Biology subjects

Farhat, M.

Publications and source records attributed to Farhat, M..

4 recordsLinked to original sources

bioRxiv2025

Sterols regulate ciliary membrane dynamics and hedgehog signaling in health and disease

The primary cilium is a specialized signaling hub whose function depends on a tightly regulated membrane composition. While its protein content is well-characterized, its lipid identity, particularly regarding sterols, remains poorly defined. Here, we used mass spectrometry-based lipidomics to map the sterol profile of isolated primary cilia from MDCK cells. We found that ciliary membranes are enriched in cholesterol and desmosterol while excluding precursors like 7-lathosterol and limiting others, suggesting a selective sterol barrier. Inhibiting cholesterol biosynthesis at distinct enzymatic steps led to sterol accumulation, altered ciliary membrane fluidity, and impaired Hedgehog signaling, including defective Smoothened (Smo) retention-- even in the presence of a constitutively active form of Smo. These findings link sterol homeostasis to ciliary membrane properties and signaling fidelity. Our work provides a molecular framework for understanding Hedgehog-related phenotypes in disorders like Smith-Lemli-Opitz Syndrome, highlighting the importance of membrane lipid composition in developmental signaling.

cell biology↗

bioRxiv2024

Leveraging Synthetic Virology for the Rapid Engineering of Vesicular Stomatitis Virus (VSV)

Vesicular stomatitis virus (VSV) is a prototype RNA virus that has been instrumental in advancing our understanding of viral molecular biology and has applications in vaccine development, cancer therapy, antiviral screening, and more. Current VSV genome plasmids for purchase or contract virus services provide limited options for modification, restricted to predefined cloning sites and insert locations. Improved methods and tools to engineer VSV will unlock further insights in long-standing virology questions and new opportunities for innovative therapies. Here, we report the design and construction of a full-length VSV genome. The 11,161 base pair synthetic VSV (synVSV) was assembled from four modularized DNA fragments. Following rescue and titration, phenotypic analysis showed no significant differences between natural and synthetic viruses. To demonstrate the utility of a synthetic virology platform, we then engineered VSV with a foreign glycoprotein, a common use case for studying viral entry and developing anti-virals. To show the freedom-of-design afforded by this platform, we then modified the genome of VSV by rearranging the gene order, switching the positions of VSV-P and VSV-M genes. This work represents a significant technical advance, providing a flexible, cost-efficient platform for the rapid construction of VSV genomes, facilitating the development of innovative therapies.

synthetic biology↗

bioRxiv2022

Mutation rates and adaptive variation among the clinically dominant clusters of Mycobacterium abscessus

Mycobacterium abscessus (Mab) is a multi-drug resistant pathogen increasingly responsible for severe pulmonary infections. Analysis of whole genome sequences (WGS) of Mab demonstrates dense genetic clustering of clinical isolates collected from disparate geographic locations. This has been interpreted as supporting patient-to-patient transmission, but epidemiological studies have contradicted this interpretation. Here we present evidence for a slowing of the Mab molecular clock rate coincident with the emergence of phylogenetic clusters. We find that clustered isolates are enriched in mutations affecting DNA repair machinery and have lower spontaneous mutation rates in vitro. We propose that Mab adaptation to the host environment through variation in DNA repair genes affects the organisms mutation rate and that this manifests as phylogenetic clustering. These results inform our understanding of niche switching for facultative pathogens and challenge the model of transmission as the major mode of dissemination of clinically dominant Mab clusters.

genomics↗

bioRxiv2018

Deep Learning Predicts Tuberculosis Drug Resistance Status from Whole-Genome Sequencing Data

BackgroundThe diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data.\n\nMethods and FindingsUsing targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3,601 Mycobacterium tuberculosis strains, 1,228 of which were multidrug resistant, we investigated the use of machine learning to predict phenotypic drug resistance to 10 anti-tuberculosis drugs. The final model, a multitask wide and deep neural network (MD-WDNN), achieved improved high predictive performance: the average AUCs were 0.979 for first-line drugs and 0.936 for second-line drugs during repeated cross-validation. On an independent validation set, the MD-WDNN showed average AUCs, sensitivities, and specificities, respectively, of 0.937, 87.9%, and 92.7% for first-line drugs and 0.891, 82.0% and 90.1% for second-line drugs. In addition to being able to learn from samples that have only been partially phenotyped, our proposed multidrug architecture shares information across different anti-tuberculosis drugs and genes to provide a more accurate phenotypic prediction. We use t-distributed Stochastic Neighbor Embedding (t-SNE) visualization and feature importance analyses to examine inter-drug similarities.\n\nConclusionsMachine learning is capable of accurately predicting resistant status using genomic information and holds promise in bringing sequencing technologies closer to the bedside.

bioinformatics↗