Biology subjects

May, P.

Publications and source records attributed to May, P..

32 records · Page 2Linked to original sources

bioRxiv2021

Ultra-rare constrained missense variants in the epilepsies: Shared and specific enrichment patterns in neuronal gene-sets

BackgroundBurden analysis in epilepsy has shown an excess of deleterious ultra-rare variants (URVs) in few gene-sets, such as known epilepsy genes, constrained genes, ion channel or GABAA receptor genes. We set out to investigate the burden of URVs in a comprehensive range of gene-sets presumed to be implicated in epileptogenesis. MethodsWe investigated several constraint and conservation-based strategies to study whole exome sequencing data from European individuals with developmental and epileptic encephalopathies (DEE, n = 1,003), genetic generalized epilepsy (GGE, n = 3,064), and non-acquired focal epilepsy (NAFE, n = 3,522), collected by the Epi25 Collaborative, compared to 3,962 ancestry-matched controls. The burden of 12 non-synonymous URVs types in 92 gene-sets was compared between epilepsy cases (DDE, GGE, NAFE) and controls using logistic regression analysis. ResultsBurden analysis of brain-expressed genes revealed an excess of different URVs types in all three epilepsy categories which was largest for missense variants in highly constrained sites (constrained missense variants). The URVs burden was prominent in neuron-specific, synaptic and developmental genes as well as genes encoding ion channels and receptors, and it was generally higher for DEE and GGE compared to NAFE. The patterns of URVs burden in gene-sets expressed in inhibitory vs. excitatory neurons or receptors suggested a high burden in both groups in DEE but a differential involvement of inhibitory genes in GGE, while excitatory genes were predominantly affected in NAFE. Top ranking susceptibility genes from a recent genome-wide association study (GWAS) of generalized and focal epilepsies displayed a higher URVs burden in constrained coding regions in GGE and NAFE, respectively. ConclusionsUsing exome-based gene-set burden analysis, we demonstrate that missense URVs affecting mainly constrained sites are enriched in neuronal genes in both common and rare severe epilepsy syndromes. Our results indicate a differential impact of these URVs in genes expressed in inhibitory vs. excitatory neurons and receptors in generalized vs. focal epilepsies. The excess of URVs in top-ranking GWAS risk-genes suggests a convergence of rare deleterious and common risk-variants in the pathogenesis of generalized and focal epilepsies.

genomics↗

bioRxiv2021

Critical Assessment of Metaproteome Investigation (CAMPI): a Multi-Lab Comparison of Established Workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carried out the first community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluated the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observed that variability at the peptide level was predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappeared at the protein group level. While differences were observed for predicted community composition, similar functional profiles were obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

microbiology↗

bioRxiv2020

Mantis: flexible and consensus-driven genome annotation

BackgroundThe past decades have seen a rapid development of the (meta-)omics fields, producing an unprecedented amount of data. Through the use of well-characterized datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation allows the identification of regions of interest (i.e. domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, some challenges remain, specifically in terms of speed, flexibility, and reproducibility. In the era of big data it also becomes increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, thus overcoming some limitations in overly relying on computationally generated data. ResultsWe implemented a protein annotation tool - Mantis, which uses text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for total customization of the reference data used, adaptable, and reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which led to an average 0.038 increase in precision when compared to sequence-wide annotation. Mantis is fast, annotating an average genome in 25-40 minutes, whilst also outputting high-quality annotations (average coverage 81.4%, average precision 0.892). ConclusionsMantis is a protein function annotation tool that produces high-quality consensusdriven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license available at https://github.com/PedroMTQ/mantis.

bioinformatics↗

bioRxiv2020

Primary and recurrent glioma patient-derived orthotopic xenografts (PDOX) represent relevant patient avatars for precision medicine

Patient-derived cancer models are essential tools for studying tumor biology and preclinical interventions. Here, we show that glioma patient-derived orthotopic xenografts (PDOXs) enable long-term propagation of patient tumors and represent clinically relevant patient avatars. We created a large collection of PDOXs from primary and recurrent gliomas with and without mutations in IDH1, which retained histopathological, genetic, epigenetic and transcriptomic features of patient tumors with no mouse-specific clonal evolution. Longitudinal PDOX models recapitulate the limited genetic evolution of gliomas observed in patient tumors following treatment. PDOX-derived standardized tumor organoid cultures enabled assessment of drug responses, which were validated in mice. PDOXs showed clinically relevant responses to Temozolomide and to targeted treatments such as EGFR and CDK4/6 inhibitors in (epi)genetically defined groups, according to MGMT promoter and EGFR/CDK status respectively. Dianhydrogalactitol, a bifunctional alkylating agent, showed promising potential against glioblastoma. Our study underlines the clinical relevance of glioma PDOX models for translational research and personalized treatment studies.

