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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Small effect-size mutations cumulatively affect yeast quantitative traits

SummaryQuantitative traits are influenced by pathways that have traditionally been defined through genes that have a large loss- or gain-of-function effect. However, in theory, a large number of small effect-size genes could cumulative play a substantial role in pathway function, potentially by acting as \"modifiers\" that tune the levels of large effect size pathway components. To understand the role of these small effect-size genes, we used a quantitative assay to determine the number, strength, and identity of all non-essential genes that affect two galactose-responsive (GAL) traits, in addition to re-analyzing two previously screened quantitative traits. Over a quarter of assayed genes have a detectable effect; approximately two thirds of the quantitative trait variation comes from small effect-size genes. The functions of small effect-size genes are partially overlapping between traits and are enriched in core cellular processes. This implies that genetic variation in one process has the potential to influence behavior or disease in seemingly unconnected processes.\n\nHighlightsO_LIFour yeast quantitative traits are affected by thousands of small effect-size genes.\nC_LIO_LISmall effect-size genes are enriched in core cellular processes\nC_LIO_LIThe effects of these genes are quantitative trait-specific.\nC_LI

systems biology↗

bioRxiv2017

Extracting Intercellular Signaling Network Of Cancer Tissues Using Ligand-Receptor Expression Patterns From Whole-Tumor And Single-Cell Transcriptomes

Many behaviors of cancer, such as progression, metastasis and drug resistance etc., cannot be fully understood by genetic mutations or intracellular signaling alone. Instead, they are emergent properties of cell community that forms a tumor. Studies of tumor heterogeneity reveal that many cancer behaviors critically depend on the intercellular communication mediated by secreted signaling ligands and their cognate receptors that take place between cancer cells and stromal cells. Owing to systematic cancer omics efforts, we studied such cell-cell interactions using data from cancer transcriptome database. We curated a list of more than 2,500 ligand-receptor pairs and developed a method to identify their enrichment in tumors from TCGA pancancer data and to build a cell interaction network from single-cell data for the case of melanoma. Using the specificity of the ligand-receptor interaction and their expressions measured in individual cells, we built a map of a cell-cell communication network which indicate what signal is exchanged between which cell types. Such networks establish a new formal phenotypes which are embodied by the cell communication structure - it may offer new opportunities to identify the molecular signatures which may influence cancer cell behaviors by changing cell population dynamics.

systems biology↗

bioRxiv2017

Intracellular metabolic circuits shape inter-species microbial interactions

Metabolite exchanges in microbial communities give rise to ecological interaction networks that influence ecosystem diversity and stability1-6. These exchanges depend on complex intracellular pathways thus raising the question of whether ecological interactions are inferable from genomes. We address these questions by integrating genome-scale models of metabolism7, to compute the fitness of interacting microbes, with evolutionary game theory, which uses these fitness values to infer evolutionarily stable interactions in multi-species microbial \"games\". After validating our approach using data on sucrose hydrolysis by S. cerevisiae, we performed over 80,000 in silico experiments to evaluate the rise of unidirectional and cross-feeding metabolic dependencies in populations of Escherichia coli secreting 189 amino acid pairs. We found that, despite the diversity of exchanged amino acids, most pairs conform to general patterns of inter-species interactions. However, several amino acid pairs deviate from these patterns due to pleiotropy and epistasis in metabolic pathways. To better understand the emergence of cross-feeding, we performed in silico invasion experiments and found possible evolutionary paths that could lead to such association. Overall, our study provides mechanistic insights into the rise of evolutionarily stable interdependencies, with important implications for biomedicine and microbiome engineering 8-10.

systems biology↗

bioRxiv2017

GlnK facilitates the dynamic regulation of bacterial nitrogen assimilation

Ammonium assimilation in E. coli is regulated by two paralogous proteins (GlnB and GlnK), which orchestrate interactions with regulators of gene expression, transport proteins and metabolic pathways. Yet how they conjointly modulate the activity of glutamine synthetase (GS), the key enzyme for nitrogen assimilation, is poorly understood. We combine experiments and theory to study the dynamic roles of GlnB and GlnK during nitrogen starvation and upshift. We measure time-resolved in vivo concentrations of metabolites, total and post-translationally modified proteins, and develop a concise biochemical model of GlnB and GlnK that incorporates competition for active and allosteric sites, as well as functional sequestration of GlnK. The model predicts the responses of GS, GlnB and GlnK under time-varying external ammonium level in the wild type and two genetic knock-outs. Our results show that GlnK is tightly regulated under nitrogen-rich conditions, yet it is expressed during ammonium run-out and starvation. This suggests a role for GlnK as a buffer of nitrogen shock after starvation, and provides a further functional link between nitrogen and carbon metabolisms.

