Abstract

Streptococcus pyogenes causes 700 million human infections annually worldwide, yet, despite a century of intensive effort, there is no licensed vaccine against this bacterium. Although a number of large-scale genomic studies of bacterial pathogens have been published, the relationships among the genome, transcriptome, and virulence in large bacterial populations remain poorly understood. We sequenced the genomes of 2,101 emm28 S. pyogenes invasive strains, from which we selected 492 phylogenetically diverse strains for transcriptome analysis and 50 strains for virulence assessment. Data integration provided a novel understanding of the virulence mechanisms of this model organism. Genome-wide association study, expression quantitative trait loci analysis, machine learning, and isogenic mutant strains identified and confirmed a one-nucleotide indel in an intergenic region that significantly alters global transcript profiles and ultimately virulence. The integrative strategy that we used is generally applicable to any microbe and may lead to new therapeutics for many human pathogens.

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Data availability

Whole-genome sequencing data for the 2,101 isolates studied have been deposited in the NCBI Sequence Read Archive under BioProject accession number PRJNA434389. The slightly updated complete genome sequence of the emm28 reference strain MGAS6180 (GenBank accession number CP000056) has been deposited in the NCBI GenBank database under the same accession number. Transcriptome data have been deposited in the Gene Expression Omnibus under accession GSE113058. The data that support the findings of this study are available from the corresponding author upon request.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

This study was supported in part by the Fondren Foundation, Houston Methodist Hospital and Research Institute (to J.M.M.), the Academy of Finland (grant 255636 to J.V.), a European Research Council grant (number 742158 to J.C.), and a National Institutes of Health grant (1R01AI109096-01A1 to M.K.). This research was also supported in part by the Intramural Research Program of the National Institute of Allergy and Infectious Disease, National Institutes of Health (to F.R.D.). We thank N. Copeland, N. Jenkins, and D. Ginsburg for critical comments and suggestions to improve the manuscript; K. Stockbauer for critical comments and editorial assistance; E. Graviss, H. Erlendsdottir, W. Hong, and S. Linson for technical assistance; H.-L. Hyyryläinen, J. Jalava, and the Finnish clinical microbiology laboratories; A. A. Shishkin for helpful suggestions regarding the RNAtag-seq protocol; M. Todorovic and J. Jonsdottir Nielsen for banking strains from the Faroe Islands; A. McGeer for Ontario strains; C. Van Beneden, B. Beall, and the Active Bacterial Core Surveillance of the CDC’s Emerging Infections Programs network; A. Ramstad Alme and A. Witsø for technical assistance; and M. Steinbakk (Norwegian Laboratory for Streptococci) for support.

Author information

Author notes

  1. These authors contributed equally: Priyanka Kachroo, Jesus M. Eraso.

Affiliations

  1. Center for Molecular and Translational Human Infectious Diseases Research, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute and Houston Methodist Hospital, Houston, TX, USA

    • Priyanka Kachroo
    • , Jesus M. Eraso
    • , Stephen B. Beres
    • , Randall J. Olsen
    • , Luchang Zhu
    • , Waleed Nasser
    • , Paul E. Bernard
    • , Concepcion C. Cantu
    • , Matthew Ojeda Saavedra
    • , María José Arredondo
    • , Benjamin Strope
    • , Hackwon Do
    • , Muthiah Kumaraswami
    • , Samantha L. Kubiak
    • , Hoang A. T. Nguyen
    • , S. Wesley Long
    •  & James M. Musser
  2. Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY, USA

    • Randall J. Olsen
    •  & James M. Musser
  3. Department of Microbiology and Immunology, Weill Cornell Medical College, New York, NY, USA

    • Randall J. Olsen
    •  & James M. Musser
  4. Institute of Biomedicine, Medical Microbiology and Immunology, University of Turku, Turku, Finland

    • Jaana Vuopio
    •  & Kirsi Gröndahl-Yli-Hannuksela
  5. National Institute for Health and Welfare, Helsinki, Finland

    • Jaana Vuopio
  6. Department of Clinical Microbiology, Landspitali University Hospital, Reykjavik, Iceland

    • Karl G. Kristinsson
  7. Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland

    • Karl G. Kristinsson
    • , Magnus Gottfredsson
    •  & Marita Debess Magnussen
  8. Department of Infectious Diseases, Landspitali University Hospital, Reykjavik, Iceland

    • Magnus Gottfredsson
  9. Helsinki Institute of Information Technology, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland

    • Maiju Pesonen
    • , Johan Pensar
    •  & Jukka Corander
  10. Department of Computer Science, Aalto University, Espoo, Finland

