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, since beginning of last month
in category systems biology
1,183 results found. For more information, click each entry to expand.
2,952 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 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 A. 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 Galonić Fujimori, Trey Ideker, Charles S. Craik, Stephen N. Floor, James S. Fraser, John D. Gross, Andrej Sali, Tanja Kortemme, Pedro Beltrao, Kevan M. 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
1,143 downloads systems biology
Payman Samavarchi-Tehrani, Hala Abdouni, James D.R. Knight, Audrey Astori, Reuben Samson, Zhen-Yuan Lin, Dae-Kyum Kim, Jennifer J Knapp, Jonathan St-Germain, Christopher D Go, Brett Larsen, Cassandra J Wong, Patricia Cassonnet, Caroline Demeret, Yves Jacob, Frederick P. Roth, Brian Raught, Anne-Claude Gingras
Viral replication is dependent on interactions between viral polypeptides and host proteins. Identifying virus-host protein interactions can thus uncover unique opportunities for interfering with the virus life cycle via novel drug compounds or drug repurposing. Importantly, many viral-host protein interactions take place at intracellular membranes and poorly soluble organelles, which are difficult to profile using classical biochemical purification approaches. Applying proximity-dependent biotinylation (BioID) with the fast-acting miniTurbo enzyme to 27 SARS-CoV-2 proteins in a lung adenocarcinoma cell line (A549), we detected 7810 proximity interactions (7382 of which are new for SARS-CoV-2) with 2242 host proteins (results available at covid19interactome.org). These results complement and dramatically expand upon recent affinity purification-based studies identifying stable host-virus protein complexes, and offer an unparalleled view of membrane-associated processes critical for viral production. Host cell organellar markers were also subjected to BioID in parallel, allowing us to propose modes of action for several viral proteins in the context of host proteome remodelling. In summary, our dataset identifies numerous high confidence proximity partners for SARS-CoV-2 viral proteins, and describes potential mechanisms for their effects on specific host cell functions. ### Competing Interest Statement The authors have declared no competing interest.
830 downloads systems biology
Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from 12,627 genes across 32 normal human tissue types prepared by the GTEx project. Known and new tissue specific or enriched proteins (5,499) were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode highly tissue specific proteins. Discordance in the sites of RNA expression and protein detection also revealed potential sites of synthesis and action of protein signaling molecules. Overall, these results provide an extraordinary resource, and demonstrate that understanding protein levels can provide insights into metabolism, regulation, secretome, and human diseases. Summary Quantitative proteome study of 32 human tissues and integrated analysis with transcriptome data revealed that understanding protein levels could provide in-depth knowledge to post transcriptional or translational regulations, human metabolism, secretome, and diseases.
685 downloads systems biology
The fate and physiology of individual cells are controlled by proteins. Yet, our ability to quantitatively analyze proteins in single cells has remained limited. To overcome this barrier, we developed SCoPE2. It substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. 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 3,042 proteins in 1,490 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 our measurements sampled 20-fold more protein copies than RNA copies per gene, and thus SCoPE2 supports quantification with improved count statistics. Joint analysis of the data illustrates how variability across single cells can reveal transcriptional and post-transcriptional gene regulation. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass-spectrometry. ![Figure]</img> ### Competing Interest Statement The authors have declared no competing interest. : pending:yes
625 downloads systems biology
Accurate measurement of the biological markers of the aging process could provide an “aging clock” measuring predicted longevity and allow for the quantification of the effects of specific lifestyle choices on healthy aging. Using modern machine learning techniques, we demonstrate that chronological age can be predicted accurately from (a) the expression level of human genes in capillary blood, and (b) the expression level of microbial genes in stool samples. The latter uses the largest existing metatranscriptomic dataset, stool samples from 90,303 individuals, and is the highest-performing gut microbiome-based aging model reported to date. Our analysis suggests associations between biological age and lifestyle/health factors, e.g., people on a paleo diet or with IBS tend to be biologically older, and people on a vegetarian diet tend to be biologically younger. We delineate the key pathways of systems-level biological decline based on the age-specific features of our model; targeting these mechanisms can aid in development of new anti-aging therapeutic strategies. ### Competing Interest Statement All authors are employees of Viome Inc, a for-profit company
618 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.
469 downloads systems biology
Cell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is critical because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress response reporters in Escherichia coli , while simultaneously monitoring growth rate. In conditions without stress, we found widespread examples of pulsatile expression. Single-cell growth rates were often anti-correlated with gene expression, with changes in growth preceding changes in expression. These pulsatile dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that pulsatile expression and growth dynamics are common in stress response networks and can have direct consequences for survival. ### Competing Interest Statement The authors have declared no competing interest.
