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.

Early prediction of liver disease using conventional risk factors and gut microbiome-augmented gradient boosting

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=>7,115) and ~15 years of electronic health register follow-up together with machine-learning to investigate the predictive capacity of gut microbial predictors, individually 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 0834 for incident liver disease and 0956 for alcoholic liver disease (AUPRCs of 0185 and 0304, 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

Microbial ecology of Atlantic salmon, Salmo salar, hatcheries: impacts of the built environment on fish mucosal microbiota

Abstract: Successful rearing of fish in hatcheries is critical for conservation, recreational fishing, and commercial fishing through wild stock enhancements, and aquaculture production. Flow through (FT) hatcheries require more water than Recirculating-Aquaculture-Systems (RAS) which enable up to 99% of water to be recycled thus significantly reducing environmental impacts. Here, we evaluated the biological and physical microbiome interactions of the built environment of a hatchery from three Atl salmon hatcheries (RAS n=2, FT n=1). Six juvenile fish were sampled from tanks in each of the hatcheries for a total of 60 fish across 10 tanks. Water and tank side biofilm samples were collected from each of the tanks along with three salmon body sites (gill, skin, and digesta) to assess mucosal microbiota using 16S rRNA sequencing. The water and tank biofilm had more microbial richness than fish mucus while skin and digesta from RAS fish had 2× the richness of FT fish. Body sites each had unique microbial communities (P IMPORTANCE Atlantic salmon, Salmo salar, is the most farmed marine fish worldwide with an annual production of 2,248 million metric tonnes in 2016. Salmon hatcheries are increasingly changing from flow through towards RAS design to accommodate more control over production along with improved environmental sustainability due to lower impacts on water consumption. To date, microbiome studies on hatcheries have focused either on the fish mucosal microbiota or the built environment microbiota, but have not combined the two to understand interactions. Our study evaluates how water and tank biofilm microbiota influences fish microbiota across three mucosal environments (gill, skin, and digesta). Results from this study highlight how the built environment is a unique source of microbes to colonize fish mucus and furthermore how this can influence the fish health. Further studies can use this knowledge to engineer built environments to modulate fish microbiota for a beneficial phenotype.

Insulin-regulated serine and lipid metabolism drive peripheral neuropathy

Abstract: Abstract Diabetes represents a spectrum of disease in which metabolic dysfunction damages multiple organ systems including liver, kidneys and peripheral nerves 1,2 . Although the onset and progression of these co-morbidities are linked with insulin resistance, hyperglycaemia and dyslipidaemia 3–7 , aberrant non-essential amino acid (NEAA) metabolism also contributes to the pathogenesis of diabetes 8–10 . Serine and glycine are closely related NEAAs whose levels are consistently reduced in patients with metabolic syndrome 10–14 , but the mechanistic drivers and downstream consequences of this metabotype remain unclear. Low systemic serine and glycine are also emerging as a hallmark of macular and peripheral nerve disorders, correlating with impaired visual acuity and peripheral neuropathy 15,16 . Here we demonstrate that aberrant serine homeostasis drives serine and glycine deficiencies in diabetic mice, which can be diagnosed with a serine tolerance test that quantifies serine uptake and disposal. Mimicking these metabolic alterations in young mice by dietary serine or glycine restriction together with high fat intake markedly accelerates the onset of small fibre neuropathy while reducing adiposity. Normalization of serine by dietary supplementation and mitigation of dyslipidaemia with myriocin both alleviate neuropathy in diabetic mice, linking serine-associated peripheral neuropathy to sphingolipid metabolism. These findings identify systemic serine deficiency and dyslipidaemia as novel risk factors for peripheral neuropathy that may be exploited therapeutically.

Pigs, Unlike Mice, Have Two Distinct Colonic Stem Cell Populations Similar to Humans That Respond to High-Calorie Diet prior to Insulin Resistance.

Abstract: Basal colonic crypt stem cells are long lived and play a role in colon homeostasis Previous evidence has shown that high-calorie diet (HCD) enhances colonic stem cell numbers and expansion of the proliferative zone, an important biomarker for colon cancer However, it is not clear how HCD drives dysregulation of colon stem cell/colonocyte proliferative kinetics We used a human-relevant pig model and developed an immunofluorescence technique to detect and quantify colonic stem cells Pigs (n = 8/group) were provided either standard diet (SD; 5% fat) or HCD (23% fat) for 13 weeks HCD- and SD-consuming pigs had similar total calorie intake, serum iron, insulin, and glucose levels However, HCD elevated both colonic proliferative zone (KI-67) and stem cell zone (ASCL-2 and BMI-1) Proliferative zone correlated with elevated innate colonic inflammatory markers TLR-4, NF-κB, IL6, and lipocalin-2 (r ≥ 062, P = 002) Elevated gut bacterial phyla proteobacteria and firmicutes in HCD-consuming pigs correlated with proliferative and stem cell zone Colonic proteome data revealed the upregulation of proteins involved in cell migration and proliferation and correlated with proliferative and stem cell zone expansion Our study suggests that pig colon, unlike mice, has two distinct stem cells (ASCL-2 and BMI-1) similar to humans, and HCD increases expansion of colonic proliferative and stem cell zone Thus, pig model can aid in the development of preventive strategies against gut bacterial dysbiosis and inflammation-promoted diseases, such as colon cancer Cancer Prev Res; 10(8); 442-50 ©2017 AACR

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.