    • Maiju Pesonen
  11. Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA

    • Emily R. Davenport
    •  & Andrew G. Clark
  12. Department of Biostatistics, University of Oslo, Oslo, Norway

    • Jukka Corander
  13. Division for Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway

    • Dominique A. Caugant
  14. Medical Department, Infectious Diseases Division, National Hospital of the Faroe Islands, Tórshavn, Denmark

    • Shahin Gaini
  15. Department of Infectious Diseases, Odense University Hospital, Odense, Denmark

    • Shahin Gaini
  16. Department of Clinical Research, University of Southern Denmark, Odense, Denmark

    • Shahin Gaini
  17. Department of Science and Technology, Centre of Health Research, University of the Faroe Islands, Tórshavn, Denmark

    • Shahin Gaini
  18. Thetis, Food and Environmental Laboratory, Torshavn, Denmark

    • Marita Debess Magnussen
  19. Laboratory of Bacteriology, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA

    • Adeline R. Porter
    •  & Frank R. DeLeo

Authors

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Contributions

J.M.M. conceptualized the study. P.K., J.M.E., and J.M.M. designed the study. P.K., J.M.E., S.B.B., R.J.O., L.Z., W.N., P.E.B., C.C.C., M.O.S., M.J.A., B.S., M.P., J.P., J.C., S.L.K., H.A.T.N., S.W.L., and A.R.P. produced the data. P.K., J.M.E., S.B.B., R.J.O., L.Z., H.D., M.K., M.P., J.P., J.C., S.W.L., and F.R.D. analyzed the data. P.K. led the analyses of the transcriptome data. M.P., J.P., E.R.D., A.G.C., and J.C. provided scholarly input on the statistical analysis and presentation strategies. J.V., K.G.-Y.-H., K.G.K., M.G., D.A.C., S.G., and M.D.M. provided strains and metadata. All authors contributed to writing the manuscript. All authors reviewed and approved the final draft. P.K. and J.M.E. contributed equally to this work, as did S.B.B., R.J.O., and L.Z.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to James M. Musser.

Integrated supplementary information

  1. Supplementary Figure 1 Distribution of emm28 isolates by country and state in the United States.

    All strains were isolated during a 26-year period, spanning 1991 through 2016. (a) Distribution of strains by country. Vertical black bars indicate the number of isolates per year. The total number of strains isolated in the USA was 952, of which 951 strains were collected as part of the Active Bacterial Core (ABC) surveillance study conducted by the Centers for Disease Control and Prevention32,68,111,112 (see https://www.cdc.gov/abcs/index.html for a complete description of the study). The one additional strain (from Texas) is strain MGAS6180, which is the genome sequence reference strain. Canadian strains are all from Ontario. The Faroe Islands are a self-governing part of Denmark. Regardless of country, all strains were recovered as part of comprehensive, population-based studies. (b) Distribution of emm28 isolates by state in the USA. All strains were isolated during a period of 18 years, spanning 1995 through 2012. Vertical black bars indicate the number of isolates per year. For the U.S. isolates, the states have been coded (A-J) at the request of the Centers for Disease Control.

  2. Supplementary Figure 2 Flowcharts depicting bacterial genome and transcriptome data analysis.

    (a) Next-generation sequencing data analysis pipeline employed for the preprocessing, read mapping, variant discovery and downstream genomic analyses of whole-genome sequencing data. aMLST: Multilocus sequence type, bSNP: Single nucleotide polymorphism, cHGT: Horizontal gene transfer. (b) Bioinformatics pipeline for demultiplexing, quality assessment, adapter trimming, read mapping, and data normalization and differential expression of transcriptome data.

  3. Supplementary Figure 3 Distribution of emm28 isolates by genetic subclade, country and year.

    Strains are represented by country and year of isolation. Only strains belonging to subclades 1A (SC1A-red), 1B (SC1B-blue), 2A (SC2A-green), and 2B (SC2B-brown) are shown. (a) Vertical bars indicate the number of isolates per year. The number (n) of strains isolated in each country is shown. Six distant outlier strains in the phylogenetic tree and 7 strains from the Faroe Islands are not shown. Thus, the number of strains does not sum to the total sample of 2,101 strains. No strains belonging to subclade SC2A or SC2B were isolated in Iceland. (b) Total number of strains belonging to each individual subclade per country. US, United States; CA, Canada (Ontario); FI, Finland; NO, Norway; IS, Iceland. Others refers to 6 distant outlier strains in the phylogenetic tree.

  4. Supplementary Figure 4 Correlation among biological replicates for 50 strains analyzed by RNA-seq.

    Comparison of biological replicates per strain at mid-exponential (a) and early-stationary phase (b). Mean correlation coefficient (Pearson) and standard deviation of normalized and log-transformed transcript counts for three biological replicates per strain are plotted.

