Biology subjects

Lum, J.

Publications and source records attributed to Lum, J..

11 recordsLinked to original sources

bioRxiv2026

Lactate epigenetically shapes tumor cell states in H3K27M diffuse midline gliomas

H3K27M diffuse midline gliomas (DMGs) are lethal and lack treatment options. These tumors exhibit tumor heterogeneity with aggressive oligodendrocyte precursor (OPC)- and mesenchymal (MES)-like cell states. Because all tumor cells bear H3K27M, tumor heterogeneity has been attributed to microenvironmental cues that remain obscure. H3K27M DMGs exhibit increased glycolysis and the Warburg effect to produce lactate. Lactate can directly modify histone lysines including H3K27Lac, and we hypothesized that lactate epigenetically regulates tumor cell states in H3K27M DMGs. We undertook a comprehensive and integrated analysis of H3K27M DMG cell lines, animal models, tumor tissues, and magnetic resonance spectroscopy in patients to decipher the epigenetic roles of lactate. Here, we report that H3K27M cells uniquely exhibit elevation of the activating mark H3K27Lac. We observed significant heterogeneity in lactate levels within tumors, and the addition of exogenous lactate resulted in marked increases in H3K27Lac and the expression of critical OPC- and MES- like gene signature genes, which correlates with poorer patient outcomes. Our therapeutic approach leverages the inhibition of CBP/p300, which deposits both H3K27Lac and H3K27ac. Targeting CBP/p300 effectively reduces both H3K27Lac and H3K27ac levels, suppresses key OPC- and MES-like signatures, and shows efficacy in preclinical models. Collectively, our findings identify lactate as a key player in the epigenetic regulation of tumor cell state heterogeneity and suggest that targeting this pathway offers promise for addressing the heterogeneous complexities of H3K27M DMGs.

molecular biology↗

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↗

bioRxiv2024

Metabolic Vulnerabilities of Temozolomide-Resistant Glioblastoma Cells: Implications for Targeted Therapies and Overcoming Chemoresistance

Chemoresistance is a major clinical challenge in the management of glioblastoma (GB), making it difficult to achieve long-term success with traditional treatments. Therefore, there is a need for the development of novel drugs. We explored the metabolic vulnerabilities of temozolomide (TMZ)-resistant GB and their potential implications for targeted therapies. In monolayer and tumoroid cultures, we found elevated reliance on oxidative phosphorylation in TMZ-resistant cells. Notably, iron reduction in TMZ-resistant cells reduced viability and proliferation, upregulated hypoxia-inducible factor 1- (Hif1-) expression, induced autophagy, inhibited autophagic flux, and increased reactive oxygen species (ROS) generation, indicating the significance of iron in metabolic vulnerabilities of these cells. Hypoxic cells showed acquired resistance to iron chelation compared to their normoxic state, suggesting an adaptive mechanism associated to hypoxia. Viability, size, and invasion were reduced in TMZ-resistant tumoroids. Additionally, we reported IC50 for the combination of TMZ with a range of DFO and DFP, making the combination therapy a promising drug candidate to improve therapeutic treatments. TeaserCombining iron reduction and chemotherapy in drug-resistant glioblastoma cells enhances therapeutic outcomes.

cancer biology↗

bioRxiv2024

A method to identify high consensus predictions of single-cell metabolic flux

Altered metabolism is a key contributor to pathology in numerous disease states, including cancer. These changes can occur within certain pathological cells, or within a population of cells. Two recently developed single-cell flux prediction tools, Single-cell Flux Estimation Analysis ("scFEA") and Compass, have shown success in predicting cellular metabolism using readily available transcriptome data. By adapting the outputs of these tools, we sought to determine if they can work in concert to identify higher confidence consensus flux predictions. We created a set of reaction modules for the two systems. By testing multiple function composites with sets of modularized Compass outputs, we identified a method that showed the highest global similarity to the outputs of scFEA. Our analysis showed broad biological areas of agreement between the results of the two systems when applied to single-cell data arising from both pathological and healthy samples, with pathological samples increasing system consensus. Consensus testing on matched transcriptome and metabolomics data suggested that agreement between the two systems could indicate at least a minimal degree of coherence between both systems and direct metabolite measurements. Overall, we demonstrated that automated Comparisons between the outputs of Compass and scFEA are possible, applicable to data arising from pathological samples, and that such a consensus approach can reveal strongly correlated predictions between these two systems. Author summaryStudying the metabolism of individual cells allows us to understand the mechanisms behind a myriad of diseases. However, single-cell metabolism cannot readily be measured. Computational tools exist to predict metabolism, but validating their outputs requires metabolic measurements. To address this circular shortcoming, we created a method to automatically adapt and compare the outputs of two popular systems used to predict single-cell metabolism from genetic data. In other fields, using predictive methods in an ensemble has provided superior accuracy, and we speculated that the same may hold true for computational predictions of single-cell metabolism. Our work demonstrated that these two systems can be used together to find agreement on a broad range of metabolic processes related to disease. Further, our results, although early, suggest that system agreement may indicate genuine shifts in the underlying biology of a cell population. Owing to the methodologies used by the two systems, such changes could be studied at both a broad or granular level. As our comparison tools provide rapid readouts of such system agreement, they could potentially be used as part of an exploratory pipeline to aid in identification of candidate metabolic mechanisms as drug targets.

