Biology subjects

Baker, R. E.

Publications and source records attributed to Baker, R. E..

At least 37 records · Page 2Linked to original sources

bioRxiv2020

Host-pathogen genetic interactions underlie tuberculosis susceptibility in genetically diverse mice

The outcome of an encounter with Mycobacterium tuberculosis depends on the pathogens ability to adapt to the variable immune pressures exerted by the host. Understanding this interplay has proven difficult, largely because experimentally tractable animal models do not recapitulate the heterogeneity of tuberculosis disease. We leveraged the genetically diverse Collaborative Cross (CC) mouse panel in conjunction with a library of Mtb mutants to associate bacterial genetic requirements with host genetics and immunity. We report that CC strains vary dramatically in their susceptibility to infection and produce qualitatively distinct immune states. Global analysis of Mtb mutant fitness across the CC panel revealed that many virulence pathways are only in specific host microenvironments, identifying the large fraction of the pathogens genome that has been maintained to ensure fitness in a diverse population. Both immunological and bacterial traits were associated with genetic variants distributed across the mouse genome, identifying the specific host-pathogen genetic interactions that influence pathogenesis.

genetics↗

bioRxiv2020

Listen to the noise: identifiability analysis for stochastic differential equation models in systems biology

Mathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Identifiability analysis is well-established for deterministic, ordinary differential equation (ODE) models, but there are no commonly-adopted methods for analysing identifiability in stochastic models. We provide an accessible introduction to identifiability analysis and demonstrate how existing ideas for analysis of ODE models can be applied to stochastic differential equation (SDE) models through four practical case studies. To assess structural identifiability, we study ODEs that describe the statistical moments of the stochastic process using open-source software tools. Using practically-motivated synthetic data and Markov-chain Monte Carlo (MCMC) methods, we assess parameter identifiability in the context of available data. Our analysis shows that SDE models can often extract more information about parameters than deterministic descriptions. All code used to perform the analysis is available on Github.

systems biology↗

bioRxiv2020

Climate and urbanization drive mosquito preference for humans

The majority of mosquito-borne illness is spread by a few mosquito species that have evolved to specialize in biting humans, yet the precise causes of this behavioral shift are poorly understood. We address this gap in the arboviral vector Aedes aegypti. We first characterize the behaviour of mosquitoes from 27 sites scattered across the species ancestral range in sub-Saharan Africa, revealing previously unrecognized diversity in female preference for human versus animal odor. We then use modelling to show that this diversity can be almost fully predicted by two ecological factors - dry season intensity and human population density. Finally we integrate this information with whole genome sequence data from 345 individual mosquitoes to identify a single underlying ancestry component linked to human preference, with genetic changes concentrated in a few key chromosomal regions. Our findings strongly suggest that human-biting in this important disease vector originally evolved as a by-product of breeding in human-stored water in areas where doing so provided the only means to survive the long, hot dry season. Our model also predicts that changes in human population density are likely to drive future mosquito evolution. Rapid urbanization may drive a shift to human-biting in many cities across Africa by 2050.

evolutionary biology↗

bioRxiv2019

Mechanical cell competition in heterogeneous epithelial tissues

Mechanical cell competition is important during tissue development, cancer invasion, and tissue ageing. Heterogeneity plays a key role in practical applications since cancer cells can have different cell stiffness and different proliferation rates than normal cells. To study this phenomenon, we propose a one-dimensional mechanical model of heterogeneous epithelial tissue dynamics that includes cell-length-dependent proliferation and death mechanisms. Proliferation and death are incorporated into the discrete model stochastically and arise as source/sink terms in the corresponding continuum model that we derive. Using the new discrete model and continuum description, we explore several applications including the evolution of homogeneous tissues experiencing proliferation and death, and competition in a heterogeneous setting with a cancerous tissue competing for space with an adjacent normal tissue. This framework allows us to postulate new mechanisms that explain the ability of cancer cells to outcompete healthy cells through mechanical differences rather than by having some intrinsic proliferative advantage. We advise when the continuum model is beneficial and demonstrate why naively adding source/sink terms to a continuum model without considering the underlying discrete model may lead to incorrect results.

