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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

The role of contact inhibition in intratumoral heterogeneity: An off-lattice individual based model

We present a model that shows how intratumoral heterogeneity, in terms of tumor cell phenotypic traits, can evolve in a tumor mass as a result of selection when space is a limited resource. This model specifically looks at the traits of proliferation rate and migration speed. The competition for space amongst individuals in the tumor mass creates a selection pressure for the cells with the fittest traits. To allow for organic movement and capture the invasive behavior, we use an off-lattice individual-based model.

Systems Biology↗

bioRxiv2016

A convex optimization approach for identification of human tissue-specific interactomes

MotivationAnalysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell-type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/ selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.\n\nResultsWe present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (ESEA, GO Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state of the art techniques. Finally, using case studies of Alzheimers and Parkinsons diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets.\n\nAvailabilityhttp://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html

Systems Biology↗

bioRxiv2016

Lifespan extension by dietary restriction in Drosophila is associated with GATA motifs and organ-specific, TOR-dependent transcriptional networks.

Animal lifespan can be extended by dietary restriction (DR), but at a cost to fitness. This phenomenon depends on essential amino acids (EAAs) and TOR signalling, but roles of specific tissues and downstream transcriptional regulators are poorly characterised. Manipulating relevant transcription factors (TFs) specifically in lifespan-limiting tissues may ameliorate ageing without costs of DR. Here we identify TFs which regulate the DR phenotype in Drosophila, analysing organs as an interacting system and reducing its transcriptional complexity by two orders of magnitude. Evolutionarily conserved GATA TFs are predicted to regulate the overlapping effects of DR and TOR on organs, and genetic analyses confirmed that these TFs interact with diet to determine lifespan. Importantly, Srp knockdown insulated fly lifespan from the pernicious effects of EAAs, but tissue-specific knockdown reduced the corrolary costs. These results provide the first indication that benefits of EAAs for early-life fitness can be decoupled from longevity by tissue-specific transcriptional reprogramming.

Systems Biology↗

bioRxiv2016

A virtual B cell lymphoma model to predict effective combination therapy

AbstractThe complexity of cancer signaling networks limits the efficacy of most single agent treatments and brings about challenges in identifying effective combinatorial therapies. Using chronic active B cell receptor (BCR) signaling in diffuse large B cell lymphoma (DLBCL) as a model system, we established a computational framework to optimize combinatorial therapy in silico. We constructed a detailed kinetic model of the BCR signaling network, which captured the known complex crosstalk between the NF{kappa}B, ERK and AKT pathways; and multiple feedback loops. Combining this signaling model with a data-derived tumor growth model we predicted viability responses of many single drug and drug combinations that are in agreement with experimental data. Under this framework, we exhaustively predicted and ranked the efficacy and synergism of all possible combinatorial inhibitions of eleven currently targetable kinases in the BCR signaling network. Our work established a detailed kinetic model of the core BCR signaling network and provides the means to explore the large space of possible drug combinations.\n\nAuthor SummaryUsing chronic active B cell receptor (BCR) signaling in diffuse large B cell lymphoma(DLBCL) as a model system, we developed a kinetic-modeling based computational framework for predicting effective combination therapy in silico. By integrative modeling of signal transduction, drug kinetics and tumor growth, we were able to directly predict drug-induced cell viability response at various dosages, which were in agreement with published cell line experimental data. We implemented computational screening methods that identified potent and synergistic combinations in silico and validated our independent predictions in vitro.

Systems Biology↗

bioRxiv2016

Transcription factor loads tend to decrease the robustness of stable gene transcription networks

Robustness of a systems behavior to changes in parameter values is a remarkable property of natural systems and especially desirable when designing de novo synthetic gene circuits. Loads on transcription factors resulting from binding to target promoters have been shown to significantly affect the effective time constants of gene transcription networks and to thus alter the overall systems behavior. Here, we employ models that explicitly account for load effects to investigate how these impact the robustness of a stable gene transcription network to parameter perturbations. By employing a combination of sampling-based methods and analytical tools from control theory, we demonstrate that the presence of loading shrinks the region of parameter space where a gene circuit performs the desired function. A number of multi-module synthetic gene circuits are analyzed to demonstrate this point, including an event detector and a molecular signature classifier. These results indicate that for designing genetic circuits that are robust to parameter uncertainty it is highly desirable to find ways to mitigate the effects of transcription factor loading.\n\nAuthor SummaryEfforts to understand how loads affect gene transcription networks have been underway in the past decade. Here we perform a numerical investigation on three synthetic gene circuits to show that loads tend to decrease the robustness of stable gene transcriptional networks. We complement the numerical findings with analytical derivations that employ the stability radius to compare the robustness of different networks to parameter perturbations near an equilibrium point. Consistent with the numerical finding, the analytical results support that systems with substantial transcription factor loading have smaller stability radius (less robustness) than systems without loading.

