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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 739 records · Page 41Linked to original sources

bioRxiv2016

The Plasmodium falciparum drugome and its polypharmacological implications

Malaria is a disease contracted by over 200 million people each year, mostly in developing countries. The primary causative agent, Plasmodium falciparum (P. falciparum) has shown increased resistance to existing drugs, hence new treatments are needed quickly. To this end we performed a high-throughput systems-level analysis, mapping existing FDA drugs with the potential for repurposing against targets from the P. falciparum structural proteome. The resulting P. falciparum drugome (P.falciparum-drugome) was used to prioritize potential new anti-malaria candidate targets and highlight some novel FDA approved drugs that have apparent anti-malaria effects for possible use as multi-target therapeutics.

Systems Biology↗

bioRxiv2016

Nature lessons: the whitefly bacterial endosymbiont is a minimal amino acid factory with unusual energetics

Bacterial lineages that establish obligate symbiotic associations with insect hosts are known to possess highly reduced genomes with streamlined metabolic functions that are commonly focused on amino acid and vitamin synthesis. We constructed a genome-scale metabolic model of the whitefly bacterial endosymbiont Candidatus Portiera aleyrodidarum to study the energy production capabilities using stoichiometric analysis. Strikingly, the results suggest that the energetic metabolism of the bacterial endosymbiont relies on the use of pathways related to the synthesis of amino acids and carotenoids. A deeper insight showed that the ATP production via carotenoid synthesis may also have a potential role in the regulation of amino acid production. The coupling of energy production to anabolism suggest that minimization of metabolic networks as a consequence of genome size reduction does not necessarily limit the biosynthetic potential of obligate endosymbionts.

Systems Biology↗

bioRxiv2016

Glycerol as the actuator of integral feedback control in yeast osmotic stress signaling

van Oudenaarden and colleagues employ elegant experiments and control theory to model perfect adaptation of the yeast osmotic stress response - precise return of turgor pressure to its optimal, steady-state value despite variation in system parameters and the continued presence of osmotic stress. Their data convincingly show that nuclear signaling and cell volume undergo \"robust perfect adaptation\" implying integral feedback must restore their steady state values. However they incorrectly map the integrator onto a minimal network that violates assumptions implicit in conventional block diagrams. Using known features of osmotic stress signaling and results presented by the authors, I argue that glycerol concentration - the integral of the rate of glycerol accumulation (synthesis minus leakage) - transforms metabolic energy into increased osmolarity that drives water influx and restoration of turgor pressure. Integral feedback control actuated through glycerol synthesis is logically positioned to provide perfect adaptation and robustness in hyperosmotic stress responses.

Systems Biology↗

bioRxiv2016

Effective Dynamic Models of Metabolic Networks

Mathematical models of biochemical networks are useful tools to understand and ultimately predict how cells utilize nutrients to produce valuable products. Hybrid cybernetic models in combination with elementary modes (HCM) is a tool to model cellular metabolism. However, HCM is limited to reduced metabolic networks because of the computational burden of calculating elementary modes. In this study, we developed the hybrid cybernetic modeling with flux balance analysis or HCM-FBA technique which uses flux balance solutions instead of elementary modes to dynamically model metabolism. We show HCM-FBA has comparable performance to HCM for a proof of concept metabolic network and for a reduced anaerobic E. coli network. Next, HCM-FBA was applied to a larger metabolic network of aerobic E. coli metabolism which was infeasible for HCM (29 FBA modes versus more than 153,000 elementary modes). Global sensitivity analysis further reduced the number of FBA modes required to describe the aerobic E. coli data, while maintaining model fit. Thus, HCM-FBA is a promising alternative to HCM for large networks where the generation of elementary modes is infeasible.

