High throughput in-situ metagenomic measurement of

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and ≤ 5% contamination) with varying degree of fragmentation ranging from 55 to 202 fragments/Mbp. The horizon- tal line represents ... Staphylococcus capitis.

High throughput in-situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage Emiola and Oh

C

B 1250

Non-growing bacteria

750

Genome Location 0

[email protected]

0

25 50 Average coverage

p = 2.49 x 10

500 1000 1500 2000 Genome Location (Kbp)

−1 −3 −4 500

1000

1500

2000

Genome Location (Kbp)

F

● PTR 6 ● OD600

p = 2.26 x 10

2000 OD600



4

2

Count

1000





Coverage

OD600

0.4x

0.2x Refined Unrefined



●●

1

2

3

Distance from ori

4

(Mb)

1.8

C. simulans

● 0.5

0.0

1.50

● ●



0 0

1.75

300

1.0

500

2.25 2.00

●● ● ● ● 100 200 Time (min)

1.5 ● PTR ● OD600



● ●

0

1

● 2

●● 3 Time (hr)





● ● ●

0 ● 0





●●

−2.5

2.50

S. epidermidis ●

1500

Log delta

Refined GRID = 1.81 Species heterogeneity (1- Refined/Unrefined) 0.515

−2

-16

0.0

Unrefined GRID = 3.73

0

75

E

D -10

−2.0

1

250

Growth rate=

Trough

−1.5

500

[email protected]

Peak

0

Log % coverage

Number of genomes

1000

−1.0

PTR

Increasing reads coverage

Growing bacteria

1.7 1.6

● ●

1.5 ● 1.4

● 4

PTR

Replication origin

Log % coverage

A

5

1.3

Supplementary Figure 1: GRiD benchmark (A) Growing bacteria have higher read coverage in regions close to the origin of replication (ori) compared to the terminus (ter) region. Growth rate can be measured as the ratio of coverage at the ori and ter regions. (B) Average coverage of genomes calculated from a metagenomic skin dataset (n = 698) with median read count of 17.9 million reads per sample10. The red vertical line represents a coverage cutoff of 5x which is required by iRep. (C) To minimize the level of noise during GRiD estimation, GRiD utilizes the lowest point of expected variance of the mean for the peak value, while the upper point of the variance of the trough mean is selected. The lower figure shows GRiD calculations of S. epidermidis in a skin sample with or without refinement. Overestimation of growth rate could occur without refinement. (D) Reproducibility of S. epidermidis GRiD estimates from a skin dataset after subsampling in the presence and absence of refinement. GRiD estimates are significantly (p < 0.001, Wilcoxon rank-sum test) more reproducible when refinement is included. (E) Barplot showing the distance of dnaA from ori in 2561 bacterial genomes obtained from the Database of Replication Origins (doriC) (http://tubic.tju.edu.cn/doric/index.php). (F) In vitro growth curve of S. epidermidis and C. simulans obtained from pure cultures and the corresponding PTR. Both microbes had an exponential doubling time of 30 min. Source data are provided as a Source Data file.

A

B

C

20 0.2

0.4 15

10

Delta

Delta 0.2

0.1

0.0

0.0

2

3 Delta

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5

Fragments/Mbp

75

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50

0

92 10 4 11 0 12 3 13 0 14 0 14 8 16 8 17 5 20 2

0

89

5

55

(dnaA/ori) x (ter/dif) / species heterogeneity

Coverage 0.2 0.4 1

Coverage 0.2 0.4 1

25

Completeness (%)

