Caiming, Xiao, Chaowei, Li, Chunyuan, Xing, Eric, Huang, Furong, Liu, Hao, Ji, Heng, Wang, Hongyi, Zhang, Huan, Yao, Huaxiu, Kellis, Manolis, Zitnik, Marinka, Jiang, Meng, Bansal, Mohit, Zou, James, Pei, Jian, Liu, Jian, Gao, Jianfeng, Han, Jiawei, Zhao, Jieyu, Tang, Jiliang, Wang, Jindong, Vanschoren, Joaquin, Mitchell, John, Shu, Kai, Xu, Kaidi, Chang, Kai-Wei, He, Lifang, Huang, Lifu, Backes, Michael, Gong, Neil Zhenqiang, Yu, Philip S., Chen, Pin-Yu, Gu, Quanquan, Xu, Ran, Ying, Rex, Ji, Shuiwang, Jana, Suman, Chen, Tianlong, Liu, Tianming, Zhou, Tianyi, Wang, William, Li, Xiang, Zhang, Xiangliang, Wang, Xiao, Xie, Xing, Chen, Xun, Wang, Xuyu, Liu, Yan, Ye, Yanfang, Cao, Yinzhi, Chen, Yong, and Zhao, Yue

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Therefore, ensuring the trustworthiness of LLMs emerges as an important topic. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. Our findings firstly show that in general trustworthiness and utility (i.e., functional effectiveness) are positively related. Secondly, our observations reveal that proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, raising concerns about the potential risks of widely accessible open-source LLMs. However, a few open-source LLMs come very close to proprietary ones. Thirdly, it is important to note that some LLMs may be overly calibrated towards exhibiting trustworthiness, to the extent that they compromise their utility by mistakenly treating benign prompts as harmful and consequently not responding. Finally, we emphasize the importance of ensuring transparency not only in the models themselves but also in the technologies that underpin trustworthiness. Knowing the specific trustworthy technologies that have been employed is crucial for analyzing their effectiveness., Comment: This work is still under work and we welcome your contribution

Published
2024

7. Massively parallel characterization of regulatory elements in the developing human cortex

Author

Deng, Chengyu, Whalen, Sean, Steyert, Marilyn, Ziffra, Ryan, Przytycki, Pawel F, Inoue, Fumitaka, Pereira, Daniela A, Capauto, Davide, Norton, Scott, Vaccarino, Flora M, Pollen, Alex A, Nowakowski, Tomasz J, Ahituv, Nadav, Pollard, Katherine S, Akbarian, Schahram, Abyzov, Alexej, Arasappan, Dhivya, Almagro Armenteros, Jose Juan, Beliveau, Brian J, Bendl, Jaroslav, Berretta, Sabina, Bharadwaj, Rahul A, Bhattacharya, Arjun, Bicks, Lucy, Brennand, Kristen, Champagne, Frances A, Chatterjee, Tanima, Chatzinakos, Chris, Chen, Yuhang, Chen, H Isaac, Cheng, Yuyan, Cheng, Lijun, Chess, Andrew, Chien, Jo-fan, Chu, Zhiyuan, Clarke, Declan, Clement, Ashley, Collado-Torres, Leonardo, Cooper, Gregory M, Crawford, Gregory E, Dai, Rujia, Daskalakis, Nikolaos P, Davila-Velderrain, Jose, Deep-Soboslay, Amy, DiPietro, Christopher P, Dracheva, Stella, Drusinsky, Shiron, Duan, Ziheng, Duong, Duc, Dursun, Cagatay, Eagles, Nicholas J, Edelstein, Jonathan, Emani, Prashant S, Fullard, John F, Galani, Kiki, Galeev, Timur, Gandal, Michael J, Gaynor, Sophia, Gerstein, Mark, Geschwind, Daniel H, Girdhar, Kiran, Goes, Fernando S, Greenleaf, William, Grundman, Jennifer, Guo, Hanmin, Guo, Qiuyu, Gupta, Chirag, Hadas, Yoav, Hallmayer, Joachim, Han, Xikun, Haroutunian, Vahram, Hawken, Natalie, He, Chuan, Henry, Ella, Hicks, Stephanie C, Ho, Marcus, Ho, Li-Lun, Hoffman, Gabriel E, Huang, Yiling, Huuki-Myers, Louise A, Hwang, Ahyeon, Hyde, Thomas M, Iatrou, Artemis, Jajoo, Aarti, Jensen, Matthew, Jiang, Lihua, Jin, Peng, Jin, Ting, Jops, Connor, Jourdon, Alexandre, Kawaguchi, Riki, Kellis, Manolis, Khullar, Saniya, Kleinman, Joel E, Kleopoulos, Steven P, and Kozlenkov, Alex

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Stem Cell Research - Embryonic - Human, Stem Cell Research, Human Genome, Genetics, Neurosciences, Underpinning research, Aetiology, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Neurological, Humans, Cerebral Cortex, Chromatin, Deep Learning, Enhancer Elements, Genetic, Gene Expression Regulation, Developmental, Neurogenesis, Neurons, Organoids, Regulatory Sequences, Nucleic Acid, Promoter Regions, Genetic, Regulatory Elements, Transcriptional, PsychENCODE Consortium‡, PsychENCODE Consortium, General Science & Technology
Abstract

Nucleotide changes in gene regulatory elements are important determinants of neuronal development and diseases. Using massively parallel reporter assays in primary human cells from mid-gestation cortex and cerebral organoids, we interrogated the cis-regulatory activity of 102,767 open chromatin regions, including thousands of sequences with cell type-specific accessibility and variants associated with brain gene regulation. In primary cells, we identified 46,802 active enhancer sequences and 164 variants that alter enhancer activity. Activity was comparable in organoids and primary cells, suggesting that organoids provide an adequate model for the developing cortex. Using deep learning we decoded the sequence basis and upstream regulators of enhancer activity. This work establishes a comprehensive catalog of functional gene regulatory elements and variants in human neuronal development.

