Cell Rep Methods. 2025 Apr 23:101030. doi: 10.1016/j.crmeth.2025.101030. Online ahead of print.

ABSTRACT

Quantification of the absolute microbial abundance in a human stool sample is crucial for a comprehensive understanding of the microbial ecosystem, but this information is lost upon metagenomic sequencing. While several methods exist to measure absolute microbial abundance, they are technically challenging and costly, presenting an opportunity for machine learning. Here, we observe a strong correlation between DNA concentration and the absolute number of 16S ribosomal RNA copies as measured by digital droplet PCR in clinical stool samples from individuals undergoing hematopoietic cell transplantation (BMT CTN 1801). Based on this correlation and additional measurements, we trained an accurate yet simple machine learning model for the prediction of absolute prokaryotic load, which showed exceptional prediction accuracy on an external cohort that includes people living with Parkinson's disease and healthy controls. We propose that, with further validation, this model has the potential to enable accurate absolute abundance estimation based on readily available sample measurements.

PMID:40300608 | DOI:10.1016/j.crmeth.2025.101030

Cell Rep Methods. 2025 Apr 23:101032. doi: 10.1016/j.crmeth.2025.101032. Online ahead of print.

ABSTRACT

Epigenomic profiling of the brain has largely been done on bulk tissues, limiting our understanding of cell type-specific epigenetic changes in disease states. Here, we introduce cell type-specific histone acetylation score (CHAS), a computational tool for inferring cell type-specific signatures in bulk brain H3K27ac profiles. We applied CHAS to >300 H3K27ac chromatin immunoprecipitation sequencing samples from studies of Alzheimer's disease, Parkinson's disease, autism spectrum disorder, schizophrenia, and bipolar disorder in bulk postmortem brain tissue. In addition to recapitulating known disease-associated shifts in cellular proportions, we identified cell type-specific biological insights into brain-disorder-associated regulatory variation. In most cases, genetic risk and epigenetic dysregulation targeted different cell types, suggesting independent mechanisms. For instance, genetic risk of Alzheimer's disease was exclusively enriched within microglia, while epigenetic dysregulation predominantly fell within oligodendrocyte-specific H3K27ac regions. In addition, reanalysis of the original datasets using CHAS enabled identification of biological pathways associated with each neurological and psychiatric disorder at cellular resolution.

PMID:40300607 | DOI:10.1016/j.crmeth.2025.101032

Cell Rep Methods. 2025 Apr 23:101031. doi: 10.1016/j.crmeth.2025.101031. Online ahead of print.

ABSTRACT

Biological tissues, as natural scattering media, inherently disrupt structural information, presenting significant challenges for optical imaging. Complex light propagation through tissue severely degrades image quality, limiting conventional fluorescence imaging techniques to superficial depths. Extracting meaningful information from random speckle patterns is, therefore, critical for deeper tissue imaging. In this study, we present RNP (robust non-negative principal matrix factorization), an approach that enables fluorescence microscopy under diverse scattering conditions. By integrating robust feature extraction with non-negativity constraints, RNP effectively addresses challenges posed by non-sparse signals and background interference in scattering tissue environments. The framework operates on a standard epi-fluorescence platform, eliminating the need for complex instrumentation or precise alignment. The results from imaging scattered cells and tissues demonstrate substantial improvements in robustness, field of view, depth of field, and image clarity. We anticipate that RNP will become a valuable tool for overcoming scattering challenges in fluorescence microscopy and driving advancements in biomedical research.

PMID:40300606 | DOI:10.1016/j.crmeth.2025.101031

Biosens Bioelectron. 2025 Apr 21;282:117475. doi: 10.1016/j.bios.2025.117475. Online ahead of print.