bioinformatics↗

bioRxiv2022

Right Anterior Theta Connectivity Predicts Autistic Social Traits in Neurotypical Children

Growing evidence supports functional network alterations in autism spectrum disorder, however much less is known about the neural mechanisms underlying autistic traits in typically developing children. Using resting-state electroencephalographic (EEG) recordings, we examined whether functional connectivity could predict autistic trait expression in 127 children aged between 4-12 years. Regression models showed that right anterior theta connectivity was a significant predictor of autistic traits (p = 0.013), with increased connectivity in this region associated with greater autistic trait expression. These results corroborate similar recent findings in adults, extending this observation to a cohort of children spanning early-to-middle childhood. These findings further highlight EEG-derived functional connectivity as a sensitive physiological correlate of autistic trait expression in neurotypical children.

neuroscience↗

bioRxiv2020

Single cell RNA-seq reveals immunosuppressive gastric stem-like cancer cells as a poor prognostic factor

ObjectiveGastric cancer (GC) tumors are highly heterogenous with different subpopulations of epithelial cells. We employed single cell RNA sequencing (scRNA-seq) to dissect the heterogeneity and identified subpopulations of cancer cells with stem-like properties. We further investigated their resistance to oxaliplatin chemotherapy and their contribution to gastric cancer outcome. DesignWe performed scRNA-seq on FACS sorted epithelial and immune cells from paired samples of GC tumors and normal adjacent tissues. We identified two epithelial subpopulations (STMN1+IQGAP3+ and STMN1+IQGAP3-) with stem-like properties. We characterized and compared them to known healthy gastric stem cell populations. We also cultivated GC derived organoids to study the chemoresistance of similarly marked populations. Lastly, we employed immunohistochemistry (IHC) staining to ascertain the predicted immunosuppressive interactions. ResultsThe STMN1+IQGAP3+ subpopulation showed a higher tumor mutation burden, upregulated proliferative pathways and transcriptomically resembled proliferative healthy gastric isthmus stem cells. The STMN1+IQGAP3- subpopulation were comparatively quiescent and transcriptomically resembled enteroendocrine cells. Both transcriptomic signatures were associated with worse mortality than other epithelial subpopulations with the quiescent being associated with the poorest patient survival. GC tissue derived organoids were dominated by STMN1+IQGAP3+ cells but the STMN1+IQGAP3- compartment was more resistant to chemotherapy. We also verified the likely suppression of CD8 T cell cytotoxicity by STMN1+IQGAP3+ cells through the NECTIN2/TIGIT interaction. ConclusionsCancer cells with stem-like characteristics are associated with poor survival through chemoresistance and immunosuppression. Reactivating the immune system through checkpoint blockade is an opportunity to eliminate these cells. What is already known on this topicMultiple gastric stem cell populations have been identified and linked to tumor initiation in rodent-based studies. However, none of them have been conclusively proven in human tumors. Isolating and characterizing tumor cells with stem-like properties will help shed light on their possible origin and possible mitigation strategies. What this study addsHere we identified two sets of stem-like gastric cancer cells that are associated with poorer patient prognosis. One set is highly proliferative and exhibits oxaliplatin susceptibility. It also engages in immunosuppressive interactions such as NECTIN2/TIGIT. The other set is quiescent and highly resistant to oxaliplatin. How this study might affect research, practice or policyThe transcriptome signatures of the identified stem-like cells can aid in patient prognosis and identify patients who can benefit from checkpoint blockade therapy to reactivate their immune response towards gastric cancer cells.

immunology↗

bioRxiv2020

Disulfiram inhibits M. tuberculosis growth by altering methionine pool, redox status and host-immune response

