Biology subjects

Tian, F.

Publications and source records attributed to Tian, F..

At least 19 recordsLinked to original sources

bioRxiv2026

PatientPheX: A Full-Text Biomedical Corpus for Patient-Specific Phenotype Information Extraction

Patient-specific phenotype information extraction identifiers and normalizes phenotype entities and links them with individual patients, supporting rare disease research and genotype-phenotype analysis. However, existing datasets rarely combine full-text biomedical content with detailed annotations of patients, phenotypes, related entities, and their associations. In this work, we present PatientPheX, a corpus for patient-specific phenotype information extraction comprising 200 full-text articles, primarily case reports and case series. The corpus provides mention annotations and normalization for seven biomedical entity types, together with explicit associations between patients and entities across sentences, paragraphs, and sections. Negated findings and composite phenotype mentions are also annotated. Corpus construction combines automated pre-annotation, large language model (LLM)-assisted verification, and manual review and adjudication. Benchmarks cover entity recognition and normalization and patient-phenotype association extraction using gold-standard and predicted entities. The overall document-level entity F1 was 86.95%, while phenotype F1 was 79.33%. The best relation extraction macro-F1 decreased from 84.14% with gold-standard entities to 54.98% with predicted entities, highlighting the combined challenges of phenotype extraction and patient attribution. PatientPheX provides a resource for developing and evaluating patient-centered information extraction methods and supports the construction of structured patient-level knowledge resources for phenotype-driven biomedical research.

bioinformatics↗

bioRxiv2026

SOLAR Mediates Lysosomal Membrane Repair and Redox Defense

Lysosomal damage is increasingly recognized as a hallmark of aging. Understanding lysosomal quality control may open new therapeutic strategies to enhance lysosomal resilience. Here, we identify SOLAR (SQSTM1/p62 Oligomer-mediated Lysosomal Antioxidant Defense And Membrane Repair), a lysosomal quality control pathway that links CASM (conjugation of ATG8 to single membranes) to p62-driven membrane repair and redox signaling. In this pathway, CASM, but not macroautophagy, recruited p62 to damaged lysosomes. Efficient lysosomal recruitment of p62 required its ATG8-binding and self-oligomerizing domains, enabling p62 assemblies to promote membrane repair. p62 mutations associated with neurodegeneration interfered with p62 recruitment and compromised lysosomal repair. Besides membrane repair, the SOLAR pathway also activated the p62-KEAP1-NRF2 redox signaling axis, driving transcriptional upregulation of antioxidative genes and cholesterol biosynthesis. By integrating membrane repair and redox defense, SOLAR establishes a coordinated lysosomal quality control pathway with implications for aging and degenerative disease.

cell biology↗

bioRxiv2026

The kinetochore proteins Ndc80 and Dsn1 are required for survival of postmitotic neurons

Dsn1 and Ndc80 are essential proteins of the kinetochore complex and required for chromosome segregation in dividing cells and for regulating development and microtubule dynamics in postmitotic neurons. With conditional deletion of floxed alleles, we here show that Dsn1 and Ndc80 are also required for the viability of postmitotic neurons, both in cultures of hippocampal and cortical neurons and in vivo in the retina. Loss of these proteins triggers apoptosis, as indicated by caspase cleavage and an increase in nuclear DNA breakage. The pro-survival function of the kinetochore components is distinct from that which was previously demonstrated for regulation of neuronal synaptogenesis. The microtubule-binding domain of Ndc80 is required for the synaptogenic functions but Ndc80 lacking this domain can nonetheless rescue the viability of neurons from which Ndc80 has been deleted. Similarly, whereas the synaptogenic function involves regulation of microtubules in axons and dendrites, a nucleus-localized Dsn1 is sufficient to rescue the viability of neurons from which Dsn1 has been deleted. Thus, postmitotic neurons retain a nuclear requirement for components of the kinetochore in order to prevent apoptosis.

neuroscience↗

bioRxiv2026

Widespread use of invalid statistical tests in biomedical machine learning

Cross-validation is routinely used to compare performance in biomedical artificial intelligence. Standard tests ignore correlation across cross-validation folds, inflating false-positive rates. In a PRISMA-guided meta-analysis of 184 studies (impact factor [≥] 15) across 30 biomedical fields, 97% use invalid tests. Among studies with abstract-level claims supported by invalid tests, 59% rely on a spurious comparison: one that loses significance after correcting for this correlation. On average, regaining significance requires a 43% larger effect. Extending these findings to the broader literature, we estimate that spurious comparisons occur in nearly two in three studies and support abstract-level claims in nearly one in three studies. Simulations confirm false-positive rates paradoxically approach 100% when cross-validation is repeated to improve stability. We introduce SHARP, a redesign of cross-validation, which best balances false-positive control and power among 13 benchmarked tests. These results reveal widespread fragility in biomedical artificial intelligence and offer a practical route to valid comparisons.

