Biology subjects

Lichtarge, O.

Publications and source records attributed to Lichtarge, O..

5 recordsLinked to original sources

bioRxiv2026

PIEZO1-mediated mechanosensation links aging to bladder dysfunction

Aging is accompanied by profound changes in bladder function, leading to increased urinary frequency and incontinence that impair quality of life in humans and are recapitulated in mouse models. Bladder filling and emptying rely on precise mechanosensory feedback, yet how aging alters this sensory control remains unclear. PIEZO ion channels convert mechanical forces into cellular signals essential for bladder fullness sensation. Using inducible smooth-muscle specific Piezo1 deletion, we find that loss of Piezo1 attenuates aging-related bladder dysfunction. Dietary enrichment with margaric acid, a membrane-active fatty acid previously shown to inhibit PIEZO channels, reduced urinary dysfunction in aged mice. In humans, genotype-phenotype analyses reveal an association between a PIEZO1 gain-of-function variant and early-onset neuromuscular bladder dysfunction. Together, these findings define a smooth-muscle, PIEZO1-mediated mechanosensory basis for aging-related bladder dysfunction and introduce a non-invasive strategy for targeting PIEZO channels in vivo.

physiology↗

bioRxiv2024

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfa-tase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among sub-missions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.

genetics↗

bioRxiv2023

Predicting the impact of rare variants on RNA splicing in CAGI6

BackgroundVariants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variants impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. ResultsThe performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum overall accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. ConclusionsSeveral challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application.

bioinformatics↗

bioRxiv2018

Automated literature mining and hypothesis generation through a network of Medical Subject Headings

The scientific literature is vast, growing, and increasingly specialized, making it difficult to connect disparate observations across subfields. To address this problem, we sought to develop automated hypothesis generation by networking at scale the MeSH terms curated by the National Library of Medicine. The result is a Mesh Term Objective Reasoning (MeTeOR) approach that tallies associations among genes, drugs and diseases from PubMed and predicts new ones.\n\nComparisons to reference databases and algorithms show MeTeOR tends to be more reliable. We also show that many predictions based on the literature prior to 2014 were published subsequently. In a practical application, we validated experimentally a surprising new association found by MeTeOR between novel Epidermal Growth Factor Receptor (EGFR) associations and CDK2. We conclude that MeTeOR generates useful hypotheses from the literature (http://meteor.lichtargelab.org/).\n\nAUTHOR SUMMARYThe large size and exponential expansion of the scientific literature forms a bottleneck to accessing and understanding published findings. Manual curation and Natural Language Processing (NLP) aim to address this bottleneck by summarizing and disseminating the knowledge within articles as key relationships (e.g. TP53 relates to Cancer). However, these methods compromise on either coverage or accuracy, respectively. To mitigate this compromise, we proposed using manually-assigned keywords (MeSH terms) to extract relationships from the publications and demonstrated a comparable coverage but higher accuracy than current NLP methods. Furthermore, we combined the extracted knowledge with semi-supervised machine learning to create hypotheses to guide future work and discovered a direct interaction between two important cancer genes.

bioinformatics↗

bioRxiv2017

Evolutionary Action of de novo missense variants across pathways prioritizes genes linked to autism and predicts patient phenotypic severity

The pathogenicity of individual de novo missense mutations in autism spectrum disorder remains difficult to validate. Here we asked in 2,384 probands whether these variants exhibited collective functional impact biases across pathways. As measured with Evolutionary Action (EA) in 368 gene groupings, we found significant biases in axonogenesis, synaptic transmission, and other neurodevelopmental pathways. Strikingly, both de novo and inherited missense variants in prioritized genes correlated with patient IQ. This general integrative approach thus detects missense variants most likely to contribute to autism pathogenesis and is the first, to our knowledge, to link missense variant impact to autism phenotypic severity.

genomics↗