Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 83,873 bioRxiv papers from 361,196 authors.
Most downloaded bioRxiv papers, since beginning of last month
in category systems biology
2,182 results found. For more information, click each entry to expand.
65,531 downloads systems biology
David E Gordon, Gwendolyn M. Jang, Mehdi Bouhaddou, Jiewei Xu, Kirsten Obernier, Matthew J. O’Meara, Jeffrey Z. Guo, Danielle L. Swaney, Tia A. Tummino, Ruth Huettenhain, Robyn M. Kaake, Alicia L. Richards, Beril Tutuncuoglu, Helene Foussard, Jyoti Batra, Kelsey Haas, Maya Modak, Minkyu Kim, Paige Haas, Benjamin J. Polacco, Hannes Braberg, Jacqueline M. Fabius, Manon Eckhardt, Margaret Soucheray, Melanie J. Bennett, Merve Cakir, Michael J. McGregor, Qiongyu Li, Zun Zar Chi Naing, Yuan Zhou, Shiming Peng, Ilsa T. Kirby, James E. Melnyk, John S. Chorba, Kevin Lou, Shizhong A. Dai, Wenqi Shen, Ying Shi, Ziyang Zhang, Inigo Barrio-Hernandez, Danish Memon, Claudia Hernandez-Armenta, Christopher J.P. Mathy, Tina Perica, Kala B. Pilla, Sai J. Ganesan, Daniel J. Saltzberg, Rakesh Ramachandran, Xi Liu, Sara B. Rosenthal, Lorenzo Calviello, Srivats Venkataramanan, Jose Liboy-Lugo, Yizhu Lin, Stephanie A. Wankowicz, Markus Bohn, Phillip P. Sharp, Raphael Trenker, Janet M. Young, Devin A. Cavero, Joseph Hiatt, Theodore L. Roth, Ujjwal Rathore, Advait Subramanian, Julia Noack, Mathieu Hubert, Ferdinand Roesch, Thomas Vallet, Björn Meyer, Kris M. White, Lisa Miorin, Oren S. Rosenberg, Kliment A Verba, David Agard, Melanie Ott, Michael Emerman, Davide Ruggero, Adolfo García-Sastre, Natalia Jura, Mark von Zastrow, Jack Taunton, Alan Ashworth, Olivier Schwartz, Marco Vignuzzi, Christophe d’Enfert, Shaeri Mukherjee, Matt Jacobson, Harmit S. Malik, Danica G. Fujimori, Trey Ideker, Charles S. Craik, Stephen Floor, James S. Fraser, John Gross, Andrej Sali, Tanja Kortemme, Pedro Beltrao, Kevan Shokat, Brian K. Shoichet, Nevan J. Krogan
An outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption,. There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 67 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains. * HC-PPIs : High confidence protein-protein interactions PPIs : protein-protein interaction AP-MS : affinity purification-mass spectrometry COVID-19 : Coronavirus Disease-2019 ACE2 : angiotensin converting enzyme 2 Orf : open reading frame Nsp3 : papain-like protease Nsp5 : main protease Nsp : nonstructural protein TPM : transcripts per million : #ref-1 : #ref-2
6,288 downloads systems biology
Katie Heiser, Peter F McLean, Chadwick T Davis, Ben Fogelson, Hannah B. Gordon, Pamela Jacobson, Brett Hurst, Ben Miller, Ronald W. Alfa, Berton A. Earnshaw, Mason L. Victors, Yolanda T. Chong, Imran S Haque, Adeline S. Low, Christopher C Gibson
To identify potential therapeutic stop-gaps for SARS-CoV-2, we evaluated a library of 1,670 approved and reference compounds in an unbiased, cellular image-based screen for their ability to suppress the broad impacts of the SARS-CoV-2 virus on phenomic profiles of human renal cortical epithelial cells using deep learning. In our assay remdesivir is the only antiviral tested with strong efficacy, that neither chloroquine nor hydroxychloroquine have any beneficial effect in this human cell model, and that a small number of compounds not currently being pursued clinically for SARS-CoV-2 have efficacy. We observed weak but beneficial class effects of 𝛃-blockers, mTOR/PI3K inhibitors and Vitamin D analogues and a mild amplification of the viral phenotype with 𝛃-agonists. ### Competing Interest Statement All authors from Recursion have real or potential ownership interest in the company. However, Recursion has committed to free non-discriminatory licensing for any of its intellectual property around discoveries related to the treatment of COVID19.
