The success in determining the whole genome sequence of a bacterial pathogen was first achieved in 1995 by determining the complete nucleotide sequence of Haemophilus influenzae Rd using the chain-termination method established by Sanger et al. in 1977 and automated by Hood et al. in 1987. However, this technology was laborious, costly, and time-consuming. Since 2004, high-throughput next-generation sequencing technologies have been developed, which are highly efficient, require less time, and are cost-effective for whole genome sequencing (WGS) of all organisms, including bacterial pathogens. In recent years, the data obtained using WGS technologies coupled with bioinformatics analyses of the sequenced genomes have been projected to revolutionize clinical bacteriology. WGS technologies have been used in the identification of bacterial species, strains, and genotypes from cultured organisms and directly from clinical specimens. WGS has also helped in determining resistance to antibiotics by the detection of antimicrobial resistance genes and point mutations. Furthermore, WGS data have helped in the epidemiological tracking and surveillance of pathogenic bacteria in healthcare settings as well as in communities. This review focuses on the applications of WGS in clinical bacteriology.

Highlights of the Study

  • Whole genome sequencing has been projected to revolutionize the practice of clinical bacteriology due to increased efficiency and reduced time and cost.

  • Whole genome sequencing can be used to identify bacterial species and genotypes from cultures and clinical specimens.

  • Whole genome sequencing can also help in the detection of antimicrobial resistance mechanisms and epidemiologic tracking and surveillance of pathogenic bacteria.

An estimated 13.7 million people died due to infections in 2019, out of which 7.7 million deaths were related to bacterial pathogens, which was 13.6% of all global deaths [1]. The global burden of bacterial infections makes them a major cause of healthcare expenses involving diagnosis, treatment, and infection control strategies [2]. The laboratory diagnosis of infectious diseases caused by bacterial pathogens requires appropriate methods for their detection and identification in clinical samples. These methods include classical approaches like microscopic examinations, culture methods by using suitable media, antigen and antibody detection assays, and genomic approaches like molecular tests for the detection of pathogen-specific nucleic acids [3]. If a pathogenic bacterium can be grown in vitro in a reasonable time (i.e., within 24 h), culture of the pathogen from clinical specimens can be considered the “gold standard” because of high sensitivity and specificity. Furthermore, pathogens isolated in cultures can be used for sensitivity/resistance testing to antimicrobial agents to provide information for selecting appropriate and effective chemotherapeutic agents. However, some bacterial pathogens cannot be cultured in vitro or require longer times (up to several weeks) to get the culture results [3]. For the detection of such bacterial pathogens in clinical samples, culture methods are being replaced or supplemented with molecular tests which help detect pathogen-specific DNA or RNA sequences [4, 5]. Furthermore, molecular tests have also been developed to determine the genotypes and drug resistance genes/determinants of bacterial pathogens [6‒12].

Molecular methods are usually more sensitive with rapid turnaround time than classical culture-based methods and can be used equally well with culturable and non-culturable organisms [13‒15]. The applications of molecular methods in infectious diseases, like probe-based hybridization, polymerase chain reaction, and Sanger sequencing, usually target a few genes in each experiment [13‒17]. More recently, several next-generation sequencing (NGS) technologies for whole genome sequencing (WGS) have been developed [18]. These NGS technologies can be exploited to provide information about the complete genome of an organism [19, 20] and can detect many pathogens in clinical specimens simultaneously [21, 22]. Data obtained from WGS can be analyzed by using a battery of bioinformatics tools [23, 24], which provide information about the quality of sequenced genomes and identify species, strains, and genotypes of the infecting organisms, as well as predications about drug susceptibility/resistance and epidemiological investigations [25‒30]. Such information is valuable for early diagnosis, effective treatment, and epidemiological investigations of known as well as unknown/new pathogens [31, 32]. Hence, application of WGS can make significant contributions to clinical bacteriology and help in the diagnosis, treatment, prevention, control, and surveillance of bacterial infections.

