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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 649 records · Page 36Linked to original sources

bioRxiv2014

Mycoplasma stress response: adaptive regulation or broken brakes?

The avian bacterial pathogen Mycoplasma gallisepticum is a good model for transcriptional regulation studies due to its small genome and relative simplicity. In this study, we used RNA-Seq experiments combined with MS-based proteomics to accurately map coding sequences (CDSs), transcription start sites (TSSs) and transcription terminators (TTs) and to decipher their roles in stress-induced transcriptional responses. We identified 1061 TSSs at an FDR (false discovery rate) of 10% and showed that almost all transcription in M. gallisepticum is initiated from classic TATAAT promoters, which are surrounded by A/T-rich sequences and rarely accompanied by a -35 element. Our analysis revealed the pronounced complexity of the operon structure: on average, each coding operon has one internal TSS and TT in addition to the primary ones. Our new transcriptomic approach based on the intervals between the two closest transcription initiators and/or terminators allowed us to identify two classes of TTs: strong, unregulated and hairpin-containing TTs and weak, heat shock-regulated and hairpinless TTs. Comparing the gene expression levels under different conditions (such as heat, osmotic and peroxide stresses) revealed widespread and divergent transcription regulation in M. gallisepticum. Modeling suggested that the structure of the core promoter plays a major role in gene expression regulation. We have shown that the heat stress activation of cryptic promoters combined with the suppression of hairpinless TTs leads to widespread, seemingly non-functional transcription.

Systems Biology↗

bioRxiv2014

Locus architecture affects mRNA expression levels in Drosophila embryos

Structural variation in the genome is common due to insertions, deletions, duplications and rearrangements. However, little is known about the ways structural variants impact gene expression. Developmental genes are controlled by multiple regulatory sequence elements scattered over thousands of bases; developmental loci are therefore a good model to test the functional impact of structural variation on gene expression. Here, we measured the effect of rearranging two developmental enhancers from the even-skipped (eve) locus in Drosophila melanogaster blastoderm embryos. We systematically varied orientation, order, and spacing of the enhancers in transgenic reporter constructs and measured expression quantitatively at single cell resolution in whole embryos to detect changes in both level and position of expression. We found that the position of expression was robust to changes in locus organization, but levels of expression were highly sensitive to the spacing between enhancers and order relative to the promoter. Our data demonstrate that changes in locus architecture can dramatically impact levels of gene expression. To quantitatively predict gene expression from sequence, we must therefore consider how information is integrated both within enhancers and across gene loci.

Systems Biology↗

bioRxiv2014

Differential stoichiometry among core ribosomal proteins

Understanding the regulation and structure of ribosomes is essential to understanding protein synthesis and its dysregulation in disease. While ribosomes are believed to have a fixed stoichiometry among their core ribosomal proteins (RPs), some experiments suggest a more variable composition. Testing such variability requires direct and precise quantification of RPs. We used mass-spectrometry to directly quantify RPs across monosomes and polysomes of mouse embryonic stem cells (ESC) and budding yeast. Our data show that the stoichiometry among core RPs in wild-type yeast cells and ESC depends both on the growth conditions and on the number of ribosomes bound per mRNA. Furthermore, we find that the fitness of cells with a deleted RP-gene is inversely proportional to the enrichment of the corresponding RP in polysomes. Together, our findings support the existence of ribosomes with distinct protein composition and physiological function.

