Biology subjects

Zhao, X.-M.

Publications and source records attributed to Zhao, X.-M..

13 recordsLinked to original sources

bioRxiv2026

Long-read metagenomic sequencing reveals novel lineages and functional diversity in urban soil microbiome

City parks and other urban green spaces can bring significant benefits to the physical and mental health of city residents. However, there is limited knowledge about the microbial communities inhabiting these urban soils. Here, we applied long-read metagenomic sequencing to 58 urban soil samples from two major cities in China, enabling genome-resolved reconstruction of microbial diversity at unprecedented contiguity. We recovered 7,949 medium- and high-quality metagenome-assembled genomes, comprising 4,171 species-level genome bins, of which over 97% represent previously undescribed species. Long-read assemblies revealed extensive secondary metabolic capacity, including more than 30,000 biosynthetic gene clusters, which were highly contiguous compared with those from fragmented short-read assemblies. Beyond secondary metabolism, we uncovered over 2 million small protein families, including hundreds that are strongly enriched in the neighbourhood of defense systems and mobile genetic elements, highlighting their overlooked role in urban soils. These findings expand our understanding of the functional diversity of urban soil microbiomes and provide new insights with implications for urban public health.

bioinformatics↗

bioRxiv2025

AEMB: a computationally efficient abundance estimation method for metagenomic binning

Metagenomic binning is a crucial step in metagenomic analysis, namely grouping together contigs that are predicted to originate from the same genome to enable the recovery of metagenome-assembled genomes (MAGs). It has been shown that using information from multiple samples yields better results than binning each sample independently. However, for N metagenomic samples, using full multi-against-multi binning requires N2 alignments, making it computationally challenging to apply in large-scale metagenomic studies. Here, we propose AEMB (Abundance Estimation for Metagenomic Binning), a novel mapping mode implemented in strobealign. AEMB is a computationally efficient abundance estimation method that uses a prefix-lookup vector as an indexing structure to reduce memory usage and randstrobes to estimate the abundance of contigs without performing base-level alignment. Compared to the hash table used in the previous version of strobealign, the indexing structure reduces peak memory usage by 25.2% with almost the same runtime. Furthermore, we implemented a fast abundance estimation method that skips base-level alignment. Altogether, AEMB reduces the runtime for abundance estimation by 88% to 96% compared to commonly used alignment methods such as Bowtie2 and BWA, while achieving similar binning results. AEMB is available as a mapping mode in strobealign https://github.com/ksahlin/strobealign and SemiBin2 (v2.1 and later) accepts its inputs for binning.

bioinformatics↗

bioRxiv2025

Proposal of two new genera and seventy-seven new species of ascomycetous yeasts isolated from China

Ascomycetous yeasts belong to Saccharomycotina and Taphrinomycotina, with about 1,200 described species distributed worldwide. More than 300 ascomycetous yeast species have been reported from China, which indicates that China possesses a hotspot with high yeast diversity. However, our knowledge of yeast diversity and distribution remains limited, and this area needs to be explored in depth. Here we describe 77 new ascomycetous yeast species with 195 isolates obtained over the past two decades during the continuous yeast diversity surveys in China, based on the phylogenetic analyses of the sequences of D1/D2 domains of the large subunit (LSU) and the internal transcribed spacer region including the 5.8S rDNA (ITS) of the nuclear ribosomal DNA (rDNA). The average nucleotide identity (ANI) analysis was used to delimitate closely related species with low sequence heterogeneity in the ITS and D1/D2 regions. Seventy-five of these new species belong to 35 genera in the Saccharaomycotina, while the other two are distributed in Taphrina of the Taphrinomycotina. In addition, we propose two new genera, namely Gaboromyces gen. nov. and Yurkovozyma gen. nov.

microbiology↗

bioRxiv2023

A catalogue of small proteins from the global microbiome

Small open reading frames (smORFs) shorter than 100 codons are widespread and perform essential roles in microorganisms, where they encode proteins active in several cell functions, including signal pathways, stress response, and antibacterial activities. However, the ecology, distribution and role of small proteins in the global microbiome remain unknown. Here, we constructed a global microbial smORFs catalogue (GMSC) derived from 63,410 publicly available metagenomes across 75 distinct habitats and 87,920 high-quality isolate genomes. GMSC contains 965 million non-redundant smORFs with comprehensive annotations. We found that archaea harbor more small proteins proportionally than bacteria. We moreover provide a tool called GMSC-mapper to identify and annotate small proteins from microbial (meta)genomes. Overall, this publicly-available resource demonstrates the immense and underexplored diversity of small proteins.