Systems Biology↗

bioRxiv2016

Dichotomy of cellular inhibition by small-molecule inhibitors revealed by single-cell analysis

Despite progress in developing small molecule inhibitors, a quantitative understanding of drug action in the physiological context of cells is lacking. Here, we apply single-cell analysis of signal transduction and proliferation to probe cellular responses to small molecule inhibitors. We use the model of cellular activation of T lymphocytes responding to cytokines and antigens. We uncover two distinct modes of drug action, in terms of signaling inhibition: digital inhibition (e.g. when the fraction of activated cells diminishes upon drug treatment, but cells remaining active appear unperturbed), and analog inhibition (e.g. when the fraction of activated cells is unperturbed while their overall activation is diminished). We introduce a computational model of the signaling cascade in order to account for such dichotomy. We test the predictions of our model in terms of the phenotypic variability of cellular responses under drug inhibition. Finally, we demonstrate that the digital/analog dichotomy of cellular response as revealed on short timescales with signal transduction, translates into similar dichotomy on long timescales. Overall, our analysis of drug action at the single cell level illustrates the strength of quantitative approaches to translate the promise of in vitro pharmacology into functionally-relevant cellular settings.

Systems Biology↗

bioRxiv2016

Integrated molecular phenotyping identifies genes and pathways disrupted in osteoarthritis

BackgroundOsteoarthritis (OA) is a common disease characterized by cartilage degeneration and joint remodeling. The underlying molecular changes underpinning disease progression are incompletely understood, but can be characterized using recent advances in genomics technologies, as the relevant tissue is readily accessible at joint replacement surgery. Here we investigate genes and pathways that mark OA progression, combining genome-wide DNA methylation, RNA sequencing and quantitative proteomics in isolated primary chondrocytes from matched intact and degraded articular cartilage samples across twelve patients with OA undergoing knee replacement surgery.\n\nResultsWe identify 49 genes differentially regulated between intact and degraded cartilage at multiple omics levels, 16 of which have not previously been implicated in OA progression. Using independent replication datasets, we replicate statistically significant signals and show that the direction of change is consistent for over 90% of differentially expressed genes and differentially methylated CpG probes. Three genes are differentially regulated across all 3 omics levels: AQP1, COL1A1 and CLEC3B, and all three have evidence implicating them in OA through animal or cellular model studies. Integrated pathway analysis implicates the involvement of extracellular matrix degradation, collagen catabolism and angiogenesis in disease progression. All data from these experiments are freely available as a resource for the scientific community.\n\nConclusionsThis work provides a first integrated view of the molecular landscape of human primary chondrocytes and identifies key molecular players in OA progression that replicate across independent datasets, with evidence for translational potential.

Systems Biology↗

bioRxiv2016

A Comparison of the Costs and Benefits of Bacterial Gene Expression

To study how a bacterium allocates its resources, we compared the costs and benefits of most of the proteins in Escherichia coli K-12 during growth in minimal glucose medium. Proteins that are important for fitness are usually highly expressed, and 95% of these proteins are expressed at above 13 parts per million (ppm). Conversely, proteins that do not measurably benefit the host tend to be weakly expressed, with a median expression of 13 ppm. In aggregate, genes with no detectable benefit account for 31% of protein production, or about 22% if we correct for genetic redundancy. Although some of the apparently unnecessary expression could have subtle benefits in minimal glucose medium, the majority of the burden is due to genes that are important in other conditions. We propose that over 10% of the cells protein is \"on standby\" in case conditions change.