Systems Biology↗

bioRxiv2016

The contribution of alternative splicing probability to the coding expansion of the genome

Alternative splicing results in the inclusion or exclusion of exons in an RNA, thereby allowing a single gene to code for multiple RNA isoforms. Genes are often composed of many exons, allowing combinatorial choice to significantly expand the coding potential of the genome. How much coding potential is gained by alternative splicing and what is the main contributor: alternative-splicing-depth or exon-count? Here we develop a splice-site-centric quantification method, allowing us to characterize transcriptome-wide alternative splicing with a simple probabilistic model, enabling species-wide comparison. We use information theory to quantify the coding potential gain and show that an increase in alternative splicing probability contributes more to transcriptome expansion than exon-count. Our results suggest that dominant isoforms are co-expressed alongside many minor isoforms. We propose that this solves two problems simultaneously, that is, expression of functional isoforms and expansion of the transcriptome landscape potentially without a direct function, but available for evolution.

Systems Biology↗

bioRxiv2016

Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer

BackgroundDiscovering patient subtypes and molecular drivers of a subtype are difficult and driving problems underlying most modern disease expression studies collected across patient populations. Expression patterns conserved across multiple expression datasets from independent disease studies are likely to represent important molecular events underlying the disease.\n\nMethodsWe present the INSPIRE (INferring Shared modules from multiPle gene expREssion datasets) method to infer highly coherent and robust modules of co-expressed genes and the dependencies among the modules from multiple expression datasets. Focusing on inferring modules and their dependencies conserved across multiple expression datasets is important for several reasons. First, using multiple datasets will increase the power to detect robust and relevant patterns (modules and dependencies among modules). Second, INSPIRE enables the use of multiple datasets that contain different sets of genes due to, e.g., the difference in microarray platforms. Many methods designed for expression data analysis cannot integrate multiple datasets with variable discrepancy to infer a single combined model, whereas INSPIRE can naturally model the dependencies among the modules even when a large proportion of genes are not observed on a certain platform.\n\nResultsWe evaluated INSPIRE on synthetically generated datasets with known underlying network structure among modules, and gene expression datasets from multiple ovarian cancer studies. We show that the model learned by INSPIRE can explain unseen data better and can reveal prior knowledge on gene functions more accurately than alternative methods. We demonstrate that applying INSPIRE to nine ovarian cancer datasets leads to the identification of a new marker and potential molecular driver of tumor-associated stroma - HOPX. We also demonstrate that the HOPXmodule strongly overlaps with the genes defining the mesenchymal patient subtype identified in The Cancer Genome Atlas (TCGA) ovarian cancer data. We provide evidence for a previously unknown molecular basis of tumor resectability efficacy involving tumor-associated mesenchymal stem cells represented by HOPX.\n\nConclusionsINSPIRE extracts a low-dimensional description from multiple gene expression data, which consists of modules and their dependencies. The discovery of a new tumor-associated stroma marker, HOPX, and its module suggests a previously unknown mechanism underlying tumor-associated stroma.

Systems Biology↗

bioRxiv2016

Transcriptome data analysis reveals correlation between developmentally altered gene expression and offspring phenotype in epigenetic inheritance

A recent study investigated sperm mediated inheritance of diet induced metabolic phenotype, reported underlying regulation of MERVL targets and ribosomal protein genes in embryos, and suggested that the altered regulation observed may cause placentation defects which can secondarily result in abnormal metabolism. An analysis of available transcriptomic data however reveals a direct link between the developmentally altered genes and the offspring phenotype.

