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Title: Measurement of bacterial replication rates in microbial communities

Abstract

Culture-independent microbiome studies have increased our understanding of the complexity and metabolic potential of microbial communities. However, to understand the contribution of individual microbiome members to community functions, it is important to determine which bacteria are actively replicating. We developed an algorithm, iRep, that uses draft-quality genome sequences and single time-point metagenome sequencing to infer microbial population replication rates. The algorithm calculates an index of replication (iRep) based on the sequencing coverage trend that results from bi-directional genome replication from a single origin of replication. We apply this method to show that microbial replication rates increase after antibiotic administration in human infants. We also show that uncultivated, groundwater-associated, Candidate Phyla Radiation bacteria only rarely replicate quickly in subsurface communities undergoing substantial changes in geochemistry. Our method can be applied to any genome-resolved microbiome study to track organism responses to varying conditions, identify actively growing populations and measure replication rates for use in modeling studies.

Authors:
ORCiD logo [1];  [1];  [1];  [2]
+ Show Author Affiliations
  1. Univ. of California, Berkeley, CA (United States)
  2. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1532211
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Nature Biotechnology
Additional Journal Information:
Journal Volume: 34; Journal Issue: 12; Journal ID: ISSN 1087-0156
Publisher:
Springer Nature
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats

Brown, Christopher T., Olm, Matthew R., Thomas, Brian C., and Banfield, Jillian F. Measurement of bacterial replication rates in microbial communities. United States: N. p., 2016. Web. doi:10.1038/nbt.3704.
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Brown, Christopher T., Olm, Matthew R., Thomas, Brian C., & Banfield, Jillian F. Measurement of bacterial replication rates in microbial communities. United States. https://doi.org/10.1038/nbt.3704
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Brown, Christopher T., Olm, Matthew R., Thomas, Brian C., and Banfield, Jillian F. Mon . "Measurement of bacterial replication rates in microbial communities". United States. https://doi.org/10.1038/nbt.3704. https://www.osti.gov/servlets/purl/1532211.
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@article{osti_1532211,
title = {Measurement of bacterial replication rates in microbial communities},
author = {Brown, Christopher T. and Olm, Matthew R. and Thomas, Brian C. and Banfield, Jillian F.},
abstractNote = {Culture-independent microbiome studies have increased our understanding of the complexity and metabolic potential of microbial communities. However, to understand the contribution of individual microbiome members to community functions, it is important to determine which bacteria are actively replicating. We developed an algorithm, iRep, that uses draft-quality genome sequences and single time-point metagenome sequencing to infer microbial population replication rates. The algorithm calculates an index of replication (iRep) based on the sequencing coverage trend that results from bi-directional genome replication from a single origin of replication. We apply this method to show that microbial replication rates increase after antibiotic administration in human infants. We also show that uncultivated, groundwater-associated, Candidate Phyla Radiation bacteria only rarely replicate quickly in subsurface communities undergoing substantial changes in geochemistry. Our method can be applied to any genome-resolved microbiome study to track organism responses to varying conditions, identify actively growing populations and measure replication rates for use in modeling studies.},
doi = {10.1038/nbt.3704},
journal = {Nature Biotechnology},
number = 12,
volume = 34,
place = {United States},
year = {Mon Nov 07 00:00:00 EST 2016},
month = {Mon Nov 07 00:00:00 EST 2016}
}
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https://doi.org/10.1038/nbt.3704
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Cited by: 207 works
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Figures / Tables:

Figure 1 Figure 1: iRep determines replication rates for bacteria using genomeresolved metagenomics. (a) Populations of bacteria undergoing rapid cell division differ from slowly growing populations in that the individual cells of a growing population are more actively in the process of replicating their genomes (purple circles). (b) Differences in genome copymore » number across a population of replicating cells can be determined based on sequencing read coverage over complete genome sequences. The ratio between the coverage at the origin (“peak”) and terminus (“trough”) of replication (PTR) relates to the replication rate of the population. The origin and terminus can be determined based on cumulative GC skew. (c,d) If no complete genome sequence is available, it is possible to calculate the replication rate based on the distribution of coverage values across a draftquality genome using the iRep method. Coverage is first calculated across overlapping segments of genome fragments. Growing populations will have a wider distribution of coverage values compared with stable populations (histograms). These values are ordered from lowest to highest, and linear regression is used to evaluate the coverage distribution across the genome in order to determine the coverage values associated with the origin and terminus of replication. iRep is calculated as the ratio of these values. (e) Genomeresolved metagenomics involves DNA extraction from a microbiome sample followed by DNA sequencing, assembly, and genome binning. Binning is the grouping together of assembled genome fragments that originated from the same genome. This can be done based on shared characteristics of each fragment, such as sequence composition, taxonomic affiliation, or abundance.« less
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