Cheminformatics approach to exploring and modeling trait-associated metabolite profiles

Seminal plasma metabolomics analysis of differences in liquid preservation ability of boar sperm

The preservation of semen is pivotal in animal reproduction to ensure successful fertilization and genetic improvement of livestock and poultry. However, investigating the underlying causes of differences in sperm liquid preservation ability and identifying relevant biomarkers remains a challenge. This study utilized liquid chromatography-mass spectrometry (LC-MS) to analyze the metabolite composition of seminal plasma (SP) from two groups with extreme differences in sperm liquid preservation ability. The two groups namely the good liquid preservation ability (GPA) and the poor preservation ability (PPA). The aim was to explore the relationship between metabolite composition in SP and sperm liquid preservation ability, and to identify candidate biomarkers associated with this ability of sperm. The results revealed the identification of 756 metabolites and 70 differentially expressed metabolites (DEM) in the SP from two groups of boar semen with differing liquid preservation abilities at 17 °C. The majority of identified metabolites in the SP belonged to organic acids and derivatives as well as lipids and lipid-like molecules. The DEM in the SP primarily consisted of amino acids, peptides, and analogs. The Kyoto Encyclopedia of Genes and Genomes analysis also demonstrated that the DEM are mainly concentrated in amino acid synthesis and metabolism-related pathways (P < 0.05). Furthermore, eleven key metabolites were identified and six target amino acids were verified, and the results were consistent with the non-targeted metabolic analysis. These findings indicated that amino acids and their associated pathways play a potential role in determining boar sperm quality and liquid preservation ability. D-proline, arginine, L-citrulline, phenylalanine, leucine, DL-proline, DL-serine, and indole may serve as potential biomarkers for early assessment of boar sperm liquid preservation ability. The findings of this study are helpful in understanding the causes and mechanisms of differences in the liquid preservation ability of boar sperm, and provide valuable insights for improving semen quality assessment methods and developing novel extenders or protocols.

Visualization of exhaled breath metabolites reveals distinct diagnostic signatures for acute cardiorespiratory breathlessness

Acute cardiorespiratory breathlessness accounts for one in eight of all emergency hospitalizations. Early, noninvasive diagnostic testing is a clinical priority that allows rapid triage and treatment. Here, we sought to find and replicate diagnostic breath volatile organic compound (VOC) biomarkers of acute cardiorespiratory disease and understand breath metabolite network enrichment in acute disease, with a view to gaining mechanistic insight of breath biochemical derangements. We collected and analyzed exhaled breath samples from 277 participants presenting acute cardiorespiratory exacerbations and aged-matched healthy volunteers. Topological data analysis phenotypes differentiated acute disease from health and acute cardiorespiratory exacerbation subtypes (acute heart failure, acute asthma, acute chronic obstructive pulmonary disease, and community-acquired pneumonia). A multibiomarker score (101 breath biomarkers) demonstrated good diagnostic sensitivity and specificity (≥80%) in both discovery and replication sets and was associated with all-cause mortality at 2 years. In addition, VOC biomarker scores differentiated metabolic subgroups of cardiorespiratory exacerbation. Louvain clustering of VOCs coupled with metabolite enrichment and similarity assessment revealed highly specific enrichment patterns in all acute disease subgroups, for example, selective enrichment of correlated C5-7 hydrocarbons and C3-5 carbonyls in heart failure and selective depletion of correlated aldehydes in acute asthma. This study identified breath VOCs that differentiate acute cardiorespiratory exacerbations and associated subtypes and metabolic clusters of disease-associated VOCs.

Consensus Prediction of Human Gut Microbiota-Mediated Metabolism Susceptibility for Small Molecules by Machine Learning, Structural Alerts, and Dietary Compounds-Based Average Similarity Methods

