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PubMed2026

Intratumoral Mycobacterium abscessus promotes cytidine deaminase mutagenesis in non-small cell lung cancer.

The intratumoral microbiota is increasingly recognized as an active component of the tumor microenvironment, yet whether it directly drives tumor mutagenesis remains unclear. Here, integrated multi-omics analysis of human non-small cell lung cancer (NSCLC) identifies Mycobacterium abscessus as a microbial determinant of APOBEC3A-associated mutagenesis. Mechanistically, the bacterial effector nucleoside diphosphate kinase (NDK) directly targets the host transcription factor IRF3 and installs a non-canonical 1-phosphohistidine modification at H263, thereby amplifying type I interferon signaling and sustaining APOBEC3A expression. This inter-kingdom phosphotransfer event links intratumoral microbial colonization to an endogenous mutational process that promotes genomic diversification. Genetic inactivation of NDK, or pharmacologic elimination using an engineered NDK-PROTAC, suppresses APOBEC3A activation and attenuates microbe driven mutagenesis. Together, these findings establish a direct microbial effector mechanism that promotes APOBEC3A-associated mutagenesis and provide a therapeutic framework to intercept microbiome driven mutagenesis in NSCLC.

Humans↗

PubMed2026

The metabolic and anatomical complexity of root microhabitats modulate their interaction with the microbiota.

Plant roots constantly communicate with their microbiota, adapting their anatomy to facilitate microbial colonisation under abiotic stresses. Microbes, in turn, can reshape root anatomy once they establish. However, the mechanisms that coordinate this interplay remain largely unknown. Working with the aquatic plant family Lemnaceae, we reveal that the inherent complexity of root anatomy determines root plasticity in response to microbial colonisation. This microbiota-driven anatomical plasticity enhances plant survival in nutrient-competitive environments. By combining synthetic root models with real roots, we also find that anatomical plasticity is associated with metabolic reprogramming during microbial establishment. Moreover, we identify a plant metabolite, N6,N6,N6-Trimethyl-L-lysine, that regulates anatomical plasticity in response to microbial colonisation. Our work generalizes the importance of microhabitat complexity for microbiome recruitment under challenging environmental conditions.

Plant Roots↗

PubMed2026

Proteo-metabolomic integration identifies stage-specific candidate biomarkers for Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder with a prolonged prodromal phase and complex motor symptoms. Despite improved clinical criteria, early diagnosis and longitudinal monitoring remain challenging. While cerebrospinal fluid (CSF) and plasma metabolites and proteins show biomarker potential, their utility in predictive models is insufficiently characterized. We employed a secondary computational approach to integrate proteometabolomic profiles from CSF and plasma samples of >1100 Parkinson's Progression Markers Initiative (PPMI) participants. Using multi-omics machine learning, we identified biofluid-specific signatures and evaluated predictive performance. Twenty-one biomarker candidates were validated across three models (SVM, GLMNET, RF); SVM and GLMNET achieved the highest recall (83-86%) and AUCs of 0.84-0.89. Longitudinal mixed-effects modeling revealed eight candidates associated with progression across diagnostic stages. We identified a three-part molecular framework characterizing neurodegeneration: a diagnostic subpanel reflecting early microbiome dysregulation (secretory granins and metabolites) and synaptic breakdown; a second subpanel monitoring phenoconversion via neurogenesis precursors and extracellular matrix proteins; and a third subpanel tracking progression through chronic neuroinflammation and immune activation. This integrated multi-omics approach provides a robust framework for stage-specific PD monitoring and potential clinical deployment.

Journal Article↗

PubMed2026

Adaptive evolution of polyploid crops.

