Author
Rob Knight
Other affiliations: Anschutz Medical Campus, University of Sydney, Veterans Health Administration ...read more
Bio: Rob Knight is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Microbiome & Gut flora. The author has an hindex of 201, co-authored 1061 publications receiving 253207 citations. Previous affiliations of Rob Knight include Anschutz Medical Campus & University of Sydney.
Topics: Microbiome, Gut flora, Medicine, Metagenomics, Biology
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors colonized mice with microbial communities from human, zebrafish, and termite guts, human skin and tongue, soil, and estuarine microbial mats.
291 citations
TL;DR: 3D mapping of mass spectrometry data and microbial 16S rRNA amplicon sequences reveals that the molecular composition of skin has diverse distributions and that the composition is defined not only by skin cells and microbes but also by the authors' daily routines, including the application of hygiene products.
Abstract: The human skin is an organ with a surface area of 1.5-2 m(2) that provides our interface with the environment. The molecular composition of this organ is derived from host cells, microbiota, and external molecules. The chemical makeup of the skin surface is largely undefined. Here we advance the technologies needed to explore the topographical distribution of skin molecules, using 3D mapping of mass spectrometry data and microbial 16S rRNA amplicon sequences. Our 3D maps reveal that the molecular composition of skin has diverse distributions and that the composition is defined not only by skin cells and microbes but also by our daily routines, including the application of hygiene products. The technological development of these maps lays a foundation for studying the spatial relationships of human skin with hygiene, the microbiota, and environment, with potential for developing predictive models of skin phenotypes tailored to individual health.
289 citations
TL;DR: High‐throughput 16S rRNA gene sequencing is used to examine the host and environmental influences on the skin microbiota of the cohabiting amphibian species Anaxyrus boreas, Pseudacris regilla, Taricha torosa and Lithobates catesbeianus from the Central Valley in California, and populations of Rana cascadae tadpoles.
Abstract: Skin-associated bacteria of amphibians are increasingly recognized for their role in defence against pathogens, yet we have little understanding of their basic ecology. Here, we use high-throughput 16S rRNA gene sequencing to examine the host and environmental influences on the skin microbiota of the cohabiting amphibian species Anaxyrus boreas, Pseudacris regilla, Taricha torosa and Lithobates catesbeianus from the Central Valley in California. We also studied populations of Rana cascadae over a large geographic range in the Klamath Mountain range of Northern California, and across developmental stages within a single site. Dominant bacterial phylotypes on amphibian skin included taxa from Bacteroidetes, Gammaproteobacteria, Alphaproteobacteria, Firmicutes, Sphingobacteria and Actinobacteria. Amphibian species identity was the strongest predictor of microbial community composition. Secondarily, within a given amphibian species, wetland site explained significant variation. Amphibian-associated microbiota differed systematically from microbial assemblages in their environments. Rana cascadae tadpoles have skin bacterial communities distinct from postmetamorphic conspecifics, indicating a strong developmental shift in the skin microbes following metamorphosis. Establishing patterns observed in the skin microbiota of wild amphibians and environmental factors that underlie them is necessary to understand skin symbiont community assembly, and ultimately, the role skin microbiota play in the extended host phenotype including disease resistance.
288 citations
University of Groningen1, Erasmus University Rotterdam2, Katholieke Universiteit Leuven3, Chinese Academy of Sciences4, University of Surrey5, King's College London6, University of Toronto7, Avera Health8, Karolinska Institutet9, University of Copenhagen10, University of Greifswald11, University of Kiel12, Yeshiva University13, Sungkyunkwan University14, University of Tartu15, Weizmann Institute of Science16, Copenhagen University Hospital17, University of Texas Health Science Center at Houston18, University of Alabama at Birmingham19, Stockholm University20, University of Michigan21, VU University Amsterdam22, University of Oxford23, University of Bristol24, University of Amsterdam25, Maastricht University26, University of California, San Diego27, University of Eastern Finland28, National Institutes of Health29, University of California, Los Angeles30, Linköping University31, Harvard University32, Radboud University Nijmegen33, University of North Carolina at Chapel Hill34, Ewha Womans University35, Fred Hutchinson Cancer Research Center36, National Research Council37
TL;DR: In this article, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts) and found high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples.
Abstract: To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10−8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10−20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10−10 < P < 5 × 10−8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.
287 citations
TL;DR: A WCST version sensitive to differences between 'efficient' and random errors was used to examine set shifting deficits in patients with focal lesions to their lateral prefrontal cortex and, as expected, patients showed abnormally high rates of perseverative errors.
283 citations
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TL;DR: An overview of the analysis pipeline and links to raw data and processed output from the runs with and without denoising are provided.
Abstract: Supplementary Figure 1 Overview of the analysis pipeline. Supplementary Table 1 Details of conventionally raised and conventionalized mouse samples. Supplementary Discussion Expanded discussion of QIIME analyses presented in the main text; Sequencing of 16S rRNA gene amplicons; QIIME analysis notes; Expanded Figure 1 legend; Links to raw data and processed output from the runs with and without denoising.
28,911 citations
28 Jul 2005
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
TL;DR: The extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
Abstract: SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
18,256 citations
University of Massachusetts Amherst1, University of Michigan2, University of New Mexico3, University of British Columbia4, Texas A&M University5, University of Minnesota6, University of Warwick7, Dalhousie University8, Colorado School of Mines9, University of Ljubljana10, Graz University of Technology11, Louisiana State University12
TL;DR: M mothur is used as a case study to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the α and β diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments.
Abstract: mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
17,350 citations
TL;DR: UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters and offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets.
Abstract: Motivation: Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification.
Results: UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets.
Availability: Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch
Contact: [email protected]
Supplementary information:Supplementary data are available at Bioinformatics online.
17,301 citations