bioinformatics↗

bioRxiv2023

SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing

MotivationMetagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental samples have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process. ResultsWe propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3%-21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing samples. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1%-26.3% more high-quality genomes than the second best binner for long-read data. Availability and ImplementationSemiBin2 is available as open source software at https://github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at https://github.com/BigDataBiology/SemiBin2_benchmark.

bioinformatics↗

bioRxiv2022

Enterotypes of the human gut mycobiome

The fungal component of the human gut microbiome, also known as the mycobiome, plays a vital role in intestinal ecology and human health. Here, we identify and characterize four mycobiome enterotypes using ITS profiling of 3,363 samples from 16 cohorts across three continents, including 572 newly profiled samples from China. These enterotypes exhibit stability across populations and geographical locations and significant correlation with bacterial enterotypes. Particularly, we notice that fungal enterotypes have a strong age preference, where the enterotype dominated by Candida (i.e., fun_C_E enterotype) is enriched in the elderly population and confers an increased risk of multiple diseases associated with compromised intestinal barrier. In addition, bidirectional mediation analysis reveals that the fungi-contributed aerobic respiration pathway associated with fun_C_E enterotype might mediate the association between the compromised intestinal barrier and aging. TeaserAs an integral part of the human gut microbiome, the fungi, which co-habit with prokaryotic microbiome in the gut, play important role in the intestinal ecology and human health. Yet, the overall structure of the human gut mycobiome and the inter-individual variation worldwide remain largely unclear. Lai et al. analyzed the fungal profiles of 3,363 samples from 16 cohorts across three continents, and identified four fungal enterotypes that exhibit stability across populations. They found that fungal enterotypes showed age preference, where a Candida dominated enterotype was enriched in the elderly population and confers an increased risk of multiple diseases and more severe compromised intestinal barrier. Furthermore, they determined one fungi-contributed aerobic respiration pathway could mediate the association between the compromised intestinal barrier and aging.

bioinformatics↗

bioRxiv2022

Vast human gut virus diversity uncovered by combined short- and long-read sequencing

Current metagenome-assembled human phage catalogs contained mostly fragmented genomes. Here, we developed a vigorous phage detection method involving phage enrichment and long-read sequencing and applied to 135 fecal samples. With ~10 times more efficient in obtaining complete genomes (~34%) than the Gut Virome Database, we identified the first megabasephage (~1.03Mb), and revealed the hidden diversity of the gut phageome including dozens of phages more prevalent than the crAssphages and Gubaphages.

bioinformatics↗

bioRxiv2021

SemiBin: Incorporating information from reference genomes with semi-supervised deep learning leads to better metagenomic assembled genomes (MAGs)

Metagenomic binning is the step in building metagenome-assembled genomes (MAGs) when sequences predicted to originate from the same genome are automatically grouped together. The most widely-used methods for binning are reference-independent, operating de novo and allow the recovery of genomes from previously unsampled clades. However, they do not leverage the knowledge in existing databases. Here, we propose SemiBin, an open source tool that uses neural networks to implement a semi-supervised approach, i.e. SemiBin exploits the information in reference genomes, while retaining the capability of binning genomes that are outside the reference dataset. SemiBin outperforms existing state-of-the-art binning methods in simulated and real microbiome datasets across three different environments (human gut, dog gut, and marine microbiomes). SemiBin returns more high-quality bins with larger taxonomic diversity, including more distinct genera and species. SemiBin is available as open source software at https://github.com/BigDataBiology/SemiBin/.

bioinformatics↗

bioRxiv2021

metaMIC: reference-free Misassembly Identification and Correction of de novo metagenomic assemblies