systems biology↗

bioRxiv2017

Learning The High-Dimensional Immunogenomic Features That Predict Public And Private Antibody Repertoires

Recent studies have revealed that immune repertoires contain a substantial fraction of public clones, which are defined as antibody or T-cell receptor (TCR) clonal sequences shared across individuals. As of yet, it has remained unclear whether public clones possess predictable sequence features that separate them from private clones, which are believed to be generated largely stochastically. This knowledge gap represents a lack of insight into the shaping of immune repertoire diversity. Leveraging a machine learning approach capable of capturing the high-dimensional compositional information of each clonal sequence (defined by the complementarity determining region 3, CDR3), we detected predictive public- and private-clone-specific immunogenomic differences concentrated in the CDR3s N1-D-N2 region, which allowed the prediction of public and private status with 80% accuracy in both humans and mice. Our results unexpectedly demonstrate that not only public but also private clones possess predictable high-dimensional immunogenomic features. Our support vector machine model could be trained effectively on large published datasets (3 million clonal sequences) and was sufficiently robust for public clone prediction across studies prepared with different library preparation and high-throughput sequencing protocols. In summary, we have uncovered the existence of high-dimensional immunogenomic rules that shape immune repertoire diversity in a predictable fashion. Our approach may pave the way towards the construction of a comprehensive atlas of public clones in immune repertoires, which may have applications in rational vaccine design and immunotherapeutics.

systems biology↗

bioRxiv2017

Not by systems alone: identifying functional outliers in rare disease pedigrees

In characterizing a disease, it is common to search for dysfunctional genes by assaying the transcriptome. The resulting differentially expressed genes are typically assessed for shared features, such as functional annotation or co-expression. While useful, the reliability of these systems methods is hard to evaluate. To better understand shared disease signals, we assess their replicability by first looking at gene-level recurrence and then pathway-level recurrence along with co-expression signals across six pedigrees of a rare homogeneous X-linked disorder, TAF1 syndrome. We find most differentially expressed genes are not recurrent between pedigrees, making functional enrichment largely distinct in each pedigree. However, we find two highly recurrent \"functional outliers\" (CACNA1I and IGFBP3), genes acting atypically with respect to co-expression and therefore absent from a systems-level assessment. We show this occurs in re-analysis of Huntingtons disease, Parkinsons disease and schizophrenia. Our results suggest a significant role for genes easily missed in systems approaches.

systems biology↗

bioRxiv2017

Efficient And Accurate Causal Inference With Hidden Confounders From Genome-Transcriptome Variation Data

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into account hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr.\n\nAuthor summaryUnderstanding how genetic variation between individuals determines variation in observable traits or disease risk is one of the core aims of genetics. It is known that genetic variation often affects gene regulatory DNA elements and directly causes variation in expression of nearby genes. This effect in turn cascades down to other genes via the complex pathways and gene interaction networks that ultimately govern how cells operate in an ever changing environment. In theory, when genetic variation and gene expression levels are measured simultaneously in a large number of individuals, the causal effects of genes on each other can be inferred using statistical models similar to those used in randomized controlled trials. We developed a novel method and ultra-fast software Findr which, unlike existing methods, takes into account the complex but unknown network context when predicting causality between specific gene pairs. Findrs predictions have a significantly higher overlap with known gene networks compared to existing methods, using both simulated and real data. Findr is also nearly a million times faster, and hence the only software in its class that can handle modern datasets where the expression levels of ten-thousands of genes are simultaneously measured in hundreds to thousands of individuals.

systems biology↗

bioRxiv2017

An Integrated Mechanistic Model of Pan-Cancer Driver Pathways Predicts Stochastic Proliferation and Death

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this context, synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD. AKT dynamics explain S-phase entry synergy between EGF and insulin, but stochastic ERK dynamics seem to drive cell-to-cell proliferation variability, which in simulations are predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations predict MEK alteration negligibly influences transformation, consistent with clinical data. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, moving towards more rational cancer combination therapy.

