Biology subjects

XU, Z.

Publications and source records attributed to XU, Z..

8 recordsLinked to original sources

bioRxiv2026

Graph in Graph (GiG): A novel graph AI framework for integrating and interpreting whole-person healthmedical and omics data

Medical records and omics data are rapidly becoming standard in healthcare settings, which characterize the whole-person from dysfunctional molecules to phenotypes, and thus offer potential for precise disease diagnosis and target discovery. Whereas, it remains an open problem to systematically integrate and interprete medical record and omics data of individual patients. In this study, for the first time, we propose a novel graph AI model framework, Graph in Graph (GiG), to integrate and interpret the whole-person medical and omic datasets. Specifically, the medical record data is modeled using a person-phenotype graph, followed by omics signaling graphs of invidival patients, which enables the integration of information learned from omic-signaling graph and medical phenotype features to characterize individual patients and to prioritize important omic biomarkers and phenotypes. As an exploratory study, we applied and evaluated the GiG model to study the type 2 diabetes (T2D) and pre-T2D vs healthy using the Long Life Family Study (LLFS) cohort, which enrolls families with exceptional longevity to uncover biological mechanisms of healthy aging with medical and omics data. The evaluation results showed that GiG not only achieve a high prediction but also can interpret the prediction by ranking the essential clinical and omic biomarkers. The GiG framework can be applied to other studies by effectively integrating and interpreting medical and omic datasets for disease diagnosis and pathogenesis discovery.

bioinformatics↗

bioRxiv2026

Interpreting Omics Data Analysis with Large Language Models for Disease Target and Drug Discovery

In biomedical scientific discovery, synthesizing prior knowledge from the literature is an essential component of interpreting numerical omics data analyses for disease target identification and drug discovery. Large language models (LLMs) alone can rapidly retrieve disease mechanisms from biomedical text, but text-only outputs are general and unreliable for target and drug prioritization without cohort-specific quantitative evidence. Herein, we propose a provenance-aware Text-to-Target framework that couples schema-constrained multi-model LLM retrieval with numeric omics data analysis. The key design is a modality-aware fusion step: candidates are partitioned into overlap-supported anchors, retrieval-only hidden hubs, and network-emergent novelty nodes, then propagated into staged hypothesis and strategy generation under topology constraints. We evaluate the model in Alzheimers disease (AD) and pancreatic ductal adenocarcinoma (PDAC). In PDAC, the workflow produced a balanced 75-gene candidate universe and a 23-strategy portfolio, with significant DepMap support at both target level and strategy level. In AD, stricter candidate controls yielded a compact 34-gene universe and 14 strategies; under an expanded CRISPRbrain registry, both target-level axes were significant, with strong strategy-level enrichment. Across both diseases, final strategies preserved full provenance closure to the candidate pool, enabling end-to-end auditability from retrieval artifacts to validation outputs. These results support a transferable discovery architecture in which omics evidence constrains biological activity, LLM retrieval expands mechanistic search space, and network-aware fusion preserves interpretability. The framework provides a reproducible basis for dual-disease target prioritization and motivates continuous literature-mechanism concordance with agentic evidence-refresh loops.

bioinformatics↗

bioRxiv2024

Orchestrating the Acquisition of Oligodendrocyte Precursor Cell versus Olfactory Bulb Interneuron Fates through Olig1/2 during Mammalian Cortical Gliogenesis and Gliomagenesis