5,959 downloads systems biology
Erica T. Prates, Michael R Garvin, Mirko Pavicic, Piet Jones, Manesh Shah, Christiane Alvarez, David Kainer, Omar Demerdash, B Kirtley Amos, Armin Geiger, John Pestian, Kang Jin, Alexis Mitelpunkt, Eric Bardes, Bruce Aronow, Daniel Jacobson
Using a Systems Biology approach, we integrated genomic, transcriptomic, proteomic, and molecular structure information to provide a holistic understanding of the COVID-19 pandemic. The expression data analysis of the Renin Angiotensin System indicates mild nasal, oral or throat infections are likely and that the gastrointestinal tissues are a common primary target of SARS-CoV-2. Extreme symptoms in the lower respiratory system likely result from a secondary-infection possibly by a comorbidity-driven upregulation of ACE2 in the lung. The remarkable differences in expression of other RAS elements, the elimination of macrophages and the activation of cytokines in COVID-19 bronchoalveolar samples suggest that a functional immune deficiency is a critical outcome of COVID-19. We posit that using a non-respiratory system as a major pathway of infection is likely determining the unprecedented global spread of this coronavirus. ### Competing Interest Statement The authors have declared no competing interest.
3,331 downloads systems biology
Tao Liu, Jianxiong Hu, Jianpeng Xiao, Guanhao He, Min Kang, Zuhua Rong, Lifeng Lin, Haojie Zhong, Qiong Huang, Aiping Deng, Weilin Zeng, Xiaohua Tan, Siqing Zeng, Zhihua Zhu, Jiansen Li, Dexin Gong, Donghua Wan, Shaowei Chen, Lingchuan Guo, Yan Li, Limei Sun, Wenjia Liang, Tie Song, Jianfeng He, Wenjun Ma
Rationale Several studies have estimated basic production number of novel coronavirus pneumonia (NCP). However, the time-varying transmission dynamics of NCP during the outbreak remain unclear. Objectives We aimed to estimate the basic and time-varying transmission dynamics of NCP across China, and compared them with SARS. Methods Data on NCP cases by February 7, 2020 were collected from epidemiological investigations or official websites. Data on severe acute respiratory syndrome (SARS) cases in Guangdong Province, Beijing and Hong Kong during 2002-2003 were also obtained. We estimated the doubling time, basic reproduction number ( R ) and time-varying reproduction number ( Rt ) of NCP and SARS. Measurements and main results As of February 7, 2020, 34,598 NCP cases were identified in China, and daily confirmed cases decreased after February 4. The doubling time of NCP nationwide was 2.4 days which was shorter than that of SARS in Guangdong (14.3 days), Hong Kong (5.7 days) and Beijing (12.4 days). The R of NCP cases nationwide and in Wuhan were 4.5 and 4.4 respectively, which were higher than R of SARS in Guangdong ( R =2.3), Hongkong ( R =2.3), and Beijing ( R =2.6). The Rt for NCP continuously decreased especially after January 16 nationwide and in Wuhan. The R for secondary NCP cases in Guangdong was 0.6, and the Rt values were less than 1 during the epidemic. Conclusions NCP may have a higher transmissibility than SARS, and the efforts of containing the outbreak are effective. However, the efforts are needed to persist in for reducing time-varying reproduction number below one. Scientific Knowledge on the Subject Since December 29, 2019, pneumonia infection with 2019-nCoV, now named as Novel Coronavirus Pneumonia (NCP), occurred in Wuhan, Hubei Province, China. The disease has rapidly spread from Wuhan to other areas. As a novel virus, the time-varying transmission dynamics of NCP remain unclear, and it is also important to compare it with SARS. What This Study Adds to the Field We compared the transmission dynamics of NCP with SARS, and found that NCP has a higher transmissibility than SARS. Time-varying production number indicates that rigorous control measures taken by governments are effective across China, and persistent efforts are needed to be taken for reducing instantaneous reproduction number below one.
