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in category systems biology

4,135 results found. For more information, click each entry to expand.

1: A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
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Posted 22 Mar 2020

A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
180,988 downloads bioRxiv systems biology

David E Gordon, Gwendolyn M Jang, Mehdi Bouhaddou, Jiewei Xu, Kirsten Obernier, Matthew James O'Meara, Jeffrey Z. Guo, Danielle L Swaney, Tia A Tummino, Ruth Hüttenhain, 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 Bharath Pilla, Sai J. Ganesan, Daniel 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 White, Lisa Miorin, Oren S. Rosenberg, Kliment A Verba, David A. Agard, Melanie Ott, Michael Emerman, Davide Ruggero, Adolfo Gastia-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 Galonic Fujimori, Trey Ideker, Charles Craik, Stephen N Floor, James S Fraser, John D 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[1][1],[2][2]. 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 [1]: #ref-1 [2]: #ref-2

2: Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China
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Posted 26 Jan 2020

Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China
44,240 downloads bioRxiv 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.

3: Revised estimates for the number of human and bacteria cells in the body
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Posted 06 Jan 2016

Revised estimates for the number of human and bacteria cells in the body
40,107 downloads bioRxiv systems biology

Ron Sender, Shai Fuchs, Ron Milo

We critically revisit the ″common knowledge″ that bacteria outnumber human cells by a ratio of at least 10:1 in the human body. We found the total number of bacteria in the ″reference man″ to be 3.9·1013, with an uncertainty (SEM) of 25%, and a variation over the population (CV) of 52%. For human cells we identify the dominant role of the hematopoietic lineage to the total count of body cells (≈90%), and revise past estimates to reach a total of 3.0·1013 human cells in the 70 kg ″reference man″ with 2% uncertainty and 14% CV. Our analysis updates the widely-cited 10:1 ratio, showing that the number of bacteria in our bodies is actually of the same order as the number of human cells. Indeed, the numbers are similar enough that each defecation event may flip the ratio to favor human cells over bacteria.

4: Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm
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Posted 24 Jan 2020

Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm
23,569 downloads bioRxiv systems biology

Qian Guo, Mo Li, Chunhui Wang, Peihong Wang, Zhencheng Fang, Jie tan, Shufang Wu, Yonghong Xiao, Huaiqiu Zhu

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. ### Competing Interest Statement The authors have declared no competing interest.

5: Thanatotranscriptome: genes actively expressed after organismal death
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Posted 10 Jun 2016

Thanatotranscriptome: genes actively expressed after organismal death
23,489 downloads bioRxiv systems biology

Alexander Pozhitkov, Rafik Neme, Tomislav Domazet-Lošo, Brian G. Leroux, Shivani Soni, Diethard Tautz, Peter A. Noble

A continuing enigma in the study of biological systems is what happens to highly ordered structures, far from equilibrium, when their regulatory systems suddenly become disabled. In life, genetic and epigenetic networks precisely coordinate the expression of genes -- but in death, it is not known if gene expression diminishes gradually or abruptly stops or if specific genes are involved. We investigated the unwinding of the clock by identifying upregulated genes, assessing their functions, and comparing their transcriptional profiles through postmortem time in two species, mouse and zebrafish. We found transcriptional abundance profiles of 1,063 genes were significantly changed after death of healthy adult animals in a time series spanning from life to 48 or 96 h postmortem. Ordination plots revealed non-random patterns in profiles by time. While most thanatotranscriptome (thanatos-, Greek defn. death) transcript levels increased within 0.5 h postmortem, some increased only at 24 and 48 h. Functional characterization of the most abundant transcripts revealed the following categories: stress, immunity, inflammation, apoptosis, transport, development, epigenetic regulation, and cancer. The increase of transcript abundance was presumably due to thermodynamic and kinetic controls encountered such as the activation of epigenetic modification genes responsible for unraveling the nucleosomes, which enabled transcription of previously silenced genes (e.g., development genes). The fact that new molecules were synthesized at 48 to 96 h postmortem suggests sufficient energy and resources to maintain self-organizing processes. A step-wise shutdown occurs in organismal death that is manifested by the apparent upregulation of genes with various abundance maxima and durations. The results are of significance to transplantology and molecular biology.

6: Multi-study inference of regulatory networks for more accurate models of gene regulation
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Posted 08 Mar 2018

Multi-study inference of regulatory networks for more accurate models of gene regulation
19,717 downloads bioRxiv systems biology

Dayanne M. Castro, Nicholas R. de Veaux, Emily R Miraldi, Richard Bonneau

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.

