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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Deciphering the tissue specificity of the protein secretory pathway in humans

Proteins that are components of the secretory machinery form a cellular pathway of paramount importance for physiological regulation, development and function of human tissues. Consistently, most secretory pathway components are ubiquitously expressed in all tissues. At the same time, recent studies identified that the largest fraction of tissue-specific proteins consists of secreted and membrane proteins and not intracellular proteins. This suggests that the secretory pathway is distinctively regulated in a tissue-specific fashion. However, a systematic analysis on how the protein secretory pathway is tuned in different tissues is lacking, and it is even largely unexplored if the secretome and membrane proteome differs in, for example, posttranslation modifications across tissues. Here, analyzing publically available transcriptome data across 30 human tissues, we discovered the expression level of key components previously categorized as housekeeping proteins were specifically over-expressed in a certain tissue compared with the average expression of their corresponding secretory pathway subsystem (e.g. protein folding). These extreme genes define an exceptional fine-tuning in specific subnetworks, which neatly differentiated for example the pancreas and liver from 30 other tissues. Moreover, the subnetwork expression tuning correlated with the nature and number of post translational modification sites in the pancreas or liver-specific secretome and membrane proteome. These patterns were recurrently observed also in other tissues, like the blood, the brain and the skeletal muscle. These findings conciliate both the housekeeping and tissue-specific nature of the protein secretory pathway, which we attribute to a fine-tuned regulation of defined subnetworks in order to support the diversity of secreted proteins and their modifications.

Systems Biology↗

bioRxiv2016

Network analysis links genome-wide phenotypic and transcriptional stress responses in a bacterial pathogen with a large pan-genome.

BackgroundBacteria modulate subcellular processes to handle stressful environments. Genome-wide profiling of gene expression (RNA-Seq) and fitness (Tn-Seq) allows two views of the same genetic network underlying these responses. However, it remains unclear how they combine, enabling a bacterium to overcome a perturbation.\n\nResultsHere we generate RNA-Seq and Tn-Seq profiles in three strains of S. pneumoniae in response to stress defined by different levels of nutrient depletion. These profiles show that genes that change their expression and/or become phenotypically important come from a diverse set of functional categories, and genes that are phenotypically important tend to be highly expressed. Surprisingly, we find that expression and fitness changes rarely occur on the same gene, which we confirmed by over 140 validation experiments. To rationalize these unexpected results we built the first genome-scale metabolic model of S. pneumoniae showing that differential expression and phenotypic importance actually correlate between nearest neighbors, although they are distinctly partitioned into small subnetworks. Moreover, a meta-analysis of 234 S. pneumoniae gene expression studies reveals that essential genes and phenotypically important subnetworks rarely change expression, indicating that they are shielded from transcriptional fluctuations and that a clear distinction exists between transcriptional and phenotypic response networks.\n\nConclusionsWe present a genome-wide computational/experimental approach that contextualizes changes that occur on transcriptomic and phenomic levels in response to stress. Importantly, this highlights the need to connect disparate response networks, for instance in antibiotic target identification, where preferred targets are phenotypically important genes that would be overlooked by transcriptomic analyses alone.

Systems Biology↗

bioRxiv2016

Unraveling the differential dynamics of developmental fate in central and peripheral nervous systems

Bone morphogenetic protein 2 (BMP2), differentially regulates the developmental lineage commitment of neural stem cells (NSCs) in central and peripheral nervous systems. However, the precise mechanism beneath such observations still remains illusive. To decipher the intricacies of this mechanism, we propose a generic mathematical model of BMP2 driven differentiation regulation of NSCs. The model efficiently captures the dynamics of the wild-type as well as various mutant and over-expression phenotypes for NSCs in central nervous system. Our model predicts that the differential developmental dynamics of the NSCs in peripheral nervous system can be reconciled by altering the relative positions of the two mutually interconnected bi-unstable switches inherently present in the steady state dynamics of the crucial developmental fate regulatory proteins as a function of BMP2 dose. This model thus provides a novel mechanistic insight and has the potential to deliver exciting therapeutic strategies for neuronal regeneration from NSCs of different origin.

