Biology subjects

Bedo, J.

Publications and source records attributed to Bedo, J..

7 recordsLinked to original sources

bioRxiv2022

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants

Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors. In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely-used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins. We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

bioinformatics↗

bioRxiv2022

CIS calibrates GM-CSF signalling strength to regulate macrophage polarization via a STAT5-IRF8 axis

The cytokine granulocyte-macrophage-colony stimulating factor (GM-CSF) possesses the ability to differentiate macrophages (MOs) with opposing functions, namely proinflammatory M1-like and immunosuppressive M2-like. Despite the importance and opposing functional outcomes of these processes, the intrinsic mechanism that regulates the functional polarization of MOs under GM-CSF signaling remains elusive. Here we show that GM-CSF induced MOs polarisation resulted in the expression of the Cytokine-inducible SH2-containing protein (CIS), and that CIS deficiency diverted differentiation of monocytes into immunosuppressive M2-like MOs expression. CIS deficiency resulted in the hyperactivation of the JAK-STAT5 signaling pathway, consequently promoting the downregulation of the transcription factor Interferon Regulatory Factor 8 (IRF8). Loss and gain of function approaches highlighted IRF8 as a critical instructor of the M1-like polarisation program. In vivo, CIS deficiency led to skewing to M2-like macrophages, which induced strong Th2 immune responses characterised by the development of severe experimental asthma. Collectively, we reveal a CIS-censored mechanism interpreting the opposing actions of GM-CSF in MO differentiation and uncovering its role in controlling allergic inflammation.

immunology↗

bioRxiv2021

svaRetro and svaNUMT: modular packages for annotation of retrotransposed transcripts and nuclear integration of mitochondrial DNA in genome sequencing data

BackgroundThe biological significance of structural variation is now more widely recognized. However, due to the lack of available tools for downstream analysis, including processing and annotating, interpretation of structural variant calls remains a challenge. FindingsHere we present svaRetro and svaNUMT, R packages that provide functions for annotating novel genomic events such as non-reference retro-copied transcripts and nuclear integration of mitochondrial DNA. We evaluate the performance of these packages to detect events using simulations and public benchmarking datasets, and annotate processed transcripts in a public structural variant database. ConclusionssvaRetro and svaNUMT provide efficient, modular tools for downstream identification and annotation of structural variant calls.

bioinformatics↗

bioRxiv2020

Ovarian carcinosarcoma genomics and pre-clinical models highlight the N-MYC pathway as a key driver and susceptibility to EMT-targeting therapy

Ovarian carcinosarcoma (OCS) is an aggressive and rare tumour type with limited treatment options. OCS is hypothesised to develop via the combination theory from a single progenitor, resulting in carcinomatous and sarcomatous components, or alternatively via the conversion theory, with the sarcomatous component developing from the carcinomatous component through epithelial-to-mesenchymal transition (EMT). We show OCS from 18 women to be monoclonal through analysis of DNA variants from isolated carcinoma and sarcoma components. RNA sequencing indicated the carcinoma components were more mesenchymal when compared with pure ovarian carcinomas, supporting the conversion theory. We used pre-clinical OCS models to test the efficacy of microtubule-targeting drugs, including eribulin, which has been shown to reverse EMT characteristics. We demonstrated that microtubule inhibitors, vinorelbine and eribulin, were more effective than standard-of-care platinum-based chemotherapy. Eribulin reduced mesenchymal characteristics, N-MYC expression and cholesterol biosynthesis. Finally, eribulin induced a strong immune response, supporting immunotherapy combinations in the clinic.

cancer biology↗

bioRxiv2019

Improved HLA-based prediction of coeliac disease identifies two novel HLA risk modifiers, DQ6.2 and DQ7.3