Crop evolution represents a fundamental biological process through which plants respond to selection in different environments. This encompasses mechanisms operating at multiple scales of biological organization, including genetic and epigenetic regulation and higher-order interactions among molecular complexes. This Review synthesizes how polyploidy shapes crop evolution by generating duplicated genes, driving genome reorganization, altering dosage relationships and promoting regulatory divergence, which together influence crop metabolism, physiology, development and environmental responses. We focus mainly on the mechanisms underlying adaptation in polyploid crops, including the consequences of gene and genome duplication, genome reorganization and subfunctionalization. We also examine how hybridization, phenotypic plasticity and crop-microbiome interactions intersect with polyploidy to expand or constrain adaptive potential. Together, these processes affect crop survival, fitness and breeding value under changing environments. We suggest that future research connect polyploid genome architecture with experimentally validated signatures of selection and field performance to make better use of polyploidy-derived variation in crop improvement.

Polyploidy↗

PubMed2025

De novo discovery of conserved gene clusters in microbial genomes with Spacedust.

Metagenomics has revolutionized environmental and human-associated microbiome studies. However, the limited fraction of proteins with known biological processes and molecular functions presents a major bottleneck. In prokaryotes and viruses, evolution favors keeping genes participating in the same biological processes colocalized as conserved gene clusters. Conversely, conservation of gene neighborhood indicates functional association. Here we present Spacedust, a tool for systematic, de novo discovery of conserved gene clusters. To find homologous protein matches, Spacedust uses fast and sensitive structure comparison with Foldseek. Partially conserved clusters are detected using novel clustering and order conservation P values. We demonstrate Spacedust's sensitivity with an all-versus-all analysis of 1,308 bacterial genomes, identifying 72,843 conserved gene clusters containing 58% of the 4.2 million genes. It recovered 95% of antiviral defense system clusters annotated by the specialized tool PADLOC. Spacedust's high sensitivity and speed will facilitate the annotation of large numbers of sequenced bacterial, archaeal and viral genomes.

Metagenomics↗

PubMed2025

Microbial genomic database of the Yangtze River, the third-longest river on Earth.

Microbes play an important role in mediating the nutrient cycling in the river ecosystem as a hotspot for biogeochemical processes. Due to scattered sampling efforts, however, there is a lack of a systematic study of the diversity of prokaryotic genomes in the Yangtze River, the third longest river on Earth. Here, we collected 602 metagenomic datasets of water, sediment and riparian soil samples spanning the Upper, Middle, and Lower basins of the Yangtze River over a 6,300 km continuum. We reconstructed 8,110 qualified genomes represented by 927 species-level genomes at the 95% ANI threshold, spanning 31 bacterial and five archaeal phyla. We further showed that more than half of these species (61.3% ~ 82.4%) were novel according to the genomic comparison against the curated databases, greatly expanding the known diversity of river prokaryotes. This dataset depicts an overview of microbial genomic diversity in the Yangtze River and provides a resource for in-depth investigation of metabolic potential, ecology, and evolution of riverine microbiomes.

Rivers↗

PubMed2025

Metagenomic and Transcriptomic Datasets of Plateau Brown Frogs (Rana kukunoris) from the Helan Mountains.

Global climate change has become a primary driving factor behind the biodiversity crisis in amphibians, making it crucial to understand how climate change affects species and their potential responses. The plateau brown frog (Rana kukunoris) is often regarded as an ideal ecological indicator species, yet research on its environmental adaptation mechanisms based on transcriptomic and microbiomic studies remains limited. Therefore, this study investigates the adaptation strategies of the plateau brown frog to environmental changes, providing extensive transcriptomic and the first comprehensive metagenomic dataset from two distinctly different environmental regions (eastern and western slopes of the Helan Mountains). We gathered transcriptomic data from three tissues (blood, liver, and muscle), resulting in 294,962 unigenes and 570,192 transcripts. Metagenomic sequencing identified major bacterial groups, including Firmicutes, Proteobacteria, Bacteroidetes, Spirochetes, and Actinobacteria. In summary, the results of this study can be used to further explore the associations among microbiota, host, and environment, which are crucial for comprehending the mechanisms of environmental adaptation in this species and contributing to the conservation of amphibian biodiversity.