Systems Biology↗

bioRxiv2014

Extensive Regulation of Metabolism and Growth during the Cell Division Cycle

Yeast cells grown in culture can spontaneously synchronize their respiration, metabolism, gene expression and cell division. Such metabolic oscillations in synchronized cultures reflect single-cell oscillations, but the relationship between the oscillations in single cells and synchronized cultures is poorly understood. To understand this relationship and the coordination between metabolism and cell division, we collected and analyzed DNA-content, gene-expression and physiological data, at hundreds of time-points, from cultures metabolically-synchronized at different growth rates, carbon sources and biomass densities. The data enabled us to extend and generalize our mechanistic model, based on ensemble average over phases (EAP), connecting the population-average gene-expression of asynchronous cultures to the gene-expression dynamics in the single-cells comprising the cultures. The extended model explains the carbon-source specific growth-rate responses of hundreds of genes. Our physiological data demonstrate that the frequency of metabolic cycling in synchronized cultures increases with the biomass density, suggesting that this cycling is an emergent behavior, resulting from the entraining of the single-cell metabolic cycle by a quorum-sensing mechanism, and thus underscoring the difference between metabolic cycling in single cells and in synchronized cultures. Measurements of constant levels of residual glucose across metabolically synchronized cultures indicate that storage carbohydrates are required to fuel not only the G1/S transition of the division cycle but also the metabolic cycle. Despite the large variation in profiled conditions and in the scale of their dynamics, most genes preserve invariant dynamics of coordination with each other and with the rate of oxygen consumption. Similarly, the G1/S transition always occurs at the beginning, middle or end of the high oxygen consumption phases, analogous to observations in human and drosophila cells. These results highlight evolutionary conserved coordination among metabolism, cell growth and division.

Systems Biology↗

bioRxiv2014

High-throughput functional annotation of influenza A virus genome at single-nucleotide resolution

A novel genome-wide genetics platform is presented in this study, which permits functional interrogation of all point mutations across a viral genome in parallel. Here we generated the first fitness profile of individual point mutations across the influenza virus genome. Critical residues on the viral genome were systematically identified, which provided a collection of subdomain data informative for structure-function studies and for effective rational drug and vaccine design. Our data was consistent with known, well-characterized structural features. In addition, we have achieved a validation rate of 68% for severely attenuated mutations and 94% for neutral mutations. The approach described in this study is applicable to other viral or microbial genomes where a means of genetic manipulation is available.

Systems Biology↗

bioRxiv2014

Modeling Cellular Information Processing Using a Dynamical Approximation of Cellular mRNA

How does the regulatory machinery of an animal cell ensure its survival during large-scale biochemical and phenotypic transitions? When a cell is strongly perturbed by an environmental stimulus, it can either die or persist with compensatory changes. But what do the dynamics of individual genes look like during this process of adaptation? In a previous technical paper, two approaches (drug treatments and polysome isolation) were used in tandem to demonstrate the effects of perturbation on cellular phenotype. In this paper, we can use these data in tandem with a discrete, first-order feedback model that incorporates leaky components to better characterize adaptive responses of mRNA regulation related to information processing in the cell. By evaluating the dynamic relationship between mRNA associated with transcription (translatome) and mRNA associated with the polysome (transcriptome) at multiple timepoints, hypothetical conditions for decay and aggregation are found and discussed. Our feedback model allows for the approximation of fluctuations and other aspects of cellular information processing, in addition to the derivation of three information processing principles. These results will lead us to a better understanding of how mRNA provides variable information over time to the complex intracellular environment, particularly in the context of large-scale phenotypic change.

Systems Biology↗

bioRxiv2014

Rewiring of genetic networks in response to modification of genetic background

Genome-scale genetic interaction networks are progressively contributing to map the molecular circuitry that determines cellular behaviour. To what extent this mapping changes in response to different environmental or genetic conditions is however largely unknown. Here we assembled a genetic network using an in silico model of metabolism in yeast to explicitly ask how separate genetic backgrounds alter network structure. Backgrounds defined by single deletions of metabolically active enzymes induce strong rewiring when the deletion corresponds to a catabolic gene, evidencing a broad redistribution of fluxes to alternative pathways. We also show how change is more pronounced in interactions linking genes in distinct functional modules, and in those connections that present weak epistasis. These patterns reflect overall the distributed robustness of catabolism. In a second class of genetic backgrounds, in which a number of neutral mutations accumulate, we dominantly observe modifications in the negative interactions that together with an increase in the number of essential genes indicate a global reduction in buffering. Notably, neutral trajectories that originate considerable changes in the wild-type network comprise mutations that diminished the environmental plasticity of the corresponding metabolism, what emphasizes a mechanistic integration of genetic and environmental buffering. More generally, our work demonstrates how the specific mechanistic causes of robustness influence the architecture of multiconditional genetic interaction maps.