biophysics↗

bioRxiv2019

Functionally overlapping variants control TB susceptibility in Collaborative Cross mice

Host genetics plays an important role in determining the outcome of Mycobacterium tuberculosis (Mtb) infection. We previously found that Collaborative Cross mouse strains differ in their susceptibility to Mtb, and that the CC042/GeniUnc (CC042) strain suffered from a rapidly progressive disease and failed to produce the protective cytokine, IFN{gamma}, in the lung. Here, we used parallel genetic and immunological approaches to investigate the basis of CC042 susceptibility. Using a population derived from a CC001/Unc (CC001) x CC042 intercross, we mapped four QTL underlying Tuberculosis ImmunoPhenotypes (Tip1-4). These included 2 major effect QTL on Chromosome 7 (Tip1 and Tip2) that were associated with bacterial burden. Tip2, along with Tip3 (Chromosome 15) and Tip4 (Chromosome 16) also correlated with IFN{gamma} production following infection, whereas Tip1 appeared to control an IFN{gamma}-independent mechanism of bacterial control. Further immunological characterization revealed that CC042 animals recruited relatively few antigen-specific T cells to the lung and these T cells failed to express the Integrin alpha L (L; i.e., CD11a), which contributes to T cell activation and migration. These defects could be explained by a CC042 private variant in the Itgal gene, which encodes CD11a, and is found within the Tip2 interval. This 15bp deletion leads to aberrant mRNA splicing and is predicted to result in a truncated protein product. The ItgalCC042 genotype was associated with all measured disease traits, indicating that this variant is a major determinant of susceptibility in CC042. The combined effect of functionally distinct Tip variants likely explains the profound susceptibility of CC042 and highlights the multigenic nature of TB control in the Collaborative Cross.\n\nImportance statementThe variable outcome of Mycobacterium tuberculosis infection observed natural populations is difficult to model in genetically homogenous small animal models. The newly-developed Collaborative Cross (CC) represents a reproducible panel of genetically-diverse mice that display a broad range of phenotypic responses to infection. We explored the genetic basis of this variation, focusing on a CC line that is highly susceptible to M. tuberculosis infection. This study identified multiple quantitative trait loci associated with bacterial control and cytokine production, including one that is caused by a novel loss-of-function mutation in the Itgal gene that is necessary for T cell recruitment to the infected lung. These studies verify the multigenic control of mycobacterial disease in the CC panel, identify genetic loci controlling diverse aspects of pathogenesis, and highlight the utility of the CC resource.

microbiology↗

bioRxiv2019

Head Mesoderm Tissue Growth, Dynamics and Neural Crest Cell Migration

Vertebrate head morphogenesis involves orchestrated cell growth and tissue movements of the mesoderm and neural crest to form the distinct craniofacial pattern. To better understand structural birth defects, it is important that we learn how these processes are controlled. Here, we examine this question during chick head morphogenesis using time-lapse imaging, computational modeling, and experiment. We find that head mesodermal cells are inherently dynamic in culture and alter cell behaviors in the presence of either ectoderm or neural crest cells. Mesodermal cells in vivo display large-scale whirling motions that rapidly transition to lateral, directed movements after neural crest cells emerge. Computer model simulations predict distinct changes in neural crest migration as the spatio-temporal growth profile of the mesoderm is varied. BrdU-labeling and photoconversion combined with cell density measurements then reveal non-uniform mesoderm growth in space and time. Chemical inhibition of head mesoderm proliferation or ablation of premigratory neural crest alters mesoderm growth and neural crest migration, implying a dynamic feedback between tissue growth and neural crest cell signaling to confer robustness to the system.\n\nSummary StatementDynamic feedback between tissue growth and neural crest cell migration ensures robust neural crest stream formation and head morphogenesis shown by time-lapse microscopy, mathematical modeling and embryo perturbations.

