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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Molecular atlas of postnatal mouse heart development

RationaleMammals lose the ability to regenerate their hearts within one week after birth. During this regenerative window, cardiac energy metabolism shifts from glycolysis to fatty acid oxidation, and recent evidence suggests that metabolism may participate in controlling cardiomyocyte cell cycle. However, the molecular mechanisms mediating the loss of postnatal cardiac regeneration are not fully understood.\n\nObjectiveThis study aims at providing an integrated resource of mRNA, protein and metabolite changes in the neonatal heart to identify metabolism-related mechanisms associated with the postnatal loss of regenerative capacity.\n\nMethods and ResultsMouse ventricular tissue samples taken on postnatal days 1, 4, 9 and 23 (P01, P04, P09 and P23, respectively) were analyzed with RNA sequencing (RNAseq) and global proteomics and metabolomics. Differential expression was observed for 8547 mRNAs and for 1199 of the 2285 quantified proteins. Furthermore, 151 metabolites with significant changes were identified. Gene ontology analysis, KEGG pathway analysis and fuzzy c-means clustering were used to identify biological processes and metabolic pathways either up- or downregulated on all three levels. Among these were branched chain amino acid degradation (upregulated at P23) and production of free saturated and monounsaturated medium- to long-chain fatty acids (upregulated at P04 and P09; downregulated at P23). Moreover, the HMG-CoA synthase (HMGCS)-mediated mevalonate pathway and ketogenesis were transiently activated. Pharmacological inhibition of HMGCS in primary neonatal rat ventricular cardiomyocytes reduced the percentage of BrdU+ cardiomyocytes, providing evidence that the mevalonate and ketogenesis routes may participate in regulating cardiomyocyte cell cycle.\n\nConclusionsThis is the first systems-level resource combining data from genome-wide transcriptomics with global quantitative proteomics and untargeted metabolomics analyses of the mouse heart throughout the early postnatal period. This integrated multi-level data of molecular changes associated with the loss of cardiac regeneration may open up new possibilities for the development of regenerative therapies.

systems biology↗
bioRxiv2016

A space-jump derivation for non-local models of cell-cell adhesion and non-local chemotaxis

Cellular adhesion provides one of the fundamental forms of biological interaction between cells and their surroundings, yet the continuum modelling of cellular adhesion has remained mathematically challenging. In 2006, Armstrong et al. proposed a mathematical model in the form of an integro-partial differential equation. Although successful in applications, a derivation from an underlying stochastic random walk has remained elusive. In this work we develop a framework by which non-local models can be derived from a space-jump process. We show how the notions of motility and a cell polarization vector can be naturally included. With this derivation we are able to include microscopic biological properties into the model. We show that particular choices yield the original Armstrong model, while others lead to more general models, including a doubly non-local adhesion model and non-local chemotaxis models. Finally, we use random walk simulations to confirm that the corresponding continuum model represents the mean field behaviour of the stochastic random walk.

systems biology↗
bioRxiv2018

Network stress test reveals novel drug potentiators in Mycobacterium tuberculosis

Deciphering molecular stress response is highly relevant to studies of microbes such as Mycobacterium tuberculosis (MTB), the causative pathogen of tuberculosis (TB) which sickens 10 million people and kills 1.8 million each year1. Prolonged therapy and unfavorable outcomes arise partially because MTB has evolved stress responses to achieve tolerance, wherein MTB persists in otherwise inhibitory drug concentrations by means independent of heritable resistance mutations2-4. Understanding these adaptations and how they are regulated can reveal new biology, including unexplored drug targets and treatment-enhancing strategies. Here, we present a novel network-based genetic screening approach: the Transcriptional Regulator Induced Phenotype (TRIP) screen, which we used to identify previously uncharacterized MTB network adaptations to the first-line drug isoniazid (INH). We found regulators that alter IN ...

systems biology↗
bioRxiv2017

CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

BackgroundOver the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.\n\nResultsCrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.\n\nConclusionCrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.\n\nAvailabilityThe web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.\n\nContactbhaibeka@uhnresearch.ca

systems biology↗
bioRxiv2018

A network of epigenomic and transcriptional cooperation encompassing an epigenomic master regulator in cancer