Published
2024

9. TDP-43 proteinopathy in ALS is triggered by loss of ASRGL1 and associated with HML-2 expression

Author

Garcia-Montojo, Marta, Fathi, Saeed, Rastegar, Cyrus, Simula, Elena Rita, Doucet-O’Hare, Tara, Cheng, Y. H. Hank, Abrams, Rachel P. M., Pasternack, Nicholas, Malik, Nasir, Bachani, Muzna, Disanza, Brianna, Maric, Dragan, Lee, Myoung-Hwa, Wang, Herui, Santamaria, Ulisses, Li, Wenxue, Sampson, Kevon, Lorenzo, Juan Ramiro, Sanchez, Ignacio E., Mezghrani, Alexandre, Li, Yan, Sechi, Leonardo Antonio, Pineda, Sebastian, Heiman, Myriam, Kellis, Manolis, Steiner, Joseph, and Nath, Avindra

Published
2024
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12. Contextualized Machine Learning

Author

Lengerich, Benjamin, Ellington, Caleb N., Rubbi, Andrea, Kellis, Manolis, and Xing, Eric P.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

We examine Contextualized Machine Learning (ML), a paradigm for learning heterogeneous and context-dependent effects. Contextualized ML estimates heterogeneous functions by applying deep learning to the meta-relationship between contextual information and context-specific parametric models. This is a form of varying-coefficient modeling that unifies existing frameworks including cluster analysis and cohort modeling by introducing two reusable concepts: a context encoder which translates sample context into model parameters, and sample-specific model which operates on sample predictors. We review the process of developing contextualized models, nonparametric inference from contextualized models, and identifiability conditions of contextualized models. Finally, we present the open-source PyTorch package ContextualizedML.

Published
2023

13. Single-cell multiregion dissection of Alzheimer’s disease

Author

Mathys, Hansruedi, Boix, Carles A., Akay, Leyla Anne, Xia, Ziting, Davila-Velderrain, Jose, Ng, Ayesha P., Jiang, Xueqiao, Abdelhady, Ghada, Galani, Kyriaki, Mantero, Julio, Band, Neil, James, Benjamin T., Babu, Sudhagar, Galiana-Melendez, Fabiola, Louderback, Kate, Prokopenko, Dmitry, Tanzi, Rudolph E., Bennett, David A., Tsai, Li-Huei, and Kellis, Manolis

Published
2024
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16. LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs

Author

Lengerich, Benjamin J., Bordt, Sebastian, Nori, Harsha, Nunnally, Mark E., Aphinyanaphongs, Yin, Kellis, Manolis, and Caruana, Rich

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components. By adopting a hierarchical approach to reasoning, LLMs can provide comprehensive model-level summaries without ever requiring the entire model to fit in context. This approach enables LLMs to apply their extensive background knowledge to automate common tasks in data science such as detecting anomalies that contradict prior knowledge, describing potential reasons for the anomalies, and suggesting repairs that would remove the anomalies. We use multiple examples in healthcare to demonstrate the utility of these new capabilities of LLMs, with particular emphasis on Generalized Additive Models (GAMs). Finally, we present the package $\texttt{TalkToEBM}$ as an open-source LLM-GAM interface.

Published
2023

17. Applications of Deep Learning to physics workflows

Author

Agarwal, Manan, Alameda, Jay, Audenaert, Jeroen, Benoit, Will, Beveridge, Damon, Bhattacharya, Meghna, Chatterjee, Chayan, Chatterjee, Deep, Chen, Andy, Cholayil, Muhammed Saleem, Chou, Chia-Jui, Choudhary, Sunil, Coughlin, Michael, Dax, Maximilian, Desai, Aman, Di Luca, Andrea, Duarte, Javier Mauricio, Farrell, Steven, Feng, Yongbin, Goodarzi, Pooyan, Govorkova, Ekaterina, Graham, Matthew, Guiang, Jonathan, Gunny, Alec, Guo, Weichangfeng, Hakenmueller, Janina, Hawks, Ben, Hsu, Shih-Chieh, Jawahar, Pratik, Ju, Xiangyang, Katsavounidis, Erik, Kellis, Manolis, Khoda, Elham E, Lahbabi, Fatima Zahra, Lian, Van Tha Bik, Liu, Mia, Malanchev, Konstantin, Marx, Ethan, McCormack, William Patrick, McLeod, Alistair, Mo, Geoffrey, Moreno, Eric Anton, Muthukrishna, Daniel, Narayan, Gautham, Naylor, Andrew, Neubauer, Mark, Norman, Michael, Omer, Rafia, Pedro, Kevin, Peterson, Joshua, Pürrer, Michael, Raikman, Ryan, Raj, Shivam, Ricker, George, Robbins, Jared, Samani, Batool Safarzadeh, Scholberg, Kate, Schuy, Alex, Skliris, Vasileios, Soni, Siddharth, Sravan, Niharika, Sutton, Patrick, Villar, Victoria Ashley, Wang, Xiwei, Wen, Linqing, Wuerthwein, Frank, Yang, Tingjun, and Yeh, Shu-Wei

