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.

Experiences and lessons learned from two virtual, hands-on microbiome bioinformatics workshops.

Abstract: In October of 2020, in response to the Coronavirus Disease 2019 (COVID-19) pandemic, our team hosted our first fully online workshop teaching the QIIME 2 microbiome bioinformatics platform. We had 75 enrolled participants who joined from at least 25 different countries on 6 continents, and we had 22 instructors on 4 continents. In the 5-day workshop, participants worked hands-on with a cloud-based shared compute cluster that we deployed for this course. The event was well received, and participants provided feedback and suggestions in a postworkshop questionnaire. In January of 2021, we followed this workshop with a second fully online workshop, incorporating lessons from the first. Here, we present details on the technology and protocols that we used to run these workshops, focusing on the first workshop and then introducing changes made for the second workshop. We discuss what worked well, what didn't work well, and what we plan to do differently in future workshops.

The impact of maternal asthma on the preterm infants' gut metabolome and microbiome (MAP study)

Abstract: Preterm infants are at a greater risk for the development of asthma and atopic disease, which can lead to lifelong negative health consequences. This may be due, in part, to alterations that occur in the gut microbiome and metabolome during their stay in the Neonatal Intensive Care Unit (NICU). To explore the differential roles of family history (i.e., predisposition due to maternal asthma diagnosis) and hospital-related environmental and clinical factors that alter microbial exposures early in life, we considered a unique cohort of preterm infants born ≤ 34 weeks gestational age from two local level III NICUs, as part of the MAP (Microbiome, Atopic disease, and Prematurity) Study. From MAP participants, we chose a sub-cohort of infants whose mothers had a history of asthma and matched gestational age and sex to infants of mothers without a history of asthma diagnosis (control). We performed a prospective, paired metagenomic and metabolomic analysis of stool and milk feed samples collected at birth, 2 weeks, and 6 weeks postnatal age. Although there were clinical factors associated with shifts in the diversity and composition of stool-associated bacterial communities, maternal asthma diagnosis did not play an observable role in shaping the infant gut microbiome during the study period. There were significant differences, however, in the metabolite profile between the maternal asthma and control groups at 6 weeks postnatal age. The most notable changes occurred in the linoleic acid spectral network, which plays a role in inflammatory and immune pathways, suggesting early metabolomic changes in the gut of preterm infants born to mothers with a history of asthma. Our pilot study suggests that a history of maternal asthma alters a preterm infants' metabolomic pathways in the gut, as early as the first 6 weeks of life.

Stool multi-omics for the study of host–microbe interactions in inflammatory bowel disease

Abstract: ABSTRACT Inflammatory Bowel Disease (IBD) is a chronic immune-mediated inflammatory disease of the gastrointestinal tract that is a growing public burden. Gut microbes and their interactions with hosts play a crucial role in disease pathogenesis and progression. These interactions are complex, spanning multiple physiological systems and data types, making comprehensive disease assessment difficult, and often overwhelming single-omic capabilities. Stool-based multi-omics is a promising approach for characterizing host-gut microbiome interactions using deep integration of technologies such as 16S rRNA sequencing, shotgun metagenomics, meta-transcriptomics, metabolomics, and metaproteomics. The wealth of information generated through multi-omic studies is poised to usher in advancements in IBD research and precision medicine. This review highlights historical and recent findings from stool-based muti-omic studies that have contributed to unraveling IBD’s complexity. Finally, we discuss common pitfalls, issues, and limitations, and how future pipelines should address them to standardize multi-omics in IBD research and beyond.

Integrated multi-cohort analysis of the Parkinson’s disease gut metagenome

Abstract: Background The gut microbiome is altered in several neurologic disorders including Parkinson’s disease (PD). Objectives Profile the fecal gut metagenome in PD for alterations in microbial composition, taxon abundance, metabolic pathways, and microbial gene products, and their relationship with disease progression. Methods Shotgun metagenomic sequencing was conducted on 244 stool donors from two independent cohorts in the United States, including individuals with PD (n=48, n=47, respectively), environmental Household Controls (HC, n=29, n=30), and community Population Controls (PC, n=41, n=49). Microbial features consistently altered in PD compared to HC and PC subjects were identified. Data were cross-referenced to public metagenomic datasets from two previous studies in Germany and China to determine generalizable microbiome features. Results The gut microbiome in PD shows significant alterations in community composition. Robust taxonomic alterations include depletion of putative “beneficial” gut commensals Faecalibacterium prausnitzii and Eubacterium and Roseburia species, and increased abundance of Akkermansia muciniphila and Bifidobacterium species. Pathway enrichment analysis and metabolic potential, constructed from microbial gene abundance, revealed disruptions in microbial carbohydrate and lipid metabolism and increased amino acid and nucleotide metabolism. These global gene-level signatures indicate an increased response to oxidative stress, decreased cellular growth and microbial motility, and disrupted inter-community signaling. Conclusions A metagenomic meta-analysis of PD shows consistent and novel alterations in taxonomic representation, functional metabolic potential, and microbial gene abundance across four independent studies from three continents. These data reveal stereotypic changes in the gut microbiome are a consistent feature of PD, highlighting potential diagnostic and therapeutic avenues for future research.

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.