bioinformatics↗

bioRxiv2026

Forest expansion and open vegetation responses during the past 14 ka at Zhagaer Co on the eastern Tibetan Plateau

Forest expansion and retreat are key drivers of alpine ecosystem dynamics, yet it remains unclear how forest expansion shapes alpine plant communities and whether alpine assemblages that re-emerge after forest decline resemble those of the Late Glacial. Here, we integrate sedimentary ancient DNA (sedaDNA) and pollen records from Zhagaer Co on the eastern Tibetan Plateau to reconstruct vegetation changes over the past [~]14 ka. Using Picea abundance as a proxy for forest cover, we apply constrained ordination of sedaDNA-inferred plant communities to identify plant taxa as "winners" and "losers" of forest expansion and compare the composition of loser taxa between the late Glacial and late Holocene cold-open phases. Our results show that forest expansion during the early to mid-Holocene favoured woody and forest-margin taxa (e.g. Rhododendron, Salicaceae), while suppressing alpine forbs and graminoids (e.g. Carex, Thalictrum), consistent with patterns expected under ecological filtering. However, late Holocene reopening did not fully restore a late Glacial-like alpine community; instead, it was characterised by a stronger contribution of alpine meadow and shrub taxa. This difference may reflect contrasting environmental backgrounds, including higher atmospheric CO2 levels during the late Holocene, ecological legacies of prior forest expansion, and increasing human influence. These findings suggest that forest expansion may lead to long-term restructuring of alpine plant assemblages, and that late Holocene cooling did not simply restore late Glacial alpine communities but instead produced a distinct alpine ecosystem state. Together, these results highlight the long-term legacy of treeline dynamics in shaping alpine ecosystem trajectories.

ecology↗

bioRxiv2025

Structural semantic evolutionary distance (SSED) unifies the selection of cancer driver genes across macroevolution and tumorigenesis.

The non-random, site-specific enrichment of somatic mutations in cancer driver genes (CDGs) suggests their emergence is governed by underlying evolutionary constraints. However, quantitative methods to define these constraints and their underlying principles remain underexplored. To address this, we introduce the Structural Semantic Evolutionary Distance (SSED), a metric leveraging the pretrained ESM-3 protein language model to quantify evolutionary divergence within a unified structural semantic space. Our analysis demonstrates that CDGs are subject to persistent structural semantic constraints across species, tolerating a significantly narrower range of structural semantic changes during evolution compared to non-CDGs. Crucially, clinically observed oncogenic mutations follow this same principle, favoring minimal structural perturbation as shaped by long-term gene evolution. Such mutations maintain core protein function while conferring a capacity for immune evasion, thereby driving clonal expansion. Guided by this "evolutionary constraint" framework, we successfully predicted and experimentally validated a previously uncharacterized oncogenic mutation, KRAS R135L, in bronchial epithelial cells. Furthermore, clinical cohort analysis demonstrated that SSED acts as an independent predictor of response to immune checkpoint blockade, offering information orthogonal to tumor mutational burden (TMB). This study unifies the evolutionary principles governing CDGs across macroevolutionary and microevolutionary (tumorigenesis) timescales, elucidates the balance between structural adaptability, functional conservation, and immune pressure, and identifies a novel predictive biomarker for cancer immunotherapy.

genetics↗

bioRxiv2025

Bio-synthesis of 15N-Labeled G-Quadruplexes to Investigate the Structure and Interactions in the Cell Lysate using Nuclear Magnetic Resonance

