Assessment of Inter-Laboratory Variation in the Characterization and Analysis of the Mucosal Microbiota in Crohn's Disease and Ulcerative Colitis.

Jake C Szamosi,Morag Graham,Christine Bonner,Charles N Bernstein,Jessica D Forbes,David S Guttman,Julia K Copeland,Natalie C Knox,Shahrokh Shekarriz,Laura Rossi,Gary Van Domselaar,Michael G Surette

doi:10.3389/fmicb.2020.02028

Abstract

BackgroundIn studies evaluating the microbiome, numerous factors can contribute to technical variability. These factors include DNA extraction methodology, sequencing protocols, and data analysis strategies. We sought to evaluate the impact these factors have on the results obtained when the sequence data are independently generated and analyzed by different laboratories.MethodsTo evaluate the effect of technical variability, we used human intestinal biopsy samples resected from individuals diagnosed with an inflammatory bowel disease (IBD), including Crohn’s disease (n = 12) and ulcerative colitis (n = 10), and those without IBD (n = 10). Matched samples from each participant were sent to three laboratories and studied using independent protocols for DNA extraction, library preparation, targeted-amplicon sequencing of a 16S rRNA gene hypervariable region, and processing of sequence data. We looked at two measures of interest – Bray–Curtis PERMANOVA R2 values and log2 fold-change estimates of the 25 most-abundant taxa – to assess variation in the results produced by each laboratory, as well the relative contribution to variation from the different extraction, sequencing, and analysis steps used to generate these measures.ResultsThe R2 values and estimated differential abundance associated with diagnosis were consistent across datasets that used different DNA extraction and sequencing protocols, and within datasets that pooled samples from multiple protocols; however, variability in bioinformatic processing of sequence data led to changes in R2 values and inconsistencies in taxonomic assignment and abundance estimates.ConclusionAlthough the contribution of DNA extraction and sequencing methods to variability were observable, we find that results can be robust to the various extraction and sequencing approaches used in our study. Differences in data processing methods have a larger impact on results, making comparison among studies less reliable and the combined analysis of bioinformatically processed samples nearly impossible. Our results highlight the importance of making raw sequence data available to facilitate combined and comparative analyses of published studies using common data processing protocols. Study methodologies should provide detailed data processing methods for validation, interpretability, reproducibility, and comparability.

Highlights

The human microbiome – the community of microorganisms that live on and within the human body – is increasingly recognized as playing a pivotal role in health and disease
We report our findings here along with a discussion of the implications our results have on conducting multicentre studies of the gut microbiome in gastrointestinal health and disease
To evaluate variability introduced at the DNA extraction step, aliquots of extracted DNA from UM and UT were shipped to MM, where they were amplified, sequenced, and data analyzed by the MM laboratory (UMEMMSP, UTEMMSP)

Summary

Introduction

The human microbiome – the community of microorganisms that live on and within the human body – is increasingly recognized as playing a pivotal role in health and disease. Associations reported for IBD and bacterial taxa at the genus and species level have been inconsistent (Matsuoka and Kanai, 2015). The technical variability arising from the lack of adherence to any standard approach for studying the microbiome is strongly implicated in the inconsistency of the results reported by studies investigating the association between gut microbiome composition and disease status (Wesolowska-Andersen et al, 2014; Choo et al, 2015; Thomas et al, 2015; Gerasimidis et al, 2016; Gohl et al, 2016; Costea et al, 2017). In studies evaluating the microbiome, numerous factors can contribute to technical variability. These factors include DNA extraction methodology, sequencing protocols, and data analysis strategies. We sought to evaluate the impact these factors have on the results obtained when the sequence data are independently generated and analyzed by different laboratories

Objectives

Methods

Discussion

Conclusion