Supplementary Figure 2: Assessment of GRiD parameters (A) The combined role of dnaA coverage, dif coverage, and species heterogeneity on GRiD accuracy. PTR was initially calculated for S. epidermidis using a closed circular reference genome from a skin dataset, and then, GRiD was calculated using the same reference genome, but fragmented into 100 Kb fragments and reshuffled. The differences in growth estimates (delta) between PTR and GRiD (x-axis) are displayed as a factor of dnaA coverage, dif coverage, and species heterogeneity (y-axis). The red vertical line represents a delta cutoff of 0.15 which we considered as the threshold for high accuracy while the horizontal line is the y-axis cutoff for high accuracy. (B) The effect of genome fragmentation on GRiD reproducibility using 12 high quality bacterial bins (≥ 95% completeness and ≤ 5% contamination) with varying degree of fragmentation ranging from 55 to 202 fragments/Mbp. The horizontal line represents a delta cutoff of 0.15. (C) The effect of genome completeness on GRiD reproducibility. A genome bin with 89 fragments/Mbp, which is at the boundary for accuracy cutoff at ultra-low coverage as shown in ‘B’ above, was randomly subsampled prior to GRiD analysis. This subsampling step was conducted 10 times. The horizontal line represents a delta cutoff of 0.15. Source data are provided as a Source Data file.

Significant

6

GRiD

Non-significant

4

2

1

0.8

0.6

0.4

0.2

0

ER R ER 59 R 4 ER 59 294 R 4 ER 59 299 R 4 ER 59 308 R5 43 ER 9 11 R 4 ER 59 318 R 4 ER 59 326 R 4 ER 59 331 R5 43 ER 9 35 R 4 ER 59 348 R 4 ER 59 349 R 9 ER 59 000 R 9 ER 59 038 R 9 ER 59 044 9 SR R59 136 R 9 SR 15 142 R 0 SR 15 698 R 0 3 SR 15 698 R 06 6 SR 20 98 R 43 8 SR 63 72 R 6 8 SR 94 581 R9 81 48 55 28 4

Acinetobacter johnsonii Acinetobacter lwoffii Acinetobacter ursingii * Actinomyces neuii * Alcanivorax hongdengensis * Anaerococcus senegalensis * Corynebacterium jeikeium Corynebacterium pseudogenitalium * Corynebacterium simulans Enhydrobacter aerosaccus * Finegoldia magna Kocuria palustris Micrococcus luteus Paracoccus sp * Pasteurella bettyae * Peptoniphilus rhinitidis * Peptostreptococcus anaerobius * Prevotella bivia * Propionibacterium acnes Rothia dentocariosa Staphylococcus aureus Staphylococcus auricularis * Staphylococcus capitis Staphylococcus cohnii * Staphylococcus epidermidis Staphylococcus haemolyticus Staphylococcus hominis * Staphylococcus lugdunensis Staphylococcus pettenkoferi * Staphylococcus saprophyticus Staphylococcus warneri Streptococcus agalactiae Streptococcus equi Tetrasphaera japonica * Turicella otitidis *

en de Ar r th rit is

0 0 0 0 0 0 0 0 0.5 0 0 0 0 0 0 0.43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.29 0 0 0 0 0 0.36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.52 0 0 0 0 0 0 0 0 0 0 0 0 −0.337 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.37 0 0 0

B

G

t

e

as

pa tie n of e

A

I

is e

D

Ag

BS

PA S 0 0 0 0 0 0.27 0 0 0 0 0 0 0 0 0.38 0 0.34 0 0.41 0 0 0.35 0 0 0 0 0 0 0 0 0 0.38 0 0 0

du ra tio n

A

E

Propionibacterium sp. KPL1844 Cutibacterium avidum Propionibacterium sp. 5 U 42AFAA Propionibacterium sp. CC003.HC2 Propionibacterium sp. MB3007 Propionibacterium sp. oral taxon 193 Propionibacterium sp. CG1 02 60 36 Propionibacterium sp. 409.HC1 Propionibacterium sp. 434.HC2 Leptospira sp. JW3.C.A1 Propionibacterium sp. HMSC078F10 Cutibacterium acnes Propionibacterium sp. KPL2008 Propionibacterium sp. HMSC062D02 Propionibacterium sp. KPL1849 Propionibacterium sp. HMSC067A02 Propionibacterium sp. KPL2003 Propionibacterium sp. KPL1854 Propionibacterium sp. KPL2009 Propionibacterium sp. KPL1847 Propionibacterium sp. HMSC068C01 Propionibacterium sp. HMSC065F07 Propionibacterium sp. HMSC069G10 Propionibacterium sp. HMSC067A01 Propionibacterium sp. HMSC075A12 Propionibacterium sp. HMSC062D05 Propionibacterium sp. HMSC078F01

