Biology subjects

Kuo, T.-H.

Publications and source records attributed to Kuo, T.-H..

7 recordsLinked to original sources

bioRxiv2026

Metabolic specialization structures gut bacterial niches and drives colorectal cancer progression

Despite the established association between the gut microbiome and colorectal cancer (CRC), the functional distinction between microbial passengers and drivers of CRC progression remains unresolved. Here, we collected stool, blood, as well as paired tumor, and normal mucosa tissues from seventy-seven CRC patients to characterize the systemic and localized impact of the gut microbiome on early- and late-stage CRC. By deep shotgun metagenomic sequencing, we identified distinct bacterial species and functions residing in tumor versus normal mucosa, highlighting an enrichment of oral-associated bacteria in tumor tissues. Several of these species remained undetected in the stool microbiome analysis. We further combined bacterial culturing with untargeted metabolomics of bacteria enriched in tumor and normal mucosa tissues, revealing distinct clusters of metabolic potential. Functional testing of multiple members from one cluster comprising both tumor- and mucosa-enriched species revealed Leptotrichia wadei as a pro-tumorigenic bacterium in a murine CRC model. Single-nucleus RNA sequencing and in vitro experiments further demonstrated that L. wadei and its secretome induces M2 macrophage polarization to promote tumor growth. Overall, our study shows that metabolic specialization structures microbial colonization niches, while species-specific metabolic outputs identify functional drivers of CRC progression, and uncovers L. wadei as an oncogenic bacterium in CRC.

microbiology↗

bioRxiv2025

Gut bacteria generate prodrugs in situ increasing systemic drug exposure

The human gut microbiome has been increasingly recognized as a metabolic compartment that influences health and therapeutic responses. While recent studies have cataloged diverse sets of drug biotransformation reactions as a result of microbial metabolism1-3, the pharmacological relevance of the resulting drug metabolites remains mostly uncharacterized. We investigated whether certain microbial drug metabolites can act as functional prodrugs. By systematically mining 871 putative drug metabolites produced by gut bacteria1, we identified microbial drug metabolites with key physicochemical properties of prodrugs. Using bacterial culturing, a genetic gain-of-function screen coupled to mass spectrometry and comparative genomics, we uncover that Bacteroidota bacteria encode conserved methyltransferases that can generate prodrugs through carboxyl methylation. As a case in point, we demonstrate that bezafibrate gets converted to a prodrug by a distinct bacterial enzyme, increasing epithelial drug permeability in vitro and systemic drug exposure in vivo in a gnotobiotic mouse model. Additionally, we tested 170 structurally and clinically diverse drugs, and demonstrate that microbial drug-prodrug conversion is a common result of gut bacterial drug biotransformation. Altogether, our findings suggest a novel mechanism of how the gut microbiota influences an individuals pharmacokinetics, and may generally cause interpersonal differences in microbiota-host metabolic interactions.

microbiology↗

bioRxiv2024

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno, and Aytan-Aktug), an ML baseline, and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amox-clav, cefoxitin, ceftazidime, and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, C. jejuni and E. faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae, and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

bioinformatics↗

bioRxiv2023

A Weakly Supervised U-Net Model for Precise Whole Brain Immunolabeled Cell Detection

Cell segmentations low precision due to the intensity differences hinders widespread use of whole brain microscopy imaging. Previous studies used ResNet or CNN to account for this problem, but are unapplicable to immunolabeled signals across samples. Here we present a semiauto ground truth generation and weakly-supervised U-Net-based Deep-learning precise segmentation pipeline for whole brain immunopositive c-FOS signals, which reveals the distinct neural activity maps with different social motivations.

neuroscience↗

bioRxiv2019

Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

The growing importance of antibiotic resistance on clinical outcomes and cost of care underscores the need for optimization of current diagnostics. For a number of bacterial species antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and gene expression profiles, we generated predictive models and identified biomarkers of susceptibility or resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (>0.9) sensitivity and predictive values, where the relative contribution of the different categories of biomarkers strongly depended on the antibiotic. For all drugs except for ciprofloxacin, gene expression information substantially improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine clinical microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

microbiology↗

bioRxiv2019

Structural Lipidomics and Molecular Imaging of C=C Positional Isomers Enabled by mCPBA Epoxidation with Tandem Mass Spectrometry

Cellular lipidome is highly regulated through lipogenesis, rendering diverse double-bond positional isomers (C=C isomer) of a given unsaturated lipid species. In recent years, there are increasing reports indicating the physiological roles of C=C isomer compositions associated with diseases, while the biochemistry has not been fully understood due to the challenge in characterizing lipid isomers inherent to conventional mass spectrometry-based lipidomics. To address this challenge, we reported a universal, user-friendly, derivatization-based strategy, MELDI (mCPBA Epoxidation for Lipid Double-bond Identification), which enables both large-scale identification and spatial mapping of biological C=C isomers using commercial mass spectrometers without any instrument modification. With the developed liquid-chromatography mass spectrometry (LC-MS) lipidomics workflow, we elucidated more than 100 isomers among mono- and poly-unsaturated fatty acids and glycerophospholipids in both human serum, where novel isomers of low abundance were unambiguously quantified for the first time. The capability of MELDI-LC-MS in lipidome analysis was further demonstrated using the differentiated 3T3-L1 adipocytes, providing an insight into the cellular lipid reprogramming upon stearoyl-coenzyme A desaturase 1 (SCD1) inhibition. Finally, we highlighted the versatility of MELDI coupled with mass spectrometry imaging to spatially resolve cancer-associated alteration of lipid isomers in a metastatic mouse tissue section. Our results suggested that MELDI will contribute to current lipidomics pipelines with a deeper level of structural information, allowing us to investigate underlying lipid biochemistry.

biochemistry↗

bioRxiv2017

The Genomic Landscape Of Tree Rot In Phellinus noxius And Its Hymenochaetales Members

The order Hymenochaetales of white rot fungi contain some of the most aggressive wood decayers causing tree deaths around the world. Despite their ecological importance and the impact of diseases they cause, little is known about the evolution and transmission patterns of these pathogens. Here, we sequenced and undertook comparative genomics analyses of Hymenochaetales genomes using brown root rot fungus Phellinus noxius, wood-decomposing fungus Phellinus lamaensis, laminated root rot fungus Phellinus sulphurascens, and trunk pathogen Porodaedalea pini. Many gene families of lignin-degrading enzymes were identified from these fungi, reflecting their ability as white rot fungi. Comparing against distant fungi highlighted the expansion of 1,3-beta-glucan synthases in P. noxius, which may account for its fast-growing attribute. We identified 13 linkage groups conserved within Agaricomycetes, suggesting the evolution of stable karyotypes. We determined that P. noxius has a bipolar heterothallic mating system, with unusual highly expanded ~60 kb A locus as a result of accumulating gene transposition. We investigated the population genomics of 60 P. noxius isolates across multiple islands of the Asia Pacific region. Whole-genome sequencing showed this multinucleate species contains abundant poly-allelic single-nucleotide-polymorphisms (SNPs) with atypical allele frequencies. Different patterns of intra-isolate polymorphism reflect mono-/heterokaryotic states which are both prevalent in nature. We have shown two genetically separated lineages with one spanning across many islands despite the geographical barriers. Both populations possess extraordinary genetic diversity and show contrasting evolutionary scenarios. These results provide a framework to further investigate the genetic basis underlying the fitness and virulence of white rot fungi.

genomics↗