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Title: A novel analysis method for paired-sample microbial ecology experiments

Abstract

Many microbial ecology experiments use sequencing data to measure a community s response to an experimental treatment. In a common experimental design, two units, one control and one experimental, are sampled before and after the treatment is applied to the experimental unit. The four resulting samples contain information about the dynamics of organisms that respond to the treatment, but there are no analytical methods designed to extract exactly this type of information from this configuration of samples. Here we present an analytical method specifically designed to visualize and generate hypotheses about microbial community dynamics in experiments that have paired samples and few or no replicates. The method is based on the Poisson lognormal distribution, long studied in macroecology, which we found accurately models the abundance distribution of taxa counts from 16S rRNA surveys. To demonstrate the method s validity and potential, we analyzed an experiment that measured the effect of crude oil on ocean microbial communities in microcosm. Our method identified known oil degraders as well as two clades, Maricurvus and Rhodobacteraceae, that responded to amendment with oil but do not include known oil degraders. Furthermore, our approach is sensitive to organisms that increased in abundance only in the experimentalmore » unit but less sensitive to organisms that increased in both control and experimental units, thus mitigating the role of bottle effects .« less

Authors:
ORCiD logo [1];  [1];  [2];  [3];  [4];  [4];  [3];  [1];  [5]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Univ. of Tennessee, Knoxville, TN (United States)
  3. Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  4. Masdar Institute of Science and Technology, Abu Dhabi (United Arab Emirates)
  5. INRA (France)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1327714
Alternate Identifier(s):
OSTI ID: 1480721
Grant/Contract Number:  
AC05-00OR22725; AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 11; Journal Issue: 5; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; oils; microbial ecology; probability distribution; community ecology; crude oil; oceans; sequence databases; sequence alignment

Citation Formats

Olesen, Scott W., Vora, Suhani, Techtmann, Stephen M., Fortney, Julian L., Bastidas-Oyanedel, Juan R., Rodriguez, Jorge, Hazen, Terry C., Alm, Eric J., and Humbert, Jean -Francois. A novel analysis method for paired-sample microbial ecology experiments. United States: N. p., 2016. Web. doi:10.1371/journal.pone.0154804.
Olesen, Scott W., Vora, Suhani, Techtmann, Stephen M., Fortney, Julian L., Bastidas-Oyanedel, Juan R., Rodriguez, Jorge, Hazen, Terry C., Alm, Eric J., & Humbert, Jean -Francois. A novel analysis method for paired-sample microbial ecology experiments. United States. doi:10.1371/journal.pone.0154804.
Olesen, Scott W., Vora, Suhani, Techtmann, Stephen M., Fortney, Julian L., Bastidas-Oyanedel, Juan R., Rodriguez, Jorge, Hazen, Terry C., Alm, Eric J., and Humbert, Jean -Francois. Fri . "A novel analysis method for paired-sample microbial ecology experiments". United States. doi:10.1371/journal.pone.0154804. https://www.osti.gov/servlets/purl/1327714.
@article{osti_1327714,
title = {A novel analysis method for paired-sample microbial ecology experiments},
author = {Olesen, Scott W. and Vora, Suhani and Techtmann, Stephen M. and Fortney, Julian L. and Bastidas-Oyanedel, Juan R. and Rodriguez, Jorge and Hazen, Terry C. and Alm, Eric J. and Humbert, Jean -Francois},
abstractNote = {Many microbial ecology experiments use sequencing data to measure a community s response to an experimental treatment. In a common experimental design, two units, one control and one experimental, are sampled before and after the treatment is applied to the experimental unit. The four resulting samples contain information about the dynamics of organisms that respond to the treatment, but there are no analytical methods designed to extract exactly this type of information from this configuration of samples. Here we present an analytical method specifically designed to visualize and generate hypotheses about microbial community dynamics in experiments that have paired samples and few or no replicates. The method is based on the Poisson lognormal distribution, long studied in macroecology, which we found accurately models the abundance distribution of taxa counts from 16S rRNA surveys. To demonstrate the method s validity and potential, we analyzed an experiment that measured the effect of crude oil on ocean microbial communities in microcosm. Our method identified known oil degraders as well as two clades, Maricurvus and Rhodobacteraceae, that responded to amendment with oil but do not include known oil degraders. Furthermore, our approach is sensitive to organisms that increased in abundance only in the experimental unit but less sensitive to organisms that increased in both control and experimental units, thus mitigating the role of bottle effects .},
doi = {10.1371/journal.pone.0154804},
journal = {PLoS ONE},
number = 5,
volume = 11,
place = {United States},
year = {Fri May 06 00:00:00 EDT 2016},
month = {Fri May 06 00:00:00 EDT 2016}
}

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Works referenced in this record:

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journal, July 2006

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