Systems Biology↗

bioRxiv2016

Metabolic signatures of birth weight in 18288 adolescents and adults

BackgroundLower birth weight is associated with increased susceptibility to cardiometabolic diseases in adulthood, but the underlying molecular pathways are incompletely understood. We examined associations of birth weight with a comprehensive metabolic profile measured in adolescents and adults.\n\nMethodsHigh-throughput nuclear magnetic resonance metabolomics and biochemical assays were used to quantify 87 circulating metabolic measures in seven cohorts from Finland and the United Kingdom comprising altogether 18 288 individuals (mean age 26 years, range 15-75). Metabolic associations with birth weight were assessed by linear regression models adjusted for sex, gestational age, and age at blood sampling. The metabolic associations with birth weight were compared to the corresponding associations with adult body mass index (BMI).\n\nResultsLower birth weight was adversely associated with cardiometabolic biomarkers, including lipoprotein subclasses, fatty acids, amino acids, and markers of inflammation and impaired liver function (P<0.0015 for 46 measures). Associations were consistent across cohorts with different ages at metabolic profiling, but the magnitudes were weak. The pattern of metabolic deviations associated with lower birth weight resembled the metabolic signature of higher adult BMI (R2=0.77). The resemblance indicated that 1-kg lower birth weight is associated with similar metabolic aberrations as caused by 0.92-units higher BMI in adulthood.\n\nConclusionLower birth weight is associated with adverse biomarker aberrations across multiple metabolic pathways. Coherent metabolic signatures between lower birth weight and higher adult adiposity suggest potentially shared underlying molecular mechanisms. However, the magnitudes of metabolic associations with birth weight are modest in comparison to the effects of adiposity, implying that birth weight is only a weak indicator of metabolic risk in adulthood.\n\nKEY POINTSO_LILower birth weight is adversely associated with a wide range of established and emerging circulating cardiometabolic biomarkers in adulthood, including lipoprotein subclasses and their lipids, fatty acid balance, amino acids, and markers of inflammation and liver function.\nC_LIO_LIThe metabolic associations are consistent across a wide age span from adolescence to retirement age, and similar for men and women.\nC_LIO_LIThe magnitudes of metabolic aberrations are weak for the variation in birth weight observed in general population cohorts. Although the metabolic associations with birth weight are statistically significant, they are likely to be of minor public health relevance.\nC_LIO_LIThe overall metabolic association pattern with lower birth weight closely resembles the metabolic signature of higher adult adiposity, suggesting that shared underlying metabolic pathways may be involved.\nC_LIO_LI1-kg lower birth weight ({approx}2 SD) is associated with similar adverse metabolic effects as caused by 0.92 higher BMI ({approx}0.25 SD) in adulthood. These findings indicate that fetal growth, as assessed by birth weight, only has minor effects on the adult metabolic risk profile in general population settings.\nC_LI

Systems Biology↗

bioRxiv2016

Transcriptional bursting explains the noise-versus-mean relationship in mRNA and protein levels

Recent analysis (Dey et al, 2015), demonstrates that the HIV-1 Long Terminal Repeat (HIV LTR) promoter exhibits a range of possible transcriptional burst sizes and frequencies for any mean-expression level. However, these results have also been interpreted as demonstrating that cell-to-cell expression variability (noise) and mean are uncorrelated, a significant deviation from previous results. Here, we re-examine the available mRNA and protein abundance data for the HIV LTR and find that noise in mRNA and protein expression scales inversely with the mean along analytically predicted transcriptional burst-size manifolds. We then experimentally perturb transcriptional activity to test a prediction of the multiple burst-size model: that increasing burst frequency will cause mRNA noise to decrease along given burst-size lines as mRNA levels increase. The data show that mRNA and protein noise decrease as mean expression increases, supporting the canonical inverse correlation between noise and mean.\n\nConflict of InterestThe authors declare that they have no conflict of interest.

Systems Biology↗

bioRxiv2016

Transplantation of Endothelial Progenitor Cells Solely Leads to Development of Functional Neo-vessels in vivo.