Systems Biology↗

bioRxiv2016

A Novel Multi-network Approach Reveals Tissue-specific Cellular Modulators of Fibrosis in Systemic Sclerosis, Pulmonary Fibrosis and Pulmonary Arterial Hypertension

We have used integrative genomics to determine if a common molecular mechanism underlies different clinical manifestations in systemic sclerosis (SSc), and the related conditions pulmonary fibrosis (PF) and pulmonary arterial hypertension (PAH). We identified a common pathogenic gene expression signature - an immune-fibrotic axis-indicative of pro-fibrotic macrophages in multiple affected tissues (skin, lung, esophagus and PBMCs) of SSc, PF, and PAH. We used this disease-associated signature to query tissue-specific functional genomic networks. This allowed us to identify common and tissue-specific pathology of SSc and related conditions. We rigorously contrasted the lung- and skin-specific gene-gene interaction networks to identify a distinct lung resident macrophage signature associated with lipid stimulation and alternative activation. In keeping with our network results, we find distinct macrophages alternative activation transcriptional programs in SSc-PF lung and in the skin of patients with an \"inflammatory\" SSc gene expression signature. Our results suggest that the innate immune system is central to SSc disease processes, but that subtle distinctions exist between tissues. Our approach provides a framework for examining molecular signatures of disease in fibrosis and autoimmune diseases and for leveraging publicly available data to understand common and tissue-specific disease processes in complex human diseases.

Systems Biology↗

bioRxiv2016

Defining the anti-Shine-Dalgarno sequence interaction and quantifying its functional role in regulating translation efficiency

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

Systems Biology↗

bioRxiv2016

Integrating regulatory information via combinatorial control of the transcription cycle

Combinatorial regulation of gene expression by multiple transcription factors (TFs) enables cells to carry out sophisticated computations that are key to cellular decision-making. How is the information contained in multiple TF binding sites integrated to dictate the rate of transcription? The dominant model is that direct or indirect physical interactions between TFs enable them to combinatorially recruit each other and RNA polymerase to the promoter. Here we develop a quantitative framework to explore an alternative model, where combinatorial gene regulation can result from TFs working on different kinetic steps of the transcription cycle. Our results clarify the null hypotheses for independent action of TFs and show that combinatorial control of the transcription cycle can generate a wide range of analog and Boolean computations without requiring the input regulators to be simultaneously co-localized in the nucleus. This work emphasizes the importance of deciphering TF function beyond activation and repression, highlights the role of the basal promoter in processing regulatory information and suggests qualitative explanations for the flexibility of regulatory evolution.

Systems Biology↗

bioRxiv2016

Genetic redundancies enhance information transfer in noisy regulatory circuits

Cellular decision making is based on regulatory circuits that associate signal thresholds to specific physiological actions. This transmission of information is subjected to molecular noise what can decrease its fidelity. Here, we show instead how such intrinsic noise enhances information transfer in the presence of multiple circuit copies. The result is due to the contribution of noise to the generation of autonomous responses by each copy, which are altogether associated with a common decision. Moreover, factors that correlate the responses of the redundant units (extrinsic noise or regulatory cross-talk) contribute to reduce fidelity, while those that further uncouple them (heterogeneity within the copies) can lead to stronger information gain. Overall, our study emphasizes how the interplay of signal thresholding, redundancy, and noise influences the accuracy of cellular decision making. Understanding this interplay provides a basis to explain collective cell signaling mechanisms, and to engineer robust decisions with noisy genetic circuits.

Systems Biology↗

bioRxiv2016

Exploitation by cheaters facilitates the preservation of essential public goods in microbial communities

How are public goods1-4 maintained in bacterial cooperative populations? The presence of these compounds is usually threatened by the rise of cheaters that do not contribute but just exploit the common resource5,6. Minimizing cheater invasions appears then as a necessary maintenance mechanism7,8. However, that invasions can instead add to the persistence of cooperation is a prospect that has yet remained largely unexplored6. Here, we show that the detrimental consequences of cheaters can actually preserve public goods, at the cost of recurrent collapses and revivals of the population. The result is made possible by the interplay between spatial constraints and the essentiality of the shared resource. We validate this counter-intuitive effect by carefully combining theory and experiment, with the engineering of an explicit synthetic community in which the public compound allows survival to a bactericidal stress. Notably, the characterization of the experimental system identifies additional factors that can matter, like the impact of the lag phase on the tolerance to stress, or the appearance of spontaneous mutants. Our work emphasizes the unanticipated consequences of the eco-evolutionary feedbacks that emerge in microbial communities relying on essential public goods to function, feedbacks that reveal fundamental for the adaptive change of ecosystems at all scales.