PurposeHuman Leukocyte Antigen (HLA) testing is useful in the clinical work-up of coeliac disease (CD), with high negative but low positive predictive value. We construct a genomic risk score (GRS) using HLA risk loci to improve CD prediction and guide exclusion criteria.\n\nMethodsImputed HLA genotypes for five European CD case-control GWAS (n>15,000) were used to construct and validate an HLA based risk models (HDQ15). Conditioning on this score, we identified novel HLA interactions which modified CD risk, and integrated these novel alleles into a new risk score (HDQ17).\n\nResultsA GRS from HLA risk allele genotypes yields performance equivalent to a state-of-the-art GRS (GRS228) using 228 single nucleotide polymorphisms (SNPs) and significantly improves upon all previous HLA based risk models. Conditioning on this model, we find two novel associations, HLA-DQ6.2 and HLA-DQ7.3, that interact significantly with HLA-DQ2.5 (p = 2.51 x 10-9, 1.99 x 10-7 for DQ6.2 and DQ7.3 respectively). These epistatic interactions yield the best performing risk score (HDQ17) which retains performance when implemented using 6 tag SNPs. Using the HDQ17 model, the positive predictive value of CD testing in high risk populations increases from 17.5% to 27.1% while maintaining a negative predictive value above 99%.\n\nConclusionOur proposed HLA-based GRS achieves state-of-the-art risk prediction, helps elucidate further risk factors and improves HLA typing exclusionary criteria, which may reduce the number of patients requiring unnecessary endoscopies.

genetics↗

bioRxiv2019

Bioshake: a Haskell EDSL for bioinformatics pipelines

BackgroundTypical bioinformatics analysis comprise long running computational pipelines. An important part of producing reproducible research is the management and execution of these computational pipelines to allow robust execution and to minimise errors. Bioshake is an embedded domain specific language embedded in Haskell for specifying and executing computational pipelines in bioinformatics that significantly reduces the possibility of errors occurring. ResultsUnlike other pipeline frameworks, Bioshake raises many properties to the type level to allow the correctness of a pipeline to be statically checked during compilation, catching errors before any lengthy execution process. Bioshake builds on the Shake build tool to provide robust dependency tracking, parallel execution, reporting, and resumption capabilities. Finally, Bioshake abstracts execution so that jobs can either be executed directly or submitted to a cluster. ConclusionsBioshake is available at http://github.com/papenfusslab/bioshake.

bioinformatics↗

bioRxiv2017

Collateral sensitivity to β-lactam drugs in drug-resistant tuberculosis is driven by the transcriptional wiring of BlaI operon genes

BackgroundThe evolution and spread of antimicrobial resistance is a major global public health threat. In some cases the evolution of resistance to one antimicrobial seemingly results in enhanced sensitivity to another (known as collateral sensitivity). This largely underexplored phenomenon represents a fascinating evolutionary paradigm that opens new therapeutic possibilities for patients infected with pathogens unresponsive to classical treatments. Intrinsic resistance to {beta}-lactams in Mycobacterium tuberculosis (Mtb, the causative agent of tuberculosis) has traditionally curtailed the use of these low-cost and easy-to-administer drugs for tuberculosis treatment. Recently, {beta}-lactam sensitivity has been reported in strains resistant to classical tuberculosis drug therapy, leading to a resurgence of interest in using {beta}-lactams in the clinic. Unfortunately though, there remains a limited understanding of the mechanisms driving {beta}-lactam sensitivity.\n\nMethodsWe used a novel combination of systems biology and computational approaches to characterize the molecular underpinnings of {beta}-lactam sensitivity in Mtb. We performed differential gene expression and coexpression analyses of genes previously associated with {beta}-lactam sensitivity and genes associated with resistance to classical tuberculosis drugs. Protein-protein interaction and gene regulatory network analyses were used to validate regulatory interactions between these genes, and random walks through the networks identified key mediators of these interactions. Further validation was obtained using functional in silico knockout of gene pairs.\n\nResultsOur results reveal up regulation of the key regulatory inhibitor of {beta}-lactamase production, blal, following treatment with classical drugs. Co-expression and network analyses showed direct co-regulation between genes associated with {beta}-lactam sensitivity and those associated with resistance to classical tuberculosis treatment. blal and its downstream genes (sigC and atpH) were found to be key mediators of these interactions.\n\nConclusionsOur results support the hypothesis that Mtb {beta}-lactam sensitivity is a collateral consequence of the evolution of resistance to classical tuberculosis drugs, mediated through changes to transcriptional regulation. These findings support continued exploration of {beta}-lactams for the treatment of tuberculosis, particularly for patients infected with strains resistant to classical therapies that are otherwise difficult to treat. Importantly, this work also highlights the potential of systems-level and network biology approaches to improve our understanding of collateral drug sensitivity.

microbiology↗