It has been believed that de novo vessel formation (neo-vasculogenesis) can be induced by co-transplantation of pericytes or mesenchymal stem/progenitor cells (MSPC) with endothelial cells or endothelial colony-forming cells (ECFC). The requirement for co-transplantation of two adult progenitor cells is one factor that can potentially complicate the process of therapeutic vasculogenesis which hampers the development of strategies for therapeutic intervention referred to as regenerative medicine. Here we employed a novel strategy for therapeutic vessel development by transplanting endothelial colony forming progenitor cells solely to immune compromised mice and detect vessel formation capacity of single ECFC transplants compared to ECFC/MSPC co-transplants. We applied umbilical cord derived and bone marrow derived-MSPC and umbilical cord derived ECFC with different total cell number for subcutaneous transplantation in matrix composites either alone or mixed at a ratio of 1:5 subcutaneously into immune deficient NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ; NSG mice. Implants were harvested one day, one, two, eight and 24 weeks after transplantation for detecting the state of vessel formation and stability of the transplants by histological assessments. Additionally, endothelial progenitor cells derived from various human tissues such as umbilical cord blood, peripheral blood and white adipose tissue were used to assess their potential for vessel formation in vivo.\n\nResults confirmed that single transplantation of ECFCs with a higher cell number and later in the time course after transplantation is as efficient as co-transplantation of ECFC with MSPC at forming stable-perfused human vessels. Amongst ECFCs isolated from different human sources, white adipose tissue derived ECFC are most potent in forming neo-vessels (micro-vessels) in vivo, thus WAT-ECFC could be an optimal cell for vasculogenesis regenerative application.\n\nCo-transplantation of ECFC and MSPC with the defined 5:1 ratio or sole ECFC with a higher cell dosage was essential for vessel generation in vivo.

Systems Biology↗

bioRxiv2016

Vasculogenesis Potential of Mesenchymal and Endothelial Stem Cells Isolated from Various Human Tissues.

Neo vessel formation can be initiated by co-transplantation of mesenchymal stem cells (MSC) with endothelial colony-forming cells (ECFC). The two adult stem cell types can be isolated and expanded from a variety of tissues to be used for regenerative applications pro-angiogenesis.\n\nHere we performed a systematic study to evaluate the neo-vasculogenesis potential of MSC and ECFC isolated from various human tissues. MSC were isolated, purified and expanded in vitro from umbilical cord (UC) and umbilical cord blood (UCB), white adipose tissue (WAT), bone marrow (BM), and amniotic membrane of placenta (AMN).\n\nECFC were isolated from UC and UCB, WAT and peripheral blood (PB). ECFC and MSC and were co-transplanted admixed with extracellular matrix (Matrigel(R)) at a ratio of 5:1 to immune-deficient NSG mice, subcutaneously. The transplants were harvested after two weeks and the state of vessel formation and stability in the explants were investigated using immune-histochemical methods. The number of created micro-vessels was quantified using Hematoxylin & Eosin (H&E) staining followed by image J quantification.\n\nResults showed that ECFC and MSC possess variable capacity in contributing to neo-vasculogenesis. WAT and UCB-derived ECFC and WAT, UCB and BM-derived MSC are most potent cells in terms of neo-vessel formation in vivo. UC-derived ECFC and AMN-derived MSC have been shown to be least potent in contributing to neo-vasculogenesis. This variability might be due to variable phenotypes, or different genetic profiles of MSC and ECFC isolated from different tissues and/or donors.\n\nThe findings might give an insight into better regenerative strategies for neo-vessel formation in vivo.

Systems Biology↗

bioRxiv2016

Chromatin structure shapes the search process of transcription factors

The diffusion of regulatory proteins within the nucleus plays a crucial role in the dynamics of transcriptional regulation. The standard model assumes a 3D plus ID diffusion process: regulatory proteins either move freely in solution or slide on DNA. This model however does not considered the 3D structure of chromatin. Here we proposed a multi-scale stochastic model that integrates, for the first time, high-resolution information on chromatin structure as well as DNA-protein interactions. The dynamics of transcription factors was modeled as a slide plus jump diffusion process on a chromatin network based on pair-wise contact maps obtained from high-resolution Hi-C experiments. Our model allowed us to uncover the effects of chromatin structure on transcription factor occupancy profiles and target search times. Finally, we showed that binding sites clustered on few topological associated domains leading to a higher local concentration of transcription factors which could reflect an optimal strategy to efficiently use limited transcriptional resources.