7: A mathematical model for simulating the transmission of Wuhan novel Coronavirus
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Posted 19 Jan 2020

A mathematical model for simulating the transmission of Wuhan novel Coronavirus
18,432 downloads bioRxiv systems biology

Tianmu Chen, Jia Rui, Qiupeng Wang, Zeyu Zhao, Jing-An Cui, Ling Yin

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.

8: Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation
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Posted 22 Dec 2020

Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation
13,757 downloads bioRxiv systems biology

Andreas-David Brunner, Marvin A Thielert, Catherine Vasilopoulou, Constantin Ammar, Fabian Coscia, Andreas Mund, Ole Bjeld Horning, Nicolai Bache, Amalia Apalategui, Markus Lubeck, Oliver Raether, Melvin Park, Sabrina Richter, David Sebastian Fischer, Florian Meier, Fabian J. Theis, Matthias Mann

Single cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing 1,2,3. However, proteins are the main drivers of cellular function and in depth characterization of individual cells by mass spectrometry (MS) based proteomics would thus be highly valuable and complementary 4,5. Chemical labeling based single cell approaches introduce hundreds of cells into the MS, but direct analysis of single cells has not yet reached the necessary sensitivity, robustness and quantitative accuracy to answer biological questions 6,7. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow rate chromatography and a novel trapped ion mobility mass spectrometer, resulting in a more than ten fold improved sensitivity. We accurately and robustly quantify proteomes and their changes in single, FACS isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators such as CDK2NA, the E2 ubiquitin ligase UBE2S, DNA topoisomerases TOP2A/B and the chromatin regulator HMGA1. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single cell proteomes to transcriptome data revealed a stable core proteome despite perturbation, while the transcriptome appears volatile. This emphasizes substantial regulation of translation and sets the stage for its elucidation at the single cell level. Our technology can readily be applied to ultra high sensitivity analyses of tissue material8, posttranslational modifications and small molecule studies to gain unprecedented insights into cellular heterogeneity in health and disease.

9: Statistical physics of liquid brains
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Posted 26 Nov 2018

Statistical physics of liquid brains
13,017 downloads bioRxiv systems biology

Jordi Piñero, Ricard Solé

Liquid neural networks (or ''liquid brains'') are a widespread class of cognitive living networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (''solid brains''), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in other ways. On one hand, the attractors found in liquid brains are not always based on connection weights but instead on population abundances. However, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role of criticality as a way of rapidly reacting to external signals.

10: Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV
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Posted 17 Jun 2020

Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV
12,722 downloads bioRxiv systems biology

Alexey Stukalov, Virginie Girault, Vincent Grass, Ozge Karayel, Valter Bergant, Christian Urban, Darya A. Haas, Yiqi Huang, Lila Oubraham, Anqi Wang, Sabri M. Hamad, Antonio Piras, Fynn M. Hansen, Maria Tanzer, Igor Paron, Luca Zinzula, Thomas Engleitner, Maria Reinecke, Teresa M. Lavacca, Rosina Ehmann, Roman Wölfel, Jörg Jores, Bernhard Küster, Ulrike A. Protzer, Roland Rad, John Ziebuhr, Volker Thiel, Pietro Scaturro, Matthias Mann, Andreas Pichlmair

The global emergence of SARS-CoV-2 urgently requires an in-depth understanding of molecular functions of viral proteins and their interactions with the host proteome. Several individual omics studies have extended our knowledge of COVID-19 pathophysiology. Integration of such datasets to obtain a holistic view of virus-host interactions and to define the pathogenic properties of SARS-CoV-2 is limited by the heterogeneity of the experimental systems. We therefore conducted a concurrent multi-omics study of SARS-CoV-2 and SARS-CoV. Using state-of-the-art proteomics, we profiled the interactome of both viruses, as well as their influence on transcriptome, proteome, ubiquitinome and phosphoproteome in a lung-derived human cell line. Projecting these data onto the global network of cellular interactions revealed crosstalk between the perturbations taking place upon SARS-CoV-2 and SARS-CoV infections at different layers and identified unique and common molecular mechanisms of these closely related coronaviruses. The TGF-{beta} pathway, known for its involvement in tissue fibrosis, was specifically dysregulated by SARS-CoV-2 ORF8 and autophagy by SARS-CoV-2 ORF3. The extensive dataset (available at https://covinet.innatelab.org) highlights many hotspots that can be targeted by existing drugs and it can guide rational design of virus- and host-directed therapies, which we exemplify by identifying kinase and MMPs inhibitors with potent antiviral effects against SARS-CoV-2.