Coordinated experiments focused on transcriptional responses and chromatin states are well-equipped to capture different epigenomic and transcriptomic levels governing the circuitry of a regulatory network. We propose a workflow for the genome-wide identification of epigenomic and transcriptional cooperation to elucidate transcriptional networks in cancer. Gene promoter annotation in combination with network analysis and sequence-resolution of enriched transcriptional motifs in epigenomic data reveals transcription factor families that act synergistically with epigenomic master regulators. A close teamwork of the transcriptional and epigenomic machinery was discovered. The network is tightly connected and includes the histone lysine demethylase KDM3A, basic helix-loop-helix factors MYC, HIF1A, and SREBF1, as well as differentiation factors AP1, MYOD1, SP1, MEIS1, ZEB1 and ELK1. In such a cooperation network, one component opens the chromatin, another one recognizes gene-specific DNA motifs, others scaffold between histones, cofactors, and the transcriptional complex. In cancer, due to the ability to team up with transcription factors, epigenetic factors concert mitogenic and metabolic gene networks, claiming the role of a cancer master regulators or epioncogenes.\n\nSpecific histone modification patterns are commonly associated with open or closed chromatin states, and are linked to distinct biological outcomes by transcriptional activation or repression. Disruption of patterns of histone modifications is associated with loss of proliferative control and cancer. There is tremendous therapeutic potential in understanding and targeting histone modification pathways. Thus, investigating cooperation of chromatin remodelers and the transcriptional machinery is not only important for elucidating fundamental mechanisms of chromatin regulation, but also necessary for the design of targeted therapeutics.

systems biology↗
bioRxiv2017

Operating regimes of covalent modification cycles at high enzyme concentrations

The Goldbeter-Koshland model has been a paradigm for ultrasensitivity in biological networks for more than 30 years. Despite its simplicity the validity of this model is restricted to conditions when the substrate is in excess over the converter enzymes - a condition that is easy to satisfy in vitro, but which is rarely satisfied in vivo. Here, we analyze the Goldbeter-Koshland model by means of the total quasi-steady state approximation which yields a comprehensive classification of the steady state operating regimes under conditions when the enzyme concentrations are comparable to or larger than that of the substrate. Where possible we derive simple expressions characterizing the input-output behavior of the system. Our analysis suggests that enhanced sensitivity occurs if the concentration of at least one of the converter enzymes is smaller (but not necessarily much smaller) than that of the substrate and if that enzyme is saturated. Conversely, if both enzymes are saturated and at least one of the enzyme concentrations exceeds that of the substrate the system exhibits concentration robustness with respect to changes in that enzyme concentration. Also, depending on the enzymes saturation degrees and the ratio between their maximal reaction rates the total fraction of phosphorylated substrate may increase, decrease or change nonmonotonically as a function of the total substrate concentration. The latter finding may aid the interpretation of experiments involving genetic manipulations of enzyme and substrate abundances.

systems biology↗
bioRxiv2018

Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

We describe the rapid and reproducible acquisition of quantitative proteome maps for the NCI-60 cancer cell lines and their use to reveal cancer biology and drug response determinants. Proteome datasets for the 60 cell lines were acquired in duplicate within 30 working days using pressure cycling technology and SWATH mass spectrometry. We consistently quantified 3,171 proteotypic proteins annotated in the SwissProt database across all cell lines, generating a data matrix with 0.1% missing values, allowing analyses of protein complexes and pathway activities across all the cancer cells. Systematic and integrative analysis of the genetic variation, mRNA expression and proteomic data of the NCI-60 cancer cell lines uncovered complementarity between different types of molecular data in the prediction of the response to 240 drugs. We additionally identified novel proteomic drug response determinants for clinically relevant chemotherapeutic and targeted therapies. We anticipate that this study represents a significant advance toward the translational application of proteotypes, which reveal biological insights that are easily missed in the absence of proteomic data.

systems biology↗
bioRxiv2017

Analysis of Copy Number Loss of the ErbB4 Receptor Tyrosine Kinase in Glioblastoma

Current treatments for glioblastoma multiforme (GBM)--an aggressive form of brain cancer--are minimally effective and yield a median survival of 14.6 months and a two-year survival rate of 30%. Given the severity of GBM and the limitations of its treatment, there is a need for the discovery of novel drug targets for GBM and more personalized treatment approaches based on the characteristics of an individuals tumor. Most receptor tyrosine kinases--such as EGFR--act as oncogenes, but publicly available data from the Cancer Cell Line Encyclopedia (CCLE) indicates copy number loss in the ERBB4 RTK gene across dozens of GBM cell lines, suggesting a potential tumor suppressor role. This loss is mutually exclusive with loss of its cognate ligand NRG1 in CCLE as well, more strongly suggesting a functional role. The availability of higher resolution copy number data from clinical GBM patients in The Cancer Genome Atlas (TCGA) revealed that a region in Intron 1 of the ERBB4 gene was deleted in 69.1% of tumor samples harboring ERBB4 copy number loss; however, it was also found to be deleted in the matched normal tissue samples from these GBM patients (n = 81). Using the DECIPHER Genome Browser, we also discovered that this mutation occurs at approximately the same frequency in the general population as it does in the disease population. We conclude from these results that this loss in Intron 1 of the ERBB4 gene is neither a de novo driver mutation nor a predisposing factor to GBM, despite the indications from CCLE. A biological role of this significantly occurring genetic alteration is still unknown. While this is a negative result, the broader conclusion is that while copy number data from large cell line-based data repositories may yield compelling hypotheses, careful follow up with higher resolution copy number assays, patient data, and general population analyses are essential to codify initial hypotheses.

