Comparison of Fecal Collection Methods for Microbiome and Metabolomics Studies.

Zheng Wang,Carmen R Isasi,Qibin Qi,Mykhaylo Usyk,Tao Wang,Irwin J Kurland,Robert D Burk,Yunping Qiu,Howard D Strickler,Robert C Kaplan,Christine P Zolnik

doi:10.3389/fcimb.2018.00301

Abstract

Background: Integrated microbiome and metabolomics analyses hold the potential to reveal interactions between host and microbiota in relation to disease risks. However, there are few studies evaluating how field methods influence fecal microbiome characterization and metabolomics profiling.Methods: Five fecal collection methods [immediate freezing at −20°C without preservative, OMNIgene GUT, 95% ethanol, RNAlater, and Flinders Technology Associates (FTA) cards] were used to collect 40 fecal samples from eight healthy volunteers. We performed gut microbiota 16S rRNA sequencing, untargeted metabolomics profiling, and targeted metabolomics focusing on short chained fatty acids (SCFAs). Metrics included α-diversity and β-diversity as well as distributions of predominant phyla. To evaluate the concordance with the “gold standard” immediate freezing, the intraclass correlation coefficients (ICCs) for alternate fecal collection systems were calculated. Correlations between SCFAs and gut microbiota were also examined.Results: The FTA cards had the highest ICCs compared to the immediate freezing method for α-diversity indices (ICCs = 0.96, 0.96, 0.76 for Shannon index, Simpson's Index, Chao-1 Index, respectively), followed by OMNIgene GUT, RNAlater, and 95% ethanol. High ICCs (all >0.88) were observed for all methods for the β-diversity metric. For untargeted metabolomics, in comparison to immediate freezing which detected 621 metabolites at ≥75% detectability level, 95% ethanol showed the largest overlapping set of metabolites (n = 430; 69.2%), followed by FTA cards (n = 330; 53.1%) and OMNIgene GUT (n = 213; 34.3%). Both OMNIgene GUT (ICCs = 0.82, 0.93, 0.64) and FTA cards (ICCs = 0.87, 0.85, 0.54) had acceptable ICCs for the top three predominant SCFAs (butyric acid, propionic acid and acetic acid). Nominally significant correlations between bacterial genera and SCFAs (P < 0.05) were observed in fecal samples collected by different methods. Of note, a high correlation between the genus Blautia (known butyrate producer) and butyric acid was observed for both immediate freezing (r = 0.83) and FTA cards (r = 0.74).Conclusions: Four alternative fecal collection methods are generally comparable with immediate freezing, but there are differences in certain measures of the gut microbiome and fecal metabolome across methods. Choice of method depends on the research interests, simplicity of fecal collection procedures and ease of transportation to the lab, especially for large epidemiological studies.

Highlights

Over the past decade, the microbiota’s potential impact on human chronic diseases has garnered increasing interest
A number of fecal collection methods, such as 95% ethanol, OMINIgene GUT Kit, RNAlater Stabilization Solution, fecal occult blood test (FOBT) cards, and fecal immunochemical test tubes, have been examined for gut microbiota profiling, and compared to immediate freezing at −20◦C, which is considered as the “gold standard” (Nechvatal et al, 2008; Flores et al, 2015; Voigt et al, 2015; Sinha et al, 2016; Song et al, 2016; Vogtmann et al, 2017b)
The β- diversity Bray Curtis distance principalcoordinate analysis (PCoA) analysis indicated that interindividual differences were responsible for the majority the microbial communities’ variability, while very small differences were observed across different collection methods within samples (Figure 1)

Summary

Introduction

The microbiota’s potential impact on human chronic diseases has garnered increasing interest. A number of fecal collection methods, such as 95% ethanol, OMINIgene GUT Kit, RNAlater Stabilization Solution, fecal occult blood test (FOBT) cards, and fecal immunochemical test tubes, have been examined for gut microbiota profiling, and compared to immediate freezing at −20◦C, which is considered as the “gold standard” (Nechvatal et al, 2008; Flores et al, 2015; Voigt et al, 2015; Sinha et al, 2016; Song et al, 2016; Vogtmann et al, 2017b). There are few studies evaluating how field methods influence fecal microbiome characterization and metabolomics profiling

Methods

Results

Conclusion