Subjects
High Energy Physics - Experiment, Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract

Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient workflows. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) can either improve or replace existing domain-specific algorithms to increase workflow efficiency. Not only can these algorithms improve the physics performance of current algorithms, but they can often be executed more quickly, especially when run on coprocessors such as GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with ML at MIT workshop, which brought together researchers from gravitational-wave physics, multi-messenger astrophysics, and particle physics to discuss and share current efforts to integrate ML tools into their workflows. The following white paper highlights examples of algorithms and computing frameworks discussed during this workshop and summarizes the expected computing needs for the immediate future of the involved fields., Comment: Whitepaper resulting from Accelerating Physics with ML@MIT workshop in Jan/Feb 2023

Published
2023

18. Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

Author

Bonev, Boyan, Castelo-Branco, Gonçalo, Chen, Fei, Codeluppi, Simone, Corces, M. Ryan, Fan, Jean, Heiman, Myriam, Harris, Kenneth, Inoue, Fumitaka, Kellis, Manolis, Levine, Ariel, Lotfollahi, Mo, Luo, Chongyuan, Maynard, Kristen R., Nitzan, Mor, Ramani, Vijay, Satijia, Rahul, Schirmer, Lucas, Shen, Yin, Sun, Na, Green, Gilad S., Theis, Fabian, Wang, Xiao, Welch, Joshua D., Gokce, Ozgun, Konopka, Genevieve, Liddelow, Shane, Macosko, Evan, Ali Bayraktar, Omer, Habib, Naomi, and Nowakowski, Tomasz J.

Published
2025
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19. SARS-CoV-2 variants evolve convergent strategies to remodel the host response

Author

Bouhaddou, Mehdi, Reuschl, Ann-Kathrin, Polacco, Benjamin J, Thorne, Lucy G, Ummadi, Manisha R, Ye, Chengjin, Rosales, Romel, Pelin, Adrian, Batra, Jyoti, Jang, Gwendolyn M, Xu, Jiewei, Moen, Jack M, Richards, Alicia L, Zhou, Yuan, Harjai, Bhavya, Stevenson, Erica, Rojc, Ajda, Ragazzini, Roberta, Whelan, Matthew VX, Furnon, Wilhelm, De Lorenzo, Giuditta, Cowton, Vanessa, Syed, Abdullah M, Ciling, Alison, Deutsch, Noa, Pirak, Daniel, Dowgier, Giulia, Mesner, Dejan, Turner, Jane L, McGovern, Briana L, Rodriguez, M Luis, Leiva-Rebollo, Rocio, Dunham, Alistair S, Zhong, Xiaofang, Eckhardt, Manon, Fossati, Andrea, Liotta, Nicholas F, Kehrer, Thomas, Cupic, Anastasija, Rutkowska, Magdalena, Mena, Ignacio, Aslam, Sadaf, Hoffert, Alyssa, Foussard, Helene, Olwal, Charles Ochieng', Huang, Weiqing, Zwaka, Thomas, Pham, John, Lyons, Molly, Donohue, Laura, Griffin, Aliesha, Nugent, Rebecca, Holden, Kevin, Deans, Robert, Aviles, Pablo, Lopez-Martin, Jose A, Jimeno, Jose M, Obernier, Kirsten, Fabius, Jacqueline M, Soucheray, Margaret, Hüttenhain, Ruth, Jungreis, Irwin, Kellis, Manolis, Echeverria, Ignacia, Verba, Kliment, Bonfanti, Paola, Beltrao, Pedro, Sharan, Roded, Doudna, Jennifer A, Martinez-Sobrido, Luis, Patel, Arvind H, Palmarini, Massimo, Miorin, Lisa, White, Kris, Swaney, Danielle L, Garcia-Sastre, Adolfo, Jolly, Clare, Zuliani-Alvarez, Lorena, Towers, Greg J, and Krogan, Nevan J

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Immunology, Infectious Diseases, Genetics, Coronaviruses, Emerging Infectious Diseases, Coronaviruses Therapeutics and Interventions, 2.1 Biological and endogenous factors, Infection, Humans, COVID-19, Immunity, Innate, Pandemics, SARS-CoV-2, innate immunity, protein-protein interactions, proteomics, systems biology, transcriptomics, variants, virus-host interactions, systems biology., Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

SARS-CoV-2 variants of concern (VOCs) emerged during the COVID-19 pandemic. Here, we used unbiased systems approaches to study the host-selective forces driving VOC evolution. We discovered that VOCs evolved convergent strategies to remodel the host by modulating viral RNA and protein levels, altering viral and host protein phosphorylation, and rewiring virus-host protein-protein interactions. Integrative computational analyses revealed that although Alpha, Beta, Gamma, and Delta ultimately converged to suppress interferon-stimulated genes (ISGs), Omicron BA.1 did not. ISG suppression correlated with the expression of viral innate immune antagonist proteins, including Orf6, N, and Orf9b, which we mapped to specific mutations. Later Omicron subvariants BA.4 and BA.5 more potently suppressed innate immunity than early subvariant BA.1, which correlated with Orf6 levels, although muted in BA.4 by a mutation that disrupts the Orf6-nuclear pore interaction. Our findings suggest that SARS-CoV-2 convergent evolution overcame human adaptive and innate immune barriers, laying the groundwork to tackle future pandemics.

