Biology subjects

Thomas, P.

Publications and source records attributed to Thomas, P..

71 records · Page 4Linked to original sources

bioRxiv2020

A physiological role for GABAA receptor desensitization - induction of long-term potentiation at inhibitory synapses

GABAA receptors (GABAARs) are pentameric ligand-gated ion channels distributed throughout the brain where they mediate synaptic and tonic inhibition. Following activation, these receptors undergo desensitization which involves entry into long-lived agonist-bound closed states. Although the kinetic effects of this state are recognised and its structural basis has been uncovered, the physiological impact of desensitization on inhibitory neurotransmission remains unknown. Here we describe an enduring new form of long-term potentiation at inhibitory synapses that elevates synaptic current amplitude for 24 hrs following desensitization of GABAARs in response to prolonged agonist exposure or allosteric modulation. Using receptor mutants and allosteric modulators we demonstrate that desensitization of GABAARs facilitates their phosphorylation by PKC, which increases the number of receptors at inhibitory synapses. These observations provide a new physiological relevance to the desensitized state of GABAARs, acting as a signal to regulate the efficacy of inhibitory synapses during prolonged periods of inhibitory neurotransmission.

neuroscience↗

bioRxiv2020

A plug and play microfluidic platform for standardized sensitive low-input Chromatin Immunoprecipitation

Epigenetic profiling by ChIP-Seq has become a powerful tool for genome-wide identification of regulatory elements, for defining transcriptional regulatory networks and for screening for biomarkers. However, the ChIP-Seq protocol for low-input samples is laborious, time-consuming and suffers from experimental variation, resulting in poor reproducibility and low throughput. Although prototypic microfluidic ChIP-Seq platforms have been developed, these are poorly transferable as they require sophisticated custom-made equipment and in-depth microfluidic and ChIP expertise, while lacking parallelisation. To enable standardized, automated ChIP-Seq profiling of low-input samples, we constructed PDMS-based plates containing microfluidic Integrated Fluidic Circuits capable of performing 24 sensitive ChIP reactions within 30 minutes hands-on time. These disposable plates can conveniently be loaded into a widely available controller for pneumatics and thermocycling, making the ChIP-Seq procedure Plug and Play (PnP). We demonstrate high-quality ChIP-seq on hundreds to few thousands of cells for multiple widely-profiled post-translational histone modifications, together allowing genome-wide identification of regulatory elements. As proof of principle, we managed to generate high-quality epigenetic profiles of rare totipotent subpopulations of mESCs using our platform. In light of the ready-to-go ChIP plates and the automated workflow, we named our procedure PnP-ChIP-Seq. PnP-ChIP-Seq allows non-expert labs worldwide to conveniently run robust, standardized ChIP-Seq, while its high-throughput, consistency and sensitivity paves the way towards large-scale profiling of precious sample types such as rare subpopulations of cells or biopsies. Reviewer link to dataAll sequencing data has been submitted to the NCBI GEO database. Reviewer link: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=klwnocicrpaxrkv&acc=GSE120673

genetics↗

bioRxiv2019

RhoC modulates metabolic networks in cervical cancer by transcriptionally regulating the expression of genes involved in metabolism

In their quest for autonomy, tumor cells are known to reroute metabolic networks to aid their proliferation and survival. These metabolic alterations are governed by the tumor sub-population, thereby contributing towards an additional layer of complexity within the already heterogeneous tumor. For instance, bulk proliferative tumor cells rely on completely different pathways for their metabolic requirements as opposed to the stem-like metastatic cells. However, the molecular switch that drives these metabolic changes remains unknown. RhoC is a well-established contributor towards multiple aspects of tumor development including proliferation, EMT, migration, invasion and metastasis. A transcriptomics-based approach on a RhoC overexpressing cervical cancer cell line unveiled distinct metabolic signatures existent in these cells. Oxidative phosphorylation, TCA cycle, nucleic acid metabolism and fatty acid elongation were some of the specific pathways that emerged as up-regulated. This study therefore provides insight into the intricate metabolic circuitry functional in aggressive RhoC-high cells and thus proposes a pivotal role for RhoC in oncometabolism.