2,879 downloads systems biology
Yiyue Ge, Tingzhong Tian, Suling Huang, Fangping Wan, Jingxin Li, Shuya Li, Hui Yang, Lixiang Hong, Nian Wu, Enming Yuan, Lili Cheng, Yipin Lei, Hantao Shu, Xiaolong Feng, Ziyuan Jiang, Ying Chi, Xiling Guo, Lunbiao Cui, Liang Xiao, Zeng Li, Chunhao Yang, Zehong Miao, Haidong Tang, Ligong Chen, Hainian Zeng, Dan Zhao, Fengcai Zhu, Xiaokun Shen, Jianyang Zeng
The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates against a specific coronavirus. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19.
2,009 downloads systems biology
Emanuel Wyler, Mösbauer Kirstin, Vedran Franke, Diag Asija, Gottula Lina Theresa, Arsie Roberto, Klironomos Filippos, Koppstein David, Ayoub Salah, Buccitelli Christopher, Richter Anja, Legnini Ivano, Ivanov Andranik, Mari Tommaso, Del Giudice Simone, Papies Jan Patrick, Müller Marcel Alexander, Niemeyer Daniela, Selbach Matthias, Altuna Akalin, Rajewsky Nikolaus, Drosten Christian, Markus Landthaler
The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health threat with more than two million infected people since its emergence in late 2019. Detailed knowledge of the molecular biology of the infection is indispensable for understanding of the viral replication, host responses, and disease progression. We provide gene expression profiles of SARS-CoV and SARS-CoV-2 infections in three human cell lines (H1299, Caco-2 and Calu-3 cells), using bulk and single-cell transcriptomics. Small RNA profiling showed strong expression of the immunity and inflammation-associated microRNA miRNA-155 upon infection with both viruses. SARS-CoV-2 elicited approximately two-fold higher stimulation of the interferon response compared to SARS-CoV in the permissive human epithelial cell line Calu-3, and induction of cytokines such as CXCL10 or IL6. Single cell RNA sequencing data showed that canonical interferon stimulated genes such as IFIT2 or OAS2 were broadly induced, whereas interferon beta (IFNB1) and lambda (IFNL1-4) were expressed only in a subset of infected cells. In addition, temporal resolution of transcriptional responses suggested interferon regulatory factors (IRFs) activities precede that of nuclear factor-κB (NF-κB). Lastly, we identified heat shock protein 90 (HSP90) as a protein relevant for the infection. Inhibition of the HSP90 charperone activity by Tanespimycin/17-N-allylamino-17-demethoxygeldanamycin (17-AAG) resulted in a reduction of viral replication, and of TNF and IL1B mRNA levels. In summary, our study established in vitro cell culture models to study SARS-CoV-2 infection and identified HSP90 protein as potential drug target for therapeutic intervention of SARS-CoV-2 infection. ### Competing Interest Statement The authors have declared no competing interest.
1,781 downloads systems biology
Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.
1,621 downloads systems biology
To predict the tropism of human coronaviruses, we profile 28 SARS-CoV-2 and coronavirus-associated receptors and factors (SCARFs) using single-cell RNA-sequencing data from a wide range of healthy human tissues. SCARFs include cellular factors both facilitating and restricting viral entry. Among adult organs, enterocytes and goblet cells of the small intestine and colon, kidney proximal tubule cells, and gallbladder basal cells appear most permissive to SARS-CoV-2, consistent with clinical data. Our analysis also suggests alternate entry paths for SARS-CoV-2 infection of the lung, central nervous system, and heart. We predict spermatogonial cells and prostate endocrine cells, but not ovarian cells, to be highly permissive to SARS-CoV-2, suggesting male-specific vulnerabilities. Early stages of embryonic and placental development show a moderate risk of infection. The nasal epithelium looks like another battleground, characterized by high expression of both promoting and restricting factors and a potential age-dependent shift in SCARF expression. Lastly, SCARF expression appears broadly conserved across human, chimpanzee and macaque organs examined. Our study establishes an important resource for investigations of coronavirus biology and pathology. ### Competing Interest Statement The authors have declared no competing interest.