Systems Biology↗

bioRxiv2016

Cell fate-decision as high-dimensional critical state transition

Cell fate choice and commitment of multipotent progenitor cells to a differentiated lineage requires broad changes of their gene expression profile. However, how progenitor cells overcome the stability of their robust gene expression configuration (attractor) and exit their state remains elusive. Here we show that commitment of blood progenitor cells to the erythroid or the myeloid lineage is preceded by the destabilization of their high-dimensional attractor state and that cells undergo a critical state transition. Single-cell resolution analysis of gene expression in populations of differentiating cells affords a new quantitative index for predicting critical transitions in a high-dimensional state space: decrease of correlation between cells with concomitant increase of correlation between genes as cells approach a tipping point. The detection of \"rebellious cells\" which enter the fate opposite to the one intended corroborates the model of preceding destabilization of the progenitor state. Thus, \"early-warning signals\" associated with critical transitions can be detected in statistical ensembles of high-dimensional systems, offering a formal tool for analyzing single-cells molecular profiles that goes beyond computational pattern recognition but is based on dynamical systems theory and can predict impending major shifts in cell populations in development and disease.

Systems Biology↗

bioRxiv2016

Metabolic profiling of alcohol consumption in 9778 young adults

BackgroundHigh alcohol consumption is a major cause of morbidity, yet alcohol is associated with both favourable and adverse effects on cardiometabolic risk markers. We aimed to characterize the associations of usual alcohol consumption with a comprehensive systemic metabolite profile in young adults.\n\nMethodsCross-sectional associations of alcohol intake with 86 metabolic measures were assessed for 9778 individuals from three population-based cohorts from Finland (age 24-45 years, 52% women). Metabolic changes associated with change in alcohol intake during 6-year follow-up were further examined for 1466 individuals. Alcohol intake was assessed by questionnaires. Circulating lipids, fatty acids and metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays.\n\nResultsIncreased alcohol intake was associated with cardiometabolic risk markers across multiple metabolic pathways, including higher lipid concentrations in HDL subclasses and smaller LDL particle size, increased proportions of monounsaturated fatty acids and decreased proportion of omega-6 fatty acids, lower concentrations of glutamine and citrate (P<0.001 for 56 metabolic measures). Many metabolic biomarkers displayed U-shaped associations with alcohol consumption. Results were coherent for men and women, consistent across the three cohorts, and similar if adjusting for body mass index, smoking and physical activity. The metabolic changes accompanying change in alcohol intake during follow-up resembled the cross-sectional association pattern (R2=0.83, slope=0.72{+/-}0.04).\n\nConclusionsAlcohol consumption is associated with a complex metabolic signature, including aberrations in multiple biomarkers for elevated cardiometabolic risk. The metabolic signature tracks with long-term changes in alcohol consumption. These results elucidate the double-edged effects of alcohol on cardiovascular risk.\n\nKey messagesO_LIAlcohol intake in young adults associates with multiple novel metabolic biomarkers for the risk of cardiovascular disease and type 2 diabetes. The metabolic aberrations are mainly adversely related to cardiometabolic risk\nC_LIO_LIProminent metabolic associations with alcohol consumption include monounsaturated fatty acids, omega-6 fatty acids, glutamine, citrate and lipoprotein particle size. Many of these cardiometabolic biomarkers were as strongly associated with alcohol intake as HDL cholesterol\nC_LIO_LIThe strongest novel biomarkers of alcohol consumption followed linear association shapes, whereas many lipid measures displayed U-shaped associations\nC_LIO_LIThe detailed metabolic signature of alcohol consumption tracked with long-term changes in alcohol use, suggesting that the observed metabolic changes are at least partly due to alcohol consumption\nC_LIO_LIThe results provide improved understanding of the diverse molecular processes related to alcohol intake. Novel metabolic biomarkers reflecting both alcohol intake and cardiovascular risk could serve as intermediates that may help to bridge the complex relation between alcohol and cardiometabolic risk\nC_LI

