Biology subjects

Wang, X.-W.

Publications and source records attributed to Wang, X.-W..

At least 19 recordsLinked to original sources

bioRxiv2026

AI-guided discovery of antimicrobial peptides for urinary tract infections leveraging a new catalogue of the human urinary microbiome

Urinary tract infections (UTIs) are common infections that pose a critical burden on healthcare and society. Despite growing recognition that the human urinary tract harbors its own microbiome, its composition, functional potential, and alterations in UTI remain limited. Here, we leveraged the publicly available whole-metagenome shotgun sequencing data from 450 urinary microbiome samples collected in four independent cohorts together with genome assembly and metagenomic binning to construct an extensive human urinary microbiome catalog consisting of [~]1.3 million non-redundant microbial genes and 705 non-redundant metagenome-assembled genomes (nrMAGs). We found that microbiomes from patients with UTI carry significantly more genes linked to antibiotic resistance and virulence vs controls. There was an enrichment of multiple Escherichia strains in patients with UTI from two independent case-control cohorts. UTIs are becoming multidrug-resistant, and we used machine learning models to identify potential antimicrobial peptides (AMPs) in 705 nrMAGs. Furthermore, we experimentally demonstrated that two of these AMPs exhibited strong inhibitory activity against uropathogenic Escherichia coli strains. Our study provides a valuable resource for studying the human urinary microbiome and suggests urinary microbiome-derived AMPs represent a source of new therapeutics for UTIs.

microbiology↗

bioRxiv2026

Machine learning-based Personalized Dietary Recommendations to Achieve Desired Gut Microbial Compositions

Dietary intervention is an effective way to alter the gut microbiome to promote human health. Yet, due to our limited knowledge of diet-microbe interactions and the highly personalized gut microbial compositions, an efficient method to prescribe personalized dietary recommendations to achieve desired gut microbial compositions is still lacking. Here, we propose a machine learning framework to resolve this challenge. Our key idea is to implicitly learn the diet-microbe interactions by training a machine learning model using paired gut microbiome and dietary intake data from a population-level cohort. The well-trained machine learning model enables us to predict the microbial composition of any given species collection and dietary intake. Next, we prescribe personalized dietary recommendations by solving an optimization problem to achieve the desired microbial compositions. We systematically validated this Machine learning-based Personalized Dietary Recommendation (MPDR) framework using synthetic data generated from an established microbial consumer-resource model. We then validated MPDR using real data collected from a diet-microbiome association study. The presented MPDR framework demonstrates the potential of machine learning for personalized nutrition.

bioinformatics↗

bioRxiv2025

Neocortical temporal patterning by a two-layered regulatory network

In the developing neocortex, a diverse array of neurons with defined types and abundances are systematically generated by a limited population of radial glial progenitors (RGPs) as they undergo successive fate changes. The molecular regulation behind this intricate temporal patterning remains elusive. We undertook in-depth single-cell multi-omics analyses, discovering a two-layered regulatory framework at the core of this process. Central to this are global temporal regulators positioned above a temporal network, consisting of series of transcriptional factor (TF) hub groups under sequential state transitions. This temporal network operates not by restricting TF expressions to discrete temporal windows, but through coordinated transcriptional and chromatin-accessibility dynamics that modulate transient TF regulatory activity. Moreover, global temporal regulators specify the duration of each cascading stage and, consequently, the number of progenies generated at each stage. Loss of global temporal regulators protracts RGP lineage progression, whereas their increased activity accelerates it. These findings suggest a two-layer temporal regulatory system controlling RGP lineage progression and neural progeny output duality in mammalian neocortical development.

neuroscience↗

bioRxiv2025

Advancing the Discovery of Phage-Host Interactions and Disease Classification from Metagenomic Profiles Using Deep Learning