  5. Supplementary Figure 5 Transcriptome alterations and genetic subclades.

    (a) Schematic depicts number of differentially expressed (DE) genes obtained by comparing transcriptome data for strains in the three major genetic subclades at mid-exponential (ME) and early-stationary (ES) phases. (b) Fold-increase in nga-ifs-slo transcript levels in SC2A (n = 15) strains compared to SC1A (n = 12) and SC1B (n = 23) strains at ME and ES phase. (c) grab gene transcript levels (normalized counts) were significantly increased in SC1B (n = 23) strains compared to SC1A (n = 12) strains at both growth phases (ME and ES). A significant increase in grab transcript levels in SC2A (n = 15) strains compared to SC1A (n = 12) strains was observed at ES phase. Statistical tests were performed using Mann-Whitney (two-tailed) test. Data are presented as box and whisker plots, where whiskers represent the minimum and maximum values. n represents the number of strains; each strain has three independent biological replicates.

  6. Supplementary Figure 6 Comparison of three replicates versus single replicate and RNA-seq versus RNAtaq-seq.

    (a) Scatterplots comparing WT-like strains from each of three major subclades (10 SC1A, 22 SC1B, 14 SC2A) using triplicates versus one randomly selected replicate from the 50-strain data. Presence of three biological replicates in the 50-strain data allowed us to simulate comparisons of averaged normalized counts when three versus one replicate were used. Strong correlation (r = 0.99) was observed for each triplicate- versus single-replicate comparison. Pearson correlation coefficient (r) is shown for each comparison. n represents number of samples (number of strains multiplied by number of replicates). (b) Seven strains were processed using the two protocols, that is, RNA-seq (three biological replicates per strain) and RNAtag-seq (singletons, that is, using single replicates). Principal component analysis of the seven strains processed using RNA-seq (three spheres colored cyan in the PCA plot) and RNAtag-seq (single sphere colored red in the PCA plot) displays overlapping spatial clustering. Expression profile of the 7 strains in the PCA plot is circled and numbered 1 through 7. Strains analyzed: 1-MGAS7888, 2-MGAS29284, 3-MGAS29553, 4-MGAS28746, 5-MGAS7914, 6-MGAS28647, and 7-MGAS28686. (c) Scatterplots were generated for the normalized counts (log-transformed) from the aforementioned seven strains processed using the two protocols, that is, RNA-seq and RNAtag-seq. For each strain, normalized transcript counts were averaged over the three biological replicates (RNA-seq protocol) and compared to RNAtag-seq normalized counts (singleton strain samples). Pearson correlation coefficient (r) is shown for each comparison.

  7. Supplementary Figure 7 Strategy used to make pools and superpools and their sequencing read content (millions).

    (a) Strategy used to make pools and superpools. Strains (small yellow circles) were grouped to form 58 distinct pools (gray circles) by labeling total RNA extracted from each strain with unique barcoded oligoribonucleotides. RNA from 8 strains was mixed to create one pool, with the exception of pool 58, which contained RNA from only 5 strains. In total there were 58 pools. cDNAs from each pool were individually barcoded with Illumina P7 index oligonucleotides. Four different P7 oligonucleotides were used in this study. Four pools were mixed to form one superpool (large yellow circles). In total there were 15 superpools. Pool 58 contained cDNA from only five strains, and superpool 15 contained only two pools. The original number of strains we performed RNAtag-seq analysis on was 461, and here we present data for 442. Data from 19 strains were not included because of low sequence coverage. (b) Average number of sequence reads per pool for each of the 15 superpools is presented. Each circle represents mean and error bars represent standard deviation (SD). Median was calculated using data for superpools 1–14 (each comprised of four pools). Superpool 15 contained only two pools. (c) Graph depicts the median number of reads per sample per pool in millions. Median reads per sample for the pools 57 and 58 are larger due to the higher sequencing depth of these pools.