1,498 downloads systems biology
The recent outbreak of pneumonia in Wuhan, China caused by the 2019 Novel Coronavirus (2019-nCoV) emphasizes the importance of detecting novel viruses and predicting their risks of infecting people. In this report, we introduced the VHP (Virus Host Prediction) to predict the potential hosts of viruses using deep learning algorithm. Our prediction suggests that 2019-nCoV has close infectivity with other human coronaviruses, especially the severe acute respiratory syndrome coronavirus (SARS-CoV), Bat SARS-like Coronaviruses and the Middle East respiratory syndrome coronavirus (MERS-CoV). Based on our prediction, compared to the Coronaviruses infecting other vertebrates, bat coronaviruses are assigned with more similar infectivity patterns with 2019-nCoVs. Furthermore, by comparing the infectivity patterns of all viruses hosted on vertebrates, we found mink viruses show a closer infectivity pattern to 2019-nCov. These consequences of infectivity pattern analysis illustrate that bat and mink may be two candidate reservoirs of 2019-nCov.These results warn us to beware of 2019-nCoV and guide us to further explore the properties and reservoir of it. One Sentence Summary It is of great value to identify whether a newly discovered virus has the risk of infecting human. Guo et al. proposed a virus host prediction method based on deep learning to detect what kind of host a virus can infect with DNA sequence as input. Applied to the Wuhan 2019 Novel Coronavirus, our prediction demonstrated that several vertebrate-infectious coronaviruses have strong potential to infect human. This method will be helpful in future viral analysis and early prevention and control of viral pathogens.
1,469 downloads systems biology
Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map in which the detailed activity of the human signaling circuits related to the viral infection and the different antiviral responses, including immune and inflammatory activities, can be inferred from gene expression experiments. Moreover, given to the mechanistic properties of the model, the effect of potential interventions, such as knock-downs, over-expression or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied in specific conditions. This tool, with the holistic, systems biology approach to the understanding of the complexities of the viral infection process, will become an important asset in the search for efficient antiviral treatments. The tool is freely available at: http://hipathia.babelomics.org/covid19/. ### Competing Interest Statement The authors have declared no competing interest.
1,464 downloads systems biology
As reported by the World Health Organization, a novel coronavirus (2019-nCoV) was identified as the causative virus of Wuhan pneumonia of unknown etiology by Chinese authorities on 7 January, 2020. In this study, we developed a Bats-Hosts-Reservoir-People transmission network model for simulating the potential transmission from the infection source (probable be bats) to the human infection. Since the Bats-Hosts-Reservoir network was hard to explore clearly and public concerns were focusing on the transmission from a seafood market (reservoir) to people, we simplified the model as Reservoir-People transmission network model. The basic reproduction number (R0) was calculated from the RP model to assess the transmissibility of the 2019-nCoV.
1,446 downloads systems biology
Yafei Wang, Gary An, Andrew Becker, Chase Cockrell, Nicholson Collier, Morgan Craig, Courtney L. Davis, James Faeder, Ashlee N. Ford Versypt, Juliano F. Gianlupi, James A. Glazier, Randy Heiland, Thomas Hillen, Mohammad Aminul Islam, Adrianne Jenner, Bing Liu, Penelope A Morel, Aarthi Narayanan, Jonathan Ozik, P. Rangamani, Jason Edward Shoemaker, Amber M. Smith, Paul Macklin
The 2019 novel coronavirus, SARS-CoV-2, is an emerging pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors like diabetes. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic inter-actions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving rapid refinements with a two-to-four week release cycle. In a sustained community effort, this consortium effort is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. ### Competing Interest Statement The authors have declared no competing interest.
1,299 downloads systems biology
The pandemic COVID-19 has spread to all over the world and greatly threatens safety and health of people. COVID-19 is highly infectious and with high mortality rate. As no effective antiviral treatment is currently available, new drugs are urgently needed. We employed transcriptional analysis to uncover potential antiviral drugs from natural products or FDA approved drugs. We found liquiritin significantly inhibit replication of SARS-CoV-2 in Vero E6 cells with EC50 = 2.39 μM. Mechanistically, we found liquiritin exerts anti-viral function by mimicking type I interferon. Upregulated genes induced by liquiritin are enriched in GO categories including type I interferon signaling pathway, negative regulation of viral genome replication and etc. In toxicity experiment, no death was observed when treated at dose of 300 mg/kg for a week in ICR mice. All the organ indexes but liver and serum biochemical indexes were normal after treatment. Liquiritin is abundant in licorice tablet (~0.2% by mass), a traditional Chinese medicine. Together, we recommend liquiritin as a competitive candidate for treating COVID-19. We also expect liquiritin to have a broad and potent antiviral function to other viral pathogens, like HBV, HIV and etc. ### Competing Interest Statement The authors have declared no competing interest.