Systems Biology↗

bioRxiv2014

Expression noise facilitates the evolution of gene regulation

In studies of gene regulation, it is often tacitly assumed that the interactions between transcriptional regulators and their target promoters are finely tuned to ensure condition-appropriate gene expression of the targets. However, how natural selection might evolve such precise regulation from an initial state without regulation, is rarely discussed. In addition, the accuracy of gene regulation is affected by noise in gene expression [1]. Expression noise varies greatly across genes [2-5], suggesting that natural selection has affected noise levels, but the role of expression noise in gene regulation is currently poorly understood [6]. Here we present a combination of experimental evidence and theoretical modeling showing that the transmission of expression noise from regulators to their targets can function as a rudimentary form of gene regulation that facilitates the evolution of more finely tuned gene regulation. To assess how natural selection has affected transcriptional noise in E. coli, we evolved a large set of synthetic promoters under carefully controlled selective conditions and found, surprisingly, that native E. coli promoters show no signs of having been selected for minimizing their noise. Instead, a subset of native promoters, which are characterized by high expression plasticity and high numbers of regulatory inputs, show elevated noise levels. A general theoretical model, which recognizes that target genes are not only affected by the condition-dependent activities of their regulators, but also by the regulators noise, explains these observations. Noise transmission from regulators to their targets is favored by selection whenever regulation is imprecise, and may even constitute the main function of coupling a promoter to a regulator. Our theory provides a novel framework for understanding the evolution of gene regulation, demonstrating that in many situations expression noise is not the mere unwanted side-effect of regulatory interactions, but a beneficial function that is key to the evolvability of regulatory interactions.

Systems Biology↗

bioRxiv2014

Negative Feedback Facilitates Temperature Robustness in Biomolecular Circuit Dynamics

Temporal dynamics in many biomolecular circuits can change with temperature because of the temperature dependence of underlying reaction rate parameters. It is generally unclear what circuit mechanisms can inherently facilitate robustness in the dynamics to variations in temperature. Here, we address this issue using a combination of mathematical models and experimental measurements in a cell-free transcription-translation system. We find that negative transcriptional feedback can reduce the effect of temperature variation on circuit dynamics. Further, we find that effective negative feedback due to first-order degradation mechanisms can also enable such a temperature robustness effect. Finally, we estimate temperature dependence of key parameters mediating such negative feedback mechanisms. These results should be useful in the design of temperature robust circuit dynamics.

Systems Biology↗

bioRxiv2014

Branching and oscillations in the epigenetic landscape of cell-fate determination

The well-known Waddingtons epigenetic landscape of cell-fate determination is not static but varies because of the dynamic gene regulation during development. However, existing mathematical models with few state variables and fixed parameters are inadequate in characterizing the temporal transformation of the landscape. Here we simulate a decision-switch model of gene regulation with more than two state variables and with time-varying repression among regulatory factors. We are able to demonstrate multi-lineage differentiation at different timescales that portrays the branching canals in Waddingtons illustration. We also present a repressilator-type system that activates suppressed genes via sustained oscillations in a flattened landscape, hence providing an alternative scheme for cellular reprogramming. The time-dependent parameters governed by gradient-based dynamics regulate cell differentiation, dedifferentiation and transdifferentiation. Our prediction integrates the theories of branching and structural oscillations in cell-fate determination, which reveals key temporal patterns of cell differentiation and associated diseases, such as cancer.

