Biology subjects

Chiu, H.-C.

Publications and source records attributed to Chiu, H.-C..

4 recordsLinked to original sources

bioRxiv2025

TypeAssembly: Copy number estimation and allele typing for haplotype assemblies

Accurately annotating complex genes in the human genome, particularly from haplotype assemblies, remains a significant challenge. To overcome this, we developed TypeAssembly, a local alignment-based framework for copy number estimation and allele typing. Operating in two modes, mode-FASTA and mode-VCF, TypeAssembly can define alleles by either sequence or variant information. We successfully applied it to annotate 41 genes in the MHC locus, 17 KIR genes, and, for the first time, 15 pharmacogenes across 466 haplotype assemblies. This study establishes TypeAssembly as a robust method for accurately annotating complex genomic regions and provides an evaluation of existing gene annotations and callers.

bioinformatics↗

bioRxiv2024

Ganoderma formosanum polysaccharides enhance antitumor immune responses by downregulating the differentiation of myeloid-derived suppressor cells and tumor-associated macrophages

Ganoderma formosanum is a native species of Ganoderma isolated in Taiwan, and our previous studies showed that a polysaccharide fraction, PS-F2, purified from the submerged culture fluid of G. formosanum ATCC 76538 exhibited immunostimulatory and antitumor property. In the current study, we investigated the immunomodulatory and antitumor effects of PS-F2 from a UV-mutated G. formosanum variant NTU-1, which produced higher yields of PS-F2 than the original strain. Oral administration of PS-F2 effectively suppressed the growth of colon 26 (CT-26) carcinoma and splenomegaly in tumor-bearing mice without adversely affecting the animals health. We found that PS-F2 treatment resulted in augmented cytotoxic T lymphocyte (CTL) while significantly reducing the accumulation of polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and regulatory T (Treg) cells in the spleen. In the tumor, PS-F2 treatment markedly enhanced CTL and Th1 responses, whereas it reduced the accumulation of tumor-associated macrophages (TAMs). Collectively, our data demonstrate that oral treatment of PS-F2 from G. formosanum NTU-1 in CT26 tumor-bearing mice activates antitumor immune responses and reduces the accumulation of immunosuppressive cells in the spleen and the tumor, leading to delayed tumor progression.

immunology↗

bioRxiv2019

Ran GTPase regulates non-centrosomal microtubule nucleation and is transported by actin waves towards the neurite tip.

Microtubule (MT) is the most abundant cytoskeleton in neurons and controls multiple facets of their development. While the organizing center of MTs in mitotic cells is typically located at the centrosome, MT nucleation in post-mitotic neurons switches to non-centrosomal sites. A handful of proteins and organelle have been shown to promote non-centrosomal MT formation in neurons, yet the regulation mechanism remains unknown. Here we demonstrate that the small GTPase Ran is a key regulator of non-centrosomal MT nucleation in neurons. The GTP-bound Ran (RanGTP) localizes to the neurite tips and around the soma. Using the RanGTP- and RanGDP-mimic mutants, we show that RanGTP promotes MT nucleation at the tip of the neurite. To demonstrate that RanGTP can promote MT nucleation in regions other than the neurite tip, an optogenetic tool called RanTRAP was constructed to enable light-induced local production of RanGTP in the neuronal cytoplasm. An increase of non-centrosomal MT nucleation can be observed by elevating the RanGTP level along the neurite using RanTRAP, establishing a new role for Ran in regulating neuronal MTs. Additionally, the mechanism of RanGTP enrichment at the neurite tip was examined. We discovered that actin waves drive the anterograde transport of RanGTP towards the neurite tip. Pharmacological disruption of actin waves abolishes the enrichment of RanGTP and reduces the non-centrosomal MT nucleation at the neurite tip. These observations provide a novel regulation mechanism of MTs and an indirect connection between the actin and MT cytoskeletons in neurons.

cell biology↗

bioRxiv2018

Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies

Correlation-based analysis of paired microbiome-metabolome datasets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach have not been comprehensively evaluated. To address this challenge, we introduce a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally use a multi-species metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome datasets. We then compare observed taxon-metabolite correlations in these datasets to calculated ground-truth taxonomic contribution values. We find that in simulations of both a model 10-species community and of complex human gut microbiota, correlation-based analysis poorly identifies key contributors, with extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.\n\nImportanceIdentifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step towards understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples, and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation, and then examining these contributions in simulated datasets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome datasets identifies true contributions with very low predictive value, and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

microbiology↗