Biology subjects

Kok, C. R.

Publications and source records attributed to Kok, C. R..

5 recordsLinked to original sources

bioRxiv2026

Bacteriophage treatment of Pseudomonas aeruginosa PA14 infection does not alter the native microbiome of Caenorhabditis elegans

Bacteriophage therapeutics are alternatives to antibiotics for treating multi-drug-resistant (MDR) bacterial infections. Bacteriophages kill host bacteria species with targeted precision, unlike the broad-spectrum nature of antibiotics. While the precision and the natural occurrence of phages in the environment have drawn attention to phages as new "drugs" in the time of accelerated antibiotic resistance, it remains poorly understood how phage treatment impacts the community of organisms composing the microbiome. Before phage therapies can be more widespread in practice, this understanding is required. Caenorhabditis elegans lends itself as an excellent model organism to address this question, as worm populations are raised under sterile conditions and the gut microbiome can be populated experimentally by the worms natural bacterivore diet. Here we have established a C. elegans infection and phage treatment model using fluorescent Pseudomonas aeruginosa PA14 to quantify infection. We isolated and whole genome sequenced PA14-specific lytic phage from wastewater samples near West Point, New York. To characterize the impact of treatment on the microbiome, we populated the C. elegans gut microbiome with 11 strains of the native C. elegans microbiome (CeMbio) and used PacBio sequencing of the full length 16S rRNA gene to characterize the microbiome during PA14 infection in the presence and absence of phage treatment (n = 100 worms per sample, 4 replicates per condition). Taxonomic classification, phylogenetic analysis, and diversity analysis performed using a HiFi 16S bioinformatics pipeline revealed that phage treatment and infection level did not change the overall composition of the C. elegans gut microbiome. ImportanceMultidrug resistant infections are emerging faster than new antibiotic compounds can be synthesized. Bacteriophages are natural viruses of bacteria that seek out and kill only their host bacteria species. Bacteriophage therapeutics are a promising solution for drug resistant infections in patients where no other treatment exists. However, while phage treatment is effective, there is little understanding about what happens within the complex community of the microbiome when a phage treatment is employed. Here we establish a new model utilizing C. elegans to integrate a multidrug resistant infection, phage treatment, and the native microbiome of the C. elegans worm to address how the microbiome changes in response to phage treatment. We find that the microbiome remains unchanged after bacteriophage treatment, highlighting the targeted specificity of phage therapy and suggests fewer side effects for phage therapy than antibiotic treatment for the same infection.

microbiology↗

bioRxiv2026

An Embeddings Fusion Approach Predicts Disease State from Microbiome Features

BackgroundDeep neural networks are a proven technique for working with high dimensional data because of their ability to draw-out meaningful patterns and create vector representations known as "embeddings", which make it easier to work with learning tasks on large inputs as they capture the semantics and variance of the data. Microbial community abundance profiles are well suited for an embeddings approach due to their high dimensionality, and in this work we introduce a novel approach for generating embeddings from visual representations which encode NCBIs taxonomic tree and microbial compositions as images, enabling the creation of embeddings that capture factors such as disease status, type, and geographical location. ResultsWe profiled 13,534 public human metagenomes spanning 85 studies, 24 disease types, 35 countries, and 31,756 microbial species using a profiling pipeline that indexes NCBIs nucleotide database (nt) across all kingdoms of life. Our model achieves an average classification performance of 84% in distinguishing healthy and disease conditions; 87% for disease types, 99% for body sites, and 88% for geographical locations. It also achieves a 97% accuracy when performing multi-label classification of the four factors combined. ConclusionOur work highlights the use of an embeddings approach that can encode multiple features and create efficient contextualization of profiled metagenomes derived from microbiome samples. The models embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples.

bioinformatics↗

bioRxiv2025

Beyond Microbial Abundance: Metadata Integration Enhances Disease Prediction in Human Microbiome Studies

Multiple studies have highlighted the human microbiomes potential as a biomarker for diagnosing diseases through its interaction with systems like the gut, immune, liver, and skin via key axes. Advances in sequencing technologies and highperformance computing have enabled the analysis of large-scale metagenomic data, facilitating the use of machine learning to predict disease likelihood from microbiome profiles. However, challenges such as compositionality, high dimensionality, sparsity, and limited sample sizes have hindered the development of actionable models. One strategy to improve these models is by incorporating key metadata from both the host and sample collection/processing protocols. In this paper, we introduce a machine learning-based pipeline for predicting human disease states by integrating host and protocol metadata with microbiome abundance profiles from 68 different studies, processed through a common pipeline. Our findings indicate that metadata can enhance machine learning predictions, particularly at higher taxonomic ranks like Kingdom and Phylum, though this effect diminishes at lower ranks. Our study leverages a large collection of microbiome datasets comprising of 11,208 samples, therefore enhancing the robustness and statistical confidence of our findings. This work is a critical step toward utilizing microbiome and metadata for predicting diseases such as gastrointestinal infections, diabetes, cancer, and neurological disorders.

microbiology↗

bioRxiv2024

Meta2DB: Curated Shotgun Metagenomic Feature Sets and Metadata for Health State Prediction

Meta2DB is a curated metagenomic and metadata database that provides structurally consistent microbiome taxonomy feature count tables for 13,897 samples across 84 studies, 23 disease states, and 34 geographical locations. All samples were uniformly processed using a streamlined metagenomic classification pipeline that employs a reference database indexed to contain all sequences across all kingdoms of life that were present in the NCBI Nucleotide (nt) database retrieved on Jan 04, 2023. This pipeline leverages high-performance computing (HPC) resources at Lawrence Livermore National Laboratory and was used to process 50TB of publicly available raw metagenomic sequence data. Extensive metadata curation was carried out through a combination of manual curation and automated parsing, producing a consistent inter-study metadata table specifically structured to facilitate training of ML models for prediction of human health.

microbiology↗

bioRxiv2024

Addressing the dynamic nature of reference data: a new nt database for robust metagenomic classification

BackgroundAccurate metagenomic classification relies on comprehensive, up-to-date, and validated reference databases. While the NCBI BLAST Nucleotide (nt) database, encompassing a vast collection of sequences from all domains of life, represents an invaluable resource, its massive size --currently exceeding 1012 nucleotides-- and exponential growth pose significant challenges for researchers seeking to maintain current nt-based indices for metagenomic classification. Recognizing that no current nt-based indices exist for the widely used Centrifuge classifier, and the last public version was released in 2018, we addressed this critical gap by leveraging advanced high-performance computing resources. ResultsWe present new Centrifuge-compatible nt databases, meticulously constructed using a novel pipeline incorporating different quality control measures, including reference decontamination and filtering. These measures demonstrably reduce spurious classifications, and through temporal comparisons, we reveal how this approach minimizes inconsistencies in taxonomic assignments stemming from asynchronous updates between public sequence and taxonomy databases. These discrepancies are particularly evident in taxa such as Listeria monocytogenes and Naegleria fowleri, where classification accuracy varied significantly across database versions. ConclusionsThese new databases, made available as pre-built Centrifuge indexes, respond to the need for an open, robust, nt-based pipeline for taxonomic classification in metagenomics. Applications such as environmental metagenomics, forensics, and clinical metagenomics, which require comprehensive taxonomic coverage, will benefit from this resource. Our new nt-based index highlights the importance of treating reference databases as dynamic entities, subject to ongoing quality control and validation akin to software development best practices. This dynamic update approach is crucial for ensuring the accuracy and reliability of metagenomic analysis, especially as databases continue to expand in size and complexity.

bioinformatics↗