Toward Standards in Clinical Microbiota Studies: Comparison of Three DNA Extraction Methods and Two Bioinformatic Pipelines

Abstract

When studying the microbiome using next-generation sequencing, the DNA extraction method, sequencing procedures, and bioinformatic processing are crucial to obtain reliable data. Method choice has been demonstrated to strongly affect the final biological interpretation. We assessed the performance of three DNA extraction methods and two bioinformatic pipelines for bacterial microbiota profiling through 16S rRNA gene amplicon sequencing, using positive and negative controls for DNA extraction and sequencing and eight different types of high-or low-biomass samples. Performance was evaluated based on quality control passing, DNA yield, richness, diversity, and compositional profiles. All DNA extraction methods retrieved the theoretical relative bacterial abundance with a maximum 3-fold change, although differences were seen between methods, and library preparation and sequencing induced little variation. Bioinformatic pipelines showed different results for observed richness, but diversity and compositional profiles were comparable. DNA extraction methods were successful for feces and oral swabs, and variation induced by DNA extraction methods was lower than intersubject (biological) variation. For low-biomass samples, a mixture of genera present in negative controls and sample-specific genera, possibly representing biological signal, were observed. We conclude that the tested bioinformatic pipelines perform equally, with pipeline-specific advantages and disadvantages. Two out of three extraction methods performed equally well, while one method was less accurate regarding retrieval of compositional profiles. Lastly, we again demonstrate the importance of including negative controls when analyzing low-bacterial-biomass samples. IMPORTANCE Method choice throughout the workflow of a microbiome study, from sample collection to DNA extraction and sequencing procedures, can greatly affect results. This study evaluated three different DNA extraction methods and two bioinformatic pipelines by including positive and negative controls and various biological specimens. By identifying an optimal combination of DNA extraction method and bioinformatic pipeline use, we hope to contribute to increased methodological consistency in microbiota studies. Our methods were applied not only to commonly studied samples for microbiota analysis, e.g., feces, but also to more rarely studied, low-biomass samples. Microbiota composition profiles of low-biomass samples (e.g., urine and tumor biopsy specimens) were not always distinguishable from negative controls, or showed partial overlap, confirming the importance of including negative controls in microbiota studies, especially when low bacterial biomass is expected.

The Bacterial Gut Microbiota of Adult Patients Infected, Colonized or Noncolonized by Clostridioides difficile

“…Read filtering, operational taxonomic unit (OTU)-picking, and taxonomic assignment were performed using the NGTax 0.4 pipeline with following settings: forward read length of 150, reverse read length of 120, ratio OTU abundance of 2.0, classify ratio of 0.9, minimum threshold of 1 × 10 −7 , identity level of 97%, and error correction of 98.5, using the Silva_132_SSU Ref database [18][19][20]. The obtained OTU-table was filtered for OTUs with a number of sequences less than 0.005% of the total number of sequences [21].…”

Multicenter assessment of microbial community profiling using 16S rRNA gene sequencing and shotgun metagenomic sequencing

“…Previous studies have shown that different DNA extraction approaches (kits) vary in efficiency for different types of microbes, especially for hard-to-lyse microbes (such as G+ bacteria), leading to significant deviations in DNA yield and bacterial composition [40] , [41] , [42] . This issue is ubiquitous in the laboratories participating in this evaluation study.…”

“…Of note, because of the complexity of microbial communities as well as uncontrollable technical biases, no normalization approaches or guidelines have been established to improve the reproducibility of microbiome research in different laboratories. Even attempting to find consensus “best practices” for microbiome studies is challenging [50] , [51] , and a great deal of research has been conducted to find solutions to these problems [42] , [52] , [53] . DNA extraction contributes a majority of the experimental variability.…”

The vaginal microbiota in the course of bacterial vaginosis treatment

“…Raw sequencing data are available in the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under study accession PRJNA524112. Read filtering, operational taxonomic unit (OTU)-picking, and taxonomic assignment were performed using the NG-Tax 0.4 pipeline and the Silva_132_SSU Ref database [12]. Statistical analysis and data visualisation were performed in R (v3.5.1) using the packages phyloseq (v1.26.…”