462 downloads systems biology
Protein phosphorylation is a key regulatory mechanism involved in nearly every eukaryotic cellular process. Increasingly sensitive mass spectrometry approaches have identified hundreds of thousands of phosphorylation sites but the functions of a vast majority of these sites remain unknown, with fewer than 5% of sites currently assigned a function. To increase our understanding of functional protein phosphorylation we developed an approach for identifying the phosphorylation-dependence of protein assemblies in a systematic manner. A combination of non-specific protein phosphatase treatment, size-exclusion chromatography, and mass spectrometry allowed us to identify changes in protein interactions after the removal of phosphate modifications. With this approach we were able to identify 316 proteins involved in phosphorylation-sensitive interactions. We recovered known phosphorylation-dependent interactors such as the FACT complex and spliceosome, as well as identified novel interactions such as the tripeptidyl peptidase TPP2 and the supraspliceosome component ZRANB2. More generally, we find phosphorylation-dependent interactors to be strongly enriched for RNA-binding proteins, providing new insight into the role of phosphorylation in RNA binding. By searching directly for phosphorylated amino acid residues in mass spectrometry data, we identified the likely regulatory phosphosites on ZRANB2 and FACT complex subunit SSRP1. This study provides both a method and resource for obtaining a better understanding of the role of phosphorylation in native macromolecular assemblies. ### Competing Interest Statement The authors have declared no competing interest.
416 downloads systems biology
Differentiation is the process whereby a cell acquires a specific phenotype, by differential gene expression as a function of time. This is thought to result from the dynamical functioning of an underlying Gene Regulatory Network (GRN). The precise path from the stochastic GRN behavior to the resulting cell state is still an open question. In this work we propose to reduce a stochastic model of gene expression, where a cell is represented by a vector in a continuous space of gene expression, to a discrete coarse-grained model on a limited number of cell types. We develop analytical results and numerical tools to perform this reduction for a specific model characterizing the evolution of a cell by a system of piecewise deterministic Markov processes (PDMP). Solving a spectral problem, we find the explicit variational form of the rate function associated to a Large deviations principle, for any number of genes. The resulting Lagrangian dynamics allows us to define a deterministic limit, the basins of attraction of which can be identified to cellular types. In this context the quasipotential, describing the transitions between these basins in the weak noise limit, can be defined as the unique solution of an Hamilton-Jacobi equation under a particular constraint. We develop a numerical method for approximating the coarse-grained model parameters, and show its accuracy for a symmetric toggle-switch network. We deduce from the reduced model an analytical approximation of the stationary distribution of the PDMP system, which appears as a beta mixture. Altogether those results establish a rigorous frame for connecting GRN behavior to the resulting cellular behavior, including the calculation of the probability of jumps between cell types. ### Competing Interest Statement The authors have declared no competing interest.
339 downloads systems biology
A general principle of biology is the self-assembly of proteins into functional complexes. Characterizing their composition is, therefore, required for our understanding of cellular functions. Unfortunately, we lack a comprehensive set of protein complexes for human cells. To address this gap, we developed a machine learning framework to identify protein complexes in over 15,000 mass spectrometry experiments which resulted in the identification of nearly 7,000 physical assemblies. We show our resource, hu.MAP 2.0, is more accurate and comprehensive than previous resources and gives rise to many new hypotheses, including for 274 completely uncharacterized proteins. Further, we identify 259 promiscuous proteins that participate in multiple complexes pointing to possible moonlighting roles. We have made hu.MAP 2.0 easily searchable in a web interface (<http://humap2.proteincomplexes.org/>), which will be a valuable resource for researchers across a broad range of interests including systems biology, structural biology, and molecular explanations of disease. ### Competing Interest Statement The authors have declared no competing interest.
