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Rxivist uses download data on preprints from bioRxiv to help you find the papers being discussed in your field. Currently indexing 100,570 bioRxiv papers from 424,791 authors.

Most downloaded bioRxiv papers, all time

in category systems biology

2,528 results found. For more information, click each entry to expand.

81: Defining the anti-Shine-Dalgarno sequence interaction and quantifying its functional role in regulating translation efficiency
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Posted to bioRxiv 06 Feb 2016

Defining the anti-Shine-Dalgarno sequence interaction and quantifying its functional role in regulating translation efficiency
2,325 downloads systems biology

Adam J. Hockenberry, Adam R Pah, Michael C. Jewett, Luís A. Nunes Amaral

Studies dating back to the 1970s established that binding between the anti-Shine-Dalgarno (aSD) sequence on prokaryotic ribosomes and mRNA helps to facilitate translation initiation. The location of aSD binding relative to the start codon, the full extents of the aSD sequence, and the functional form of the relationship between aSD binding and translation efficiency are important parameters that remain ill defined in the literature. Here, we leverage genome-wide estimates of translation efficiency to determine these parameters and show that anti-Shine-Dalgarno sequence binding increases the translation of endogenous mRNAs on the order of 50%. Our findings highlight the non-linearity of this relationship, showing that translation efficiency is maximized for sequences with intermediate aSD binding strengths. These mechanistic insights are highly robust; we find nearly identical results in ribosome profiling datasets from 3 highly diverged bacteria, as well as independent genome-scale estimates and controlled experimental data using recombinant GFP expression.

82: Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results
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Posted to bioRxiv 30 Apr 2018

Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results
2,315 downloads systems biology

Yansheng Liu, Yang Mi, Torsten Mueller, Saskia Kreibich, Evan Williams, Audrey Van Drogen, Christelle Borel, Pierre-Luc Germain, Max Frank, Isabell Bludau, Martin Mehnert, Michael Seifert, Mario Emmenlauer, Isabel Sorg, Fedor Bezrukov, Frederique Sloan Bena, Hu Zhou, Christoph Dehio, Giuseppe Testa, Julio Saez-Rodriguez, Stylianos E. Antonarakis, Wolf-Dietrich Hardt, Ruedi Aebersold

The independent reproduction of research results is a cornerstone of experimental research, yet it is beset by numerous challenges, including the quality and veracity of reagents and materials. Much of life science research depends on life materials, including human tissue culture cells. In this study we aimed at determining the degree of variability in the molecular makeup and the ensuing phenotypic consequences in commonly used human tissue culture cells. We collected 14 stock HeLa aliquots from 13 different laboratories across the globe, cultured them in uniform conditions and profiled the genome-wide copy numbers, mRNAs, proteins and protein turnover rates via genomic techniques and SWATH mass spectrometry, respectively. We also phenotyped each cell line with respect to the ability of transfected Let7 mimics to modulate Salmonella infection. We discovered significant heterogeneity between HeLa variants, especially between lines of the CCL2 and Kyoto variety. We also observed progressive divergence within a specific cell line over 50 successive passages. From the aggregate multi-omic datasets we quantified the response of the cells to genomic variability across the transcriptome and proteome. We discovered organelle-specific proteome remodeling and buffering of protein abundance by protein complex stoichiometry, mediated by the adaptation of protein turnover rates. By associating quantitative proteotype and phenotype measurements we identified protein patterns that explained the varying response of the different cell lines to Salmonella infection. Altogether the results indicate a striking degree of genomic variability, the rapid evolution of genomic variability in culture and its complex translation into distinctive expressed molecular and phenotypic patterns. The results have broad implications for the interpretation and reproducibility of research results obtained from HeLa cells and provide important basis for a general discussion of the value and requirements for communicating research results obtained from human tissue culture cells.

