Controlling for Contaminants in Low-Biomass 16S rRNA Gene Sequencing Experiments

Abstract

Microbial communities are commonly studied using culture-independent methods, such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination, particularly in low-microbial-biomass niches. Computational approaches to identify contaminant sequences have been proposed, but their performance has not been independently evaluated. To identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we created a mock microbial community dilution series. We evaluated four computational approaches to identify and remove contaminants, as follows: (i) filtering sequences present in a negative control, (ii) filtering sequences based on relative abundance, (iii) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam, and (iv) predicting the sequence proportion arising from defined contaminant sources implemented in SourceTracker. As expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 80.1% of the most diluted sample arising from contaminant sequences. Inclusion of contaminant sequences led to overinflated diversity estimates and distorted microbiome composition. All methods for contaminant identification successfully identified some contaminant sequences, which varied depending on the method parameters used and contaminant prevalence. Notably, removing sequences present in a negative control erroneously removed >20% of expected sequences. SourceTracker successfully removed over 98% of contaminants when the experimental environments were well defined. However, SourceTracker misclassified expected sequences and performed poorly when the experimental environment was unknown, failing to remove >97% of contaminants. In contrast, the Decontam frequency method did not remove expected sequences and successfully removed 70 to 90% of the contaminants. IMPORTANCE The relative scarcity of microbes in low-microbial-biomass environments makes accurate determination of community composition challenging. Identifying and controlling for contaminant bacterial DNA are critical steps in understanding microbial communities from these low-biomass environments. Our study introduces the use of a mock community dilution series as a positive control and evaluates four computational strategies that can identify contaminants in 16S rRNA gene sequencing experiments in order to remove them from downstream analyses. The appropriate computational approach for removing contaminant sequences from an experiment depends on prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

Gut Microbiome, Intestinal Permeability, and Tissue Bacteria in Metabolic Disease: Perpetrators or Bystanders?

“…A growing need to evaluate computational approaches prior to testing can be done using mock microbial communities in dilution series. There is indeed growing evidence for this kind of contamination control being necessary [216,217,219,220]. Bacterial DNA is present in all tested adipose tissue depots as well we blood, with dissimilarities between tissues being influenced by overall host inflammation and insulin resistance.…”

“…Beyond the ones stated in the letter including low amounts of bacterial DNA, bacterial contamination from environment and material used, as well as high amounts of PCR inhibitors in human samples, we believe studies should go beyond experimental bacterial reduction and control to include rigorous bioinformatic steps to handle contaminating operational taxonomic units and taxa in downstream analyses. Although the jury is out on how best to tackle this issue, some suggestions from the community included-among others-completely discarding taxa seen in negative controls (on operational taxonomic unit (OTU) level), leading to a significant reduction in taxa possibly biologically relevant [217], but new more elegant methods taking distribution of taxa in negative controls on a frequency or prevalence basis have emerged as viable, more moderate alternatives [218]. In general, the importance for contamination reduction and control cannot be overstated.…”

Topical Application of Adult Cecal Contents to Eggs Transplants Spore-Forming Microbiota but Not Other Members of the Microbiota to Chicks

“…Bacterial DNA from Enterobacteriaceae, Actinomycetales, Bifidobacteriales, and Lachnospiraceae was detected using terminal restriction fragment length polymorphism (T-RFLP) of the entire gastrointestinal tract of chicken embryos (15), raising the possibility that in ovo microbial colonization occurs in proximal parts of the gastrointestinal tract as well as the cecum. However, skepticism about such results is not unwarranted, as low microbial biomass samples are known to be prone to contamination leading to false-positive results and inflated microbial diversity (16). The presence of bacteria within embryos and eggs would pose a question as to their origin.…”

Re‐assessing microbiomes in the low‐biomass reproductive niche

“…Such scenarios have resulted in more rigorous data analysis and the recent reporting of an absent microbiome within the placenta . A number of methods exist for identifying and removing contaminating sequences: negative control filtering; relative abundance threshold filtering; removal by correlation with DNA concentration and presence in negative controls (decontam); and prediction modelling (Sourcetracker) . For low‐biomass samples where the levels of contaminants are comparatively high (>50%), all methods fail to identify all contaminants accurately.…”