The Great Microbiome Extraction Anomaly: How Extraction Bias Distorts Microbial Community Profiles

Abstract

Research on the human microbiome has become one of the most frequently published and highly cited areas in science. In parallel, environmental DNA (eDNA) and RNA (eRNA) microbiome studies have expanded rapidly, often using similar technical workflows. Numerous studies optimizing these workflows have demonstrated that nucleic acid purification can significantly influence bacterial metagenomic outcomes. Although this issue is widely acknowledged and efforts are often made to ensure workflow consistency, such protocol standardization may obscure a deeper problem. Specifically, even when protocols are consistent, the resulting data is unlikely to reflect the true microbial community. This systemic bias, herein described as The Great Microbiome Extraction Anomaly, refers to the widespread distortion of observed microbial community composition caused by variation in nucleic acid extraction efficiencies across microbial nucleic acid states, species biology, and specimen composition. The term draws inspiration from The Great Plate Count Anomaly, a long-recognized discrepancy between observable and cultivable microbial diversity. The Great Microbiome Extraction Anomaly presents a parallel methodological challenge which, like the original, will likely require novel technological innovation to resolve, as to date, no current protocol has achieved truly unbiased microbial DNA recovery. Currently, this limitation can only be addressed with carefully considered analytical controls that enable transparent reporting of microbial DNA recovery biases. However, the rate of control use in microbiome research remains very low. This review examines the evidence for nucleic acid state, microbe biology, and specimen-specific nucleic acid extraction efficiency biases specifically within eDNA and eRNA microbiome workflows, while citing evidence of extraction bias from human microbiome and molecular diagnostic research to demonstrate the broader constraints underlying differential microbial nucleic acid recovery. In addition, this review evaluates the extent to which controls are implemented in microbiome research, outlines explicit examples of analytical controls that are essential for inclusion in microbiome research, and argues that implementing these robust analytical control strategies, especially the use of positive controls, is essential to detect and mitigate the biases of The Great Microbiome Extraction Anomaly.