Evaluating the quality of metagenomic assemblies is important for constructing reliable metagenome-assembled genomes and downstream analyses. Here, we present metaMIC (https://github.com/ZhaoXM-Lab/metaMIC), a machine-learning based tool for identifying and correcting misassemblies in metagenomic assemblies. Benchmarking results on both simulated and real datasets demonstrate that metaMIC outperforms existing tools when identifying misassembled contigs. Furthermore, metaMIC is able to localize the misassembly breakpoints, and the correction of misassemblies by splitting at misassembly breakpoints can improve downstream scaffolding and binning results.

bioinformatics↗

bioRxiv2020

Age-specific gene signatures underlying the transcriptomes and functional connectomes of human cerebral cortex

The human cerebral cortex undergoes profound structural and functional dynamic variations across the lifespan, whereas the underlying molecular mechanisms remain unclear. Here, with a novel method TCA (Transcriptome-connectome Correlation Analysis), which integrates the brain functional MR magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood, and late adulthood. The ASC gene signatures are significantly correlated with the cortical thickness (P-value <2.00e-3) and myelination (P-value <1.00e-3), two key brain structural features that vary in accordance with brain development. In addition to the molecular underpinning of age-related brain functions, the ASC gene signatures allow delineation of the molecular mechanisms of neuropsychiatric disorders, such as the regulation between ARNT2 and its target gene ETF1 involved in Schizophrenia. We further validate the ASC gene signatures with published gene sets associated with the adult cortex, and confirm the robustness of TCA on other brain image datasets.

neuroscience↗

bioRxiv2020

nMAGMA: a network enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia

MotivationAnnotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multi-marker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. However, SNPs may also affect genes in a distance, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue-specificity of signals have not been taken into account. ResultsWe propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals, and tissue-specific gene networks. When applied to schizophrenia, nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are reasonably involved in schizophrenia compared to MAGMA and H-MAGMA. Some disease-related functions (e.g. the ATPase pathway in Cortex) tissues are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multi-tissue origins.

bioinformatics↗

bioRxiv2019

FACS: antimicrobial peptide screening in genomes and metagenomes

MotivationAntimicrobial peptides (AMPs) have the potential to tackle multidrug-resistant pathogens in both clinical and non-clinical contexts. The recent growth in the availability of genomes and metagenomes provides an opportunity for in silico prediction of novel AMP molecules. However, due to the small size of these peptides, standard gene prospection methods cannot be applied in this domain and alternative approaches are necessary. In particular, standard gene prediction methods have low precision for short peptides, and functional classification by homology results in low recall. ResultsHere, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime). We demonstrate that Macrel recovers high-quality AMP candidates using realistic simulations and real data. AvailabilityMacrel is implemented in Python 3. It is available as open source at https://github.com/BigDataBiology/macrel and through bioconda. Classification of peptides or prediction of AMPs in contigs can also be performed on the webserver: http://big-data-biology.org/software/macrel.

bioinformatics↗

bioRxiv2018

Consistent alterations of human fecal microbes after transplanted to germ-free mice

Fecal microbiota transplant (FMT) of human fecal samples to germ-free (GF) mice is useful for establishing causal relationships between altered gut microbiota and human phenotypes. However, due to intrinsic differences between human and mouse intestines, and distinct diets of choice between the two organisms, replicating human phenotypes in mouse through FMT is not guaranteed; similarly, treatments that are effective in mouse models do not guarantee their success in human either. In this study, we aimed to identify human gut microbes that have undergone significant and consistent changes after transplanted to GF mice across multiple experimental settings. By comparing gut microbiota profiles in 1,713 human-mouse pairs, we found strikingly on average <50% of the human gut microbes can be re-established in mice at the species level; among which, more than 1/3 have undergone significant changes (referred as to \"variable microbes\"), most of which were consistent across multiple human-mouse pairs and experimental settings. Consistently, one-third of human samples had changed their enterotypes, i.e. significant changes in their leading species after FMT. Mice fed with controlled diet showed significant decrease in the enterotype change rate (~25%) as compared those with non-controlled diet (~50%), suggesting a possible solution for rescue. Strikingly, most of the variable microbes have been implicated in human diseases, with some being recognized as causing species. Our results highlighted the challenges of using mouse model in replicating human gut microbiota-associated phenotypes, provided useful information for researchers using mice in their gut microbiota studies and call for additional validations after FMT.

bioinformatics↗