G-quadruplexes (G4) play key roles in biology, making it critical to understand their structure and ligand-binding behavior in cellular environments for advancing G4-targeted therapeutics. While in-cell nuclear magnetic resonance (NMR) is a powerful technique for studying G4 in situ, its application is limited by the challenge of producing isotope-labeled single-stranded DNA (ssDNA). Here, we introduce Restriction Endonuclease Digestion (RED), a simple and cost-effective method to generate 15N-labeled ssDNA. This approach combines molecular cloning and enzymatic design processing by propagating plasmids in E. coli cultured with 15NH4Cl, followed by double restriction digestion and isolation target 15N-ssDNA. Using RED, we produced milligram-scale quantities of 96%-enriched 15N-labeled human telomeric G4 ssDNA (wtTel23c, CTAGGG(TTAGGG)3), ideal for NMR analysis. The NMR spectra revealed that wtTel23c adopts G4 topology and undergoes multiple conformations of wtTel23c in potassium-containing solutions and in Xenopus laevis cell lysate. Interaction studies with the ligand TMPyP4 showed distinct binding profiles in cellular and dilute environments. In dilute solution, TMPyP4 binds to the top tetrad of wtTel23c, while it binds to the loop in cellular environments. The RED method offers an efficient strategy for producing stable isotope-labeled ssDNA, opening new avenues for studying G4 structures and their ligand interactions in complex biological contexts.

biochemistry↗

bioRxiv2025

Sub-daily Bermuda Atlantic Time Series virus sampling reveals taxonomy, host, and functional differences at the population, but not community level

Ocean microbes contribute to biogeochemical cycles and ecosystem function, but they do so under top-down pressure imposed by viruses. While viruses are increasingly understood spatially and beginning to be incorporated into predictive modeling, high-frequency ocean virus dynamics remain understudied due to methodological challenges. Here we sampled stratified Bermuda Atlantic Time Series (BATS) waters for 112 hours at sub-daily 4-(surface) or 12-(deep chlorophyll maximum) hour intervals, purified viral particles from these samples, sequenced their metagenomes, and used the resulting data to characterize high-frequency virus community dynamics. Aggregated community diversity metrics changed with depth, but were not statistically significant temporally at a fixed location. However, finer-scale population-level analyses revealed both depth and temporal change, including physicochemical depth-driven differences and, in surface waters, thousands of viral populations that exhibited statistically significant diel rhythms. Statistical analyses revealed three main archetypes of temporal dynamics that themselves differed in abundance patterns, host predictions, viral taxonomy, and gene functions. Among these, highlights include viruses resembling an archetype with a night peaking pattern in activity that include an over-representation of viruses that putatively infect Prochlorococcus, a phototrophic cyanobacteria. Together, these efforts provide baseline community-and population-scale short-time-frame observations relevant to future climate state modeling.

microbiology↗

bioRxiv2025

High glucose levels impair the body's renal protective function during water deprivation by blunting the FXR-TonEBP axis

BackgroundDehydration often leads to kidney-related complications, yet its impact on diabetic patients remains underexplored. This study aimed to investigate the effects of dehydration on diabetic nephropathy(DN) and the underlying mechanisms of injury. MethodsWe analyzed clinical outcomes in 81 dehydration-induced acute kidney injury patients (35 with diabetes) and validated findings in streptozotocin-induced diabetic mice subjected to intermittent water deprivation (WD). Renal function, histopathology, and molecular markers (FXR, TonEBP, AQP2, gp91, apoptosis regulators) were assessed. ResultsKidney injury was aggravated after water deprivation in patients with diabetics and it is difficult to recover after water supplementation, with poor prognosis. Animal experiments have shown that compared with mice in a normal state, dehydration challenge aggravated renal damage in diabetic mice with increased serum creatinine, pathological damage, and renal interstitial cell apoptosis. Mechanistically, in a normal state FXR-TonEBP axis was enhanced to protect the kidney after water deprivation challenge, but this compensatory protective response was blunted in the diabetic state. ConclusionHyperglycemia blunts the FXR-TonEBP-osmolyte pathway during dehydration, compromising renal medullary protection. Targeting this axis may mitigate dehydration risks in diabetic nephropathy.

physiology↗

bioRxiv2025

Optimizing Biophysical Large-Scale Brain Circuit Models With Deep Neural Networks

Biophysical modeling provides mechanistic insights into brain function, spanning single-neuron dynamics to large-scale circuit models. These models are governed by biologically meaningful parameters, many of which can be experimentally measured. Some parameters are unknown, and optimizing them improves fit to experimental data, enhancing biological plausibility. However, existing methods require repeated, computationally expensive numerical integration of differential equations, limiting scalability to population-level datasets. Here, we introduce DELSSOME (DEep Learning for Surrogate Statistics Optimization in MEan field modeling), a framework that bypasses numerical integration by directly predicting whether parameter sets produce realistic brain dynamics. Across three large-scale circuit models, DELSSOME achieves a 1500-8000x speedup over numerical integration in predicting model realism. When embedded within an evolutionary optimization strategy, DELSSOME enables 50-100x faster parameter estimation without sacrificing agreement with numerical integration. Because of computational constraints, most studies simulate large-scale circuit models only at the group level. DELSSOME enables efficient individual-level optimization of the feedback inhibition control model. By collating 12,005 individuals across 14 datasets, we derive - for the first time - normative trajectories of cortical E/I ratio across the lifespan, revealing new insights into sex differences and network-specific patterns. This acceleration enables population-scale mechanistic modeling and unlocks new opportunities for understanding brain function.

