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Measurement of bacterial replication rates in microbial communities


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.

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Figure 1: iRep determines replication rates for bacteria using genome-resolved metagenomics.
Figure 2: iRep is an accurate measure of in situ replication rates.
Figure 3: iRep and bPTR calculations agree for a novel Deltaproteobacterium sampled from groundwater.
Figure 4: Replication rates were determined for CPR and human microbiome-associated organisms.
Figure 5: Elevated replication rates are associated with antibiotic administration and were detected before onset of necrotizing enterocolitis (NEC) in premature infants.
Figure 6: Absolute abundance (bars, left axis) and iRep (scatter plot, right axis) values for bacterial species associated with two premature infants.

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Funding was provided by NIH grant R01AI092531 Sloan Foundation grant APSF-2012-10-05, and by the US Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research under award number DE-AC02-05CH11231 (Sustainable Systems Scientific Focus Area and DOE-JGI) and award number DE-SC0004918 (Systems Biology Knowledge Base Focus Area). We thank T. Raveh-Sadka, B. Brooks, and D. Burstein for helpful discussions, and M. Albertsen for comments regarding GC sequencing bias.

Author information

Authors and Affiliations



C.T.B. and J.F.B. developed the iRep and bPTR methods. M.R.O. ordered and oriented draft genome sequences for bPTR calculations and conducted kPTR analyses. C.T.B. conducted the iRep, bPTR, and kPTR comparisons, and determined the accuracy of the iRep method. J.F.B. binned the adult human metagenome and curated the Deltaproteobacterium genome, with input from C.T.B. C.T.B. implemented the iRep method. B.C.T. provided bioinformatics support. C.T.B. and J.F.B. drafted the manuscript. All authors contributed to iRep development, reviewed results, and approved the manuscript.

Corresponding author

Correspondence to Jillian F Banfield.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Schematic showing steps involved in a genome-resolved metagenomics study that includes iRep analysis.

Microbiome sample collection and DNA extraction methods should be determined on a per-project basis, and metagenome sequencing can be conducted on the Illumina, PacBio, or another sequencing platform. Sequencing reads are trimmed based on quality scores (e.g. using SickleSickle18) and filtered for contamination (e.g. removal of human genome sequences). High-quality reads are then assembled (e.g. using IDBA_UDUD19), and the resulting scaffolds are binned either manually (e.g. based on GC content, taxonomic affiliation, coverage), and/or using a clustering algorithm such as ESOMESOM20,29,30) or using an automated binning program (e.g. MaxBinMaxBin21, CONCOCTCONCOCT22, or ABAWACAABAWACA15). Genome bins can then be assessed for completion and contamination based on inventory of expected single copy genes (SCGs), either based on identification of these genes from genome annotations (seesee15,29,55), or using software such as CheckMCheckM23. High-quality genomes are then compared with one another and grouped into clusters based on average nucleotide identity (ANI; e.g., based on sharing 98% ANI determined using MashMash54). A representative of each cluster should be included in a genome database that will be used for iRep analysis, along with genomes from other projects that may be appropriate for the analysis. Reads from each metagenome are then mapped to the genome database (e.g. using Bowtie2Bowtie247), and iRep is calculated from the read mapping data (see Online Methods).

Supplementary Figure 2 Evaluation of iRep method parameters.

(a) Gamma distribution used to simulate genome fragmentation for genome completeness analyses. The frequency of genome fragment sizes from all genomes analyzed in this study are compared with genome fragment sizes simulated using a gamma distribution with parameters: alpha = 0.1, beta = 21,000, min. = 5,000, max. = 200,000. These parameters were first estimated by fitting to the genome data, and then manually adjusted. Similarity between the two distributions shows that this gamma distribution can be used to approximate the level of genome fragmentation expected for draft-quality genome sequences. (b) iRep was calculated from random genome fragmentation simulations in order to survey a range of fragmentation levels (Supplementary Table 1). The analysis was conducted for an L. gasseri sample from the Korem et al.8 study in which iRep was determined to be 2.01 using the complete genome with 25x sequencing coverage. This known iRep value was then compared with iRep values determined from each genome fragmentation simulation after subsampling to 75% of the genome and using only 5x sequencing coverage. This enabled analysis of the influence of fragmentation on iRep calculations at the completeness and coverage limits of the method. Results show that 91.8% of iRep values are within the expected range of 0.15 when genomes have fewer than 175 fragments/Mbp of genome sequence. (c) Four L. gasseri samples from the Korem et al.8 study that represent iRep values between 1.50 and 2.01 were selected in order to test different coverage sliding window calculation methods (see Online Methods for description of each methods) and window sizes. For each sample, 100 random genome fragmentations and subsets were conducted in order to assess each method based on various levels of genome completion. The results show that the “iRep” and “median iRep” methods using 5 Kbp windows exhibited the least amount of variation. (d) Because the iRep method involves randomly combining coverage data from different genome fragments prior to calculating coverage sliding windows, some sliding windows will include coverage values from different locations on the complete genome sequence. In order to evaluate the variation introduced by the (random) order in which scaffolds are combined, iRep calculations were conducted for ten random orderings of 100 random genome fragmentations conducted using the sample set described in (c). Results show a very minimal amount of variation in iRep values as described by the difference between the lowest and highest values determined from each of the ten orderings (“iRep range”). Because of this, we chose not to implement the “median iRep” strategy. (e) Using the sample set described in (c), the iRep method was implemented using 5 Kbp windows using different window slide values in order to test whether or not the slide value would change the results. Because both 10 and 100 bp window slides produced similar results, we implemented the iRep method using a 100 bp window slide. (f) iRep is not as strongly correlated with bPTR without the GC sequencing bias correction for five genome sequences assembled from premature infant metagenomes (Supplementary Table 4; compare with GC corrected data in Fig. 2e).

