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Biology subjects

Owens, M.

Publications and source records attributed to Owens, M..

2 recordsLinked to original sources

Sexually dimorphic RNA helicases DDX3X and DDX3Y differentially regulate hollow condensates of DDX3X disease variants

Mutations in the RNA helicase DDX3X, implicated in various cancers and neurodevelopmental disorders, often impair RNA unwinding and translation. However, the mechanisms underlying this impairment and the differential interactions of DDX3X mutants with wild-type (WT) X-linked DDX3X and Y-linked homolog DDX3Y remain elusive. This study reveals that specific DDX3X mutants more frequently found in disease form distinct hollow condensates in cells. Using a combined structural, biochemical, and single-molecule microscopy study, we show that reduced ATPase and RNA release activities contribute to condensate formation and the catalytic deficits result from inhibiting the catalytic cycle at multiple steps. Proteomic investigations further demonstrate that these hollow condensates sequester WT DDX3X/DDX3Y and other proteins crucial for diverse signaling pathways. WT DDX3X enhances the dynamics of heterogeneous mutant/WT hollow condensates more effectively than DDX3Y. These findings offer valuable insights into the catalytic defects of specific DDX3X mutants and their differential interactions with wild-type DDX3X and DDX3Y, potentially explaining sex biases in disease.

molecular biology↗

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning

IntroductionMultidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens. MethodsWe present Translational Genomics platform for Tuberculosis (GenTB), a web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTBs predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. ResultsAll four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs was 77.6% (95% CI 76.6 - 78.5%) and 75.4% (95% CI 74.5 - 76.4%) respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4 - 75.3%) and Mykrobe at 71.9% (95% CI 70.9 - 72.9%). The higher sensitivities were at an expense of [&le;]1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5 - 97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (<10x across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variants ConclusionGenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu, and the source code is available at https://github.com/farhat-lab/gentb-site.

bioinformatics↗