n-6 High Fat Diet Induces Gut Microbiome Dysbiosis and Colonic Inflammation.

Background: Concerns are emerging that a high-fat diet rich in n-6 PUFA (n-6HFD) may alter gut microbiome and increase the risk of intestinal disorders. Research is needed to model the relationships between consumption of an n-6HFD starting at weaning and development of gut dysbiosis and colonic inflammation in adulthood. We used a C57BL/6J mouse model to compare the effects of exposure to a typical American Western diet (WD) providing 58.4%, 27.8%, and 13.7% energy (%E) from carbohydrates, fat, and protein, respectively, with those of an isocaloric and isoproteic soybean oil-rich n-6HFD providing 50%E and 35.9%E from total fat and carbohydrates, respectively on gut inflammation and microbiome profile. Methods: At weaning, male offspring were assigned to either the WD or n-6HFD through 10-16 weeks of age. The WD included fat exclusively from palm oil whereas the n-6HFD contained fat exclusively from soybean oil. We recorded changes in body weight, cyclooxygenase-2 (COX-2) expression, colon histopathology, and gut microbiome profile. Results: Compared to the WD, the n-6HFD increased plasma levels of n-6 fatty acids; colonic expression of COX-2; and the number of colonic inflammatory and hyperplastic lesions. At 16 weeks of age, the n-6HFD caused a marked reduction in the gut presence of Firmicutes, Clostridia, and Lachnospiraceae, and induced growth of Bacteroidetes and Deferribacteraceae. At the species level, the n-6HFD sustains the gut growth of proinflammatory Mucispirillum schaedleri and Lactobacillus murinus. Conclusions: An n-6HFD consumed from weaning to adulthood induces a shift in gut bacterial profile associated with colonic inflammation.

Colonization of the live biotherapeutic product VE303 and modulation of the microbiota and metabolites in healthy volunteers.

Manipulation of the gut microbiota via fecal microbiota transplantation (FMT) has shown clinical promise in diseases such as recurrent Clostridioides difficile infection (rCDI). However, the variable nature of this approach makes it challenging to describe the relationship between fecal strain colonization, corresponding microbiota changes, and clinical efficacy. Live biotherapeutic products (LBPs) consisting of defined consortia of clonal bacterial isolates have been proposed as an alternative therapeutic class because of their promising preclinical results and safety profile. We describe VE303, an LBP comprising 8 commensal Clostridia strains under development for rCDI, and its early clinical development in healthy volunteers (HVs). In a phase 1a/b study in HVs, VE303 is determined to be safe and well-tolerated at all doses tested. VE303 strains optimally colonize HVs if dosed over multiple days after vancomycin pretreatment. VE303 promotes the establishment of a microbiota community known to provide colonization resistance.

Correction to: Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.

Following publication of the original article [1], the authors would like to highlight the following two corrections.

Assessment of REPLI-g Multiple Displacement Whole Genome Amplification (WGA) Techniques for Metagenomic Applications.

Amplification of minute quantities of DNA is a fundamental challenge in low-biomass metagenomic and microbiome studies because of potential biases in coverage, guanine-cytosine (GC) content, and altered species abundances. Whole genome amplification (WGA), although widely used, is notorious for introducing artifact sequences, either by amplifying laboratory contaminants or by nonrandom amplification of a sample's DNA. In this study, we investigate the effect of REPLI-g multiple displacement amplification (MDA; Qiagen, Valencia, CA, USA) on sequencing data quality and species abundance detection in 8 paired metagenomic samples and 1 titrated, mixed control sample. We extracted and sequenced genomic DNA (gDNA) from 8 environmental samples and compared the quality of the sequencing data for the MDA and their corresponding non-MDA samples. The degree of REPLI-g MDA bias was evaluated by sequence metrics, species composition, and cross-validating observed species abundance and species diversity estimates using the One Codex and MetaPhlAn taxonomic classification tools. Here, we provide evidence of the overall efficacy of REPLI-g MDA on retaining sequencing data quality and species abundance measurements while providing increased yields of high-fidelity DNA. We find that species abundance estimates are largely consistent across samples, even with REPLI-g amplification, as demonstrated by the Spearman's rank order coefficient (R2 > 0.8). However, REPLI-g MDA often produced fewer classified reads at the species, genera, and family level, resulting in decreased species diversity. We also observed some areas with the PCR "jackpot effect," with varying input DNA values for the Metagenomics Research Group (MGRG) controls at specific genomic loci. We visualize this effect in whole genome coverage plots and with sequence composition analyses and note these caveats of the MDA method. Despite overall concordance of species abundance between the amplified and unamplified samples, these results demonstrate that amplification of DNA using the REPLI-g method has some limitations. These concerns could be addressed by future improvements in the enzymes or methods for REPLI-g to be considered a >99% robust method for increasing the amount of high-fidelity DNA from low-biomass samples or at the very least, accounted for during computational analysis of MDA samples.

Determining the cause of recurrent Clostridium difficile infection using whole genome sequencing.

Understanding the contribution of relapse and reinfection to recurrent Clostridium difficile infection (CDI) has implications for therapy and infection prevention, respectively. We used whole genome sequencing to determine the relation of C. difficile strains isolated from patients with recurrent CDI at an academic medical center in the United States. Thirty-five toxigenic C. difficile isolates from 16 patients with 19 recurrent CDI episodes with median time of 53.5days (range, 13-362) between episodes were whole genome sequenced on the Illumina MiSeq platform. In 84% (16) of recurrences, the cause of recurrence was relapse with prior strain of C. difficile. In 16% (3) of recurrent episodes, reinfection with a new strain of C. difficile was the cause. In conclusion, the majority of CDI recurrences at our institution were due to infection with the same strain rather than infection with a new strain.

Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.

BACKGROUND: One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. RESULTS: In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. CONCLUSIONS: This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

Genomic Methods and Microbiological Technologies for Profiling Novel and Extreme Environments for the Extreme Microbiome Project (XMP).

The Extreme Microbiome Project (XMP) is a project launched by the Association of Biomolecular Resource Facilities Metagenomics Research Group (ABRF MGRG) that focuses on whole genome shotgun sequencing of extreme and unique environments using a wide variety of biomolecular techniques. The goals are multifaceted, including development and refinement of new techniques for the following: 1) the detection and characterization of novel microbes, 2) the evaluation of nucleic acid techniques for extremophilic samples, and 3) the identification and implementation of the appropriate bioinformatics pipelines. Here, we highlight the different ongoing projects that we have been working on, as well as details on the various methods we use to characterize the microbiome and metagenome of these complex samples. In particular, we present data of a novel multienzyme extraction protocol that we developed, called Polyzyme or MetaPolyZyme. Presently, the XMP is characterizing sample sites around the world with the intent of discovering new species, genes, and gene clusters. Once a project site is complete, the resulting data will be publically available. Sites include Lake Hillier in Western Australia, the "Door to Hell" crater in Turkmenistan, deep ocean brine lakes of the Gulf of Mexico, deep ocean sediments from Greenland, permafrost tunnels in Alaska, ancient microbial biofilms from Antarctica, Blue Lagoon Iceland, Ethiopian toxic hot springs, and the acidic hypersaline ponds in Western Australia.