Biology subjects

Tsai, H.-K.

Publications and source records attributed to Tsai, H.-K..

8 recordsLinked to original sources

bioRxiv2025

A Chemoproteomic Atlas of the Human Purine Interactome for Regioselective Ligand Discovery

Purines are essential bioactive molecules that interact with a large fraction of the human proteome. Despite their importance, the scope of actionable purine-binding pockets for ligand discovery remains limited. Here, we developed a quantitative chemoproteomics platform using sulfonyl-purine (SuPUR) chemistry to produce a massive and functional map of the human purine interactome. The SuPUR platform captured 31,000+ targetable tyrosine and lysine sites, representing the most comprehensive beyond cysteine chemoproteomics database for enabling protein ligand discovery. SuPUR ligands that bind through a regioselective fashion serve as enabling starting points for developing potent (nanomolar) and proteome-wide-selective modulators of enzymatic and protein-protein interaction function. Phenotypic screening identified a site-specific (Y237) and regioselective SuPUR ligand of ACAT2 to reveal an unexpected metabolic dependency in cancer cells. A crystal structure of SuPUR ligand-bound ACAT2 revealed the purine group binds deep in the CoA pocket forming key interactions with catalytic residues via a water bridge to guide future structure-based ligand design.

biochemistry↗

bioRxiv2023

PGSbuilder: An end-to-end platform for human genome association analysis and polygenic risk score predictions

Understanding the genetic basis of human complex diseases is increasingly important in the development of precision medicine. Over the last decade, genome-wide association studies (GWAS) have become a key technique for detecting associations between common diseases and single nucleotide polymorphisms (SNPs) present in a cohort of individuals. Alternatively, the polygenic risk score (PRS), which often applies results from GWAS summary statistics, is calculated for the estimation of genetic propensity to a trait at the individual level. Despite many GWAS and PRS tools being available to analyze a large volume of genotype data, most clinicians and medical researchers are often not familiar with the bioinformatics tools and lack access to a high-performance computing cluster resource. To fill this gap, we provide a publicly available web server, PGSbuilder, for the GWAS and PRS analysis of human genomes with variant annotations. The user-friendly and intuitive PGSbuilder web server is developed to facilitate the discovery of the genetic variants associated with complex traits and diseases for medical professionals with limited computational skills. For GWAS analysis, PGSbuilder provides the most renowned analysis tool PLINK 2.0 package. For PRS, PGSbuilder provides six different PRS methods including Clumping and Thresholding, Lassosum, LDPred2, GenEpi, PRS-CS, and PRSice2. Furthermore, PGSbuilder provides an intuitive user interface to examine the annotated functional effects of variants from known biomedical databases and relevant literature using advanced natural language processing approaches. In conclusion, PGSbuilder offers a reliable platform to aid researchers in advancing the public perception of genomic risk and precision medicine for human disease genetics. PGSbuilder is freely accessible at http://pgsb.tw23.org.

bioinformatics↗

bioRxiv2023

Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning

BackgroundAlternative splicing is a crucial mechanism of post-transcriptional modification responsible for the transcriptome plasticity and proteome diversity of a metazoan cell. Although many splicing regulations around the exon/intron regions have been discovered, the relationship between promoter-bound transcription factors and the downstream alternative splicing remains largely unexplored. ResultsIn this study, we present computational approaches to decipher the regulation relationship connecting the promoter-bound transcription factor binding sites (TFBSs) and the splicing patterns. We curated a fine data set, including DNase I hypersensitive sites sequencing and transcriptome in fifteen human tissues from ENCODE. Specifically, we proposed different representations of TF binding context and splicing patterns to tackle the associations between the promoter and downstream splicing events. Our results demonstrated that the convolutional neural network (CNN) models learned from the TF binding changes in the promoter to predict the splicing pattern changes. Furthermore, through an in silico perturbation-based analysis of the CNN models, we identified several TFs that considerably reduced the model performance of splicing prediction. ConclusionIn conclusion, our finding highlights the potential role of promoter-bound TFBSs in influencing the regulation of downstream splicing patterns and provides insights for discovering alternative splicing regulations.

bioinformatics↗

bioRxiv2021

Identification and comparative analysis of long non-coding RNAs in the brain of fire ant queens in two different reproductive states

