Biology subjects

Simpson, M.

Publications and source records attributed to Simpson, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Monotreme transcriptomes shed light on evolution of lactation and placentation

Placentation and lactation are hallmark mammalian reproductive traits and key processes facilitating the transfer of nutrients from the mother to the offspring. The basal mammalian lineage of egg-laying monotremes features a short gestation period supported by a simple placenta and an elongated lactation, similar to marsupials. Eutherian mammals by contrast evolved longer gestation facilitated by extensive placentation and shorter lactation. To understand the gene expression underpinning maternal-foetal nutrient transfer across mammals, we generated transcriptome datasets for the monotreme reproductive tract and mammary gland and compared this to expression in representative species from the other mammalian clades: marsupials (Tammar wallaby) and eutherians (mouse). This revealed extensive overlapping gene expression between the therian placenta and monotreme reproductive tract, including the first observation of retroviral gene expression in the echidna reproductive tract. Interestingly, we discovered that transport and development functions supporting foetal growth are expressed in echidna mammary gland and eutherian placenta, likely a signature of the shift from prolonged lactation to longer placentation. Together, this demonstrates that diverse modes of maternal-foetal nutrient transfer are underpinned by similar gene networks across mammals.

evolutionary biology↗

bioRxiv2026

Intestinal bacteria hijack an evolutionarily conserved epithelial repair signal

Apoptosis in the gut triggers an expansion in the Enterobacteriaceae family of bacteria, causing both prolonged tissue injury and delayed repair. However, the mechanisms linking the Enterobacteriaceae bloom and subsequent deleterious tissue response are relatively unknown. Here, we establish purines as a major component of the apoptotic secretome that are consumed by bacteria. Explicitly, we identify hypoxanthine as a critical metabolite that is taken up and metabolised by both pathogenic and commensal species within the Enterobacteriaceae family. Epithelial cells release hypoxanthine into the extracellular space during early stages of apoptosis via the upregulation of equilibrative nucleoside transporters 1/2 (ENT1/2). Critically, beyond simply linking host and microbe, we delineate a connection between the release of hypoxanthine from the dying cell and the ability of the host to repair damaged epithelial tissue. Hypoxanthine is a potent promoter of epithelial cell repair in both gut and skin across the phylogenetic tree including humans, mice, zebrafish, and fruit flies and promotes similar ATP production and cellular proliferation in eukaryotic and microbial recipients. Thus, the preferential utilisation of hypoxanthine by the Enterobacteriaceae directly competes with the host for a core reparative signal.

microbiology↗

bioRxiv2025

Confidence: A Web App for Cross-Platform Differential Gene Expression Analysis, Gene Scoring, and Enrichment Analysis

RNA sequencing (RNA-seq) is used to quantify transcript levels through measurement of nucleotide sequences. To evaluate statistically significant changes in gene expression, transcript counts between samples are compared using differential expression analysis methods. However, three of the most pressing challenges in transcriptomics analyses are: 1) analytical packages produce a distinct number of differentially expressed genes with varied P-value and fold-change values; 2) the effective use of these analytical packages requires substantial knowledge of programming and bioinformatics; and 3) there are a lack of intuitive methods to select target genes for further investigation in an unbiased manner. To address these challenges, we developed Confidence, a web-based application to perform simultaneous statistical analysis of RNA-seq count data. Confidence incorporates the Confidence Score (CS), ranging from 1 to 4 to aid in gene prioritization, where 1 represents low confidence and 4 represents high confidence. The Confidence web-based application was designed for rapid and intuitive analysis of standard experimental metadata and gene count inputs. Confidence provided a web-based, wide-net approach to differential gene expression analysis. Gene scoring allows for unbiased gene selection and identification of novel genes strongly associated with disease and treatment models across multiple species. Additionally, pathway analysis tools have been integrated so that highly confident genes can be placed into biological context in terms of functions and pathways. Confidence provides a new strategy for target prioritization in RNA-seq analysis and the generation of publication-quality figures.

bioinformatics↗

bioRxiv2022

Comprehensive single-cell transcriptomic analysis of embryonic melanoblasts uncovers lineage-specific mechanisms of melanoma metastasis and therapy resistance

