Biology subjects

Bustad, E.

Publications and source records attributed to Bustad, E..

3 recordsLinked to original sources

bioRxiv2026

Modulation of Ferroptosis During Early Mycobacterium tuberculosis Infection Contributes to Beijing Lineage Strain SA161 Virulence

Although spread of internalized Mtb from the initial infected alveolar macrophages (AMs) is a crucial determinant of infection outcomes, the role of cell death in facilitating this spread, and how it is regulated by Mtb remains poorly understood. Ferroptosis, a lipid peroxidation-mediated form of necrotic cell death, contributes to pathology during later stages of infection. However, the role of ferroptosis in early AM cell death remains inadequately defined, and its induction during infection has been primarily studied with the laboratory strain, H37Rv. Using gene set variation analysis of single cell RNAseq data profiling Mtb-infected murine lungs, we found that the hypervirulent Beijing sublineage clinical strain SA161 is associated with an elevated pro-ferroptotic transcriptional response in AMs compared to H37Rv by 17 days post infection. Consistent with these transcriptional profiles, we found that SA161 induced increased lipid peroxidation in comparison to H37Rv during infection in vitro and in vivo. Administration of the lipid peroxidation inhibitor, ferrostatin-1 (Fer-1), reduces this Mtb-induced lipid peroxidation. Notably, we found that administration of Fer-1 to Mtb-infected mice significantly reduced bacterial burden for SA161 at 14 dpi while having no effect on H37Rv. Microscopic analysis of SA161-infected lung lesions at 14 dpi suggests that inhibition of ferroptosis-driving lipid peroxidation results in a greater proportion of AMs amongst infected cells and decreased neutrophil-associated IFN signaling. Collectively, these findings reveal that ferroptosis plays an important role during early infection with virulent clinical strains of Mtb by influencing bacterial spread and signaling of immune cell responders, potentially informing host-directed intervention strategies.

immunology↗

bioRxiv2024

Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis disease, the greatest source of global mortality by a bacterial pathogen. Mtb adapts and responds to diverse stresses such as antibiotics by inducing transcriptional stress-response regulatory programs. Understanding how and when these mycobacterial regulatory programs are activated could enable novel treatment strategies for potentiating the efficacy of new and existing drugs. Here we sought to define and analyze Mtb regulatory programs that modulate bacterial fitness. We assembled a large Mtb RNA expression compendium and applied these to infer a comprehensive Mtb transcriptional regulatory network and compute condition-specific transcription factor activity profiles. We utilized transcriptomic and functional genomics data to train an interpretable machine learning model that can predict Mtb fitness from transcription factor activity profiles. We demonstrated that this transcription factor activity-based model can successfully predict Mtb growth arrest and growth resumption under hypoxia and reaeration using only RNA-seq expression data as a starting point. These integrative network modeling and machine learning analyses thus enable the prediction of mycobacterial fitness under different environmental and genetic contexts. We envision these models can potentially inform the future design of prognostic assays and therapeutic intervention that can cripple Mtb growth and survival to cure tuberculosis disease.

systems biology↗

bioRxiv2023

In vivo screening for toxicity-modulating drug interactions identifies antagonism that protects against ototoxicity in zebrafish

Ototoxicity is a debilitating side effect of over 150 medications with diverse mechanisms of action, many of which could be taken concurrently to treat multiple conditions. Approaches for preclinical evaluation of drug interactions that might impact ototoxicity would facilitate design of safer multi-drug regimens and mitigate unsafe polypharmacy by flagging combinations that potentially cause adverse interactions for monitoring. They may also identify protective agents that antagonize ototoxic injury. To address this need, we have developed a novel workflow that we call Parallelized Evaluation of Protection and Injury for Toxicity Assessment (PEPITA), which empowers high-throughput, semi-automated quantification of ototoxicity and otoprotection in zebrafish larvae. By applying PEPITA to characterize ototoxic drug interaction outcomes, we have discovered antagonistic interactions between macrolide and aminoglycoside antibiotics that confer protection against aminoglycoside-induced damage to lateral line hair cells in zebrafish larvae. Co-administration of either azithromycin or erythromycin in zebrafish protected against damage from a broad panel of aminoglycosides, at least in part via inhibiting drug uptake into hair cells via a mechanism independent from hair cell mechanotransduction. Conversely, combining macrolides with aminoglycosides in bacterial inhibition assays does not show antagonism of antimicrobial efficacy. The proof-of-concept otoprotective antagonism suggests that combinatorial interventions can potentially be developed to protect against other forms of toxicity without hindering on-target drug efficacy.

pharmacology and toxicology↗