systems biology↗

bioRxiv2017

Genomics of cellular proliferation under periodic stress

Living systems control cell growth dynamically by processing information from their environment. Although responses to one environmental change have been intensively studied, little is known about how cells react to fluctuating conditions. Here we address this question at the genomic scale by measuring the relative proliferation rate (fitness) of 3,568 yeast gene deletion mutants in out-of-equilibrium conditions: periodic oscillations between two salinity conditions. Fitness and its genetic variance largely depended on the stress period. Surprisingly, dozens of mutants displayed pronounced hyperproliferation at short periods, identifying unexpected controllers of growth under fast dynamics. We validated the implication of the high-affinity cAMP phosphodiesterase and of a regulator of protein translocation to mitochondria in this control. The results illustrate how natural selection acts on mutations in a fluctuating environment, highlighting unsuspected genetic vulnerabilities to periodic stress in molecular processes that are conserved across all eukaryotes.

systems biology↗

bioRxiv2017

System Identification Using Compressed Sensing Reveals Signaling-Decoding System By Gene Expression

Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system identification method with such different time scales exists. Here we used compressed sensing technology and developed a system identification method using data of different time scales by recovering signals of missing time points. We measured phosphorylation of ERK and CREB, immediate early gene expression products, and mRNAs of decoder genes for neurite elongation in PC12 cell differentiation and performed system identification, revealing the input-output relationships between signaling and gene expression with sensitivity such as graded or switch-like response and with time delay and gain, representing signal transfer efficiency. We predicted and validated the identified system using pharmacological perturbation. Thus, we provide a versatile method for system identification using data with different time scales.\n\nHighlightsO_LIWe developed a system identification method using compressed sensing.\nC_LIO_LIThis method allowed us to find a pathway using data of different time scales.\nC_LIO_LIWe identified a selective signaling-decoding system by gene expression.\nC_LIO_LIWe validated the identified system by pharmacological perturbation.\nC_LI\n\neTOC BlurbWe describe a system identification method of molecular networks with different time-scale data using a signal recovery technique in compressed sensing.

systems biology↗

bioRxiv2017

Quantitative Assessment Of Cell Fate Decision Between Autophagy And Apoptosis

Autophagy and apoptosis regulate cell survival and death, and are implicated in the pathogenesis of many diseases. The same type of stress signals can induce either process, but it is unclear how cells assess cellular damage and make a life or death decision by activating autophagy or apoptosis. A computational model of coupled apoptosis and autophagy is built here to study the systems-level dynamics of the underlying signaling network. The model explains the differential dynamics of autophagy and apoptosis in response to various experimental stress signals. Autophagic response dominates at low-to-moderate stress; whereas the response shifts from autophagy (graded activation) to apoptosis (switch-like activation) with increasing intensity of stress. The model reveals that this dynamic cell fate decision is conferred by a core regulatory network involving cytoplasmic Ca2+ as a rheostat that fine-tunes autophagic and apoptotic responses. A G-protein signaling-mediated feedback loop maintains cytoplasmic Ca2+ level, which in turn governs autophagic response through an AMP-activated protein kinase (AMPK)-mediated feedforward loop. The model identified Ca2+/calmodulin-dependent kinase kinase {beta} (CaMKK{beta}) as a determinant of the opposite roles of cytoplasmic Ca2+ in autophagy regulation. The results also demonstrated that the model could contribute to the development of pharmacological strategies modulate cell fate decisions.

systems biology↗

bioRxiv2017

Faster Growth With Shorter Antigens Explains A VSG Hierarchy During African Trypanosome Infections: A Feint Attack By Parasites

The parasitic African trypanosome, Trypanosoma brucei, evades the adaptive host immune response by a process of antigenic variation that involves the clonal switching of variant surface glycoproteins (VSGs). The VSGs that periodically come to dominate in vivo display a hierarchy, but how this hierarchy arises is not well-understood. Combining publicly available genetic data with mathematical modelling, we report a VSG-length-dependent hierarchical timing of clonal VSG dominance in a mouse model, revealing an inverse correlation between VSG length and trypanosome growth-rate. Our analysis indicates that, among parasites switching to new VSGs, those expressing shorter VSGs preferentially accumulate to a detectable level that is sufficient to trigger an effective immune response. Subsequent elimination of faster-growing parasites then allows slower parasites with longer VSGs to accumulate. This interaction between the host and parasite is able by itself to explain the temporal distribution of VSGs observed in vivo. Thus, our findings reveal a length-dependent hierarchy that operates during T. brucei infection, representing a feint attack diversion tactic utilised during infection by these persistent parasites to out-maneuver the host immune system.\n\nSignificance StatementThe protozoan parasite Trypanosoma brucei causes devastating and lethal diseases in humans and livestock. This parasite continuously evades the host adaptive immune response by drawing on a library of variant surface proteins but the mechanisms determining the timing of surface protein - host interactions are not understood. We report a simple mechanism, based on differential growth of parasites with surface proteins of different lengths, which can explain the hierarchy of variants over time. This allows parasites to evade host immune responses for extended timeframes using limited cohorts of surface proteins. We liken this strategy to a military feint attack, that enhances the parasites ability to evade the host immune response. A similar mechanism may also operate in other important pathogens.

