Biology subjects

Pavlidis, P.

Publications and source records attributed to Pavlidis, P..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

In silico discovery of small molecules for efficient stem cell differentiation into definitive endoderm

Improving methods for human embryonic stem cell differentiation represents a challenge in modern regenerative medicine research. Using drug repurposing approaches, we discover small molecules that regulate the formation of definitive endoderm. Among them are inhibitors of known processes involved in endoderm differentiation (mTOR, PI3K, and JNK pathways) and a new compound, with an unknown mechanism of action, capable of inducing endoderm formation in the absence of growth factors in the media. Optimization of the classical protocol by including this compound achieves the same differentiation efficiency with a 90% cost reduction. The gene expression profile induced by the compound suggests that it is an inhibitor of the MYC pathway. The proposed in silico procedure for candidate molecule selection has broad potential for improving stem cell differentiation protocols.

bioinformatics↗

bioRxiv2021

ModelMatcher: A scientist-centric online platform to facilitate collaborations between stakeholders of rare and undiagnosed disease research

Next-generation sequencing is a prevalent diagnostic tool for undiagnosed diseases, and has played a significant role in rare disease gene discovery. While this technology resolves some cases, others are given a list of possibly damaging genetic variants necessitating functional studies. Productive collaborations between scientists, clinicians, and patients can help resolve such medical mysteries, and provide insights into in vivo function of human genes. Furthermore, facilitating interactions between scientists and research funders, including non-profit organizations or commercial entities, can dramatically reduce the time to translate discoveries from bench to bedside. Several systems designed to connect clinicians and researchers with a shared gene of interest have been successful. However, these platforms exclude some stakeholders based on their role or geography. Here we describe ModelMatcher, a global online matchmaking tool designed to facilitate cross-disciplinary collaborations, especially between scientists and other stakeholders of rare and undiagnosed disease research. ModelMatcher is integrated into the Rare Diseases Models and Mechanisms Network and Matchmaker Exchange, allowing users to identify potential collaborators in other registries. This living database decreases the time from when a scientist or clinician is making discoveries regarding their genes of interest, to when they identify collaborators and sponsors to facilitate translational and therapeutic research.

genetics↗

bioRxiv2021

Experiment level curation identifies high confidence transcriptional regulatory interactions in neurodevelopment

To facilitate the development of large-scale transcriptional regulatory networks (TRNs) that may enable in-silico analyses of disease mechanisms, a reliable catalogue of experimentally verified direct transcriptional regulatory interactions (DTRIs) is needed for training and validation. There has been a long history of using low-throughput experiments to validate single DTRIs. Therefore, we hypothesize that a reliable set of DTRIs could be produced by curating the published literature for such evidence. In our survey of previous curation efforts, we identified the lack of details about the quantity and the types of experimental evidence to be a major gap, despite the importance of such details for the identification of bona fide DTRIs. We developed a curation protocol to inspect the published literature for support of DTRIs at the experiment level, focusing on genes important to the development of the mammalian nervous system. We sought to record three types of low-throughput experiments: Transcription factor (TF) perturbation, TF-DNA binding, and TF-reporter assays. Using this protocol, we examined a total of 1,310 papers to assemble a collection of 1,499 unique DTRIs, involving 251 TFs and 825 target genes, many of which were not reported in any other DTRI resource. The majority of DTRIs (965, 64%) were supported by two or more types of experimental evidence and 27% were supported by all three. Of the DTRIs with all three types of evidence, 170 had been tested using primary tissues or cells and 44 had been tested directly in the central nervous system. We used our resource to document research biases among reports towards a small number of well-studied TFs. To demonstrate a use case for this resource, we compared our curation to a previously published high-throughput perturbation screen and found significant enrichment of the curated targets among genes differentially expressed in the developing brain in response to Pax6 deletion. This study demonstrates a proof-of-concept for the assembly of a high confidence DTRI resource in order to support the development of large-scale TRNs. Author SummaryThe capacity to computationally reconstruct gene regulatory networks using large-scale biological data is currently limited by the absence of a high confidence set of one-to-one regulatory interactions. Given the lengthy history of using small scale experimental assays to investigate individual interactions, we hypothesize that a reliable collection of gene regulatory interactions could be compiled by systematically inspecting the published literature. To this end, we developed a curation protocol to examine and record evidence of regulatory interactions at the individual experiment level. Focusing on the area of brain development, we applied our pipeline to 1,310 publications. We identified 3,601 individual experiments, providing detailed information about 1,499 regulatory interactions. Many of these interactions have verified activity specifically in the embryonic brain. By capturing reports of regulatory interactions at this level of granularity, we present a resource that is more interpretable than other similar resources.