cancer biology↗

bioRxiv2019

Automated recognition of functional compound-protein relationships in literature

MotivationMuch effort has been invested in the identification of protein-protein interactions using text mining and machine learning methods. The extraction of functional relationships between chemical compounds and proteins from literature has received much less attention, and no ready-to-use open-source software is so far available for this task.\n\nMethodWe created a new benchmark dataset of 2,753 sentences from abstracts containing annotations of proteins, small molecules, and their relationships. Two kernel methods were applied to classify these relationships as functional or non-functional, named shallow linguistic and all-paths graph kernel. Furthermore, the benefit of interaction verbs in sentences was evaluated.\n\nResultsThe cross-validation of the all-paths graph kernel (AUC value: 84.2%, F1 score: 81.8%) shows slightly better results than the shallow linguistic kernel (AUC value: 81.6%, F1 score: 79.7%) on our benchmark dataset. Both models achieve state-of-the-art performance in the research area of relation extraction. Furthermore, the combination of shallow linguistic and all-paths graph kernel could further increase the overall performance. We used each of the two kernels to identify functional relationships in all PubMed abstracts (28 million) and provide the results, including recorded processing time.\n\nAvailabilityThe software for the tested kernels, the benchmark, the processed 28 million PubMed abstracts, all evaluation scripts, as well as the scripts for processing the complete PubMed database are freely available at https://github.com/KerstenDoering/CPI-Pipeline.\n\nAuthor summaryText mining aims at organizing large sets of unstructured text data to provide efficient information extraction. Particularly in the area of drug discovery, the knowledge about small molecules and their interactions with proteins is of crucial importance to understand the drug effects on cells, tissues, and organisms. This data is normally hidden in written articles, which are published in journals with a focus on life sciences. In this publication, we show how text mining methods can be used to extract data about functional interactions between small molecules and proteins from texts. We created a new dataset with annotated sentences of scientific abstracts for the purpose of training two diverse machine learning methods (kernels), and successfully classified compound-protein pairs as functional and non-functional relations, i.e. no interactions. Our newly developed benchmark dataset and the pipeline for information extraction are freely available for download. Furthermore, we show that the software can be easily up-scaled to process large datasets by applying the approach to 28 million abstracts.

bioinformatics↗

bioRxiv2019

Targeting the dysfunctional mitochondrial respiration in drug resistant B-cell acute lymphoblastic leukemia

Reprogramming of cellular pathways is a crucial mechanism of drug resistance and survival in refractory acute lymphoblastic leukemia (ALL) cells. In the present study, we performed an unbiased gene expression analysis and identified a dysfunctional mitochondrial respiration program in drug-resistant ALL cells grown in a co-culture system with bone marrow stromal cells (BMSC). Specifically, the activity of the complexes within the electron transport chain was significantly downregulated, correlated with decreased mitochondrial mass and ATP production in drug-resistant ALL cells. To validate mitochondrial respiration as a druggable target, we utilized pyrvinium pamoate (PP), a known inhibitor of mitochondrial respiration and documented its anti-leukemic activity in several ALL cell lines grown alone or in co-culture with BMSC. To increase the bioavailability profile of PP, we successfully encapsulated PP in a nanoparticle drug delivery system and demonstrated that it retained its anti-leukemic activity in a hemosphere assay. PP anti-leukemic activity was decreased by the addition of sodium pyruvate, and furthermore, PP was found to have an additive anti-leukemic effect when used in combination with rotenone, a mitochondrial complex I inhibitor with activity similar to PP on the mitochondrial respiration. Importantly, PPs cell death activity was found to be specific for leukemic cells as primary normal immune cells were resistant to PP-mediated cell death. In conclusion, we have demonstrated that PP is a novel therapeutic lead compound that counteracts the respiratory reprogramming found in refractory ALL cells.

cancer biology↗

bioRxiv2019

Single cell transcriptomics identifies master regulators of dysfunctional pathways in SOD1 ALS motor neurons