Methionine biosynthetic pathway, essential for the growth of Mycobacterium tuberculosis (Mtb) in the host, represents an attractive target for the development of novel anti-tuberculars. Here, we have biochemically characterized homoserine acetyl transferase (HSAT viz. MetA) of Mtb, which catalyses the first committed step of methionine and S-adenosylmethionine (SAM) biosynthesis. High-throughput screening of a 2300 compound library resulted in identification of thiram, an anti-fungal organosulfur compound, as the most potent MetA inhibitor. Further analysis of thiram analogs led to the identification of orally bioavailable disulfiram (DIS, an anti-alcoholism FDA approved drug) as a novel inhibitor of MetA. Both thiram and DIS restricted the growth of drug-sensitive and drug-resistant Mtb strains in a bactericidal manner. ThermoFlour assay demonstrated direct binding of DIS with MetA. Metabolomic and transcriptomic studies showed DIS mediated perturbation of methionine and redox homeostasis, respectively, in Mtb. In concordance, the effect of DIS on Mtb growth was partially rescued by supplementation with either L-methionine as well as N-acetyl cysteine, suggesting a multi-target killing mechanism. In Mtb-infected mice, DIS administration restricted bacterial growth, increased efficacy of isoniazid, ameliorated lung pathology, modulated lung immune cell landscape and protective immune response. Taken together, our results demonstrate that DIS can be repurposed for designing an effective anti-tubercular therapy.

microbiology↗

bioRxiv2020

Metformin enhances anti-mycobacterial responses by educating immunometabolic circuits of CD8+ T cells

Diabetic patients taking metformin have lower risk for Mycobacterium tuberculosis (Mtb) infection, progression from infection to tuberculosis (TB) disease, TB morality and TB recurrence. However, a detailed mechanistic understanding of metformins protective immunological benefits on host resistance to TB is lacking. In this study, using mass cytometry we show that metformin treatment expands memory-like antigen-inexperienced CD8+CXCR3+ T cells in naive mice, and in healthy and diabetic humans. Metformin-educated CD8+ T cells have increased (i) mitochondrial mass, oxidative phosphorylation, and fatty acid oxidation; (ii) survival capacity; and (iii) anti-mycobacterial properties. CD8+ T cells from CXCR3-/- mice did not exhibit metformin-mediated metabolic programming. In BCG-vaccinated mice and guinea pigs, metformin enhanced immunogenicity and protective efficacy against Mtb challenge. Collectively, our results demonstrate an important role of CD8+ T cells in metformin-derived host metabolic-fitness towards Mtb infection.

immunology↗

bioRxiv2019

Host cholesterol dependent activation of VapC12 toxin enriches persister population during Mycobacterium tuberculosis infection

A worldwide increase in the frequency of multidrug-resistant and extensively drug-resistant cases of tuberculosis is mainly due to therapeutic noncompliance associated with a lengthy treatment regimen. Depending on the drug susceptibility profile, the treatment duration can extend from 6 months to 2 years. This protracted regimen is attributed to a supposedly non-replicating and metabolically inert subset of the Mycobacterium tuberculosis (Mtb) population, called persisters. The mechanism underlying stochastic generation and enrichment of persisters is not fully known. We have previously reported that the utilization of host cholesterol is essential for mycobacterial persistence. In this study, we have demonstrated that cholesterol-induced activation of a ribonuclease toxin (VapC12) inhibits translation by targeting proT tRNA in Mtb. This results in cholesterol-specific growth modulation that increases the frequency of the generation of persisters in a heterogeneous Mtb population. Also, a null mutant strain of this toxin ({Delta}vapC12) failed to persist and demonstrated an enhanced growth phenotype in a guinea pig model of Mtb infection. Thus, we have identified a novel strategy through which cholesterol-specific activation of a toxin-antitoxin (TA) module in Mtb enhances persister formation during infection. In addition to identifying the mechanism, the study provides opportunity for targeting persisters, a new paradigm facilitating tuberculosis drug development.

microbiology↗

bioRxiv2019

Histone acetylome-wide association study of tuberculosis

Host-cell chromatin changes are thought to play an important role in the pathogenesis of infectious diseases. Here, we describe the first histone acetylome-wide association study (HAWAS) of an infectious disease, based on genome-wide H3K27 acetylation profiling of peripheral granulocytes and monocytes from subjects with active Mycobacterium tuberculosis (Mtb) infection and healthy controls. We detected >2,000 differentially acetylated loci in either cell type in a Chinese discovery cohort, which were validated in a subsequent multi-ethnic cohort, thus demonstrating that HAWAS can be independently corroborated. Acetylation changes were correlated with differential gene expression in a third cohort. Differential acetylation was enriched near potassium channel genes, including KCNJ15, which modulated Akt-mTOR signaling and promoted Mtb clearance in vitro. We performed histone acetylation QTL analysis on the dataset and identified candidate causal variants for immune phenotypes. Our study serves as proof-of-principle for HAWAS to infer mechanisms of host response to pathogens.

immunology↗