Biology subjects

Rockstroh, A.

Publications and source records attributed to Rockstroh, A..

5 recordsLinked to original sources

bioRxiv2024

Valine Catabolism Drives Bioenergetic and Lipogenic Fuel Plasticity in Prostate Cancer

Metabolic reprogramming is a hallmark of cancer and fundamental for disease progression. The remodelling of oxidative phosphorylation and enhanced lipogenesis are key characteristics of prostate cancer (PCa). Recently, succinate-dependent mitochondrial reprogramming was identified in high-grade prostate tumours with upregulation of enzymes associated with branched-chain amino acid (BCAA) catabolism. We hypothesised that the degradation of BCAAs, particularly valine may play a critical role in anapleurotic refuelling of the mitochondrial succinate pool. Through suppression of valine availability, we report strongly reduced lipid content despite compensatory upregulation of fatty acid uptake, indicating valine is an important lipogenic fuel in PCa. Inhibition of the enzyme 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) also resulted in selective inhibition of cellular proliferation of malignant but not benign prostate cells and impaired succinate production. In combination with a comprehensive multi-omic investigation of patient and cell line data, our work highlights a therapeutic target for selective inhibition of metabolic reprogramming in PCa.

cancer biology↗

bioRxiv2023

Whole transcriptome profiling of placental pathobiology in SARS-CoV-2 pregnancies identifies a preeclampsia-like gene signature

ObjectivesSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus infection in pregnancy is associated with higher incidence of placental dysfunction, referred to by a few studies as a "preeclampsia-like syndrome". However, the mechanisms underpinning SARS-CoV-2-induced placental malfunction are still unclear. Here, we investigated whether the transcriptional architecture of the placenta is altered in response to SARS-CoV-2 infection. MethodsWe utilized whole-transcriptome, digital spatial profiling, to examine gene expression patterns in placental tissues from participants who contracted SARS-CoV-2 in the third trimester of their pregnancy (n=7) and those collected prior to the start of the coronavirus disease 2019 (COVID-19) pandemic (n=9). ResultsThrough comprehensive spatial transcriptomic analyses of the trophoblast and villous core stromal cell subpopulations in the placenta, we identified signatures associated with hypoxia and placental dysfunction during SARS-CoV-2 infection in pregnancy. Notably, genes associated with vasodilation (NOS3), oxidative stress (GDF15, CRH), and preeclampsia (FLT1, EGFR, KISS1, PAPPA2), were enriched with SARS-CoV-2. Pathways related to increased nutrient uptake, vascular tension, hypertension, and inflammation, were also enriched in SARS-CoV-2 samples compared to uninfected controls. ConclusionsOur findings demonstrate the utility of spatially resolved transcriptomic analysis in defining the underlying pathogenic mechanisms of SARS-CoV-2 in pregnancy, particularly its role in placental dysfunction. Furthermore, this study highlights the significance of digital spatial profiling in mapping the intricate crosstalk between trophoblasts and villous core stromal cells, thus shedding light on pathways associated with placental dysfunction in pregnancies with SARS-CoV-2 infection. Graphical abstractIn this study, using spatial digital profiling transcriptomic approaches, we demonstrate that SARS-CoV-2 infection in pregnancy disrupts optimal placental function by altering the genomic architecture of trophoblasts and villous core stromal cells.

developmental biology↗

bioRxiv2022

Latent representation of single-cell transcriptomes enables algebraic operations on cellular phenotypes

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For tumor cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR-/NElow (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.

bioinformatics↗

bioRxiv2021

Gene expression based inference of drug resistance in cancer

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

cancer biology↗

bioRxiv2020

Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer

BackgroundMetabolic reprograming, non-mutational epigenetic changes, increased cell plasticity and multidrug tolerance are early hallmarks of therapy resistance in cancer. In this temporary, therapy-tolerant state, cancer cells are highly sensitive to ferroptosis, a form of regulated cell death that is caused by oxidative stress through excess levels of iron-dependent peroxidation of polyunsaturated fatty acids (PUFA). However, mechanisms underpinning therapy-induced ferroptosis hypersensitivity remain to be elucidated. MethodsWe used quantitative single cell imaging of fluorescent metabolic probes, transcriptomics, proteomics and lipidomics to perform a longitudinal analysis of the adaptive response to androgen receptor-targeted therapies (androgen deprivation and enzalutamide) in prostate cancer (PCa). ResultsWe discovered that cessation of cell proliferation and a robust reduction in bioenergetic processes were associated with multidrug tolerance and a strong accumulation of lipids. The gain in lipid biomass was fueled by enhanced lipid uptake through cargo non-selective (macropinocytosis, tunneling nanotubes) and cargo-selective mechanisms (lipid transporters), whereas de novo lipid synthesis was strongly reduced. Enzalutamide induced extensive lipid remodeling of all major phospholipid classes at the expense of storage lipids, leading to increased desaturation and acyl chain length of membrane lipids. The rise in membrane PUFA levels enhanced membrane fluidity and lipid peroxidation, causing hypersensitivity to glutathione peroxidase (GPX4) inhibition and ferroptosis. Combination treatments against AR and fatty acid desaturation, lipase activities or growth medium supplementation with antioxidants or PUFAs altered GPX4 dependence. Despite multidrug tolerance, PCa cells displayed an enhanced sensitivity to inhibition of lysosomal processing of exogenous lipids, highlighting an increased dependence on lipid uptake in the therapy-tolerant state. ConclusionsOur work provides mechanistic insight into processes of lipid metabolism that underpin the acquisition of therapy-induced GPX4 dependence and ferroptosis hypersensitivity to standard of care therapies in PCa. It demonstrated novel strategies to suppress the therapy-tolerant state that may have potential to delay and combat resistance to androgen receptor-targeted therapies, a currently unmet clinical challenge of advanced PCa. Since enhanced GPX4 dependence is an adaptive phenotype shared by several types of cancer in response to different therapies, our work might have universal implications for our understanding of metabolic events that underpin resistance to cancer therapies.

cancer biology↗