Systems Biology↗

bioRxiv2016

13C metabolic flux ratio analysis by direct measurement of free metabolic intermediates in L. mexicana using gas chromatography-mass spectrometry

Until recently, 13C-based flux analyses have almost exclusively relied on analysis of labelled amino acids in proteins. This approach is not directly applicable to Leishmania, as these parasites scavenge most of their amino acids from the media. Leishmania are also unusual in that they i) share little genomic similarity with other organisms ii) constitutively express their metabolic genes and iii) display minimal changes in the enzyme levels throughout their life cycle stages. The three factors have contributed to an early development of comprehensive and reproducible 13C-based metabolomics approaches in these parasites. The work presented here contributes to the creation of new 13C-based metabolic flux approaches based on the isotopologue analysis of free metabolite pools in Leishmania mexicana. Namely, a new approach is presented for simultaneous calculation of in vivo fractional fluxes (or flux ratios) into two or more metabolite nodes with carbon dioxide condensation, based on isotopologue analysis of free metabolite pools. This method is used to perform the first quantitative in vivo fractional flux calculation of central carbon metabolism in any human parasite.

Systems Biology↗

bioRxiv2016

A living vector field reveals constraints on galactose network induction in yeast

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell timecourses of the inducers Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of GAL inducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection - a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.\n\nOne Sentence SummarySingle-cell galactose induction timecourses reveal that cellular resources and stochastic events determine which yeast cells outcompete their peers.

Systems Biology↗

bioRxiv2016

Automated Visualization of Rule-based Models

Rule-based modeling frameworks provide a specification format in which kinetic interactions are modeled as \"reaction rules\". These rules are specified on phosphorylation motifs, domains, binding sites and other sub-molecular structures, and have proved useful for modeling signal transduction. Visual representations are necessary to understand individual rules as well as analyze interactions of hundreds of rules, which motivates the need for automated diagramming tools for rule-based models. Here, we present a theoretical framework that unifies the layers of information in a rule-based model and enables automated visualization of (i) the mechanism encoded in a rule, (ii) the regulatory interaction of two or more rules, and (iii) the emergent network architecture of a large rule set. Specifically, we present a compact rule visualization that conveys the action of a rule explicitly (unlike conventional visualizations), a regulatory graph visualization that conveys regulatory interactions between rules, and a set of graph compression methods that synthesize informative pathway diagrams from complex regulatory graphs. These methods enable inference of network motifs (such as feedback and feed-forward loops), automated generation of signal flow diagrams for hundreds of rules, and tunable network compression using heuristics and graph analysis, all of which are advances over the state of the art for rule-based models. These methods also produce more readable diagrams than currently available tools as we show with an empirical comparison across 27 published rule-based models of various sizes. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are applicable to all current rule-based models in BioNetGen, Kappa and Simmune frameworks. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into whole cell models.\n\nAuthor SummarySignaling in living cells is mediated through a complex network of chemical interactions. Current predictive models of signal pathways have hundreds of reaction rules that specify chemical interactions, and a comprehensive model of a stem cell or cancer cell would be expected to have many more. Visualizations of rules and their interactions are needed to navigate, organize, communicate and analyze large signaling models. In this work, we have developed: (i) a novel visualization for individual rules that compactly conveys what each rule does, (ii) a comprehensive visualization of a set of rules as a network of regulatory interactions called a regulatory graph, and (iii) a set of procedures for compressing the regulatory graph into a pathway diagram that highlights underlying signaling motifs such as feedback and feedforward loops. We show that these visualizations are compact and informative across models of widely varying sizes. The methods developed here not only improve the understandability of current models, but also establish principles for organizing the much larger models of the future.

Systems Biology↗

bioRxiv2016

Comparing individual-based approaches to modelling the self-organization of multicellular tissues

The coordinated behaviour of populations of cells plays a central role in tissue growth and renewal. Cells react to their microenvironment by modulating processes such as movement, growth and proliferation, and signalling. Alongside experimental studies, computational models offer a useful means by which to investigate these processes. To this end a variety of cell-based modelling approaches have been developed, ranging from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes. It is difficult to accurately compare between different modelling approaches, since one cannot distinguish between differences in behaviour due to the underlying model assumptions and those due to differences in the numerical implementation of the model. Here, we exploit the availability of an implementation of five popular cell-based modelling approaches within a consistent computational framework, Chaste (http://www.cs.ox.ac.uk/chaste). This framework allows one to easily change constitutive assumptions within these models. In each case we provide full details of all technical aspects of our model implementations. We compare model implementations using four case studies, chosen to reflect the key cellular processes of proliferation, adhesion, and short-and long-range signalling. These case studies demonstrate the applicability of each model and provide a guide for model usage.\n\nAuthors contributionsJO and AF conceived of the study, designed the study, coordinated the study, carried out the computational modelling and drafted the manuscript. JP contributed to the computational modelling and helped draft the manuscript. PM and DG conceived of the study, designed the study and helped draft the manuscript. All authors gave final approval for publication.

Systems Biology↗

bioRxiv2016

Properties of compostite feedback-feedforward pulse generating motifs.

