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Comparative genomic analysis of ten clinical Streptococcus pneumoniae collected from a Malaysian hospital reveal 31 new unique drug-resistant SNPs using whole genome sequencing

  • Hassan Mahmood Jindal
  • Babu Ramanathan
  • Cheng Foh Le
  • Ranganath Gudimella
  • Rozaimi Razali
  • Rishya Manikam
  • Shamala Devi Sekaran
Open Access
Research
  • 1k Downloads

Abstract

Background

Streptococcus pneumoniae or pneumococcus is a leading cause of morbidity and mortality worldwide, specifically in relation to community-acquired pneumonia. Due to the overuse of antibiotics, S. pneumoniae has developed a high degree of resistance to a wide range of antibacterial drugs.

Methods

In this study, whole genome sequencing (WGS) was performed for 10 clinical strains of S. pneumoniae with different levels of sensitivity to standard antibiotics. The main objective was to investigate genetic changes associated with antibiotic resistance in S. pneumoniae.

Results

Our results showed that resistant isolates contain a higher number of non-synonymous single nucleotide polymorphisms (SNPs) as compared to susceptible isolates. We were able to identify SNPs that alter a single amino acid in many genes involved in virulence and capsular polysaccharide synthesis. In addition, 90 SNPs were only presented in the resistant isolates, and 31 SNPs were unique and had not been previously reported, suggesting that these unique SNPs could play a key role in altering the level of resistance to different antibiotics.

Conclusion

Whole genome sequencing is a powerful tool for comparing the full genome of multiple isolates, especially those closely related, and for analysing the variations found within antibiotic resistance genes that lead to differences in antibiotic sensitivity. We were able to identify specific mutations within virulence genes related to resistant isolates. These findings could provide insights into understanding the role of single nucleotide mutants in conferring drug resistance.

Keywords

Streptococcus Pneumoniae Antibiotic-resistance Whole genome sequencing Single nucleotide polymorphism Penicillin-binding proteins 

Background

Streptococcus pneumoniae is a Gram–positive human pathogen naturally inhabiting the human nasopharynx (which considered to be the reservoir as this pathogen has no animal or insect vectors) and is responsible for invasive and noninvasive diseases including meningitis, pneumonia, bacteremia, otitis media, and sinusitis [1, 2, 3]. According to the World Health Organization (WHO), this bacterium is responsible for 1.6 million deaths annually, including 0.7–1 million in children less than 5 years old and mostly in developing countries [4, 5]. In the United States, the annual number of deaths caused by pneumococcal pneumonia or meningitis is 40,000 [6, 7]. In Asia, S. pneumoniae is the major cause of acute respiratory infections (ARIs) in children under 5 years old [8]. Five of the 10 countries with the largest number of deaths caused by pneumococcal infections in children below 5 years old are located in Asia, including India, China, Bangladesh, Pakistan, and Afghanistan [9]. Currently, more than 93 different S. pneumoniae serotypes have been identified based on the immunochemical differences in the capsular polysaccharides [9, 10, 11].

The mechanism used by S. pneumoniae to become pathogenic is still poorly understood, and most likely it depends on the interaction between pneumococcal virulence factors and the host’s immunological response [12, 13]. For decades, penicillin has been the principle option for the treatment of infections associated with S. pneumoniae [14]. The main targets for penicillin and other β-lactams antibiotics are the penicillin-binding proteins (PBPs). These enzymes are essential for the synthesis of the bacterial cell wall. β-lactams act by binding to these enzymes and reducing peptidoglycan synthesis and remodeling. Subsequently, this leads to disruption of cell wall integrity and cell lysis [15, 16].

Like other Gram-positive bacteria, S. pneumoniae has developed significant resistance over the last few decades against a wide range of antibiotics due to extensive over-use. Mutating the target proteins such as PBPs to reduce their affinity to β-lactam antibiotics is the main resistance mechanism developed by pneumococci to resist β-lactams [17]. Moreover, S. pneumoniae has developed powerful resistance tools against erythromycin and other macrolides by modifying the target site (23S ribosomal RNA) using the erm gene or by efflux of the antibiotic from the bacterial cell through acquisition of the mef gene [18]. Tetracyclines are bacteriostatic agents that stop bacteria from reproducing by binding to the 30S subunit of the bacterial ribosome. Pneumococcal resistance to tetracycline occurs through ribosomal protection mediated by tet(O) and tet(M) genes [19]. There has been a huge increase in the number of penicillin-resistant pneumococcal isolates over the past three decades, and many strains are now resistant against common antibacterial drugs such as β-lactams, macrolides, and fluoroquinolones [20, 21]. A study conducted by Hackel and his co-workers on 2173 worldwide pneumococcal isolates, showed that 33.3% of the isolates were resistant to penicillin, 22.9% to erythromycin, and 16.2% to both erythromycin and penicillin [22].

Whole genome sequencing (WGS) has become a powerful tool for drug development by allowing researchers to investigate the mode of action of antibiotics and the mechanisms involved in bacterial resistance [23, 24]. Additionally, WGS can be utilised to investigate the evolution of resistance in real-time under a range of conditions [25]. In this study, we report the whole genome sequencing for 10 pneumococcal isolates with a range of susceptibility and resistance to different antimicrobial drugs to elucidate the association between antibiotic resistance and the underlying genetic changes.

