Biology subjects

Thomson, J.

Publications and source records attributed to Thomson, J..

11 recordsLinked to original sources

bioRxiv2024

Diploid assembly of the Mexican lime genome reveals extensive heterozygosity

Clonally propagated crops have long been recognized for their high levels of heterozygosity both between subgenomes within a somatic cell and between cells within an individual clone. Recent developments in long read sequencing technologies have accelerated our ability to identify this diversity and it is increasingly clear that these sources of diversity are abundant in clonal varieties and can contribute to variation in traits of interest to breeders. In this work, we assemble both subgenomes of Mexican lime (Citrus x aurantifolia), an interspecific hybrid between C. hystrix var. micrantha and C. medica. Using this chromosome-level assembly, we find extensive divergence between haplotypes, with at least 89% of the annotated genes harboring polymorphisms at an average rate of 13 per kilobase of coding sequence. Additionally, using high coverage PacBio HiFi libraries from leaf tissue of four individuals we identified multiple large structural variants differing between thorned and thornless lineages, and evidence for mosaicism at hundreds of loci. Many of these variants are found in the promoters and bodies of genes and may act as standing variation for continued improvement of this cultivar.

plant biology↗

bioRxiv2023

Molecular mechanism of α-synuclein aggregation on lipid membranes revealed.

The central hallmark of Parkinsons disease pathology is the aggregation of the -synuclein protein, which, in its healthy form, is associated with lipid membranes. Purified monomeric -synuclein is relatively stable in vitro, but its aggregation can be triggered by the presence of lipid vesicles. Despite this central importance of lipids in the context of -synuclein aggregation, their mechanistic role in this process has not been established to date. Here, we use chemical kinetics to develop a detailed mechanistic model that is able to globally describe the aggregation behaviour of -synuclein in the presence of DMPS lipid vesicles, across a range of lipid and protein concentrations. Through the application of our kinetic model to experimental data, we find that the reaction is a co-aggregation process involving both protein and lipids and that lipids promote aggregation predominantly by enabling the elongation process. Moreover, we find that the initial formation of aggregates, via primary nucleation, takes place not on the surface of lipid vesicles but at the interfaces present in vitro. Our model will enable mechanistic insights, also in other lipid-protein co-aggregation systems, which will be crucial in the rational design of drugs that inhibit aggregate formation and act at the key points in the -synuclein aggregation cascade.

biophysics↗

bioRxiv2023

Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models

BackgroundThe PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: 1) they identify a relationship but not the type of relationship, 2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, 3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or 4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. ResultsWe demonstrate SKiMs ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface (https://skim.morgridge.org) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. ConclusionsSKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

bioinformatics↗

bioRxiv2022

Fibrillin microfibril structure identifies long-range effects of inherited pathogenic mutations affecting a key regulatory TGFβ-binding site

Genetic mutations in fibrillin microfibrils cause a range of serious inherited diseases such as Marfan syndrome (MFS) and Weill-Marchesani syndrome (WMS). These diseases typically show major dysregulation of tissue development and growth, particularly in skeletal long bones, but links between the mutations and the diseases are unknown. In this study we reveal the detailed cryo-EM structure of native fibrillin microfibrils from mammalian tissue. The major bead region showed pseudo 8-fold symmetry and a buried protease resistant N-terminal core. Based on this structure, we show a WMS deletion mutant induces a rearrangement with long-range effects blocking interaction with latent TGF{beta}-binding protein (LTBP)-1 at a remote site. Separate deletion of this binding site resulted in the assembly of shorter fibrillin microfibrils with structural alterations. These results establish that in complex extracellular protein assemblies, such as in fibrillin, mutations may have long-range structural consequences to disrupt growth factor signalling and cause disease.

biochemistry↗

bioRxiv2020

SKiM - A generalized literature-based discovery system for uncovering novel biomedical knowledge from PubMed