BackgroundBulk RNA-Seq has been extensively utilized to investigate the molecular changes accompanying motor neuron degeneration in Amyotrophic Lateral Sclerosis (ALS). However, due to the heterogeneity and degenerating phenotype of the neurons, it has proved difficult to assign specific changes to neuronal subtypes and identify which factors drive these changes. Consequently, we have utilized single cell transcriptomics of degenerating motor neurons derived from ALS patients to uncover key transcriptional drivers of dysfunctional pathways. ResultsSingle cell analysis of spinal neuronal cultures derived from SOD1 E100G ALS and isogenic iPSCs allowed us to classify cells into neural subtypes including motor neurons and interneurons. Differential expression analysis between disease and control motor neurons revealed downregulation of genes involved in synaptic structure, neuronal cytoskeleton, mitochondrial function and autophagy. Interestingly, interneurons did not show similar suppression of these homeostatic functions. Single cell expression data enabled us to derive a context-specific transcriptional network relevant to ALS neurons. Master regulator analysis based on this network identified core transcriptional factors driving the ALS MN gene dysregulation. Specifically, we identified activation of SMAD2, a downstream mediator of the TGF-{beta} signalling pathway as a potential driving factor of ALS motor neuron degeneration. Our phenotypic analysis further confirmed that an activated TGF-{beta} signalling is major driver of motor neuron loss in SOD1 ALS. Importantly, expression analysis of TGF{beta} target genes and computational analysis of publicly available datasets indicates that activation of TGF{beta} signalling may be a common mechanism shared between SOD1, FUS and sporadic ALS. ConclusionsOur results demonstrate the utility of single cell transcriptomics in mapping disease-relevant gene regulatory networks driving neurodegeneration in ALS motor neurons. We find that ALS-associated mutant SOD1 targets transcriptional networks that perturb motor neuron homeostasis.

genomics↗

bioRxiv2019

Emergence of non-genetic heterogeneity in metabolic reactions

Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.

systems biology↗

bioRxiv2019

Locally Fixed Alleles: A method to localize gene drive to island populations

Invasive species pose a major threat to biodiversity on islands. While successes have been achieved using traditional removal methods, such as toxicants aimed at rodents, these approaches have limitations and various off-target effects on island ecosystems. Gene drive technologies designed to eliminate a population provide an alternative approach, but the potential for drive-bearing individuals to escape from the target release area and impact populations elsewhere is a major concern. Here we propose the \"Locally Fixed Alleles\" approach as a novel means for localizing elimination by a drive to an island population that exhibits significant genetic isolation from neighboring populations. Our approach is based on the assumption that in small island populations of rodents, genetic drift will lead to multiple genomic alleles becoming fixed. In contrast, multiple alleles are likely to be maintained in larger populations on mainlands. Utilizing the high degree of genetic specificity achievable using homing drives, for example based on the CRISPR/Cas9 system, our approach aims at employing one or more locally fixed alleles as the target for a gene drive on a particular island. Using mathematical modeling, we explore the feasibility of this approach and the degree of localization that can be achieved. We show that across a wide range of parameter values, escape of the drive to a neighboring population in which the target allele is not fixed will at most lead to modest transient suppression of the non-target population. While the main focus of this paper is on elimination of a rodent pest from an island, we also discuss the utility of the locally fixed allele approach for the goals of population suppression or population replacement. Our analysis also provides a threshold condition for the ability of a gene drive to invade a partially resistant population.

evolutionary biology↗

bioRxiv2018

Brain neurosteroids are natural anxiolytics targeting α2 subunit γ-aminobutyric acid type-A receptors