systems biology↗

bioRxiv2017

Systemic Activation Coordinates The Heat Shock Response Of The Insulin/IGF-1 Pathway In Caenorhabditis elegans

Exposure to high temperatures has an adverse effect on cellular processes and results in activation of the cellular heat shock response (HSR), a highly conserved program of inducible genes to maintain protein homeostasis1. The insulin/IGF-1 signaling (IIS) pathway, which has diverse roles from metabolism to stress response and longevity, is activated as part of the HSR2-4. Recent evidence suggest that the IIS pathway is able to affect proteostasis non-autonomously5,6, yet it is not known if it is activated autonomously in stressed cells or systemically as part of an organismic program. In Caenorhabditis elegans, the single forkhead box O (FOXO) homologue DAF-16 functions as the major target of the IIS pathway7 and, together with the heat-shock factor HSF-1, induce the expression of small heat shock proteins in response to heat shock8-10,3. Here we use a novel microfluidic device that allows precise control of the spatiotemporal temperature profile to show that cellular activation of DAF-16 integrates local temperature sensation with systemic signals. We demonstrate that DAF-16 activation in head sensory neurons is essential for DAF-16 activation in other tissues, but show that no known thermosensory neuron is individually required. Our findings demonstrate that systemic and cell-autonomous aspects of stress response act together to facilitate a coordinated cellular response at the organismic level.

systems biology↗

bioRxiv2017

Bistability In Fluorescence From A Purple Non-Sulfur Bacteria

Bistable optical emission has been observed for photosynthetic purple non-sulfur bacteria Rhodobacter capsulatus SB1003. The microbes respond to UV excitation (at 395nm) in a bifurcating way one branch corresponding to increase and the other corresponding to diminishing fluorescent emission in the range 590-685nm.The switching between such bifurcating branches can be observed when parameters like concentration, temperature are varied or static magnetic field is applied. Thus switching from amplification to reduction occurs if fluorophore concentration lowered. Again if temperature is lowered a steady quenching (instead of amplification) of fluorescence is observed. However presence of magnetic field of the order of 0.5 T reverts this and once again the systems resumes its fluoresence amplifying state. We propose that aggregation of bacterial porphyrin and regulation of such aggregation by photon excitation may explain this bistablity. Possible ecological implication of the photosynthetic bistability is suggested.

systems biology↗

bioRxiv2017

Network Reconstruction And Systems Analysis Of Plant Cell Wall Deconstruction By Neurospora crassa

Plant biomass degradation by fungal derived enzymes is rapidly expanding in economic importance as a clean and efficient source for biofuels. The ability to rationally engineer filamentous fungi would facilitate biotechnological applications for degradation of plant cell wall polysaccharides. However, incomplete knowledge of biomolecular networks responsible for plant cell wall deconstruction impedes experimental efforts in this direction. To expand this knowledge base, a detailed network of reactions important for deconstruction of plant cell wall polysaccharides into simple sugars was constructed for the filamentous fungus Neurospora crassa. To reconstruct this network, information was integrated from five heterogeneous data types: functional genomics, transcriptomics, proteomics, genetics, and biochemical characterizations. The combined information was encapsulated into a feature matrix and the evidence weighed to assign annotation confidence scores for each gene within the network. Comparative analyses of RNA-seq and ChIP-seq data shed light on the regulation of the plant cell wall degradation network (PCWDN), leading to a novel hypothesis for degradation of the hemicellulose mannan. The transcription factor CLR-2 was subsequently experimentally shown to play a key role in the mannan degradation pathway of Neurospora crassa. Our network serves as a scaffold for integration of diverse experimental data, leading to elucidation of regulatory design principles for plant cell wall deconstruction by filamentous fungi, and guiding efforts to rationally engineer industrially relevant hyper-production strains.

