ORCID Profile
0000-0002-3568-6271
Current Organisation
Arizona State University
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Publisher: Cold Spring Harbor Laboratory
Date: 20-12-2022
DOI: 10.1101/2022.12.19.520774
Abstract: 16S rRNA and shotgun metagenomics studies typically yield different results, usually attributed to biases in PCR lification of 16S rRNA genes. Here, we introduce Greengenes2 and show that differences in reference phylogeny are more important. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same s les agree in principal coordinates space, taxonomy, and in phenotype effect size when analyzed with the same tree.
Publisher: Cold Spring Harbor Laboratory
Date: 03-09-2021
Abstract: The number of publicly available microbiome s les is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic ersity (Faith's PD) is a highly utilized phylogenetic alpha ersity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic ersity (SFPhD) enables calculation of this widely adopted ersity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting ersity differences between younger and older populations in the FINRISK study's metagenomic data.
Publisher: Cold Spring Harbor Laboratory
Date: 13-09-2020
DOI: 10.1101/2020.09.12.20193045
Abstract: Co-evolution between humans and the microbial communities colonizing them has resulted in an intimate assembly of thousands of microbial species mutualistically living on and in their body and impacting multiple aspects of host physiology and health. Several studies examining whether human genetic variation can affect gut microbiota suggest a complex combination of environmental and host factors. Here, we leverage a single large-scale population-based cohort of 5,959 genotyped in iduals with matched gut microbial shotgun metagenomes, dietary information and health records up to 16 years post-s ling, to characterize human genetic variations associated with microbial abundances, and predict possible causal links with various diseases using Mendelian randomization (MR). Genome-wide association study (GWAS) identified 583 independent SNP-taxon associations at genome-wide significance ( p .0×10 -8 ), which included notable strong associations with LCT ( p =5.02×10 -35 ), ABO ( p =1.1×10 -12 ), and MED13L ( p =1.84×10 -12 ). A combination of genetics and dietary habits was shown to strongly shape the abundances of certain key bacterial members of the gut microbiota, and explain their genetic association. Genetic effects from the LCT locus on Bifidobacterium and three other associated taxa significantly differed according to dairy intake. Variation in mucin-degrading Faecalicatena lactaris abundances were associated with ABO , highlighting a preferential utilization of secreted A/B/AB-antigens as energy source in the gut, irrespectively of fibre intake. Enterococcus faecalis levels showed a robust association with a variant in MED13L , with putative links to colorectal cancer. Finally, we identified putative causal relationships between gut microbes and complex diseases using MR, with a predicted effect of Morganella on major depressive disorder that was consistent with observational incident disease analysis. Overall, we present striking ex les of the intricate relationship between humans and their gut microbial communities, and highlight important health implications.
Publisher: Cold Spring Harbor Laboratory
Date: 06-04-2021
DOI: 10.1101/2021.04.04.438427
Abstract: We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to in idual reference genomes as the minimum unit for assessing the ersity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene licon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene licon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate OGU adoption in future metagenomics studies. Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene licon sequencing for decoding the composition and structure of microbial communities. However, current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution compared to 16S rRNA licon sequence variant analysis. To solve these challenges, we introduce Operational Genomic Units (OGUs), which are the in idual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition while (ii) permitting use of phylogeny-aware tools. Our analysis of real-world datasets shows several advantages over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGU as standard practice in metagenomic studies.
Publisher: Springer Science and Business Media LLC
Date: 27-07-2023
DOI: 10.1038/S41587-023-01845-1
Abstract: Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR lification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same s les agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.
