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Somma, A.

Publications and source records attributed to Somma, A..

2 recordsLinked to original sources
bioRxiv2023

Transcription Factor EB (TFEB) activity increases resistance of TNBC stem cells to metabolic stress

Breast Cancer Stem Cells (CSCs) are difficult to therapeutically target, but continued efforts are critical given their contribution to tumor heterogeneity and treatment resistance in Triple-Negative Breast Cancer (TNBC). CSC properties are influenced by metabolic stress, but specific mechanisms are lacking for effective drug intervention. Our previous work on TFEB suggested a key function in CSC metabolism. Indeed, TFEB knockdown (KD) inhibited mammosphere formation in vitro and tumor initiation/growth in vivo. These phenotypic effects were accompanied by a decline in CD44high/CD24low cells. Glycolysis inhibitor 2-deoxy-D-glucose (2-DG) induced TFEB nuclear translocation, indicative of TFEB transcriptional activity. TFEB KD blunted, whereas TFEB (S142A) augmented 2-DG-driven UPR mediators, notably BiP/HSPA5 and CHOP. Like TFEB KD, silencing BiP/HSPA5 inhibited CSC self-renewal, suggesting that TFEB augments UPR-related survival. Further studies showed that TFEB KD attenuated 2-DG-directed autophagy, suggesting a mechanism whereby TFEB protects CSCs against 2-DG-induced stress. Our data indicate that TFEB modulates CSC metabolic stress response via autophagy and UPR. These findings reveal the novel role of TFEB in regulating CSCs during metabolic stress in TNBC. Financial SupportThis work was supported by CPRIT Grant RR160093 (to S.G. Eckhardt), CPRIT Grant RP210088 (to K.N. Dalby), UT College of Pharmacy Non-discretionary Funds (to C. Van Den Berg), and UT Graduate Continuing Fellowship (to M. Soleimani).

cancer biology↗
bioRxiv2023

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. We present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single cell/nuclei RNA-seq data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperforms 12 other gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPIONs scalability to population-level analyses using a single-cell RNA-seq atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences detected by SCORPION between tumor regions are consistent across population cohorts, as well as with our understanding of disease progression and elucidate phenotypic regulators that may impact patient survival.

bioinformatics↗