High-Throughput flaA Short Variable Region Sequencing to Assess Campylobacter Diversity in Fecal Samples From Birds

Current approach to identify sources of human pathogens is largely dependent on the cultivation and isolation of target bacteria. For rapid pathogen source identification, culture-independent strain typing method is necessary. In this study, we designed new primer set that broadly covers flaA short variable region (SVR) of various Campylobacter species, and applied the flaA SVR sequencing method to examine the diversity of Campylobacter spp. in geese fecal samples (n = 16) with and without bacteria cultivation. Twenty-three Campylobacter strains isolated from the 16 geese fecal samples were grouped similarly by conventional flaA restriction fragment length polymorphism (RFLP) method and by the flaA SVR sequencing method, but higher discriminant power was observed in the flaA SVR sequencing approach. For culture-independent flaA SVR sequencing analysis, we developed and optimized the sequence data analysis pipeline to identify as many genotypes as possible, while minimizing the detection of genotypes generated by sequencing errors. By using this pipeline, 51,629 high-quality flaA sequence reads were clustered into 16 operational taxonomic units (=genotypes) by using 98% sequence similarity and >50 sequence duplicates. Almost all flaA genotypes obtained by culture-dependent method were also identified by culture-independent flaA SVR MiSeq sequencing method. In addition, more flaA genotypes were identified probably due to high throughput nature of the MiSeq sequencing. These results suggest that the flaA SVR sequencing could be used to analyze the diversity of Campylobacter spp. without bacteria isolation. This method is promising to rapidly identify potential sources of Campylobacter pathogens

Complex host genetics influence the microbiome in inflammatory bowel disease

Background: Human genetics and host-associated microbial communities have been associated independently with a wide range of chronic diseases. One of the strongest associations in each case is inflammatory bowel disease (IBD), but disease risk cannot be explained fully by either factor individually. Recent findings point to interactions between host genetics and microbial exposures as important contributors to disease risk in IBD. These include evidence of the partial heritability of the gut microbiota and the conferral of gut mucosal inflammation by microbiome transplant even when the dysbiosis was initially genetically derived. Although there have been several tests for association of individual genetic loci with bacterial taxa, there has been no direct comparison of complex genome-microbiome associations in large cohorts of patients with an immunity-related disease. Methods: We obtained 16S ribosomal RNA (rRNA) gene sequences from intestinal biopsies as well as host genotype via Immunochip in three independent cohorts totaling 474 individuals. We tested for correlation between relative abundance of bacterial taxa and number of minor alleles at known IBD risk loci, including fine mapping of multiple risk alleles in the Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) gene exon. We identified host polymorphisms whose associations with bacterial taxa were conserved across two or more cohorts, and we tested related genes for enrichment of host functional pathways. Results: We identified and confirmed in two cohorts a significant association between NOD2 risk allele count and increased relative abundance of Enterobacteriaceae, with directionality of the effect conserved in the third cohort. Forty-eight additional IBD-related SNPs have directionality of their associations with bacterial taxa significantly conserved across two or three cohorts, implicating genes enriched for regulation of innate immune response, the JAK-STAT cascade, and other immunity-related pathways. Conclusions: These results suggest complex interactions between genetically altered host functional pathways and the structure of the microbiome. Our findings demonstrate the ability to uncover novel associations from paired genome-microbiome data, and they suggest a complex link between host genetics and microbial dysbiosis in subjects with IBD across independent cohorts. Electronic supplementary material The online version of this article (doi:10.1186/s13073-014-0107-1) contains supplementary material, which is available to authorized users

Associations Between Nutrition, Gut Microbiome, and Health in A Novel Nonhuman Primate Model.