The human gut microbiota (HGM) colonizing human gastrointestinal tract (HGT) confers a repertoire of dynamic and unique metabolic capacities that are not possessed by the host and therefore is tentatively perceived as an alternative metabolic ″organ″ besides the liver in the host. Nevertheless, the significant contribution of HGM to the overall human metabolism is often overlooked in the modern drug discovery pipeline. Hence, a systematic evaluation of HGM-mediated drug metabolism is gradually important, and its computational prediction becomes increasingly necessary. In this work, a new data set containing both the HGM-mediated metabolism susceptible (HGMMS) and insusceptible (HGMMI) compounds (329 vs 320) was manually curated. Based on this data set, the first machine learning (ML) model, a new structural alerts (SA) model, and the K-nearest neighboring dietary compounds-based average similarity (AS) model were proposed to directly predict the HGM-mediated metabolism susceptibility for small molecules, and exhibit promising performance on three independent test sets. Finally, consensus prediction (ML/SA/AS) for DrugBank molecules revealed an intriguing phenomenon that a typical Michael acceptor ″α,β-unsaturated carbonyl group″ is a very common warhead for the design of covalent inhibitors and inclined to be metabolized by HGM in anaerobic HGT to generate the reduced metabolite without the reactive warhead, which could be a new concern to medicinal chemists. To the best of our knowledge, we gleaned the first HGMMS/HGMMI data set, developed the first HGMMS/HGMMI classification model, implemented a relatively comprehensive program based on ML/SA/AS approaches, and found a new phenomenon on the HGM-mediated deactivation of an extensively used warhead for covalent inhibitors.

From Prevention to Disease Perturbations: A Multi-Omic Assessment of Exercise and Myocardial Infarctions

While a molecular assessment of the perturbations and injury arising from diseases is essential in their diagnosis and treatment, understanding changes due to preventative strategies is also imperative. Currently, complex diseases such as cardiovascular disease (CVD), the leading cause of death worldwide, suffer from a limited understanding of how the molecular mechanisms taking place following preventive measures (e.g., exercise) differ from changes occurring due to the injuries caused from the disease (e.g., myocardial infarction (MI)). Therefore, this manuscript assesses lipidomic changes before and one hour after exercise treadmill testing (ETT) and before and one hour after a planned myocardial infarction (PMI) in two separate patient cohorts. Strikingly, unique lipidomic perturbations were observed for these events, as could be expected from their vastly different stresses on the body. The lipidomic results were then combined with previously published metabolomic characterizations of the same patients. This integration provides complementary insights into the exercise and PMI events, thereby giving a more holistic understanding of the molecular changes associated with each.

Unveiling molecular signatures of preeclampsia and gestational diabetes mellitus with multi-omics and innovative cheminformatics visualization tools

To fully enable the development of diagnostic tools and progressive pharmaceutical drugs, it is imperative to understand the molecular changes occurring before and during disease onset and progression. Systems biology assessments utilizing multi-omic analyses (e.g. the combination of proteomics, lipidomics, genomics, etc.) have shown enormous value in determining molecules prevalent in diseases and their associated mechanisms. Herein, we utilized multi-omic evaluations, multi-dimensional analysis methods, and new cheminformatics-based visualization tools to provide an in depth understanding of the molecular changes taking place in preeclampsia (PRE) and gestational diabetes mellitus (GDM) patients. Since PRE and GDM are two prevalent pregnancy complications that result in adverse health effects for both the mother and fetus during pregnancy and later in life, a better understanding of each is essential. The multi-omic evaluations performed here provide new insight into the end-stage molecular profiles of each disease, thereby supplying information potentially crucial for earlier diagnosis and treatments.

Structural-based connectivity and omic phenotype evaluations (SCOPE): a cheminformatics toolbox for investigating lipidomic changes in complex systems

Since its inception, the main goal of the lipidomics field has been to characterize lipid species and their respective biological roles. However, difficulties in both full speciation and biological interpretation have rendered these objectives extremely challenging and as a result, limited our understanding of lipid mechanisms and dysregulation. While mass spectrometry-based advancements have significantly increased the ability to identify lipid species, less progress has been made surrounding biological interpretations. We have therefore developed a Structural-based Connectivity and Omic Phenotype Evaluations (SCOPE) cheminformatics toolbox to aid in these evaluations. SCOPE enables the assessment and visualization of two main lipidomic associations: structure/biological connections and metadata linkages either separately or in tandem. To assess structure and biological relationships, SCOPE utilizes key lipid structural moieties such as head group and fatty acyl composition and links them to their respective biological relationships through hierarchical clustering and grouped heatmaps. Metadata arising from phenotypic and environmental factors such as age and diet is then correlated with the lipid structures and/or biological relationships, utilizing Toxicological Prioritization Index (ToxPi) software. Here, SCOPE is demonstrated for various applications from environmental studies to clinical assessments to showcase new biological connections not previously observed with other techniques.