Melanoma plasticity, driven by phenotype state switching, underlies clinically relevant traits such as metastasis and therapy resistance. As melanoma progression is thought to recapitulate aspects of neural crest cell (NCC) development, understanding embryonic melanocyte specification and lineage fate decisions of closely related NCCs may illuminate the pathways co-opted during disease evolution. Here, we use a mouse model to isolate and sequence Dopachrome tautomerase (Dct) expressing NCCs, the precursors of melanocytes, at two key developmental stages. We classify these lineages and devise a Developmental Gene Module (DGM) scoring system to interrogate lineage state switching in melanoma samples. In bulk transcriptomes, activation of DGMs representing embryonic Schwann Cell Precursors (SCPs)--multipotent stem cells--in patient tumors predicts poor response to immune checkpoint inhibitors (ICI). Co-activation of SCP and Mesenchymal-like (Mes.) modules further correlates with resistance to MAPK inhibitors. Notably, single-cell analyses reveal that melanoma cells can simultaneously express multiple DGMs, forming "hybrid" states. Cells in a hybrid Neural/SCP state are enriched in early metastasis and ICI-resistant tumors and are insensitive to inflammatory stimuli. We demonstrate that targeting Hdac2, a histone deacetylase associated with this Neural/SCP hybrid state, promotes a mesenchymal-like state switch, remodels the tumor microenvironment, and sensitizes melanoma cells to TNF and tumors to ICI therapy. Our methodology thus reveals dynamic patterns of lineage state switching correlated with melanoma tumor evolution to drive insight into new therapeutic targets. TeaserNewly identified melanoblast cell states are reawakened in metastasizing and therapy-resistant melanomas.

cancer biology↗

bioRxiv2022

Escape From Oncogene-Induced Senescence is Controlled by POU2F2 and Memorized by Chromatin Scars

Although oncogene-induced senescence (OIS) is a potent tumor-suppressor mechanism, recent studies revealed that cells can escape from OIS with features of transformed cells. However, the mechanisms that promote OIS escape remain unclear, and evidence of post-senescent cells in human cancers is missing. Here, we unravel the regulatory mechanisms underlying OIS escape using dynamic multidimensional profiling. We demonstrate a critical role for AP1 and POU2F2 transcription factors for escape from OIS and identify senescence-associated chromatin scars (SACS) as an epigenetic memory of OIS, detectable during colorectal cancer progression. POU2F2 levels are elevated already in precancerous lesions and as cells escape from OIS, and its expression and binding activity to cis-regulatory elements are associated with decreased patient survival. Our results support a model in which POU2F2 exploits a precoded enhancer landscape to promote senescence escape and reveal POU2F2 gene signatures and SACS as valuable biomarkers with diagnostic and prognostic potential.

genomics↗

bioRxiv2021

Clostridioides difficile binary toxin binding component (CDTb) increases virulence in a hamster model

Clostridioides difficile is the leading cause of hospital-acquired gastrointestinal infection, in part due to the existence of binary toxin (CDT)-expressing hypervirulent strains. We have previously shown that CDT interacts with the TLR2/6 heterodimer to induce inflammation, and in this study we further explore this interaction as well as the contribution of the separate components of CDT, CDTa and CDTb. We found that the binding component, CDTb, is capable of inducing inflammation. Additionally, complementation of a CDT-deficient C. difficile strain with CDTb alone restored virulence in a hamster model of C. difficile infection. Overall, this study demonstrates that the binding component of C. difficile binary toxin contributes to virulence during infection.