Microbial communities are essential for sustaining ecosystem functions in diverse environments, including the human gut. Phages interact dynamically with their prokaryotic hosts and play a crucial role in shaping the structure and function of microbial communities. Previous approaches for inferring phage-host interactions (PHIs) from metagenomic data are constrained by low sensitivity and the inability to accurately capture ecological relationships. To overcome these limitations, we developed PHILM (Phage-Host Interaction Learning from Metagenomic profiles), a deep learning framework that predicts PHIs directly from the taxonomic profiles of metagenomic data. We validated PHILM on both synthetic datasets generated by ecological models and real-world data, finding that it consistently outperformed the co-abundance-based approach for inferring PHIs. When applied to a large-scale metagenomic dataset comprising 7,016 stool samples from healthy individuals, PHILM identified 90% more genus-level PHIs than the traditional assembly-based approach. In a longitudinal dataset tracking PHI dynamics, PHILMs latent representations recapitulated microbial succession patterns originally described using taxonomic abundances. Furthermore, we demonstrated that PHILMs latent representations served as more discriminative features than taxonomic abundance-based features for disease classifications. In summary, PHILM represents a novel computational framework for predicting phage-host interactions from metagenomic data, offering valuable insights for both microbiome science and translational medicine.

bioinformatics↗

bioRxiv2025

PEARL: Integrative multi-omics classification and omics feature discovery via deep graph learning

Integrating multi-omics data provides valuable insights into biological processes by capturing information across multiple molecular layers, enabling a comprehensive understanding of complex diseases and driving advancements in precision medicine. However, existing computational methods for multi-omics integration face significant challenges, such as low reliability and poor generalizability, due to the high dimensionality and low sample size nature of omics data. To address these challenges, we present PEARL (Pearson-Enhanced spectrAl gRaph convoLutional networks), a novel deep graph learning method for biomedical classification and functional important omics features identification. PEARL leverages a simple yet effective learning architecture to achieve superior and robust performance in high-dimensional, low-sample-size multi-omics settings. Our results demonstrate that PEARL significantly outperforms existing state-of-the-art methods on both synthetic and real biomedical datasets. Furthermore, applied to Alzheimers disease (AD) brain multi-omics data, features prioritized by PEARL lead to functionally important genes that demonstrate significant enrichment in AD-related pathways. These findings highlight PEARLs practical utility in biomedical research and its potential to enhance biological interpretability in multi-omics studies.

bioinformatics↗

bioRxiv2024

Optimization-based dietary recommendations for healthy eating

Various diet scores have been developed to assess compliance with dietary guidelines. Yet, enhancing those diet scores is very challenging. Here, we tackle this issue by formalizing an optimization problem and solving it with simulated annealing. Our optimization-based dietary recommendation (ODR) approach, evaluated using Diet-Microbiome Association study data, provides efficient and reasonable recommendations for different diet scores. ODR has the potential to enhance nutritional counseling and promote dietary adherence for healthy eating.

bioinformatics↗

bioRxiv2024

Auxin-salicylic acid seesaw regulates the age-dependent balance between plant growth and herbivore defense

Plants must fine-tune their needs for growth and defense. According to the Plant Vigor Hypothesis, younger, more vigorous plants tend to be more susceptible to herbivores compared to older, mature plants, yet the molecular mechanisms underlying this dynamic remain elusive. Here, we uncover a hormonal crosstalk framework that orchestrates the age-related balance between plant growth and herbivore defense. We demonstrate that the accumulation of salicylic acid (SA), synthesized by Nicotiana benthamiana phenylalanine ammonia-lyase 6 (NbPAL6), dictates insect resistance in adult plants. The expression of NbPAL6 is driven by the key transcription factor, NbMYB42, which is regulated by two interacting auxin response factors, NbARF18La/b. In juvenile plants, higher auxin levels activate NbmiR160c, a microRNA that simultaneously silences NbARF18La/b, subsequently reducing NbMYB42 expression, lowering SA accumulation, and thus weakening herbivore defense. Furthermore, excessive SA accumulation in juvenile plants enhances defense but antagonizes auxin signaling, impairing early growth. Our findings suggest a seesaw-like model that balances growth and herbivore defense depending on the plants developmental stage.

plant biology↗

bioRxiv2024

Mapping Somatosensory Afferent Circuitry to Bone Identifies Neurotrophic Signals Required for Fracture Healing