systems biology↗
bioRxiv2018

FPtool a software tool to obtain in silico genotype-phenotype signatures and fingerprints based on massive model simulations

Fptool is an intuitive tool that provides to the user a preliminary fingerprint of the behaviour simulated by a mathematical model of a biochemical network when comparing two biological scenarios defined by the user. Here we present the tool and we applied to an already published mathematical model of lung legionella infection. The fingerprint obtained correlates with the results obtained in the original article. This tool is optimal for the users that would like to obtain a fast and preliminary view of the qualitative behaviour of a mathematical model before deciding for more elaborate analyses.

systems biology↗
bioRxiv2018

Determining protein structures using genetics

Determining the three dimensional structures of macromolecules is a major goal of biological research because of the close relationship between structure and function. Structure determination usually relies on physical techniques including x-ray crystallography, NMR spectroscopy and cryo-electron microscopy. Here we present a method that allows the high-resolution three-dimensional structure of a biological macromolecule to be determined only from measurements of the activity of mutant variants of the molecule. This genetic approach to structure determination relies on the quantification of genetic interactions (epistasis) between mutations and the discrimination of direct from indirect interactions. This provides a new experimental strategy for structure determination, with the potential to reveal functional and in vivo structural conformations at low cost and high throughput.

systems biology↗
bioRxiv2018

Protein phase separation provides long-term memory of transient spatial stimuli

Protein/RNA clusters arise frequently in spatially-regulated biological processes, from the asymmetric distribution of P granules and PAR proteins in developing embryos to localized receptor oligomers in migratory cells. This co-occurrence suggests that protein clusters might possess intrinsic properties that make them a useful substrate for spatial regulation. Here, we demonstrate that protein droplets show a robust form of spatial memory, maintaining the spatial pattern of an inhibitor of droplet formation long after it has been removed. Despite this persistence, droplets can be highly dynamic, continuously exchanging monomers with the diffuse phase. We investigate the principles of biophysical spatial memory in three contexts: a computational model of phase separation; a novel optogenetic system where light can drive rapid, localized dissociation of liquid-like protein droplets; and membrane-localized signal transduction from clusters of receptor tyrosine kinases. Our results suggest that the persistent polarization underlying many cellular and developmental processes could arise through a simple biophysical process, without any additional requirement for biochemical positive and negative feedback loops.\n\nHighlightsO_LIWe introduce PixELLs, an optogenetic system for protein droplet disassembly.\nC_LIO_LIModeling and experiments demonstrate long-term memory of local droplet dissociation.\nC_LIO_LIDroplets remember spatial stimuli in nuclei, the cytosol and on cell membranes.\nC_LIO_LIFGFR-optoDroplets convert transient local inputs to persistent cytoskeletal responses.\nC_LI

systems biology↗
bioRxiv2018

Regeneration-Associated Cells Improve Recovery from Myocardial Infarction through Enhanced Vasculogenesis, Anti-inflammation, and Cardiomyogenesis