83: Fundamental limits on dynamic inference from single cell snapshots
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Posted to bioRxiv 30 Jul 2017

Fundamental limits on dynamic inference from single cell snapshots
2,311 downloads systems biology

Caleb Weinreb, Samuel Wolock, Betsabeh K. Tusi, Merav Socolovsky, Allon M. Klein

Single cell profiling methods are powerful tools for dissecting the molecular states of cells, but the destructive nature of these methods has made it difficult to measure single cell expression over time. When cell dynamics are asynchronous, they can form a continuous manifold in gene expression space whose structure is thought to encode the trajectory of a typical cell. This insight has spurred a proliferation of methods for single cell trajectory discovery that have successfully ordered cell states and identified differentiation branch-points. However, all attempts to infer dynamics from static snapshots of cell state face a common limitation: for any measured distribution of cells in high dimensional state space, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we enumerate from first principles the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone, but nonetheless have a profound influence on temporal ordering and fate probabilities of cells. On the basis of these unknowns, we identify assumptions necessary to constrain a unique solution for the dynamics and translate these constraints into a practical algorithmic approach, called Population Balance Analysis (PBA). At its core, PBA invokes a new method based on spectral graph theory for solving a certain class of high dimensional differential equation. We show the strengths and limitations of PBA using simulations and validate its accuracy on single cell profiles of hematopoietic progenitor cells. Altogether, these results provide a rigorous basis for dynamic interpretation of a gene expression continuum, and the pitfalls facing any method of dynamic inference. In doing so they clarify experimental designs to minimize these shortfalls.

84: Senescent cells and the dynamics of aging
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Posted to bioRxiv 14 Nov 2018

Senescent cells and the dynamics of aging
2,284 downloads systems biology

Omer Karin, Amit Agrawal, Ziv Porat, Valery Krizhanovsky, Uri Alon

A causal factor in mammalian aging is the accumulation of senescent cells (SnCs) with age. SnCs cause chronic inflammation, and removing SnCs decelerates aging in mice. Despite their importance, however, the production and removal rates of SnCs are not known, and their connection to aging dynamics is unclear. Here we use longitudinal SnC measurements and SnC induction experiments to show that SnCs turn over rapidly in young mice, with a half-life of days, but slow their own removal rate to a half-life of weeks in old mice. This leads to a critical slowing-down that generates persistent SnC fluctuations. We further demonstrate that a mathematical model, in which death occurs when fluctuating SnC populations cross a threshold, quantitatively recapitulates the Gompertz law of survival curves in mice and humans. The concept of a causal factor for aging with rapid turnover which slows its own removal can go beyond SnCs to explain the effects of interventions that modulate lifespan in Drosophila and C. elegans , including survival-curve scaling and rapid effects of dietary shifts on mortality.

85: DeepCell Kiosk: Scaling deep learning-enabled cellular image analysis with Kubernetes
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Posted to bioRxiv 22 Dec 2018

DeepCell Kiosk: Scaling deep learning-enabled cellular image analysis with Kubernetes
2,284 downloads systems biology

Dylan Bannon, Erick Moen, Morgan Schwartz, Enrico Borba, Takamasa Kudo, Noah Greenwald, Vibha Vijayakumar, Brian Chang, Edward Pao, Erik Osterman, William Graf, David Van Valen

Deep learning is transforming the analysis of biological images but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5 h for ~$250, with a sub-$100 cost achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at <https://github.com/vanvalenlab/kiosk-console>; a persistent deployment is available at <https://deepcell.org>. ### Competing Interest Statement The authors have filed a provisional patent for the described work; the software described here is available under a modified Apache license and is free for non-commercial uses.

86: Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach
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Posted to bioRxiv 03 Jan 2019

Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach
2,280 downloads systems biology

Benjamin A Logsdon, Thanneer M Perumal, Vivek Swarup, Minghui Wang, Cory Funk, Chris Gaiteri, Mariet Allen, Xue Wang, Eric Dammer, Gyan Srivastava, Sumit Mukherjee, Solveig K. Sieberts, Larsson Omberg, Kristen D. Dang, James A. Eddy, Phil Snyder, Yooree Chae, Sandeep Amberkar, Wenbin Wei, Winston Hide, Christoph Preuss, Ayla Ergun, Phillip J. Ebert, David C. Airey, Gregory W. Carter, Sara Mostafavi, Lei Yu, Hans-Ulrich Klein, the AMP-AD Consortium, David A. Collier, Todd Golde, Allan Levey, David A. Bennett, Karol Estrada, Michael Decker, Zhandong Liu, Joshua M. Shulman, Bin Zhang, Eric Schadt, Phillip L. De Jager, Nathan D. Price, Nilüfer Ertekin-Taner, Lara M. Mangravite

