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.

Individuals with substance use disorders have a distinct oral microbiome pattern.

Abstract: Background Substance use disorder emerges from a complex interaction between genetic predisposition, life experiences, exposure, and subsequent adaptation of biological systems to the repeated use of drugs. Recently, investigators have proposed that the human microbiota may play a role in brain health and disease. In particular, the human oral microbiome is a distinct and diverse ecological niche with its composition influenced by external factors such as lifestyle, diet, and oral hygiene. This investigation examined whether individuals with substance use disorder (SU) show a different oral microbiome pattern and whether this pattern is sufficient to delineate the SU group from healthy comparison (HC) subjects. Methods Participants were a sub-sample (N ​= ​177) of the Tulsa 1000 (T-1000) project. We analyzed 123 SU and 54 HC subjects using 16S rRNA marker gene sequencing to characterize the oral microbiome. Results The groups differed significantly based on the UniFrac distance, a phylogenetic-based measure of beta diversity, but did not differ in alpha diversity. Using a machine learning approach, microbiome features combined with socio-demographic variables successfully categorized group membership with 87%–92% accuracy, even after controlling for external factors such as smoking or alcohol consumption. SU individuals with relatively lower diversity also reported higher levels of negative reinforcement experiences associated with their primary substance of abuse. Conclusions Oral microbiome features are useful to sufficiently differentiate SU from HC subjects. There is some evidence that subjects whose drug use is driven by negative reinforcement show an impoverished oral microbiome. Taken together, the oral microbiome may help to understand the dysfunctional biological processes that promote substance use or may be pragmatically useful as a risk or severity biological marker.

Does protein structure influence trypsin miscleavage

Abstract: In this article, a new database that relates structural information from proteins in protein data bank to closely related protein sequences in humans was developed. Because the match criteria are extremely stringent, the structure of proteins in other species to infer characteristics of the human proteins was used. As a demonstration of the approach, this database has been applied to the problem of identifying likely trypsin miscleavage sites, a significant problem in proteomics. However, the approach is very general, and can be used to answer many kinds of structural questions (including questions related to posttranslational modifications). The study found that both the surface area and the secondary structure of cleavage sites have highly statistically significant effects on trypsin cleavage. The results of this analysis do not, however, suggest that surface area or secondary structure properties of particular peptides can be used to predict miscleavage sites, at least at a global level. This analysis of cleavage sites demonstrates the general power of homology-based techniques, in which the characteristics of a single protein that has a structure that has been solved can be used to infer properties of other proteins. We expect that our database of related proteins, structures, and sequences and our ability to query experimentally determined sets of peptides against this database will allow us to answer many other questions relation to global protein expression and modification.

Fecal Microbiome Composition Does Not Predict Diet-Induced TMAO Production in Healthy Adults.

Abstract: Background Trimethylamine‐N‐oxide (TMAO) is a small molecule derived from the metabolism of dietary nutrients by gut microbes and contributes to cardiovascular disease. Plasma TMAO increases follow...

Microbiome for Mars: surveying microbiome connections to healthcare with implications for long-duration human spaceflight, virtual workshop, July 13, 2020.

Abstract: The inaugural "Microbiome for Mars" virtual workshop took place on July 13, 2020. This event assembled leaders in microbiome research and development to discuss their work and how it may relate to long-duration human space travel. The conference focused on surveying current microbiome research, future endeavors, and how this growing field could broadly impact human health and space exploration. This report summarizes each speaker's presentation in the order presented at the workshop.

A lightweight, scalable grid computing framework for parallel bioinformatics applications

Abstract: In recent years our society has witnessed an unprecedented growth in computing power available to tackle important problems in science, engineering and medicine. For example, the SHARCNET network links large computing resources in 11 leading academic institutions in South Central Ontario, thus providing access to thousands of compute processors. It is a continuous challenge to develop efficient and scalable algorithms and methods for solving large scientific and engineering problems on such parallel and distributed computers. If the computing power available in such computational grids can be unleashed effectively in a scalable way, large scientific problems can be solved that would otherwise be hard to solve using the machines available in a stand-alone way. This paper describes techniques and software developed that allow to apply the power of computational grids to large-scale, loosely coupled parallel bioinformatics problems. Our approach is based on decentralization and implemented in Java, leading to a flexible, portable and scalable software solution for parallel bioinformatics. We discuss advantages and disadvantages of this approach, and demonstrate seamless performance on an ad-hoc grid composed of a wide variety of hardware for a real-life parallel bioinformatics problem. The bioinformatics problem described consists of virtual experiments in RNA folding executed on hundreds of compute processors concurrently, which may establish one of the missing links in the chain events that led to the origin of life.

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.