Biology subjects

Goudey, B.

Publications and source records attributed to Goudey, B..

3 recordsLinked to original sources

bioRxiv2018

A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature

As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available in the scientific literature, and is then later manually curated into more standardized genomic databases. This curation process is expensive, time-consuming and many variants do not end up being fully curated, if at all. Detecting mutations in the literature is the first key step towards automating this process. However, most of the current methods have focused on identifying mutations that follow existing nomenclatures. In this work, we show that there is a large number of mutations that are missed by using this standard approach. Furthermore, we implement the first mutation annotator to cover an extended mutation landscape, and we show that its F1 performance is the same performance as human annotation (F1 78.29 for manual annotation vs F1 79.56 for automatic annotation).

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↗

bioRxiv2017

A blood-based signature of cerebral spinal fluid Aβ1-42 status

It is increasingly recognized that Alzheimers disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebral spinal fluid (CSF) amyloid {beta}1-42 (A{beta}1-42) may be an earlier indicator of Alzheimers disease risk than measures of amyloid obtained from Positron Emission Topography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individuals CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF A{beta}1-42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOE{varepsilon}4 carrier status and four analytes are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF A{beta}1-42 levels transitioned to an AD diagnosis over 120 months significantly faster than those predicted with normal CSF A{beta}1-42 levels and that the resulting model also performs reasonably across PET A{beta}1-42 status.\n\nThis is the first study to show that a machine learning approach, using plasma protein levels, age and APOE{varepsilon}4 carrier status, is able to predict CSF A{beta}1-42 status, the earliest risk indicator for AD, with high accuracy.

neuroscience↗