developmental biology↗

bioRxiv2019

Neural crest cells bulldoze through the microenvironment using Aquaporin-1 to stabilize filopodia

Neural crest migration requires cells to move through an environment filled with dense extracellular matrix and mesoderm to reach targets throughout the vertebrate embryo. Here, we use high-resolution microscopy, computational modeling, and in vitro and in vivo cell invasion assays to investigate the function of Aquaporin-1 (AQP-1) signaling. We find that migrating lead cranial neural crest cells express AQP-1 mRNA and protein, implicating a biological role for water channel protein function during invasion. Differential AQP-1 levels affect neural crest cell speed, direction, and the length and stability of cell filopodia. Further, AQP-1 enhances matrix metalloprotease (MMP) activity and colocalizes with phosphorylated focal adhesion kinases (pFAK). Co-localization of AQP-1 expression with EphB guidance receptors in the same migrating neural crest cells raises novel implications for the concept of guided bulldozing by lead cells during migration.

developmental biology↗

bioRxiv2019

Accurate and efficient discretisations for stochastic models of cell migration and cell proliferation with crowding

Understanding how cells proliferate, migrate, and die in various environments is essential in determining how organisms develop and repair themselves. Continuum mathematical models, such as the logistic equation and the Fisher-Kolmogorov equation, can describe the global characteristics observed in commonly-used cell biology assays, such as proliferation and scratch assays. However, these continuum models do not account for single-cell-level mechanics observed in high-throughput experiments. Mathematical modelling frameworks that represent individual cells, often called agent-based models, can successfully describe key single-cell-level features of these assays, but are computationally infeasible when dealing with large populations. In this work, we propose an agent-based model with crowding effects that is computationally efficient and matches the logistic and Fisher-Kolmogorov equations in parameter regimes relevant to proliferation and scratch assays, respectively. This stochastic agent-based model allows multiple agents to be contained within compartments on an underlying lattice, thereby reducing the computational storage compared to existing agent-based models that allow one agent per site only. We propose a systematic method to determine a suitable compartment size. Implementing this compartment-based model with this compartment size provides a balance between computational storage, local resolution of agent behaviour, and agreement with classical continuum descriptions.

biophysics↗

bioRxiv2019

Statistical Analysis of Variability in TnSeq Data Across Conditions Using Zero-Inflated Negative Binomial Regression

Deep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality of genomic loci under different environmental conditions. Various analytical methods have been described for identifying conditionally essential genes whose tolerance for insertions varies between two conditions. However, for large-scale experiments involving many conditions, a method is needed for identifying genes that exhibit significant variability in insertions across multiple conditions. In this paper, we introduce a novel statistical method for identifying genes with significant variability of insertion counts across multiple conditions based on Zero-Inflated Negative Binomial (ZINB) regression. Using likelihood ratio tests, we show that the ZINB fits TnSeq data better than either ANOVA or a Negative Binomial (in a generalized linear model). We use ZINB regression to identify genes required for infection of M. tuberculosis H37Rv in C57BL/6 mice. We also use ZINB to perform a retrospective analysis of genes conditionally essential in H37Rv cultures exposed to multiple antibiotics. Our results show that, not only does ZINB generally identify most of the genes found by pairwise resampling (and vastly out-performs ANOVA), but it also identifies additional genes where variability is detectable only when the magnitudes of insertion counts are treated separately from local differences in saturation, as in the ZINB model.

bioinformatics↗

bioRxiv2019

Testing models of mRNA localization reveals robustness regulated by reducing transport between cells