The hijacking of developmental gliogenesis programs is a hallmark of glioblastoma (GBM), in which glial precursor cells (GPCs) typically differentiate into both neurons and glial cells. In GBM, this process is disrupted, leading to the overproduction of proliferative glial-like cells. Our study demonstrates that the knockout of Olig1/2 in both normal development and gliomagenesis causes GPCs to shift from generating highly proliferative oligodendrocyte precursor cells to producing non-proliferative olfactory bulb interneurons. Mechanistically, Olig1/2 play dual roles by orchestrating distinct transcriptional programs in GPCs, particularly inhibiting the expression of Gsx2 through direct binding to its multiple enhancers. Additionally, we provide compelling evidence that human H3.3G34R/V-mutant tumors, a subtype of high-grade gliomas, originate from dorsal cortical-derived GPCs rather than from the previously assumed progenitors in the ventral basal ganglia. Collectively, our findings reveal a previously unrecognized role of Olig1/2 in both gliogenesis and gliomagenesis, offering deeper insights into the connections between normal neural development and tumorigenesis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=159 HEIGHT=200 SRC="FIGDIR/small/623106v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@10ea118org.highwire.dtl.DTLVardef@1725006org.highwire.dtl.DTLVardef@1e3ce25org.highwire.dtl.DTLVardef@f300ee_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

Newly discovered and conserved role of IgM against viral infection in an early vertebrate

IgM emerged in jawed vertebrates 500 Mya and remains the most evolutionarily conserved antibody class. However, despite extensive studies on IgM as an ancient antiviral weapon in warm-blooded vertebrates, its role and mechanisms in combating viral infections in early vertebrates remain poorly understood. Here, we found that significant virus-specific sIgM titers were generated in serum and gut mucus of teleost fish that survived infection, and fish lacking sIgM were more susceptible to viral infection. These results challenge the paradigm that IgM and IgT are specialized to systemic and mucosal immunity, respectively. More crucially, our study provides further insight into the role of sIgM in viral neutralization and firstly clarified the mechanism through which teleost sIgM blocks viral infection by directly targeting viral particles. From an evolutionary perspective, our findings indicate that sIgM in both primitive and modern vertebrates follow conserved principles in the development of specialized antiviral immunity.

immunology↗

bioRxiv2024

GeneSurv: Multi-Gene Cancer Survival Analysis Tool

SummaryTraditional cancer survival analysis tools often restrict their analysis to single genes, thereby limiting their analytical scope. GeneSurv, a novel Web-Based Multi-Gene Cancer Survival Analysis Tool, addresses this limitation by enabling multi-gene analyses that correlates the expression of multiple genes, without restriction on their number, in various combinations, with patient survival outcomes. GeneSurv offers dynamic functionalities and an intuitive interface for flexible and precise adjustments of gene expression thresholds. Among its demonstrated applications, GeneSurv identified a significant association between high expression of CCND1 and CDK4 with poor breast cancer prognosis, and related the combined expression of five genes to patient survival, showcasing its capacity to unravel the complexities of genetic interactions and their impact on patient outcomes. ConclusionAvailable with user-friendly tutorials at https://can-sat.erc.monash.edu/, GeneSurv is an invaluable addition to the existing suite of bioinformatic tools, significantly enhancing the investigation of cancer prognosis.

bioinformatics↗

bioRxiv2022

Genomic dissection of the microevolution of Australian epidemic Bordetella pertussis

Whooping cough (pertussis) is a highly contagious respiratory disease caused by the bacterium Bordetella pertussis. Despite high vaccine coverage, pertussis has re-emerged in many countries and caused two large epidemics in Australia since 2007. Here, we undertook a genomic and phylogeographic study of 385 Australian B. pertussis isolates collected from 2008 to 2017. The Australian B. pertussis population was found to be composed of mostly ptxP3 strains carrying different fim3 alleles, with ptxP3-fim3A genotype expanded far more than ptxP3-fim3B. Within the former, there were six co-circulating epidemic lineages (EL1 to EL6). The multiple ELs emerged, expanded, and then declined at different time points over the two epidemics, likely driven by immune selection from pertussis vaccination and natural infection in addition to local and global transmission events. Both hard and soft selective sweeps through vaccine selection pressures determined the current B. pertussis population dynamics. Relative risk analysis found that once a new B. pertussis lineage emerged, it was more likely to spread locally within the first 1.5 years. However, after 1.5 years, any new lineage was likely to expand to a wider region and became no longer spatially structured across the country. Phylogenetic analysis revealed the expansion of ptxP3 strains was also associated with replacement of the type III secretion system allele bscI1 with bscI3. This study advanced our understanding of the epidemic population structure and spatial and temporal dynamics of B. pertussis in a highly immunised population.

