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Kanchi, R.

Publications and source records attributed to Kanchi, R..

2 recordsLinked to original sources

Cell type-independent timekeeping gene modules enable embryonic stage prediction in zebrafish

Gene expression changes across embryonic development reflect both differentiation and genes whose expression varies strictly with developmental time, independent of cell type. Multiple embryonic timing systems set the onset and pace of developmental events, and blocking transcription arrests many of these events. However, the genes comprising the vertebrate embryonic timing system(s) remain largely unknown. To identify genes whose expression changes with time alone, we examine naive zebrafish embryonic explants that form only two tissue types yet maintain developmental timing, thus uncoupling developmental age from most differentiation programs. By comparing longitudinal gene expression in naive explants with Nodal-induced explants that differentiate into all three germ layers, we identify "timekeeping" genes whose temporal expression patterns vary strictly with developmental age. Consensus clustering of temporally dynamic genes identified 20 gene clusters, termed "chrono-constitutive modules" (CCMs), that maintain distinct schedules of expression regardless of tissue type. These CCM trajectories are similar in intact zebrafish embryos and single embryonic cells of multiple distinct lineages. Enrichment analysis of microRNA targets and transcription factor regulons within the CCMs further reveal distinct putative regulators of several modules. Strikingly, CCM expression patterns are also largely conserved during early development of another fish species, Japanese medaka. Machine learning models trained on only zebrafish CCM transcript levels accurately predict the developmental age of embryonic explants, intact embryos, and even individual embryonic cells, demonstrating their utility in developmental timekeeping. These results support the existence of transcriptional timekeeping during early development and demonstrate its utility in embryonic stage prediction.

developmental biology↗

MicroRNA-mRNA networks are dysregulated in opioid use disorder postmortem brain: further evidence for opioid-induced neurovascular alterations

To understand mechanisms and identify potential targets for intervention in the current crisis of opioid use disorder (OUD), postmortem brains represent an under-utilized resource. To refine previously reported gene signatures of neurobiological alterations in OUD from the dorsolateral prefrontal cortex (Brodmann Area 9, BA9), we explored the role of microRNAs (miRNA) as powerful epigenetic regulators of gene function. Building on the growing appreciation that miRNAs can cross the blood-brain barrier, we carried out miRNA profiling in same-subject postmortem samples from BA9 and blood tissues. miRNA-mRNA network analysis showed that even though miRNAs identified in BA9 and blood were fairly distinct, their target genes and corresponding enriched pathways were highly overlapping, with tube development and morphogenesis, and pathways related to endothelial cell function and vascular organization, among the dominant enriched biological processes. These findings point to robust, redundant, and systemic opioid-induced miRNA dysregulation with potential functional impact on transcriptomic changes. Further, using correlation network analysis we identified cell-type specific miRNA targets, specifically in astrocytes, neurons, and endothelial cells, associated with OUD transcriptomic dysregulation. Our refined miRNA-mRNA networks enabled identification of novel pharmaco-chemical interventions for OUD, particularly targeting the TGF beta-p38MAPK signaling pathway. Finally, leveraging a collection of control brain transcriptomes from the Genotype-Tissue Expression (GTEx) project, we identified correlation of OUD miRNA targets with TGF beta, hypoxia, angiogenesis, coagulation, immune system and inflammatory pathways. These findings support previous reports of neurovascular and immune system alterations as a consequence of opioid abuse.

bioinformatics↗