genomics↗

bioRxiv2020

Can machine learning aid in identifying disease genes? The case of autism spectrum disorder

Discovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: Can machine learning aid in the discovery of disease genes? We collected thirteen published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

bioinformatics↗

bioRxiv2020

Structural classification of proteins based on the computationally efficient recurrence quantification analysis and horizontal visibility graphs

MotivationProtein structure prediction is one of the most significant problems in bioinformatics, as it has a prominent role in understanding the function and evolution of proteins. Designing a computationally efficient but at the same time accurate prediction method remains a pressing issue, especially for sequences that we cannot obtain a sufficient amount of homologous information from existing protein sequence databases. Several studies demonstrate the potential of utilizing chaos game representation (CGR) along with time series analysis tools such as recurrence quantification analysis (RQA), complex networks, horizontal visibility graphs (HVG) and others. However, the majority of existing works involve a large amount of features and they require an exhaustive, time consuming search of the optimal parameters. To address the aforementioned problems, this work adopts the generalized multidimensional recurrence quantification analysis (GmdRQA) as an efficient tool that enables to process concurrently a multidimensional time series and reduce the number of features. In addition, two data-driven algorithms, namely average mutual information (AMI) and false nearest neighbors (FNN), are utilized to define in a fast yet precise manner the optimal GmdRQA parameters. ResultsThe classification accuracy is improved by the combination of GmdRQA with the HVG. Experimental evaluation on a real benchmark dataset demonstrates that our methods achieve similar performance with the state-of-the-art but with a smaller computational cost. AvailabilityThe code to reproduce all the results is available at https://github.com/aretiz/protein_structure_classification/tree/main. Contactedoutsi@ics.forth.gr Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains

This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize, genetic data from all strains available from GISAID and countries regional information such as deaths and cases per million as well as covid-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.

bioinformatics↗

bioRxiv2020

Curation of over 10,000 transcriptomic studies to enable data reuse

Vast amounts of transcriptomic data reside in public repositories, but effective reuse remains challenging. Issues include unstructured dataset metadata, inconsistent data processing and quality control, and inconsistent probe-gene mappings across microarray technologies. Thus, extensive curation and data reprocessing is necessary prior to any reuse. The Gemma bioinformatics system was created to help address these issues. Gemma consists of a database of curated transcriptomic datasets, analytical software, a web interface, and web services. Here we present an update on Gemmas holdings, data processing and analysis pipelines, our curation guidelines, and software features. As of June 2020, Gemma contains 10,811 manually curated datasets (primarily human, mouse, and rat), over 395,000 samples and hundreds of curated transcriptomic platforms (both microarray and RNA-sequencing). Dataset topics were represented with 10,215 distinct terms from 12 ontologies, for a total of 54,316 topic annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of conditions and tissues, it captures a large majority of available brain-related datasets, accounting for 34% of its holdings. Users can access the curated data and differential expression analyses through the Gemma website, RESTful service, and an R package. Database URL: https://gemma.msl.ubc.ca/home.html