microbiology↗

bioRxiv2021

Detection of pathogenic splicing events from RNA-sequencing data using dasper

Although next-generation sequencing technologies have accelerated the discovery of novel gene-to-disease associations, many patients with suspected Mendelian diseases still leave the clinic without a genetic diagnosis. An estimated one third of these patients will have disorders caused by mutations impacting splicing. RNA-sequencing has been shown to be a promising diagnostic tool, however few methods have been developed to integrate RNA-sequencing data into the diagnostic pipeline. Here, we introduce dasper, an R/Bioconductor package that improves upon existing tools for detecting aberrant splicing by using machine learning to incorporate disruptions in exon-exon junction counts as well as coverage. dasper is designed for diagnostics, providing a rank-based report of how aberrant each splicing event looks, as well as including visualization functionality to facilitate interpretation. We validate dasper using 16 patient-derived fibroblast cell lines harbouring pathogenic variants known to impact splicing. We find that dasper is able to detect pathogenic splicing events with greater accuracy than existing LeafCutterMD or z-score approaches. Furthermore, by only applying a broad OMIM gene filter (without any variant-level filters), dasper is able to detect pathogenic splicing events within the top 10 most aberrant identified for each patient. Since using publicly available control data minimises costs associated with incorporating RNA-sequencing into diagnostic pipelines, we also investigate the use of 504 GTEx fibroblast samples as controls. We find that dasper leverages publicly available data effectively, ranking pathogenic splicing events in the top 25. Thus, we believe dasper can increase diagnostic yield for a pathogenic splicing variants and enable the efficient implementation of RNA-sequencing for diagnostics in clinical laboratories.

bioinformatics↗

bioRxiv2020

Clostridium difficile binary toxin (CDT) is recognized by the TLR2/6 heterodimer to induce an NF-κB response

Clostridioides difficile infection (CDI) represents a significant burden on the health care system, one that is exacerbated by the emergence of binary toxin (CDT)-producing hypervirulent C. difficile strains. Previous work from our lab has shown that TLR2 recognizes CDT to induce inflammation. Here we explore the interactions of CDT with TLR2 and the impact on host immunity during CDI. We found that the TLR2/6 heterodimer, not TLR2/1, is responsible for CDT recognition, and that gene pathways including NF-{kappa}B and MAPK downstream of TLR2/6 are upregulated in mice with intact TLR2/6 signaling during CDI.

immunology↗

bioRxiv2019

Mathematical models incorporating a multi-stage cell cycle explain synchronisation in proliferation experiments

We present a suite of experimental data showing that cell proliferation assays, prepared using standard methods thought to produce asynchronous cell populations, persistently exhibit inherent synchronisation. Our experiments use fluorescent cell cycle indicators to reveal the normally-hidden cell synchronisation by highlighting oscillatory subpopulations within the total cell population. These oscillatory subpopulations would never be observed without these cell cycle indicators. On the other hand, our experimental data show that the total cell population appears to grow exponentially, as in an asynchronous population. We reconcile these seemingly inconsistent observations by employing a multi-stage mathematical model of cell proliferation that can replicate the oscillatory subpopulations. Our study has important implications for understanding and improving experimental reproducibility. In particular, inherent synchronisation may affect the experimental reproducibility of studies aiming to investigate cell cycle-dependent mechanisms, including changes in migration and drug response.

biophysics↗

bioRxiv2019

Inbreeding depression causes reduced fecundity in Golden Retrievers

Inbreeding depression has been demonstrated to impact vital rates, productivity, and performance in many domestic species. Many in the field have demonstrated the value of genomic measures of inbreeding compared to pedigree-based estimates of inbreeding; further, standardized, high-quality phenotype data on all individuals is invaluable for longitudinal analyses of a study cohort. We compared measures of reproductive fitness in a small cohort of Golden Retrievers enrolled in the Golden Retriever Lifetime Study (GRLS) to a genomic measurement of inbreeding, FROH. We demonstrate a statistically significant negative correlation between fecundity and FROH.This work sets the stage for larger scale analyses to investigate genomic regions associated with fecundity and other measures of fitness.

genomics↗

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

Gut microbiome communication with bone marrow regulates susceptibility to amebiasis