ABSTRACT

Extracellular vesicles (EVs) have emerged as promising biomarkers for various diseases. Encapsulating biomolecules reflective of their parental cells, EVs are readily accessible in bodily fluids. The prospect for minimally invasive, repeatable molecular testing has stimulated significant research; however, challenges persist in isolating EVs from complex biological matrices and characterizing their limited molecular cargo. Technical advances have been pursued to address these challenges, producing innovative EV-specific platforms. This review highlights recent technological developments, focusing on EV isolation and molecular detection methodologies. Furthermore, it explores the translation of these laboratory innovations to clinical applications through the analysis of patient samples, providing insights into the potential diagnostic and prognostic utility of EV-based technologies.

PMID:40300344 | DOI:10.1016/j.bios.2025.117475

Pac Symp Biocomput. 2025;30:473-487. doi: 10.1142/9789819807024_0034.

ABSTRACT

CDK4/6 inhibitors such as palbociclib block cell cycle progression and improve outcomes for many ER+/HER2- breast cancer patients. Unfortunately, many patients are initially resistant to the drug or develop resistance over time in part due to heterogeneity among individual tumor cells. To better understand these mechanisms of resistance, we used multiplex, single-cell imaging to profile cell cycle proteins in ER+ breast tumor cells under increasing palbociclib concentrations. We then applied spherical principal component analysis (SPCA), a dimensionality reduction method that leverages the inherently cyclical nature of the high-dimensional imaging data, to look for changes in cell cycle behavior in resistant cells. SPCA characterizes data as a hypersphere and provides a framework for visualizing and quantifying differences in cell cycles across treatment-induced perturbations. The hypersphere representations revealed shifts in the mean cell state and population heterogeneity. SPCA validated expected trends of CDK4/6 inhibitor response such as decreased expression of proliferation markers (Ki67, pRB), but also revealed potential mechanisms of resistance including increased expression of cyclin D1 and CDK2. Understanding the molecular mechanisms that allow treated tumor cells to evade arrest is critical for identifying targets of future therapies. Ultimately, we seek to further SPCA as a tool of precision medicine, targeting treatments by individual tumors, and extending this computational framework to interpret other cyclical biological processes represented by high-dimensional data.

PMID:40299610 | DOI:10.1142/9789819807024_0034

Pac Symp Biocomput. 2025;30:412-425. doi: 10.1142/9789819807024_0030.

ABSTRACT

Down syndrome (DS), caused by the triplication of chromosome 21 (T21), is a prevalent genetic disorder with a higher incidence of obesity. Traditional approaches have struggled to differentiate T21-specific molecular dysregulation from general obesity-related processes. This study introduces the omni-PLIER framework, combining the Pathway-Level Information ExtractoR (PLIER) with the omnigenic model, to uncover molecular mechanisms underlying obesity in DS. The PLIER framework aligns gene expression data with biological pathways, facilitating the identification of relevant molecular patterns. Using RNA sequencing data from the Human Trisome Project, omni-PLIER identified latent variables (LVs) significantly associated with both T21 and body mass index (BMI). Elastic net regression and causal mediation analysis revealed LVs mediating the effect of karyotype on BMI. Notably, LVs involving glutathione peroxidase-1 (GPX1) and MCL1 apoptosis regulator, BCL2 family members emerged as crucial mediators. These findings provide insights into the molecular interplay between DS and obesity. The omni-PLIER model offers a robust methodological advancement for dissecting complex genetic disorders, with implications for understanding obesity-related processes in both DS and the general population.

PMID:40299606 | DOI:10.1142/9789819807024_0030

Pac Symp Biocomput. 2025;30:360-376. doi: 10.1142/9789819807024_0026.

ABSTRACT

Patients experiencing adverse drug events (ADE) from polypharmaceutical regimens present a huge challenge to modern healthcare. While computational efforts may reduce the incidence of these ADEs, current strategies are typically non-generalizable for standard healthcare systems. To address this, we carried out a retrospective study aimed at developing a statistical approach to detect and quantify potential ADEs. The data foundation comprised of almost 2 million patients from two health regions in Denmark and their drug and laboratory data during the years 2011 to 2016. We developed a series of multistate Cox models to compute hazard ratios for changes in laboratory test results before and after drug exposure. By linking the results to data from a drug-drug interaction database, we found that the models showed potential for applications for medical safety agencies and improved efficiency for drug approval pipelines.