Methods

Bacteria and MIC determinations

Ten pneumococcal clinical strains were collected from the microbiological specimens of patients cared for at University of Malaya Medical Centre (UMMC) over a three-year period from September 2010 to May 2012 (Table 1). All the isolated strains were stored in brain heart infusion (BHI) broth at − 80 °C. Pneumococcal isolates were grown in blood agar containing 5% defibrinated sheep blood as previously described [26]. Cultures were incubated for 16–24 h at 37 °C under 5% CO2. Multiplex PCR was performed to identify each strain serotype as previously described [27]. Minimal inhibitory concentration (MIC) was determined following the broth microdilution assay as described by the Clinical and Laboratory Standards Institute (CLSI) guidelines. Cation-adjusted Mueller–Hinton broth with lysed horse blood was inoculated with a 5 × 105 cfu/mL bacterial suspension. The MIC was recorded as the lowest dilution showing no visible growth. All of the results were obtained from three independent trials.
Table 1

Bacterial strains and sources used for the genomic comparison of S. pneumoniae strains

Isolate

Isolation date

Sex

Source

Serotypea

SPS1

15/9/2010

NA

Nasopharyngeal swab

NT

SPS2

21/5/2011

Female

Nasopharyngeal swab

1

SPS3

21/5/2011

Male

Nasopharyngeal swab

19F

SPS4

20/2/2012

Female

Nasopharyngeal swab

14

SPS5

16/3/2012

Female

Swab from eye

23F

SPS6

18/5/2012

Male

Nasopharyngeal swab

15B/C

SPS7

9/5/2011

Male

Blood

1

SPS8

8/3/2011

Female

Nasopharyngeal swab

14

SPS9

26/4/2011

Male

Blood

18

SPS10

10/5/2011

Male

Blood

8

aall serotypes were identified using multiplex PCR as described before

(Pai et al., [27])

Abbreviations: NA not available, NT non-typeable

Library preparation and whole genome sequencing

A DNeasy Blood & Tissue Kit (Qiagen) was used to extract genomic DNA from pneumococcal cells cultured overnight following the manufacturer’s guidelines. Whole genome sequencing was performed using the Illumina HiSeq 2000 platform consisting of 1 lane 100 bp paired-end reads. Briefly, Covaris S2 was used to fragment all genomic DNA at the temperature of 5.5 to 6 °C for 40 s. The fragmented DNAs were ends repaired, added with dA base and ligated with Illumina indexed adapters. Invitrogen 2% agarose E-gel was used for size selections of the samples. The selected DNA fragments with adapter molecules on both ends underwent ten cycles of PCR for amplification of prepared material. The samples were then diluted to 10 nM and pooled together. The libraries were loaded onto one lane of Illumina HiSeq 2000 flow cell v3 for sequencing. Illumina adapter sequences were trimmed on both ends of the reads which resulted in low quality bases on the 5′ end of the reads. Low quality bases were removed with a quality score filter of ≥ 30 using PRINSEQ version 0.20.3 [28].

Assembly

Assembly was performed utilising SPAdes assembler version 3.8.1 [29] by using metaSPAdes option specific for metagenome assemblies. Assembler was run using iterative kmer lengths ranging from 27 to 77. The 10 assembled genomes were compared to the reference genome S. pneumoniae TIGR4 (NC_003028.3) using MetaQuast [30].

Gene prediction and clustering

Genes prediction on the draft assemblies was performed by using the Prokka (Prokaryotic annotation) tool. Functionally, Prokka predicts genes based on available annotation information such as CDS and proteins. It builds HMM databases which are searched by using HMMER3. Prokka was run using customised parameters that were set to annotate against reference genome S. pneumoniae TIGR4 with an evalue of 1e-10. To create gene clusters among 10 isolates and the reference genome all the amino acid sequences in fasta format were retrieved. All proteins were subjected for BLASTp (e.value <1e-5) against the same set of sequences in order to perform all versus all blast. A connection (edge) between two genes was assigned if more than one third of the region aligned to both genes. An h-score (0 to 100) was used to weight the similarity (edge). For two genes G1 and G2, the Hscore was defined as score (G1G2) / max (score (G1G1), score (G2G2); the score used here was the BLAST raw score. Gene families were identified by using clustering by Hcluster_sg [31]. We used the average distance for the hierarchical clustering algorithm, with the parameters of minimum edge weight set to 5 and the minimum edge density (total number of edges / theoretical number of edges) set to 0.35.

Variant calling and phylogeny

Single nucleotide polymorphisms (SNPs) were identified using kSNP3 program (version 3.021) which identifies pan-genome SNPs in a set of genome sequences and builds a phylogenetic tree based on the SNPs [32]. kSNP3 was run using the standard mode of SNP detection and annotation using S. pneumoniae TIGR4 as reference with Kmer size of 11. Kmer was calculated by Kchooser program which accurately defines a kmer size based on the draft genome assemblies. Phylogeny trees are parsimony trees based on consensus trees from different samples. Although parsimony trees do not define evolution lineage, they do help to define the nearer samples based on changes per number of SNPs. A complex heat map package from Bioconductor was used to generate heat maps in R. Clusters are predicted using Euclidean distance method.

Statistical analysis

Statistical analysis testing the difference in SNP number between the antibiotic resistant and susceptible isolates was performed using two-sample Student’s t-test with a significant level at p < 0.05.