The gut microbiome provides resistance to infection. However, the mechanisms for this are poorly understood. Colonization with the intestinal bacterium Clostridium scindens provided protection from the parasite Entamoeba histolytica via innate immunity. Introduction of C. scindens into the gut microbiota epigenetically altered and expanded bone marrow granulocyte-monocyte-progenitors (GMPs) and provided neutrophil-mediated protection against subsequent challenge with E. histolytica. Adoptive transfer of bone-marrow from C. scindens colonized-mice into naive-mice protected against ameba infection and increased intestinal neutrophils. Because of the known ability of C. scindens to metabolize the bile salt cholate, we measured deoxycholate and discovered that it was increased in the sera of C. scindens colonized mice, as well as in children protected from amebiasis. Administration of deoxycholate alone (in the absence of C. scindens) increased the epigenetic mediator JMJD3 and GMPs and provided protection from amebiasis. In conclusion the microbiota was shown to communicate to the bone marrow via microbially-metabolized bile salts to train innate immune memory to provide antigen-nonspecific protection from subsequent infection. This represents a novel mechanism by which the microbiome protects from disease.\n\nOne Sentence SummaryIntroduction of the human commensal bacteria Clostridium scindens into the intestinal microbiota epigenetically alters bone marrow and protects from future parasite infection.

microbiology↗

bioRxiv2018

Spatial structure arising from chase-escape interactions with crowding

Movement of individuals, mediated by localised interactions, plays a key role in numerous processes including cell biology and ecology. In this work, we investigate an individual-based model accounting for various intraspecies and interspecies interactions in a community consisting of two distinct species. In this framework we consider one species to be chasers and the other species to be escapees, and we focus on chase-escape dynamics where the chasers are biased to move towards the escapees, and the escapees are biased to move away from the chasers. This framework allows us to explore how individual-level directional interactions scale up to influence spatial structure at the macroscale. To focus exclusively on the role of motility and directional bias in determining spatial structure, we consider conservative communities where the number of individuals in each species remains constant. To provide additional information about the individual-based model, we also present a mathematically tractable deterministic approximation based on describing the evolution of the spatial moments. We explore how different features of interactions including interaction strength, spatial extent of interaction, and relative density of species influence the formation of the macroscale spatial patterns.

biophysics↗

bioRxiv2018

Using experimental data and information criteria to guide model selection for reaction--diffusion problems in mathematical biology

Reaction-diffusion models describing the movement, reproduction and death of individuals within a population are key mathematical modelling tools with widespread applications in mathematical biology. A diverse range of such continuum models have been applied in various biological contexts by choosing different flux and source terms in the reaction-diffusion framework. For example, to describe collective spreading of cell populations, the flux term may be chosen to reflect various movement mechanisms, such as random motion (diffusion), adhesion, haptotaxis, chemokinesis and chemotaxis. The choice of flux terms in specific applications, such as wound healing, is usually made heuristically, and rarely is it tested quantitatively against detailed cell density data. More generally, in mathematical biology, the questions of model validation and model selection have not received the same attention as the questions of model development and model analysis. Many studies do not consider model validation or model selection, and those that do often base the selection of the model on residual error criteria after model calibration is performed using nonlinear regression techniques. In this work, we present a model selection case study, in the context of cell invasion, with a very detailed experimental data set. Using Bayesian analysis and information criteria, we demonstrate that model selection and model validation should account for both residual errors and model complexity. These considerations are often overlooked in the mathematical biology literature. The results we present here provide a clear methodology that can be used to guide model selection across a range of applications. Furthermore, the case study we present provides a clear example where neglecting the role of model complexity can give rise to misleading outcomes.

biophysics↗

bioRxiv2018

Optimal control of acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a blood cancer affecting the haematopoietic stem cells of the myeloid cell line. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corresponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagins maximum principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available.

synthetic biology↗

bioRxiv2018

Self-organization controls expression more than abundance of molecular components of transcription and translation in confined cell-free gene expression

Cell-free gene expression using purified components or cell extracts has become an important platform for synthetic biology that is finding a growing numBer of practical applications. Unfortunately, at cell-relevant reactor volumes, cell-free expression suffers from excessive variability (noise) such that protein concentrations may vary by more than an order of magnitude across a population of identically constructed reaction chambers. Consensus opinion holds that variability in expression is due to the stochastic distribution of expression resources (DNA, RNAP, ribosomes, etc.) across the population of reaction chambers. In contrast, here we find that chamber-to-chamber variation in the expression efficiency generates the large variability in protein production. Through analysis and modeling, we show that chambers self-organize into expression centers that control expression efficiency. Chambers that organize into many centers, each having relatively few expression resources, exhibit high expression efficiency. Conversely, chambers that organize into just a few centers where each center has an abundance of resources, exhibit low expression efficiency. A particularly surprising finding is that diluting expression resources reduces the chamber-to-chamber variation in protein production. Chambers with dilute pools of expression resources exhibit higher expression efficiency and lower expression noise than those with more concentrated expression resources. In addition to demonstrating the means to tune expression noise, these results demonstrate that in cell-free systems, self-organization may exert even more influence over expression than the abundance of the molecular components of transcription and translation. These observations in cell-free platform may elucidate how self-organized, membrane-less structures emerge and function in cells.