PMID:40299602 | DOI:10.1142/9789819807024_0026

Discov Oncol. 2025 Apr 29;16(1):632. doi: 10.1007/s12672-025-02395-5.

ABSTRACT

OBJECTIVE: Invasive lung cancer staging poses significant challenges, often requiring painful and costly biopsy procedures. This study aims to identify non-invasive biomarkers for detecting bronchogenic carcinoma and its various stages by analyzing gene expression data using bioinformatics and machine learning techniques. By leveraging these advanced computational methods, we seek to eliminate the need for surgical intervention in the diagnostic process.

METHODS: We utilized the TCGA-LUAD dataset, including gene expression data from healthy and cancerous samples. To identify robust biomarkers, we applied eight metaheuristic algorithms for feature selection, combined with four classification methods and two data fusion techniques to optimize performance.

RESULTS: Our approach achieved 100% accuracy in distinguishing healthy samples from cancerous ones, outperforming existing methods that reported 97% accuracy. Notably, while prior methods have struggled to separate bronchogenic carcinoma stages effectively, our research achieved an approximate accuracy of 77% in stage classification. Furthermore, using gene enrichment methods, we identified 5, 7, and 16 diagnostic biomarker candidates for stages I, II, III, and IV, respectively.

CONCLUSION: This study demonstrates that integrating bioinformatics, gene set enrichment, and biological pathway analysis can enable non-invasive diagnostics for bronchogenic carcinoma stages. These findings hold promise for developing alternatives to traditional, invasive staging systems, potentially improving patient outcomes and reducing healthcare costs.

PMID:40299256 | DOI:10.1007/s12672-025-02395-5

Antioxidants (Basel). 2025 Apr 18;14(4):486. doi: 10.3390/antiox14040486.

ABSTRACT

Transforming Growth Factor-Beta 1 (TGF-β1) plays a pivotal role in the differentiation of fibroblasts into myofibroblasts, which is a critical process in tissue repair, fibrosis, and wound healing. Upon exposure to TGF-β1, fibroblasts acquire a contractile phenotype and secrete collagen and extracellular matrix components. Numerous studies have identified hydrogen peroxide (H2O2) as a key downstream effector of TGF-β1 in this pathway. H2O2 functions as a signalling molecule, regulating various cellular processes mostly through post-translational redox modifications of cysteine thiol groups of specific proteins. In this study, we used primary human skin fibroblast cultures to investigate the oxidative mechanisms triggered by TGF-β1. We analyzed the expression of redox-related genes, evaluated the effects of the genetic and pharmacological inhibition of H2O2-producing enzymes, and employed an unbiased redox proteomics approach (OxICAT) to identify proteins undergoing reversible cysteine oxidation. Our findings revealed that TGF-β1 treatment upregulated the expression of oxidant-generating genes while downregulating antioxidant genes. Low concentrations of diphenyleneiodonium mitigated myofibroblast differentiation and mitochondrial oxygen consumption, suggesting the involvement of a flavoenzyme in this process. Furthermore, we identified the increased oxidation of highly conserved cysteine residues in key proteins such as the epidermal growth factor receptor, filamin A, fibulin-2, and endosialin during the differentiation process. Collectively, this study provides insights into the sources of H2O2 in fibroblasts and highlights the novel redox mechanisms underpinning fibroblast-to-myofibroblast differentiation.

PMID:40298862 | DOI:10.3390/antiox14040486

J Acquir Immune Defic Syndr. 2025 Apr 29. doi: 10.1097/QAI.0000000000003689. Online ahead of print.

ABSTRACT

BACKGROUND: Pulmonary complications in people living with HIV (PWH) have shifted away from infectious disease and towards chronic disease. HIV is an independent risk factor for chronic obstructive pulmonary disease (COPD), with PWH developing COPD younger and declining faster in pulmonary function. As an accelerated decline is associated with greater mortality, there is a need to identify individuals at high risk of longitudinal decline.