bioinformatics↗

bioRxiv2020

Population genomics insights into the recent evolution of SARS-CoV-2

The current coronavirus disease 2019 (COVID-19) pandemic is caused by the SARS-CoV-2 virus and is still spreading rapidly worldwide. Full-genome-sequence computational analysis of the SARS-CoV-2 genome will allow us to understand the recent evolutionary events and adaptability mechanisms more accurately, as there is still neither effective therapeutic nor prophylactic strategy. In this study, we used population genetics analysis to infer the mutation rate and plausible recombination events that may have contributed to the evolution of the SARS-CoV-2 virus. Furthermore, we localized targets of recent and strong positive selection. The genomic regions that appear to be under positive selection are largely co-localized with regions in which recombination from non-human hosts appeared to have taken place in the past. Our results suggest that the pangolin coronavirus genome may have contributed to the SARS-CoV-2 genome by recombination with the bat coronavirus genome. However, we find evidence for additional recombination events that involve coronavirus genomes from other hosts, i.e., Hedgehog and Sparrow. Even though recombination events within human hosts cannot be directly assessed, due to the high similarity of SARS-CoV-2 genomes, we infer that recombinations may have recently occurred within human hosts using a linkage disequilibrium analysis. In addition, we employed an Approximate Bayesian Computation approach to estimate the parameters of a demographic scenario involving an exponential growth of the size of the SARS-CoV-2 populations that have infected European, Asian and Northern American cohorts, and we demonstrated that a rapid exponential growth in population size can support the observed polymorphism patterns in SARS-CoV-2 genomes.

evolutionary biology↗

bioRxiv2020

Multi-parametric analysis of 58 SYNGAP1 variants reveal impacts on GTPase signaling, localization and protein stability

SYNGAP1 is a Ras and Rap GTPase with important roles in regulating excitatory synaptic plasticity. While many SYNGAP1 missense and nonsense mutations have been associated with intellectual disability, epilepsy, schizophrenia and autism spectrum disorder (ASD), there are many variants of unknown significance (VUS). In this report, we characterize 58 variants in nine assays that examine multiple aspects of SYNGAP1 function. Specifically, we used multiplex phospho-flow cytometry to measure the impact of variants on pERK, pGSK3{beta} and pCREB and high-content imaging to examine their subcellular localization. We find variants ranging from complete loss-of-function (LoF) to wildtype (WT)-like in their ability to regulate pERK and pGSK3{beta}, while all variants retain at least partial ability to regulate pCREB. Interestingly, our assays reveal that a high percentage of variants located within the disordered domain of unknown function that makes up the C-terminal half of SYNGAP1 exhibited LoF, compared to the more well studied catalytic domain. Moreover, we find protein instability to be a major contributor to dysfunction only for two missense variants both located within the catalytic domain. Using high-content imaging, we find variants with nuclear enrichment/exclusion and aberrant nuclear speckle localization. These variants are primarily located within the C2 domain known to mediate membrane lipid interactions. We find that mislocalization is distinct from altered catalytic activity, highlighting multiple independent molecular mechanisms underlying variant dysfunction. Our multidimensional dataset allows clustering of variants based on functional phenotypes and provides high-confidence pathogenicity classification.

genetics↗

bioRxiv2020

ERCC1-XPF Interacts with Topoisomerase IIβ to Facilitate the Repair of Activity-induced DNA Breaks

Type II DNA Topoisomerases (TOP II) generate transient double-strand DNA breaks (DSBs) to resolve topological constraints during transcription. Using genome-wide mapping of DSBs and functional genomics approaches, we show that, in the absence of exogenous genotoxic stress, transcription leads to DSB accumulation and to the recruitment of the structure-specific ERCC1-XPF endonuclease on active gene promoters. Instead, we find that the complex is released from regulatory or gene body elements in UV-irradiated cells. Abrogation of ERCC1 or re-ligation blockage of TOP II-mediated DSBs aggravates the accumulation of transcription-associated {gamma}H2Ax and 53BP1 foci, which dissolve when TOP II-mediated DNA cleavage is inhibited. An in vivo biotinylation tagging strategy coupled to a high-throughput proteomics approach reveals that ERCC1-XPF interacts with TOP II{beta} and the CTCF/cohesin complex, which co-localize with the heterodimer on DSBs. Together; our findings provide a rational explanation for the remarkable clinical heterogeneity seen in human disorders with ERCC1-XPF defects.