Literature-based discovery (LBD) uncovers undiscovered public knowledge by linking terms A to C via a B intermediate. Existing LBD systems are limited to process certain A, B, and C terms, and many are not maintained. We present SKiM (Serial KinderMiner), a generalized LBD system for processing any combination of A, Bs, and Cs. We evaluate SKiM via the rediscovery of discoveries by Don Swanson, who pioneered LBD. Using only literature from the 19th century up to a year before Swansons discoveries, SKiM uncovers all five discoveries. We apply SKiM to repurposing drugs for 26 conditions of high prevalence. Manual analysis confirmed 65 discoveries useful for four diseases from Swansons discoveries from one to 31 years prior to their first validation by clinical trials. SKiM predicts many new potential drug candidates representing prime targets for wet lab validation. SKiM can be applied to any biomedical inquiry sufficiently mentioned in the literature.

bioinformatics↗

bioRxiv2020

Maternal high fiber diet protects offspring on diabetogenic diet in the Nile rat

Previous studies have reported that maternal malnutrition is linked to increased risk of developing type 2 diabetes in adulthood. Although several diabetic risk factors associated with early life environment have been identified, protective factors remain elusive. Here, we modulate the early life environment using a maternal high fiber diet to investigate whether the offspring are protected from type 2 diabetes. We examined the interplay between early life environment (maternal diet) and later life environment (offspring diet) using a longitudinal study with 671 Nile rats. We found that exposure to a high fiber maternal diet prior to weaning significantly lowers the risk of diet-induced diabetes in the offspring. Interestingly, offspring consuming a high fiber diet after weaning do not get diet-induced diabetes, even when exposed to a diabetogenic maternal diet. Here, we provide the first evidence that the protective effect of high fiber can be transmitted to the offspring through a maternal diet, which has important implications in diabetes prevention.

developmental biology↗

bioRxiv2020

Interspecies Chimeric Conditions Affect the Developmental Rate of Human Pluripotent Stem Cells

Human pluripotent stem cells hold significant promise for regenerative medicine. However, long differentiation protocols and immature characteristics of stem cell-derived cell types remain challenges to the development of many therapeutic applications. In contrast to the slow differentiation of human stem cells in vitro that mirrors a nine-month gestation period, mouse stem cells develop according to a much faster three-week gestation timeline. Here, we tested if co-differentiation with mouse pluripotent stem cells could accelerate the differentiation speed of human embryonic stem cells. Following a six-week RNA-sequencing time course of neural differentiation, we identified 929 human genes that were upregulated earlier and 535 genes that exhibited earlier peaked expression profiles in chimeric cell cultures than in human cell cultures alone. Genes with accelerated upregulation were significantly enriched in Gene Ontology terms associated with neurogenesis, neuron differentiation and maturation, and synapse signaling. Moreover, chimeric mixed samples correlated with in utero human embryonic samples earlier than human cells alone, and acceleration was dose-dependent on human-mouse co-culture ratios. Differences in the timing and expression levels of genes corresponding to neuron cell types and brain region identity under chimeric conditions were also observed. The altered developmental rates and lineage outcomes described in this report have implications for accelerating human stem cell differentiation and the use of interspecies chimeric embryos in developing human organs for transplantation. Author SummaryHuman pluripotent stem cells often require long in vitro protocols to form mature cell types of clinical relevance for potential regenerative therapies, a ramification of a nine-month developmental clock in utero that also runs ex utero. What controls species-specific developmental time and whether the timer is amenable to acceleration is unknown. Further, interspecies chimeric embryos are increasingly being created to study early human development or explore the potential growth of human organs for transplantation. How the conflicting developmental speeds of cells from different species co-differentiating together affect each other is not understood. Here, using genome-wide transcriptional analysis of RNA-sequencing time courses, we show that 1) co-differentiating human embryonic stem cells intermixed with mouse stem cells accelerated elements of human developmental programs, 2) the acceleration was dose-dependent on the proportion of mouse cells, and 3) human cells in chimeric samples correlated to in utero samples earlier than human only samples. Our results provide evidence that some components of species-specific developmental clocks may be susceptible to acceleration.