Published
2023

20. Author Correction: APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes

Author

Blanchard, Joel W., Akay, Leyla Anne, Davila-Velderrain, Jose, von Maydell, Djuna, Mathys, Hansruedi, Davidson, Shawn M., Effenberger, Audrey, Chen, Chih-Yu, Maner-Smith, Kristal, Hajjar, Ihab, Ortlund, Eric A., Bula, Michael, Agbas, Emre, Ng, Ayesha, Jiang, Xueqiao, Kahn, Martin, Blanco-Duque, Cristina, Lavoie, Nicolas, Liu, Liwang, Reyes, Ricardo, Lin, Yuan-Ta, Ko, Tak, R’Bibo, Lea, Ralvenius, William T., Bennett, David A., Cam, Hugh P., Kellis, Manolis, and Tsai, Li-Huei

Published
2024
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21. Integrative multi-omics profiling in human decedents receiving pig heart xenografts

Author

Schmauch, Eloi, Piening, Brian, Mohebnasab, Maedeh, Xia, Bo, Zhu, Chenchen, Stern, Jeffrey, Zhang, Weimin, Dowdell, Alexa K., Kim, Jacqueline I., Andrijevic, David, Khalil, Karen, Jaffe, Ian S., Loza, Bao-Li, Gragert, Loren, Camellato, Brendan R., Oliveira, Michelli F., O’Brien, Darragh P., Chen, Han M., Weldon, Elaina, Gao, Hui, Gandla, Divya, Chang, Andrew, Bhatt, Riyana, Gao, Sarah, Lin, Xiangping, Reddy, Kriyana P., Kagermazova, Larisa, Habara, Alawi H., Widawsky, Sophie, Liang, Feng-Xia, Sall, Joseph, Loupy, Alexandre, Heguy, Adriana, Taylor, Sarah E. B., Zhu, Yinan, Michael, Basil, Jiang, Lihua, Jian, Ruiqi, Chong, Anita S., Fairchild, Robert L., Linna-Kuosmanen, Suvi, Kaikkonen, Minna U., Tatapudi, Vasishta, Lorber, Marc, Ayares, David, Mangiola, Massimo, Narula, Navneet, Moazami, Nader, Pass, Harvey, Herati, Ramin S., Griesemer, Adam, Kellis, Manolis, Snyder, Michael P., Montgomery, Robert A., Boeke, Jef D., and Keating, Brendan J.

Published
2024
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22. Multisensory gamma stimulation promotes glymphatic clearance of amyloid

Author

Murdock, Mitchell H., Yang, Cheng-Yi, Sun, Na, Pao, Ping-Chieh, Blanco-Duque, Cristina, Kahn, Martin C., Kim, TaeHyun, Lavoie, Nicolas S., Victor, Matheus B., Islam, Md Rezaul, Galiana, Fabiola, Leary, Noelle, Wang, Sidney, Bubnys, Adele, Ma, Emily, Akay, Leyla A., Sneve, Madison, Qian, Yong, Lai, Cuixin, McCarthy, Michelle M., Kopell, Nancy, Kellis, Manolis, Piatkevich, Kiryl D., Boyden, Edward S., and Tsai, Li-Huei

Published
2024
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26. Standardized annotation of translated open reading frames

Author

Mudge, Jonathan M, Ruiz-Orera, Jorge, Prensner, John R, Brunet, Marie A, Calvet, Ferriol, Jungreis, Irwin, Gonzalez, Jose Manuel, Magrane, Michele, Martinez, Thomas F, Schulz, Jana Felicitas, Yang, Yucheng T, Albà, M Mar, Aspden, Julie L, Baranov, Pavel V, Bazzini, Ariel A, Bruford, Elspeth, Martin, Maria Jesus, Calviello, Lorenzo, Carvunis, Anne-Ruxandra, Chen, Jin, Couso, Juan Pablo, Deutsch, Eric W, Flicek, Paul, Frankish, Adam, Gerstein, Mark, Hubner, Norbert, Ingolia, Nicholas T, Kellis, Manolis, Menschaert, Gerben, Moritz, Robert L, Ohler, Uwe, Roucou, Xavier, Saghatelian, Alan, Weissman, Jonathan S, and van Heesch, Sebastiaan

Subjects
Molecular Sequence Annotation, Open Reading Frames, Protein Biosynthesis, Ribosomes
Published
2022

27. NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters

Author

Lengerich, Ben, Ellington, Caleb, Aragam, Bryon, Xing, Eric P., and Kellis, Manolis

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) identify context-dependent relationships between variables, but the non-convexity induced by the acyclicity requirement makes it difficult to share information between context-specific estimators (e.g. with graph generator functions). For this reason, existing methods for inferring context-specific Bayesian networks have favored breaking datasets into subsamples, limiting statistical power and resolution, and preventing the use of multidimensional and latent contexts. To overcome this challenge, we propose NOTEARS-optimized Mixtures of Archetypal DAGs (NOTMAD). NOTMAD models context-specific Bayesian networks as the output of a function which learns to mix archetypal networks according to sample context. The archetypal networks are estimated jointly with the context-specific networks and do not require any prior knowledge. We encode the acyclicity constraint as a smooth regularization loss which is back-propagated to the mixing function; in this way, NOTMAD shares information between context-specific acyclic graphs, enabling the estimation of Bayesian network structures and parameters at even single-sample resolution. We demonstrate the utility of NOTMAD and sample-specific network inference through analysis and experiments, including patient-specific gene expression networks which correspond to morphological variation in cancer.

