Biology subjects

Segre, A.

Publications and source records attributed to Segre, A..

7 recordsLinked to original sources

bioRxiv2026

Time-dependent Glucocorticoid-Induced Transcriptomic Changes in Human Trabecular Meshwork and Schlemm's Canal

PurposeTo identify the transcriptomic changes induced by dexamethasone (DEX) in trabecular meshwork (TM) and Schlemms canal endothelial (SCE) cells with RNA-sequencing (RNA-seq). MethodsHuman TM (n=10) and SCE cell strains (n=5) were isolated from healthy donor eyes and exposed to DEX 100nM and vehicle (control). Three DEX exposure times were evaluated: 1-hour, 6-hours, and 2 days. RNA-seq was performed on Illuminas TruSeq platform and gene expression was quantified using featureCount. DESeq2 paired (treated and untreated) sample test was applied to identify genes transcriptionally responsive to DEX (DEGs) at false discovery rate <0.05. Gene-set enrichment analyses were performed on DEGs. DEGs were tested for association with glaucoma (POAG) and intraocular pressure (IOP). ResultsNine TM and 4 SCE strains passed quality control. After 2-day DEX exposure, there were 857 and 2,086 DEGs in TM and SCE, respectively. Of these, 411 genes were differentially expressed in both TM and SCE, including FKBP5 (17.3-fold-change, p=6.9x10-53) and FAM107A (25.1-fold-change, p=4.0x10-240), the most significant DEG after 2-day DEX exposure in TM and SCE, respectively. The 2-day DEX DEGs in TM and SCE were enriched in cell adhesion, extracellular matrix, and response to stimulus in Gene Ontologies (p<3.7x10-6). Early response DEGs were enriched in immune-related processes. Thirteen DEGs in TM were significant at all three time points, including PER1. LTBP2 is a TM-only DEG and FAM105A a SCE-only DEG associated with IOP and POAG risk. ConclusionsThis study identified candidate genes and pathways for glucocorticoid-induced ocular hypertension which can be further explored in human genetic analyses.

genomics↗

bioRxiv2023

Transcriptomic Analysis Of The Ocular Posterior Segment Completes A Cell Atlas Of The Human Eye

Although the visual system extends through the brain, most vision loss originates from defects in the eye. Its central element is the neural retina, which senses light, processes visual signals, and transmits them to the rest of the brain through the optic nerve (ON). Surrounding the retina are numerous other structures, conventionally divided into anterior and posterior segments. Here we used high-throughput single nucleus RNA sequencing (snRNA-seq) to classify and characterize cells in the extraretinal components of the posterior segment: ON, optic nerve head (ONH), peripheral sclera, peripapillary sclera (PPS), choroid, and retinal pigment epithelium (RPE). Defects in each of these tissues are associated with blinding diseases - for example, glaucoma (ONH and PPS), optic neuritis (ON), retinitis pigmentosa (RPE), and age-related macular degeneration (RPE and choroid). From [~]151,000 single nuclei, we identified 37 transcriptomically distinct cell types, including multiple types of astrocytes, oligodendrocytes, fibroblasts, and vascular endothelial cells. Our analyses revealed a differential distribution of many cell types among distinct structures. Together with our previous analyses of the anterior segment and retina, the new data complete a "Version 1" cell atlas of the human eye. We used this atlas to map the expression of >180 genes associated with the risk of developing glaucoma, which is known to involve ocular tissues in both anterior and posterior segments as well as neural retina. Similar methods can be used to investigate numerous additional ocular diseases, many of which are currently untreatable.

cell biology↗

bioRxiv2021

A tissue-aware machine learning framework enhances the mechanistic understanding and genetic diagnosis of Mendelian and rare diseases

Genetic studies of Mendelian and rare diseases face the critical challenges of identifying pathogenic gene variants and their modes-of-action. Previous efforts rarely utilized the tissue-selective manifestation of these diseases for their elucidation. Here we introduce an interpretable machine learning (ML) platform that utilizes heterogeneous and large-scale tissue-aware datasets of human genes, and rigorously, concurrently and quantitatively assesses hundreds of candidate mechanisms per disease. The resulting tissue-aware ML platform is applicable in gene-specific, tissue-specific, or patient-specific modes. Application of the platform to selected Mendelian disease genes pinpointed mechanisms that lead to tissue-specific disease manifestation. When applied jointly to diseases that manifest in the same tissue, the models revealed common known and previously underappreciated factors that underlie tissue-selective disease manifestation. Lastly, we harnessed our ML platform toward genetic diagnosis of tissue-selective rare diseases. Patient-specific models of candidate disease-causing genes from 50 patients successfully prioritized the pathogenic gene in 86% of the cases, implying that the tissue-selectivity of rare diseases aids in filtering out unlikely candidate genes. Thus, interpretable tissue-aware ML models can boost mechanistic understanding and genetic diagnosis of tissue-selective heritable diseases. A webserver supporting gene prioritization is available at https://netbio.bgu.ac.il/trace/.

genetics↗

bioRxiv2020

A large cross-ancestry meta-analysis of genome-wide association studies identifies 69 novel risk loci for primary open-angle glaucoma and includes a genetic link with Alzheimer's disease

We conducted a large multi-ethnic meta-analysis of genome-wide association studies for primary open-angle glaucoma (POAG) on a total of 34,179 cases vs 349,321 controls, and identified 127 independent risk loci, almost doubling the number of known loci for POAG. The majority of loci have broadly consistent effect across European, Asian and African ancestries. We identify a link, both genome-wide and at specific loci, between POAG and Alzheimers disease. Gene expression data and bioinformatic functional analyses provide further support for the functional relevance of the POAG risk genes. Several drug compounds target these risk genes and may be potential candidates for developing novel POAG treatments.

genetics↗

bioRxiv2019

Widespread dose-dependent effects of RNA expression and splicing on complex diseases and traits

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2,519 out of 5,385) of the GWAS loci examined. Our results demonstrate the translational relevance of the GTEx resources and highlight the need to increase their resolution and breadth to further our understanding of the genotype-phenotype link.

genetics↗

bioRxiv2019

Cell type specific genetic regulation of gene expression across human tissues

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci (cis-QTLs) for the majority of genes across a wide range of human tissues. However, the interpretation of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. Here, we map interactions between computational estimates of cell type abundance and genotype to identify cell type interaction QTLs for seven cell types and show that cell type interaction eQTLs provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations to 87 complex traits show a contribution from cell type interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.\n\nOne Sentence SummaryEstimated cell type abundances from bulk RNA-seq across tissues reveal the cellular specificity of quantitative trait loci.

genomics↗

bioRxiv2018

A comprehensive analysis of RNA sequences reveals macroscopic somatic clonal expansion across normal tissues

Cancer genome studies have significantly advanced our knowledge of somatic mutations. However, how these mutations accumulate in normal cells and whether they promote pre-cancerous lesions remains poorly understood. Here we perform a comprehensive analysis of normal tissues by utilizing RNA sequencing data from [~]6,700 samples across 29 normal tissues collected as part of the Genotype-Tissue Expression (GTEx) project. We identify somatic mutations using a newly developed pipeline, RNA-MuTect, for calling somatic mutations directly from RNA-seq samples and their matched-normal DNA. When applied to the GTEx dataset, we detect multiple variants across different tissues and find that mutation burden is associated with both the age of the individual and tissue proliferation rate. We also detect hotspot cancer mutations that share tissue specificity with their matched cancer type. This study is the first to analyze a large number of samples across multiple normal tissues, identifying clones with genomic aberrations observed in cancer.

genomics↗