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.

Quantifying and understanding well-to-well contamination in microbiome research

Abstract: Microbial sequences inferred as belonging to one sample may not have originated from that sample. Such contamination may arise from laboratory or reagent sources or from physical exchange between samples. This study seeks to rigorously assess the behavior of this often-neglected between-sample contamination. Using unique bacteria each assigned a particular well in a plate, we assess the frequency at which sequences from each source appears in other wells. We evaluate the effects of different DNA extraction methods performed in two labs using a consistent plate layout including blanks, low biomass, and high biomass samples. Well-to-well contamination occurred primarily during DNA extraction, and to a lesser extent in library preparation, while barcode leakage was negligible. Labs differed in the levels of contamination. DNA extraction methods differed in their occurrences and levels of well-to-well contamination, with robotic methods having more well-to-well contamination while manual methods having higher background contaminants. Well-to-well contamination was observed to occur primarily in neighboring samples, with rare events up to 10 wells apart. The effect of well-to-well was greatest in samples with lower biomass, and negatively impacted metrics of alpha and beta diversity. Our work emphasizes that sample contamination is a combination of crosstalk from nearby wells and background contaminants. To reduce well-to-well effects, samples should be randomized across plates, and samples of similar biomass processed together. Researchers should evaluate well-to-well contamination in study design and avoid removal of taxa or OTUs appearing in negative controls, as many will be microbes from other samples rather than reagent contaminants. Importance Microbiome research has uncovered magnificent biological and chemical stories across nearly all areas of life science, at times creating controversy when findings reveal fantastic descriptions of microbes living and even thriving in once thought to be sterile environments. Scientists have refuted many of these claims because of contamination, which has led to robust requirements including use of controls for validating accurate portrayals of microbial communities. In this study, we describe a previously undocumented form of contamination, well-to-well contamination and show that contamination primarily occurs during DNA extraction rather than PCR, is highest in plate-based methods as compared to single tube extraction, and occurs in higher frequency in low biomass samples. This finding has profound importance on the field as many current techniques to ‘decontaminate’ a dataset simply relies on an assumption that microbial reads found in blanks are contaminants from ‘outside’ namely the reagents or consumables.

Technology and Techniques for Microbial Ecology via DNA Sequencing

Abstract: High-throughput sequencing technology, coupled with the use of conserved marker genes, has allowed for the understanding of communities of microbes (both culturable and unculturable) as well as their phylogenetic placement. The recent explosion of sequencing data prompted the development of software that could process the vast amount of data generated and phylogenetically differentiate groups of samples. Host-associated microbial studies have revealed that microbes are highly varied between individuals and fluctuate within an individual. Large-scale studies are being undertaken that include collection of extensive environmental data to help uncover the forces that shape microbial communities.

Three-dimensional culture of oral progenitor cells: Effects on small extracellular vesicles production and proliferative function.

Abstract: Background Small extracellular vesicles (SEVs) have a diameter between 30 and 150 nm and play a key role in cell‐cell communication. As cells cultured in 3D vs 2D behave differently, this project aimed to assess whether there were differences in SEVs derived from human oral mucosa lamina propria‐progenitor cells (OMLP‐PCs) cultured in a 3D matrix compared with traditional 2D monolayer cultures. Methods OMLP‐PCs were cultured in 3D type I collagen matrices or on traditional 2D tissue culture plastic. Cell morphology and viability were assessed by light microscopy, actin staining, and trypan blue staining. SEVs secreted by OMLP‐PCs were purified and quantitatively analyzed by a BCA assay and nanoparticle tracking analysis (NTA; nanosight™). SEVs were further characterized by flow cytometry. SEV proliferative function was assessed by a 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide (MTT) assay. Results Cells cultured in 3D grew well as observed by light microscopy and phalloidin staining with cells branching in three dimensions (as opposed to the cells grown as monolayers on tissue culture plastic). NTA demonstrated a significantly higher number of SEV‐sized particles in the conditioned medium of cells grown in 3D type I collagen matrices vs a 2D monolayer (P < .01). Like SEVs from 2D culture, SEVs from 3D culture demonstrated a particle size within the expected SEV range. Tetraspanin analysis confirmed that 3D‐derived SEVs were positive for typical, expected tetraspanins. Cell proliferation analysis demonstrated that SEVs produced through 3D cell culture conditions significantly reduced the proliferation of skin fibroblasts when compared with SEVs from 2D monolayers (P < .05). Conclusion 3D culture of OMLP‐PCs produced typical SEVs but in a greater amount than when the same cells were cultured in 2D. The downstream proliferative potential of the SEVs was influenced by the initial culture methodology. Future work should now assess the potential effects of 3D SEVs on key wound healing activities.

The Microbiome and Its Potential for Pharmacology.

Abstract: The human microbiota (the microscopic organisms that inhabit us) and microbiome (their genes) hold considerable potential for improving pharmacological practice. Recent advances in multi-“omics” techniques have dramatically improved our understanding of the constituents of the microbiome and their functions. The implications of this research for human health, including microbiome links to obesity, drug metabolism, neurological diseases, cancer, and many other health conditions, have sparked considerable interest in exploiting the microbiome for targeted therapeutics. Links between microbial pathways and disease states further highlight a rich potential for companion diagnostics and precision medicine approaches. For example, the success of fecal microbiota transplantation to treat Clostridium difficile infection has already started to redefine standard of care with a microbiome-directed therapy. In this review we briefly discuss the nature of human microbial ecosystems and with pathologies and biological processes linked to the microbiome. We then review emerging computational metagenomic, metabolomic, and wet lab techniques researchers are using today to learn about the roles host-microbial interactions have with respect to pharmacological purposes and vice versa. Finally, we describe how drugs affect the microbiome, how the microbiome can impact drug response in different people, and the potential of the microbiome itself as a source of new therapeutics.

Paroxetine Administration Affects Microbiota and Bile Acid Levels in Mice.

Abstract: Recent interest in the role of microbiota in health and disease has implicated gut microbiota dysbiosis in psychiatric disorders including major depressive disorder. Several antidepressant drugs that belong to the class of selective serotonin reuptake inhibitors have been found to display antimicrobial activities. In fact, one of the first antidepressants discovered serendipitously in the 1950s, the monoamine-oxidase inhibitor Iproniazid, was a drug used for the treatment of tuberculosis. In the current study we chronically treated DBA/2J mice for 2 weeks with paroxetine, a selective serotonin reuptake inhibitor, and collected fecal pellets as a proxy for the gut microbiota from the animals after 7 and 14 days. Behavioral testing with the forced swim test revealed significant differences between paroxetine- and vehicle-treated mice. Untargeted mass spectrometry and 16S rRNA profiling of fecal pellet extracts showed several primary and secondary bile acid level, and microbiota alpha diversity differences, respectively between paroxetine- and vehicle-treated mice, suggesting that microbiota functions are altered by the drug. In addition to their lipid absorbing activities bile acids have important signaling activities and have been associated with gastrointestinal diseases and colorectal cancer. Antidepressant drugs like paroxetine should therefore be used with caution to prevent undesirable side effects.

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.