cancer biology↗

bioRxiv2020

PathoFact: A pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data

BackgroundPathogenic microorganisms cause disease by invading, colonizing and damaging their host. Virulence factors including bacterial toxins contribute to their pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is therefore essential to identify virulence factors and antimicrobial resistance genes in metagenomic datasets. At present, there is a clear lack of computational approaches to simultaneously identifying these factors. Here we present PathoFact, a tool for the contextualized prediction of virulence factors and antimicrobial resistance genes in metagenomic data. ResultsPathoFact predicts virulence factors, bacterial toxins and antimicrobial resistance genes with high accuracy (0.92, 0.83 and 0.99) and specificity (0.96, 0.99 and 0.98), respectively. The performance of PathoFact was furthermore demonstrated on three publicly available case-control metagenomic datasets representing an actual infection as well as chronic diseases in which either pathogenic potential or bacterial toxins were predicted to play a role. With PathoFact, we identified virulence factors (including toxins) and antimicrobial resistance genes, and identified signature genes which differentiated between the disease and control groups. ConclusionPathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, toxins and antimicrobial resistance genes in metagenomic data. Additionally, PathoFact combines the prediction of these pathogenicity factors with the identification of mobile genetic elements. This provides further depth to the analysis by considering the genomic context of the pertinent genes. Furthermore, each module (virulence factors, toxin and antimicrobial resistance genes) of PathoFact is also a standalone component, making it a flexible and versatile tool. PathoFact is freely available online at https://git-r3lab.uni.lu/laura.denies/PathoFact.

microbiology↗

bioRxiv2019

Insights into protein structural, physicochemical, and functional consequences of missense variants in 1,330 disease-associated human genes

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variants on protein structure and function being especially challenging. Here we evaluated the burden of amino acids affected in pathogenic variants (n=32,923) compared to the variants (n=164,915) from the general population in 1,330 disease-associated genes on forty protein features using over 14,000 experimentally-solved 3D structures. By analyzing the whole gene/variant set jointly, we identified 18 features associated with 3D mutational hotspots that are generally important for protein fitness and stability. Individual analyses performed for twenty-four protein functional classes further revealed 240 characteristics of mutational hotspots in total, including new associations recapitulating the sheer diversity across proteins essential structural regions. We demonstrated that the function-specific features of variants correspond to the readouts of mutagenesis experiments and positively correlate with clinically-interpreted pathogenic and benign missense variants. Finally, we made our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step towards translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the pathogenicity of variants in terms of the perturbed molecular mechanisms.

genetics↗

bioRxiv2019

The 14q32.31 DLK1-DIO3 MIR300 tumor suppressor promotes leukemogenesis by inducing cancer stem cell quiescence and inhibiting NK cell anti-cancer immunity.

Drug-resistance of tumor-initiating cells, impaired NK cell immune-response, PP2A loss-of-function and aberrant miRNA expression are cancer features resulting from microenvironmental- and tumor-specific signals. Here we report that genomic-imprinted MIR300 is a cell context-independent dual function tumor suppressor which is upregulated in quiescent leukemic stem (LSC) and NK cells by microenvironmental signals to induce quiescence and impair immune-response, respectively, but inhibited in CML and AML proliferating blasts to prevent PP2A-induced apoptosis. MIR300 anti-proliferative and PP2A-activating functions are differentially activated through dose-dependent CCND2/CDK6 and SET inhibition, respectively. LSCs escape PP2A-mediated apoptosis through TUG1 lncRNA that uncouples and limits MIR300 functions to cytostasis by regulating unbound-MIR300 levels. Halting MIR300 homeostasis restores NK cell activity and suppresses leukemic but not normal hematopoiesis by eradicating nearly all LSCs. Thus, MIR300 tumor suppressor activity is essential and therapeutically important for LSC-driven leukemias.

cancer biology↗

bioRxiv2019

Predicting Functional Effects of Missense Variants in Voltage-Gated Sodium and Calcium Channels