  8. Supplementary Figure 8 PCA plot of singleton strains and analysis of Cluster A ropB mutant strains.

    (a) The two major clusters identified by DBSCAN are shown. (b) No subclade-specific clustering was evident within the two clusters. (c) Twenty strains with ropB mutations are outliers (colored yellow) and group away from the other strains with ropB mutations (colored orange). ropB-non-outlier strains cluster with WT-like strains (colored light blue) and strains with mutations in other major regulator genes (colored blue). (d) Cluster A ropB mutant strains separated into two groups validated by k-means clustering and were designated arbitrarily as Group I and Group II. (e) Group II ropB mutant strains had significantly decreased speB transcript levels compared to Group I strains (Mann-Whitney, two-tailed, P < 0.0001). (f) Mutations were mapped onto the crystal structure of the C-terminal region of the RopB protein. Variant amino acid positions associated with Group I or Group II organisms are labeled in red and pink, respectively. Amino acid residues present in inferred functional domains are demarcated with ovals. Mutations located in RopB functional domains were present at significantly increased frequency (test of proportions-one-tailed, P < 0.05) in Group II strains (pink labels within ovals) compared to Group I strains (red labels within ovals). PBD: peptide binding domain, NTD: N-terminal domain. The crystal structure of the NTD has not been solved. (g) Kaplan-Meier curve showing that the Group I (n = 3) and Group II (n = 4) strains differ significantly (log-rank test) in virulence in a mouse necrotizing myositis infection model (40 mice per strain). (h) Gross pathology images of infected mouse hindlimbs (n = 5 mice per strain) reflect the difference in virulence between the Group I (top) and Group II (bottom) strains, and representative images are displayed. Boxed areas demarcated in white illustrate major lesion areas.

  9. Supplementary Figure 9 Lack of significant relationship between extent of transcriptome remodeling (number of DE genes) and genetic distance.

    (a) Scatterplot comparing the number of differentially expressed genes (DE) and the genetic distance of the 442 singleton strains. For each of the strains, genes were called differentially expressed compared to reference strain MGAS28737. Genetic distance was measured as the number of core chromosomal SNPs compared to strain MGAS28737. Red line represents the line of regression. No significant correlation was observed between genetic distance and extent of transcriptome remodeling (number of DE genes) with R2 value of 0.0046. (b) No improvement in correlation (R2 = 0.0040) was observed when the analysis was conducted using only data for the 188 strains that have wild-type alleles for all known major regulatory genes. Red, SC1A; blue, SC1B; green, SC2A; yellow, SC2B. R2 value was calculated by linear regression analysis.

  10. Supplementary Figure 10 Genome-wide association analysis and eQTL analysis of 442 strains

    (a) Genome-wide association analysis was performed on 442 strains. Manhattan plot showing statistical significance (y-axis) of each k-mer (red circles) positively associated with high transcript expression of genes Spy1336/R28 and Spy1337, and their position along the 1.9 Mb GAS genome. Significant k-mers mapped to only one region of the chromosome, corresponding to the intergenic region between the Spy1336/R28 and Spy1337 genes. The top part is a schematic of the GAS genome, with vertical blue lines corresponding to open reading frames (ORFs) encoded by each strand of the chromosome. The bottom part shows an enlargement of the genome location corresponding to Spy1336/R28 and Spy1337, and the intergenic region. P values were computed by SEER software (Methods) (b) eQTL analysis identifies significant association between genotype (9T versus 10T) and expression level of genes Spy1336/R28 and Spy1337 in 50 strains at mid-exponential phase (left panel) and in 442 strains at early-stationary phase (right panel). Horizontal black bars represent mean transcript expression and standard deviation. PeQTL refers to q-values (False discovery rate, FDR) as reported by MatrixEQTL package. The threshold used for genome-wide significance was adjusted P value < 10e-8.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–10, Supplementary Note and Supplementary Tables 1, 7, 9, 10, 13 and 17–19

  2. Reporting Summary

  3. Supplementary Table 2

    SNPs largely present in SC2A but absent in SC1A post Gubbins

  4. Supplementary Table 3

    Inferred MGE content for the 20 most prevalent MGE genotypes in the S. pyogenes emm28 cohort

  5. Supplementary Table 4

    MGE genotype based on the presence or absence of 50 phage and ICE encoded genes, 31 integrases and 19 secreted virulence factors, derived from MGEs identified in 60 complete S. pyogenes

  6. Supplementary Table 5

    SRST2 MGE-50 absence/presence matrix and genotype

  7. Supplementary Table 6

  8. Supplementary Table 8

    List of differentially expressed genes comparing the three major genetic subclades at midexponential and stationary phase

  9. Supplementary Table 11

    Regulatory gene mutation prediction by machine learning

  10. Supplementary Table 12

    List of differentially expressed genes comparing transcriptomic clusters within CovR/CovS mutant strains

  11. Supplementary Table 14

    List of differentially expressed genes between group II versus group I ropB mutant strains

  12. Supplementary Table 15

    List of differentially expressed genes comparing the isogenic strains with either 9Ts or 10Ts in the intergenic region between the Spy1336/R28 and Spy1337 genes

  13. Supplementary Table 16

    Results of eQTL analysis

  14. Supplementary Table 20

    Data quality metrics for the 2101 emm28 cohort

About this article

Publication history

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DOI

https://doi.org/10.1038/s41588-018-0343-1