Biology subjects

Dharmaratne, M.

Publications and source records attributed to Dharmaratne, M..

3 recordsLinked to original sources

bioRxiv2022

Redox signaling regulates breast cancer metastasis via HIF1alpha-stimulated EMT dynamics and metabolic reprogramming

Metastasis is orchestrated by phenotypic and metabolic reprogramming underlying tumor aggressiveness. Redox signaling by mammary tumor knockdown (KD) of the antioxidant glutathione peroxidase 2 (GPx2) enhanced metastasis via dynamic changes in epithelial-to-mesenchymal transition. Single cell RNA sequencing (scRNA-seq) of the control and PyMT/GPx2 KD mammary tumor revealed six luminal and one basal/mesenchymal like (cluster 3) subpopulations. Remarkably, GPx2 KD enhanced the size and basal/mesenchymal gene signature of cluster 3 as well as induced epithelial/mesenchymal (E/M) clusters which expressed markers of oxidative phosphorylation and glycolysis, indicative of hybrid metabolism. These data were validated in human breast cancer xenografts and were supported by pseudotime cell trajectory analysis. Moreover, the E/M and M states were both attenuated by GPx2 gain of function or HIF1 inhibition, leading to metastasis suppression. Collectively, these results demonstrate that redox/HIF1 signaling promotes mesenchymal gene expression, resulting in E/M clusters and a mesenchymal root subpopulation, driving phenotypic and metabolic heterogeneity underlying metastasis. SignificanceBy leveraging single cell RNA analysis, we were able to demonstrate that redox signaling by GPx2 loss in mammary tumors results in HIF1 signaling, which promotes partial and full EMT conversions, represented by distinct tumor cell subpopulations, which in turn express hybrid and binary metabolic states. These data underscore a phenotypic and metabolic co-adaptation in cancer, arguing in favor of the GPx2-HIF1 axis as a therapeutic platform for targeting tumor cell metastasis.

cancer biology↗

bioRxiv2022

scShapes: A statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

BackgroundSingle cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell-cell variability therefore are useful for going beyond changes based on average expression and instead, identifying genes with homogenous expression versus those that vary widely from cell to cell. ResultsWe present a novel statistical framework scShapes for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single cell data are driven by over-dispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that scShapes identifies subtle variations that are independent of altered mean expression and detects biologically-relevant genes that were not discovered through standard approaches. ConclusionsThis analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from scShapes helps to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework scShapes is incorporated into Bioconductor R package (https://github.com/Malindrie/scShapes).

bioinformatics↗

bioRxiv2022

Deconstructing replicative senescence heterogeneity of human mesenchymal stem cells at single cell resolution reveals therapeutically targetable senescent cell sub-populations

Cellular senescence is characterised by a state of permanent cell cycle arrest. It is accompanied by often variable release of the so-called senescence-associated secretory phenotype (SASP) factors, and occurs in response to a variety of triggers such as persistent DNA damage, telomere dysfunction, or oncogene activation. While cellular senescence is a recognised driver of organismal ageing, the extent of heterogeneity within and between different senescent cell populations remains largely unclear. Elucidating the drivers and extent of variability in cellular senescence states is important for discovering novel targeted seno-therapeutics and for overcoming cell expansion constraints in the cell therapy industry. Here we combine cell biological and single cell RNA-sequencing approaches to investigate heterogeneity of replicative senescence in human ESC-derived mesenchymal stem cells (esMSCs) as MSCs are the cell type of choice for the majority of current stem cell therapies and senescence of MSC is a recognized driver of organismal ageing. Our data identify three senescent subpopulations in the senescing esMSC population that differ in SASP, oncogene expression, and escape from senescence. Uncovering and defining this heterogeneity of senescence states in cultured human esMSCs allowed us to identify potential drug targets that may delay the emergence of senescent MSCs in vitro and perhaps in vivo in the future.

bioinformatics↗