Zhenjiang Zech Xu

The human microbiome is inherently structured by phylogeny, yet most predictive models treat microbial taxa as independent features, thereby underusing evolutionary information that may improve disease classification. While recent deep learning approaches have attempted to incorporate phylogeny, they generally rely on projecting phylogenetic trees into Euclidean spaces, which can distort the intrinsic topology of evolutionary relationships. To address this limitation, we propose PhyloGCNE, a framework that models microbiome samples directly as graphs and employs edge-aware graph convolution to integrate phylogeny. Unlike previous methods that rely on fixed, distance-based aggregation, PhyloGCNE learns how phylogeny-informed edge attributes should influence signal propagation across evolutionary hierarchies. We further introduce a Phylogenetic Saliency Propagation (PSP) framework for model interpretation, which attributes importance scores to microbial taxa by integrating gradient sensitivity with evolutionary context. Benchmarked against one synthetic and eight real-world data sets spanning inflammatory bowel disease, colorectal cancer, type 2 diabetes, oral squamous cell carcinoma, gastric cancer, and dietary fiber intervention, PhyloGCNE consistently outperforms existing state-of-the-art approaches. Together, these results establish PhyloGCNE as an accurate and interpretable phylogeny-aware framework for microbiome-based host phenotype prediction. IMPORTANCE: The human microbiome is a complex ecosystem closely linked to physiological health, yet traditional analysis often treats microbes as isolated features, ignoring their shared evolutionary history. This study introduces PhyloGCNE, a novel framework that integrates the evolutionary tree directly into the analysis of microbiome data. By modeling microbial communities as interconnected networks rather than independent entities, this approach captures shared biological traits across related lineages. We demonstrate that this method significantly improves the accuracy of predicting host phenotypes, such as inflammatory bowel disease and colorectal cancer. Crucially, unlike many “black box” artificial intelligence models, this tool identifies specific, biologically relevant microbial signatures driving these predictions. This advancement provides a powerful, interpretable approach for deciphering the complex links between the human microbiome and host phenotypes.

Background: Bacterial transcription termination is a critical yet underexplored layer of gene regulation in microbial ecosystems. Existing computational tools, however, primarily focus on predicting transcript 3′ ends generated by Rho-independent terminators (RITs) in a few model species, leaving gaps in understanding those generated by Rho-dependent terminators (RDTs) and their diversity across Bacteria. Results: We developed BATTER (Bacteria Transcript Three Prime End Recognizer), a deep learning-based framework for predicting bacterial transcript 3′ termini. BATTER leverages the observation that conserved stem-loop structures are frequently associated with 3′ ends of primary transcripts terminated by both RIT and RDT mechanisms across diverse bacterial clades. Compared with existing approaches, BATTER demonstrated superior performance and scalability, enabling a comprehensive analysis of 42,905 representative bacterial genomes. This large-scale application revealed that stem-loop structures exhibit clade-specific properties with greater variations between species than between gene families. Notably, BATTER uncovered that certain Cyanobacteria lineages, despite lacking rho homologs, harbor Rho utilization (RUT)-like sequences near 3′ ends, and preliminary experimental validation in E. coli supports their partial functionality in transcription termination. Additionally, BATTER systematically identified pervasive premature termination events in antimicrobial resistance (AMR) genes. Conclusions: BATTER enables large-scale comparative genomic analyses of transcription termination, providing a powerful framework to investigate termination-associated transcriptional regulation in microbial communities. The BATTER tool is available at https://github.com/xu-research-lab/BATTER .

