Biology subjects

Young, W. C.

Publications and source records attributed to Young, W. C..

3 recordsLinked to original sources

bioRxiv2022

Single-cell profiling uncovers a Muc4-expressing metaplastic gastric cell type sustained by Helicobacter pylori-specific inflammation

AbstractMechanisms for Helicobacter pylori (Hp)-driven stomach cancer are not fully understood. In a transgenic mouse model of gastric preneoplasia, concomitant Hp infection and induction of constitutively active KRAS (Hp+KRAS+) alters metaplasia phenotypes and elicits greater inflammation than either perturbation alone. Gastric single-cell RNA-seq showed that Hp+KRAS+ mice had a large population of metaplastic pit cells that expressed the intestinal mucin Muc4 and the growth factor amphiregulin. Metaplastic pit cells were associated with macrophage and T cell inflammation and prevented by gastric immunosuppression. Lineage tracing showed that Muc4 was not dependent on cell-intrinsic KRAS activity, and lineage-derived cells had a limited propensity for growth as organoids, demonstrating that metaplastic pit cells are largely not self- renewing. Finally, MUC4 expression was significantly associated with proliferation in human gastric cancer samples. These studies identify an Hp-associated metaplastic pit cell lineage, also found in human gastric cancer tissues, whose expansion is driven by Hp-dependent inflammation. Statement of SignificanceUsing a mouse model, we have delineated metaplastic pit cells as a pre-cancerous cell type whose expansion requires H. pylori-driven inflammation. In humans, metaplastic pit cells show enhanced proliferation as well as enrichment in precancer and early cancer tissues, highlighting an early step in the gastric metaplasia to cancer cascade.

microbiology↗

bioRxiv2017

Integration of multiple data sources for gene network inference using genetic perturbation data

BackgroundThe inference of gene regulatory networks is of great interest and has various applications. The recent advances in high-throughout biological data collection have facilitated the construction and understanding of gene regulatory networks in many model organisms. However, the inference of gene networks from large-scale human genomic data can be challenging. Generally, it is difficult to identify the correct regulators for each gene in the large search space, given that the high dimensional gene expression data only provides a small number of observations for each gene.\n\nResultsWe present a Bayesian approach integrating external data sources with knockdown data from human cell lines to infer gene regulatory networks. In particular, we assemble multiple data sources including gene expression data, genome-wide binding data, gene ontology, known pathways and use a supervised learning framework to compute prior probabilities of regulatory relationships. We show that our integrated method improves the accuracy of inferred gene networks. We apply our method to two different human cell lines, which illustrates the general scope of our method.\n\nConclusionsWe present a flexible and systematic framework for external data integration that improves the accuracy of human gene network inference while retaining efficiency. Integrating various data sources of biological information also provides a systematic way to build on knowledge from existing literature.

systems biology↗

bioRxiv2017

fastBMA: Scalable Network Inference and Transitive Reduction

BACKGROUNDInferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a novel and computationally efficient method for eliminating redundant indirect edges in the network.\n\nFINDINGSWe evaluated the performance of fastBMA on synthetic data and experimental genome-wide yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory efficient, parallel and distributed application that scales to human genome wide expression data. A 10,000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster.\n\nCONCLUSIONSfastBMA is a significant improvement over its predecessor ScanBMA. It is orders of magnitude faster and more accurate than other fast network inference methods such as LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable timeframe. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/).

systems biology↗