Malfunctions of voltage-gated sodium and calcium channels (SCN and CACNA1 genes) have been associated with severe neurologic, psychiatric, cardiac and other diseases. Altered channel activity is frequently grouped into gain or loss of ion channel function (GOF or LOF, respectively) which is not only corresponding to clinical disease manifestations, but also to differences in drug response. Experimental studies of channel function are therefore important, but laborious and usually focus only on a few variants at a time. Based on known gene-disease-mechanisms, we here infer LOF (518 variants) and GOF (309 variants) of likely pathogenic variants from disease phenotypes of variant carriers. We show regional clustering of inferred GOF and LOF variants, respectively, across the alignment of the entire gene family, suggesting shared pathomechanisms in the SCN/CACNA1 genes. By training a machine learning model on sequence- and structure-based features we predict LOF- or GOF- associated disease phenotypes (ROC = 0.85) of likely pathogenic missense variants. We then successfully validate the GOF versus LOF prediction on 87 functionally tested variants in SCN1/2/8A and CACNA1I (ROC = 0.73) and in exome-wide data from > 100.000 cases and controls. Ultimately, functional prediction of missense variants in clinically relevant genes will facilitate precision medicine in clinical practice.

genetics↗

bioRxiv2019

Identification of pathogenic variant enriched regions across genes and gene families

Missense variant interpretation is challenging. Essential regions for protein function are conserved among gene family members, and genetic variants within these regions are potentially more likely to confer risk to disease. Here, we generated 2,871 gene family protein sequence alignments involving 9,990 genes and performed missense variant burden analyses to identify novel essential protein regions. We mapped 2,219,811 variants from the general population into these alignments and compared their distribution with 65,034 missense variants from patients. With this gene family approach, we identified 398 regions enriched for patient variants spanning 33,887 amino acids in 1,058 genes. As a comparison, testing the same genes individually we identified less patient variant enriched regions involving only 2,167 amino acids and 180 genes. Next, we selected de novo variants from 6,753 patients with neurodevelopmental disorders and 1,911 unaffected siblings, and observed a 5.56-fold enrichment of patient variants in our identified regions (95% C.I. =2.76-Inf, p-value = 6.66x10-8). Using an independent ClinVar variant set, we found missense variants inside the identified regions are 111-fold more likely to be classified as pathogenic in comparison to benign classification (OR = 111.48, 95% C.I = 68.09-195.58, p-value < 2.2e-16). All patient variant enriched regions identified (PERs) are available online through a user-friendly platform for interactive data mining, visualization and download at http://per.broadinstitute.org. In summary, our gene family burden analysis approach identified novel patient variant enriched regions in protein sequences. This annotation can empower variant interpretation.

genetics↗

bioRxiv2019

Ultra-rare genetic variation in the epilepsies: a whole-exome sequencing study of 17,606 individuals

Sequencing-based studies have identified novel risk genes for rare, severe epilepsies and revealed a role of rare deleterious variation in common epilepsies. To identify the shared and distinct ultra-rare genetic risk factors for rare and common epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,364 controls of European ancestry. We focused on three phenotypic groups; the rare but severe developmental and epileptic encephalopathies (DEE), and the commoner phenotypes of genetic generalized epilepsy (GGE) and non-acquired focal epilepsy (NAFE). We observed that compared to controls, individuals with any type of epilepsy carried an excess of ultra-rare, deleterious variants in constrained genes and in genes previously associated with epilepsy, with the strongest enrichment seen in DEE and the least in NAFE. Moreover, we found that inhibitory GABAA receptor genes were enriched for missense variants across all three classes of epilepsy, while no enrichment was seen in excitatory receptor genes. The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEE and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the top associations, including CACNA1G, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study confirms a convergence in the genetics of common and rare epilepsies associated with ultra-rare coding variation and highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology in the largest epilepsy WES study to date.

genetics↗

bioRxiv2017

Isolation of nucleic acids from low biomass samples: detection and removal of sRNA contaminants