11: Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2
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Posted 23 Apr 2020

Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2
11,687 downloads bioRxiv 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.

12: Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity
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Posted 09 Jun 2019

Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity
11,632 downloads bioRxiv systems biology

Harrison Specht, Edward Emmott, Aleksandra A. Petelski, R. Gray Huffman, David H. Perlman, Marco Serra, Peter Kharchenko, Antonius Koller, Nikolai Slavov

Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of limitations of quantitative single-cell protein analysis. To overcome this limitation, we developed SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantified over 3,042 proteins in 1,490 single monocytes and macrophages in ten days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. This gradient correlates to the inflammatory axis of classically and alternatively activated macrophages. Parallel measurements of transcripts by 10x Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus SCoPE2 supports quantification with improved count statistics. The joint distributions of proteins and transcripts allowed exploring regulatory interactions, such as between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. Our methodology lays the foundation for quantitative single-cell analysis of proteins by mass-spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC="FIGDIR/small/665307v5_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@18ead97org.highwire.dtl.DTLVardef@26b51aorg.highwire.dtl.DTLVardef@13bd6a8org.highwire.dtl.DTLVardef@189cf00_HPS_FORMAT_FIGEXP M_FIG C_FIG

13: Mapping Transcriptomic Vector Fields of Single Cells
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Posted 09 Jul 2019

Mapping Transcriptomic Vector Fields of Single Cells
11,605 downloads bioRxiv systems biology

Xiaojie Qiu, Yan Dora Zhang, Shayan Hosseinzadeh, Dian Yang, Angela N Pogson, Li Wang, Mattew Shurtleff, Ruoshi Yuan, Song Xu, Yian Ma, Joseph Replogle, Spyros Darmanis, Jianhua Xing, Jonathan Weissman

Single-cell RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires dynamical models capable of predicting cell fate and unveiling the governing regulatory mechanisms. Here, we introduce dynamo, an analytical framework that reconciles intrinsic splicing and labeling kinetics to estimate absolute RNA velocities, reconstructs continuous velocity vector fields that predict future cell fates, and finally employs differential geometry analyses to elucidate the underlying regulatory networks. We applied dynamo to a wide range of disparate biological processes including prediction of future states of differentiating hematopoietic stem cell lineages, deconvolution of glucocorticoid responses from orthogonal cell-cycle progression, characterization of regulatory networks driving zebrafish pigmentation, and identification of possible routes of resistance to SARS-CoV-2 infection. Our work thus represents an important step in going from qualitative, metaphorical conceptualizations of differentiation, as exemplified by Waddington's epigenetic landscape, to quantitative and predictive theories.

14: A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19
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Posted 12 Mar 2020

A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19
11,217 downloads bioRxiv 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.

15: Learning inverse folding from millions of predicted structures
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Posted 10 Apr 2022

Learning inverse folding from millions of predicted structures
10,293 downloads bioRxiv systems biology

Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives

We consider the problem of predicting a protein sequence from its backbone atom coordinates. Machine learning approaches to this problem to date have been limited by the number of available experimentally determined protein structures. We augment training data by nearly three orders of magnitude by predicting structures for 12M protein sequences using AlphaFold2. Trained with this additional data, a sequence-to-sequence transformer with invariant geometric input processing layers achieves 51% native sequence recovery on structurally held-out backbones with 72% recovery for buried residues, an overall improvement of almost 10 percentage points over existing methods. The model generalizes to a variety of more complex tasks including design of protein complexes, partially masked structures, binding interfaces, and multiple states.

16: Partial reprogramming restores youthful gene expression through transient suppression of cell identity
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Posted 23 May 2021

Partial reprogramming restores youthful gene expression through transient suppression of cell identity
9,837 downloads bioRxiv systems biology

Antoine Roux, Chunlian Zhang, Jonathan Paw, José-Zavalara Solorio, Twaritha Vijay, Ganesh Kolumam, Cynthia Kenyon, Jacob C Kimmel

Transient induction of pluripotent reprogramming factors has been reported to reverse some features of aging in mammalian cells and tissues. However, the impact of transient reprogramming on somatic cell identity programs and the necessity of individual pluripotency factors remain unknown. Here, we mapped trajectories of transient reprogramming in young and aged cells from multiple murine cell types using single cell transcriptomics to address these questions. We found that transient reprogramming restored youthful gene expression in adipocytes and mesenchymal stem cells but also temporarily suppressed somatic cell identity programs. We further screened Yamanaka Factor subsets and found that many combinations had an impact on aging gene expression and suppressed somatic identity, but that these effects were not tightly entangled. We also found that a transient reprogramming approach inspired by amphibian regeneration restored youthful gene expression in aged myogenic cells. Our results suggest that transient pluripotent reprogramming poses a neoplastic risk, but that restoration of youthful gene expression can be achieved with alternative strategies.