Background: Porphyromonas gingivalis (Pg), a key pathogen in periodontitis, is implicated in various systemic diseases such as pancreatic cancer and Alzheimer’s disease. However, as a periodontal pathogen that can directly enter the lower gastrointestinal tract via saliva, its potential impact on the gut microbiome, intestinal inflammation, and its underlying mechanisms remains largely elusive. Results: Here, we observed that oral administration of Pg exacerbates intestinal inflammation in mice by inducing gut microbiome dysbiosis, increasing Th17 cells and the release of pro-inflammatory cytokines. Inhibition of Th17 activity with GSK805 or an anti-IL-17A blocking antibody mitigated this inflammatory response, highlighting the mediating role of Th17 cells. Gingipains, the virulence factors of Pg, played a crucial role in this process. Sequential knockout of gingipain genes revealed a gradual reduction in inflammatory phenotypes, with statistically significant alleviation observed when all three gingipain genes were deleted. Co-housing experiments showed that gut microbiota remodeling effectively protected against Th17-driven inflammatory response. Furthermore, immunization with inactivated Pg effectively prevented gut microbiome dysbiosis and Th17 cell-mediated inflammation. Conclusion: Our findings suggest that Pg may exacerbate intestinal inflammation, potentially via its gingipain virulence proteases, which are linked to gut microbiota dysbiosis and enhanced Th17-mediated immune responses. These results suggest that gingipains could be promising targets for further investigation in Pg-associated intestinal disorders.

Microbial degradation of organic matter is a fundamental Earth process, yet a mechanistic understanding of microbial metabolisms and successional ecology involved in decomposition remains poorly understood. Here, we announce the recovery of 277 cadaver-associated soil metagenome-assembled genomes to enhance our understanding of vertebrate decomposition microbial processes.

Species-level quantitative PCR (qPCR) provides in-depth knowledge of oral probiotics in the human gastrointestinal tract (GIT). However, it lacks the capability to differentiate exogenous strains from native microbiota, nor can it distinguish between live and dead bacteria. In this study, we employed improved propidium monoazide (PMAxx)-qPCR to evaluate the survival and colonization of Bifidobacterium longum subsp . longum ZS-8 (designated ZS-8) on the strain level in the GIT and its impact on human gut microbiota. By spiking in live and dead ZS-8, we demonstrated that strain-level PMAxx-qPCR could identify and quantify the viable ZS-8 in fecal samples accurately. Using this method, we found that, in healthy humans, oral administration of ZS-8 can transiently survive in the GIT, and multi-layer seamless capsules (MLSC) significantly improve the gastrointestinal tolerance and survivability of ZS-8 compared to its powder form. Furthermore, through selective cultivation and PMAxx-microbiome sequencing, we investigated the response of gut viable microbiome to ZS-8. Results showed that, while the microbiota diversity and total viable counts of Bifidobacterium and Lactobacillus remained stable, certain indigenous species of Bifidobacterium and Lactobacillus increased in abundance, confirming ZS-8's probiotic potential in healthy individuals. Overall, our study demonstrates the effectiveness of combining strain-specific comparative genomics with PMAxx-qPCR for evaluating probiotic survival and colonization in the human gut and highlights the safety of ZS-8 oral administration in healthy individuals. IMPORTANCE: The survival and colonization of probiotics in the gut are critical for their functional efficacy, yet conventional species-level quantitative PCR (qPCR) fails to distinguish exogenous strains from native microbiota or differentiate live from dead bacteria. By integrating strain-specific comparative genomics with propidium monoazide (PMAxx)-qPCR, we precisely quantified the viability of Bifidobacterium longum ZS-8 at the strain level in the human gut after its oral administration. Our study demonstrated that 1.53–6.90% of cells surviving transit and multi-layer seamless capsules (MLSC) significantly enhanced the gastrointestinal tolerance of ZS-8. While ZS-8 administration did not alter gut microbiota diversity or total viable counts of Bifidobacterium and Lactobacillus , it selectively increased the abundance of specific indigenous beneficial species. This method overcomes the dual limitations of traditional techniques (strain-level specificity and viability discrimination), providing a robust tool for probiotic research. Furthermore, our findings confirm the safety of ZS-8 in healthy individuals and its potential to modulate gut ecology, offering a scientific foundation for personalized probiotic development and clinical translation.