Title: Simulated rRNA/DNA Ratios Show Potential To Misclassify Active Populations as Dormant

Abstract

ABSTRACT The use of rRNA/DNA ratios derived from surveys of rRNA sequences in RNA and DNA extracts is an appealing but poorly validated approach to infer the activity status of environmental microbes. To improve the interpretation of rRNA/DNA ratios, we performed simulations to investigate the effects of community structure, rRNA amplification, and sampling depth on the accuracy of rRNA/DNA ratios in classifying bacterial populations as “active” or “dormant.” Community structure was an insignificant factor. In contrast, the extent of rRNA amplification that occurs as cells transition from dormant to growing had a significant effect ( P < 0.0001) on classification accuracy, with misclassification errors ranging from 16 to 28%, depending on the rRNA amplification model. The error rate increased to 47% when communities included a mixture of rRNA amplification models, but most of the inflated error was false negatives (i.e., active populations misclassified as dormant). Sampling depth also affected error rates ( P < 0.001). Inadequate sampling depth produced various artifacts that are characteristic of rRNA/DNA ratios generated from real communities. These data show important constraints on the use of rRNA/DNA ratios to infer activity status. Whereas classification of populations as active based on rRNA/DNA ratios appears generally valid, classificationmore » of populations as dormant is potentially far less accurate. IMPORTANCE The rRNA/DNA ratio approach is appealing because it extracts an extra layer of information from high-throughput DNA sequencing data, offering a means to determine not only the seedbank of taxa present in communities but also the subset of taxa that are metabolically active. This study provides crucial insights into the use of rRNA/DNA ratios to infer the activity status of microbial taxa in complex communities. Our study shows that the approach may not be as robust as previously supposed, particularly in complex communities composed of populations employing different growth strategies, and identifies factors that inflate the erroneous classification of active populations as dormant.« less

Authors:

Steven, Blaire ^[1]; Hesse, Cedar ^[2]; Soghigian, John ^[1]; Gallegos-Graves, La Verne ^[2]; Dunbar, John  ^[2];

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Löffler, ed., Frank E.

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+ Show Author Affiliations

1. Department of Environmental Sciences, Connecticut Agricultural Experiment Station, New Haven, Connecticut, USA
2. Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA

Publication Date:

Thu Jun 01 00:00:00 EDT 2017

Research Org.:

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE; USDOE Office of Science (SC). Biological and Environmental Research (BER) (SC-23)

OSTI Identifier:

1617750

Alternate Identifier(s):

OSTI ID: 1361481

Report Number(s):

LA-UR-16-26998
Journal ID: ISSN 0099-2240; e00696-17

Grant/Contract Number:  

AC52-06NA25396

Resource Type:

Published Article

Journal Name:

Applied and Environmental Microbiology

Additional Journal Information:

Journal Name: Applied and Environmental Microbiology Journal Volume: 83 Journal Issue: 11; Journal ID: ISSN 0099-2240

Publisher:

American Society for Microbiology

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; Biological Science; microbial activity