neuroscience↗

bioRxiv2025

Bacteriophage protein Dap2 inhibits bacterial type III secretion system and synergizes with Dap1 to evade anti-phage immunity

The evolutionary arms race between bacterial immunity and phages has driven the emergence of sophisticated anti-defense systems (ADSs). However, certain ADSs exhibit incomplete suppression of their cognate defense systems, suggesting functional cooperation between multiple ADSs targeting the same bacterial safeguard. In this study, we characterize Dap2, a protein encoded by a Pseudomonas aeruginosa phage PaoP5, which directly binds to the Lon protease to prevent the degradation of the phage-encoded HNH endonuclease. Deletion of dap2 in PaoP5 exhibits significantly impaired genome packaging due to insufficient levels of HNH. Strikingly, Dap2 synergizes with its genomically adjacent partner Dap1, a previously identified HNH-binding protein providing partial Lon resistance, to achieve complete protection of HNH. Beyond anti-defense activity, Dap2 disrupts host virulence by sequestering the type III secretion system (T3SS) transcriptional activator ExsA, suppressing bacterial pathogenicity while redirecting metabolic resources toward phage progeny production. This study unveils a dual functional ADS that simultaneously modulates bacterial virulence and anti-phage immunity, both aimed at ensuring phage survival and maximizing progeny production. Furthermore, it elucidates a novel mechanism whereby phages employ synergistic ADS pairs (Dap1/Dap2) to achieve complete neutralization of a bacterial defense system, when individual components provide only partial protection. These findings significantly enhance our understanding of the intricate evolutionary arms race between phages and their bacterial hosts.

microbiology↗

bioRxiv2025

Planetary-scale marine community modeling predicts metabolic synergy and viral impacts

The oceans buffer against climate change via biogeochemical cycles underpinned by microbial metabolic activities. While planetary-scale surveys provide baseline microbiome data, inferring metabolic and biogeochemical impacts remains challenging. Genome-scale modeling has addressed analogous issues at the cellular level, highlighting key metabolic reactions contingent upon specific environmental conditions. Here we adapt this mechanistic modeling framework towards analyzing global ocean microbial communities to reveal metabolic processes predicted to maintain ecosystem functioning. To achieve this, we developed a genome-scale superorganism metabolic model for each TARA Ocean metagenome or metatranscriptome (i.e., limited to reactions known from heterotrophic prokaryotes and viruses), and evaluated these models to establish a community-wide metabolic phenotype for each sample. To validate, we showed that even with reaction-mappable genes only ([~]1/4 of the total genes), model composition revealed metabolism-inferred ecological zones that matched taxonomy-inferred zones. Model inferred metabolic phenotypes revealed reaction cooperation associated with microbial metabolism and organism diversity. These phenotypes also suggest elevated ecological roles for viruses as model predictions suggest they genomically target community-critical metabolic reactions that underpin metabolic phenotype stability, and also demonstrate that, as metabolites are better understood, immediate estimates could be made for where viruses remineralize versus sink carbon. While this new constraints-based, agile, and mechanistic modeling framework is highly upgradable, it already begins to convert molecular-scale environmental omics data to ecological and even planetary-scale biogeochemical features that will better bring microbes and their viruses into Earth system and climate models.

systems biology↗

bioRxiv2025

Mobile genetic elements that shape microbial diversity and functions inthawing permafrost soils

The worlds ecosystems are shaped by microbiota. Their niches and their impacts depend on functional profiles influenced by gene gains and losses. While culture-based experiments demonstrate that mobile genetic elements (MGEs) can mediate gene flux, quantitative field data on the rates and impacts of MGE activity remains scarce. Here we leverage large-scale soil meta-omic data to develop and apply analytics for studying MGEs in complex natural systems. In our model permafrost-thaw ecosystem, Stordalen Mire, we identify [~]2.1 million MGE recombinases across 89 microbial phyla to assess ecological distributions, affected functions, past mobility, and current activity. This revealed MGEs shaping natural genetic diversity via differential impacts on major phyla; affecting a wide range of functions, including diverse regulatory and metabolic genes affecting carbon flux and nutrient cycling; and moving at rates that should significantly influence the realized functional profiles of natural microbiomes. These findings and this systematic meta-omic framework open new avenues to better investigate MGE diversity, activity, mobility, and impacts in nature.