Results

Selection and whole-genome sequencing of S. pneumoniae

Ten isolates were selected from a larger collection of pneumococcal isolates according to their susceptibility to four different antibiotics: penicillin, cefotaxime, erythromycin, and tetracycline. Table 2 summarises the MICs for all 10 isolates. Isolates SPS1, SPS2, and SPS3 were non-susceptible to all antibiotics; isolate SPS4 was susceptible to penicillin, cefotaxime, and erythromycin; SPS5 exhibited susceptibility to cefotaxime and erythromycin, but it showed resistance to penicillin and tetracycline. Isolate SPS6 was susceptible to all four antibiotics; conversely, isolates SPS7 and SPS10 were resistant to all four antibiotics. Isolates SPS8 and SPS9 were resistant to all antibiotics but they showed susceptibility to penicillin.
Table 2

Antibiotic susceptibility profiles of S. pneumoniae isolates

Isolate a

MIC (μg/ml)b

PEN c

CTX c

ERY c

TET c

SPS1

2

1

2

16

SPS2

2

1

> 2

> 16

SPS3

4

1

> 2

> 16

SPS4

0.06

≤0.063

≤0.016

> 16

SPS5

1

0.125

0.031

4

SPS6

0.06

≤0.063

≤0.016

≤0.125

SPS7

2

2

> 2

> 16

SPS8

0.5

> 8

2

> 16

SPS9

0.25

8

2

16

SPS10

2

2

> 2

16

aIsolates SPS1, SPS2, and SPS3 are non-susceptible to all antibiotics. Isolate SPS4 is susceptible to penicillin, cefotaxime, and erythromycin, but resistant to tetracycline. SPS5 is susceptible to cefotaxime and erythromycin, but resistant to penicillin and tetracycline. SPS6 is susceptible to all four antibiotics. SPS7 and SPS10 are resistant to all four antibiotics, SPS8 and SPS9 were resistant to all antibiotics except penicillin

bMIC Minimum inhibitory concentration

cPEN Penicillin, CTX Cefotaxime, ERY Erythromycin, TET Tetracycline

The WGSs of all 10 isolates were conducted to investigate the association between antibiotic-resistance and the underlying genomics variations. The genomic DNA of all the isolates was sequenced using the Illumina HiSeq 2000 platform. The draft genome assemblies for the 10 isolates have been submitted to the NCBI BioProject under the project accession number PRJNA317517 (http://www.ncbi.nlm.nih.gov/bioproject/317517). The sequencing consisted of one lane 100 bp paired-end reads, yielding approximately 0.6Gbp to 3.6Gbp for S. pneumoniae. More than 80% of the reads were above a Phred quality score of 30 indicating the high-quality of the sequencing data. The overall GC% content for all 10 isolates ranged from 39.12 to 39.72% and was similar to that of the TIGR4 reference genome (39.7%) [33]. The number of genes was 2352 and 2159 for SPS1 and SPS2, respectively. SPS3, SPS4, SPS5, and SPS6 had gene contents of 1983, 1983, 2020, and 1984, respectively. SPS7, SPS8, SPS9, and SPS10 showed gene contents of 2064, 1980, 2035, and 1924, respectively (Additional file 1). The number of tRNAs was also similar for all the 10 isolates in the range of 41-46 (Additional file 1). Figure 1 represents a circular map of the ten pneumococcal clinical isolates compared to the reference genome of isolate TIGR4. All 10 isolates showed > 90% identity with the reference genome. Different colours represent the BLASTn matches between 70% to 100% nucleotide identities. The full assembly and gene content for each pneumococcal isolate can be found in Additional file 1.
Fig. 1

Circular genome map of 10 S. pneumoniae isolates compared to reference genome TIGR4. Rings from the outside inward: SP10, SP09, SP08, SP07, SP06, SP05, SP04, SP03, SP02, SP01, and reference genome S. pneumoniae TIGR4 (NC_003028.3). The blank spaces in the rings represent matches with less than 70% or no BLAST matches to the reference genome. The image was prepared using Blast Ring Image Generator [51]

Core genome polymorphism

To identify sequence variations, WGS reads from each strain were mapped to the TIGR4 reference genome of S. pneumoniae using the Bowtie2 software [34]. The genome sequences of all the clinical isolates revealed a high level of similarity and the virulence genes that are known to be involved in drug-resistance are well conserved among all the 10 isolates (Table 3). In order to identify differences that alter the level of resistance of these clinical isolates we focused on identifying SNPs in genes engaged with antibiotics pathways. Table 4 represents the total number of SNPs identified for each isolate, which ranged from 3600 to 6548 SNPs. SNPs that cause a change in amino acids, start codons, and stop codons were classified as “non-synonymous SNPs”. Figure 2 illustrates the distribution of all SNPs in both antibiotic resistant and susceptible isolates. The majority of these non-synonymous SNPs associated with pneumococcal essential genes were present in antibiotic resistant strains (p = 0.016). Penicillin-resistant isolates showed 3301 SNPs, while susceptible isolates had only 281 SNPs. 6343 SNPs were associated with tetracycline-resistant isolates compared to only 21 SNPs associated with isolate SPS6 (Fig. 2). Similarly, ceftriaxone and erythromycin resistant isolates showed greater number of SNPs (5234) compared to 111 SNPs in susceptible isolates (Fig. 2). The complete list of SNPs in the 10 isolates sequenced and the TIGR4 reference genome can be found in Additional file 2. To explore the potential link between sequence variants with virulence characteristics, non-synonymous polymorphisms were extracted from genes annotated as virulence factors or involved in bacterial resistance [35, 36] (Table 4). The conserved non-synonymous polymorphisms in all resistant pneumococcal isolates were identified. These SNPs could possibly play an important biological role as they result in stop codons or frame shifts in protein sequence (Table 5). A total of 16 genes with 90 non-synonymous SNPs found only in the resistant isolates were identified. The presence of some of these SNPs in more than one resistant isolate suggests that these SNPs or a subset of them might have potential roles in antibiotic resistance. For example, the same mutation (G597E) associated with penicillin binding protein PBP2b was found in four different resistant isolates SPS7, SPS8, SPS9, and SPS10. Similarly, mutation (T23A) associated with virulent gene pneumococcal surface protein A (PspA) was identified in four resistant isolates SPS1, SPS7, SPS8, and SPS9 (Additional file 3). By blasting our sequences, we were able to identify 31 unique non-synonymous SNPs associated with penicillin binding proteins (PBPs) and other virulent genes that were not previously published (Table 6).
Table 3