developmental biology↗

bioRxiv2020

Dynamic regulatory module networks for inference of cell type specific transcriptional networks

Multi-omic datasets with parallel transcriptomic and epigenomic measurements across time or cell types are becoming increasingly common. However, integrating these data to infer regulatory network dynamics is a major challenge. We present Dynamic Regulatory Module Networks (DRMNs), a novel approach that uses multi-task learning to infer cell type-specific cis-regulatory networks dynamics. Compared to existing approaches, DRMN integrates expression, chromatin state and accessibility, accurately predicts cis-regulators of context-specific expression and models network dynamics across linearly and hierarchically related contexts. We apply DRMN to three dynamic processes of different experimental designs and predict known and novel regulators driving cell type-specific expression patterns.

bioinformatics↗

bioRxiv2019

Age-dependent effects of reduced mTor signalling on life expectancy through distinct physiology

Research on the mechanisms of ageing has identified ways via which lifespan can be extended in model organisms, increasing the potential for translation of these findings to our own species. However, the large majority of research on animal models involves dietary, genetic or pharmacological treatments throughout life - limiting translational potential and ignoring age-dependent effects. Previously, we have suggested using demographic meta-analysis that reduced mTor signalling has the potential to instantly rejuvenate. We have now tested this prediction experimentally using large-scale demographic data (N > 10,000) combined with conditional knockdown of mTor in Drosophila melanogaster. Indeed, reduced mTor decreased mortality rate when applied during old age. Interestingly, we found that transient treatment during early adult life had long-lasting benefits. Age-dependent deep-RNAseq indicated that these effects arose from distinct physiology and implicate alternative splicing as a potential mechanism for the long-lasting benefits of transient mTor reduction. These findings suggest that reducing mTor short term or during old age could be used to combat ageing. In addition, our findings suggest that the results from experimental research on mTor signalling, and potentially other mechanisms of ageing, that employ life-long interventions are likely to be a complex composite of age-dependent effects that counteract or enhance each other.

physiology↗

bioRxiv2017

CATaDa reveals global remodelling of chromatin accessibility during stem cell differentiationin vivo

Regulation of eukaryotic gene expression is coordinated by dynamic changes to chromatin states throughout development. Measurements of accessible chromatin are used extensively to identify genomic regulatory elements. Whilst the chromatin landscapes of pluripotent stem cells are well characterised, chromatin accessibility changes in the development of somatic stem cell lineages are not well defined. Here we show that tissue specific chromatin accessibility data can be produced via ectopic expression of E. coli Dam methylase in vivo, without the requirement for cell-sorting. We have profiled chromatin accessibility in individual cell types of the Drosophila neural and midgut stem cell lineages. Functional cell-type specific enhancers were identified, as well as novel motifs enriched at diferent stages of development. Finally, we show global changes in the accessibility of chromatin between stem-cells and their diferentiated progeny. Our results demonstrate the dynamic nature of chromatin accessibility in somatic tissues during stem cell diferentiation and provide a novel approach to understanding the gene regulatory mechanisms underlying development.

developmental biology↗

bioRxiv2017

Trendy: Segmented regression analysis of expression dynamics for high-throughput ordered profiling experiments

AbstractHigh throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for profiling detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time-points. We present an R package, Trendy, which utilizes segmented regression models to simultaneously characterize each genes expression pattern and summarize overall dynamic activity in ordered condition experiments. For each gene, Trendy finds the optimal segmented regression model and provides the location and direction of dynamic changes in expression. We demonstrate the utility of Trendy to provide biologically relevant results on both microarray and RNA-seq datasets. Trendy is a flexible R package which characterizes gene-specific expression patterns and summarizes changes of global dynamics over ordered conditions. Trendy is freely available as an R package with a full vignette at https://github.com/rhondabacher/Trendy.

genomics↗