Alzheimer's disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related biological processes. This complexity contributes, in part, to existing difficulties in the identification of successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research studies. From this analysis, five consensus clusters were identified that described the major sources of AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. AD genetic associations, previously studied AD-related biological processes, and AD targets under active investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated strong heterogeneity between males and females in AD-related expression that was consistently observed across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, which was specific for genes that showed the strongest association with changes in AD-related gene expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been previously identified and was enriched for genes related to proteostasis. This study provides an atlas to map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery efforts.

87: Gene Expression-Based Drug Repurposing to Target Ageing
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Posted to bioRxiv 24 Jan 2018

Gene Expression-Based Drug Repurposing to Target Ageing
2,262 downloads systems biology

Handan Melike Donertas, Matías Fuentealba Valenzuela, Linda Partridge, Janet M Thornton

Ageing is the largest risk factor for a variety of non-communicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both life- and health-span. Ageing is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target-centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat ageing in human brain. Using multiple gene expression datasets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterise ageing. Then, we compared these changes in gene expression with drug perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as anti-ageing drugs by reversing the detrimental changes that occur during ageing, others by mimicking the cellular defense mechanisms. The drugs that we identified included significant number of already identified pro-longevity drugs, indicating that the method can discover de novo drugs that meliorate ageing. The approach has the advantages that, by using data from human brain ageing data it focuses on processes relevant in human ageing and that it is unbiased, making it possible to discover new targets for ageing studies.

88: Systematic detection of amino acid substitutions in proteome reveals a mechanistic basis of ribosome errors
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Posted to bioRxiv 29 Jan 2018

Systematic detection of amino acid substitutions in proteome reveals a mechanistic basis of ribosome errors
2,250 downloads systems biology

Ernest Mordret, Avia Yehonadav, Georgina D Barnabas, Jürgen Cox, Orna Dahan, Tamar Geiger, Ariel B Lindner, Yitzhak Pilpel

Translation errors limit the accuracy of information transmission from DNA to proteins. Selective pressures shape the way cells produce their proteins: the translation machinery and the mRNA sequences it decodes co-evolved to ensure that translation proceeds fast and accurately in a wide range of environmental conditions. Our understanding of the causes of amino acid misincorporations and of their effect on the evolution of protein sequences is largely hindered by the lack of experimental methods to observe errors at the full proteome level. Here, we systematically detect and quantify errors in entire proteomes from mass spectrometry data. Following HPLC MS-MS data acquisition, we identify E. coli and S. cerevisiae peptides whose mass and fragment ion spectrum are consistent with that of a peptide bearing a single amino acid substitution, and verify that such spectrum cannot result from a post-translational modification. Our analyses confirm that most substitutions occur due to codon-to-anticodon mispairing within the ribosome. Patterns of errors due to mispairing were similar in bacteria and yeast, suggesting that the error spectrum is chemically constrained. Treating E. coli cells with a drug known to affect ribosomal proofreading increased the error rates due to mispairing at the wobble codon position. Starving bacteria for serine resulted in specific patterns of substitutions reflecting the amino acid deficiency. Overall, translation errors tend to occur at positions that are less evolutionarily conserved, and that minimally affect protein energetic stability, indicating that they are selected against. Genome wide ribosome density data suggest that errors occur at sites where ribosome velocity is relatively high, supporting the notion of a trade-off between speed and accuracy as predicted by proofreading theories. Together our results reveal a mechanistic basis for ribosome errors in translation.

89: Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers
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Posted to bioRxiv 26 Dec 2018

Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers
2,236 downloads systems biology

David J Wooten, Sarah M Groves, Darren R. Tyson, Qi Liu, Jing S Lim, Carlos F. Lopez, Julien Sage, Vito Quaranta

Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes, while the fourth is a previously undescribed neuroendocrine variant (NEv2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.

