MARTIN, B. | Search bioRxiv

bioRxiv2026

Metabolic Targeting of SREBP1 Reprograms the Obesity-Driven Ascites Immune Microenvironment and Enhances Chemotherapy Response in Obesity-Associated Ovarian Cancer

Obesity has an adverse effect on survival of women with ovarian cancer (OC), supporting a link between obesity, metastatic progression, and therapeutic response, but comprehensive mechanistic insight is lacking. In pre-clinical models of diet-induced obesity and OC, mice fed a high fat diet (HFD) exhibit substantially increased tumor burden relative to low fat diet (LFD) mice and respond poorly to standard-of-care (SOC) chemotherapy. Both HFD murine tumors and tumors from women with BMI>30 show increased expression and nuclear localization of sterol regulatory element binding protein 1 (SREBP1), a master regulator of lipogenesis and lipid transport. The objective of this study was to assess whether inhibition of SREBP1 processing could improve SOC chemotherapy response in obesity-associated OC. We evaluated the FDA-approved drug Nelfinavir (NFV), a pharmacologic inhibitor of site-2 protease that blocks SREBP1 processing, in combination with SOC (paclitaxel+carboplatin) chemotherapy in preclinical models of diet-induced obesity and OC. Our results demonstrate significantly improved therapeutic response, corresponding changes in intra-tumoral adipocytes, and reprogramming of the obesity-driven ascites immune microenvironment. These findings support a model in which NFV-mediated inhibition of SREBP1 processing disrupts obesity-associated metabolic pathways linked to immune suppression and therapeutic resistance, thereby enhancing response to SOC chemotherapy in obesity-associated OC. Collectively, these results highlight metabolic targeting as a strategy to improve therapeutic outcomes in obesity-associated ovarian cancer.

cancer biology↗

bioRxiv2019

Optimization and quantitative evaluation of fluorescence in situ hybridization chain reaction in intact fresh-frozen brain tissues

Transcript labelling in intact tissues using in situ hybridization chain reaction has potential to provide vital spatiotemporal information for molecular characterization of heterogeneous neuronal populations. However, it remains relatively unexplored in fresh-frozen brain which provides more flexible utilization than perfused tissue. In the present study, we optimized the combination of in situ hybridization chain reaction in fresh-frozen rodent brains and then evaluated the uniformity of neuronal labelling between two clearing methods, CLARITY and iDISCO+. We found that CLARITY yielded higher signal-to-noise ratios but more limited imaging depth and required longer clearing times, whereas, iDISCO+ resulted in better tissue clearing, greater imaging depth and a more uniform labelling of larger samples. Based on these results, we used iDISCO+-cleared fresh-frozen rodent brains to further validate this combination and map the expression of a few genes of interest pertaining to mood disorders. We then examined the potential of in situ hybridization chain reaction to label transcripts in cleared postmortem human brain tissues. The combination failed to produce adequate mRNA labelling in postmortem human cortical slices but produced visually adequate labeling in the cerebellum tissues. We next, investigated the multiplexing ability of in situ hybridization chain reaction in cleared tissues which revealed inconsistent fluorescence output depending upon the fluorophore conjugated to the hairpins. Finally, we applied our optimized protocol to assess the effect of glucocorticoid receptor overexpression on basal somatostatin expression in the mouse cortex. The constitutive glucocorticoid receptor overexpression resulted in lower number density of somatostatin-expressing neurons compared to wild type. Overall, the combination of in situ hybridization chain reaction with clearing methods especially iDISCO+ may find broad application in the transcript analysis in rodent studies but its limited use in postmortem human tissues can be improved by further optimizations.

neuroscience↗