Vasuk Gautam

Chronic kidney disease (CKD) is a progressive condition associated with metabolic disturbances, systemic inflammation, and the accumulation of gut-derived uremic toxins. Increasing evidence highlights the role of gut microbiota dysbiosis in the progression of CKD through the gut–kidney axis. Consequently, microbiome-targeted nutritional strategies, including probiotics, prebiotics, and synbiotics, have emerged as promising complementary approaches to modulate intestinal microbial composition and metabolic functions. This review summarizes and critically evaluates the current clinical evidence regarding the use of these interventions in CKD patients. Clinical studies indicate that supplementation with probiotics, prebiotics, and synbiotic formulations may promote beneficial shifts in the composition of the gut microbiota, enhance saccharolytic fermentation, and increase the production of short-chain fatty acids (SCFAs). These changes have been associated with reduced circulating levels of gut-derived uremic toxins such as indoxyl sulfate and p-cresyl sulfate, as well as with the attenuation of systemic inflammation and oxidative stress. However, available trials remain heterogeneous in terms of study design, probiotic strains, prebiotic substrates, dosing regimens, and patient populations, and are frequently limited by small sample sizes and short intervention durations. As a result, evidence for improvements in renal function and long-term clinical outcomes remains inconclusive. While synbiotics may offer theoretical advantages by combining microbial supplementation with targeted substrates that support microbial growth and metabolic activity, current evidence does not consistently demonstrate superior clinical efficacy. Overall, these interventions often improve surrogate biomarkers, but their effects on renal function and hard clinical outcomes remain uncertain. Larger, longer-duration multicenter randomized controlled trials with standardized formulations are needed to establish their clinical utility and to better elucidate microbiota–host interactions in CKD. Advancing this field may support the development of personalized microbiome-based therapeutic strategies aimed at modulating the gut–kidney axis and ultimately improving clinical outcomes in CKD patients.

Optimizing surgical decisions in breast cancer is critical. Choosing between mastectomy and breast-conserving surgery (BCS) is complex due to heterogeneous pre-operative clinical factors. We developed BrCaM ( Br east Ca ncer M odel), a machine learning–based Clinical Prediction Model designed to analyze pre-operative surgical decision patterns. A dataset of 5100 patients (age range: 18–96 years) treated at a Breast Unit with standardized protocols was used. Surgeon-guided feature selection and an end-to-end machine learning pipeline were implemented. Multiple algorithms were evaluated; AdaBoost performed best using 10-fold cross-validation. BrCaM achieved an overall accuracy of 95% in distinguishing BCS from mastectomy. Feature selection identified clinically meaningful predictors that reflect established criteria influencing surgical decisions. In this retrospective setting, BrCaM captures real-world surgical decision patterns based on clinical factors. These findings support the consistency of current clinical practice and provide a foundation for future prospective validation as a clinical decision-support adjunct.

Background: The aging evolution of Taurasi, an Aglianico-based DOCG wine from southern Italy, has been comprehensively characterized here for the first time. Known for its high levels of bioactive metabolites with potential health benefits, this variety was analyzed using an integrated analytical and computational approach to explore the evolution of its composition during bottle aging. Methods: The analytical framework encompassed physicochemical profiling (density, pH, °Brix) and targeted spectrophotometric analyses for polyphenols (Folin–Ciocâlteu), antioxidant capacity (DPPH, ABTS), and anthocyanins (TAC). The phenolic and volatile fractions were analyzed in detail qualitatively and quantitatively using HPLC-UV-DAD and HS-SPME-GC/MS, respectively. Results: The aging process was characterized by a profound phenolic reorganization, with a significant decrease in monomeric phenols and an accumulation of key phenolic acids and stilbenes. The net result of these opposing trends was an overall increase in antioxidant capacity, a significant decrease in total anthocyanins, and an aromatic shift from primary fruity esters to a complex tertiary bouquet. Additionally, machine learning techniques were employed to predict aging-related variations in antioxidant activity and chemical parameters, establishing links between compositional shifts and potential bioactivity. Conclusions: This study marks the first-ever investigation of Taurasi Aglianico wine aging, offering a novel multidisciplinary approach that provides comprehensive insights into the wine’s chemical evolution. The findings emphasize the potential of Taurasi Aglianico in both enological and health-related contexts, highlighting its unique aging dynamics and bioactive potential.

The analysis of metabolic profiles using high resolution mass spectrometry (MS) data gives deep insights into the biological processes. In metabolomics, MS generates a large number of features that represent metabolites. However, identifying specific metabolites from these features can be challenging. One of the major bottlenecks in the metabolomics field is the identification of MS features, which is a prerequisite for any biochemical interpretation. By identifying similarities and differences within a family, evaluating MS features at the metabolite family level can help assign functional roles to individual MS features. This data can help interpret metabolic pathways and processes within a biological system. For the assignment of metabolite families to MS features, it is very important to have good quality, reliable, and diverse spectral libraries. We initiated a global effort to collect high-resolution MS/MS spectra of metabolites from all biological origins, including mammals, microorganisms, and plants. The mFAM-MS/MS Collection delivers valuable training data to assign machine-readable classified information on the unknown metabolites. The mFam Consortium used a standardized metadata template and has developed a globally curated MS/MS spectral library of 7,872 spectra with 2,126 unique metabolites. This library was compiled from 47 datasets contributed by 25 laboratories and leverages 12 instrument types, including QTOF, Orbitrap, and Ion Mobility systems. It comprises 4,646 spectra in positive mode and 3,226 in negative mode. This standardized resource significantly enhances metabolite identification capabilities, supports the development of machine learning-based annotation tools, and accelerates the discovery of novel metabolites. All spectra are available under the collective contributor label mFam in the MassBank system, including the web interface and the 2025.10 data release available at GitHub and Zenodo.