Published
2021

30. Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes

Author

Moore, Jill E, Purcaro, Michael J, Pratt, Henry E, Epstein, Charles B, Shoresh, Noam, Adrian, Jessika, Kawli, Trupti, Davis, Carrie A, Dobin, Alexander, Kaul, Rajinder, Halow, Jessica, Van Nostrand, Eric L, Freese, Peter, Gorkin, David U, Shen, Yin, He, Yupeng, Mackiewicz, Mark, Pauli-Behn, Florencia, Williams, Brian A, Mortazavi, Ali, Keller, Cheryl A, Zhang, Xiao-Ou, Elhajjajy, Shaimae I, Huey, Jack, Dickel, Diane E, Snetkova, Valentina, Wei, Xintao, Wang, Xiaofeng, Rivera-Mulia, Juan Carlos, Rozowsky, Joel, Zhang, Jing, Chhetri, Surya B, Zhang, Jialing, Victorsen, Alec, White, Kevin P, Visel, Axel, Yeo, Gene W, Burge, Christopher B, Lécuyer, Eric, Gilbert, David M, Dekker, Job, Rinn, John, Mendenhall, Eric M, Ecker, Joseph R, Kellis, Manolis, Klein, Robert J, Noble, William S, Kundaje, Anshul, Guigó, Roderic, Farnham, Peggy J, Cherry, J Michael, Myers, Richard M, Ren, Bing, Graveley, Brenton R, Gerstein, Mark B, Pennacchio, Len A, Snyder, Michael P, Bernstein, Bradley E, Wold, Barbara, Hardison, Ross C, Gingeras, Thomas R, Stamatoyannopoulos, John A, and Weng, Zhiping

Subjects
ENCODE Project Consortium, General Science & Technology
Abstract

In the version of this article initially published, two members of the ENCODE Project Consortium were missing from the author list. Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) are now included in the author list. These errors have been corrected in the online version of the article.

Published
2022

31. Evolution of enhanced innate immune evasion by SARS-CoV-2

Author

Thorne, Lucy G, Bouhaddou, Mehdi, Reuschl, Ann-Kathrin, Zuliani-Alvarez, Lorena, Polacco, Ben, Pelin, Adrian, Batra, Jyoti, Whelan, Matthew VX, Hosmillo, Myra, Fossati, Andrea, Ragazzini, Roberta, Jungreis, Irwin, Ummadi, Manisha, Rojc, Ajda, Turner, Jane, Bischof, Marie L, Obernier, Kirsten, Braberg, Hannes, Soucheray, Margaret, Richards, Alicia, Chen, Kuei-Ho, Harjai, Bhavya, Memon, Danish, Hiatt, Joseph, Rosales, Romel, McGovern, Briana L, Jahun, Aminu, Fabius, Jacqueline M, White, Kris, Goodfellow, Ian G, Takeuchi, Yasu, Bonfanti, Paola, Shokat, Kevan, Jura, Natalia, Verba, Klim, Noursadeghi, Mahdad, Beltrao, Pedro, Kellis, Manolis, Swaney, Danielle L, García-Sastre, Adolfo, Jolly, Clare, Towers, Greg J, and Krogan, Nevan J

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Immunology, Medical Microbiology, Infectious Diseases, Coronaviruses, Emerging Infectious Diseases, Genetics, 2.1 Biological and endogenous factors, 2.2 Factors relating to the physical environment, Infection, COVID-19, Coronavirus Nucleocapsid Proteins, Evolution, Molecular, Humans, Immune Evasion, Immunity, Innate, Interferons, Mitochondrial Precursor Protein Import Complex Proteins, Phosphoproteins, Phosphorylation, Proteomics, RNA, Viral, RNA-Seq, SARS-CoV-2, General Science & Technology
Abstract

The emergence of SARS-CoV-2 variants of concern suggests viral adaptation to enhance human-to-human transmission1,2. Although much effort has focused on the characterization of changes in the spike protein in variants of concern, mutations outside of spike are likely to contribute to adaptation. Here, using unbiased abundance proteomics, phosphoproteomics, RNA sequencing and viral replication assays, we show that isolates of the Alpha (B.1.1.7) variant3 suppress innate immune responses in airway epithelial cells more effectively than first-wave isolates. We found that the Alpha variant has markedly increased subgenomic RNA and protein levels of the nucleocapsid protein (N), Orf9b and Orf6-all known innate immune antagonists. Expression of Orf9b alone suppressed the innate immune response through interaction with TOM70, a mitochondrial protein that is required for activation of the RNA-sensing adaptor MAVS. Moreover, the activity of Orf9b and its association with TOM70 was regulated by phosphorylation. We propose that more effective innate immune suppression, through enhanced expression of specific viral antagonist proteins, increases the likelihood of successful transmission of the Alpha variant, and may increase in vivo replication and duration of infection4. The importance of mutations outside the spike coding region in the adaptation of SARS-CoV-2 to humans is underscored by the observation that similar mutations exist in the N and Orf9b regulatory regions of the Delta and Omicron variants.