SETTING: 59 adults with HIV enrolled from the Pittsburgh Lung HIV study cohort.

METHODS: Targeted metabolite profiling was performed on baseline bronchoalveolar lavage fluid (BALF, n=35) and serum samples (n=54) using liquid chromatography-high resolution mass spectrometry. Longitudinal pulmonary function tests (median 3 measurements over 2.95 years with a follow-up interval of 1.34 years) were used to determine rates of decline. Predictive modeling and feature selection algorithms identified baseline clinical and metabolomic factors associated with longitudinal decline across forced expiratory volume, forced vital capacity, and diffusing capacity of the lung.

RESULTS: Predictive models found the BALF metabolome to successfully predict outcomes more consistently than serum. Key BALF metabolites such as elevated carnitine and reduced pyruvate predicted greater risk of longitudinal decline. Low serum citrate levels were a robust predictor of decline across multiple tests. Probabilistic graphical models supported direct relationships between these metabolites and lung function decline.

CONCLUSION: Baseline metabolomic profiling, especially using BALF, can help identify PWH at risk for accelerated lung function decline. Key metabolic pathways related to glucose oxidation, fatty acid metabolism, and amino acid metabolism underlie observed lung function changes.

PMID:40298290 | DOI:10.1097/QAI.0000000000003689

Protein Sci. 2025 May;34(5):e70146. doi: 10.1002/pro.70146.

ABSTRACT

Canonical serine protease inhibitor proteins occupy the substrate-binding groove of their target enzyme via a surface loop. Unlike true substrates, inhibitors are cleaved by the target protease extremely slowly. Here, we applied an unbiased directed evolution approach to investigate which loop residues hamper proteolytic cleavage while maintaining high-affinity binding. As a protease inhibitor model system, we used human chymotrypsin C (CTRC) and Schistocerca gregaria protease inhibitor 2 (SGPI-2). We created an SGPI-2 library displayed on M13 phage by randomizing the binding loop amino acid positions, with the exception of the structurally indispensable Cys residues. We selected binding phage clones against active CTRC and the inactive mutant Ser195Ala. All CTRC-selected binders inhibited CTRC activity and also bound to the inactive Ser195Ala mutant, but the Ser195Ala-selected clones proved to be either inhibitors or substrates of active CTRC. Substrate-like behavior of SGPI-2 variants was associated with the absence of the P2 Thr, the residue next to the specificity determinant P1 amino acid. The selected SGPI-2 variants containing a P2 Thr bound strongly to CTRC even if the other loop residues deviated from the optimal inhibitory consensus sequence. In the absence of a P2 Thr, however, SGPI-2 variants became substrates unless all other loop residues were optimal for binding. Structural modeling confirmed that P2 Thr is important for organizing a stabilizing H-bond network. The observations indicate that binding loops of canonical serine protease inhibitors evolved amino acids not only to support tight binding to the target enzyme but also to inhibit proteolytic cleavage.

PMID:40298105 | DOI:10.1002/pro.70146

New Phytol. 2025 Apr 29. doi: 10.1111/nph.70159. Online ahead of print.

ABSTRACT

Tomato fruit size is a crucial trait in domestication, determined by cell division and cell expansion. Despite the identification of several quantitative trait loci associated with fruit size in tomatoes, the underlying molecular mechanisms that govern cell division and expansion to control fruit size remain unclear. CRISPR/Cas9 gene editing was used to generate single and double loss-of-function mutants of the tomato STERILE APETALA1 (c) and SlSAP2. The results demonstrate that the two SlSAP genes function redundantly in regulating leaf and fruit size by positively regulating cell proliferation and expansion, with SlSAP1 having the predominant effect. Consistently, overexpression of either SlSAP1 or SlSAP2 leads to enlarged fruits due to an increase in both cell layers and cell size in the pericarp. Biochemical evidence suggests that both SlSAP1 and SlSAP2 can form an SCF complex and physically interact with SlKIX8 and SlKIX9, which are crucial negative regulators of fruit size. Further results reveal that SlSAP1 and SlSAP2 target them for degradation. This study uncovers that the ubiquitination pathway plays an important role in the determination of tomato fruit size, and offers new genetic loci for improving fruit yield and biomass by manipulating pericarp thickness.