17: Methods for High-Throughput Drug Combination Screening and Synergy Scoring
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Posted 05 May 2016

Methods for High-Throughput Drug Combination Screening and Synergy Scoring
8,466 downloads bioRxiv systems biology

Liye He, Evgeny Kulesskiy, Jani Saarela, Laura Turunen, Krister Wennerberg, Tero Aittokallio, Jing Tang

Gene products or pathways that are aberrantly activated in cancer but not in normal tissue hold great promises for being effective and safe anticancer therapeutic targets. Many targeted drugs have entered clinical trials but so far showed limited efficacy mostly due to variability in treatment responses and often rapidly emerging resistance. Towards more effective treatment options, we will critically need multi-targeted drugs or drug combinations, which selectively inhibit the cancer cells and block distinct escape mechanisms for the cells to become resistant. Functional profiling of drug combinations requires careful experimental design and robust data analysis approaches. At the Institute for Molecular Medicine Finland (FIMM), we have developed an experimental-computational pipeline for high-throughput screening of drug combination effects in cancer cells. The integration of automated screening techniques with advanced synergy scoring tools allows for efficient and reliable detection of synergistic drug interactions within a specific window of concentrations, hence accelerating the identification of potential drug combinations for further confirmatory studies.

18: Functional Immune Deficiency Syndrome via Intestinal Infection in COVID-19
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Posted 10 Apr 2020

Functional Immune Deficiency Syndrome via Intestinal Infection in COVID-19
8,171 downloads bioRxiv systems biology

Erica 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 A. 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.

19: Ordinary Differential Equations in Cancer Biology
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Posted 23 Aug 2016

Ordinary Differential Equations in Cancer Biology
7,990 downloads bioRxiv systems biology

Margaret P Chapman, Claire J. Tomlin

Ordinary differential equations (ODEs) provide a classical framework to model the dynamics of biological systems, given temporal experimental data. Qualitative analysis of the ODE model can lead to further biological insight and deeper understanding compared to traditional experiments alone. Simulation of the model under various perturbations can generate novel hypotheses and motivate the design of new experiments. This short paper will provide an overview of the ODE modeling framework, and present examples of how ODEs can be used to address problems in cancer biology.

20: AI-driven Deep Visual Proteomics defines cell identity and heterogeneity
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Posted 27 Jan 2021

AI-driven Deep Visual Proteomics defines cell identity and heterogeneity
7,987 downloads bioRxiv systems biology

Andreas Mund, Fabian Coscia, Reka Hollandi, Ferenc Kovacs, Andras Kriston, Andreas-David Brunner, Michael Bzorek, Soraya Naimy, Lise Mette Rahbek Gjerdrum, Beatrice Dyring-Andersen, Jutta Maria Bulkescher, Claudia Lukas, Christian Gnann, Emma Lundberg, Peter Horvath, Matthias Mann

The systems-wide analysis of biomolecules in time and space is key to our understanding of cellular function and heterogeneity in health and disease1. Remarkable technological progress in microscopy and multi-omics technologies enable increasingly data-rich descriptions of tissue heterogeneity2,3,4,5. Single cell sequencing, in particular, now routinely allows the mapping of cell types and states uncovering tremendous complexity6. Yet, an unaddressed challenge is the development of a method that would directly connect the visual dimension with the molecular phenotype and in particular with the unbiased characterization of proteomes, a close proxy for cellular function. Here we introduce Deep Visual Proteomics (DVP), which combines advances in artificial intelligence (AI)-driven image analysis of cellular phenotypes with automated single cell laser microdissection and ultra-high sensitivity mass spectrometry7. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. Individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and novel proteins. AI also discovered rare cells with distinct morphology, whose potential function was revealed by proteomics. Applied to archival tissue of salivary gland carcinoma, our generic workflow characterized proteomic differences between normal-appearing and adjacent cancer cells, without admixture of background from unrelated cells or extracellular matrix. In melanoma, DVP revealed immune system and DNA replication related prognostic markers that appeared only in specific tumor regions. Thus, DVP provides unprecedented molecular insights into cell and disease biology while retaining spatial information.

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