Published
2022

32. Transcriptomic and spatial dissection of human ex vivo right atrial tissue reveals proinflammatory microvascular changes in ischemic heart disease

Author

Linna-Kuosmanen, Suvi, Schmauch, Eloi, Galani, Kyriakitsa, Ojanen, Johannes, Boix, Carles A., Örd, Tiit, Toropainen, Anu, Singha, Prosanta K., Moreau, Pierre R., Harju, Kristiina, Blazeski, Adriana, Segerstolpe, Åsa, Lahtinen, Veikko, Hou, Lei, Kang, Kai, Meibalan, Elamaran, Agudelo, Leandro Z., Kokki, Hannu, Halonen, Jari, Jalkanen, Juho, Gunn, Jarmo, MacRae, Calum A., Hollmén, Maija, Hartikainen, Juha E.K., Kaikkonen, Minna U., García-Cardeña, Guillermo, Tavi, Pasi, Kiviniemi, Tuomas, and Kellis, Manolis

Published
2024
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33. Single-cell dissection of the human motor and prefrontal cortices in ALS and FTLD

Author

Pineda, S. Sebastian, Lee, Hyeseung, Ulloa-Navas, Maria J., Linville, Raleigh M., Garcia, Francisco J., Galani, Kyriakitsa, Engelberg-Cook, Erica, Castanedes, Monica C., Fitzwalter, Brent E., Pregent, Luc J., Gardashli, Mahammad E., DeTure, Michael, Vera-Garcia, Diana V., Hucke, Andre T.S., Oskarsson, Bjorn E., Murray, Melissa E., Dickson, Dennis W., Heiman, Myriam, Belzil, Veronique V., and Kellis, Manolis

Published
2024
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36. Structural variants involved in high-altitude adaptation detected using single-molecule long-read sequencing

Author

Shi, Jinlong, Jia, Zhilong, Sun, Jinxiu, Wang, Xiaoreng, Zhao, Xiaojing, Zhao, Chenghui, Liang, Fan, Song, Xinyu, Guan, Jiawei, Jia, Xue, Yang, Jing, Chen, Qi, Yu, Kang, Jia, Qian, Wu, Jing, Wang, Depeng, Xiao, Yuhui, Xu, Xiaoman, Liu, Yinzhe, Wu, Shijing, Zhong, Qin, Wu, Jue, Cui, Saijia, Bo, Xiaochen, Wu, Zhenzhou, Park, Minsung, Kellis, Manolis, and He, Kunlun

Published
2023
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38. The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

Author

Schreiber, Jacob Matthew, Boix, Carles A., wook Lee, Jin, Li, Hongyang, Guan, Yuanfang, Chang, Chun-Chieh, Chang, Jen-Chien, Hawkins-Hooker, Alex, Schölkopf, Bernhard, Schweikert, Gabriele, Carulla, Mateo Rojas, Canakoglu, Arif, Guzzo, Francesco, Nanni, Luca, Masseroli, Marco, Carman, Mark James, Pinoli, Pietro, Hong, Chenyang, Yip, Kevin Y., Spence, Jefrey P., Batra, Sanjit Singh, Song, Yun S., Mahony, Shaun, Zhang, Zheng, Tan, Wuwei, Shen, Yang, Sun, Yuanfei, Shi, Minyi, Adrian, Jessika, Sandstrom, Richard S., Farrell, Nina P., Halow, Jessica M., Lee, Kristen, Jiang, Lixia, Yang, Xinqiong, Epstein, Charles B., Strattan, J. Seth, Bernstein, Bradley E., Snyder, Michael P., Kellis, Manolis, Noble, William S., and Kundaje, Anshul Bharat

Published
2023
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43. Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer’s disease pathology

Author

Mathys, Hansruedi, Peng, Zhuyu, Boix, Carles A., Victor, Matheus B., Leary, Noelle, Babu, Sudhagar, Abdelhady, Ghada, Jiang, Xueqiao, Ng, Ayesha P., Ghafari, Kimia, Kunisky, Alexander K., Mantero, Julio, Galani, Kyriaki, Lohia, Vanshika N., Fortier, Gabrielle E., Lotfi, Yasmine, Ivey, Jason, Brown, Hannah P., Patel, Pratham R., Chakraborty, Nehal, Beaudway, Jacob I., Imhoff, Elizabeth J., Keeler, Cameron F., McChesney, Maren M., Patel, Haishal H., Patel, Sahil P., Thai, Megan T., Bennett, David A., Kellis, Manolis, and Tsai, Li-Huei

Published
2023
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44. Human microglial state dynamics in Alzheimer’s disease progression

Author

Sun, Na, Victor, Matheus B., Park, Yongjin P., Xiong, Xushen, Scannail, Aine Ni, Leary, Noelle, Prosper, Shaniah, Viswanathan, Soujanya, Luna, Xochitl, Boix, Carles A., James, Benjamin T., Tanigawa, Yosuke, Galani, Kyriaki, Mathys, Hansruedi, Jiang, Xueqiao, Ng, Ayesha P., Bennett, David A., Tsai, Li-Huei, and Kellis, Manolis

Published
2023
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45. Translation Initiation Site Profiling Reveals Widespread Synthesis of Non-AUG-Initiated Protein Isoforms in Yeast

Author

Eisenberg, Amy R, Higdon, Andrea L, Hollerer, Ina, Fields, Alexander P, Jungreis, Irwin, Diamond, Paige D, Kellis, Manolis, Jovanovic, Marko, and Brar, Gloria A

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Codon, Peptide Chain Initiation, Translational, Protein Biosynthesis, Protein Isoforms, Saccharomyces cerevisiae, alternate protein isoforms, codon, gene expression, genome annotation, meiosis, near-cognate, ribosome profiling, translation, translation initiation site, yeast, Biochemistry and Cell Biology, Biochemistry and cell biology
Abstract