Neurosteroids are naturally-occurring molecules in the brain that modulate neurotransmission. They are physiologically important since disrupting their biosynthesis precipitates neurological disorders, such as anxiety and depression. The endogenous neurosteroids, allopregnanolone and tetrahydro-deoxycorticosterone are derived from sex and stress hormones respectively, and exhibit therapeutically-useful anxiolytic, analgesic, sedative, anticonvulsant and antidepressant properties. Their main target is the {gamma}-aminobutyric acid type-A inhibitory neurotransmitter receptor (GABAAR), whose activation they potentiate. However, whether specific GABAAR isoforms and neural circuits differentially mediate endogenous neurosteroid effects is unknown. By creating a knock-in mouse that removes neurosteroid potentiation from 2-GABAAR subunits, we reveal that this isoform is a key target for neurosteroid modulation of phasic and tonic inhibition, and is essential for the anxiolytic role of endogenous neurosteroids, but not for their anti-depressant or analgesic properties. Overall, 2-GABAAR targeting neurosteroids may act as selective anxiolytics for the treatment of anxiety disorders, providing new therapeutic opportunities for drug development.

neuroscience↗

bioRxiv2018

bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data

Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability and high amounts of missing observations typical of scRNA-seq datasets make this task particularly challenging. Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The methods likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared to other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalisation, imputation and true count recovery of gene expression measurements from scRNA-seq data.

bioinformatics↗

bioRxiv2018

Population growth affects intrinsic and extrinsic noise in gene expression

Clonal cells of exponentially growing populations vary substantially from cell to cell. The main drivers of this heterogeneity are the population dynamics and stochasticity in the intracellular reactions, which are commonly studied separately. Here we develop an agent-based framework that allows tracking of the biochemical dynamics in every single cell of a growing population that accounts for both of these factors. Apart from the common intrinsic variability of the biochemical reactions, the framework also predicts extrinsic noise arising from fluctuations in the histories of cells without the need to introduce fluctuating rate constants. Instead, these extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age, which are ubiquitously observed in growing populations. We give explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics as measured in two-colour experiments. We find that these statistics may differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) snapshots of a growing population with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots of unknown cell ages as measured from static images. Our integrated approach applies to arbitrary biochemical networks and generation time distributions. By employing models of stochastic gene expression and feedback regulation, we elucidate that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.

systems biology↗

bioRxiv2018

Analysis of cell size homeostasis at the single-cell and population level

Growth pervades all areas of life from single cells to cell populations to tissues. However, cell size often fluctuates significantly from cell to cell and from generation to generation. Here we present a unified framework to predict the statistics of cell size variations within a lineage tree of a proliferating population. We analytically characterise (i) the distributions of cell size snapshots, (ii) the distribution within a population tree, and (iii) the distribution of lineages across the tree. Surprisingly, these size distributions differ significantly from observing single cells in isolation. In populations, cells seemingly grow to different sizes, typically exhibit less cell-to-cell variability and often display qualitatively different sensitivities to cell cycle noise and division errors. We demonstrate the key findings using recent single-cell data and elaborate on the implications for the ability of cells to maintain a narrow size distribution and the emergence of different power laws in these distributions.

systems biology↗

bioRxiv2018

Stochasticity of cellular growth: sources, propagation and consequences

Cellular growth impacts a range of phenotypic responses. Identifying the sources of fluctuations in growth and how they propagate across the cellular machinery can unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. In addition to several population-averaged data, the model quantitatively recovers how growth fluctuations in single cells change across nutrient conditions. We develop a framework to analyse stochastic chemical reactions coupled with cell divisions and use it to identify sources of growth heterogeneity. By visualising cross-correlations we then determine how such initial fluctuations propagate to growth rate and affect other cell processes. We further study antibiotic responses and find that complex drug-nutrient interactions can both enhance and suppress heterogeneity. Our results provide a predictive framework to integrate single-cell and bulk data and draw testable predictions with implications for antibiotic tolerance, evolutionary biology and synthetic biology.