Robust control of gene expression in both space and time is of central importance in the regulation of cellular processes, and for multicellular development. However, the mechanisms by which robustness is achieved are generally not identified or well understood. For example, mRNA localization by molecular-motor-driven transport is crucial for cell polarization in numerous contexts, but the regulatory mechanisms that enable this process to take place in the face of noise or significant perturbations are not fully understood. Here we use a combined experimental-theoretical approach to characterize the robustness of gurken/TGF-alpha mRNA localization in Drosophila egg chambers, where the oocyte and 15 surrounding nurse cells are connected in a stereotypic network via intracellular bridges known as ring canals. We construct a mathematical model that encodes simplified descriptions of the range of steps involved in mRNA localization, including production and transport between and within cells until the final destination in the oocyte. Using Bayesian inference, we calibrate this model using quantitative single molecule fluorescence in situ hybridization data. By analyzing both the steady state and dynamic behaviours of the model, we provide estimates for the rates of different steps of the localization process, as well as the extent of directional bias in transport through the ring canals. The model predicts that mRNA synthesis and transport must be tightly balanced to maintain robustness, a prediction which we tested experimentally using an over-expression mutant. Surprisingly, the over-expression mutant fails to display the anticipated degree of overaccumulation of mRNA in the oocyte predicted by the model. Through careful model-based analysis of quantitative data from the over-expression mutant we show evidence of saturation of transport of mRNA through ring canals. We conclude that this saturation engenders robustness of the localization process, in the face of significant variation in the levels of mRNA synthesis. Statement of significanceFor development to function correctly and reliably across a population, gene expression must be controlled robustly in a repeatable manner. How this robustness is achieved is not well understood. We use modelling to better study the localization of polarity determining transcripts (RNA) in fruit fly development. By calibrating our model with quantitative imaging data we are able to make experimentally testable predictions, comparison of which with data from a genetic mutant, reveals evidence that saturation of RNA transport contributes to the robustness of RNA localization.

developmental biology↗

bioRxiv2019

Process noise distinguishes between indistinguishable population dynamics

Model selection is becoming increasingly important in mathematical biology. Model selection often involves comparing a set of observations with predictions from a suite of continuum mathematical models and selecting the model that provides the best explanation of the data. In this work we consider the more challenging problem of model selection in a stochastic setting. We consider five different stochastic models describing population growth. Through simulation we show that all five stochastic models gives rise to classical logistic growth in the limit where we consider a large number of identically prepared realisations. Therefore, comparing mean data from each of the models gives indistinguishable predictions and model selection based on population-level information is impossible. To overcome this challenge we extract process noise from individual realisations of each model and identify properties in the process noise that differ between the various stochastic models. Using a Bayesian framework, we show how process noise can be used successfully to make a probabilistic distinction between the various stochastic models. The relative success of this approach depends upon the identification of appropriate summary statistics and we illustrate how increasingly sophisticated summary statistics can lead to improved model selection, but this improvement comes at the cost of requiring more detailed summary statistics.

synthetic biology↗

bioRxiv2018

A free boundary model of epithelial dynamics

In this work we analyse a one-dimensional, cell-based model of an epithelial sheet. In this model, cells interact with their nearest neighbouring cells and move deterministically. Cells also proliferate stochastically, with the rate of proliferation specified as a function of the cell length. This mechanical model of cell dynamics gives rise to a free boundary problem. We construct a corresponding continuum-limit description where the variables in the continuum limit description are expanded in powers of the small parameter 1/N, where N is the number of cells in the population. By carefully constructing the continuum limit description we obtain a free boundary partial differential equation description governing the density of the cells within the evolving domain, as well as a free boundary condition that governs the evolution of the domain. We show that care must be taken to arrive at a free boundary condition that conserves mass. By comparing averaged realisations of the cell-based model with the numerical solution of the free boundary partial differential equation, we show that the new mass-conserving boundary condition enables the coarsegrained partial differential equation model to provide very accurate predictions of the behaviour of the cell-based model, including both evolution of the cell density, and the position of the free boundary, across a range of interaction potentials and proliferation functions in the cell based model.

systems biology↗

bioRxiv2017

Discrete and continuum approximations for collective cell migration in a scratch assay with cell size dynamics