The profound pain accompanying bone fracture is mediated by somatosensory neurons, which also appear to be required to initiate bone regeneration following fracture. Surprisingly, the precise neuroanatomical circuitry mediating skeletal nociception and regeneration remains incompletely understood. Here, we characterized somatosensory dorsal root ganglia (DRG) afferent neurons innervating murine long bones before and after experimental long bone fracture in mice. Retrograde labeling of DRG neurons by an adeno-associated virus with peripheral nerve tropism showed AAV-tdT signal. Single cell transcriptomic profiling of 6,648 DRG neurons showed highest labeling across CGRP+ neuron clusters (6.9-17.2%) belonging to unmyelinated C fibers, thinly myelinated A{delta} fibers and A{beta}-Field LTMR (9.2%). Gene expression profiles of retrograde labeled DRG neurons over multiple timepoints following experimental stress fracture revealed dynamic changes in gene expression corresponding to the acute inflammatory (S100a8, S100a9) and mechanical force (Piezo2). Reparative phase after fracture included morphogens such as Tgfb1, Fgf9 and Fgf18. Two methods to surgically or genetically denervate fractured bones were used in combination with scRNA-seq to implicate defective mesenchymal cell proliferation and osteodifferentiation as underlying the poor bone repair capacity in the presence of attenuated innervation. Finally, multi-tissue scRNA-seq and interactome analyses implicated neuron-derived FGF9 as a potent regulator of fracture repair, a finding compatible with in vitro assessments of neuron-to-skeletal mesenchyme interactions.

cell biology↗

bioRxiv2024

Analyses of trait evolution and diversification reveal multiple adaptive periods in the fungal order Hymenochaetales

Mushroom-forming fungi (Agaricomycetes) represent one of the most speciose and morphologically diverse life forms, which radiated into most niches on Earth and evolved diverse morphologies and life histories. The order Hymenochaetales comprises a species-rich group of important wood decayers and tree parasites, however, patterns of macroevolution across the order and thus the origins of key traits, such as pathogenicity are currently unknown. Here, using a novel, nearly comprehensive phylogeny of the Hymenochaetales, we show that its evolution has been shaped by an early adaptive period, followed by a general rate slowdown interrupted by local rapid radiations in the Cretaceous. We inferred that the ancestor of the order has undergone rapid phenotypic diversification into a range of morphologies and lifestyles, of which crust-like morphologies and pileate-sessile forms typical of bracket-fungi became dominant among extant taxa. Net diversification rate showed significant correlations with paleoclimate, morphological and ecological traits, however, teasing apart the contributions of individual clades revealed that these were mostly driven by the Mid-late Cretaceous rapid radiation of the genus Phylloporia, a diverse group of plant parasites. Together, this study unraveled a complex evolutionary history of the Hymenochaetales and uncovered novel patterns of phenotype evolution and diversification in mushroom-forming fungi.

evolutionary biology↗

bioRxiv2023

Herbivore insect small RNA effector suppress plant defense by cross-kingdom gene silencing

Herbivore insects deploy salivary effectors to manipulate the defense of their host plants, however, whether insect small RNAs (sRNAs) act as effectors to regulate plant-insect interaction is currently unclear. Here, we report that a microRNA (miR29-b) from the saliva of phloem-feeding insects can transfer into the host plant phloem and fine-tune the host defense. The salivary glands abundant miR29-b was produced by insect Dicer 1 and insect salivary exosome is involved in its transferring and releasing into the host plant. Insect miR29-b effector hijacks plant Argonaute 1 to silence host defense gene Bcl-2-associated athanogene 4 (BAG4). Silencing of BAG4 suppressed the expression of phenylalanine ammonia-lyase and the accumulation of salicylic acid (SA), therefore negatively regulating host defense against herbivore insects. miR29-b is highly conserved in Hemiptera, Coleoptera, Hymenoptera, Orthoptera, and Blattaria insects and also targets the BAG4 gene. Notably, BAG4 orthologs exist in a wide range of plant species and may as the target of insect miR29-b. Our work provides new insight into the intriguing defense and counter-defense between herbivores and plants. TeaserPhloem-feeding insects produce and transfer small RNA into the host plants to fine-tune plant basal defense by cross-kingdom gene silencing.