systems biology↗

bioRxiv2018

USP7 cooperates with NOTCH1 to drive the oncogenic transcriptionalprogram in T cell leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive disease, affecting children and adults. Treatments1-6 show high response rates but have debilitating effects and carry risk of relapse5,7,8. Previous work implicated NOTCH1 and other oncogenes1,2,9-20. However, direct inhibition of these pathways affects healthy tissues and cancer alike. Here, we demonstrate that ubiquitin-specific protease 7 (USP7)21-32 controls leukemia growth by stabilizing the levels of the NOTCH1 and JMJD3 demethylase. USP7 is overexpressed T-ALL and is transcriptionally regulated by NOTCH1. In turn, USP7 controls NOTCH1 through deubiquitination. USP7 is bound to oncogenic targets and controls gene expression through H2B ubiquitination and H3K27me3 changes via stabilization of NOTCH1 and JMJD3. We also show that USP7 and NOTCH1 bind T-ALL superenhancers, and USP7 inhibition alters associated gene activity. These results provide a new model for deubiquitinase activity through recruitment to oncogenic chromatin loci and regulation of both oncogenic transcription factors and chromatin marks to promote leukemia. USP7 inhibition33 significantly blocked T-ALL cell growth in vitro and in vivo. Our studies also show that USP7 is upregulated in the aggressive high-risk cases of T-ALL and suggest that USP7 expression might be a prognostic marker in ALL and its inhibition could be a therapeutic tool against aggressive leukemia.

cancer biology↗

bioRxiv2017

Cell size control driven by the circadian clock and environment in cyanobacteria

How cells maintain their size has been extensively studied under constant conditions. In the wild, however, cells rarely experience constant environments. Here, we examine how the 24-hour circadian clock and environmental cycles modulate cell size control and division timings in the cyanobacterium Synechococcus elongatus using single-cell time-lapse microscopy. Under constant light, wild type cells follow an apparent sizer-like principle. Closer inspection reveals that the clock generates two subpopulations, with cells born in the subjective day following different division rules from cells born in subjective night. A stochastic model explains how this behaviour emerges from the interaction of cell size control with the clock. We demonstrate that the clock continuously modulates the probability of cell division throughout day and night, rather than solely applying an on-off gate to division as previously proposed. Iterating between modelling and experiments, we go on to show that the combined effects of the environment and the clock on cell division are explained by an effective coupling function. Under naturally graded light-dark cycles, this coupling shifts cell division away from dusk and dawn, when light levels are low and cell growth is reduced. Our analysis allows us to disentangle, and predict the effects of, the complex interactions between the environment, clock, and cell size control.

systems biology↗

bioRxiv2017

Abnormal cell sorting underlies the unique X-linked inheritance of PCDH19 Epilepsy

X-linked diseases typically exhibit more severe phenotypes in males than females. In contrast, Protocadherin 19 (PCDH19) mutations cause epilepsy in heterozygous females but spare hemizygous males. The cellular mechanism responsible for this unique pattern of X-linked inheritance is unknown. We show that PCDH19 contributes to highly specific combinatorial adhesion codes such that mosaic expression of Pcdh19 in heterozygous female mice leads to striking sorting between WT PCDH19- and null PCDH19-expressing cells in the developing cortex, correlating with altered network activity. Complete deletion of PCDH19 in heterozygous mice abolishes abnormal cell sorting and restores normal network activity. Furthermore, we identify variable cortical malformations in PCDH19 epilepsy patients. Our results highlight the role of PCDH19 in determining specific adhesion codes during cortical development and how disruption of these codes is associated with the unique X-linked inheritance of PCDH19 epilepsy.

developmental biology↗

bioRxiv2017

Single-cell histories in growing populations: relating physiological variability to population growth

Cell size and individual growth rates vary substantially across genetically identical cell populations. This variation cannot entirely be explained by asynchronous cell division cycles, but also needs to take into account the differences in the histories that cells experience during their lifespan. We describe a stochastic framework to characterise cell size histories in an exponentially growing population. We show that these histories differ from cells observed in isolation, such as observed in mother machines. Quantifying these historical fluctuations allows us to predict the population growth rate. We highlight that the maximum attainable population growth cannot exceed the rate at which an average cell grows, but the population doubles faster than an average cell doubles its size. We validate this prediction using recent single-cell data. The theory thus provides fundamental limits on population fitness in terms of individual cell properties.

systems biology↗