Presence and absence of genes involved in virulence and antibiotic-resistance in each of the 10 clinical isolates

Locus namea

Gene name and/or description

Presence of virulent genes in Pneumococcal isolates

SPS01

SPS02

SPS03

SPS04

SPS05

SPS06

SPS07

SPS08

SPS09

SPS10

SP_0531

blpI; bacteriocin

1

1

0

0

0

0

0

0

1

0

SP_0041

blpU; bacteriocin

1

1

1

1

1

1

1

1

0

1

SP_0109

bacteriocin

1

1

0

0

0

0

1

1

1

1

SP_0544

immunity protein BlpX

1

1

0

0

0

0

1

1

0

1

SP_1315

V-type ATP synthase subunit D

1

1

0

0

0

0

1

0

0

0

SP_1318

V-type ATP synthase subunit F

1

1

0

0

0

0

1

0

0

0

SP_1319

V-type ATP synthase subunit C

1

1

0

0

0

0

1

0

0

0

SP_1320

V-type ATP synthase subunit E

1

1

0

0

0

0

1

0

0

0

SP_1321

V-type ATP synthase subunit K

1

1

0

0

0

0

1

0

0

0

SP_1322

V-type ATP synthase subunit I

1

1

0

0

0

0

1

0

0

0

SP_0346

cpsA; capsular polysaccharide biosynthesis protein

1

1

1

1

1

1

1

1

1

1

SP_0347

cpsB; capsular polysaccharide biosynthesis protei

1

1

1

1

1

1

1

1

1

1

SP_0348

cpsC; capsular polysaccharide biosynthesis protein

1

1

1

1

1

1

1

1

1

1

SP_0349

cpsD; capsular polysaccharide biosynthesis protein

1

1

1

1

1

1

1

1

1

1

SP_0350

cpsE; capsular polysaccharide biosynthesis protein

1

1

1

1

1

1

1

1

1

1

SP_0117

pspA; pneumococcal surface protein A

1

1

1

1

1

1

1

1

1

1

SP_0377

cbpC; choline-binding protein C

1

1

1

1

1

1

1

1

1

1

SP_0378

cbpJ; choline-binding protein J

1

1

1

1

1

1

1

1

1

1

SP_0390

cbpG; choline-binding protein G

1

1

1

1

1

1

1

1

1

1

SP_0391

cbpF; choline-binding protein F

1

1

1

1

1

1

1

1

1

1

SP_0533

blpK; bacteriocin associated protein

1

1

1

1

1

1

1

1

1

1

SP_0545

blpY; Immunity protein

1

1

1

1

1

1

1

1

1

1

SP_0641

prtA; protective antigen A

1

1

1

1

1

1

1

1

1

1

SP_1638

PsaR; transcriptional regulator

1

1

1

1

1

1

1

1

1

1

SP_0966

pavA; adherence and virulence protein A

1

1

1

1

1

1

1

1

1

1

SP_1923

pln; pneumolysin

1

1

1

1

1

1

1

1

1

1

SP_1937

lytA; autolysin

1

1

1

1

1

1

1

1

1

1

SP_2190

chpA; choline-binding protein A

1

1

1

1

1

1

1

1

1

1

SP_2201

chpD; choline-binding protein D

1

1

1

1

1

1

1

1

1

1

SP_0457

bacA; bacitracin resistance protein

1

1

1

1

1

1

1

1

1

1

SP_0615

fibA; beta-lactam resistance factor

1

1

1

1

1

1

1

1

1

1

SP_1673

penA; penicillin-binding protein 2B

1

1

1

1

1

1

1

1

1

1

SP_0369

penicillin-binding protein 1A

1

1

1

1

1

1

1

1

1

1

SP_2099

penicillin-binding protein 1B

1

1

1

1

1

1

1

1

1

1

SP_2010

penicillin-binding protein 2A

1

1

1

1

1

1

1

1

1

1

SP_0336

penicillin-binding protein 2X

1

1

1

1

1

1

1

1

1

1

SP_0616

beta-lactam resistance factor

1

1

1

1

1

1

1

1

1

1

SP_0798

ciaR; DNA-binding response regulator

1

1

1

1

1

1

1

1

1

1

SP_0799

ciaH; sensor histidine kinase

1

1

1

1

1

1

1

1

1

1

SP_0972

pmrA; multi-drug resistance efflux pump

1

1

1

1

1

1

1

1

1

1

SP_0461

transcriptional regulator

0

0

1

1

1

1

0

0

0

0

All S. pneumoniae loci number (sp_#) are according to the annotation of reference strain TIGR4. 1 indicates presence and 0 indicates absence of the gene from the pneumococcal isolates