Scratch assays are routinely used to study the collective spreading of cell populations. In general, the rate at which a population of cells spreads is driven by the combined effects of cell migration and proliferation. To examine the effects of cell migration separately from the effects of cell proliferation, scratch assays are often performed after treating the cells with a drug that inhibits proliferation. Mitomycin-C is a drug that is commonly used to suppress cell proliferation in this context. However, in addition to suppressing cell proliferation, Mitomycin-C also causes cells to change size during the experiment, as each cell in the population approximately doubles in size as a result of treatment. Therefore, to describe a scratch assay that incorporates the effects of cell-to-cell crowding, cell-to-cell adhesion, and dynamic changes in cell size, we present a new stochastic model that incorporates these mechanisms. Our agent-based stochastic model takes the form of a system of Langevin equations that is the system of stochastic differential equations governing the evolution of the population of agents. We incorporate a time-dependent interaction force that is used to mimic the dynamic increase in size of the agents. To provide a mathematical description of the average behaviour of the stochastic model we present continuum limit descriptions using both a standard mean-field approximation, and a more sophisticated moment dynamics approximation that accounts for the density of agents and density of pairs of agents in the stochastic model. Comparing the accuracy of the two continuum descriptions for a typical scratch assay geometry shows that the incorporation of agent growth in the system is associated with a decrease in accuracy of the standard mean-field description. In contrast, the moment dynamics description provides a more accurate prediction of the evolution of the scratch assay when the increase in size of individual agents is included in the model.

biophysics↗

bioRxiv2017

Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues

The growth and dynamics of epithelial tissues govern many morphogenetic processes in embryonic development. A recent quantitative transition in data acquisition, facilitated by advances in genetic and live-imaging techniques, is paving the way for new insights to these processes. Computational models can help us understand and interpret observations, and then make predictions for future experiments that can distinguish between hypothesised mechanisms. Increasingly, cell-based modelling approaches such as vertex models are being used to help understand the mechanics underlying epithelial morphogenesis. These models typically seek to reproduce qualitative phenomena, such as cell sorting or tissue buckling. However, it remains unclear to what extent quantitative data can be used to constrain these models so that they can then be used to make quantitative, experimentally testable predictions. To address this issue, we perform an in silico study to investigate whether vertex model parameters can be inferred from imaging data, and explore methods to quantify the uncertainty of such estimates. Our approach requires the use of summary statistics to estimate parameters. Here, we focus on summary statistics of cellular packing and of laser ablation experiments, as are commonly reported from imaging studies. We find that including data from repeated experiments is necessary to generate reliable parameter estimates that can facilitate quantitative model predictions.

developmental biology↗

bioRxiv2017

N-terminal sumoylation of centromeric histone H3 variant Cse4 regulates its proteolysis to prevent mislocalization to non-centromeric chromatin

Stringent regulation of cellular levels of evolutionarily conserved centromeric histone H3 variant (CENP-A in humans, CID in flies, Cse4 in yeast) prevents its mislocalization to non-centromeric chromatin. Overexpression and mislocalization of CENP-A has been observed in cancers and leads to aneuploidy in yeast, flies, and human cells. Ubiquitin-mediated proteolysis of Cse4 by E3 ligases such as Psh1 and Sumo-Targeted Ubiquitin Ligase (STUbL) Slx5 prevent mislocalization of Cse4. Previously, we identified Siz1 and Siz2 as the major E3 ligases for sumoylation of Cse4. In this study, we identify lysine 65 (K65) in Cse4 as a SUMO site and show that sumoylation of Cse4 K65 regulates its ubiquitin-mediated proteolysis by Slx5. Strains expressing cse4 K65R exhibit reduced levels of sumoylated and ubiquitinated Cse4 in vivo. Furthermore, co-immunoprecipitation experiments reveal reduced interaction of cse4 K65R with Slx5. Defects in sumoylation of cse4 K65R contribute to increased stability and mislocalization of cse4 K65R under normal physiological conditions. Based on the increased stability of cse4 K65R in psh1{triangleup} strains but not in slx5{triangleup} strains, we conclude that Slx5 targets sumoylated Cse4 K65 for ubiquitination-mediated proteolysis independent of Psh1. In summary, we have identified and characterized the physiological role of Cse4 sumoylation and determined that sumoylation of Cse4 K65 regulates ubiquitin-mediated proteolysis and prevents mislocalization of Cse4 which is required for genome stability.