Systems Biology↗

bioRxiv2014

Shadow enhancers enable Hunchback bifunctionality in the Drosophila embryo

Hunchback (Hb) is a bifunctional transcription factor that activates and represses distinct enhancers. Here, we investigate the hypothesis that Hb can activate and repress the same enhancer. Computational models predicted that Hb bifunctionally regulates the even-skipped (eve) stripe 3+7 enhancer (eve3+7) in Drosophila blastoderm embryos. We measured and modeled eve expression at cellular resolution under multiple genetic perturbations and found that the eve3+7 enhancer could not explain endogenous eve stripe 7 behavior. Instead, we found that eve stripe 7 is controlled by two enhancers: the canonical eve3+7 and a sequence encompassing the minimal eve stripe 2 enhancer (eve2+7). Hb bifunctionally regulates eve stripe 7, but it executes these two activities on different pieces of regulatory DNA-it activates the eve2+7 enhancer and represses the eve3+7 enhancer. These two \"shadow enhancers\" use different regulatory logic to create the same pattern.\n\nSignificance statementEnhancers are regions of regulatory DNA that control gene expression and cell fate decisions during development. Enhancers compute the expression pattern of their target gene by reading the concentrations of input regulatory proteins. Many developmental genes contain multiple enhancers that control the same output pattern, but it is unclear if these enhancers all compute the pattern in the same way. We use measurements in single cells and computational models in Drosophila embryos to demonstrate that two enhancers that encode the same gene expression pattern compute differently: the same regulatory protein represses one enhancer and activates the other. Pairs of enhancers that output the same pattern by performing different computations may impart special properties to developmental systems.

Systems Biology↗

bioRxiv2014

Dual autogenous control of the multiple antibiotic resistance phenotype in Escherichia coli

Bacteria can defend against diverse antibiotics by mounting a multiple antibiotic resistance (mar) phenotype. The resistance is linked to a chromosomal locus that encodes an activator and a repressor regulating their own expression. Here, we investigated how this dual autogenous control determines the dynamics of the response. We found that the regulatory architecture provides a mechanism to enable rapid induction, generate pulses of activation, and increase the range of sensing. The response is also graded and homogeneous across the population. Moreover, the interaction of a third regulator with the core module fine tunes the previous features, while limiting the cross-talk with metabolic signals. A minimal model accurately anticipates these properties, and emphasizes how specific attributes of the circuit components constrain the appearance of other potential behaviors associated to the regulatory design. Our results integrate both molecular and circuit-level characteristics to fully elucidate the dynamic emergence of the mar phenotype.

Systems Biology↗

bioRxiv2014

Extensive Decoupling of Metabolic Genes in Cancer

Tumorigenesis involves, among other factors, the alteration of metabolic gene expression to support malignant, unrestrained proliferation. Here, we examine how the altered metabolism of cancer cells is reflected in changes in co-expression patterns of metabolic genes between normal and tumor tissues. Our emphasis on changes in the interactions of pairs of genes, rather than on the expression levels of individual genes, exposes changes in the activity of metabolic pathways which do not necessarily show clear patterns of over- or under-expression. We report the existence of key metabolic genes which act as hubs of differential co-expression, showing significantly different co-regulation patterns between normal and tumor states. Notably, we find that the extent of differential co-expression of a gene is only weakly correlated with its differential expression, suggesting that the two measures probe different features of metabolism. By leveraging our findings against existing pathway knowledge, we extract networks of functionally connected differentially co-expressed genes and the transcription factors which regulate them. Doing so, we identify a previously unreported network of dysregulated metabolic genes in clear cell renal cell carcinoma transcriptionally controlled by the transcription factor HNF4A. While HNF4A shows no significant differential expression, the co-expression HNF4A and several of its regulated target genes in normal tissue is completely abrogated in tumor tissue. Finally, we aggregate the results of differential co-expression analysis across seven distinct cancer types to identify pairs of metabolic genes which may be recurrently dysregulated. Among our results is a cluster of four genes, all located in the mitochondrial electron transport chain, which show significant loss of co-expression in tumor tissue, pointing to potential mitochondrial dysfunction in these tumor types.

Systems Biology↗

bioRxiv2014

Cooperative development of logical modelling standards and tools with CoLoMoTo

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour, and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Here, we outline the logical modelling framework and present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. This open community welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments.