microbiology↗

bioRxiv2025

RNA virus ecogenomics along a subarctic permafrost thaw gradient

Climate change thaws permafrost, which releases greenhouse gases partly from dormant microorganisms awakening and metabolizing organic matter. Though DNA viruses that infect these soil microbes have been studied, little is known on soil RNA viruses, which typically infect microeukaryotes. Here we identify and characterize 2,651 RNA viruses from a 4-year time series of bulk soil metatranscriptomes derived from the climatically fragile Stordalen Mire ecosystem --a long studied permafrost peatland. RNA virus diversity was structured by habitat (palsa, bog, and fen), and these patterns correlated with pH and carbon dioxide and methane emissions. Further, host prediction, virus-encoded metabolite-transforming and information-processing functions suggested roles in ecosystem-scale carbon fluxes and contribute to greenhouse gases emissions. Together, these RNA virus ecogenomic data in permafrost provide essential baseline information for integration into predictive models to support hypothesis testing.

microbiology↗

bioRxiv2025

Age-Dependent Patterns of Gut Bifidobacterium Microbiome Diversity across child Cohorts in a Multi-Ethnic population, Western China

High-throughput sequencing has provided unprecedented opportunities to elucidate the role of human gut microbiota. However, current studies have primarily focused on infant and adult cohorts, leaving biomarkers associated with the development of transitional children unclear, particularly in revealing geographic, multi-ethnic group underrepresented. Here, we analyzed fecal samples from 776 infants and children aged 0 to 15 years with amplicon sequencing of the 16S rRNA and groEL gene, representing multi-ethnic groups from different residential locations in the Yining region of Xinjiang. First, we systematically characterized the gut microbiota Spectrum of children in the Ili region. As age increases, the diversity and functional composition of the microbiota become richer and more stable, with Bifidobacterium consistently dominating the community. Utilizing Bayesian regression models, we identified a cluster of microbiota strongly associated with growth and development in children, which phylogenetically corresponds to Bifidobacterium breve. Finally, we included another similar study in the species-level of Bifidobacterium, and metrics of the age-related effect sizes showed heterogeneity across ethnic groups (Uyghur vs Han: {beta}Deming = 4.40 and Kazakh vs Han: {beta}Deming = 4.33). In conclusion, this study provides novel insights into the developmental trajectories of the gut microbiome in children and its relationship with health. IMPORTANCEThe intestinal microbial markers associated with the development of geographically diverse, multi-ethnic children, particularly those represented by Bifidobacterium species, have been elucidated in this study. Through the application of Bayesian regression models, we identified a microbiome profile closely linked to childhood growth and development, specifically phylogenetically aligned with Bifidobacterium breve. When incorporating a comparable study conducted at the species level of another Bifidobacterium, it was observed that age-related effect sizes varied among different ethnic groups, offering novel insights into the developmental trajectory of childrens gut microbiota and its implications for health.

microbiology↗

bioRxiv2025

OPTN protects retinal ganglion cells and ameliorates neuroinflammation in optic neuropathies

Optineurin (OPTN) is a crucial component of the homeostatic pathway, playing a pivotal role in regulating a number of essential signaling pathways including NF-{kappa}B, interferon, autophagy, and vesicular trafficking. The dysfunction of OPTN has been implicated in the pathogenesis of several diseases, such as primary open angle glaucoma (POAG), amyotrophic lateral sclerosis (ALS), frontotemporal lobar dementia, and Pagets disease of bone. Interestingly, mutations in OPTN are implicated as gain-of-function in glaucoma pathology and loss-of-function in ALS. However, the role of loss-of-function OPTN in glaucoma pathology remains unclear. Here, we demonstrate that OPTN dysfunction contributes to chronic neuroinflammation, leading to sustained RGC death, which may represent a shared pathological mechanism in both normal tension glaucoma (NTG) and high-tension glaucoma (HTG). Retinal conditioned OPTN knockout contributes to short-term astrogliosis and long-term microglia activation, with the propagation of microglia activation spreading to the optic nerve. Moreover, OPTN loss of function does not further exacerbate RGC death in the ocular hypertension mouse model induced by viscobead injection. Combined with the downregulation of OPTN in glaucoma patients, we have revealed an IOP-independent mechanism of glaucoma pathogenesis. Furthermore, we found that OPTN-driven NPY upregulation may suppress the CHOP-associated neurodegeneration. Our findings reveal a neuroprotective role for the OPTN-NPY signaling pathway, and its dysfunction promotes RGC loss in glaucoma pathology. The OPTN-NPY-mediated neuroinflammatory pathway provides a potential therapy for IOP-resistant glaucoma and highlights a druggable target for CHOP-associated neurodegeneration.