Systems Biology↗

bioRxiv2016

Predicting Drug Synergy and Antagonism from Genetic Interaction Neighborhoods

Although drug combinations have proven efficacious in a variety of diseases, the design of such regimens often involves extensive experimental screening due to the myriad choice of drugs and doses. To address these challenges, we utilize the budding yeast Saccharomyces cerevisiae as a model organism to evaluate whether drug synergy or antagonism is mediated through genetic interactions between their target genes. Specifically, we hypothesize that if the inhibition targets of one chemical compound are in close proximity to those of a second compound in a genetic interaction network, then the compound pair will exhibit synergy or antagonism. Graph metrics are employed to make precise the notion of proximity in a network. Knowledge of genetic interactions and small-molecule targets are compiled through literature sources and curated databases, with predictions validated according to experimentally determined gold standards. Finally, we test whether genetic interactions propagate through networks according to a \"guilt-by-association\" framework. Our results suggest that close proximity between the target genes of one drug and those of another drug does not strongly predict synergy or antagonism. In addition, we find that the extent to which the growth of a double gene mutant deviates from expectation is moderately anti-correlated with their distance in a genetic interaction network.

Systems Biology↗

bioRxiv2016

Inferring interaction partners from protein sequences

Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multi-protein complexes, and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners. Hence, the sequences of interacting partners are correlated. Here we exploit these correlations to accurately identify which proteins are specific interaction partners from sequence data alone. Our general approach, which employs a pairwise maximum entropy model to infer direct couplings between residues, has been successfully used to predict the three-dimensional structures of proteins from sequences. Building on this approach, we introduce an iterative algorithm to predict specific interaction partners from among the members of two protein families. We assess the algorithm's performance on histidine kinases and response regulators from bacterial two-component signaling systems. The algorithm proves successful without any a priori knowledge of interaction partners, yielding a striking 0.93 true positive fraction on our complete dataset, and we uncover the origin of this surprising success. Finally, we discuss how our method could be used to predict novel protein-protein interactions.

Systems Biology↗

bioRxiv2016

Methods for High-Throughput Drug Combination Screening and Synergy Scoring

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

Systems Biology↗

bioRxiv2016

Shear-induced nitric oxide production by endothelial cells

We present a biochemical model of the wall shear stress (WSS)-induced activation of endothelial nitric oxide synthase (eNOS) in an endothelial cell (EC). The model includes three key mechanotransducers: mechanosensing ion channels, integrins and G-protein-coupled receptors. The reaction cascade consists of two interconnected parts. The first is rapid activation of calcium, which results in formation of calcium-calmodulin complexes, followed by recruitment of eNOS from caveolae. The second is phosphoryaltion of eNOS by protein kinases PKC and AKT. The model also includes a negative feedback loop due to inhibition of calcium influx into the cell by cyclic guanosine monophosphate (cGMP). In this feedback, increased nitric oxide (NO) levels cause an increase in cGMP levels, so that cGMP inhibition of calcium influx can limit NO production. The model was used to predict the dynamics of NO production by an EC subjected to a step increase of WSS from zero to a finite physiologically relevant value. Among several experimentally observed features, the model predicts a highly nonlinear, bipha-sic transient behavior of eNOS activation and NO production: a rapid initial activation due to the very rapid influx of calcium into the cytosol (occurring within 1 to 5 minutes) is followed by a sustained period of activation due to protein kinases.\n\nAcronymsAKT, protein kinase B; [Ca2+]c, [Ca2+]s, [Ca2+]e and [Ca2+]b, cytosolic, stored, external and buffer concentrations of calcium ions, respectively; Ca3-CaM and Ca4-CaM, calcium-calmodulin complexes with 3 and 4 calcium ions bound to CaM, respectively; CaM, calmodulin; CCE, capacitative calcium entry; cGMP, cyclic guanosine monophosphate; EC, endothelial cell; ECM, extracellular matrix; eNOS, endothelial nitric oxide synthase; eNOScav, eNOS bound to caveolin; eNOS*, eNOS-CaM complex phosphorylated at Ser-1197; eNOS0, caveolin-bound eNOS phosphorylated at Thr-495; ER, endoplasmic reticulum; FAK, focal adhesion kinase; G, active G proteins; Gt, total G proteins; GPCR, G-protein-coupled receptors; Hsp90, heat shock protein 90; GTP, guanosine triphosphate; IP3, inositol triphosphate; L-Arg, L-form of arginine; MSIC, mechanosensing ion channel; NO, nitric oxide; O2, oxygen; PIP2, phosphatidylinositol 4,5-bisphosphate; PIP3, phos-phatidylinositol (3,4,5)-triphosphate; PI3K, phosphatidylinositide 3-kinases; PKC, protein kinase C; RBC, red blood cell; sGC, soluble guanylate cyclase; WSS, wall shear stress