PMID:40298065 | DOI:10.1111/nph.70159

Front Mol Biosci. 2025 Apr 14;12:1561464. doi: 10.3389/fmolb.2025.1561464. eCollection 2025.

ABSTRACT

Breast cancer (BC) remains a complex and widespread problem, affecting millions of women worldwide, Among the various subtypes of BC, triple-negative breast cancer (TNBC) is particularly challenging, representing approximately 20% of all BC cases, and the survival rate of TNBC patients is generally worse than other subtypes of BC. TNBC is a heterogeneous disease characterized by lack of expression of three receptors: estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2 (HER2), resulting conventional hormonal therapies are ineffective for its management. Despite various therapeutic approaches have been explored, but no definitive solution has been found yet for TNBC. Current treatments options are chemotherapy, immunotherapy, radiotherapy and surgery, although, these therapies have some limitations, such as the development of resistance to anti-cancer drugs, and off-target toxicity, which remain primary obstacles and significant challenges for TNBC. Several findings have shown that EVs exhibit significant therapeutic promise in many diseases, and a similar important role has been observed in various types of tumor. Studies suggest that EVs may offer a potential solution for the management of TNBC. This review highlights the multifaceted roles of EVs in TNBC, emphasizing their involvement in disease progression, diagnosis and therapeutic approach, as well as their potential as biomarkers and drug delivery.

PMID:40297849 | PMC:PMC12034555 | DOI:10.3389/fmolb.2025.1561464

Front Physiol. 2025 Apr 14;16:1535331. doi: 10.3389/fphys.2025.1535331. eCollection 2025.

ABSTRACT

BACKGROUND: Assessing real-time stress in individuals to prevent the accumulation of stress is necessary due to the adverse effects of excessive psychological stress on health. Since both stress and circadian rhythms affect the excitability of the nervous system, the influence of circadian rhythms needs to be considered during stress assessment. Most studies train classifiers using physiological data collected during fixed short time periods, overlooking the assessment of stress levels at other times.

METHODS: In this work, we propose a method for training a classifier capable of identifying stress and resting states throughout the day, based on 10 short-term heart rate variability (HRV) feature data obtained from morning, noon, and evening. To characterize the circadian rhythms of HRV features, heartbeat interval data were collected and analyzed from 50 volunteers over three consecutive days. The circadian rhythm trends in the HRV features were then removed using the Smoothness Priors Approach (SPA), and XGBoost models were trained to assess stress.

RESULTS: The results show that all HRV features exhibit 12-h and 24-h circadian rhythms, and the circadian rhythm differences across different days for individuals are relatively small. Furthermore, training classifiers on detrended data can improve the overall accuracy of stress assessment across all time periods. Specifically, when combining data from different time periods as the training dataset, the accuracy of the classifier trained on detrended data increases by 13.67%.

DISCUSSION: These findings indicate that using HRV features with circadian rhythm trends removed is an effective method for assessing stress at all times throughout the day.

PMID:40297780 | PMC:PMC12034550 | DOI:10.3389/fphys.2025.1535331

Bioinform Adv. 2025 Apr 4;5(1):vbaf073. doi: 10.1093/bioadv/vbaf073. eCollection 2025.

ABSTRACT

MOTIVATION: The increasing use of big data and optimized prediction methods in metabolomics requires techniques aligned with biological assumptions to improve early symptom diagnosis. One major challenge in predictive data analysis is handling confounding factors-variables influencing predictions but not directly included in the analysis.

