Identification and Removal of Contaminant Sequences From Ribosomal Gene Databases: Lessons From the Census of Deep Life

Abstract

Earth’s subsurface environment is one of the largest, yet least studied, biomes on Earth, and many questions remain regarding what microorganisms are indigenous to the subsurface. Through the activity of the Census of Deep Life (CoDL) and the Deep Carbon Observatory, an open access 16S ribosomal RNA gene sequence database from diverse subsurface environments has been compiled. However, due to low quantities of biomass in the deep subsurface, the potential for incorporation of contaminants from reagents used during sample collection, processing, and/or sequencing is high. Thus, to understand the ecology of subsurface microorganisms (i.e., the distribution, richness, or survival), it is necessary to minimize, identify, and remove contaminant sequences that will skew the relative abundances of all taxa in the sample. In this meta-analysis, we identify putative contaminants associated with the CoDL dataset, recommend best practices for removing contaminants from samples, and propose a series of best practices for subsurface microbiology sampling. The most abundant putative contaminant genera observed, independent of evenness across samples, were Propionibacterium, Aquabacterium, Ralstonia, and Acinetobacter. While the top five most frequently observed genera were Pseudomonas, Propionibacterium, Acinetobacter, Ralstonia, and Sphingomonas. The majority of the most frequently observed genera (high evenness) were associated with reagent or potential human contamination. Additionally, in DNA extraction blanks, we observed potential archaeal contaminants, including methanogens, which have not been discussed in previous contamination studies. Such contaminants would directly affect the interpretation of subsurface molecular studies, as methanogenesis is an important subsurface biogeochemical process. Utilizing previously identified contaminant genera, we found that ∼27% of the total dataset were identified as contaminant sequences that likely originate from DNA extraction and DNA cleanup methods. Thus, controls must be taken at every step of the collection and processing procedure when working with low biomass environments such as, but not limited to, portions of Earth’s deep subsurface. Taken together, we stress that the CoDL dataset is an incredible resource for the broader research community interested in subsurface life, and steps to remove contamination derived sequences must be taken prior to using this dataset.

Hyperdiverse archaea near life limits at the polyextreme geothermal Dallol area

“…By contrast, amplicons derived from Dallol ponds, Black and Yellow lakes but also first-PCR 'negative'-controls were dominated by bacterial sequences. Most of them were related 25 to well-known kit and laboratory contaminants (17,18), other were human-related bacteria likely introduced during intensive afar and tourist daily visits to the site; a few archaeal sequences might result from aerosol cross-contamination despite extensive laboratory precautions (see Supplementary Methods). After removal of contaminant sequences (grey bars, Fig.…”

Rare Biosphere Archaea Assimilate Acetate in Precambrian Terrestrial Subsurface at 2.2 km Depth

“…Bacterial RNA data hosted sequences that are clearly deriving from the human contamination e.g., Streptococcaceae (Table S4) or have been detected as contaminants in molecular biology kits, such as Sphingomonas spp. [59,60]. Amplicon PCR controls were negative on all PCR runs.…”

Microbial residents of the Atlantis Massif’s shallow serpentinite subsurface

“…We identified laboratory contaminants as amplicon sequence variants (ASVs) that occurred in samples of both air and serpentinites. This simple overlap (SO) approach 345 is commonly used in environmental microbiology studies to remove all sequences (or taxonomic categories) that are shared between any sources of contamination and the sample of interest (39). This approach can be useful for removing ASVs from We considered the possibility of "reverse contamination" from our core samples into our air samples to be unlikely for multiple reasons.…”

“…Pseudomonas, Staphylococcus, Acinetobacter). Our list of laboratory air taxa (Data 365 set S4) may be useful for identifying contaminants in future studies, as a complement to previously reported lists of contaminants from laboratory reagents (37)(38)(39).…”

“…No matter how cleanly DNA is prepared, contaminant sequences can appear for a myriad of reasons (37)(38). Sheik et al (39) recently reviewed practices for identifying 115 contaminants in DNA sequence datasets, highlighting the importance of collecting and sequencing control samples representing potential sources of environmental and laboratory contamination. These studies have highlighted the need to complement careful laboratory protocols with computational methods that can distinguish true residents from multiple potential environmental and laboratory 120 contaminants (43-45).…”