biophysics↗

bioRxiv2018

Mechanistic and experimental models of cell migration reveal the importance of intercellular interactions in cell invasion

Moving fronts of cells are essential for development, repair and disease progression. Therefore, understanding and quantifying the details of the mechanisms that drive the movement of cell fronts is of wide interest. Quantitatively identifying the role of intercellular interactions, and in particular the role of cell pushing, remains an open question. Indeed, perhaps the most common continuum mathematical idealization of moving cell fronts is to treat the population of cells, either implicitly or explicitly, as a population of point particles undergoing a random walk that neglects intercellular interactions. In this work, we report a combined experimental-modelling approach showing that intercellular interactions contribute significantly to the spatial spreading of a population of cells. We use a novel experimental data set with PC-3 prostate cancer cells that have been pretreated with Mitomycin-C to suppress proliferation. This allows us to experimentally separate the effects of cell migration from cell proliferation, thereby enabling us to focus on the migration process in detail as the population of cells recolonizes an initially-vacant region in a series of two-dimensional experiments. We quantitatively model the experiments using a stochastic modelling framework, based on Langevin dynamics, which explicitly incorporates random motility and various intercellular forces including: (i) long range attraction (adhesion); and (ii) finite size effects that drive short range repulsion (pushing). Quantitatively comparing the ability of this model to describe the experimentally observed population-level behaviour provides us with quantitative insight into the roles of random motility and intercellular interactions. To quantify the mechanisms at play, we calibrate the stochastic model to match experimental cell density profiles to obtain estimates of cell diffusivity, D, and the amplitude of intercellular forces, f0. Our analysis shows that taking a standard modelling approach which ignores intercellular forces provides a poor match to the experimental data whereas incorporating intercellular forces, including short-range pushing and longer range attraction, leads to a faithful representation of the experimental observations. These results demonstrate a significant role for intercellular interactions in cell invasion.\n\nAuthor summaryMoving cell fronts are routinely observed in various physiological processes, such as wound healing, malignant invasion and embryonic morphogenesis. We explore the effects of a previously overlooked mechanism that contributes to population-level front movement: pushing. Our framework is flexible and incorporates range of reasonable biological phenomena, such as random motility, cell-to-cell adhesion, and pushing. We find that neglecting finite size effects and intercellular forces, such as cell pushing, reduces our ability to mimic and predict our experimental observations.

biophysics↗

bioRxiv2018

A Bayesian sequential learning framework to parameterise continuum models of melanoma invasion into human skin

We present a novel framework to parameterise a mathematical model of cell invasion that describes how a population of melanoma cells invades into human skin tissue. Using simple experimental data extracted from complex experimental images, we estimate three model parameters:(i) the melanoma cell proliferation rate,{lambda} ; (ii) the melanoma cell diffusivity, D; and (iii){delta} , a constant that determines the rate that melanoma cells degrade the skin tissue. The Bayesian sequential learning frame-work involves a sequence of increasingly-sophisticated experimental data from:(i) a spatially uniform cell proliferation assay; (ii) a two-dimensional circular barrier assay; and, (iii) a three-dimensional invasion assay. The Bayesian sequential learning approach leads to well-defined parameter estimates. In contrast, taking a naive approach that attempts to estimate all parameters from a single set of images from the same experiment fails to produce meaningful results. Overall our approach to inference is simple-to-implement, computationally efficient, and well-suited for many cell biology phenomena that can be described by low dimensional continuum models using ordinary differential equations and partial differential equations. We anticipate that this Bayesian sequential learning framework will be relevant in other biological contexts where it is challenging to extract detailed, quantitative biological measurements from experimental images and so we must rely on using relatively simple measurements from complex images.

biophysics↗