BackgroundConsidering the impaired function of regenerative cells in patients with comorbidities and associated risk factors, cell therapy to enhance the regenerative microenvironment was designed using regeneration-associated cells (RACs), including endothelial progenitor cells (EPCs) and anti-inflammatory cells.\n\nMethodsRACs were prepared by quality and quantity control culture of blood mononuclear cells (QQMNCs). The peripheral blood mononuclear cells (PBMNCs) were isolated from Lewis rats and conditioned for 5 days using a medium containing stem cell factors, thrombopoietin, Flt-3 ligand, vascular endothelial growth factor, and interleukin-6 to generate QQMNCs.\n\nResultsIn vitro EPC colony forming assays demonstrated a 5.3-fold increase in the definitive colony-forming EPCs and vasculogenic EPCs, in comparison to naive PBMNCs. Flow cytometry analysis revealed that QQMNCs were enriched with RACs, such as EPCs (28.9-fold, P<0.0019) and M2 macrophages (160.3-fold, P<0.0002). Cell transcriptome analysis revealed that angiogenesis (angpt1, angpt2, and vegfb), stem/progenitor (c-kit and sca-1) and anti-inflammation related (arg-1, erg-2, tgfb, and foxp3) genes were highly expressed in QQMNCs. For in vivo cell transplantation experiments, 1x105 cells were administered via the tail vein into syngeneic rat models of myocardial infarction (MI). Echocardiographic data showed that QQMNCs-transplanted group (QQ-Tx) preserved cardiac function and fraction shortening (45.5{+/-}4.6%) at 28-days post-MI in comparison with PBMNCs-transplanted (PB-Tx) (30.9{+/-}6.4%, P<0.0001) and Control (32.2{+/-}7.7%, P<0.0008) groups. Histological analysis revealed that QQ-Tx showed enhanced angiogenesis and reduced interstitial left ventricular fibrosis, along with a decrease in neutrophils and an increase in M2 macrophages in the acute phase of MI. Cell tracing studies revealed that intravenously administered QQMNCs preferentially homed to ischemic tissues via blood circulation, while PBMNCs did not. QQ-Tx showed markedly upregulated early cardiac transcriptional cofactors (Nkx2-5, 29.8-fold, and Gata-4, 5.2- fold) as well as c-kit (4.5-fold) while these markers were downregulated in PB-Tx. In QQ-Tx animals, de novo blood vessels formed a \"Biological Bypass\" as observed macroscopically and microscopically, while PB-Tx and Control-Tx groups never developed epicardial blood vessels but showed severe fibrotic adhesion to the surrounding tissues.\n\nConclusionQQMNCs, as RACs derived from rat PBMNCs, conferred potent angiogenic and anti-inflammatory properties to the regenerative microenvironment, enhancing myocardiogenesis and functional recovery of rat MI hearts.

systems biology↗
bioRxiv2018

iOmicsPASS: a novel method for integration of multi-omics data over biological networks and discovery of predictive subnetworks

We developed iOmicsPASS, an intuitive method for network-based multi-omics data integration and detection of biological subnetworks for phenotype prediction. The method converts abundance measurements into co-expression scores of biological networks and uses a powerful phenotype prediction method adapted for network-wise analysis. Simulation studies show that the proposed data integration approach considerably improves the quality of predictions. We illustrate iOmicsPASS through the integration of quantitative multi-omics data using transcription factor regulatory network and protein-protein interaction network for cancer subtype prediction. Our analysis of breast cancer data identifies network signatures surrounding established markers of molecular subtypes. The analysis of colorectal cancer data highlights a protein interactome surrounding key proto-oncogenes as predictive features of subtypes, rendering them more biologically interpretable than the approaches integrating data without a priori relational information. However, the results indicate that current molecular subtyping is overly dependent on transcriptomic data and crude integrative analysis fails to account for molecular heterogeneity in other -omics data. The analysis also suggest that tumor subtypes are not mutually exclusive and future subtyping should therefore consider multiplicity in assignments.\n\nAvailability: https://github.com/cssblab/iOmicsPASS

systems biology↗
bioRxiv2018

Master regulators of signaling pathways coordinate key processes of embryonic development in breast cancer

Signal transduction pathways allow cells to respond to environmental cues and can induce intracellular changes. In some contexts, like embryonic development, signal transduction plays crucial roles in cell fate determination and differentiation, while in developed organisms some of this processes contribute in the maintenance of the structural integrity of tissues.\n\nTumor cells are recognized as having deregulated signaling which leads to a series of abnormal behaviors known as the hallmarks of cancer. Although gene regulation is often viewed as the last step in signal transduction, transcriptional regulation of the components of a pathway may impact in the long term deregulation observed in tumors. The study of gene regulatory networks centered around genes of the signal transduction pathways allows the identification of transcriptional regulators with the greatest influence over the signal transduction gene signature, also denominated Master Regulators.\n\nIn this work we identify, the master regulators that regulate the expression of genes of 25 relevant pathways grouped in KEGG within the category of signal transduction in a breast cancer dataset. For this purpose we implemented a modified MARINa algorithm that identifies, from a network of regulons, those that possess more differentially expressed genes related to the process to be studied. We identified CLOCK, TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3 and GLI2 as the top 10 master regulators of signaling pathways in breast cancer. Nine of them are recognized for taking part in embryonic development associated processes.\n\nIndividual enrichment GO biological function for each TMR regulons showed to be significantly enriched in embryonic development related processes. Hedgehog signaling pathway was shown as enriched and also highly deregulated. The genes of the HOXA family are shared among most of the TMRs. Overall, this suggests the importance of the aberrant reprogramming of mechanisms present during embryonic development, being coopted in favor of tumor development.

