Biology subjects

Taravella Oill, A. M.

Publications and source records attributed to Taravella Oill, A. M..

3 recordsLinked to original sources

bioRxiv2025

Dynamic responses to rejection in the transplanted human heart revealed through spatial transcriptomics

Allograft rejection following solid-organ transplantation is a major cause of graft dysfunction and mortality. Current approaches to diagnosis rely on histology, which exhibits wide diagnostic variability and lacks access to molecular phenotypes that may stratify therapeutic response. Here, we leverage image-based spatial transcriptomics at sub-cellular resolution in longitudinal human cardiac biopsies to characterize transcriptional heterogeneity in 62 adult and pediatric heart transplant (HT) recipients during and following histologically-diagnosed rejection. Across 28 cell types, we identified significant differences in abundance in CD4+ and CD8+ T cells, fibroblasts, and endothelial cells across different biological classes of rejection (cellular, mixed, antibody-mediated). We observed a broad overlap in cellular transcriptional states across histologic rejection severity and biological class and significant heterogeneity within rejection severity grades that would qualify for immunomodulatory treatment. Individuals who had resolved rejection after therapy had a distinct transcriptomic profile relative to those with persistent rejection, including 216 genes across 6 cell types along pathways of inflammation, IL6-JAK-STAT3 signaling, IFN/IFN{gamma} response, and TNF signaling. Spatial transcriptomics also identified genes linked to long-term prognostic outcomes post-HT. These results underscore importance of subtyping immunologic states during rejection to stratify immune-cardiac interactions following HT that are therapeutically relevant to short- and long-term rejection-related outcomes.

genomics↗

bioRxiv2021

Genomic analysis reveals geography rather than culture as the predominant factor shaping genetic variation in northern Kenyan human populations

ObjectivesThe aim of this study was to characterize the genetic relationships within and among four neighboring populations in northern Kenya in light of cultural relationships to understand the extent to which geography and culture shape patterns of genetic variation. Materials and MethodsWe collected DNA and demographic information pertaining to aspects of social identity and heritage from 572 individuals across the Turkana, Samburu, Waso Borana, and Rendille of northern Kenya. We sampled individuals across a total of nine clans from these four groups and, additionally, three territorial sections within the Turkana and successfully genotyped 376 individuals. ResultsHere we report that geography predominately shapes genetic variation within and among human groups in northern Kenya. We observed a clinal pattern of genetic variation that mirrors the overall geographic distribution of the individuals we sampled. We also found relatively higher rates of intermarriage between the Rendille and Samburu and evidence of gene flow between them that reflect these higher rates of intermarriage. Among the Turkana, we observed strong recent genetic substructuring based on territorial section affiliation. Within ethnolinguistic groups, we found that Y chromosome haplotypes do not consistently cluster by natal clan affiliation. Finally, we found that sampled populations that are geographically closer have lower genetic differentiation, and that cultural similarity does not predict genetic similarity as a whole across these northern Kenyan populations. DiscussionOverall, the results from this study highlight the importance of geography, even on a local geographic scale, in shaping observed patterns of genetic variation in human populations.

genetics↗

bioRxiv2019

PopInf: An approach for reproducibly visualizing and assigning population affiliation in genomic samples of uncertain origin

Germline genetic variation contributes to cancer etiology, but self-reported race is not always consistent with genetic ancestry, and samples may not have identifying ancestry information. Here we describe a flexible computational pipeline, PopInf, to visualize principal components analysis output and assign ancestry to samples with unknown genetic ancestry, given a reference population panel of known origins. PopInf is implemented as a reproducible workflow in Snakemake with a tutorial on GitHub. We provide a pre-processed reference population panel that can be quickly and efficiently implemented in cancer genetics studies. We ran PopInf on TCGA liver cancer data and identify discrepancies between reported race and inferred genetic ancestry.\n\nSignificanceThe PopInf pipeline facilitates visualization and identification of genetic ancestry across samples, so that this ancestry can be accounted for in studies of disease risk. All code and a tutorial are available on Github: https://github.com/SexChrLab/PopInf.

genomics↗