Biology subjects

Powell, D. R.

Publications and source records attributed to Powell, D. R..

5 recordsLinked to original sources

bioRxiv2025

Single-Cell Pharmacogenomic Landscapes of Epigenetic Therapy in Gastric Cancer

Gene expression signatures ("molecular phenotypes") are extensively utilized in cancer research. To study how gastric cancer (GC) molecular phenotypes are shaped by cell-intrinsic genetic alterations interacting with cell-extrinsic therapeutic pressures, we performed direct capture Perturb-seq (dcPerturb-seq) to interrogate >200 GC-related genes across 4 distinct epigenetic drug classes in multiple gastric lines. We captured 17.7 million pharmocogenomic expression interactions in 625,866 cells representing baseline and post-therapeutic molecular phenotypes. This single-cell pharmacogenomic compendium confirmed previously known gene-driven molecular phenotypes, elucidated poorly characterized genes, and uncovered novel gene dosage-molecular phenotype relationships. Molecular phenotypes in post-therapeutic surviving cells revealed diverse gene perturbation-associated pathways causing convergent drug resistance (EMT plasticity, cell cycle alterations, metabolic reprogramming), highlighting combinatorial strategies for restoring sensitivity. Mapping of in vitro molecular phenotypes to primary human GCs imparted prognostic information and insights into spatial heterogeneity. Comparative analysis of gene perturbations across therapies and lines revealed both conserved and context-specific molecular alterations. Our results illustrate how Perturb-seq approaches can systematically map diverse cancer-associated molecular phenotypes across multiple gene/drug/cell line interactions, yielding translational insights.

cancer biology↗

bioRxiv2024

Long-lived chondroprogenitors are generated by fetal-limb Gli1+ cells and are replenished upon mosaic cell-cycle arrest in the cartilage

The growth-plate cartilage of the developing long bones is a well-known system of spatially segregated stem/progenitor, transient amplifying and terminally differentiated cells. However, the regulation of the number and activity of long-lived cartilage progenitors (LLCPs) is poorly understood, despite its relevance for understanding human-height variation, the evolution of limb size and proportions and the aetiology of skeletal growth disorders. Moreover, whether their behaviour can adapt to developmental perturbations, generating robustness, has not been explored. Here, we show that Gli1+ cells are the fetal precursors of postnatal LLCPs. Moreover, while during normal growth they remain mostly dormant until postnatal stages, in response to cartilage-targeted cell-cycle arrest they increase their cartilage contribution, compensating for the perturbation. We further show that, during the response to challenge, some of the reparative Gli1+ cells originate from Pdgfra+ cells outside the cartilage anlage. Elucidating how stromal cells become Gli1+ LLCPs could shed light on developmental robustness and lead to growth-boosting therapies.

developmental biology↗

bioRxiv2022

Contrasting effects of Ksr2, an obesity gene, on trabecular bone volume and bone marrow adiposity

Pathological obesity and its complications are associated with an increased propensity for bone fractures. Humans with certain genetic polymorphisms at the kinase suppressor of ras2 (Ksr2) locus develop severe early-onset obesity and type 2 diabetes (T2D). Both conditions are phenocopied in mice with Ksr2 deleted, but whether this affects bone health remains unknown. Here we studied the bones of global Ksr2 null mice and found that Ksr2 negatively regulates femoral, but not vertebral, bone mass in two genetic backgrounds, while the paralogous gene, Ksr1, was dispensable for bone homeostasis. Mechanistically, KSR2 regulates bone formation by influencing adipocyte differentiation at the expense of osteoblasts in the bone marrow. Compared with Ksr2s known role as a regulator of feeding by its function in the hypothalamus, pair feeding and osteoblast-specific conditional deletion of Ksr2 reveals that Ksr2 can regulate bone formation autonomously. Despite the gains in appendicular bone mass observed in absence of Ksr2, bone strength, as well as fracture healing response remains compromised in these mice. This study highlights the interrelationship between adiposity and bone health and provides mechanistic insights into how Ksr2, an adiposity and diabetic gene, regulates bone metabolism.

genetics↗

bioRxiv2018

Widespread cytoplasmic polyadenylation programs asymmetry in the germline and early embryo

BACKGROUNDThe program of embryonic development is launched by selective activation of a silent maternal transcriptome. In Caenorhabditis elegans, nuclei of the adult germline are responsible for the synthesis of at least two distinct mRNA populations; those required for housekeeping functions, and those that program the oocyte-to-embryo transition. We mapped this separation by changes to the length-distribution of poly(A)-tails that depend on GLD-2 mediated cytoplasmic polyadenylation and its regulators genome-wide.\n\nRESULTSMore than 1000 targets of cytoplasmic polyadenylation were identified by differential polyadenylation. Amongst mRNA with the greatest dependence on GLD-2 were those encoding RNA binding proteins with known roles in spatiotemporal patterning such as mex-5 and pos-1. In General, the 3 UTR of GLD-2 targets were longer, contained cytosine-patches, and were enriched for non-standard polyadenylation-motifs. To identify the deadenylase that initiated transcript silencing, we depleted the known deadenylases in the gld-2(0) mutant background. Only the loss of CCF-1 suppressed the short-tailed phenotype of GLD-2 targets suggesting that in addition to its general role in RNA turnover, this is the major deadenylase for regulatory silencing of maternal mRNA. Analysis of poly(A)-tail length-change in the embryo lacking specific RNA-binding proteins revealed new candidates for asymmetric expression in the first embryonic divisions.\n\nCONCLUSIONThe concerted action of RNA binding proteins exquisitely regulates GLD-2 activity in space and time. We present our data as interactive web resources for a model where GLD-2 mediated cytoplasmic polyadenylation regulates target mRNA at each stage of worm germline and early embryonic development.

developmental biology↗

bioRxiv2018

Topconfects: a package for confident effect sizes in differential expression analysis provides improved usability ranking genes of interest

BackgroundA differential gene expression analysis may produce a set of significantly differentially expressed genes that is too large to easily investigate, so that a means of ranking genes by their biological interest level is desirable. The life-sciences have grappled with the abuse of p-values to rank genes for this purpose. As an alternative, a lower confidence bound on the magnitude of Log Fold Change (LFC) could be used to rank genes, but it has been unclear how to reconcile this with the need to perform False Discovery Rate (FDR) correction. The TREAT test of McCarthy and Smyth is a step in this direction, finding genes significantly exceeding a specified LFC threshold. Here we describe the use of test inversion on TREAT to present genes ranked by a confidence bound on the LFC, while still controlling FDR.\n\nResultsTesting the Topconfects R package with simulated gene expression data shows the method outperforming current statistical approaches across a wide range of experiment sizes in the identification of genes with largest LFCs. Applying the method to a TCGA breast cancer data-set shows the method ranks some genes with large LFC higher than would traditional ranking by p-value. Importantly these two ranking methods lead to a different biological emphasis, in terms both of specific highly ranked genes and gene-set enrichment.\n\nConclusionsThe choice of ranking method in differential expression analysis can affect the biological interpretation. The common default of ranking by p-value is implicitly by an effect size in which each gene is standardized to its own variability, rather than comparing genes on a common scale, which may not be appropriate. The Topconfects approach of presenting genes ranked by confident LFC effect size is a variation on the TREAT method with improved usability, removing the need to fine-tune a threshold parameter and removing the temptation to abuse p-values as a de-facto effect size.

bioinformatics↗