The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients?

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Abstract

The gut microbiota is emerging as a promising target for the management or prevention of inflammatory and metabolic disorders in humans. Many of the current research efforts are focused on the identification of specific microbial signatures, more particularly for those associated with obesity, type 2 diabetes, and cardiovascular diseases. Some studies have described that the gut microbiota of obese animals and humans exhibits a higher Firmicutes/Bacteroidetes ratio compared with normal-weight individuals, proposing this ratio as an eventual biomarker. Accordingly, the Firmicutes/Bacteroidetes ratio is frequently cited in the scientific literature as a hallmark of obesity. The aim of the present review was to discuss the validity of this potential marker, based on the great amount of contradictory results reported in the literature. Such discrepancies might be explained by the existence of interpretative bias generated by methodological differences in sample processing and DNA sequence analysis, or by the generally poor characterization of the recruited subjects and, more particularly, the lack of consideration of lifestyle-associated factors known to affect microbiota composition and/or diversity. For these reasons, it is currently difficult to associate the Firmicutes/Bacteroidetes ratio with a determined health status and more specifically to consider it as a hallmark of obesity.

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Meta-analyses of human gut microbes associated with obesity and IBD.

William Walters, Zech Xu, Rob Knight (2014)

Recent studies have linked human gut microbes to obesity and inflammatory bowel disease, but consistent signals have been difficult to identify. Here we test for indicator taxa and general features of the microbiota that are generally consistent across studies of obesity and of IBD, focusing on studies involving high-throughput sequencing of the 16S rRNA gene (which we could process using a common computational pipeline). We find that IBD has a consistent signature across studies and allows high classification accuracy of IBD from non-IBD subjects, but that although subjects can be classified as lean or obese within each individual study with statistically significant accuracy, consistent with the ability of the microbiota to experimentally transfer this phenotype, signatures of obesity are not consistent between studies even when the data are analyzed with consistent methods. The results suggest that correlations between microbes and clinical conditions with different effect sizes (e.g. the large effect size of IBD versus the small effect size of obesity) may require different cohort selection and analysis strategies.

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Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform

Melanie Schirmer, Umer Z. Ijaz, Rosalinda D'Amore … (2015)

With read lengths of currently up to 2 × 300 bp, high throughput and low sequencing costs Illumina's MiSeq is becoming one of the most utilized sequencing platforms worldwide. The platform is manageable and affordable even for smaller labs. This enables quick turnaround on a broad range of applications such as targeted gene sequencing, metagenomics, small genome sequencing and clinical molecular diagnostics. However, Illumina error profiles are still poorly understood and programs are therefore not designed for the idiosyncrasies of Illumina data. A better knowledge of the error patterns is essential for sequence analysis and vital if we are to draw valid conclusions. Studying true genetic variation in a population sample is fundamental for understanding diseases, evolution and origin. We conducted a large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data. We tested state-of-the-art library preparation methods for amplicon sequencing and showed that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns. Furthermore we tested the efficiency of various error correction strategies and identified quality trimming (Sickle) combined with error correction (BayesHammer) followed by read overlapping (PANDAseq) as the most successful approach, reducing substitution error rates on average by 93%.

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Effects of gut microbes on nutrient absorption and energy regulation.

Rosa Krajmalnik-Brown, Zehra-Esra Ilhan, Dae-Wook Kang … (2012)

Malnutrition may manifest as either obesity or undernutrition. Accumulating evidence suggests that the gut microbiota plays an important role in the harvest, storage, and expenditure of energy obtained from the diet. The composition of the gut microbiota has been shown to differ between lean and obese humans and mice; however, the specific roles that individual gut microbes play in energy harvest remain uncertain. The gut microbiota may also influence the development of conditions characterized by chronic low-level inflammation, such as obesity, through systemic exposure to bacterial lipopolysaccharide derived from the gut microbiota. In this review, the role of the gut microbiota in energy harvest and fat storage is explored, as well as differences in the microbiota in obesity and undernutrition.