neuroscience↗

bioRxiv2024

Dynamic responses of human lung innate and adaptive immune cells highlight the roles of genes at asthma risk loci

RationaleThe lung is a unique immunological niche with diverse immune cell types. The effects of stimulation through innate and adaptive immune receptors on human lung immune cells has largely been extrapolated from studies of blood immune cells. While multiple immune cell types and many genes have been implicated as contributing to asthma, the dynamics of these in human lung immune cells following activation will yield insights into asthma pathogenesis and lung immunity more broadly. Methods and MeasurementsHuman lung immune cells from 6 donors were isolated. Mixed leukocytes were treated separately with lipopolysaccharide (LPS), F(ab)2-anti-human-IgM/IgG + IL4 and anti-CD3/CD28 for 4 and 18 hours and underwent single cell RNA sequencing (scRNAseq). Lung immune cell types were annotated, and gene expression compared across conditions. Genes at prior asthma-associated genetic loci were characterized across cell types, treatments and timepoints. Expression of non-classical class II genes associated with asthma, HLA-DQA2 and HLA-DQB2, and their protein products was characterized with immunohistochemistry. Main ResultsWe characterized gene expression in 116,697 lung immune cells. Cell-, treatment-, and timepoint-specific effects on gene expression were detected in all lung immune cell populations. Correlation of gene expression between lung and blood lymphocyte populations decreased following stimulation. Among the genes that were differentially expressed, 97 receptor:ligand pairs had changes with treatments. 96.0% of genes at asthma risk loci demonstrated differential expression in at least one cell type and at least one treatment. B cells were the cell type with the highest expression of HLA-DQA2 and HLA-DQB2 which increased with anti-IgM/IgG treatment and the HLA-DQ{beta}2 protein was identified in lung B cells from a donor with asthma. ConclusionsHuman lung immune activation elicits a broad range of cellular responses that deviate from those of blood immune cells and are relevant to asthma. Lung B cells expressing HLA-DQA2 and HLA-DQB2 appear to be involved in a novel antigen presentation pathway that contributes to asthma risk.

immunology↗

bioRxiv2024

Association of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model

The exponential growth of High-Throughput Sequencing (HTS) data on the microbial communities presents researchers with an unparalleled opportunity to delve deeper into the association of microorganisms with host phenotype. However, this growth also poses a challenge, as microbial data is complex, sparse, discrete, and prone to zero-inflation. Moreover, current methods for integrating microbiome data and other covariates are severely lacking. Hence, we propose a Bayesian zero-inflated negative binomial (ZINB) regression model that is capable of identifying differentially abundant taxa with distinct phenotypes and quantifying the effects of covariates on these taxa. Our model exhibits excellent performance when tested on simulated data. Upon successfully applying our model to a real multi-ethnic cohort study, we discovered that the prevailing understanding of microbial count data from previous research was overly dogmatic, because only a subset of taxa demonstrated zero inflation in real data. Moreover, we have discovered that dispersion parameters significantly influence the accuracy of model results, and increasing sample size can alleviate this issue. In all, we have presented an innovative integrative Bayesian regression model and a comprehensive pipeline for conducting a multi-ethnic cohort study of children, which facilitates bacterial differential abundance analysis and quantification of microbiome-covariate effects. This approach can be applied to general microbiome studies. IMPORTANCEMicrobiome are closely associated with physical indicators of the body, such as height, weight, age and BMI, which can be used as measures of human health. How to accurately identify which taxa in the microbiome are closely related to indicators of physical development is valuable as microbial markers of local child growth. Complex biological systems can be effectively modeled with ZINB model which is a Bayesian Generalized Linear Model. However, the potential of the ZINB model in the microbiome field has not yet been fully utilized in practice. Microbial count data are more complex than other scenarios, and our model captures this complexity. Our study is the first to discuss the effects of zero inflation and the degree of overdispersion in microbiome data on the results of model solutions. Finally, our work successfully applied it to a real multi-ethnic cohort study.

bioinformatics↗