Table 4

The total number of non-synonymous SNPs for each pneumococcal isolate

Isolate

No. of SNPs

SNPs in protein coding

SPS1

3600

3180

SPS2

2847

2522

SPS3

6297

5611

SPS4

6339

5668

SPS5

6359

5675

SPS6

6411

5731

SPS7

6704

5998

SPS8

6440

5761

SPS9

6452

5776

SPS10

6548

5875

Fig. 2

The Venn diagram summarizes the number of SNPs among the resistant and susceptible isolates for each antibiotic. PEN (Penicillin), CTX (Cefotaxime), ERY (Erythromycin), and TET (Tetracycline)

Table 5

Non-synonymous SNPs among S. pneumoniae isolates found in genes associated with virulence, antibiotic resistance, and other regulatory functions

Locus namea

Gene name and/or description

No. of SNPs in Pneumococcal isolates

SPS01

SPS02

SPS03

SPS04

SPS05

SPS06

SPS07

SPS08

SPS09

SPS10

SP_0041

blpU; bacteriocin

1

1

1

1

1

1

1

1

2

SP_0346

cpsA; capsular polysaccharide biosynthesis protein

7

5

4

4

4

4

8

8

8

8

SP_0347

cpsB; capsular polysaccharide biosynthesis protein

1

2

1

1

1

5

SP_0348

cpsC; capsular polysaccharide biosynthesis protein

1

3

3

3

3

3

2

2

2

6

SP_0349

cpsD; capsular polysaccharide biosynthesis protein

7

11

11

12

12

5

5

9

SP_0350

cpsE; capsular polysaccharide biosynthesis protein

SP_0351

cpsF; capsular polysaccharide biosynthesis protein

SP_0352

cpsG; capsular polysaccharide biosynthesis protein

SP_0353

cpsH; capsular polysaccharide biosynthesis protein

SP_1837

capsular polysaccharide biosynthesis protein

4

3

3

3

4

4

SP_0117

pspA; pneumococcal surface protein A

2

4

2

3

4

5

5

4

3

SP_0377

cbpC; choline-binding protein C

6

3

5

5

5

5

1

1

1

SP_0930

cbpE; choline-binding protein E

7

8

12

11

13

12

3

3

11

5

SP_0378

cbpJ; choline-binding protein J

1

3

2

2

2

2

1

1

1

SP_0390

cbpG; choline-binding protein G

3

6

4

4

2

4

4

4

3

1

SP_0391

cbpF; choline-binding protein F

5

6

SP_0069

cbpI; choline-binding protein I

SP_0533

blpK; bacteriocin associated protein

SP_0545

blpY; Immunity protein

2

3

3

3

2

2

2

2

1

SP_0641

prtA; protective antigen A

16

14

13

12

12

13

14

14

9

13

SP_0730

spxB; pyruvate oxidase

1

1

1

SP_1002

lmb; adhesion lipoprotein

3

SP_1003

Conserved hypothetical protein

7

6

5

3

4

5

8

8

4

4

SP_1207

xseA; exodeoxyribonuclease VII, large subunit

SP_1923

pln; pneumolysin

1

1

2

2

2

3

SP_1937

lytA; autolysin

1

4

1

1

1

1

1

1

1

SP_2190

chpA; choline-binding protein A

6

11

9

10

7

8

2

2

4

9

SP_2201

chpD; choline-binding protein D

3

4

4

4

4

4

6

6

3

8

SP_2239

htrA; serine protease

1

1

1

2

2

SP_2240

spoJ; homologous to sporulation protein

1

2

2

2

2

1

1

1

1

SP_0457

bacA; bacitracin resistance protein

SP_0615

fibA; beta-lactam resistance factor

1

1

2

2

1

2

1

1

4

SP_1673

penA; penicillin-binding protein 2B

1

6

3

1

5

5

2

2

3

2

SP_0369

penicillin-binding protein 1A

3

2

1

2

1

1

2

2

3

SP_2099

penicillin-binding protein 1B

3

2

4

4

4

4

3

3

4

4

SP_2010

penicillin-binding protein 2A

4

5

5

5

5

5

1

1

7

5

SP_0336

penicillin-binding protein 2X

1

1

3

2

1

2

2

2

4

3

SP_0616

beta-lactam resistance factor

2

1

1

2

2

1

4

4

3

2

SP_1075

CpoA; glycosyl transferase

1

1

1

1

1

1

1

1

SP_0798

ciaR; DNA-binding response regulator

1

1

SP_0799

ciaH; sensor histidine kinase

1

1

SP_2084

pstS; phosphate ABC transporter, phosphate-bind

1

SP_2085

pstC; phosphate ABC transporter, permease protein

2

3

3

3

3

2

2

1

1

SP_2086

pstA; phosphate ABC transporter, permease protein

SP_2087

pstB; phosphate ABC transporter, ATP-binding protein

3

3

1

1

1

1

1

SP_1890

amiC; oligopeptide ABC transporter permease

2

3

3

3

3

3

4

4

4

2

SP_0010

beta-lactamase class C

4

1

1

1

1

1

1

SP_0314

hyaluronate lyase

14

14

15

15

15

15

16

16

10

14

SP_0071

metalloprotease ZmpC

SP_1650

manganese ABC transporter substrate-binding lipoprotein

1

1

1

1

1

1

1

1

1

1

SP_1687

neuraminidase B

7

7

8

4

4

8

5

5

5

4

SP_0357

UDP-N-acetyl glucosamine 2-epimerase CpsI

SP_0358

capsular polysaccharide biosynthesis protein CpsJ

SP_0359

capsular polysaccharide biosynthesis protein CpsK

SP_0360

UDP-N-acetyl glucosamine 2-epimerase CpsL

SP_0463

cell wall surface anchor protein

1

1

1

1

SP_0461

transcriptional regulator

1

SP_0468

sortase

SP_0965

endo-beta-N-acetylglucosaminidase LytB

2

2

2

2

2

2

1

1

1

2

SP_1783

DNA mismatch repair protein MutT

1

1

1

1

1

SP_0173

DNA mismatch repair protein HexB

SP_2218