Genomic analyses in budding yeast have helped define the foundational principles of eukaryotic gene expression. However, in the absence of empirical methods for defining coding regions, these analyses have historically excluded specific classes of possible coding regions, such as those initiating at non-AUG start codons. Here, we applied an experimental approach to globally annotate translation initiation sites in yeast and identified 149 genes with alternative N-terminally extended protein isoforms initiating from near-cognate codons upstream of annotated AUG start codons. These isoforms are produced in concert with canonical isoforms and translated with high specificity, resulting from initiation at only a small subset of possible start codons. The non-AUG initiation driving their production is enriched during meiosis and induced by low eIF5A, which is seen in this context. These findings reveal widespread production of non-canonical protein isoforms and unexpected complexity to the rules by which even a simple eukaryotic genome is decoded.

Published
2020

46. Expanded encyclopaedias of DNA elements in the human and mouse genomes

Author

Moore, Jill E, Purcaro, Michael J, Pratt, Henry E, Epstein, Charles B, Shoresh, Noam, Adrian, Jessika, Kawli, Trupti, Davis, Carrie A, Dobin, Alexander, Kaul, Rajinder, Halow, Jessica, Van Nostrand, Eric L, Freese, Peter, Gorkin, David U, Shen, Yin, He, Yupeng, Mackiewicz, Mark, Pauli-Behn, Florencia, Williams, Brian A, Mortazavi, Ali, Keller, Cheryl A, Zhang, Xiao-Ou, Elhajjajy, Shaimae I, Huey, Jack, Dickel, Diane E, Snetkova, Valentina, Wei, Xintao, Wang, Xiaofeng, Rivera-Mulia, Juan Carlos, Rozowsky, Joel, Zhang, Jing, Chhetri, Surya B, Zhang, Jialing, Victorsen, Alec, White, Kevin P, Visel, Axel, Yeo, Gene W, Burge, Christopher B, Lécuyer, Eric, Gilbert, David M, Dekker, Job, Rinn, John, Mendenhall, Eric M, Ecker, Joseph R, Kellis, Manolis, Klein, Robert J, Noble, William S, Kundaje, Anshul, Guigó, Roderic, Farnham, Peggy J, Cherry, J Michael, Myers, Richard M, Ren, Bing, Graveley, Brenton R, Gerstein, Mark B, Pennacchio, Len A, Snyder, Michael P, Bernstein, Bradley E, Wold, Barbara, Hardison, Ross C, Gingeras, Thomas R, Stamatoyannopoulos, John A, and Weng, Zhiping

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, 1.1 Normal biological development and functioning, Animals, Chromatin, DNA, DNA Footprinting, DNA Methylation, DNA Replication Timing, Databases, Genetic, Deoxyribonuclease I, Genome, Genome, Human, Genomics, Histones, Humans, Mice, Mice, Transgenic, Molecular Sequence Annotation, RNA-Binding Proteins, Registries, Regulatory Sequences, Nucleic Acid, Transcription, Genetic, Transposases, ENCODE Project Consortium, General Science & Technology
Abstract

The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

Published
2020

47. Expanded encyclopaedias of DNA elements in the human and mouse genomes.

Author

ENCODE Project Consortium, Moore, Jill E, Purcaro, Michael J, Pratt, Henry E, Epstein, Charles B, Shoresh, Noam, Adrian, Jessika, Kawli, Trupti, Davis, Carrie A, Dobin, Alexander, Kaul, Rajinder, Halow, Jessica, Van Nostrand, Eric L, Freese, Peter, Gorkin, David U, Shen, Yin, He, Yupeng, Mackiewicz, Mark, Pauli-Behn, Florencia, Williams, Brian A, Mortazavi, Ali, Keller, Cheryl A, Zhang, Xiao-Ou, Elhajjajy, Shaimae I, Huey, Jack, Dickel, Diane E, Snetkova, Valentina, Wei, Xintao, Wang, Xiaofeng, Rivera-Mulia, Juan Carlos, Rozowsky, Joel, Zhang, Jing, Chhetri, Surya B, Zhang, Jialing, Victorsen, Alec, White, Kevin P, Visel, Axel, Yeo, Gene W, Burge, Christopher B, Lécuyer, Eric, Gilbert, David M, Dekker, Job, Rinn, John, Mendenhall, Eric M, Ecker, Joseph R, Kellis, Manolis, Klein, Robert J, Noble, William S, Kundaje, Anshul, Guigó, Roderic, Farnham, Peggy J, Cherry, J Michael, Myers, Richard M, Ren, Bing, Graveley, Brenton R, Gerstein, Mark B, Pennacchio, Len A, Snyder, Michael P, Bernstein, Bradley E, Wold, Barbara, Hardison, Ross C, Gingeras, Thomas R, Stamatoyannopoulos, John A, and Weng, Zhiping

Subjects
ENCODE Project Consortium, Chromatin, Animals, Mice, Transgenic, Humans, Mice, Deoxyribonuclease I, Transposases, RNA-Binding Proteins, Histones, DNA, Registries, DNA Footprinting, Genomics, DNA Methylation, DNA Replication Timing, Transcription, Genetic, Regulatory Sequences, Nucleic Acid, Genome, Genome, Human, Databases, Genetic, Molecular Sequence Annotation, Human Genome, HIV/AIDS, Vaccine Related, Biotechnology, Genetics, Immunization, Vaccine Related (AIDS), Prevention, 1.1 Normal biological development and functioning, Generic health relevance, General Science & Technology
Abstract