Negative feedbacks and incoherent feedforward loops are known to give rise to a pulse in response to a step change in the input. In this article, I present a study of composite motifs made of coupled feedback and feedforward loops, acting via different regulatory mechanisms. In these motifs, the effect of input and output on the controller is realized via either an AND-gate or an OR-gate. Using a simplistic model of gene expression and a common parameter set, I have studied the effect of global parameter variation on the dynamic and steady-state properties of different motifs, in response to a step change in the input. These metrics include steady state gain, response time, overshoot, peak time and peak duration. For the motifs with a negative feedback component, it can be seen that AND-gated motifs show a \"feedforward-like\" property whereas the OR-gated motifs show a \"feedback-like\" property. Motifs with a positive feedback component show hypersensitivity of gain, to parameters. Overshoot correlates negatively with peak time whereas peak duration correlates concavely with peak time, a property that is also observed for uncoupled feedback and feedforward motifs. This indicates that this relationship between overshoot, peak duration and peak time, seems to be a universal property of pulse-generating motifs.

Systems Biology↗

bioRxiv2016

Stochastic dynamics of genetic broadcasting networks

The complex genetic programs of eukaryotic cells are often regulated by key transcription factors occupying or clearing out of a large number of genomic locations. Orchestrating the residence times of these factors is therefore important for the well organized functioning of a large network. The classic models of genetic switches sidestep this timing issue by assuming the binding of transcription factors to be governed entirely by thermodynamic protein-DNA affinities. Here we show that relying on passive thermodynamics and random release times can lead to a \"time-scale crisis\" of master genes that broadcast their signals to large number of binding sites. We demonstrate that this \"time-scale crisis\" can be resolved by actively regulating residence times through molecular stripping. We illustrate these ideas by studying the stochastic dynamics of the genetic network of the central eukaryotic master regulator NF{kappa}B which broadcasts its signals to many downstream genes that regulate immune response, apoptosis etc.

Systems Biology↗

bioRxiv2016

Design principles of autocatalytic cycles constrain enzyme kinetics and force over-expression at flux branch points

A set of chemical reactions that require a metabolite to synthesize more of that metabolite is an autocatalytic cycle. Here we show that most of the reactions in the core of central carbon metabolism are part of compact autocatalytic cycles. Such metabolic designs must meet specific conditions to support stable fluxes, hence avoiding depletion of intermediate metabolites. As such, they are subjected to constraints that may seem counter-intuitive: the enzymes of branch reactions out of the cycle must be overexpressed and the affinity of these enzymes to their substrates must be relatively weak. We use recent quantitative proteomics and fluxomics measurements to show that the above conditions hold for functioning cycles in central carbon metabolism of E.coli. This work demonstrates that the topology of a metabolic network can shape kinetic parameters of enzymes and lead to seemingly wasteful enzyme usage.

Systems Biology↗

bioRxiv2016

Universal attenuators and their interactions with feedback loops in gene regulatory networks

Using a combination of mathematical modelling, statistical simulation and large-scale data analysis we study the properties of linear regulatory chains (LRCs) within gene regulatory networks (GRNs). Our modelling indicates that downstream genes embedded within LRCs are highly insulated from the variation in expression of upstream genes, and thus LRCs act as attenuators. This observation implies a progressively weaker functionality of LRCs as their length increases. When analysing the preponderance of LRCs in the GRNs of E. coli K12 and several other organisms, we find that very long LRCs are essentially absent. In both E. coli and M. tuberculosis we find that four-gene LRCs are intimately linked to identical feedback loops that are involved in potentially chaotic stress response, indicating that the dynamics of these potentially destabilising motifs are strongly restrained under homeostatic conditions. The same relationship is observed in a human cancer cell line (K562), and we postulate that four-gene LRCs act as \"universal attenuators\". These findings suggest a role for long LRCs in dampening variation in gene expression, thereby protecting cell identity, and in controlling dramatic shifts in cell-wide gene expression through inhibiting chaos-generating motifs.\n\nIn briefWe present a general principle that linear regulatory chains exponentially attenuate the range of expression in gene regulatory networks. The discovery of a universal interplay between linear regulatory chains and genetic feedback loops in microorganisms and a human cancer cell line is analysed and discussed.\n\nHighlightsWithin gene networks, linear regulatory chains act as exponentially strong attenuators of upstream variation\n\nBecause of their exponential behaviour, linear regulatory chains beyond a few genes provide no additional functionality and are rarely observed in gene networks across a range of different organisms\n\nNovel interactions between four-gene linear regulatory chains and feedback loops were discovered in E. coli, M. tuberculosis and human cancer cells, suggesting a universal mechanism of control.