Sequencing-based analyses of low-biomass samples are known to be prone to misinterpretation due to the potential presence of contaminating molecules derived from laboratory reagents and environments. Due to its inherent instability, contamination with RNA is usually considered to be unlikely. Here we report the presence of small RNA (sRNA) contaminants in widely used microRNA extraction kits and means for their depletion. Sequencing of sRNAs extracted from human plasma samples was performed and significant levels of non-human (exogenous) sequences were detected. The source of the most abundant of these sequences could be traced to the microRNA extraction columns by qPCR-based analysis of laboratory reagents. The presence of artefactual sequences originating from the confirmed contaminants were furthermore replicated in a range of published datasets. To avoid artefacts in future experiments, several protocols for the removal of the contaminants were elaborated, minimal amounts of starting material for artefact-free analyses were defined, and the reduction of contaminant levels for identification of bona fide sequences using ultra-clean extraction kits was confirmed. In conclusion, this is the first report of the presence of RNA molecules as contaminants in laboratory reagents. The described protocols should be applied in the future to avoid confounding sRNA studies.

molecular biology↗

bioRxiv2017

Gene family information facilitates variant interpretation and identification of disease-associated genes

Differentiating risk-conferring from benign missense variants, and therefore optimal calculation of gene-variant burden, represent a major challenge in particular for rare and genetic heterogeneous disorders. While orthologous gene conservation is commonly employed in variant annotation, approximately 80% of known disease-associated genes are paralogs and belong to gene families. It has not been thoroughly investigated how gene family information can be utilized for disease gene discovery and variant interpretation. We developed a paralog conservation score to empirically evaluate whether paralog conserved or nonconserved sites of in-human paralogs are important for protein function. Using this score, we demonstrate that disease-associated missense variants are significantly enriched at paralog conserved sites across all disease groups and disease inheritance models tested. Next, we assessed whether gene family information could assist in discovering novel disease-associated genes. We subsequently developed a gene family de novo enrichment framework that identified 43 exome-wide enriched gene families including 98 de novo variant carrying genes in more than 10k neurodevelopmental disorder patients. 33 gene family enriched genes represent novel candidate genes which are brain expressed and variant constrained in neurodevelopmental disorders.

genetics↗

bioRxiv2017

Reassessment Of Lesion-Associated Gene And Variant Pathogenicity In Focal Human Epilepsies

PurposeIncreasing availability of surgically resected brain tissue from Focal Cortical Dysplasia and low-grade epilepsy-associated tumor patients fostered large-scale genetic examination. However, assessment of germline and somatic variant pathogenicity remains difficult.\n\nMethodsHere, we critically reevaluated the pathogenicity for all neuropathology-associated variants reported to date in the PubMed and ClinVar databases, including 12 disease-related genes and 88 neuropathology-associated missense variants. We (1) assessed evolutionary gene constraint using the pLI and missense z scores, (2) applied guidelines by the American College of Medical Genetics and Genomics (ACMG), and (3) predicted pathogenicity by using PolyPhen-2, CADD, and GERP.\n\nResultsConstraint analysis classified only seven out of 12 genes to be likely disease-associated, while 35 (40%) of those 88 variants were classified as being variants of unknown significance (VUS) and 53 (60%) as being likely pathogenic (LPII). Pathogenicity prediction yielded discrimination between neuropathology-associated variants (LPII and VUS) and rare variant scores obtained from individuals present in the Genome Aggregation Database (gnomAD).\n\nConclusionWe conclude that several VUS are likely disease-associated and will be reclassified by future molecular evidence. In summary, interpretation of lesion-associated gene variants remains complex while the application of current ACMG guidelines including bioinformatic pathogenicity prediction will help improving interpretation and prediction.

genetics↗

bioRxiv2017

The Spectrum Of De Novo Variants In Neurodevelopmental Disorders With Epilepsy

Epilepsy is a frequent feature of neurodevelopmental disorders (NDD) but little is known about genetic differences between NDD with and without epilepsy. We analyzed de novo variants (DNV) in 6753 parent-offspring trios ascertained for different NDD. In the subset of 1942 individuals with NDD with epilepsy, we identified 33 genes with a significant excess of DNV, of which SNAP25 and GABRB2 had previously only limited evidence for disease association. Joint analysis of all individuals with NDD also implicated CACNA1E as a novel disease gene. Comparing NDD with and without epilepsy, we found missense DNV, DNV in specific genes, age of recruitment and severity of intellectual disability to be associated with epilepsy. We further demonstrate to what extent our results impact current genetic testing as well as treatment, emphasizing the benefit of accurate genetic diagnosis in NDD with epilepsy.

genetics↗