systems biology↗
bioRxiv2017

Rule-based modeling using wildcards

Many biological molecules exist in multiple variants, such as proteins with different post-translational modifications, DNAs with different sequences, and phospholipids with different chain lengths. Representing these variants as distinct species, as most biochemical simulators do, leads to the problem that the number of species, and chemical reactions that interconvert them, typically increase combinatorially with the number of ways that the molecules can vary. This can be alleviated by \"rule-based modeling methods,\" in which software generates the chemical reaction network from relatively simple \"rules.\" This article presents a new approach to rule-based modeling. It is based on wildcards that match to species names, much as wildcards can match to file names in computer operating systems. It is much simpler to use than the formal rule-based modeling approaches developed previously but can also lead to unintended consequences if not used carefully. This article demonstrates rule-based modeling with wildcards through examples for: signaling systems, protein complexation, polymerization, nucleic acid sequence copying and mutation, the \"SMILES\" chemical notation, and others. The method is implemented in Smoldyn, a spatial and stochastic biochemical simulator, for both the generate-first and on-the-fly expansion, meaning whether the reaction network is generated before or during the simulation.

systems biology↗
bioRxiv2017

The Fes tyrosine kinase guides CD19 receptor fate in B-cells by shaping regulatory Src phosphorylation networks

The c-Fes protein tyrosine kinase is a proto-oncogene that can also act as a tumor suppressor. We implemented an unbiased phosphoproteomics-based analysis that establishes cognate kinase-substrate associations, and revealed that c-Fes directly phosphorylates Dok1, Ptpn18 and Sts1, facilitating recruitment of the Src inhibitory kinase Csk to these substrates. These interactions resulted in modulation of Src signaling following B-cell receptor (BCR) stimulation and subsequent alteration of the protein levels of CD19, a membrane-localized BCR co-receptor and emerging key protein affecting the development of B- and plasma cell-lymphoma. Strikingly, manipulating c-Fes expression levels drove opposing biological outcomes. Low-level exogenous c-Fes expression led to a strong increase in CD19 protein levels while high c-Fes expression abolished CD19 protein levels. Thus, we propose that a balance of c-Fes and Src signaling can regulate receptor maintenance, which may influence cellular outcome such as tumorigenesis or tumor suppression.

systems biology↗
bioRxiv2018

Hierarchical organization of the human cell from a cancer coessentiality network

Genetic interactions mediate the emergence of phenotype from genotype. Systematic survey of genetic interactions in yeast showed that genes operating in the same biological process have highly correlated genetic interaction profiles, and this observation has been exploited to infer gene function in model organisms. Systematic surveys of digenic perturbations in human cells are also highly informative, but are not scalable, even with CRISPR-mediated methods. As an alternative, we developed an indirect method of deriving functional interactions. We show that genes having correlated knockout fitness profiles across diverse, non-isogenic cell lines are analogous to genes having correlated genetic interaction profiles across isogenic query strains, and similarly implies shared biological function. We constructed a network of genes with correlated fitness profiles across 400 CRISPR knockout screens in cancer cell lines into a \"coessentiality network,\" with up to 500-fold enrichment for co-functional gene pairs, enabling strong inference of human gene function. Modules in the network are connected in a layered web that gives insight into the hierarchical organization of the cell.

systems biology↗
bioRxiv2018

Harnessing formal concepts of biological mechanism to analyze human disease

Mechanism is a widely used concept in biology: in 2017 more than 10% of PubMed abstracts used the term. Thus, searching for and reasoning about mechanisms is fundamental to much of biomedical research, but until now there has been almost no computational infrastructure for this purpose. Recent work in the philosophy of science has explored the central role that the search for mechanistic accounts of biological phenomena plays in biomedical research, providing a conceptual basis for representing and analyzing biological mechanism. The foundational categories for components of mechanisms - entities and activities - guide the development of general, abstract types of biological mechanism parts. Building on that analysis, we have developed a formal framework for describing and representing biological mechanism, MecCog, and applied it to describing mechanisms underlying human genetic disease. Mechanisms are depicted using a graphical notation. Key features are assignment of mechanism components to stages of biological organization and classes; visual representation of uncertainty, ignorance, and ambiguity; and tight integration with literature sources. The MecCog framework facilitates analysis of many aspects of disease mechanism, including the prioritization of future experiments, probing of gene-drug and gene-environment interactions, identification of possible new drug targets, personalized drug choice, analysis of non-linear interactions between relevant genetic loci, and classification of disease based on mechanism.

systems biology↗