Systems Biology↗

bioRxiv2016

Temporal mixture modelling of single-cell RNA-seq data resolves a CD4+ T cell fate bifurcation

Differentiation of naive CD4+ T cells into functionally distinct T helper subsets is crucial for the orchestration of immune responses. Due to multiple levels of heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a challenge for systematic dissection in vivo. By using single-cell RNA transcriptomics and computational modelling of temporal mixtures, we reconstructed the developmental trajectories of Th1 and Tfh cell populations during Plasmodium infection in mice at single-cell resolution. These cell fates emerged from a common, highly proliferative and metabolically active precursor. Moreover, by tracking clonality from T cell receptor sequences, we infer that ancestors derived from the same naive CD4+ T cell can concurrently populate both Th1 and Tfh subsets. We further found that precursor T cells were coached towards a Th1 but not a Tfh fate by monocytes/macrophages. The integrated genomic and computational approach we describe is applicable for analysis of any cellular system characterized by differentiation towards multiple fates.\n\nOne Sentence SummaryUsing single-cell RNA sequencing and a novel unsupervised computational approach, we resolve the developmental trajectories of two CD4+ T cell fates in vivo, and show that uncommitted T cells are externally influenced towards one fate by inflammatory monocytes.

Systems Biology↗

bioRxiv2016

Noise-processing by signaling networks

Signaling networks mediate environmental information to the cell nucleus. To perform this task effectively they must be able to integrate multiple stimuli and distinguish persistent signals from transient environmental fluctuations. However, the ways in which signaling networks process environmental noise are not well understood. Here we outline a mathematical framework that relates a networks structure to its capacity to process noise, and use this framework to dissect the noise-processing ability of signaling networks. We find that complex networks that are dense in directed paths are poor noise processors, while those that are sparse and strongly directional process noise well. These results suggest that while cross-talk between signaling pathways may increase the ability of signaling networks to integrate multiple stimuli, too much cross-talk may compromise the ability of the network to distinguish signal from noise. To illustrate these general results we consider the structure of the signaling network that maintains pluripotency in mouse embryonic stem cells, and find an incoherent feedforward loop structure involving Stat3, Tfcp2l1, Esrrb, Klf2 and Klf4 is particularly important for noise-processing. Taken together these results suggest that noise-processing is an important function of signaling networks and they may be structured in part to optimize this task.

Systems Biology↗

bioRxiv2016

Tracking single-cell gene regulation in dynamically controlled environments using an integrated microfluidic and computational setup

Bacteria adapt to changes in their environment by regulating gene expression, often at the level of transcription. However, since the molecular processes underlying gene regulation are subject to thermodynamic and other stochastic fluctuations, gene expression is inherently noisy, and identical cells in a homogeneous environment can display highly heterogeneous expression levels. To study how stochasticity affects gene regulation at the single-cell level, it is crucial to be able to directly follow gene expression dynamics in single cells under changing environmental conditions. Recently developed microfluidic devices, used in combination with quantitative fluorescence time-lapse microscopy, represent a highly promising experimental approach, allowing tracking of lineages of single cells over long time-scales while simultaneously measuring their growth and gene expression. However, current devices do not allow controlled dynamical changes to the environmental conditions which are needed to study gene regulation. In addition, automated analysis of the imaging data from such devices is still highly challenging and no standard software is currently available. To address these challenges, we here present an integrated experimental and computational setup featuring, on the one hand, a new dual-input microfluidic chip which allows mixing and switching between two growth media and, on the other hand, a novel image analysis software which jointly optimizes segmentation and tracking of the cells and allows interactive user-guided fine-tuning of its results. To demonstrate the power of our approach, we study the lac operon regulation in E. coli cells grown in an environment that switches between glucose and lactose, and quantify stochastic lag times and memory at the single cell level.

Systems Biology↗

bioRxiv2016

Multi-algorithm particle simulations with Spatiocyte

As quantitative biologists get more measurements of spatially regulated systems such as cell division and polarization, simulation of reaction and diffusion of proteins using the data is becoming increasingly relevant to uncover the mechanisms underlying the systems. Spatiocyte is a lattice-based stochastic particle simulator for biochemical reaction and diffusion processes. Simulations can be performed at single molecule and compartment spatial scales simultaneously. Molecules can diffuse and react in 1D (filament), 2D (membrane) and 3D (cytosol) compartments. The implications of crowded regions in the cell can be investigated because each diffusing molecule has spatial dimensions. Spatiocyte adopts multi-algorithm and multi-timescale frameworks to simulate models that simultaneously employ deterministic, stochastic and particle reaction-diffusion algorithms. Comparison of light microscopy images to simulation snapshots is supported by Spatiocyte microscopy visualization and molecule tagging features. Spatiocyte is open-source software and is freely available at http://spatiocyte.org.

