Biology subjects

Tang, Y.-C.

Publications and source records attributed to Tang, Y.-C..

4 recordsLinked to original sources

bioRxiv2023

Transcriptome and Mutant Analysis of Neuronal Genes for Memory Formation and Retrieval in Caenorhabditis elegans

Although it is generally believed that the formation and retrieval of memory require distinct sets of genes, little is known about the identity and temporal specificity of genes that act at different memory stages. Here we characterize the transcriptomes for the formation and retrieval of stress-induced aversive memory in the nematode Caenorhabditis elegans. Our RNA-sequencing analysis show that neuronal genes for calcium homeostasis, membrane excitability, synaptic function and signaling are progressively activated during the formation of aversive memory. Stage-specific transcriptomes further reveal that memory formation and retrieval display distinct gene activation patterns. We carried out a candidate screen of mutants for these memory genes and verified the temporal requirement of memory function for several of them. Further in-depth characterization of casy-1/calsyntenin show that while the short CASY-1B isoform acts in memory retrieval, the long CASY-1A isoform functions during memory formation, and its shed extracellular N-terminus fragment is likely a critical signal for memory regulation. Our work uncovers gene expression patterns associated with distinct memory stages and provides a foundation for future mechanistic interrogation of memory functions.

neuroscience↗

bioRxiv2020

PathDSP: Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment

Computational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Using a deep neural network, we outperforming state-of-the-art deep learning models, while demonstrating good generalizability a separate dataset of the Cancer Cell Line Encyclopedia (CCLE) as well as provide explainable results, demonstrated through case studies that are in line with current knowledge. Additionally, our pathway-based model achieved a good performance when predicting unseen drugs and cells, with potential utility for drug development and for guiding individualized medicine.

bioinformatics↗

bioRxiv2018

TF-TWAS: Transcription-factor polymorphism associated with tissue-specific gene expression

Transcriptional regulation is associated with a broad range of diseases. Methods associating genetic polymorphism with gene transcription levels offer key insights for understanding the transcriptional regulation plan. The majority of gene imputation methods focus on modeling polymorphism in the cis regions of the gene, partially owing to the large genetic search space. We hypothesize that polymorphism within transcription factors (TFs) may help explain transcription levels of their transcribed genes.\n\nHere, we test this hypothesis by developing TF-TWAS: imputation models that integrate transcription factor information with transcription-wide association study methodology. By comparing TF-TWAS models to base models that use only gene cis information, we are able to estimate possible mechanisms of the TF polymorphism effect - TF expression or binding affinity within four tissues - whole blood, liver, brain hippocampus and coronary artery.\n\nWe identified 48 genes where the TF-TWAS models explain significantly better their expression than cis models alone in at least one of the four tissues. Sixteen of these genes are associated with various diseases, including cancer, neurological, psychiatric and rare genetic diseases. Our method is a new expansion to transcriptome-wide association studies and enables the identification of new associations between polymorphism in transcription factor and gene transcription levels.

genomics↗

bioRxiv2017

Mupirocin-associated temporal changes in the nasal microbiota and host’s antimicrobial responses: A pilot study in healthy staphylococcal carriers

BackgroundHow mupirocin affects the human nasal microbiota over time remains uncharacterized.\n\nMethodsWe repeatedly sampled the anterior nares of four healthy staphylococcal carriers before and after mupirocin use. By sequencing bacterial 16S ribosomal cDNA, we characterized sequential changes in the carriage status, the nasal microbiota, and the hosts antimicrobial peptide expression up to 90 days after decolonization.\n\nResultsBefore mupirocin use, the nasal microbiota differed by the initial, culture-based staphylococcal carriage status, with Firmicutes (54.1%) being the most predominant in carriers and Proteobacteria (75.8%) in the only noncarrier. The nasal microbiota became less diverse (Shannon diversity: 1.33, 95% confidence interval [CI]: 1.06-1.54) immediately after decolonisation than that before decolonisation (1.78, 95%CI: 0.58-1.93). Based on results of differential abundance analysis, Firmicutes were significantly enriched (log2 fold changes [≥] 4, Benjamini-Hochberg adjusted P < .01) while Actinobacteria, particularly Corynbebacterium, were relatively depleted in samples from staphylococcal carriers. Results of nonmetric multidimensional scaling (NMDS) and constrained correspondence analysis (CCA) also suggested that the initial staphylococcal carriage status, human neutrophil peptide 1 levels, and sampling times were major contributors to the between-community dissimilarities (P for marginal permutation test: .014) though the significance attenuated when within-group correlation was considered (P for blocked permutation test: .047).\n\nConclusionThese findings suggest that large-scale investigations on antibiotic effects on the human nasal microbiota are warranted.

microbiology↗