Streptococcus sp. UMB0029 Propionibacterium sp. 434.HC2 Propionibacterium sp. CG1 02 60 36 Cutibacterium avidum Propionibacterium sp. KPL1847 Propionibacterium sp. 5 U 42AFAA Propionibacterium sp. CC003.HC2 Corynebacterium pseudogenitalium Propionibacterium sp. MB3007 Propionibacterium sp. 409.HC1 Propionibacterium sp. HMSC078F01 Propionibacterium sp. HMSC078F10 Propionibacterium sp. HMSC068C01 Propionibacterium sp. KPL1849 Propionibacterium sp. oral taxon 193 Leptospira sp. JW3.C.A1 Corynebacterium aurimucosum Propionibacterium sp. HMSC065F07 Propionibacterium sp. HMSC069G10 Propionibacterium sp. HMSC062D05 Propionibacterium sp. HMSC067A01 Propionibacterium sp. HMSC075A12 Propionibacterium sp. KPL2008 Propionibacterium sp. HMSC062D02 Propionibacterium sp. HMSC067A02 Propionibacterium sp. KPL1854 Streptococcus sp. CCUG 49591 Propionibacterium sp. KPL2009 Cutibacterium acnes Propionibacterium sp. KPL2003 Corynebacterium sp. S5S1 Corynebacterium tuberculostearicum

Propionibacterium sp. KPL1844 Cutibacterium avidum Propionibacterium sp. 5 U 42AFAA Propionibacterium sp. CC003.HC2 Propionibacterium sp. MB3007 Propionibacterium sp. oral taxon 193 Propionibacterium sp. CG1 02 60 36 Propionibacterium sp. 409.HC1 Propionibacterium sp. 434.HC2 Leptospira sp. JW3.C.A1 Propionibacterium sp. HMSC078F10 Cutibacterium acnes Propionibacterium sp. KPL2008 Propionibacterium sp. HMSC062D02 Propionibacterium sp. KPL1849 Propionibacterium sp. HMSC067A02 Propionibacterium sp. KPL2003 Propionibacterium sp. KPL1854 Propionibacterium sp. KPL2009 Propionibacterium sp. KPL1847 Propionibacterium sp. HMSC068C01 Propionibacterium sp. HMSC065F07 Propionibacterium sp. HMSC069G10 Propionibacterium sp. HMSC067A01 Propionibacterium sp. HMSC075A12 Propionibacterium sp. HMSC062D05 Propionibacterium sp. HMSC078F01

D

1 0.8 0.6 0.4 0.2 0 −0.2 −0.4 −0.6 −0.8 −1

1 0.8 0.6 0.4 0.2 0 −0.2 −0.4 −0.6 −0.8 −1

Propionibacterium sp. oral taxon 193 Propionibacterium sp. 5 U 42AFAA Propionibacterium sp. CC003.HC2 Propionibacterium sp. MB3007 Propionibacterium sp. CG1 02 60 36 Propionibacterium sp. 409.HC1 Propionibacterium sp. 434.HC2 Propionibacterium sp. KPL1854 Propionibacterium sp. KPL2009 Corynebacterium sp. S5S1 Corynebacterium aurimucosum Corynebacterium pseudogenitalium Corynebacterium tuberculostearicum Propionibacterium sp. KPL2008 Propionibacterium sp. HMSC067A02 Propionibacterium sp. KPL2003 Propionibacterium sp. HMSC062D05 Propionibacterium sp. HMSC078F01 Propionibacterium sp. HMSC062D02 Cutibacterium acnes Propionibacterium sp. HMSC078F10 Propionibacterium sp. KPL1849 Propionibacterium sp. HMSC068C01 Propionibacterium sp. HMSC065F07 Propionibacterium sp. HMSC075A12 Propionibacterium sp. HMSC067A01 Propionibacterium sp. HMSC069G10 Cutibacterium avidum Propionibacterium sp. KPL1847

