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.

Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks.

Abstract: 1 Design Lab, UC San Diego, La Jolla, California, United States of America, 2 Center for Computational Biology and Bioinformatics, UC San Diego, La Jolla, California, United States of America, 3 Department of Pediatrics, UC San Diego, La Jolla, California, United States of America, 4 Data Science Hub, San Diego Supercomputer Center, UC San Diego, La Jolla, California, United States of America, 5 Departments of Bioengineering, and Computer Science and Engineering, and Center for Microbiome Innovation, UC San Diego, La Jolla, California, United States of America, 6 Bioinformatics and Systems Biology Graduate Program, UC San Diego, La Jolla, California, United States of America, 7 Department of Statistics and Berkeley Institute for Data Science, UC Berkeley, and Lawrence Berkeley National Laboratory, Berkeley, California, United States of America

Inhibitory bacteria reduce fungi on early life stages of endangered Colorado boreal toads (Anaxyrus boreas).

Abstract: Increasingly, host-associated microbiota are recognized to mediate pathogen establishment, providing new ecological perspectives on health and disease. Amphibian skin-associated microbiota interact with the fungal pathogen, Batrachochytrium dendrobatidis (Bd), but little is known about microbial turnover during host development and associations with host immune function. We surveyed skin microbiota of Colorado's endangered boreal toads (Anaxyrus boreas), sampling 181 toads across four life stages (tadpoles, metamorphs, subadults and adults). Our goals were to (1) understand variation in microbial community structure among individuals and sites, (2) characterize shifts in communities during development and (3) examine the prevalence and abundance of known Bd-inhibitory bacteria. We used high-throughput 16S and 18S rRNA gene sequencing (Illumina MiSeq) to characterize bacteria and microeukaryotes, respectively. Life stage had the largest effect on the toad skin microbial community, and site and Bd presence also contributed. Proteobacteria dominated tadpole microbial communities, but were later replaced by Actinobacteria. Microeukaryotes on tadpoles were dominated by the classes Alveolata and Stramenopiles, while fungal groups replaced these groups after metamorphosis. Using a novel database of Bd-inhibitory bacteria, we found fewer Bd-inhibitory bacteria in post-metamorphic stages correlated with increased skin fungi, suggesting that bacteria have a strong role in early developmental stages and reduce skin-associated fungi.

Suppression of the gut microbiome ameliorates age-related arterial dysfunction and oxidative stress in mice.

Abstract: KEY POINTS Age-related arterial dysfunction, characterized by oxidative stress- and inflammation-mediated endothelial dysfunction and arterial stiffening, is the primary risk factor for cardiovascular diseases. To investigate whether age-related changes in the gut microbiome may mediate arterial dysfunction, we suppressed gut microbiota in young and old mice with a cocktail of broad-spectrum, poorly-absorbed antibiotics in drinking water for 3-4 weeks. In old mice, antibiotic treatment reversed endothelial dysfunction and arterial stiffening and attenuated vascular oxidative stress and inflammation. To provide insight into age-related changes in gut microbiota that may underlie these observations, we show that ageing altered the abundance of microbial taxa associated with gut dysbiosis and increased plasma levels of the adverse gut-derived metabolite trimethylamine N-oxide. The results of the present study provide the first proof-of-concept evidence that the gut microbiome is an important mediator of age-related arterial dysfunction and therefore may be a promising therapeutic target for preserving arterial function with ageing, thereby reducing the risk of cardiovascular diseases. ABSTRACT Oxidative stress-mediated arterial dysfunction (e.g. endothelial dysfunction and large elastic artery stiffening) is the primary mechanism driving age-related cardiovascular diseases. Accumulating evidence suggests the gut microbiome modulates host physiology because dysregulation ('gut dysbiosis') has systemic consequences, including promotion of oxidative stress. The present study aimed to determine whether the gut microbiome modulates arterial function with ageing. We measured arterial function in young and older mice after 3-4 weeks of treatment with broad-spectrum, poorly-absorbed antibiotics to suppress the gut microbiome. To identify potential mechanistic links between the gut microbiome and age-related arterial dysfunction, we sequenced microbiota from young and older mice and measured plasma levels of the adverse gut-derived metabolite trimethylamine N-oxide (TMAO). In old mice, antibiotics reversed endothelial dysfunction [area-under-the-curve carotid artery dilatation to acetylcholine in young: 345 ± 16 AU vs. old control (OC): 220 ± 34 AU, P < 0.01; vs. old antibiotic-treated (OA): 334 ± 15 AU; P < 0.01 vs. OC] and arterial stiffening (aortic pulse wave velocity in young: 3.62 ± 0.15 m s-1 vs. OC: 4.43 ± 0.38 m s-1 ; vs. OA: 3.52 ± 0.35 m s-1 ; P = 0.03). These improvements were accompanied by lower oxidative stress and greater antioxidant enzyme expression. Ageing altered the abundance of gut microbial taxa associated with gut dysbiosis. Lastly, plasma TMAO was higher with ageing (young: 2.6 ± 0.4 μmol L-1 vs. OC: 7.2 ± 2.0 μmol L-1 ; P < 0.0001) and suppressed by antibiotic treatment (OA: 1.2 ± 0.2 μmol L-1 ; P < 0.0001 vs. OC). The results of the present study provide the first evidence for the gut microbiome being an important mediator of age-related arterial dysfunction and oxidative stress and suggest that therapeutic strategies targeting gut microbiome health may hold promise for preserving arterial function and reducing cardiovascular risk with ageing in humans.

High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County

Abstract: Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead-based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-min run. The method compared favorably to conventionally used methods for viral wastewater concentrations with higher recovery efficiencies from input sample volumes as low as 10 ml and can enable the processing of over 100 wastewater samples in a day. The sensitivity of the high-throughput protocol was shown to detect 1 asymptomatic individual in a building of 415 residents. Using the high-throughput pipeline, samples from the influent stream of the primary wastewater treatment plant of San Diego County (serving 2.3 million residents) were processed for a period of 13 weeks. Wastewater estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome copies in raw untreated wastewater correlated strongly with clinically reported cases by the county, and when used alongside past reported case numbers and temporal information in an autoregressive integrated moving average (ARIMA) model enabled prediction of new reported cases up to 3 weeks in advance. Taken together, the results show that the high-throughput surveillance could greatly ameliorate comprehensive community prevalence assessments by providing robust, rapid estimates.IMPORTANCE Wastewater monitoring has a lot of potential for revealing coronavirus disease 2019 (COVID-19) outbreaks before they happen because the virus is found in the wastewater before people have clinical symptoms. However, application of wastewater-based surveillance has been limited by long processing times specifically at the concentration step. Here we introduce a much faster method of processing the samples and show its robustness by demonstrating direct comparisons with existing methods and showing that we can predict cases in San Diego by a week with excellent accuracy, and 3 weeks with fair accuracy, using city sewage. The automated viral concentration method will greatly alleviate the major bottleneck in wastewater processing by reducing the turnaround time during epidemics.

Measuring Causal Specificity

Abstract: Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well defined without a probability distribution over the states of that variable. We demonstrate the tractability and interest of our proposed measure by measuring the specificity of coding DNA and other factors in a simple model of the production of mRNA.

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.