RESULTS: Detecting and correcting confounding factors enhances prediction accuracy, reducing false negatives that contribute to diagnostic errors. This study reviews concept drift detection methods in metabolomic predictions and selects the most appropriate ones. We introduce a new implementation of concept drift analysis in predictive classifiers using metabolomics data. Known confounding factors were confirmed, validating our approach and aligning it with conventional methods. Additionally, we identified potential confounding factors that may influence biomarker analysis, which could introduce bias and impact model performance.

AVAILABILITY AND IMPLEMENTATION: Based on biological assumptions supported by detected concept drift, these confounding factors were incorporated into correction of prediction algorithms to enhance their accuracy. The proposed methodology has been implemented in Semi-Automated Pipeline using Concept Drift Analysis for improving Metabolomic Predictions (SAPCDAMP), an open-source workflow available at https://github.com/JanaSchwarzerova/SAPCDAMP.

PMID:40297776 | PMC:PMC12037104 | DOI:10.1093/bioadv/vbaf073

Oncol Res. 2025 Apr 18;33(5):1229-1248. doi: 10.32604/or.2025.059411. eCollection 2025.

ABSTRACT

OBJECTIVES: Currently, there exist two approaches to the treatment of malignant neoplasms: the Karanahan technology and in situ vaccination, which are based on chronometric delivery of therapeutic agents to the tumor depending on the characteristics of tumor cells, as well as the immune status. The main purpose of this study was to experimentally prove the feasibility of combining the Karanahan technology and in situ vaccination with αOX40 antibodies into a single therapeutic platform to achieve a potent additive antitumor therapeutic effect.

METHODS: BALB/c mice grafted with B-cellular lymphoma A20 were treated using the Karanahan technology consisting of intraperitoneal cyclophosphamide administrations and intratumoral DNA injections according to an individually determined therapeutic regimen, together with in situ vaccination with αOX40. A pathomorphological analysis of the organs of experimental animals that died during the initial attempt to combine the two technologies was carried out. An analysis of blood cell populations was performed to determine the safe time for antibody administration: the number of immune cells capable of activating systemic inflammation (CD11b+Ly-6C+, CD11b+Ly-6G+, CD3-NKp46+CD11b+), the presence of Fc receptor and OX40 on the surface of these cells, and the number of neutrophils activated to NETosis were analyzed. Based on the analysis results, the antitumor efficacy of various modes of combining the Karanahan technology and in situ vaccination was studied.

RESULTS: When αOX40 was administered 5 h after each treatment using the Karanahan technology, mass death of mice caused by systemic inflammation and multiple organ failure was observed. The state of blood cells after the treatment using the Karanahan technology at the time points corresponding to antibody injections was analyzed to elucidate the reasons for this effect. It was found that at some time points, there occurs activation of the immune system and a powerful release (up to 16%) of monocytes and granulocytes carrying Fc receptor and OX40 on their surface into blood; when interacting with αOX40, they can activate the lytic potential of these cells. Activation of neutrophils to NETosis was also observed. Based on these findings, a study was carried out in different time regimes to combine the Karanahan technology and αOX40 injections. When αOX40 was injected into the points of minimal release of myeloid cells into the blood, increased survival rate and the greatest antitumor efficacy were observed: 37% of animals survived without relapses on day 100 after experiment initiation. Conclusions: The results obtained indicate that it is possible to combine the Karanahan technology and in situ vaccination with αOX40, with obligatory constant monitoring of the number of myeloid cells in peripheral blood to determine the safe time for antibody injection.

PMID:40296901 | PMC:PMC12034020 | DOI:10.32604/or.2025.059411

Front Toxicol. 2025 Apr 14;7:1605402. doi: 10.3389/ftox.2025.1605402. eCollection 2025.

NO ABSTRACT

PMID:40296895 | PMC:PMC12034682 | DOI:10.3389/ftox.2025.1605402

J Agric Food Chem. 2025 Apr 29. doi: 10.1021/acs.jafc.5c02722. Online ahead of print.