Animals↗

PubMed2026

META-DIFF: a k-mer-based pipeline that detects differentially abundant sequences in metagenomics whole genome sequencing.

Traditional case-control metagenomic studies are constrained by their dependence on taxonomic and functional databases. Because annotation occurs before differential analysis, they are limited to known elements and keep function and taxonomy separate. Although binning strategies have emerged to reconstruct genomes and mitigate this issue, they still require an assembly step, preventing the use of all available sequencing data. Here, we introduce META-DIFF, a pipeline based on differentially abundant k-mers independently of any prior annotation. From those k-mers, it reconstructs longer sequences and provides biological context, as well as the best set of unitigs to discriminate between conditions. Across both taxonomy-centric and functionally-centric benchmarks, it showed robust performance and displayed great reproducibility. It also behaved more conservatively than did other univariate methodologies, i.e. it maintained a high precision at the expense of recall, particularly in conditions of low fold-change and limited sequencing depth. The efficacy of META-DIFF was further validated through its application to a real-world colorectal cancer dataset, which produced both confirmatory and novel results compared with those of previous publications. The pipeline is able to exploit all reads and identify differentially abundant elements, including unknown DNA, prior to annotation. With the guidelines provided, META-DIFF provides users with great exploratory power to unravel microbiome changes.

Metagenomics↗

PubMed2002

The host-microbe interface within the gut.

Colonization with bacteria is critical for the normal structural and functional development and optimal function of the mucosal immune system. Unrestrained mucosal immune activation in response to bacterial signals from the lumen is, however, a risk factor for inflammatory bowel disease. Therefore, mucosal immune responses to indigenous flora require precise control and an immunosensory capacity for distinguishing commensals from pathogens. The use of germ-free animal models with selective colonization strategies combined with modern molecular techniques promises to clarify the molecular signals responsible for host-flora interactions in health and disease. At least half of the resident flora cannot be cultured by conventional techniques but are identifiable by molecular methods. Collectively, the resident flora represent a virtual organ with a metabolic activity in excess of the liver and a microbiome in excess of the human genome. An improved understanding of this hidden organ holds secrets relevant to several infectious, inflammatory and neoplastic disease mechanisms.

Animals↗

PubMed2026

Parental niche construction buffers microbial and competitive challenges and drives offspring dependence in burying beetles.

Parents across diverse taxa modify the biotic or abiotic environments of their offspring. Such modifications may constitute ecological inheritance and are central to developmental niche construction, whereby organisms shape developmental conditions and selective pressures experienced by the next generation. Despite its theoretical importance, parental niche construction is often studied under simplified conditions or by focusing on single components of care, limiting our understanding of how multiple parental modifications interact in ecologically relevant contexts, whether they buffer environmental heterogeneity, and how this shapes offspring development and evolutionary trajectories. Using the burying beetle Nicrophorus vespilloides, we investigated how parents jointly modify chemical and microbial properties of vertebrate carcasses, a highly contested resource on which offspring develop. We show that under natural microbial and competitive conditions, prehatch care enhances larval survival and growth, alters cadaveric volatile emissions, and reduces carcass attractiveness to competitors. While soil type shapes carcass-associated microbial communities, parental care buffers these environmental effects, creating a more consistent microbiome and reducing environmentally induced larval mortality. Larvae of the related species Ptomascopus morio, which lacks prehatch carcass preparation, survived equally well on prepared and unmodified carcasses, whereas N. vespilloides larvae showed reduced survival on unmodified carcasses. This contrast is consistent with the hypothesis that N. vespilloides larvae have evolved a reliance on a parentally constructed developmental environment. Together, these findings show that parental care can constitute an integrated form of niche construction that reshapes developmental environments, enhances offspring performance, and may promote evolutionary feedback leading to increased offspring dependence on parental care.

Animals↗

PubMed2026

Lpb. plantarum inhalation powders for the reduction of lung inflammation and S. aureus growth control in non-CF bronchiectasis.