328 downloads systems biology
A major biomedical challenge is the interpretation of genetic variation and the ability to design functional novel sequences. Since the space of all possible genetic variation is enormous, there is a concerted effort to develop reliable methods that can capture genotype to phenotype maps. State-of-art computational methods rely on models that leverage evolutionary information and capture complex interactions between residues. However, current methods are not suitable for a large number of important applications because they depend on robust protein or RNA alignments. Such applications include genetic variants with insertions and deletions, disordered proteins, and functional antibodies. Ideally, we need models that do not rely on assumptions made by multiple sequence alignments. Here we borrow from recent advances in natural language processing and speech synthesis to develop a generative deep neural network-powered autoregressive model for biological sequences that captures functional constraints without relying on an explicit alignment structure. Application to unseen experimental measurements of 43 deep mutational scans predicts the effect of insertions and deletions while matching state-of-art missense mutation prediction accuracies. We then test the model on single domain antibodies, or nanobodies, a complex target for alignment-based models due to the highly variable complementarity determining regions. We fit the model to a naïve llama immune repertoire and generate a diverse, optimized library of 105 nanobody sequences for experimental validation. Our results demonstrate the power of the 'alignment-free' autoregressive model in mutation effect prediction and design of traditionally challenging sequence families.
301 downloads systems biology
Eukaryotic genes are combinatorially regulated by a diversity of factors, including specific DNA-binding proteins called transcription factors (TFs). Physical interactions between regulatory factors have long been known to mediate synergistic behaviour, commonly defined as deviation from additivity when TFs or sites act in combination. Beyond binding-based interactions, the possibility of synergy emerging from functional interactions between TFs was theoretically proposed, but its governing principles have remained largely unexplored. Theoretically, the interplay between the binding of TFs and their effects over transcription has been challenging to integrate. Experimentally, probing kinetic synergy is easily confounded by physical interactions. Here we circumvent both of these limitations by focusing on a scenario where only one TF can be specifically bound at any given time, which we build using a synthetic biology approach in a mammalian cell line. We develop and analyze a mathematical model that explicitly incorporates the details of the binding of the TFs and their effects over transcription. The model reveals that synergy depends not only on the biochemical activities of the TFs, but also on their binding kinetics. We find experimental evidence for this result in a reporter-based system where fusions of mammalian TFs with engineered zinc fingers bind to a single, shared site. A complex synergy landscape emerges where TF activity, concentration and binding affinity shape the expression response. Our results highlight the relevance of an integrated understanding of TF function in eukaryotic transcriptional control. ### Competing Interest Statement The authors have declared no competing interest.
281 downloads systems biology
Helena García-Castro, Nathan J. Kenny, Patricia Álvarez-Campos, Vincent Mason, Anna Schönauer, Victoria A Sleight, Jakke Neiro, Aziz Aboobaker, Jon Permanyer, Marta Iglesias, Manuel Irimia, Arnau Sebé-Pedrós, Jordi Solana
Single-cell sequencing technologies are revolutionizing biology, but are limited by the need to dissociate fresh samples that can only be fixed at later stages. We present ACME (ACetic-MEthanol) dissociation, a cell dissociation approach that fixes cells as they are being dissociated. ACME-dissociated cells have high RNA integrity, can be cryopreserved multiple times, can be sorted by Fluorescence-Activated Cell Sorting (FACS) and are permeable, enabling combinatorial single-cell transcriptomic approaches. As a proof of principle, we have performed SPLiT-seq with ACME cells to obtain around ∼34K single cell transcriptomes from two planarian species and identified all previously described cell types in similar proportions. ACME is based on affordable reagents, can be done in most laboratories and even in the field, and thus will accelerate our knowledge of cell types across the tree of life. ### Competing Interest Statement The authors have declared no competing interest.
277 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. ### Competing Interest Statement The authors have declared no competing interest.
273 downloads systems biology
RNA hybridization based spatial transcriptomics provides unparalleled detection sensitivity. However, inaccuracies in segmentation of image volumes into cells cause misassignment of mRNAs which is a major source of errors. Here we develop JSTA, a computational framework for Joint cell Segmentation and cell Type Annotation that utilizes prior knowledge of cell-type specific gene expression. Simulation results show that leveraging existing cell type taxonomy increases RNA assignment accuracy by more than 45%. Using JSTA we were able to classify cells in the mouse hippocampus into 133 (sub)types revealing the spatial organization of CA1, CA3, and Sst neuron subtypes. Analysis of within cell subtype spatial differential gene expression of 80 candidate genes identified 43 with statistically significant spatial differential gene expression across 61 (sub)types. Overall, our work demonstrates that known cell type expression patterns can be leveraged to improve the accuracy of RNA hybridization based spatial transcriptomics while providing highly granular cell (sub)type information. The large number of newly discovered spatial gene expression patterns substantiates the need for accurate spatial transcriptomics measurements that can provide information beyond cell (sub)type labels. ### Competing Interest Statement The authors have declared no competing interest.