Systems Biology↗

bioRxiv2016

Cell-cycle coupled expression minimizes random fluctuations in gene product levels

Expression of many genes varies as a cell transitions through different cell-cycle stages. How coupling between stochastic expression and cell cycle impacts cell-to-cell variability (noise) in the level of protein is not well understood. We analyze a model, where a stable protein is synthesized in random bursts, and the frequency with which bursts occur varies within the cell cycle. Formulas quantifying the extent of fluctuations in the protein copy number are derived and decomposed into components arising from the cell cycle and stochastic processes. The latter stochastic component represents contributions from bursty expression and errors incurred during partitioning of molecules between daughter cells. These formulas reveal an interesting trade-off: cell-cycle dependencies that amplify the noise contribution from bursty expression also attenuate the contribution from partitioning errors. We investigate existence of optimum strategies for coupling expression to the cell cycle that minimize the stochastic component. Intriguingly, results show that a zero production rate throughout the cell cycle, with expression only occurring just before cell division minimizes noise from bursty expression for a fixed mean protein level. In contrast, the optimal strategy in the case of partitioning errors is to make the protein just after cell division. We provide examples of regulatory proteins that are expressed only towards the end of cell cycle, and argue that such strategies enhance robustness of cell-cycle decisions to the intrinsic stochasticity of gene expression.

Systems Biology↗

bioRxiv2016

Exact Bayesian lineage tree-based inference identifies Nanog negative autoregulation in mouse embryonic stem cells

The autoregulatory motif of Nanog, a heterogeneously expressed core pluripotency factor in mouse embryonic stem cells, remains debated. Although recent time-lapse microscopy data provide the unparalleled ability to monitor Nanog expression at the single-cell level, the extraction of mechanistic knowledge is precluded by the lack of inference techniques suitable for noisy, incomplete and heterogeneous data obtained from proliferating cell populations.\n\nThis work identifies Nanogs autoregulatory motif from quantified time-lapse fluorescence line-age trees with STILT (Stochastic Inference on Lineage Trees), a novel particle-filter based algorithm for exact Bayesian parameter inference and model selection of stochastic models. We first verify STILTs ability to accurately infer parameters and select the correct autoregulatory motif from simulated data. We then apply STILT to time-lapse microscopy movies of a fluorescent Nanog fusion protein reporter and reject the possibility of positive autoregulation. Finally, we use STILT for experimental design, perform in silico overexpression simulations, and experimentally validate model predictions via exogenous Nanog overexpression. We finally conclude that the protein expression dynamics and overexpression experiments strongly suggest a weak negative feedback from the protein on the DNA activation rate.\n\nWe find that a simple autoregulatory mechanism can explain the observed heterogeneous Nanog dynamics. This finding has implications on the understanding of the core pluripotency network, such as supporting the ability of mESC populations to diversify their proteomic profile to respond to a spectrum of differentiation cues. Beyond this application STILT constitutes a generally applicable fully Bayesian approach for model selection of gene regulatory models on the basis of time-lapse imaging data of proliferating cell populations. STILT is freely available at: http://www.imsb.ethz.ch/research/claassen/Software/stilt--stochastic-inference-on-lineage-trees.html

Systems Biology↗