microbiology↗

bioRxiv2021

Linking Parasitism to Network Centrality and the Impact of Sampling Bias in its Interpretation

AO_SCPLOWBSTRACTC_SCPLOWGroup living is beneficial for individuals, but also comes with costs. One such cost is the increased possibility of pathogen transmission because increased numbers or frequencies of social contacts are often associated with increased parasite abundance or diversity. The social structure of a group or population is paramount to patterns of infection and transmission. Yet, for various reasons, studies investigating the links between sociality and parasitism in animals, especially in primates, have only accounted for parts of the group (e.g., only adults), which is likely to impact the interpretation of results. Here, we investigated the relationship between social network centrality and an estimate of gastrointestinal helminth infection intensity in a whole group of Japanese macaques (Macaca fuscata). We then tested the impact of omitting parts of the group on this relationship. We aimed to test: (1) whether social network centrality - in terms of the number of partners (degree), frequency of interactions (strength), and level of social integration (eigenvector) - was linked to parasite infection intensity (estimated by eggs per gram of faeces, EPG); and, (2) to what extent excluding portions of individuals within the group might influence the observed relationship. We conducted social network analysis on data collected from one group of Japanese macaques over three months on Koshima Island, Japan. We then ran a series of knock-out simulations. General linear mixed models showed that, at the whole-group level, network centrality was positively associated with geohelminth infection intensity. However, in partial networks with only adult females, only juveniles, or random subsets of the group, the strength of this relationship - albeit still generally positive - lost statistical significance. Furthermore, knock-out simulations where individuals were removed but network metrics were retained from the original whole-group network showed that these changes are partly a power issue and partly an effect of sampling the incomplete network. Our study indicates that sampling bias can thus hamper our ability to detect real network effects involving social interaction and parasitism. In addition to supporting earlier results linking geohelminth infection to Japanese macaque social networks, this work introduces important methodological considerations for research into the dynamics of social transmission, with implications for infectious disease epidemiology, population management, and health interventions.

animal behavior and cognition↗

bioRxiv2021

Whole-genome-based Helicobacter pylori geographic surveillance: a visualized and expandable webtool

Helicobacter pylori exhibits specific geographic distributions that related to the clinical outcomes. Despite the high infection rate of H. pylori throughout the world, the genetic epidemiology surveillance of H. pylori still needs to be improved. Here, we used single nucleotide polymorphisms (SNPs) profiling approach based on whole genome sequencing (WGS) that facilitates genomic population analyses of H. pylori and encourages the dissemination of microbial genotyping strategies worldwide. A total number of 1,211 public H. pylori genomes were downloaded and used to construct the typing tool, named as HPTT (H. pylori Typing Tool). Combined with the metadata, we developed two levels of genomic typing, including a continent scale and a country scale that nested in the continent scale. Results showed that Asia was the largest isolates source in our dataset, while isolates from Europe and Oceania were comparatively more widespread. More specifically, Switzerland and Australia are the main source of widespread isolates in their corresponding continents. To integrate all the typing information and enable researchers to compare their own dataset against the existing global database in an easy and rapid way, a user-friendly website (https://db.cngb.org/HPTT/) was developed with both genomic typing tool and visualization tool. To further confirm the validity of the website, ten newly assembled genomes were downloaded and tested precisely located on the branch as we expected. In summary, H. pylori typing tool (HPTT) is a novel genomic epidemiological tool that can achieve high resolution analysis of genomic typing and visualizing simultaneously, providing insights into the genetic population structure analysis, evolution analysis and epidemiological surveillance of H. pylori.

genomics↗