The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

Published
2020

48. Analysis of Genetically Regulated Gene Expression Identifies a Prefrontal PTSD Gene, SNRNP35, Specific to Military Cohorts

Author

Huckins, Laura M, Chatzinakos, Chris, Breen, Michael S, Hartmann, Jakob, Klengel, Torsten, da Silva Almeida, Ana C, Dobbyn, Amanda, Girdhar, Kiran, Hoffman, Gabriel E, Klengel, Claudia, Logue, Mark W, Lori, Adriana, Maihofer, Adam X, Morrison, Filomene G, Nguyen, Hoang T, Park, Yongjin, Ruderfer, Douglas, Sloofman, Laura G, van Rooij, Sanne JH, Consortium, PTSD Working Group of Psychiatric Genomics, Baker, Dewleen G, Chen, Chia-Yen, Cox, Nancy, Duncan, Laramie E, Geyer, Mark A, Glatt, Stephen J, Im, Hae Kyung, Risbrough, Victoria B, Smoller, Jordan W, Stein, Dan J, Yehuda, Rachel, Liberzon, Israel, Koenen, Karestan C, Jovanovic, Tanja, Kellis, Manolis, Miller, Mark W, Bacanu, Silviu-Alin, Nievergelt, Caroline M, Buxbaum, Joseph D, Sklar, Pamela, Ressler, Kerry J, Stahl, Eli A, and Daskalakis, Nikolaos P

Subjects
Genetics, Brain Disorders, Post-Traumatic Stress Disorder (PTSD), Mental Health, Neurosciences, 1.1 Normal biological development and functioning, Underpinning research, Neurological, Animals, Case-Control Studies, Cohort Studies, Dexamethasone, Down-Regulation, Gene Expression Regulation, Gene Regulatory Networks, Genetic Predisposition to Disease, Humans, Leukocytes, Male, Mice, Mice, Inbred C57BL, Military Personnel, Prefrontal Cortex, RNA Interference, RNA, Small Interfering, Repressor Proteins, Ribonucleoproteins, Small Nuclear, Stress Disorders, Post-Traumatic, PTSD Working Group of Psychiatric Genomics Consortium, GWAS, PTSD, blood, civilian, glucocorticoid, military, prefrontal cortex, sex, splicing, transcriptomic imputation, genetics, Transcriptomic Imputation, trauma, Biochemistry and Cell Biology, Medical Physiology
Abstract

To reveal post-traumatic stress disorder (PTSD) genetic risk influences on tissue-specific gene expression, we use brain and non-brain transcriptomic imputation. We impute genetically regulated gene expression (GReX) in 29,539 PTSD cases and 166,145 controls from 70 ancestry-specific cohorts and identify 18 significant GReX-PTSD associations corresponding to specific tissue-gene pairs. The results suggest substantial genetic heterogeneity based on ancestry, cohort type (military versus civilian), and sex. Two study-wide significant PTSD associations are identified in European and military European cohorts; ZNF140 is predicted to be upregulated in whole blood, and SNRNP35 is predicted to be downregulated in dorsolateral prefrontal cortex, respectively. In peripheral leukocytes from 175 marines, the observed PTSD differential gene expression correlates with the predicted differences for these individuals, and deployment stress produces glucocorticoid-regulated expression changes that include downregulation of both ZNF140 and SNRNP35. SNRNP35 knockdown in cells validates its functional role in U12-intron splicing. Finally, exogenous glucocorticoids in mice downregulate prefrontal Snrnp35 expression.

Published
2020

49. Publisher Correction: Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries.

Author

Zekavat, Seyedeh M, Ruotsalainen, Sanni, Handsaker, Robert E, Alver, Maris, Bloom, Jonathan, Poterba, Timothy, Seed, Cotton, Ernst, Jason, Chaffin, Mark, Engreitz, Jesse, Peloso, Gina M, Manichaikul, Ani, Yang, Chaojie, Ryan, Kathleen A, Fu, Mao, Johnson, W Craig, Tsai, Michael, Budoff, Matthew, Vasan, Ramachandran S, Cupples, L Adrienne, Rotter, Jerome I, Rich, Stephen S, Post, Wendy, Mitchell, Braxton D, Correa, Adolfo, Metspalu, Andres, Wilson, James G, Salomaa, Veikko, Kellis, Manolis, Daly, Mark J, Neale, Benjamin M, McCarroll, Steven, Surakka, Ida, Esko, Tonu, Ganna, Andrea, Ripatti, Samuli, Kathiresan, Sekar, Natarajan, Pradeep, and NHLBI TOPMed Lipids Working Group

Subjects
NHLBI TOPMed Lipids Working Group
Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2020

50. Exercise training remodels inguinal white adipose tissue through adaptations in innervation, vascularization, and the extracellular matrix

Author

Nigro, Pasquale, Vamvini, Maria, Yang, Jiekun, Caputo, Tiziana, Ho, Li-Lun, Carbone, Nicholas P., Papadopoulos, Danae, Conlin, Royce, He, Jie, Hirshman, Michael F., White, Joseph D., Robidoux, Jacques, Hickner, Robert C., Nielsen, Søren, Pedersen, Bente K., Kellis, Manolis, Middelbeek, Roeland J.W., and Goodyear, Laurie J.

Published
2023
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