Publisher: Springer Science and Business Media LLC
Date: 09-08-2019
DOI: 10.1038/S41587-019-0252-6
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Publisher: Cold Spring Harbor Laboratory
Date: 25-06-2020
DOI: 10.1101/2020.06.24.20138933
Abstract: Gut microbiome sequencing has shown promise as a predictive biomarker for a wide range of diseases, including classification of liver disease and severity grading. However, the potential of gut microbiota for prospective risk prediction of liver disease has not been assessed. Here, we utilise shallow gut metagenomic sequencing data of a large population-based cohort (N= ,115) and ∼15 years of electronic health register follow-up together with machine-learning to investigate the predictive capacity of gut microbial predictors, in idually and in conjunction with conventional risk factors, for incident liver disease and alcoholic liver disease. Separately, conventional and microbiome risk factors showed comparable predictive capacity for incident liver disease. However, microbiome augmentation of conventional risk factor models using gradient boosted classifiers significantly improved performance, with average AUROCs of 0.834 for incident liver disease and 0.956 for alcoholic liver disease (AUPRCs of 0.185 and 0.304, respectively). Disease-free survival analysis showed significantly improved stratification using microbiome-augmented risk models as compared to conventional risk factors alone. Investigation of predictive microbial signatures revealed a wide range of bacterial taxa, including those previously associated with hepatic function and disease. This study supports the potential clinical validity of gut metagenomic sequencing to complement conventional risk factors for risk prediction of liver diseases.
Publisher: Springer Science and Business Media LLC
Date: 28-11-2022
DOI: 10.1038/S41564-022-01266-X
Abstract: Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and h ers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a erse set of 880 microbial community s les collected for the Earth Microbiome Project. We include licon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore ersity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite ersity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth’s environments (for ex le, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from erse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.
Publisher: Springer Science and Business Media LLC
Date: 18-10-2023
Publisher: American Society for Microbiology
Date: 26-04-2022
DOI: 10.1128/MSYSTEMS.00167-22
Abstract: Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene licon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution.
Publisher: Cold Spring Harbor Laboratory
Date: 23-03-2022
DOI: 10.1101/2022.03.22.22272736
Abstract: The gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults. To investigate the associations between gut microbiome and respiratory disease and to construct predictive models from baseline gut microbiome profiles for incident asthma or chronic obstructive pulmonary disease (COPD). Shallow metagenomic sequencing was performed for stool s les from a prospective, population-based cohort (FINRISK02 N=7,115 adults) with linked national administrative health register derived classifications for incident asthma and COPD up to 15 years after baseline. Generalised linear models and Cox regressions were utilised to assess associations of microbial taxa and ersity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances in idually and in combination with other risk factors, including sex, age, body mass index and smoking status. A total of 695 and 392 significant microbial associations at different taxonomic levels were found with incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome predicted incident asthma and COPD with mean area under the curves of 0.608 and 0.780, respectively. For both incident asthma and COPD, the baseline gut microbiome had C-indices of 0.623 for asthma and 0.817 for COPD, which were more predictive than other conventional risk factors. The integration of gut microbiome and conventional risk factors further improved prediction capacities. Subgroup analyses indicated gut microbiome was significantly associated with incident COPD in both current smokers and non-smokers, as well as in in iduals who reported never smoking. The gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.
Publisher: Springer Science and Business Media LLC
Date: 11-2017
DOI: 10.1038/NATURE24621
Abstract: Our growing awareness of the microbial world’s importance and ersity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community s les collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of ersity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial ersity.
Publisher: Elsevier BV
Date: 05-2022
Publisher: Springer Science and Business Media LLC
Date: 02-2022
DOI: 10.1038/S41588-021-00991-Z
Abstract: Human genetic variation affects the gut microbiota through a complex combination of environmental and host factors. Here we characterize genetic variations associated with microbial abundances in a single large-scale population-based cohort of 5,959 genotyped in iduals with matched gut microbial metagenomes, and dietary and health records (prevalent and follow-up). We identified 567 independent SNP-taxon associations. Variants at the LCT locus associated with Bifidobacterium and other taxa, but they differed according to dairy intake. Furthermore, levels of Faecalicatena lactaris associated with ABO, and suggested preferential utilization of secreted blood antigens as energy source in the gut. Enterococcus faecalis levels associated with variants in the MED13L locus, which has been linked to colorectal cancer. Mendelian randomization analysis indicated a potential causal effect of Morganella on major depressive disorder, consistent with observational incident disease analysis. Overall, we identify and characterize the intricate nature of host-microbiota interactions and their association with disease.
Publisher: Springer Science and Business Media LLC
Date: 24-07-2019
Publisher: American Chemical Society (ACS)
Date: 09-03-2021
Location: United States of America
Location: United States of America
No related grants have been discovered for Qiyun Zhu.