Systems Biology↗

bioRxiv2014

Comprehensive comparison of large-scale tissue expression datasets

For tissues to carry out their functions, they rely on the right proteins to be present. Several high-throughput technologies have been used to map out which proteins are expressed in which tissues; however, the data have not previously been systematically compared and integrated. We present a comprehensive evaluation of tissue expression data from a variety of experimental techniques and show that these agree surprisingly well with each other and with results from literature curation and text mining. We further found that most datasets support the assumed but not demonstrated distinction between tissue-specific and ubiquitous expression. By developing comparable confidence scores for all types of evidence, we show that it is possible to improve both quality and coverage by combining the datasets. To facilitate use and visualization of our work, we have developed the TISSUES resource (http://tissues.jensenlab.org), which makes all the scored and integrated data available through a single user-friendly web interface.

Systems Biology↗

bioRxiv2014

Phenotypic heterogeneity implements a game theoretic mixed strategy in a clonal microbial population

Genetically identical cells in microbial populations often exhibit a remarkable degree of phenotypic heterogeneity even in homogenous environments. While such heterogeneity is often thought to be a bet-hedging strategy against unpredictable environments, evolutionary game theory also predicts phenotypic heterogeneity as a stable response to evolutionary \"hawk-dove\" games, in which rare strategies are favored over common ones. Here we provide experimental evidence for this game theoretic explanation in the context of the well-studied yeast GAL network. In an environment containing the two sugars glucose and galactose, the yeast GAL network displays stochastic bimodal activation. We show that genetic mutants playing the \"pure\" strategies of GAL-ON or GAL-OFF can each invade the opposite strategy when rare, indicating a hawk-dove game between the two. Consistent with the Nash equilibrium of an evolutionary game, the stable mix of pure strategists does not necessarily maximize the growth of the overall population. We also find that the wild type GAL network can invade populations of both pure strategists while remaining uninvasible by either. Taken together, our results provide experimental evidence that evolutionary hawk-dove games between identical cells can explain the phenotypic heterogeneity found in clonal microbial populations.

Systems Biology↗

bioRxiv2014

Natural Variation in Preparation for Nutrient Depletion Reveals a Cost-Benefit Tradeoff

Maximizing growth and survival in the face of a complex, time-varying environment is a common problem for single-celled organisms in the wild. When offered two different sugars as carbon sources, microorganisms first consume the preferred sugar, then undergo a transient growth delay, the \"diauxic lag\", while inducing genes to metabolize the less preferred sugar. This delay is commonly assumed to be an inevitable consequence of selection to maximize use of the preferred sugar. Contrary to this view, we found that many natural isolates of Saccharomyces cerevisiae display short or non-existent diauxic lags when grown in mixtures of glucose (preferred) and galactose. These strains induce galactose-utilization (GAL) genes hours before glucose exhaustion, thereby \"preparing\" for the transition from glucose to galactose metabolism. The extent of preparation varies across strains, and seems to be determined by the steady-state response of GAL genes to mixtures of glucose and galactose rather than by induction kinetics. Although early GAL induction gives strains a competitive advantage once glucose runs out, it comes at a cost while glucose is still present. Costs and benefits correlate with the degree of preparation: strains with higher expression of GAL genes prior to glucose exhaustion experience a larger upfront growth cost but also a shorter diauxic lag. Our results show that classical diauxic growth is only one extreme on a continuum of growth strategies constrained by a cost-benefit tradeoff. This type of continuum is likely to be common in nature, as similar tradeoffs can arise whenever cells evolve to use mixtures of nutrients.

Systems Biology↗

bioRxiv2014

Stochasticity and negative feedback lead to pulsed dynamics and distinct gene activity patterns

Gene expression is an inherently stochastic process that depends on the structure of the biochemical regulatory network in which the gene is embedded. Here we study the interplay between stochastic gene switching and the widespread negative feedback regulatory loop. Using a simple hybrid model, we find that stochasticity in gene switching by itself can induce pulses in the system. Furthermore, we find that our simple model is able to reproduce both exponential and peaked distributions of gene active and inactive times similar to those that have been observed experimentally. Our hybrid modelling approach also allows us to link these patterns to the dynamics of the system for each gene state.

Systems Biology↗