1,153 downloads systems biology
Molecular differences between individual cells can lead to dramatic differences in cell fate, such as the difference between death versus survival of cancer cells upon treatment with anti-cancer drugs. These originating differences have remained hidden, however, due to our inability to precisely determine what variable molecular features lead to what cellular fates. Here, we trace drug-resistant cell fates back to differences in the molecular profiles of their drug-naive melanoma precursors, revealing a rich substructure of variability underlying a number of resistant phenotypes at the single cell level. We make these connections using Rewind, a methodology that combines genetic barcoding with an RNA-based readout to directly capture rare cells that give rise to cellular behaviors of interest. We performed extensive single cell analysis to identify differences in gene expression and MAP-kinase signaling that mark a rare population of drug-naive cells (initial frequency of ~1:1000-1:10,000 cells) that ultimately gives rise to drug resistant clones. We demonstrate that this rare subpopulation has rich substructure and is composed of several distinct subpopulations, and the molecular differences between these subpopulations predict future differences in phenotypic behavior, such as the ultimate proliferative capacity of drug resistant cells. Similarly, we show that treatments that modify the frequency of resistance can allow otherwise non-resistant cells in the drug-naive population to become resistant, and that these new populations are marked by the variable expression of distinct genes. Together, our results reveal the presence of hidden, rare-cell variability that can underlie a range of latent phenotypic outcomes upon drug exposure. ### Competing Interest Statement AR receives consulting income and AR and SMS receive royalties related to Stellaris RNA FISH probes.
1,122 downloads systems biology
COVID-19 (Coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While the pathophysiology of this deadly virus is complex and largely unknown, we employ a network biology-fueled approach and integrated multiomics data pertaining to lung epithelial cells-specific co-expression network and human interactome to generate Calu-3-specific human-SARS-CoV-2 Interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 target central nodes of host-viral network that participate in core functional pathways. Network centrality analyses discover 28 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host modifying responses and cytokine storm. Overall, our network centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into SARS-CoV-2 pathogenicity. ### Competing Interest Statement The authors have declared no competing interest.
880 downloads systems biology
Katja Luck, Dae-Kyum Kim, Luke Lambourne, Kerstin Spirohn, Bridget E Begg, Wenting Bian, Ruth Brignall, Tiziana Cafarelli, Francisco J Campos-Laborie, Benoit Charloteaux, Dongsic Choi, Atina G. Cote, Meaghan Daley, Steven Deimling, Alice Desbuleux, Amélie Dricot, Marinella Gebbia, Madeleine F Hardy, Nishka Kishore, Jennifer J Knapp, István A. Kovács, Irma Lemmens, Miles W Mee, Joseph C. Mellor, Carl Pollis, Carles Pons, Aaron D Richardson, Sadie Schlabach, Bridget Teeking, Anupama Yadav, Mariana Babor, Dawit Balcha, Omer Basha, Christian Bowman-Colin, Suet-Feung Chin, Soon Gang Choi, Claudia Colabella, Georges Coppin, Cassandra D’Amata, David De Ridder, Steffi De Rouck, Miquel Duran-Frigola, Hanane Ennajdaoui, Florian Goebels, Liana Goehring, Anjali Gopal, Ghazal Haddad, Elodie Hatchi, Mohamed Helmy, Yves Jacob, Yoseph Kassa, Serena Landini, Roujia Li, Natascha van Lieshout, Andrew MacWilliams, Dylan Markey, Joseph N. Paulson, Sudharshan Rangarajan, John Rasla, Ashyad Rayhan, Thomas Rolland, Adriana San-Miguel, Yun Shen, Dayag Sheykhkarimli, Gloria M. Sheynkman, Eyal Simonovsky, Murat Taşan, Alexander Tejeda, Jean-Claude Twizere, Yang Wang, Robert J. Weatheritt, Jochen Weile, Yu Xia, Xinping Yang, Esti Yeger-Lotem, Quan Zhong, Patrick Aloy, Gary D Bader, Javier De Las Rivas, Suzanne Gaudet, Tong Hao, Janusz Rak, Jan Tavernier, Vincent Tropepe, David E. Hill, Marc Vidal, Frederick P. Roth, Michael A. Calderwood
Global insights into cellular organization and function require comprehensive understanding of interactome networks. Similar to how a reference genome sequence revolutionized human genetics, a reference map of the human interactome network is critical to fully understand genotype-phenotype relationships. Here we present the first human “all-by-all” binary reference interactome map, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI is approximately four times larger than the information curated from small-scale studies available in the literature. Integrating HuRI with genome, transcriptome and proteome data enables the study of cellular function within essentially any physiological or pathological cellular context. We demonstrate the use of HuRI in identifying specific subcellular roles of PPIs and protein function modulation via splicing during brain development. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI thus represents an unprecedented, systematic reference linking genomic variation to phenotypic outcomes.