BackgroundMany long non-coding RNAs (lncRNAs) have been extensively identified in many higher eukaryotic species. The function of lncRNAs has been reported to play important roles in diverse biological processes, including developmental regulation and behavioral plasticity. However, there are no reports of systematic characterization of long non-coding RNAs in the fire ant Solenopsis invicta. ResultsIn this study, we performed a genome-wide analysis of lncRNAs in the brains of S. invicta from RNA-seq. In total, 1,393 novel lncRNA transcripts were identified in the fire ant. In contrast to the annotated lncRNA transcripts having at least two exons, novel lncRNAs are monoexonic transcripts with a shorter length. Besides, the transcriptome from virgin alate and dealate mated queens were analyzed and compared. The results showed 295 differentially expressed mRNA genes (DEGs) and 65 differentially expressed lncRNA genes (DELs) between virgin and mated queens, of which 17 lncRNAs were highly expressed in the virgin alates and 47 lncRNAs were highly expressed in the mated dealates. By identifying the DEL:DEG pairs with high association in their expression (Spearmans |rho| > 0.8 and p-value < 0.01), many DELs were co-regulated with DEGs after mating. Furthermore, several remarkable lncRNAs (MSTRG.6523, MSTRG.588, and nc909) that were found to associate with particular coding genes may play important roles in the regulation of brain gene expression in reproductive transition in fire ants. ConclusionThis study provides the first genome-wide identification of S. invicta lncRNAs in the brains in different reproductive states and will contribute to a fuller understanding of the transcriptional regulation underpinning reproductive changes.

genomics↗

bioRxiv2021

Connecting MHC-I-binding motifs with HLA alleles via deep learning

The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned.

bioinformatics↗

bioRxiv2020

Differential Hsp90-dependent gene expression is strain-specific and common among yeast strains

Enhanced phenotypic diversity increases the likelihood of a population surviving catastrophic conditions. It has been suggested that Hsp90, an essential molecular chaperone in eukaryotes, can suppress (i.e., buffer) or enhance (i.e., potentiate) the effects of genetic variation, enabling organisms to adjust their levels of phenotypic diversity in response to environmental cues. Many Hsp90-interacting proteins are involved in signaling transduction pathways and transcriptional regulation. However, it remains unclear if Hsp90-dependent differential gene expression is common in natural populations. By examining the gene expression profiles of five diverse yeast strains, we identified many genes exhibiting Hsp90-dependent strain-specific differential expression. We employed an analysis pipeline to identify transcription factors (TFs) potentially contributing to variable expression. We found that upon Hsp90 inhibition or heat stress, activities or abundances of Hsp90-dependent TFs varied among strains, resulting in differential strain-specific expression of their target genes, which consequently led to phenotypic diversity. We provide evidence that individual populations can readily display specific Hsp90-dependent gene expression, suggesting that the evolutionary impacts of Hsp90 are widespread in nature. HighlightsO_LIHsp90-dependent gene expression varies among different yeast strains. C_LIO_LIHsp90 differentially influences transcriptional activity or protein abundances of transcription factors among yeast strains. C_LIO_LIDifferential strain-specific gene expression is correlated with phenotypic variations upon Hsp90 inhibition. C_LIO_LIHsp90-mediated strain-specific regulation manifests under environmental stress. C_LI

evolutionary biology↗

bioRxiv2018

The borders of cis-regulatory DNA sequences harbor the divergent transcription factor binding motifs in the human genome

Changes in the cis-regulatory DNA sequences and transcription factor (TF) repertoires provide major sources that shape the gene regulatory evolution in eukaryotes. However, it is currently unclear how dynamic change of DNA sequences introduce various divergence level of TF binding motifs in the genome over evolutionary time. Here, we estimated the evolutionary divergence level of the TF binding motifs, and quantified their occurrences in the DNase I hypersensitive sites. Results from our in silico motif scan and empirical TF-ChIP (chromatin immunoprecipitation) demonstrate that the divergent motifs tend to be introduced at the borders of the cis-regulatory regions, that are likely accompanied with the expansion through evolutionary time. Accordingly, we propose that an expansion by incorporating divergent motifs within the cis-regulatory regions provides a rationale for the evolutionary divergence of regulatory circuits.

genomics↗

bioRxiv2017

Comparative genomic analyses highlight the contribution of pseudogenized protein-coding genes to human lincRNAs

BackgroundThe regulatory roles of long intergenic noncoding RNAs (lincRNAs) in humans have been revealed through the use of advanced sequencing technology. Recently, three possible scenarios of lincRNA origin have been proposed: de novo origination from intergenic regions, duplication from long noncoding RNA, and pseudogenization from protein. The first two scenarios are largely studied and supported, yet few studies focused on the evolution from pseudo genized protein-coding sequence to lincRNA. Due to the non-mutually exclusive nature that these three scenarios have, accompanied by the need of systematic investigation of lincRNA origination, we conduct a comparative genomics study to investigate the evolution of human lincRNAs.\n\nResultsCombining with syntenic analysis and stringent Blastn e-value cutoff, we found that the majority of lincRNAs are aligned to the intergenic regions of other species. Interestingly, 193 human lincRNAs could have protein-coding orthologs in at least two of nine vertebrates. Transposable elements in these conserved regions in human genome are much less than expectation. Moreover, 19% of these lincRNAs have overlaps with or are close to pseudogenes in the human genome.\n\nConclusionsWe suggest that a notable portion of lincRNAs could be derived from pseudogenized protein-coding genes. Furthermore, based on our computational analysis, we hypothesize that a subset of these lincRNAs could have potential to regulate their paralogs by functioning as competing endogenous RNAs. Our results provide evolutionary evidence of the relationship between human lincRNAs and protein-coding genes.

bioinformatics↗