Community ecology studies how species interact in their ecosystems, influenced by environmental and phenotypic factors. Analyzing these complex interactions requires specialized software or proficiency in statistical programming. While many stand-alone community ecology software tools exist, there is a gap for a free and widely available webserver to support community ecology analysis. To address this shortcoming we have developed WEGAN (Web-based Ecological Group Analysis), an easy-to-use webserver for analyzing and visualizing community ecology data. WEGAN is designed to provide features offered by popular programs such as vegan through a point-and-click web interface. Specifically, WEGAN provides a wide range of community ecology methods to support the analysis and visualization of trends in dispersal, diversity, and taxonomy as well as univariate and multivariate statistics for clustering, classification, correlation, and ordination analysis. WEGAN offers intuitive workflows and generates detailed tables, publication quality figures and a complete (reproducible) R coding history of all inputs, operations and outputs for every user session, together with comprehensive tutorials. WEGAN was developed to help with the teaching and training of community ecology and to encourage wider use of sophisticated community ecology techniques. WEGAN is freely available at https://www.wegan.ca.

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 the context of the chemical space being considered. Neither are they easily automated nor 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 database that matches 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 multiproperty reference libraries constructed from a subset of 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.

The Natural Products Magnetic Resonance Database (NP-MRD; https://np-mrd.org) is a comprehensive, freely accessible, web-based resource for the deposition, distribution, extraction, and retrieval of nuclear magnetic resonance (NMR) data on natural products (NPs). The NP-MRD was initially established to support compound de-replication and data dissemination for the NP community. However, that community has now grown to include many users from the metabolomics, microbiomics, foodomics, and nutrition science fields. Indeed, since its launch in 2022, the NP-MRD has expanded enormously in size, scope, and popularity. The current version of NP-MRD now contains nearly 7× more compounds (281 859 versus 40 908) and 7× more NMR spectra (5.5 million versus 817 278) than the first release. More specifically, an additional 4.6 million predicted spectra and another 11 000 spectra simulated from experimental chemical shifts were deposited into the database. Likewise, the number of NMR raw spectral data depositions has grown from 165 spectra per year to >10 000 per year. As a result of this expansion, the number of monthly webpage views has grown from 55 to 20 000 and the number of monthly visitors has increased from 7 to 2500. To address this growth and to better support the expanding needs of its diverse community of users, many additional improvements to the NP-MRD have been made. These include significant enhancements to the data submission process, notable updates to the database’s spectral search utilities and useful additions to support better NMR spectral analysis/prediction. Significant efforts have also been undertaken to remediate and update many of NP-MRD’s database entries. This manuscript describes these database improvements and expansion efforts, along with how they have been implemented and what future upgrades to the NP-MRD are planned.

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.

MarkerDB (https://markerdb.ca) has become a leading resource for comprehensive information on molecular biomarkers. Over the past 3 years, the database has evolved significantly, reflecting the dynamic landscape of biomarker research and increasing demands from its user community. This year’s update, which is called MarkerDB 2.0, introduces key improvements to enhance the database’s usability, consistency and the range of biomarkers covered. These improvements include (i) the addition of thousands of new biomarkers and associated health conditions, (ii) the inclusion of many new biomarker types and categories, (iii) upgraded searches and data filtering functionalities, (iv) new features for exploring and understanding biomarker panels and (v) significantly expanded and improved descriptions. These upgrades, along with numerous minor improvements in content, interface, layout and overall website performance, have greatly enhanced MarkerDB’s usability and capacity to facilitate biomarker interpretation across various research domains. MarkerDB remains committed to providing a free, publicly accessible platform for consolidated information on a wide range of molecular (protein, genetic, chromosomal and chemical/small molecule) biomarkers, covering diagnostic, prognostic, risk, monitoring, safety and response-related biomarkers. We are confident that these upgrades and updates will improve MarkerDB’s user friendliness, increase its utility and greatly expand its potential applications to many other areas of clinical medicine and biomedical research.

GCMS-ID (Gas Chromatography Mass Spectrometry compound IDentifier) is a webserver designed to enable the identification of compounds from GC–MS experiments. GC–MS instruments produce both electron impact mass spectra (EI-MS) and retention index (RI) data for as few as one, to as many as hundreds of different compounds. Matching the measured EI-MS, RI or EI-MS + RI data to experimentally collected EI-MS and/or RI reference libraries allows facile compound identification. However, the number of available experimental RI and EI-MS reference spectra, especially for metabolomics or exposomics-related studies, is disappointingly small. Using machine learning to accurately predict the EI-MS spectra and/or RIs for millions of metabolomics and/or exposomics-relevant compounds could (partially) solve this spectral matching problem. This computational approach to compound identification is called in silico metabolomics. GCMS-ID brings this concept of in silico metabolomics closer to reality by intelligently integrating two of our previously published webservers: CFM-EI and RIpred. CFM-EI is an EI-MS spectral prediction webserver, and RIpred is a Kovats RI prediction webserver. We have found that GCMS-ID can accurately identify compounds from experimental RI, EI-MS or RI + EI-MS data through matching to its own large library of >1 million predicted RI/EI-MS values generated for metabolomics/exposomics-relevant compounds. GCMS-ID can also predict the RI or EI-MS spectrum from a user-submitted structure or annotate a user-submitted EI-MS spectrum. GCMS-ID is freely available at https://gcms-id.ca/.