791 downloads systems biology
SARS-CoV-2 infections are rapidly spreading around the globe. The rapid development of therapies is of major importance. However, our lack of understanding of the molecular processes and host cell signaling events underlying SARS-CoV-2 infection hinder therapy development. We employed a SARS-CoV-2 infection system in permissible human cells to study signaling changes by phospho-proteomics. We identified viral protein phosphorylation and defined phosphorylation-driven host cell signaling changes upon infection. Growth factor receptor (GFR) signaling and downstream pathways were activated. Drug-protein network analyses revealed GFR signaling as key pathway targetable by approved drugs. Inhibition of GFR downstream signaling by five compounds prevented SARS-CoV-2 replication in cells, assessed by cytopathic effect, viral dsRNA production, and viral RNA release into the supernatant. This study describes host cell signaling events upon SARS-CoV-2 infection and reveals GFR signaling as central pathway essential for SARS-CoV-2 replication. It provides with novel strategies for COVID-19 treatment. ### Competing Interest Statement The authors filed a patent application on the use of GFR signaling inhibitors for the treatment of COVID-19.
732 downloads systems biology
Numerous fundamental biological processes require individual cells to correctly interpret and accurately respond to incoming cues. How intracellular signaling networks achieve the integration of complex information from various contexts remains unclear. Here we quantify epidermal growth factor-induced heterogeneous activation of multiple signaling proteins, as well as cellular state markers, in the same single cells across multiple spatial scales. We find that the acute response of each node in a signaling network is tightly coupled to the cellular state in a partially non-redundant manner. This generates a multimodal response that senses the diversity of cellular states better than any individual response alone and allows individual cells to accurately place growth factor concentration in the context of their cellular state. We propose that the non-redundant multimodal property of signaling networks in mammalian cells underlies specific and context-aware cellular decision making in a multicellular setting.
695 downloads systems biology
mRNA splicing is one of the key processes in eukaryotic gene expression. Most Intron-containing genes are constitutively spliced, and hence must undergo splicing in order to produce a functional mature mRNA. Therefore, regulation of splicing efficiency greatly affects broader gene expression regulation. Here we use a large synthetic oligo library of ~25,000 variants to explore how different intronic sequence determinants affect splicing efficiency and mRNA expression levels in yeast. We found that the three splice sites (donor, acceptor, and branching point) differ in how deviations from the consensus sequence affect functionality. We also use intronic sequences from other yeast species with modified splicing machinery to show that intron architecture has co-evolved with the splicing machinery to adapt to the presence or absence of a specific splicing factor. Finally, we show that synthetic sequences containing two introns give rise to diverse RNA isoforms, which enables us to elucidate intronic features that control and enable alternative splicing. Our study reveals novel mechanisms by which introns are shaped in evolution to allow cells to regulate their transcriptome. ### Competing Interest Statement The authors have declared no competing interest.
689 downloads systems biology
The fate and physiology of individual cells are controlled by protein interactions. Yet, our ability to quantitatively analyze proteins in single cells has remained limited. To overcome this barrier, we developed SCoPE2. It lowers cost and hands-on time by introducing automated and miniaturized sample preparation while substantially increasing quantitative accuracy. These advances enabled us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiated into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantified over 2,700 proteins in 1,018 single monocytes and macrophages in ten days of instrument time, and the quantified proteins allowed us to discern single cells by cell type. Furthermore, the data uncovered a continuous gradient of proteome states for the macrophage-like cells, suggesting that macrophage heterogeneity may emerge even in the absence of polarizing cytokines. Parallel measurements of transcripts by 10x Genomics scRNA-seq suggest that SCoPE2 samples 20-fold more copies per gene, thus supporting quantification with improved count statistics. Joint analysis of the data indicated that most genes had similar responses at the protein and RNA levels, though the responses of hundreds of genes differed. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass-spectrometry. ![Figure]</img> : pending:yes
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