genetics↗

bioRxiv2019

Evaluation of Connectivity Map shows limited reproducibility in drug repositioning

The Connectivity Map (CMap) is a popular resource designed for data-driven drug repositioning using a large transcriptomic compendium. However, evaluations of its performance are limited. We used two iterations of CMap (CMap 1 and 2) to assess their comparability and reliability. We queried CMap 2 with CMap 1-derived signatures, expecting CMap 2 would highly prioritize the queried compounds; success rate was 17%. Analysis of previously published prioritizations yielded similar results. Low recall is caused by low differential expression (DE) reproducibility both between CMaps and within each CMap. DE strength was predictive of reproducibility, and is influenced by compound concentration and cell-line responsiveness. Reproducibility of CMap 2 sample expression levels was also lower than expected. We attempted to identify the "better" CMap by comparison with a third dataset, but they were mutually discordant. Our findings have implications for CMap usage and we suggest steps for investigators to limit false positives.

bioinformatics↗

bioRxiv2019

Proximal and Distal Gut Mucosa Adapt Differently to Westernized Diet, Promoting an Insulin-Resistant Dysmetabolic State

ObjectiveCombinatorial gut hormone therapy is one of the more promising strategies for identifying improved treatments for metabolic disease. Many approaches combine the established benefits of glucagon-like peptide-1 (GLP-1) agonism with one or more additional molecules with the aim of improving metabolic outcomes. Recent attention has been drawn to the glucose-dependent insulinotropic polypeptide (GIP) system due to compelling pre-clinical evidence describing the metabolic benefits of antagonising the GIP receptor (GIPR). We rationalised that benefit might be accrued from combining GIPR antagonism with GLP-1 agonism. To this end we investigated the metabolic effects of co-administration of previously reported peptide-based GIPR antagonists with the GLP-1 agonist liraglutide. MethodsTwo GIPR peptide antagonists, GIPA-1 (mouse GIP(3-30)NH2) and GIPA-2 (NAc-K10[{gamma}E{gamma}E-C16]-Arg18-hGIP(5-42)), were pharmacologically characterised in vitro in an assay measuring cAMP production in CHO-K1 cells overexpressing the mouse GIPR. These peptides were then characterised in vivo in lean mice for their effect on oral glucose tolerance, as well as their ability to antagonize exogenous GIP action. Finally, a mouse model of diet-induced obesity (DIO) was used to investigate the potential metabolic benefits of chronic dosing of peptide-based GIPR antagonists, alone or in combination with liraglutide. ResultsIn vitro, both GIPR peptides exhibited potent antagonistic properties, with GIPA-2 being the more potent of the two. Acute in vivo administration of GIPA-1 during an oral glucose tolerance test (OGTT) had negligible effects on glucose tolerance and circulated insulin in lean mice. In contrast, GIPA-2 impaired glucose tolerance and attenuated circulating insulin levels, with offsetting effects on glycemia noted with co-administration with exogenous mouse GIP, suggesting true antagonism via GIPA-2 at the GIP receptor. Chronic administration studies in a DIO mouse model showed expected effects of GLP-1 agonism (via liraglutide), lowering food intake, body weight, fasting blood glucose and plasma insulin concentrations while improving glucose sensitivity, whereas delivery of either GIPR antagonist alone had negligible effects on these parameters. Interestingly, chronic dual therapy with the GIPR antagonists and GLP-1 showed separation from single intervention arms though augmented insulin sensitizing effects (modestly lowering insulin and HOMA-IR) and lowering plasmas triglycerides and free-fatty acids, with more notable effects observed with GIPA-1 compared to GIPA-2. ConclusionWe conclude that, in contrast to the well-documented effects of GLP-1R agonism, systemic administration of peptide-based GIPR antagonists demonstrate minimal benefit on metabolic parameters in DIO mice, exhibiting no major effects on body weight, food intake and glycaemic parameters. However, the co-administration of both a GIPR antagonist together with a GLP1 agonist uncovers interesting synergistic and beneficial effects on measures of insulin sensitivity, circulating lipids and certain adipose stores that seem influenced by the degree or nature of GIP receptor antagonism.