INTRODUCTION: The reduced diversity of the lung microbiome in respiratory conditions, including bronchiectasis, promotes bacterial infection and inflammation, contributing to worsening clinical outcomes. Traditional treatments often fail to address both infection and inflammation. Because of their potential dual-action, lactic acid bacteria (LAB) directly administered to the lungs could represent an innovative therapeutic approach for these diseases. RESEARCH DESIGN AND METHODS: Two powders for inhalation containing Lpb. plantarum, lactose, l-leucine with and without raffinose, a prebiotic, were produced by spray drying and in vitro tested. The focus was on investigating their potential in vitro anti-inflammatory and anti-microbial activities against S. aureus. RESULTS: The powders showed a fine particle fraction (<5 µm) of >40% and allowed the maintenance of anti -inflammatory activity in vitro for both treatment and prevention. Moreover, both powders led to a significant reduction in S. aureus growth. The stability study of the powders in capsules at different storage conditions showed the preservation of the LAB up to 90 days in refrigerated conditions (4°C/-20°C). CONCLUSIONS: This proof-of-concept study shows that inhalable spray-dried live LABs retain biological activity and are suitable for pulmonary delivery, supporting their potential as a microbiota-modulating therapy, which, however, requires further preclinical validation.

Inhalation↗

PubMed2025

Bacteriophages as vaccine platforms: Opportunities and challenges in translation.

Bacteriophages (phages) have recently received increased interest as versatile candidates for vaccine development. Their inherent characteristics, such as ease of genetic manipulation, high-density antigen display, intrinsic immunostimulatory properties, demonstrated human safety, and scalability in bacterial hosts, make them attractive as next-generation vaccine platforms. Additionally, their cost-effective production, stability, and existing regulatory approval for food and compassionate phage therapy provide a strong foundation for further development of phage-based vaccines. This commentary summarizes the types of phages, the strategies used, and current advances in phage-based vaccine development for viral and bacterial targets, and discusses the promises and challenges of this platform for novel vaccine development. Phage-based vaccines represent an innovative and promising platform for vaccine development to address significant medical and public health challenges, particularly in antimicrobial resistance, pandemic preparedness, and One Health. Accumulative experimental data have demonstrated that phage-based vaccines induce specific cellular, humoral, and mucosal immune responses at magnitudes comparable to those induced by other vaccine platforms. However, a better understanding of phage biology (interactions with the human immune system and microbiome), more carefully designed preclinical studies, Good Manufacturing Practice production development, the regulatory framework, and ultimately clinical trials are needed before the full potential of this platform is realized.

Animals↗

PubMed2002

New technologies, human-microbe interactions, and the search for previously unrecognized pathogens.

Evidence suggests that a significant number of clinically important microbial pathogens remain unrecognized. Observations from the natural world, from patterns of disease in human populations, from the bedside, and from the clinical laboratory all contribute to this body of evidence. A variety of acute and chronic neurologic syndromes illustrate this point; despite features of infection, most cases of aseptic meningitis, encephalitis, and cerebral vasculitis cannot be assigned a microbiologic diagnosis. The development and clinical application of molecular methods have led to the discovery of novel members of the endogenous normal flora as well as putative disease agents. Current challenges include the establishment of criteria for disease causation and further characterization of the human microbiome during states of health. These challenges and the goal of understanding microbial contributions to inflammatory disease may be addressed effectively through the thoughtful integration of modern technologies and clinical insight.

Bacteria↗

PubMed2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing.

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Deep Learning↗

PubMed2025

PLSKO: a robust knockoff generator to control false discovery rate in omics variable selection.