molecular biology↗

bioRxiv2023

X chromosome encoded histone demethylase UTX regulates mammalian axon regeneration via microRNA-124

Epigenetic regulation of neuronal transcriptomic landscape is emerging to be a key coordinator of mammalian neural regeneration. Here we investigated roles of two histone 3 lysine 27 (H3K27) demethylases Kdm6a/b in controlling neuroprotection and axon regeneration. Deleting either Kdm6a or Kdm6b led to enhanced sensory axon regeneration in the PNS, whereas in the CNS only deleting Kdm6a in retinal ganglion cells (RGCs) significantly enhanced optic nerve regeneration. Moreover, both Kdm6a and Kdm6b functioned to regulate RGC survival but with different mechanisms. Mechanistically, Kdm6a regulates RGC regeneration via distinct pathway from that of Pten and co-deleting Kdm6a and Pten resulted in long distance optic nerve regeneration passing the optic chiasm. In addition, RNA-seq profiling revealed that Kdm6a deletion switched the RGC transcriptomics into a developmental-like state and suppressed several known repressors of neural regeneration. Klf4 was identified as a direct downstream target of Kdm6a-H3K27me3 signaling in both sensory neurons and RGCs to regulate axon regeneration. These findings not only revealed different roles of Kdm6a and Kdm6b in regulation of neural regeneration and their underlying mechanisms, but also identified Kdm6a- mediated histone demethylation signaling as a novel epigenetic target for supporting CNS neural regeneration.

neuroscience↗

bioRxiv2023

A New Feature of the Laboratory Model Plant Nicotiana benthamiana: Dead-End Trap for Sustainable Field Pest Control

O_LIHemiptera and Thysanoptera insects pose persistent threats to agricultural production. Conventional management strategies involve the release of chemical or plastic agents, causing adverse environmental and global health issues. Notably, Nicotiana benthamiana, a globally utilized model plant, exhibits remarkable lethal effects and attraction towards these pests. C_LIO_LIIn this study, we explored the potential of using N. benthamiana for Hemiptera and Thysanoptera pest control in the laboratory and field. Through net cover and three field assays over two years, we demonstrated the efficacy and benefits of using N. benthamiana as a field-deployed pest control dead-end trap. C_LIO_LIN. benthamiana demonstrated nearly 100% lethality to whiteflies, aphids, and thrips, with emitted volatiles attracting these insects. Field trials showed that potted and planted N. benthamiana blocks and traps whiteflies and thrips from several Solanaceae and Cucurbitaceae crops effectively, comparable to common commercial yellow and blue sticky boards. Moreover, N. benthamiana in the field exhibits robust growth in commercial greenhouses without negatively impacting crop growth, natural enemies, and pollinators. C_LIO_LIOur study introduces an innovative, easily implementable, and sustainable approach for controlling Hemiptera and Thysanoptera pests. Moreover, it unveils the novel utility of N. benthamiana in field-based pest management. C_LI

plant biology↗

bioRxiv2023

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium. The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

ecology↗

bioRxiv2023

Identifying keystone species in microbial communities using deep learning

Previous studies suggested that microbial communities harbor keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. This is mainly due to our limited knowledge of microbial dynamics and the experimental and ethical difficulties of manipulating microbial communities. Here, we propose a Data-driven Keystone species Identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep learning model using microbiome samples collected from this habitat. The well-trained deep learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data generated from a classical population dynamics model in community ecology. We then applied DKI to analyze human gut, oral microbiome, soil, and coral microbiome data. We found that those taxa with high median keystoneness across different communities display strong community specificity, and many of them have been reported as keystone taxa in literature. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

microbiology↗

bioRxiv2022

A plant virus causes symptoms through the deployment of a host-mimicking protein domain to attract the insect vector

