Are bacteria, fungi, and archaea present in the midtrimester amniotic fluid?

OBJECTIVES

This study was conducted to determine whether bacteria, fungi, or archaea are detected in the amniotic fluid of patients who underwent midtrimester amniocentesis for clinical indications.

METHODS

Amniotic fluid samples from 692 pregnancies were tested by using a combination of culture and end-point polymerase chain reaction (PCR) techniques. Intra-amniotic inflammation was defined as an interleukin-6 concentration >2,935 pg/mL.

RESULTS

Microorganisms were detected in 0.3% (2/692) of cases based on cultivation, 1.73% (12/692) based on broad-range end-point PCR, and 2% (14/692) based on the combination of both methods. However, most (13/14) of these cases did not have evidence of intra-amniotic inflammation and delivered at term. Therefore, a positive culture or end-point PCR in most patients appears to have no apparent clinical significance.

CONCLUSIONS

Amniotic fluid in the midtrimester of pregnancy generally does not contain bacteria, fungi, or archaea. Interpretation of amniotic fluid culture and molecular microbiologic results is aided by the assessment of the inflammatory state of the amniotic cavity. The presence of microorganisms, as determined by culture or a microbial signal in the absence of intra-amniotic inflammation, appears to be a benign condition.

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

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.