MreC; rod shape-determining protein

SP_rrnaA23S

23S ribosomal RNA; K01980 23S ribosomal RNA

SP_rrnaB23S

23S ribosomal RNA; K01980 23S ribosomal RNA

SP_rrnaC23S

23S ribosomal RNA; K01980 23S ribosomal RNA

SP_rrnaD23S

23S ribosomal RNA; K01980 23S ribosomal RNA

aAll S. pneumoniae loci number (sp_#) are according to the annotation of reference strain TIGR4

Table 6

Unique non-synonymous single nucleotide polymorphisms (SNPs) associated with penicillin binding proteins (PBPs) and other virulent genes found in all ten pneumococcal isolates isolates

Locus Name

Putative Identification

Reference

Position

TIGR4

SNP

Pneumococcal isolate

Amino Acid Change

SP_0346

cpsA; capsular polysaccharide biosynthesis protein

320,234

C

T

SPS7, SPS8, SPS10

A53V

320,204

C

T

SPS7, SPS8

A43V

320,872

C

T

SPS10

P266S

321,451

G

A

SPS10

V459 M

320,657

C

T

SPS9, SPS10

S194 L

321,460

A

G

SPS2

I462V

321,485

T

C

SPS7, SPS8

V470A

320,710

A

G

SPS1

T212A

322,321

A

G

SPS10

K20E

322,360

G

A

SPS2

G33S

323,191

A

C

SPS1, SPS7, SPS8

N76H

SP_1837

capsular polysaccharide biosynthesis protein

1,746,914

T

C

SPS1, SPS9, SPS10

K212R

SP_0117

pspA; pneumococcal surface protein A

118,490

C

T

SPS9

T23 M

120,431

C

A

SPS7, SPS8

A670D

118,496

A

C

SPS7, SPS8, SPS10

Q25P

SP_0799

ciaH; sensor histidine kinase ClaH

753,163

C

G

SPS7, SPS8

H180D

SP_1923

pln; pneumolysin

1,832,851

G

A

SPS9

T154 M

SP_0369

penicillin-binding protein 1A

347,479

C

T

SPS2

E512K

348,706

T

A

SPS2

T103S

SP_2099

penicillin-binding protein 1B

2,006,807

A

G

SPS10

V787A

SP_2010

penicillin-binding protein 2A

1,917,863

T

C

SPS9, SPS10

E17G

1,916,459

T

G

SPS9

A485E

1,916,166

C

T

SPS9

A583T

SP_1673

penA; penicillin-binding protein 2B

1,573,212

C

A

SPS9

L609F

1,574,933

C

T

SPS3

V36I

1,573,493

C

A

SPS2

A516S

1,574,288

C

A

SPS2, SPS3

A251S

1,574,461

G

A

SPS2

A193V

SP_0336

penicillin-binding protein 2X

308,341

G

A

SPS9

D488N

307,393

G

A

SPS2, SPS3

A172T

309,113

C

A

SPS3

T745 K

The numbers of non-synonymous SNPs corresponding to the selected genes essential for bacterial survival and virulence based on previous literatures [37, 38, 39] in each isolate are presented in Table 5. Figure 3 represents the number of non-synonymous SNPs that are in genes associated with virulence and antibiotics resistance. Resistant isolates to all four antibiotics have higher numbers of SNPs associated with virulent genes than susceptible isolates (SPS4 and SPS6), suggesting that the presence of certain SNPs could be more related to drug-resistance (Fig. 4). Genes encoding capsular polysaccharide (CPS) biosynthesis proteins Cps4E, Cps4F, Cps4G, and Cps4H did not possess any mutations amongst all the pneumococcal isolates (Table 5).
Fig. 3

Heatmap represents the number of Non-synonymous SNPs from S. pneumoniae isolates from antibiotic-resistance genes

Fig. 4

Heatmap represents the presence and absence of SNPs (numbers on the right side) in some of virulent genes in all ten isolates. Annotation on the right side of the heat map is the gene names. (Blue color represents absence and pink color represents presence)

Phylogenetic analysis of S. pneumoniae isolates

A parsimony tree with respect to reference and 10 isolates was generated from the kSNP3 pipeline, parsimony tree is consensus tree based on all of the SNPs identified between the reference genome TIGR4 and the 10 isolates. Branch lengths are expressed in terms of changes per number of SNPS. Our result showed that isolates SPS3, SPS4, SPS5, and SPS6 were closest to each other, while isolates SPS8, SPS9, and SPS10 were closely related to each other. All these eight isolates formed one clade. On the other hand isolates SPS1, SPS2, and SPS7 were closely related to each other (Fig. 5).
Fig. 5

Parsimony tree with respect to reference and ten isolates was generated from kSNP3 pipeline. Parsimony tree is consensus tree based all of the SNPs identified between the reference and isolates. Branch lengths are expressed in terms of changes per number of SNP

Discussion

WGS has become an essential tool to elucidate the mechanisms used by bacteria to resist various antibiotics. In the present study, we have investigated the genomic variations and mutations among genes associated with virulence and antibiotic resistance in 10 clinical isolates of S. pneumoniae selected based on their susceptibility profiles against four antibiotics. Using WGS technique, the full genomic sequences of the 10 isolates were compared to that of the S. pneumoniae reference genome TIGR4.