90: Cellular state determines the multimodal signaling response of single cells
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Posted to bioRxiv 19 Dec 2019

Cellular state determines the multimodal signaling response of single cells
2,171 downloads systems biology

Bernhard A. Kramer, Lucas Pelkmans

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.

91: A Logic-Based Dynamic Modeling Approach to Explicate the Evolution of the Central Dogma of Molecular Biology
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Posted to bioRxiv 26 Jan 2017

A Logic-Based Dynamic Modeling Approach to Explicate the Evolution of the Central Dogma of Molecular Biology
2,169 downloads systems biology

Mohieddin Jafari, Naser Ansari-Pour, Sadegh Azimzadeh, Mehdi Mirzaie

It is nearly half a century past the age of the introduction of the Central Dogma (CD) of molecular biology. This biological axiom has been developed and currently appears to be all the more complex. In this study, we modified CD by adding further species to the CD information flow and mathematically expressed CD within a dynamic framework by using Boolean network based on its present-day and 1965 editions. We show that the enhancement of the Dogma not only now entails a higher level of complexity, but it also shows a higher level of robustness, thus far more consistent with the nature of biological systems. Using this mathematical modeling approach, we put forward a logic-based expression of our conceptual view of molecular biology. Finally, we show that such biological concepts can be converted into dynamic mathematical models using a logic-based approach and thus may be useful as a framework for improving static conceptual models in biology.

92: Network-based prediction of protein interactions
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Posted to bioRxiv 02 Mar 2018

Network-based prediction of protein interactions
2,127 downloads systems biology

István A. Kovács, Katja Luck, Kerstin Spirohn, Yang Wang, Carl Pollis, Sadie Schlabach, Wenting Bian, Dae-Kyum Kim, Nishka Kishore, Tong Hao, Michael A. Calderwood, Marc Vidal, Albert-László Barabási

As biological function emerges through interactions between a cell's molecular constituents, understanding cellular mechanisms requires us to catalogue all physical interactions between proteins. Despite spectacular advances in high-throughput mapping, the number of missing human protein-protein interactions (PPIs) continues to exceed the experimentally documented interactions. Computational tools that exploit structural, sequence or network topology information are increasingly used to fill in the gap, using the patterns of the already known interactome to predict undetected, yet biologically relevant interactions. Such network-based link prediction tools rely on the Triadic Closure Principle (TCP), stating that two proteins likely interact if they share multiple interaction partners. TCP is rooted in social network analysis, namely the observation that the more common friends two individuals have, the more likely that they know each other. Here, we offer direct empirical evidence across multiple datasets and organisms that, despite its dominant use in biological link prediction, TCP is not valid for most protein pairs. We show that this failure is fundamental - TCP violates both structural constraints and evolutionary processes. This understanding allows us to propose a link prediction principle, consistent with both structural and evolutionary arguments, that predicts yet uncovered protein interactions based on paths of length three (L3). A systematic computational cross-validation shows that the L3 principle significantly outperforms existing link prediction methods. To experimentally test the L3 predictions, we perform both large-scale high-throughput and pairwise tests, finding that the predicted links test positively at the same rate as previously known interactions, suggesting that most (if not all) predicted interactions are real. Combining L3 predictions with experimental tests provided new interaction partners of FAM161A, a protein linked to retinitis pigmentosa, offering novel insights into the molecular mechanisms that lead to the disease. Because L3 is rooted in a fundamental biological principle, we expect it to have a broad applicability, enabling us to better understand the emergence of biological function under both healthy and pathological conditions.

