Biology subjects

Garcia-Rivera, E.

Publications and source records attributed to Garcia-Rivera, E..

4 recordsLinked to original sources

bioRxiv2023

Mechanism-guided quantification of LINE-1 reveals p53 regulation of both retrotransposition and transcription

Somatic activity of LINE-1 (L1) mobile elements has been implicated in cancer etiology, which may be related to the loss of p53-mediated regulation as a result of TP53 mutations. Quantifying the mechanisms of L1 regulation in cancer has been challenging. Here, we build a statistical model of L1 regulation by simultaneously quantifying L1 retrotransposition, L1 expression, and the fitness costs of mutated TP53 with precision. We first developed Total ReCall, an algorithm specifically tailored to the mechanisms of L1 reintegration, to detect L1 insertions from short-read whole-genome sequencing. Applying Total ReCall to high-quality data consisting of >750 paired tumor and normal samples from The Cancer Genome Atlas (TCGA) shows high L1 insertion heterogeneity among tumor types, with increased retrotransposition burden in lung squamous cell carcinoma, head and neck, and colon cancers. We next assessed the active RNA expression of intact L1 in >9,000 TCGA tumor samples, establishing, for the first time, a clear correlation between L1 expression and retrotransposition. Finally, we integrated the number of L1 insertions, L1 expression and a mathematical model of TP53 fitness into a multi-modal model of p53- mediated mechanisms of L1 regulation. We show that TP53 mutations enable retrotransposition both by disinhibiting L1 expression and enabling its reintegration and quantify the relative weights of this dual regulatory role. We demonstrate how mechanism-based multi-modal modeling applied at scale can statistically disentangle the complex interplay between canonical driver events in tumor evolution and retrotransposon activity.

cancer biology↗

bioRxiv2021

A literature-derived knowledge graph augments the interpretation of single cell RNA-seq datasets

Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have rapidly advanced in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p < 6.15x10-76, r = 0.24; cohens D = 2.6). Building on this, we developed an augmented annotation algorithm that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 185 clusters in 13 datasets from human blood, pancreas, lung, liver, kidney, retina, and placenta. With the optimized settings, the true cellular identity matched the top prediction in 66% of tested clusters and was present among the top five predictions for 94% of clusters. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of established cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.

genomics↗

bioRxiv2020

Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes, airway club cells, and ciliated cells are likely underappreciated targets of SARS-CoV-2 infection, in addition to type II pneumocytes and olfactory epithelial cells. We further identify mature small intestinal enterocytes as a possible hotspot of COVID-19 fecal-oral transmission, where an intriguing maturation-correlated transcriptional signature is shared between ACE2 and the other coronavirus receptors DPP4 (MERS-CoV) and ANPEP (-coronavirus). This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses. The nferX Platform Single-cell resource - https://academia.nferx.com/

genomics↗

bioRxiv2019

Recapitulation and Retrospective Prediction of Biomedical Associations Using Temporally-enabled Word Embeddings

The recent explosion of biomedical knowledge presents both a major opportunity and challenge for scientists tackling complex problems in healthcare. Here we present an approach for synthesizing biomedical knowledge based on a combination of word-embeddings and select cooccurrences. We evaluated our ability to recapitulate and retrospectively predict disease-gene associations from the Online Mendelian Inheritance in Man (OMIM) resource. Our metrics achieved an area under the curve (AUC) value of 0.981 at the recapitulation task for 2,400 disease-gene associations. At the most stringent cutoff, our metrics predicted 13.89% of these associations before their first cooccurrence in the literature, with a median time of 4 years between prediction and first cooccurrence. Finally, our literature metrics can be combined with human genetics data to retrospectively predict disease-gene associations, IL-6 and Giant Cell Arteritis provided as an example. We believe this framework can provide robust biomedical hypotheses at a much faster pace than current standard practices.

bioinformatics↗