Whole genome sequencing of the 10 isolates has revealed a high degree of sequence conservation between the pneumococcal isolates regardless of their susceptibility to antibiotics. This high sequence similarity of the isolates could possibly be explained by the low number of isolates and also by the fact that all the isolates had been collected from the same hospital. Nevertheless, these results are in agreement with previous studies showing that closely related isolates may possess different levels of resistance to antibiotics [40]. The genes known to be involved in antibiotic resistance are well conserved among all the 10 isolates; however, we were able to identify many mutations that differentiate resistant form susceptible isolates.

Our results showed that the majority of the SNPs occur in the resistant strains rather than the susceptible strains (Fig. 3). For instance, penicillin-resistant isolates showed a greater number of SNPs (3301) compared to susceptible isolates (281 SNPs). Figure 3 reveals that the highest numbers of SNPs among all the isolates are present in prtA, CpsD, CbpE, CbpA, CbpD, and CpsA. These genes could play a role in pneumococcal resistance to antibiotics. We were able to identify 90 non-synonymous SNPs associated with the essential genes of the resistant isolates, and some of them have reappeared in more than one resistant isolate, while none of them have occurred in susceptible strains (Additional file 3). Figure 4 shows that resistant isolates possess a higher number of SNPs associated with virulent genes than the susceptible isolates (SPS4 and SPS6). These results suggest that the presence of particular SNPs could play a role in conferring resistance to antibiotics. Out of these 90 SNPs, 31 were unique and found in penicillin binding proteins (PBP1A, PBP1B, PBP2A, PBP2B, and PBP2X), virulent genes (pneumolysin and PspA), sensor histidine kinase (ciaH), and CpsA; capsular polysaccharide biosynthesis protein (Table 6). However, the role of these SNPs in antibiotic resistance need to be investigated as some of them especially those related to penicillin binding proteins were also present in isolates SPS8 and SPS9 that are susceptible to penicillin.

In our study, we observed that genes encoding capsular polysaccharide biosynthesis proteins CpsE, CpsF, CpsG, and CpsH did not contribute to antibiotic resistance in all the resistant-types (Table 5). On the other hand, our results revealed that several novel mutations are present within capsular biosynthesis genes CpsA, CpsB, CpsC, and CpsD associated with resistant isolates (Table 3). The synthesis of capsular polysaccharides is regulated by a set of genes located at the same locus (cps) between dexB and aliA. Except for serotypes 3 and 37, the first four genes of cps locus (CpsA-D) are common in all pneumococcal serotypes. These four genes encode proteins that affect the level of CPS expression [41]. Although CpsA has no impact on the transcription of CPS in S. pneumoniae, a mutant of pneumococcus lacking CpsA has been shown to produce a reduced level of CPS [42]. CpsB is a manganese-dependent phosphotyrosine-protein phosphatase; it has been shown that CpsB is necessary for the dephosphorylation of CpsD. Mutants with CpsB deletions tend to have an increased level of phosphorylated CpsD, which leads to a significant decrease in production of CPS [43]. CpsC is a membrane protein required for CpsD tyrosine autophosphorylation. A novel role for CpsC in the attachment of CPS to the pneumococcal cell wall has been identified recently [44]. CpsD is an auto-phosphorylating tyrosine kinase. Mutations in CpsD affecting the ATP-binding domain eliminate CPS production in S. pneumococcus. Therefore, the capsular genes CpsB, CPsC and CpsD work together to regulate CPS biosynthesis [43, 44].

S. pneumoniae resists penicillin and other β-lactams by altering PBPs, the main enzymes involved in the final stage of cell wall synthesis. Six PBPs have been identified in pneumococcus PBP1a, 1b, 2×, 2a, 2b, and 3 [15]. Mutations in three of the PBPs (PBP2b, PBP2x, and PBP1a) have the most significant effect on β-lactams resistance. Several groups have reported mutations in genes encoding PBPs [45, 46, 47]. The MIC levels for isolates SPS1 and SPS2 were 2 μg/ml for penicillin and 1 μg/ml for cefotaxime. On the other hand, SPS7 and SPS10 showed the same level of resistance toward penicillin but an increased resistance for cefotaxime (2 μg/ml). Through our analysis, we were able to identify a non-synonymous SNP (G597E) in both SPS7 and SPS10 associated with penicillin binding protein PBP2B. The same SNP was found in isolates SPS8 and SPS9, and both of these two isolates showed high MICs toward cefotaxime (> 8 μg/ml and 8 μg/ml, respectively) (Additional file 3). However, our results showed that all the pneumococcal isolates regardless of their sensitivity to penicillin possess same mutations in the genes encoding penicillin-binding proteins, confirming previous reports that showed that these proteins are not the only determinants of penicillin resistance [48]. Furthermore, we were able to identify a unique mutation (H180D) in the sensor histidine kinase gene (ciaH) in resistant isolates only (Additional file 3). Mutations in ciaH increase resistance to β-lactams, as this gene is involved in the biosynthesis of cell wall components [49].