Systems Biology↗

bioRxiv2016

The Effects of Ribosome Autocatalysis and Negative Feedback in Resource Competition

BackgroundResource competition, and primarily competition for ribosomes, can lead to unexpected behavior of genetic circuits and has recently gained renewed attention with both experimental and theoretical studies. Current models studying the effects of resource competition assume a constant production of ribosomes and these models describe the experimental results well. However, ribosomes are also autocatalytic since they are partially made of protein and autocatalysis has been shown to have detrimental effects on a systems stability and robustness. Additionally, there are known feedback regulations on ribosome synthesis such as inhibition of rRNA synthesis via ppGpp.\n\nResultsHere, we develop two-state models of ribosome and protein synthesis incorporating autocatalysis and feedback to investigate conditions under which these regulatory actions have a significant effect in situations of increased ribosome demand. Our modeling results indicate that for sufficiently low demand, defined by the mRNA level of synthetic genes, autocatalysis has little or no effect. However, beyond a certain demand level, the system goes through a transcritical bifurcation at which the only non-negative steady state is at zero ribosome concentration. The presence of negative feedback, in turn, can shift this point to higher demand values, thus restoring the qualitative behavior observed in a model with a constant ribosome production at low demand. However, autocatalysis affects the dynamics of the system and can lead to an overshoot in the temporal response of the synthetic genes to changes in induction level.\n\nConclusionOur results show that ribosome autocatalysis has a significant effect on the system robustness to increases in ribosome demand, however the existing negative feedback on ribosome production compensates for the effects of the necessary autocatalytic loop and restores the behavior seen in the system with constant ribosome production. These findings explain why previous models with constant ribosome production reproduce the steady state behavior well, however incorporating autocatalysis and feedback is needed to capture the transient behavior.

Systems Biology↗

bioRxiv2016

Environmental gene regulatory influence networks in rice (Oryza sativa): response to water deficit, high temperature and agricultural environments

Environmental Gene Regulatory Influence Networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental and developmental signals. EGRINs encompass many layers of regulation, which culminate in changes in the level of accumulated transcripts. Here we infer EGRINs for the response of five tropical Asian rice cultivars to high temperatures, water deficit, and agricultural field conditions, by systematically integrating time series transcriptome data (720 RNA-seq libraries), patterns of nucleosome-free chromatin (18 ATAC-seq libraries), and the occurrence of known cis-regulatory elements. First, we identify 5,447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes with known cis-regulatory motifs in nucleosome-free chromatin regions proximal to transcriptional start sites (TSS) of genes. We then use network component analysis to estimate the regulatory activity for these TFs from the expression of these putative target genes. Finally, we inferred an EGRIN using the estimated TFA as the regulator. The EGRIN included regulatory interactions between 4,052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of a large TF family, including a putative regulatory connection between abiotic stress and the circadian clock, as well as specific regulatory functions for TFs in the drought response. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference and that supplementing data from controlled experimental conditions with data from outdoor field conditions increases the resolution for EGRIN inference.

Systems Biology↗

bioRxiv2016

Limiting the Development of Anti-Cancer Drug Resistance in a Spatial Model of Micrometastases

While chemoresistance in primary tumors is well-studied, much less is known about the influence of systemic chemotherapy on the development of drug resistance at metastatic sites. In this work, we use a hybrid spatial model of tumor response to a DNA damaging drug to study how the development of chemoresistance in micrometastases depends on the drug dosing schedule. We separately consider cell populations that harbor pre-existing resistance to the drug, and those that acquire resistance during the course of treatment. For each of these independent scenarios, we consider one hypothetical cell line that is responsive to metronomic chemotherapy, and another that with high probability cannot be eradicated by a metronomic protocol. Motivated by experimental work on ovarian cancer xenografts, we consider all possible combinations of a one week treatment protocol, repeated for three weeks, and constrained by the total weekly drug dose. Simulations reveal a small number of fractionated-dose protocols that are at least as effective as metronomic therapy in eradicating micrometastases with acquired resistance (weak or strong), while also being at least as effective on those that harbor weakly pre-existing resistant cells. Given the responsiveness of very different theoretical cell lines to these few fractionated-dose protocols, these may represent more effective ways to schedule chemotherapy with the goal of limiting metastatic tumor progression.

Systems Biology↗