MOTIVATION: Integrating the knockoff framework with any variable-selection method delivers stringent false discovery rate (FDR) control without recourse to p-values, offering a powerful alternative for differential expression analysis of high-throughput omics datasets. However, existing knockoff generators rely on restrictive modelling assumptions or coarse approximations that often inflate the FDR when applied to real-world data. RESULTS: We introduce Partial Least Squares Knockoff (PLSKO), an efficient, assumption-free generator that remains robust across diverse omics platforms. Our extensive simulations show that PLSKO is the only method to maintain FDR control with sufficient power in complex non-linear settings. Our semi-simulation studies drawn from RNA-seq, proteomics, metabolomics, and microbiome experiments confirm PLSKO generates valid knockoff variables. In pre-eclampsia multi-omics case studies, we combine PLSKO with Aggregation Knockoff to address the randomness of knockoffs and improve power, and demonstrate the method's ability to recover biologically meaningful features. AVAILABILITY AND IMPLEMENTATION: Our proposed algorithm is available on Github (https://github.com/guannan-yang/PLSKO) and Zenodo (https://doi.org/10.5281/zenodo.16879594).

Algorithms↗

PubMed2025

Enhancing genome recovery across metagenomic samples using MAGmax.

SUMMARY: The number of metagenome-assembled genomes (MAGs) is rapidly increasing with the growing scale of metagenomic studies, driving fast progress in microbiome research. Sample-wise assembly has become the standard due to its computational efficiency and strain-level resolution. It requires dereplication, the removal of near-identical genomes assembled in different metagenomic samples. We present MAGmax, an efficient dereplication tool that enhances both the quantity and quality of MAGs through a strategy of bin merging and reassembly. Unlike dRep, which selects a single representative bin per genome cluster, MAGmax merges multiple bins within a cluster and reassembles them to increase coverage. MAGmax produces more dereplicated, higher-quality MAGs than dRep at 1.6× its speed and using three times less memory. AVAILABILITY AND IMPLEMENTATION: The MAGmax open source software, implemented in Rust, is available under the GPLv3 license at https://github.com/soedinglab/MAGmax.

Metagenomics↗

PubMed2025

SPARKI: a tool for the statistical analysis of pathogen identification results.

MOTIVATION: Many pathogen identification and microbiome analysis tools have been developed in recent years, with Kraken 2 being one of the most popular. While tools downstream of Kraken 2 can assist in the interpretation of its outputs, a statistical framework to assess the likelihood that a taxon/organism is present in a single sample alongside an automated end-to-end analysis pipeline has not yet been fully implemented. RESULTS: Here, we introduce SPARKI, an R package that performs statistical analysis of Kraken 2 outputs and aids in the identification of pathogens present in next-generation sequencing samples. SPARKI adds to the field by bringing a probabilistic view to Kraken 2 data, serving as a discovery tool and complementing other methods such as KrakenTools, Bracken, and Pavian. AVAILABILITY AND IMPLEMENTATION: SPARKI code is available on GitHub at https://github.com/team113sanger/sparki. SPARKI is also part of an end-to-end pathogen identification pipeline, sparki-nf, which is available at https://github.com/team113sanger/sparki-nf. An additional pipeline for further exploration and validation of SPARKI results is also available at https://github.com/team113sanger/map-to-genome.

Software↗

PubMed2025

HoloFoodR: a statistical programming framework for holo-omics data integration workflows.

SUMMARY: Holo-omics is an emerging research area that integrates multi-omic datasets from the host organism and its microbiome to study their interactions. Recently, curated and openly accessible holo-omic databases have been developed. The HoloFood database, for instance, provides nearly 10 000 holo-omic profiles for salmon and chicken under controlled treatments. However, bridging the gap between holo-omic data resources and algorithmic frameworks remains a challenge. Combining the latest advances in statistical programming with curated holo-omic data sets can facilitate the design of open and reproducible research workflows in the emerging field of holo-omics. AVAILABILITY AND IMPLEMENTATION: HoloFoodR R/Bioconductor package and the source code are available under the open-source Artistic License 2.0 at the package homepage https://doi.org/10.18129/B9.bioc.HoloFoodR.

Software↗