C

Streptococcus sp. UMB0029 Propionibacterium sp. 434.HC2 Propionibacterium sp. CG1 02 60 36 Cutibacterium avidum Propionibacterium sp. KPL1847 Propionibacterium sp. 5 U 42AFAA Propionibacterium sp. CC003.HC2 Corynebacterium pseudogenitalium Propionibacterium sp. MB3007 Propionibacterium sp. 409.HC1 Propionibacterium sp. HMSC078F01 Propionibacterium sp. HMSC078F10 Propionibacterium sp. HMSC068C01 Propionibacterium sp. KPL1849 Propionibacterium sp. oral taxon 193 Leptospira sp. JW3.C.A1 Corynebacterium aurimucosum Propionibacterium sp. HMSC065F07 Propionibacterium sp. HMSC069G10 Propionibacterium sp. HMSC062D05 Propionibacterium sp. HMSC067A01 Propionibacterium sp. HMSC075A12 Propionibacterium sp. KPL2008 Propionibacterium sp. HMSC062D02 Propionibacterium sp. HMSC067A02 Propionibacterium sp. KPL1854 Streptococcus sp. CCUG 49591 Propionibacterium sp. KPL2009 Cutibacterium acnes Propionibacterium sp. KPL2003 Corynebacterium sp. S5S1 Corynebacterium tuberculostearicum

Spearman correlation coefficient

Propionibacterium sp. oral taxon 193 Propionibacterium sp. 5 U 42AFAA Propionibacterium sp. CC003.HC2 Propionibacterium sp. MB3007 Propionibacterium sp. CG1 02 60 36 Propionibacterium sp. 409.HC1 Propionibacterium sp. 434.HC2 Propionibacterium sp. KPL1854 Propionibacterium sp. KPL2009 Corynebacterium sp. S5S1 Corynebacterium aurimucosum Corynebacterium pseudogenitalium Corynebacterium tuberculostearicum Propionibacterium sp. KPL2008 Propionibacterium sp. HMSC067A02 Propionibacterium sp. KPL2003 Propionibacterium sp. HMSC062D05 Propionibacterium sp. HMSC078F01 Propionibacterium sp. HMSC062D02 Cutibacterium acnes Propionibacterium sp. HMSC078F10 Propionibacterium sp. KPL1849 Propionibacterium sp. HMSC068C01 Propionibacterium sp. HMSC065F07 Propionibacterium sp. HMSC075A12 Propionibacterium sp. HMSC067A01 Propionibacterium sp. HMSC069G10 Cutibacterium avidum Propionibacterium sp. KPL1847

1 0.8 0.6 0.4 0.2 0 −0.2 −0.4 −0.6 −0.8 −1

Supplementary Figure 3: GRiD analysis from metagenomic dataset (A) Inter-individual bacterial growth differences and association with psoriasis patient characteristics. Statistical differences between population groups were determined using the Wilcoxon rank-sum test. (B) GRiD values of Bdellovibrio species in each environmental sample. (C, D, E) Growth rate correlation between bacterial species in dry (C), moist (D) and oily (E) sites from a skin metagenomic dataset. Blue and red circles indicate positive and negative Spearman correlation respectively. Larger circles and darker colors indicate a higher correlation. Source data are provided as a Source Data file.

Concatenate all samples

Sample pool

Contigs Extract contigs > 1 Kb Contigs pool

Bowtie mapping

De novo assembly (MEGAHIT)

Unmapped reads

Concatenate unmapped reads De novo assembly (SPAdes) Extract contigs/scaffolds > 1 Kb, concatenate with previous contig pool Binning (MetaBAT) Genome bins Quality check, cutoff at 75% completeness and 5% contamination (CheckM) High quality bins

GRiD

Supplementary Figure 4: Flowchart for the identification of uncultivated bacteria prior to GRiD analysis.

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