Biology subjects

MacPherson, S.

Publications and source records attributed to MacPherson, S..

4 recordsLinked to original sources

bioRxiv2026

PRDX1 regulates T cell effector function in the ovarian tumor microenvironment

T cell-based immunotherapies have remained ineffective against high-grade serous ovarian carcinoma (HGSOC). The metabolic environment of HGSOC suppresses the activity of cellular therapies, however, the metabolites that enhance or suppress T cell antitumor activity are not fully understood. Here, a pooled CRISPR-Cas9 knockout screen in primary human T cells cultured with patient-derived ascites was used to identify metabolic enzymes that inhibit effector cytokine production and cytolytic function. The screen identified PRDX1 as a negative regulator of T cell effector function. Targeted deletion of PRDX1 increased the frequency of IFN-{gamma}-producing T cells, enhanced glucose uptake, increased mitochondrial mass, and improved T cell viability under suppressive ascites conditions. Mechanistically, PRDX1 deficiency increased intracellular reactive oxygen species (ROS) and impaired autophagic flux. The effects of PRDX1 deletion enhanced aspects of T cell function, while its effects on chimeric antigen receptor (CAR)-T cell cytotoxicity were donor dependent. Collectively, this study identifies PRDX1 as a regulator of T cell activation, metabolism, and effector function in the inhibitory physiological suppressive environment of HGSOC ascites.

immunology↗

bioRxiv2025

A-LAVA: Detecting impact of germline variants on metabolic pathways in cancer genomes

The metabolic landscape of cancer has been widely studied, especially in the context of somatic mutations. However, the impact of inherited germline variants upon the metabolic genes interaction still remains unexplored. In this work, we present a computational pipeline named A-LAVA for the detection and analysis of germline variants that affect metabolic pathways in cancer. Our pipeline enables analysis at three different levels: SNP, gene, and pathway-based analysis. The first steps consist of detecting statistically significant SNPs through standardized GWAS pipelines and, subsequently, genes associated with metabolic traits through gene-level analysis. Then, A-LAVA performs gene set analysis (GSA) to further explore the effect of detected associations on metabolic pathways. This analysis is done through a statistical model that newly corrects for the confounding effects arising from overlapping gene sets, in addition to other corrections performed by the current best practices. Our analysis conducted on TCGA data shows that SNP and gene-level results identified key associations and that A-LAVAs GSA approach improved the overall accuracy both on synthetic and real data by correctly correcting for overlapping genes, refining significance thresholds, and reducing false positives, thus leading to more reliable metabolic pathway rankings and a more robust framework for gene set analysis. CCS CONCEPTSO_LIApplied computing [->]Bioinformatics; Metabolomics / metabonomics. C_LI

bioinformatics↗

bioRxiv2021

Clinically-relevant T cell expansion protocols activate distinct cellular metabolic programs and phenotypes

Ex vivo expansion conditions used to generate T cells for immunotherapy are thought to adopt metabolic phenotypes that impede therapeutic efficacy in vivo. The comparison of five different culture media used for clinical T cell expansion revealed unique optima based on different output variables including proliferation, differentiation, function, activation and mitochondrial phenotypes. T cells adapted their metabolism to match their media expansion condition as shown by glucose and glutamine uptake, and patterns of glucose isotope labeling. However, adoption of these metabolic phenotypes was uncoupled to T cell function. Furthermore, T cell products cultured in ascites from ovarian cancer patients displayed suppressed mitochondrial activity and function irrespective of the ex vivo expansion media. In one case, culturing in ascites resulted in increased glucose uptake which was insufficient to rescue T cell function. Thus, ex vivo T cell expansion conditions have profound impacts on metabolism and function.

immunology↗

bioRxiv2020

1-Methylnicotinamide is an immune regulatory metabolite in human ovarian cancer

Immune regulatory metabolites are key features of the tumor microenvironment (TME), yet with a few notable exceptions, their identities remain largely unknown. We uncovered the immune regulatory metabolic states and metabolomes of sorted tumor and stromal, CD4+, and CD8+ cells from the tumor and ascites of patients with high-grade serous ovarian cancer (HGSC) using high-dimensional flow cytometry and metabolomics supplemented with single cell RNA sequencing. Flow cytometry revealed that tumor cells show a consistently greater uptake of glucose than T cells, but similar mitochondrial activity. Cells within the ascites and tumor had pervasive metabolite differences, with a striking enrichment in 1-methylnicotinamide (MNA) in T cells infiltrating the tumor compared to ascites. Despite the elevated levels of MNA in T cells, the expression of nicotinamide N-methyltransferase, the gene encoding the enzyme that catalyses the transfer of a methyl group from S-adenosylmethionine to nicotinamide, was restricted to fibroblasts and tumor cells. Treatment of T cells with MNA resulted in an increase in T cell-mediated secretion of the tumor promoting cytokine tumor necrosis factor alpha. Thus, the TME-derived metabolite MNA contributes to an alternative and non-cell autonomous mechanism of immune modulation of T cells in HGSC. Collectively, uncovering the tumor-T cell metabolome may reveal metabolic vulnerabilities that can be exploited using T cell-based immunotherapies to treat human cancer.

immunology↗