molecular biology↗

bioRxiv2019

Multi-model functionalization of disease-associated PTEN missense mutations identifies multiple molecular mechanisms underlying protein dysfunction

Functional variomics provides the foundation for personalized medicine by linking genetic variation to disease expression, outcome and treatment, yet its utility is dependent on appropriate assays to evaluate mutation impact on protein function. To fully assess the effects of 106 missense and nonsense variants of PTEN associated with autism spectrum disorder, somatic cancer and PHTS, we take a deep phenotypic profiling approach using 18 assays in 5 model systems spanning diverse cellular environments ranging from molecular function to neuronal morphogenesis and behavior. Variants inducing instability occurred across the protein, resulting in partial to complete loss of function (LoF), which was well correlated across models. However, assays were selectively sensitive to variants located in substrate binding and catalytic domains, which exhibited complete LoF or dominant negativity independent of effects on stability. Our results indicate that full characterization of variant impact requires assays sensitive to instability and a range of protein functions.

genetics↗

bioRxiv2019

ISX-9 manipulates endocrine progenitor fate revealing conserved intestinal lineages in mouse and human.

Enteroendocrine cells (EECs) survey the gut luminal environment and co-ordinate hormonal, immune and neuronal responses to it. They exhibit well characterised physiological roles ranging from the control of local gut function to whole body metabolism, but little is known regarding the regulatory networks controlling their differentiation, especially in human gut.\n\nThe small molecule Isoxazole-9 (ISX-9) stimulates neuronal and pancreatic beta-cell differentiation, both closely related to EEC differentiation. We used ISX-9 as a tool to explore EEC specification in mouse and human intestinal organoids. ISX-9 increased the number of neurogenin3 (Ngn3) positive endocrine progenitor cells and upregulated NeuroD1 and Pax4, transcription factors which play roles in mouse EEC specification. Single cell analysis revealed induction of Pax4 expression in a developmentally late Ngn3+ population of cells and potentiation of genes associated with progenitors biased towards serotonin-producing enterochromaffin (EC) cells. This coincided with enrichment of organoids with functional EC cells which was partly dependent on stimulation of calcium signalling in a population of cells residing outside the crypt base. Inducible Pax4 overexpression, in ileal organoids, uncovered its importance as a component of early human endocrine specification and highlighted the potential existence of two major endocrine lineages, the early appearing enterochromaffin lineage and the later developing peptidergic lineage which contains classical gut hormone cell types.\n\nOur data provide proof-of-concept for the controlled manipulation of specific endocrine lineages with small molecules, whilst also shedding new light on human EEC differentiation and its similarity to mouse. Given their diverse roles, understanding endocrine lineage plasticity and its control could have multiple therapeutic implications.

cell biology↗

bioRxiv2019

Genome Scans for Selection and Introgression based on k-nearest Neighbor Techniques

In recent years, genome-scan methods have been extensively used to detect local signatures of selection and introgression. Here, we introduce a series of versatile genome-scan methods that are based on non-parametric k-nearest neighbors (kNN) techniques, while incorporating pairwise Fixation Index (FST) estimates and pairwise nucleotide differences (dxy) as features. Simulations were performed for both positive directional selection and introgression, with varying parameters, such as recombination rates, population background histories, the proportion of introgression, and the time of gene flow. We find that kNN-based methods perform remarkably well while yielding stable results almost over the entire range of k. We provide a GitHub repository (pievos101/kNN-Genome-Scans) containing R source code to demonstrate how to apply the proposed methods to real-world genomic data using the population genomics R-package PopGenome.