Viruses are obligate intracellular parasites with limited proteomes that heavily rely on the cell molecular machinery for their multiplication and spread. Plant viruses frequently cause symptoms through interference with host developmental programs. Despite the agricultural relevance of symptom development in virus-infected crops, the molecular mechanisms underlying these viral effects remain elusive. Here, we show that the symptoms triggered by tomato yellow leaf curl virus (TYLCV) depend on the physical interaction between the host-mimicking domain of a virus-encoded protein, C4, and a plant-specific family of RCC1-like domain-containing (RLD) proteins. C4 outcompetes endogenous interactors of RLDs, disrupting RLD function in the regulation of endomembrane trafficking and polar auxin transport, ultimately leading to the developmental alterations recognized as symptoms of the viral infection. Importantly, symptoms do not have a detectable effect on the performance of the virus in the plant host, but they serve as attractants for the viral insect vector, the whitefly Bemisia tabaci, hence promoting pathogen spread. Our work uncovers the molecular underpinnings of the viral manipulation that leads to symptom development in the TYLCV-tomato pathosystem, and suggests that symptoms have evolved as a strategy to promote viral transmission by the insect vector. Given that most plant viruses are insect-transmitted, the principles described here might have broad applicability to crop-virus interactions.

plant biology↗

bioRxiv2022

Association Rule Mining of the Human Gut Microbiome

The human gut carries a vast and diverse microbial community that is essential for human health. Understanding the structure of this complex community requires quantitative approaches. Traditional co-occurrence and correlation analyses typically focus on pair-wise relationships and ignore higher-order relationships. Association rule mining (ARM) is a well-developed technique in data mining and has been applied to human microbiome data to identify higher-order relationships. Yet, existing attempts suffer from small sample sizes and low taxonomic resolution. Here we leverage the curatedMetagenomic Database (CMD) to resolve those issues. We first infer association rules from gut microbiome samples of a large cohort of healthy individuals (n=2,815) in CMD. Then we compare those rules with that inferred from samples of individuals with different diseases: Inflammatory Bowel Disease (IBD, n=768), Colorectal cancer (CRC, n=368), Impaired Glucose Tolerance (IGT, n=199), and Type 2 Diabetes (T2D, n=164). Finally, we demonstrate that using ARM as a feature selection tool can improve the performance of microbiome-based disease classification. Together, this study provides a comprehensive study of higher-order microbial relationships in the human gut microbiome and highlights the importance of incorporating association rules in microbiome-based disease classification.

microbiology↗

bioRxiv2022

The transcriptional regulator JAZ8 interacts with the C2 protein from geminiviruses and limits the viral infection in Arabidopsis

Jasmonates (JAs) are phytohormones that finely regulate critical biological processes, including plant development and defense. JASMONATE ZIM-DOMAIN (JAZ) proteins are crucial keeping JA-responsive genes in a repressed state. In the presence of JA-Ile, JAZ repressors are ubiquitinated and targeted for degradation by the ubiquitin/proteasome system, allowing the activation of the downstream transcription factors and, consequently, the activation of JA-responsive genes. A growing body of evidence has shown that JA signalling is crucial in defending against plant viruses and their insect vectors. Here, we describe the interaction of C2 proteins from two geminiviruses from the genus Begomovirus, tomato yellow curl Sardinia virus (TYLCSaV) and tomato yellow leaf curl virus (TYLCV), with the transcriptional repressor JAZ8 from Arabidopsis thaliana and its closest orthologue in tomato, SlJAZ9. Both JAZ and C2 proteins colocalize in the nucleus, forming discrete nuclear speckles. Overexpression of JAZ8 did not lead to altered responses to TYLCV infection; however, knock-down of JAZ8 favours the geminiviral infection in plants. Low levels of JAZ8 likely affect the viral infection specifically since JAZ8-silenced plants do not display developmental phenotypes nor present differences in their interaction with the viral insect vector. Our results show that JAZ8 interacts with geminiviral C2 proteins and exerts an anti-geminiviral effect.

plant biology↗

bioRxiv2022

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a new method -- mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

microbiology↗