The phylogenetic relationships among different clinical isolates of S. pneumonaie were examined using the parsimony tree based on SNPs from whole genome sequencing. From the results, we observed that isolates SPS1 and SPS2 were clustered in one clade; isolates SPS8, SPS9, and SPS10 grouped in one clade; and isolates SPS4, SPS5 and SPS6 clustered in different clade (Fig. 5). These results are consistent with the MIC profile of the 10 isolates (Table 2). The observations that pneumococcal isolates with similar MIC profile were grouped together in a phylogenetic tree suggest that they possess common mutations and were probably originated from the common clone. It is possible that these strains could have evolved and acquired mutations in a similar manner due to selection pressures. Surprisingly, our results revealed that resistant isolate SPS3 was closely related to the susceptible isolates SPS4, SPS5, and SPS6. Among all the resistant isolates, SPS10 showed the highest number of SNPs (34 SNPs) (Table 3). On the other hand, SPS3 was the least resistant isolate having non-synonymous SNPs compared to other resistant isolates. Moreover, three non-synonymous SNPs (I178T, V22A, and T19A) were common among all the resistant isolates except isolate SPS3 (Additional file 3). This finding suggests that SPS3 could resist antibiotics using a unique mechanism as compared to other resistant isolates. The high phylogenetic relatedness among the clinical pneumococcal isolates with similar MIC profile is related to the specific SNPs in the mutated genes. The presence of identical uncommon mutations, as well as certain genes in the grouped isolates in the phylogenetic tree, is indicative of a single cluster of strains circulating in the population. For instance, the mutations S29A in cpsB, H197L in cpsC, M79I in cpsD, and Q136K in Ply were all found in isolates SPS1, SPS7, and SPS8 (Table 4). All three isolates are closely related to each other in the phylogenetic tree (Fig. 5). Similarly, certain genes such as SP_0461, SP_0463, SP_0357, and SP_1765 were only found in pneumococcal isolates SPS3, SPS4, SPS5, and SPS6 (Additional file 1).

Conclusion

In conclusion, this study compared the genomic sequences of 10 pneumococcal isolates with different susceptibility to multiple antibiotics. The high degree of sequence conservation and the presence of the same SNPs especially those related to genes involved in β-lactam resistance in both sensitive and resistant isolates, makes it a difficult task to identify distinct mechanisms of resistance that differentiate strains with different drug-sensitivities, and that antibiotic resistance cannot be only linked to the presence of certain genes. These results are in agreement with previous assumptions that bacterial virulence is the result of a gathering of pathogenicity-related genes that interact in various combinations [50] and that multi-drug resistance could be a result of combinations of mutations that lead to overexpression of several multi-drug efflux pumps; outer membrane porins, β-lactam acylases and enzymes and structural components involved in peptidoglycan stability (targets of β-lactams); gyrase mutations; and aminoglycoside phosphotransferases and –acetylases [40]. We were able to identify unique SNPs associated with virulent genes that could have a possible role in resistance to various antibiotics. To confirm these results future studies on virulence gene knockouts are needed to link the role of antibiotic resistance with these genes. This study was also limited by the relatively small number of isolates included in the analysis. Moreover, all resistant genes have yet to be subjected to individual mutational analysis. This can be achieved by introducing the SNPs on the resistant genes by site-directed mutagenesis and further expression analysis. The development of bacterial resistance towards antibiotics is a complex mechanism and multiple genetic alterations such as addition/deletion of specific genes, mutations, or a combination of both could be involved in the process. Whole genome sequencing can be utilised in conjunction with current epidemiological studies, diagnostic assays, and antimicrobial susceptibility tests to understand the genetic variation and pathogen biology of “high-risk” bacteria. It is also important to note that t.

Notes

Acknowledgements

Not applicable.

Funding

This study was supported by University of Malaya High Impact Research Grant (reference number: UM.C/HIR/MOHE/MED/40, account number: H-848 20001-E000079) and University of Malaya Research Grant (UMRG Project no. RP020C-14AFR and RP001C-13ICT).

Availability of data and materials

The draft genome assemblies for the ten isolates have been submitted to NCBI bioproject under the project accession number PRJNA317517 (http://www.ncbi.nlm.nih.gov/bioproject/317517).

Transparency declarations

None to declare.

Authors’ contributions

SDS designed the experiments, selected the isolates, funded the project and helped write and approve the manuscript, LCF assisted in the design and testing of laboratory tests on the isolates, HMJ performed the experiments, assisted in analysis and drafting of the manuscript, BR assisted in analysis and sequence allignment, RM helped fund the salaries of research assistants, designing and in manuscript drafting and RR performed the statistical analysis. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

12929_2018_414_MOESM1_ESM.xlsx (289 kb)
Additional file 1: Assembly and gene content of all the ten pneumococcal clinical isolates. (XLSX 288 kb)
12929_2018_414_MOESM2_ESM.xlsx (8.6 mb)
Additional file 2: Full list of non-synonymous single nucleotide polymorphisms (SNPs) in all ten pneumococcal clinical isolates. (XLSX 8827 kb)
12929_2018_414_MOESM3_ESM.docx (30 kb)
Additional file 3: Conserved non-synonymous single nucleotide polymorphisms (SNPs) associated with penicillin binding proteins (PBPs) and other virulent genes found in resistant isolates. (DOCX 29 kb)

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© The Author(s). 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Department of Medical MicrobiologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Biological SciencesSchool of Science and Technology, Sunway UniversityKuala LumpurMalaysia
  3. 3.School of Pharmacy, University of Nottingham Malaysia CampusSemenyihMalaysia
  4. 4.Sengenics HIR, University MalayaKuala LumpurMalaysia
  5. 5.Department of Trauma and EmergencyUniversity Malaya Medical CentreKuala LumpurMalaysia
  6. 6.Department of Microbiology, Faculty of MedicineMAHSA UniversityJenjaromMalaysia

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