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Midha, M.

Publications and source records attributed to Midha, M..

3 recordsLinked to original sources

Hepatic Vagal Afferents Convey Clock-Dependent Signals to Regulate Circadian Food Intake

Circadian desynchrony induced by shiftwork or jetlag is detrimental to metabolic health, but how synchronous/desynchronous signals are transmitted among tissues is unknown. Here we report that liver molecular clock dysfunction is signaled to the brain via the hepatic vagal afferent nerve (HVAN), leading to altered food intake patterns that are corrected by ablation of the HVAN. Hepatic branch vagotomy also prevents food intake disruptions induced by high-fat diet feeding and reduces body weight gain. Our findings reveal a previously unrecognized homeostatic feedback signal that relies on synchrony between the liver and the brain to control circadian food intake patterns. This identifies the hepatic vagus nerve as a therapeutic target for obesity in the setting of chrono-disruption. One Sentence SummaryThe hepatic vagal afferent nerve signals internal circadian desynchrony between the brain and liver to induce maladaptive food intake patterns.

physiology↗

SerpinB3 drives cancer stem cell survival in glioblastoma

Despite therapeutic interventions for glioblastoma (GBM), cancer stem cells (CSCs) drive recurrence. The precise mechanisms underlying CSC therapeutic resistance, namely inhibition of cell death, are unclear. We built on previous observations that the high cell surface expression of junctional adhesion molecule-A drives CSC maintenance and identified downstream signaling networks, including the cysteine protease inhibitor SerpinB3. Using genetic depletion approaches, we found that SerpinB3 is necessary for CSC maintenance, survival, and tumor growth, as well as CSC pathway activation. The knockdown of SerpinB3 also increased apoptosis and susceptibility to radiation therapy. Mechanistically, SerpinB3 was essential to buffer cathepsin L-mediated cell death, which was enhanced with radiation. Finally, we found that SerpinB3 knockdown dramatically increased the efficacy of radiation in pre-clinical models. Taken together, our findings identify a novel GBM CSC-specific survival mechanism involving a previously uninvestigated cysteine protease inhibitor, SerpinB3, and provide a potential target to improve the efficacy of standard-of-care GBM therapies against therapeutically resistant CSCs. SummaryLauko et al. demonstrate a functional role for SerpinB3, which is elevated in glioblastoma cancer stem cells and protects against lysosomal-mediated cell death. SerpinB3 can be targeted to increase the efficacy of radiation in glioblastoma pre-clinical models.

cancer biology↗

PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline

Spectral matching sequence database search engines commonly used on mass spectrometry-based proteomics experiments excel at identifying peptide sequence ions, and in addition, possible sequence ions carrying post-translational modifications (PTMs), but most do not provide confidence metrics for the exact localization of those PTMs when several possible sites are available. Localization is absolutely required for downstream molecular cell biology analysis of PTM function in vitro and in vivo. Therefore, we developed PTMProphet, a free and open-source software tool integrated into the Trans-Proteomic Pipeline, which reanalyzes identified spectra from any search engine for which pepXML output is available to provide localization confidence to enable appropriate further characterization of biologic events. Localization of any type of mass modification (e.g., phosphorylation) is supported. PTMProphet applies Bayesian mixture models to compute probabilities for each site/peptide spectrum match where a PTM has been identified. These probabilities can be combined to compute a global false localization rate at any threshold to guide downstream analysis. We describe the PTMProphet tool, its underlying algorithms and demonstrate its performance on ground-truth synthetic peptide reference datasets, one previously published small dataset, one new larger dataset, and also on a previously published phospho-enriched dataset where the correct sites of modification are unknown. Data have been deposited to ProteomeXchange with identifier PXD013210.\n\nO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

bioinformatics↗