Biology subjects

Wishart, D. S.

Publications and source records attributed to Wishart, D. S..

7 recordsLinked to original sources

bioRxiv2026

Age-related differences in metabolome composition in a healthy pediatric population: A pilot analysis

Metabolic profiling of biofluids provides a comprehensive overview of physiological processes occurring at the molecular, cellular, tissue, and organ levels. Accounting for age-related variation in the metabolome is critical to accurately interpret these effects in blood-based metabolomics research. In this study, we sought to characterize the blood metabolome of healthy pediatric patients and assess whether metabolite concentrations differ across age groups and stages of sexual maturation. A total of 44 plasma samples from individuals aged 0-19 years were analyzed using a targeted quantitative metabolomics approach, combining direct injection with reverse-phase liquid chromatography mass spectrometry. Samples were stratified by age: infants (0-2 years), pre-teens (10-12 years), and post-pubertal adolescents (16-19 years). Asymmetric dimethylarginine, carnosine, N-acetyl-serine, and quinolinic acid had a negative association with age, whereas creatinine, homoarginine, lysoPC a C16:1, guanidoacetic acid and 5-Hydroxyindoleacetic acid had a positive association with age. Comparisons between the pre-teen and infant age groups yielded the greatest number of differences followed by the post-pubertal versus infant and post-pubertal versus pre-teen groups. These findings demonstrate age-associated heterogeneity in the pediatric plasma metabolome. Establishing normative reference ranges for metabolite concentrations will be essential to improve the accuracy of metabolomic analyses that include pediatric participants.

developmental biology↗

bioRxiv2026

TransXplorer: An automated translational discovery platform for RNA-seq data

RNA-seq experiments routinely identify thousands of differentially expressed genes, but translating these into biological insights and therapeutic hypotheses often requires integrating multiple tools. Existing web platforms such as iDEP, NetworkAnalyst, and GEPIA2 address individual steps, differential expression, network visualization, or TCGA queries, but lack a unified environment spanning raw data processing to clinical and pharmacological interpretation. TransXplorer (https://transxplorer.org) is a freely available web platform that addresses this limitation by integrating the complete RNA-seq analytical workflow. It supports processing from raw FASTQ files using HISAT2 or Salmon, as well as direct GEO dataset import with automated metadata handling. Differential expression analysis is implemented via DESeq2, edgeR, and limma-voom, followed by functional enrichment across more than 1,800 species using Bioconductor resources. Batch effects are automatically detected and corrected using a composite of PVCA, kBET, and Silhouette metrics without requiring predefined batch annotations. Downstream analyses include co-expression network construction (WGCNA), protein-protein interaction mapping (STRING), cell-type deconvolution, and transcription factor inference using integrated DoRothEA and TFLink resources. The platform further links gene signatures to drug candidates through DGIdb and OpenTargets and enables survival and tumour-normal comparisons across TCGA cohorts. Application to cardiac endothelial differentiation (GSE151427) and kidney renal papillary cell carcinoma (TCGA-KIRP) datasets demonstrates accurate batch correction, biologically consistent pathway enrichment, recovery of expected cell-type proportions, and identification of clinically relevant genes and drug candidates. TransXplorer is freely available without a login.

bioinformatics↗

bioRxiv2026

The NMR Exchange Format (NEF): Specification and Applications

The NMR Exchange Format (NEF) is a community-driven standard for representing NMR experimental data in a consistent, interoperable, and machine-readable form. Built on the STAR syntax, NEF provides a structured framework for storing and exchanging chemical shifts, peak lists, various types of structural restraints, and related metadata, thus allowing for data exchange across software platforms. By enabling direct, lossless transfer of information, NEF simplifies multi-software workflows, improves reproducibility, and supports FAIR (Findable, Accessible, Interoperable, Reusable) data principles. We describe the NEF specification, its current implementation across commonly used NMR software packages, and its application in areas including biomolecular structure determination, metabolomics, and ligand screening. Testing demonstrates that NEF can be used to exchange complete datasets between programs without loss of information or functionality. We also outline recent developments and future directions, such as inclusion of NMR relaxation data and support for non-standard residue topologies. NEFs growing adoption highlights its potential as a unifying standard for NMR data, enabling more efficient, transparent and collaborative research.

biochemistry↗

bioRxiv2025

BioTransformer4.0 a comprehensive computational tool for small molecule metabolism prediction

BioTransformer 4.0, the successor to BioTransformer 3.0, is a freely available in silico metabolism prediction tool. It integrates both knowledge-based and machine learning approaches to predict metabolites for small molecules using one of seven modules: abiotic, environmental, CYP450, phase II, enzyme commission-based, human gut microbial, and all human metabolism. It also provides a customizable sequence prediction module that allows users to simulate multi-step metabolic transformations by chaining among the first six different modules. BioTransformer 4.0 can make predictions more efficiently and accurately than the previous version, as it includes more than 130 new reaction rules, and also an optional validation module to improve the efficiency by restricting the number of predicted metabolites, due to their similarity among real human metabolites. We evaluated its performance by running the six-step all-human metabolism prediction on the DrugBank dataset of 2,457 known biotransformations, and the PhytoHub dataset of 633 known biotransformations - achieving recall values of 87.2% (resp., 91.6%) for the DrugBank (resp., PhytoHub) datasets.

bioinformatics↗

bioRxiv2024

Language model-guided anticipation and discovery of unknown metabolites

Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks within human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains a low-throughput endeavor. Biochemical large language models have transformed the interpretation of DNA, RNA, and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns the latent biosynthetic logic embedded within the structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective chemical synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We harness DeepMet to discover several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to accelerate the mapping of the metabolome.

bioinformatics↗

bioRxiv2024

Introducing identification probability for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

biochemistry↗

bioRxiv2019

Assessing the performance of genome-wide association studies for predicting disease risk

To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.

genomics↗