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Phylogenetic microbiota profiling in fecal samples depends on combination of sequencing depth and choice of NGS analysis method.

ABSTRACT: The human gut microbiota is well established as an important factor in health and disease. Fecal sample microbiota are often analyzed as a proxy for gut microbiota, and characterized with respect to their composition profiles. Modern approaches employ whole genome shotgun next-generation sequencing as the basis for these analyses. Sequencing depth as well as choice of next-generation sequencing data analysis method constitute two main interacting methodological factors for such an approach. In this study, we used 200 million sequence read pairs from one fecal sample for comparing different taxonomy classification methods, using default and custom-made reference databases, at different sequencing depths. A mock community data set with known composition was used for validating the classification methods. Results suggest that sequencing beyond 60 million read pairs does not seem to improve classification. The phylogeny prediction pattern, when using the default databases and the consensus database, appeared to be similar for all three methods. Moreover, these methods predicted rather different species. We conclude that the choice of sequencing depth and classification method has important implications for taxonomy composition prediction. A multi-method-consensus approach for robust gut microbiota NGS analysis is recommended.

SUBMITTER: Rajan SK

PROVIDER: S-EPMC6748435 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Phylogenetic microbiota profiling in fecal samples depends on combination of sequencing depth and choice of NGS analysis method.

Rajan Sukithar K SK Lindqvist Mårten M Brummer Robert Jan RJ Schoultz Ida I Repsilber Dirk D

PloS one 20190917 9

The human gut microbiota is well established as an important factor in health and disease. Fecal sample microbiota are often analyzed as a proxy for gut microbiota, and characterized with respect to their composition profiles. Modern approaches employ whole genome shotgun next-generation sequencing as the basis for these analyses. Sequencing depth as well as choice of next-generation sequencing data analysis method constitute two main interacting methodological factors for such an approach. In t ...[more]

PMID: 31527871

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Project description:ObjectiveTo study the protective effect of fecal microbiota transplantation (FMT) on experimental autoimmune encephalomyelitis (EAE) and reveal its potential intestinal microflora-dependent mechanism through analyses of the intestinal microbiota and spinal cord transcriptome in mice.MethodWe measured the severity of disease by clinical EAE scores and H&E staining. Gut microbiota alteration in the gut and differentially expressed genes (DEGs) in the spinal cord were analyzed through 16S rRNA and transcriptome sequencing. Finally, we analyzed associations between the relative abundance of intestinal microbiota constituents and DEGs.ResultsWe observed that clinical EAE scores were lower in the EAE+FMT group than in the EAE group. Meanwhile, mice in the EAE+FMT group also had a lower number of infiltrating cells. The results of 16S rRNA sequence analysis showed that FMT increased the relative abundance of Firmicutes and Proteobacteria and reduced the abundance of Bacteroides and Actinobacteria. Meanwhile, FMT could modulate gut microbiota balance, especially via increasing the relative abundance of g_Adlercreutzia, g_Sutterella, g_Prevotella_9, and g_Tyzzerella_3 and decreasing the relative abundance of g_Turicibacter. Next, we analyzed the transcriptome of mouse spinal cord tissue and found that 1476 genes were differentially expressed between the EAE and FMT groups. The analysis of these genes showed that FMT mainly participated in the inflammatory response. Correlation analysis between gut microbes and transcriptome revealed that the relative abundance of Adlercreutzia was correlated with the expression of inflammation-related genes negatively, including Casp6, IL1RL2 (IL-36R), IL-17RA, TNF, CCL3, CCR5, and CCL8, and correlated with the expression of neuroprotection-related genes positively, including Snap25, Edil3, Nrn1, Cpeb3, and Gpr37.ConclusionAltogether, FMT may selectively regulate gene expression to improve inflammation and maintain the stability of the intestinal environment in a gut microbiota-dependent manner.

Project description:Recent evidence suggests there is a link between metabolic diseases and gut microbiota. To investigate the gut microbiota composition and fecal metabolic phenotype in diabetic retinopathy (DR) patients. DNA was extracted from 50 fecal samples (21 individuals with type 2 diabetes mellitus-associated retinopathy (DR), 14 with type 2 diabetes mellitus but without retinopathy (DM) and 15 sex- and age-matched healthy controls) and then sequenced by high-throughput 16S rDNA analysis. Liquid chromatography mass spectrometry (LC-MS)-based metabolomics was simultaneously performed on the samples. A significant difference in the gut microbiota composition was observed between the DR and healthy groups and between the DR and DM groups. At the genus level, Faecalibacterium, Roseburia, Lachnospira and Romboutsia were enriched in DR patients compared to healthy individuals, while Akkermansia was depleted. Compared to those in the DM patient group, five genera, including Prevotella, were enriched, and Bacillus, Veillonella, and Pantoea were depleted in DR patients. Fecal metabolites in DR patients significantly differed from those in the healthy population and DM patients. The levels of carnosine, succinate, nicotinic acid and niacinamide were significantly lower in DR patients than in healthy controls. Compared to those in DM patients, nine metabolites were enriched, and six were depleted in DR patients. KEGG annotation revealed 17 pathways with differentially abundant metabolites between DR patients and healthy controls, and only two pathways with differentially abundant metabolites were identified between DR and DM patients, namely, the arginine-proline and α-linolenic acid metabolic pathways. In a correlation analysis, armillaramide was found to be negatively associated with Prevotella and Subdoligranulum and positively associated with Bacillus. Traumatic acid was negatively correlated with Bacillus. Our study identified differential gut microbiota compositions and characteristic fecal metabolic phenotypes in DR patients compared with those in the healthy population and DM patients. Additionally, the gut microbiota composition and fecal metabolic phenotype were relevant. We speculated that the gut microbiota in DR patients may cause alterations in fecal metabolites, which may contribute to disease progression, providing a new direction for understanding DR.

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Phylogenetic microbiota profiling in fecal samples depends on combination of sequencing depth and choice of NGS analysis method.

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Phylogenetic microbiota profiling in fecal samples depends on combination of sequencing depth and choice of NGS analysis method.

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