Rob Knight

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.

Taxonomic Signatures of Long-Term Mortality Risk in Human Gut Microbiota

Abstract: The collection of fecal material and developments in sequencing technologies have enabled cost-efficient, standardized, and non-invasive gut microbiome profiling. As a result, microbiome composition data from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases.1–5 In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterized due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data.6–8 Here, we analyse the long-term association between gut microbiome variation and mortality in a large, well-phenotyped, and representative population cohort (n = 7211, FINRISK 2002; Finland).9 We report specific taxonomic and functional signatures related to the Enterobacteriaceae family in the human gut microbiome that predict mortality during a 15-year follow-up. These associations can be observed both in the Eastern and Western Finns who have differing genetic backgrounds, lifestyles, and mortality rates.10,11 Our results supplement previously reported cross-sectional associations,1–4,12 and help to establish a methodological and conceptual basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status. These findings could serve as a solid framework for microbiome profiling in clinical risk prediction, paving the way towards clinical applications of human microbiome sequencing aimed at prediction, prevention, and treatment of disease.

Eggshell Bacterial Load Is Related to Antimicrobial Properties of Feathers Lining Barn Swallow Nests

Abstract: The use of feathers to line bird’s nests has traditionally been interpreted as having a thermoregulatory function. Feather-degrading bacteria growing on feathers lining nests may have antimicrobial properties, which may provide an additional benefit to lining nests with feathers. We test the hypothesis that the production of antimicrobial substances by feather bacteria affects the microbiological environment of the nest, and therefore the bacterial density on eggshells and, indirectly, hatching success. These effects would be expected to differ between nests lined with pigmented and white feathers, because bacteria grow differently on feathers of different colors. We experimentally manipulated the composition of pigmented and unpigmented feathers in nests of the barn swallow (Hirundo rustica) and studied the antimicrobial properties against the keratin-degrading bacterium Bacillus licheniformis of bacteria isolated from feathers of each color. Analyzed feathers were collected at the end of the incubation period, and antimicrobial activity was defined as the proportion of bacteria from the feathers that produce antibacterial substances effective against B. licheniformis. Our experimental manipulation affected antimicrobial activity, which was higher in nests with only white feathers at the beginning of incubation. Moreover, white feathers showed higher antimicrobial activity than black ones. Interestingly, antimicrobial activity in feathers of one of the colors correlated negatively with bacterial density on feather of the opposite color. Finally, antimicrobial activity of white feathers was negatively related to eggshell bacterial load. These results suggest that antimicrobial properties of feathers in general and of white feathers in particular affect the bacterial environment in nests. This environment in turn affects the bacterial load on eggshells, which may affect hatching success.

QIIME allows analysis of high-throughput community sequencing data.

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.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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。

The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

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.

Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities

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.

Search and clustering orders of magnitude faster than BLAST

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.