molecular biology↗

bioRxiv2017

The impact of experimental design choices on parameter inference for models of growing cell colonies

To better understand development, repair and disease progression it is useful to quantify the behaviour of proliferative and motile cell populations as they grow and expand to fill their local environment. Inferring parameters associated with mechanistic models of cell colony growth using quantitative data collected from carefully designed experiments provides a natural means to elucidate the relative contributions of various processes to the growth of the colony. In this work we explore how experimental design impacts our ability to infer parameters for simple models of the growth of proliferative and motile cell populations. We adopt a Bayesian approach, which allows us to characterise the uncertainty associated with estimates of the model parameters. Our results suggest that experimental designs that incorporate initial spatial heterogeneities in cell positions facilitate parameter inference without the requirement of cell tracking, whilst designs that involve uniform initial placement of cells require cell tracking for accurate parameter inference. As cell tracking is an experimental bottleneck in many studies of this type, our recommendations for experimental design provide for significant potential time and cost savings in the analysis of cell colony growth.

cell biology↗

bioRxiv2017

Semblance Of Heterogeneity In Collective Cell Migration

Cell population heterogeneity is increasingly a focus of inquiry in biological research. For example, cell migration studies have investigated the heterogeneity of invasiveness and taxis in development, wound healing, and cancer. However, relatively little effort has been devoted to explore when heterogeneity is mechanistically relevant and how to reliably measure it. Statistical methods from the animal movement literature offer the potential to analyse heterogeneity in collections of cell tracking data. A popular measure of heterogeneity, which we use here as an example, is the distribution of delays in directional cross-correlation. Using a suitably generic, yet minimal, model of collective cell movement in three dimensions, we show how using such measures to quantify heterogeneity in tracking data can result in the inference of heterogeneity where there is none. Our study highlights a potential pitfall in the statistical analysis of cell population heterogeneity, and we argue this can be mitigated by the appropriate choice of null models.\n\nHighlightsO_LIgroups of identical cells appear heterogeneous due to limited sampling and experimental repeatability\nC_LIO_LIheterogeneity bias increases with attraction/repulsion between cells\nC_LIO_LImovement in confined environments decreases apparent heterogeneity\nC_LIO_LIhypothetical applications in neural crest and in vitro cancer systems\nC_LI\n\nIn BriefWe use a mathematical model to show how cell populations can appear heterogeneous in their migratory characteristics, even though they are made up of identically-behaving individual cells. This has important consequences for the study of collective cell migration in areas such as embryo development or cancer invasion.

systems biology↗

bioRxiv2017

Continuum approximations for lattice-free multi-species models of collective cell migration

Abstract\n\nCell migration within tissues involves the interaction of many cells from distinct subpopulations. In this work, we present a discrete model of collective cell migration where the motion of individual cells is driven by random forces, short range repulsion forces to mimic crowding, and longer range attraction forces to mimic adhesion. This discrete model can be used to simulate a population of cells that is composed of K [≥] 1 distinct subpopulations. To analyse the discrete model we formulate a hierarchy of moment equations that describe the spatial evolution of the density of agents, pairs of agents, triplets of agents, and so forth. To solve the hierarchy of moment equations we introduce two forms of closure: (i) the mean field approximation, which effectively assumes that the distributions of individual agents are independent; and (ii) a moment dynamics description that is based on the Kirkwood superposition approximation. The moment dynamics description provides an approximate way of incorporating spatial patterns, such as agent clustering, into the continuum description. Comparing the performance of the two continuum descriptions confirms that both perform well when adhesive forces are sufficiently weak. In contrast, the moment dynamics description outperforms the mean field model when adhesive forces are sufficiently large. This is a first attempt to provide an accurate continuum description of a lattice-free, multi-species model of collective cell migration.

biophysics↗