systems biology↗

bioRxiv2017

Cells Interpret Temporal Information From TGF-β Through A Nested Relay Mechanism

The detection and transmission of the temporal quality of intracellular and extracellular signals is an essential cellular mechanism. It remains largely unexplored how cells interpret the duration information of a stimulus. In this paper, through an integrated quantitative and computational approach we demonstrate that crosstalk among multiple TGF-{beta} activated pathways forms a relay from SMAD to GLI1 that initializes and maintains SNAILl expression, respectively. This transaction is smoothed and accelerated by another temporal switch from elevated cytosolic GSK3 enzymatic activity to reduced nuclear GSK3 enzymatic activity. The intertwined network places SNAIL1 as a key integrator of information from TGF-{beta} signaling subsequently distributed through upstream divergent pathways; essentially cells generate a transient or sustained expression of SNAIL1 depending on TGF-{beta} duration. Other signaling pathways may use similar network structure to encode duration information.

systems biology↗

bioRxiv2017

Mathematical Model For Plant-Insect Interaction With Dynamic Response To PAD4-BIK1 Interaction And Effect Of BIK1 Inhibition

Plant-insect interaction system has been a widely studied model of the ecosystem. Attempts have long been made to understand the numerical behaviour of this counter system and make improvements in it from initial simple analogy based approach with predator-prey model to the recently developed mathematical interpretation of plant-insect interaction including concept of plant immune interventions Caughley and Lawton (1981). In our current work, we propose an improvement in the model, based on molecular interactions behind plant defense mechanism and its effect on the plant growth and insect herbivory. Motivated from an interaction network of plant biomolecules given by Louis and Shah (2014) and extending the model of Chattopadhyay, et al (2001), we propose here a mathematical model to show how plant insect interaction system is governed by the molecular components inside. Insect infestation mediated induction of Botrytis Induced Kinase-1 (BIK-1) protein causes inhibition of Phyto Alexin Deficient-4 (PAD4) protein. Lowered PAD4, being responsible for initiating plant defense mechanism, results in degraded plant immune potential and thus causes loss of plant quality. We adapt these interactions in our model to show how they influence the plant insect interaction system and also to reveal how silencing BIK-1 may aid in enhanced production of plant biomass by increasing plant immunity mediated by increase in PAD4 and associated antixenotic effects. We hypothesize the significance of BIK-1 inhibition which could result in the improvement of the plant quality. We explain the interaction system in BIK-1 inhibition using mathematical model. Further, we adopted the plethora of computational modeling and simulations techniques to identify the mechanisms of molecular inhibition.

systems biology↗

bioRxiv2017

Theory For Transitions Between Exponential And Stationary Phases: Universal Laws For Lag Time

The quantitative characterization of bacterial growth has attracted substantial research attention since Monods pioneering study. Theoretical and experimental work have uncovered several laws for describing the exponential growth phase, in which the number of cells grows exponentially. However, microorganism growth also exhibits lag, stationary, and death phases under starvation conditions, in which cell growth is highly suppressed, for which quantitative laws or theories are markedly underdeveloped. In fact, the models commonly adopted for the exponential phase that consist of autocatalytic chemical components, including ribosomes, can only show exponential growth or decay in a population, and thus phases that halt growth are not realized. Here, we propose a simple, coarse-grained cell model that includes an extra class of macromolecular components in addition to the autocatalytic active components that facilitate cellular growth. These extra components form a complex with the active components to inhibit the catalytic process. Depending on the nutrient condition, the model exhibits the typical transitions among the lag, exponential, stationary, and death phases. Furthermore, the lag time needed for growth recovery after starvation follows the square root of the starvation time and is inversely related to the maximal growth rate. This is in agreement with experimental observations, in which the length of time of cell starvation is memorized in the slow accumulation of molecules. Moreover, the lag time distributed among cells is skewed with a long time tail. If the starvation time is longer, an exponential tail appears, which is also consistent with experimental data. Our theory further predicts a strong dependence of lag time on the speed of substrate depletion, which can be tested experimentally. The present model and theoretical analysis provide universal growth laws beyond the exponential phase, offering insight into how cells halt growth without entering the death phase.

systems biology↗