269 downloads systems biology
James W. Opzoomer, Jessica Timms, Kevin Blighe, Thanos P. Mourikis, Nicolas Chapuis, Richard Bekoe, Sedigeh Kareemaghay, Paola Nocerino, Benedetta Apollonio, Alan G. Ramsay, Mahvash Tavassoli, Claire Harrison, Francesca Ciccarelli, Peter Parker, Michaela Fontenay, Paul R. Barber, James N. Arnold, Shahram Kordasti
High dimensional cytometry is an innovative tool for immune monitoring in health and disease, it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of multiparametric “big data” usually requires specialist computational knowledge. Here we describe ImmunoCluster (<https://github.com/kordastilab/ImmunoCluster>) an R package for immune profiling cellular heterogeneity in high dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a non-specialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users’ needs. The protocol consists of three core computational stages: 1, data import and quality control, 2, dimensionality reduction and unsupervised clustering; and 3, annotation and differential testing, all contained within an R-based open-source framework. ### Competing Interest Statement Dr Shahram Kordasti: Honoraria: Beckman Coulter, GWT-TUD, Alexion; Consulting or Advisory Role: Syneos Health; Research Funding: Celgene, Novartis pharmaceutical.
269 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.
259 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 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.
253 downloads systems biology
Nerves in bone play well-established roles in pain and vasoregulation and have been associated with progression of skeletal disorders including osteoporosis, fracture, arthritis and tumor metastasis. However, isolation of the region-specific mechanisms underlying these relationships is limited by our lack of comprehensive maps of skeletal innervation. To overcome this, we mapped sympathetic adrenergic and sensory peptidergic axons within the limb in two strains of mice (B6 and C3H). In the periosteum, these maps were related to the surrounding musculature, including entheses and myotendinous attachments to bone. Locally, three distinct patterns of innervation (Type I, II, III) were defined within established sites that are important for bone pain, bone repair, and skeletal homeostasis. In addition, we mapped the major nerve branches and areas of specialized mechanoreceptors. This work is intended to serve as a guide during the design, implementation, and interpretation of future neuroskeletal studies and was compiled as a resource for the field as part of the NIH SPARC consortium. ### Competing Interest Statement The authors have declared no competing interest.
242 downloads systems biology
Evan Williams, Niklas Pfister, Suheeta Roy, Cyril Statzer, Jesse Ingels, Casey Bohl, Moaraj Hasan, Jelena Cuklina, Peter Buhlmann, Nicola Zamboni, Lu Lu, Collin Y. Ewald, Robert W. Williams, Ruedi Aebersold
Systems biology approaches often use networks of gene expression and metabolite data to identify regulatory factors and pathways connected with phenotypic variance. Separating upstream causal mechanisms, downstream biomarkers, and incidental correlations remains a significant challenge, yet it is essential for designing mechanistic experiments. To address this, we first designed a population following 2157 individual mice from 89 isogenic strains of BXD mice across their lifespans to identify molecular interactions between genotype, environment, age (GxExA) and metabolic fitness. Each strain was separated into two cohorts, fed low fat (6% cal/fat) or high fat (60% cal/fat) diets. One-third of individuals (662) were sacrificed at ~6, 12, 18, or 24 months-of-age, with the remainder monitored until natural death. Transcriptome, proteome, and metabolome profiles were generated from liver samples. These multi-omic measurements were deconvolved into metabolic networks, where we observed varying network connectivity as a function of GxExA. The multiple independent study variables permitted causal inference analysis for the network variants using stability selection. This calculates the strength and directionality of the interactions between molecular measurements and metabolic networks as a function of age, diet, and genotype, and assigns each gene a score for its relative position to the target pathway. At 1% FDR, 94% of novel connections were stable across age and diet, such as the connection between Rdh11 with cholesterol biosynthesis and Mut with mitochondrial translation. 6% of discovered candidate genes were unstable, indicating a clear causal relationship between the segregating independent variable, the gene, and the pathway. For instance, age drives variation in proteasomal genes (e.g. Psmb3, Psmb4), which in turn drive changes in the mitochondrial ribosome. Conversely, COX7A2L malformation drives variation in OXPHOS genes, but both are downstream of changes in mitochondrial translation. Finally, we examined all data for connections with the longevity and known longevity-related pathways, identifying several dozen novel candidate genes. Specific C. elegans orthologs for the top two candidates, Ctsd and St7, were knocked down with RNAi and found to reduce longevity both in wildtype worms and in mutant long-lived strains. ### Competing Interest Statement The authors have declared no competing interest.
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