93: Towards an Integrated Map of Genetic Interactions in Cancer Cells
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Posted to bioRxiv 27 Mar 2017

Towards an Integrated Map of Genetic Interactions in Cancer Cells
2,119 downloads systems biology

Benedikt Rauscher, Florian Heigwer, Luisa Henkel, Thomas Hielscher, Oksana Voloshanenko, Michael Boutros

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and, as a side effect, create new vulnerabilities for potential therapeutic exploitation. To systematically identify genotype- dependent vulnerabilities and synthetic lethal interactions, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework that integrates CRISPR/Cas9 screens originating from many different libraries and laboratories to build genetic interaction maps. It builds on analytical approaches that were established for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cell lines combining functional data with information on genetic variants to explore the relationships of more than 2.1 million gene-background relationships. In addition to known dependencies, our analysis identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities associated with aberrant Wnt/β-catenin signaling identified GANAB and PRKCSH as new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data is included.

94: Gene regulatory network inference from single-cell data using multivariate information measures
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Posted to bioRxiv 20 Oct 2016

Gene regulatory network inference from single-cell data using multivariate information measures
2,097 downloads systems biology

Thalia E Chan, Michael P.H. Stumpf, Ann C Babtie

While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell data sets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available here: https://github.com/Tchanders/network_inference_tutorials. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.

95: Automated sample preparation for high-throughput single-cell proteomics
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Posted to bioRxiv 25 Aug 2018

Automated sample preparation for high-throughput single-cell proteomics
2,082 downloads systems biology

Harrison Specht, Guillaume Harmange, David H. Perlman, Edward Emmott, Zachary Niziolek, Bogdan Budnik, Nikolai Slavov

A major limitation to applying quantitative LC-MS/MS proteomics to small samples, such as single cells, are the losses incured during sample cleanup. To relieve this limitation, we developed a Minimal ProteOmic sample Preparation (mPOP) method for culture-grown mammalian cells. mPOP obviates cleanup and thus eliminates cleanup-related losses while expediting sample preparation and simplifying its automation. Bulk SILAC samples processed by mPOP or by conventional urea-based methods indicated that mPOP results in complete cell lysis and accurate relative quantification. We integrated mPOP lysis with the Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) sample preparation, and benchmarked the quantification of such samples on a Q-exactive instrument. The results demonstrate low noise and high technical reproducibility. Then, we FACS sorted single U-937, HEK-293, and mouse ES cells into 96-well plates and analyzed them by automated mPOP and SCoPE-MS. The quantified proteins enabled separating the single cells by cell-type and cell-division-cycle phase.

96: Molecular Choreography of Acute Exercise
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Posted to bioRxiv 14 Jan 2020

Molecular Choreography of Acute Exercise
2,076 downloads systems biology

Kévin Contrepois, Si Wu, Kegan J Moneghetti, Daniel Hornburg, Sara Ahadi, Ming-Shian Tsai, Ahmed A. Metwally, Eric Wei, Brittany Lee-McMullen, Jeniffer V Quijada, Songjie Chen, Jeffrey W. Christle, Mathew Ellenberger, Brunilda Balliu, Shalina Taylor, Matthew Durrant, Alexis Battle, Hany Choudhry, Melanie Ashland, Amir Bahmani, Brooke Enslen, Myriam Amsallem, Yukari Kobayashi, Monika Avina, Dalia Perelman, Sophia Miryam Schüssler-Fiorenza Rose, Wenyu Zhou, Euan A. Ashley, Stephen B. Montgomery, Hassan Chaib, Francois Haddad, Michael P. Snyder

Exercise testing is routinely used in clinical practice to assess fitness - a strong predictor of survival - as well as causes of exercise limitations. While these studies often focus on cardiopulmonary response and selected molecular pathways, the dynamic system-wide molecular response to exercise has not been fully characterized. We performed a longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including transcriptome, immunome, proteome, metabolome and lipidome in 36 well-characterized volunteers before and after a controlled bout of acute exercise (2, 15, 30 min and 1 hour in recovery). Integrative analysis revealed an orchestrated choreography of biological processes across key tissues. Most of these processes were dampened in insulin resistant participants. Finally, we discovered biological pathways involved in exercise capacity and developed prediction models revealing potential resting blood-based biomarkers of fitness.