Supplementary Figure 3 Coverage, GC skew patterns, and bPTR measurements for reconstructed genomes oriented and ordered based on complete reference genome sequences.

(a-e) Read mapping was conducted using sequences from the sample used for genome recovery. bPTR was calculated after determining the origin and terminus of replication based on cumulative GC skew. Coverage was calculated for 10 Kbp windows calculated every 100 bp (extremely low and high coverage windows were filtered out; see Online Methods). bPTR was calculated as the ratio between the coverage at the origin and terminus after applying a median filter. Cumulative GC skew and coverage patterns confirm the ordering of genome fragments.

Supplementary Figure 4 Reference genomes are not representative of organisms surveyed in the premature infant microbiome study.

Reads were mapped to both reconstructed genomes and closely related reference genomes (Supplementary Table 4), and the percent of each genome covered by sequencing reads is reported. Average nucleotide identity (ANI) is reported between each reconstructed genome and the paired reference genome. The large fractions of reference genomes not represented by metagenome sequencing show that extensive genomic variation is present between surveyed and reference genomes, despite high ANI values in some cases.

Supplementary Figure 5 Replication rates determined by iRep and kPTR are not in strong agreement for the premature infant study.

iRep values were determined based on reconstructed genomes, and kPTR values based on complete reference genomes (r = Pearson’s r value; Supplementary Tables 5 and 8).

Supplementary Figure 6 Coverage, cumulative GC skew, and bPTR measurements for complete reference genomes with similarity to genomes from the adult human microbiome sample.

(a-e) Reads from the adult human microbiome were mapped to complete reference genome sequences. Coverage was calculated for 10 Kbp windows every 100 bp (extremely low and high coverage windows were filtered out; see Online Methods). The origin and terminus of replication were determined based on coverage. bPTR was calculated as the ratio between the coverage at the origin and terminus after applying a median filter. Cumulative GC skew and coverage patterns suggest the presence of genomic variation or assembly errors for some genomes (b-c, e).

Supplementary Figure 7 Absolute abundance (bars, left axis) and iRep (scatter plot, right axis) for bacterial species associated with premature infants.

The five days following antibiotic administration are indicated using a color gradient (DOL = day of life). Half of the infants in the study developed necrotizing enterocolitis (NEC; dotted red lines) during the study period.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 9721 kb)

Supplementary Table 1

Analysis of the impact of genome completeness on iRep replication rate measurements. (XLSX 6986 kb)

Supplementary Table 2

Comparison of iRep, bPTR, and kPTR measurements. (XLSX 47 kb)

Supplementary Table 3

iRep, bPTR, and kPTR measurements for minimum genome sequencing coverage analyses. (XLSX 69 kb)

Supplementary Table 4

Comparison of iRep and bPTR measurements for draft-qualitygenomes ordered and oriented based on complete genome sequences. (XLSX 87 kb)

Supplementary Table 5

iRep measurements for organisms associated with prematureinfant microbiomes. (XLSX 163 kb)

Supplementary Table 6

Single copy gene inventory for genomes reconstructed from anadult human gut metagenome. (XLSX 38 kb)

Supplementary Table 7

iRep measurements for organisms associated with an adulthuman microbiome. (XLSX 48 kb)

Supplementary Table 8

kPTR values determined from the premature infantmetagenomes. (XLSX 46 kb)

Supplementary Table 9

kPTR values determined from the adult human metagenome. (XLSX 33 kb)

Supplementary Table 10

iRep measurements for Candidate Phyla Radiation (CPR)organisms. (XLSX 199 kb)

Supplementary Code (ZIP 89004 kb)

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Brown, C., Olm, M., Thomas, B. et al. Measurement of bacterial replication rates in microbial communities. Nat Biotechnol 34, 1256–1263 (2016).

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