evolutionary biology↗

bioRxiv2019

Phylogenomics investigation of sparids (Teleostei: Spariformes) using high-quality proteomes highlights the importance of taxon sampling

Sparidae (Teleostei: Spariformes) are a family of fish constituted by approximately 150 species with high popularity and commercial value, such as porgies and seabreams. Although the phylogeny of this family has been investigated multiple times, its position among other teleost groups remains ambiguous. Most studies have used a single or few genes to decipher the phylogenetic relationships of sparids. Here, we conducted a phylogenomic attempt to resolve the position of the family using five recently available Sparidae gene-sets and 26 available fish proteomes from species with a sequenced genome, to ensure higher quality of the predicted genes. A thorough phylogenomic analysis suggested that Tetraodontiformes (puffer fish, sunfish) are the closest relatives to sparids than all other groups used, a finding that contradicts our previous phylogenomic analysis that proposed the yellow croaker and the european seabass as closest taxa of sparids. By analytically comparing the methodologies applied in both cases, we show that this discordance is not due to different orthology assignment algorithms; on the contrary, we prove that it is caused by the increased taxon sampling of the present study, outlining the great importance of this aspect in phylogenomic analyses in general.

evolutionary biology↗

bioRxiv2019

Untangling the effects of cellular composition on coexpression analysis

BackgroundCoexpression analysis is one of the most widely used methods in genomics, with applications to inferring regulatory networks, predicting gene function, and interpretation of transcriptome profiling studies. Most studies use data collected from bulk tissue, where the effects of cellular composition present a potential confound. However, the impact of composition on coexpression analysis have not been studied in detail. Here we examine this issue for the case of human brain RNA analysis.\n\nResultsWe found that for most genes, differences in expression levels across cell types account for a large fraction of the variance of their measured RNA levels in brain (median R2 = 0.64). We then show that genes that have similar expression patterns across cell types will have correlated RNA levels in bulk tissue, due to the effect of variation in cellular composition. We demonstrate that much of the coexpression in the bulk tissue can be attributed to this effect. We further show how this composition-induced coexpression masks underlying intra-cell-type coexpression observed in single-cell data. Attempt to correct for composition yielded mixed results.\n\nConclusionsThe dominant coexpression signal in brain can be attributed to cellular compositional effects, rather than intra-cell-type regulatory relationships, and this is likely to be true for other tissues. These results have important implications for the relevance and interpretation of coexpression in many applications.

bioinformatics↗

bioRxiv2019

Systematic phenomics analysis of ASD-associated genes reveals shared functions and parallel networks underlying reversible impairments in habituation learning

A major challenge facing the genetics of Autism Spectrum Disorders (ASD) is the large and growing number of candidate risk genes and gene variants of unknown functional significance. Here, we used Caenorhabditis elegans to systematically functionally characterize ASD-associated genes in vivo. Using our custom machine vision system we quantified 26 phenotypes spanning morphology, locomotion, tactile sensitivity, and habituation learning in 87 strains each carrying a mutation in an ortholog of an ASD-associated gene. We identified hundreds of novel genotype-phenotype relationships ranging from severe developmental delays and uncoordinated movement to subtle deficits in sensory and learning behaviors. We clustered genes by similarity in phenomic profiles and used epistasis analysis to discover parallel networks centered on CHD8*chd-7 and NLGN3*nlg-1 that underlie mechanosensory hyper-responsivity and impaired habituation learning. We then leveraged our data for in vivo functional assays to gauge missense variant effect. Expression of wild-type NLG-1 in nlg-1 mutant C. elegans rescued their sensory and learning impairments. Testing the rescuing ability of all conserved ASD-associated neuroligin variants revealed varied partial loss-of-function despite proper subcellular localization. Finally, we used CRISPR-Cas9 auxin inducible degradation to determine that phenotypic abnormalities caused by developmental loss of NLG-1 can be reversed by adult expression. This work charts the phenotypic landscape of ASD-associated genes, offers novel in vivo variant functional assays, and potential therapeutic targets for ASD.

neuroscience↗