ABSTRACT

Mycoprotein, a filamentous fungi-based protein substitute for traditional meat products, plays a crucial role in ensuring global food security. While genetic manipulation of mycoprotein-producing fungi holds promise for improving key traits, such as higher protein content and biomass yield, substantial progress has yet to be made. In this study, we investigated the function of genes related to conidiogenesis to identify valuable genetic elements for increasing fungal biomass yields in Fusarium venenatum. In the FvFLBD knockout mutant of F. venenatum, fungal biomass increased compared to the wild-type strain, with conidia formation completely abolished. Nutrient profiling further revealed elevated amino acid content in this mutant, likely due to metabolic changes. Additionally, we observed synergistic effects on biomass yield in the FvFLBD and FvUBQ14 double knockout, suggesting that this approach could serve as a comprehensive strategy for enhancing fungal food production. Our findings provide valuable genetic insights and broaden the horizon of mycoprotein applications in the food industry.

PMID:40296655 | DOI:10.1021/acs.jafc.5c02722

J Cheminform. 2025 Apr 28;17(1):62. doi: 10.1186/s13321-025-01009-0.

ABSTRACT

With over 3000 representatives, the monoterpene indole alkaloids (MIAs) class is among the most diverse families of plant natural products. The MS/MS spectral space exploration of these complex compounds using chemoinformatic and computational mass spectrometry tools offers a valuable opportunity to extract and share chemical insights from this emblematic family of natural products (NPs). In this work, we first present a substantially updated version of the MIADB, a database now containing 422 MS/MS spectra of MIAs that has been uploaded to the GNPS library versus 172 initial entries. We then introduce an innovative workflow that leverages hundreds of fragmentation spectra to support the FAIRification, extraction and dissemination of chemical knowledge. This workflow aims at the extraction of spectral patterns matching finely defined MIA skeletons. These extracted signatures can then be queried against complex biological extract datasets using MassQL. By applying this strategy to an LC-MS/MS dataset of 75 plant extracts, our results demonstrated the efficiency of this approach in identifying the diversity of MIA skeletons present in the analyzed samples. Additionally, our work enabled the digitization of structural data for diverse MIA skeletons by converting them into machine-readable formats and thereby enhancing their dissemination for the scientific community.Scientific contribution A comprehensive investigation of the monoterpene indole alkaloid chemical space, aiming to highlight skeleton-dependent fragmentation similarity trends and to generate valuable spectrometric signatures that could be used as queries.

PMID:40296170 | DOI:10.1186/s13321-025-01009-0

Microbiome. 2025 Apr 28;13(1):106. doi: 10.1186/s40168-025-02105-x.

ABSTRACT

BACKGROUND: Microbial taxonomic diversity declines with increased environmental stress. Yet, few studies have explored whether phylogenetic and functional diversities track taxonomic diversity along the stress gradient. Here, we investigated microbial communities within an aquifer in Oak Ridge, Tennessee, USA, which is characterized by a broad spectrum of stressors, including extremely high levels of nitrate, heavy metals like cadmium and chromium, radionuclides such as uranium, and extremely low pH (< 3).

RESULTS: Both taxonomic and phylogenetic α-diversities were reduced in the most impacted wells, while the decline in functional α-diversity was modest and statistically insignificant, indicating a more robust buffering capacity to environmental stress. Differences in functional gene composition (i.e., functional β-diversity) were pronounced in highly contaminated wells, while convergent functional gene composition was observed in uncontaminated wells. The relative abundances of most carbon degradation genes were decreased in contaminated wells, but genes associated with denitrification, adenylylsulfate reduction, and sulfite reduction were increased. Compared to taxonomic and phylogenetic compositions, environmental variables played a more significant role in shaping functional gene composition, suggesting that niche selection could be more closely related to microbial functionality than taxonomy.

CONCLUSIONS: Overall, we demonstrated that despite a reduced taxonomic α-diversity, microbial communities under stress maintained functionality underpinned by environmental selection. Video Abstract.

PMID:40296156 | DOI:10.1186/s40168-025-02105-x