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Biology subjects

Harmon, T.

Publications and source records attributed to Harmon, T..

3 recordsLinked to original sources

SCD1 inhibition synergizes with TMZ to improve IDH1 MUT glioma survival of mice via lipotoxic stress

Mutations in the isocitrate dehydrogenase enzyme (IDH1) are prevalent in low-grade gliomas, such as oligodendrogliomas. Other than surgery, radiation, and chemotherapy, few options for treatment exist. The standard of care for patients currently consists of maximal safe resection, as well as the potential use of radiation and chemotherapy, typically followed by radiographic surveillance. Although significant advances have been achieved, these have not translated into meaningful improvements in overall survival, warranting the development of novel therapies. Herein, we demonstrate that the inhibition of SCD1 in vitro leads to decreased colony formation and is more specific to IDH1MUT glioma than the IDH1WT cells, We further identified MF-438 as an active inhibitor of this enzyme and showed that this inhibitor is linked with iron transport and ferroptosis. MF438 depleted oleic (C18:1) and palmitoleic acid (C16:1) levels, driving saturated phosphatidylcholine (PC) accumulation and ER stress (INSIG1, SEL1L upregulation). TS603 exhibited selective polyunsaturated phosphatidylcholine reduction with downregulation of GPX4, FTH1, and KEAP1, and upregulation of NCOA4, SLC11A2, ALDH7A1, and DPP4, consistent with ferroptosis priming via ferritinophagy-driven expansion of the labile iron pool, as confirmed by FerroOrange flow cytometry. Neutral lipid metabolism genes (LPIN1, LDLR, PNPLA3, ACSL1), intracellular lipid transport genes (TMEM41B, OSBP, STARD4), and lipid droplet organization genes (SQLE, CHKA, AUP1) were coordinately upregulated in TS603 after treatment with MF-438. MF438+TMZ activated the integrated stress response (ATF3, DDIT3, IRF1, CDKN1A, GADD45B) and synergistically suppressed TS603 neurosphere growth (p=0.0326). In vivo combination between MF-438 and TMZ showed improved survival versus the TMZ-alone group, in an IDH1-mutant oligodendroglioma model. Tissue analyses showed significant reduced Ki67 expression, a marker of cellular proliferation, in the combination treatment. Collectively, these findings suggest that targeting lipid metabolism may enhance the efficacy of standard-of-care therapy in IDH1-mutant oligodendroglioma.

Cancer Biology↗

Targeting RUNX1 in Macrophages Facilitates Cardiac Recovery

Despite advances in disease treatment, our understanding of how damaged organs recover and the mechanisms governing this process remain poorly defined. Here, we mapped the transcriptional and regulatory landscape of human cardiac recovery using single cell multiomics. Macrophages emerged as the most reprogrammed cell type. Deep learning identified the transcription factor RUNX1 as a key regulator of this process. Macrophage-specific Runx1 deletion recapitulated the human cardiac recovery phenotype in a chronic heart failure model. Runx1 deletion reprogrammed macrophages to a reparative phenotype, reduced fibrosis, and promoted cardiomyocyte adaptation. RUNX1 chromatin profiling revealed a conserved regulon that diminished during recovery. Mechanistically, the epigenetic reader BRD4 controlled Runx1 expression in macrophages. Chromatin activity mapping, combined with CRISPR perturbations, identified the precise regulatory element governing Runx1 expression. Therapeutically, small molecule Runx1 inhibition was sufficient to promote cardiac recovery. Our findings uncover a druggable RUNX1 epigenetic mechanism that orchestrates recovery of heart function.

immunology↗

Discrepancies in Biomarker Identification in Different Peak-Picking Strategies in Untargeted Metabolomics Analyses of Cells, Tissues, and Biofluids

Different software and algorithms are available for peak picking in nontargeted metabolomics and each may have its own strengths and limitations. The choice of pick picking method can significantly influence the results obtained, including the number and identity of metabolites detected, their quantification, and subsequent biomarker analysis. The impact of peak picking by different tools in an untargeted metabolomics-based biomarker study is largely understated. This study compares two popular open-source software tools for peak picking in untargeted metabolomics of cancer cells, tissues, and bio-fluids: XCMS and MZmine 2. The investigation evaluates the impact of these peak-picking algorithms on biomarker identification. We found significant discrepancy between the results obtained from XCMS and MZmine 2, regardless of the sample types, solvent gradient phases, retention time (RT), or mass-to-charge ratio (m/z) tolerances used. Notably, this study revealed significant disagreement between peak picking tools in the context of metabolite-based biomarker study and highlights the importance of carefully evaluating and selecting appropriate peak picking tools to ensure reliable and accurate results in untargeted metabolomics research. O_FIG O_LINKSMALLFIG WIDTH=172 HEIGHT=200 SRC="FIGDIR/small/641559v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@af718dorg.highwire.dtl.DTLVardef@481bd4org.highwire.dtl.DTLVardef@1b10fborg.highwire.dtl.DTLVardef@f80215_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