Red-shanked doucs (Pygathrix nemaeus) are endangered, foregut-fermenting colobine primates which are difficult to maintain in captivity. There are critical gaps in our understanding of their natural lifestyle, including dietary habits such as consumption of leaves, unripe fruit, flowers, seeds, and other plant parts. There is also a lack of understanding of enteric adaptations, including their unique microflora. To address these knowledge gaps, we used the douc as a model to study relationships between gastrointestinal microbial community structure and lifestyle. We analyzed published fecal samples as well as detailed dietary history from doucs with four distinct lifestyles (wild, semi-wild, semi-captive, and captive) and determined gastrointestinal bacterial microbiome composition using 16S rRNA sequencing. A clear gradient of microbiome composition was revealed along an axis of natural lifestyle disruption, including significant associations with diet, biodiversity, and microbial function. We also identified potential microbial biomarkers of douc dysbiosis, including Bacteroides and Prevotella, which may be related to health. Our results suggest a gradient-like shift in captivity causes an attendant shift to severe gut dysbiosis, thereby resulting in gastrointestinal issues

NINJA-OPS: Fast Accurate Marker Gene Alignment Using Concatenated Ribosomes

<div><p>The explosion of bioinformatics technologies in the form of next generation sequencing (NGS) has facilitated a massive influx of genomics data in the form of short reads. Short read mapping is therefore a fundamental component of next generation sequencing pipelines which routinely match these short reads against reference genomes for contig assembly. However, such techniques have seldom been applied to microbial marker gene sequencing studies, which have mostly relied on novel heuristic approaches. We propose NINJA Is Not Just Another OTU-Picking Solution (NINJA-OPS, or NINJA for short), a fast and highly accurate novel method enabling reference-based marker gene matching (picking Operational Taxonomic Units, or OTUs). NINJA takes advantage of the Burrows-Wheeler (BW) alignment using an artificial reference chromosome composed of concatenated reference sequences, the “concatesome,” as the BW input. Other features include automatic support for paired-end reads with arbitrary insert sizes. NINJA is also free and open source and implements several pre-filtering methods that elicit substantial speedup when coupled with existing tools. We applied NINJA to several published microbiome studies, obtaining accuracy similar to or better than previous reference-based OTU-picking methods while achieving an order of magnitude or more speedup and using a fraction of the memory footprint. NINJA is a complete pipeline that takes a FASTA-formatted input file and outputs a QIIME-formatted taxonomy-annotated BIOM file for an entire MiSeq run of human gut microbiome 16S genes in under 10 minutes on a dual-core laptop.</p></div

Alignment accuracy of NINJA vs USEARCH 8 (where both reported a match).

<p>Each point on the graph represents a sequence for which both tools found a valid alignment. A point’s position along the X axis corresponds to alignment score (in %ID) for the match chosen by USEARCH 8, and its position on the Y axis corresponds to the alignment score against the match chosen by NINJA. Points along the diagonal represent sequences for which both tools picked the same quality match. Points above the diagonal correspond to sequences for which NINJA produced more accurate hits, and points below the diagonal represent sequences for which USEARCH 8 produced more accurate hits. Note the presence of a line at the top of the graph showing a number of sequences for which NINJA selected a perfect match from the database while USEARCH 8 could not.</p

Schematic of the NINJA pipeline.

<p>NINJA core programs are represented by pentagons, data files by cylinders, processes within a program as lists, index operations as rounded rectangles, and other swappable programs by other shapes. The entire upper-left branch of the schematic (from input references to bowtie-build and TaxMap) does not need to be performed if using an existing database, such as that supplied with NINJA. The python wrapper encompasses the remaining two branches (bottom and right) for convenience. In general, Ninja_prep prepares the concatesome, Ninja_filter prepares the reads for alignment, bowtie2 (or any BWT-enabled aligner) performs the alignment, and Ninja_parse merges the various pieces into a complete OTU table.</p

Benchmark of runtimes for NINJA compared to USEARCH 8 in a single-threaded environment.

<p>Multi-threaded alignments are faster. For NINJA only, this represents the entire time from parsing the initial FASTA file to the completion of the OTU table. The sortMeRNA program took substantially longer than USEARCH 8 (approx. 8000s; bar not shown to preserve scale).</p