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Biology subjects

Green, A.

Publications and source records attributed to Green, A..

2 recordsLinked to original sources

Single cell RNA-seq reveals profound transcriptional similarity between Barretts esophagus and esophageal glands

Barretts esophagus is a precursor of esophageal adenocarcinoma. In this common condition, squamous epithelium in the esophagus is replaced by columnar epithelium in response to acid reflux. Barretts esophagus is highly heterogeneous and its relationships to normal tissues are unclear. We investigated the cellular complexity of Barretts esophagus and the upper gastrointestinal tract using RNA-sequencing of 2895 single cells from multiple biopsies from four patients with Barretts esophagus and two patients without esophageal pathology. We found that uncharacterised cell populations in Barretts esophagus, marked by LEFTY1 and OLFM4, exhibit a profound transcriptional overlap with a subset of esophageal cells, but not with gastric or duodenal cells. Additionally, SPINK4 and ITLN1 mark cells that precede morphologically identifiable goblet cells in colon and Barretts esophagus, potentially aiding the identification of metaplasia. Our findings reveal striking transcriptional relationships between normal tissue populations and cells in a premalignant condition, with implications for clinical practice.

genetics

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

bioinformatics