97: Optimized cross-linking mass spectrometry for in situ interaction proteomics
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Posted to bioRxiv 17 Aug 2018

Optimized cross-linking mass spectrometry for in situ interaction proteomics
2,062 downloads systems biology

Zheng Ser, Paolo Cifani, Alex Kentsis

Recent development of mass spectrometer cleavable protein cross-linkers and algorithms for their spectral identification now permits large-scale cross-linking mass spectrometry (XL-MS). Here, we optimized the use of cleavable disuccinimidyl sulfoxide (DSSO) cross-linker for labeling native protein complexes in live human cells. We applied a generalized linear mixture model to calibrate cross-link peptide-spectra matching (CSM) scores to control the sensitivity and specificity of large-scale XL-MS. Using specific CSM score thresholds to control the false discovery rate, we found that higher-energy collisional dissociation (HCD) and electron transfer dissociation (ETD) can both be effective for large-scale XL-MS protein interaction mapping. We found that the density and coverage of protein-protein interaction maps can be significantly improved through the use of multiple proteases. In addition, the use of sample-specific search databases can be used to improve the specificity of cross-linked peptide spectral matching. Application of this approach to human chromatin labeled in live cells recapitulated known and revealed new protein interactions of nucleosomes and other chromatin-associated complexes in situ. This optimized approach for mapping native protein interactions should be useful for a wide range of biological problems.

98: An atlas of protein-protein interactions across mammalian tissues
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Posted to bioRxiv 20 Jun 2018

An atlas of protein-protein interactions across mammalian tissues
2,039 downloads systems biology

Michael A. Skinnider, Nichollas E Scott, Anna Prudova, Nikolay Stoynov, Richard Greg Stacey, Joerg Gsponer, Leonard J. Foster

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the complete network of biologically relevant protein-protein interactions, the interactome, has therefore been a central objective of high-throughput biology. Yet, because widely used methods for high-throughput interaction discovery rely on heterologous expression or genetically manipulated cell lines, the dynamics of protein interactions across physiological contexts are poorly understood. Here, we use a quantitative proteomic approach combining protein correlation profiling with stable isotope labelling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide the first proteome-scale survey of interactome dynamics across mammalian tissues, revealing over 27,000 unique interactions with an accuracy comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewiring of protein interactions across tissues is widespread, and is poorly predicted by gene expression or coexpression. Rewired proteins are tightly regulated by multiple cellular mechanisms and implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

99: Growth factor receptor signaling inhibition prevents SARS-CoV-2 replication
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Posted to bioRxiv 14 May 2020

Growth factor receptor signaling inhibition prevents SARS-CoV-2 replication
2,034 downloads systems biology

Kevin Klann, Denisa Bojkova, Georg Tascher, Sandra Ciesek, Christian Münch, Jindrich Cinatl

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.

100: Changes in epithelial proportions and transcriptional state underlie major premenopausal breast cancer risks
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Posted to bioRxiv 29 Sep 2018

Changes in epithelial proportions and transcriptional state underlie major premenopausal breast cancer risks
2,034 downloads systems biology

Lyndsay M Murrow, Robert J Weber, Joseph Caruso, Christopher S. McGinnis, Kiet Phong, Philippe Gascard, Alexander D Borowsky, Tejal A Desai, Matthew Thomson, Thea D Tlsty, Zev Jordan Gartner

The human breast undergoes lifelong remodeling in response to estrogen and progesterone, but hormone exposure also increases breast cancer risk. Here, we use single-cell analysis to identify distinct mechanisms through which breast composition and cell state affect hormone signaling. We show that prior pregnancy reduces the transcriptional response of hormone-responsive (HR+) epithelial cells, whereas high body mass index (BMI) reduces overall HR+ cell proportions. These distinct changes both impact neighboring cells by effectively reducing the magnitude of paracrine signals originating from HR+ cells. Because pregnancy and high BMI are known to protect against hormone-dependent breast cancer in premenopausal women, our findings directly link breast cancer risk with person-to-person heterogeneity in hormone responsiveness. More broadly, our findings illustrate how cell proportions and cell state can collectively impact cell communities through the action of cell-to-cell signaling networks. ### Competing Interest Statement The authors have declared no competing interest.

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