Systems Biology↗

bioRxiv2016

Single Cell Phenotyping Reveals Heterogeneity among Haematopoietic Stem Cells Following Infection

The haematopoietic stem cell (HSC) niche provides essential micro-environmental cues for the production and maintenance of HSCs within the bone marrow. During inflammation, haematopoietic dynamics are perturbed, but it is not known whether changes to the HSC-niche interaction occur as a result. We visualise HSCs directly in vivo, enabling detailed analysis of the 3D niche dynamics and migration patterns in murine bone marrow following Trichinella spiralis infection. Spatial statistical analysis of these HSC trajectories reveals two distinct modes of HSC behaviour: (i) a pattern of revisiting previously explored space, and (ii) a pattern of exploring new space. Whereas HSCs from control donors predominantly follow pattern (i), those from infected mice adopt both strategies. Using detailed computational analyses of cell migration tracks and life-history theory, we show that the increased motility of HSCs following infection can, perhaps counterintuitively, enable mice to cope better in deteriorating HSC-niche micro-environments following infection.\n\nAuthor SummaryHaematopoietic stem cells reside in the bone marrow where they are crucially maintained by an incompletely-determined set of niche factors. Recently it has been shown that chronic infection profoundly affects haematopoiesis by exhausting stem cell function, but these changes have not yet been resolved at the single cell level. Here we show that the stem cell-niche interactions triggered by infection are heterogeneous whereby cells exhibit different behavioural patterns: for some, movement is highly restricted, while others explore much larger regions of space over time. Overall, cells from infected mice display higher levels of persistence. This can be thought of as a search strategy: during infection the signals passed between stem cells and the niche may be blocked or inhibited. Resultantly, stem cells must choose to either cling on, or to leave in search of a better environment. The heterogeneity that these cells display has immediate consequences for translational therapies involving bone marrow transplant, and the effects that infection might have on these procedures.

Systems Biology↗

bioRxiv2016

Analysis of noise mechanisms in cell size control

At the single-cell level, noise features in multiple ways through the inherent stochasticity of biomolecular processes, random partitioning of resources at division, and fluctuations in cellular growth rates. How these diverse noise mechanisms combine to drive variations in cell size within an isoclonal population is not well understood. To address this problem, we systematically investigate the contributions of different noise sources in well-known paradigms of cell-size control, such as the adder (division occurs after adding a fixed size from birth) and the sizer (division occurs upon reaching a size threshold). Analysis reveals that variance in cell size is most sensitive to errors in partitioning of volume among daughter cells, and not surprisingly, this process is well regulated among microbes. Moreover, depending on the dominant noise mechanism, different size control strategies (or a combination of them) provide efficient buffering of intercellular size variations. We further explore mixer models of size control, where a timer phase precedes/follows an adder, as has been proposed in Caulobacter crescentus. While mixing a timer with an adder can sometimes attenuate size variations, it invariably leads to higher-order moments growing unboundedly over time. This results in the cell size following a power-law distribution with an exponent that is inversely dependent on the noise in the timer phase. Consistent with theory, we find evidence of power-law statistics in the tail of C. crescentus cell-size distribution, but there is a huge discrepancy in the power-law exponent as estimated from data and theory. However, the discrepancy is removed after data reveals that the size added by individual newborns from birth to division itself exhibits power-law statistics. Taken together, this study provides key insights into the role of noise mechanisms in size homeostasis, and suggests an inextricable link between timer-based models of size control and heavy-tailed cell size distributions.

Systems Biology↗

bioRxiv2016

Dynamic proteomics of HSV1 infection reveals molecular events that govern non-stochastic infection outcomes

Viral infection is usually studied at the level of cell populations, averaging over hundreds of thousands of individual cells. Moreover, measurements are typically done by analyzing a few time points along the infection process. While informative, such measurements are limited in addressing how cell variability affects infection outcome. Here we employ dynamic proteomics to study virus-host interactions, using the human pathogen Herpes Simplex virus 1 as a model. We tracked >50,000 individual cells as they respond to HSV1 infection, allowing us to model infection kinetics and link infection outcome (productive or not) with the cell state at the time of initial infection. We find that single cells differ in their preexisting susceptibility to HSV1, and that this is partially mediated by their cell-cycle position. We also identify specific changes in protein levels and localization in infected cells, attesting to the power of the dynamic proteomics approach for studying virus-host interactions.

systems biology↗