The details of whole genome sequence data of two free-living bacterial pathogens, i.e., H. influenzae Rd and Mycoplasma genitalium, were published in 1995 [33, 34]. M. genitalium is considered to have the smallest genome among pathogenic bacteria, with a genome size of 580 kb. Based on the whole genome sequence information, genes responsible for DNA replication, transcription and translation, DNA repair, cellular transport, and energy metabolism were identified in M. genitalium and H. influenzae Rd [33, 34]. Furthermore, comparisons of their genomes suggested that differences in genome content were responsible for differences in the physiology and metabolic capacity of the two organisms [34]. These two genomes were sequenced using the chain-termination or dideoxynucleotide method established by Sanger et al. [35] in 1977 and automated by Hood et al. [36] in 1987 by using fluorescence-based detection of DNA fragments.

The Sanger method of sequencing is considered the “gold standard” for DNA sequencing, and WGS of many bacterial genomes has been carried out using this method [37]. However, since 2004, high-throughput NGS technologies have been developed, which generate sequences of several orders of magnitude more compared to the Sanger method and take less time (Table 1) [18]. The Sanger sequencing technique required 3 months to work out the sequence of M. genitalium in 1995 [34], whereas NGS using sequencing-by-synthesis approach allowed the complete genome sequencing of M. genitalium in 4 h with approximately 100-fold increase in throughput versus the Sanger sequencing method [38]. Furthermore, with the widespread use of NGS technologies for sequencing of whole genomes since 2008, the cost of DNA sequencing has drastically gone down (Table 2) [39]. Moreover, the Sanger method can sequence only a limited number of DNA fragments at a time [40‒42], whereas NGS leads to massive parallel sequencing and results in the sequencing of millions of fragments simultaneously in a single run (Table 1) [18]. This process results in sequencing of many genes (hundreds to thousands of genes) at one time [43]. Because of the massive parallel sequencing and reading capability, sequencing of the entire biome in a sample is possible using NGS technologies but not with the Sanger method [44].

Table 1.

Summary of currently available WGS technologies [18]

WGS technologyTechnology/companyRead lengthTime per runRead output per run
First generation 
 Short-read sequencing Sanger sequencing/Fischer Scientific 500–1,000 bp 7 h 0.44 Mbp 
Next generation 
 Short-read sequencing Pyrosequencing/Roche 50–500 bp 4–24 h 35–700 Mbp 
 Sequencing by synthesis/Illumina  56 h–14 days 15–600 Gbp 
 Ligation-based sequencing/Life Technologies  15 days 32 Gbp 
 Semiconductor sequencing/Ion Torrent  4 h 200 Mbp–2.5 Gbp 
 Long-read sequencing Single-molecule real-time/PacBio 10 –>50 kb 0.5–4 h 0.5–1 Gbp 
 Electonic signal sequencing/Oxford Nanopore  0.5–2 h 15–30 Gbp 
WGS technologyTechnology/companyRead lengthTime per runRead output per run
First generation 
 Short-read sequencing Sanger sequencing/Fischer Scientific 500–1,000 bp 7 h 0.44 Mbp 
Next generation 
 Short-read sequencing Pyrosequencing/Roche 50–500 bp 4–24 h 35–700 Mbp 
 Sequencing by synthesis/Illumina  56 h–14 days 15–600 Gbp 
 Ligation-based sequencing/Life Technologies  15 days 32 Gbp 
 Semiconductor sequencing/Ion Torrent  4 h 200 Mbp–2.5 Gbp 
 Long-read sequencing Single-molecule real-time/PacBio 10 –>50 kb 0.5–4 h 0.5–1 Gbp 
 Electonic signal sequencing/Oxford Nanopore  0.5–2 h 15–30 Gbp 
Table 2.

Data from the National Human Genome Research Institute (NHGRI), USA, for cost of DNA sequencing with time and technological developments [39]

Month and yearCost (US$) per million basesCost (US$) per human genome
First generation Sanger methodology 
 Sep 2001 5,292.39 95,263,072 
 Sep 2002 3,413.80 70,175,437 
 Oct 2003 2,230.98 40,157,554 
 Oct 2004 1,028.85 18,519,312 
 Oct 2005 766.73 13,801,124 
 Oct 2006 581.92 10,474,556 
 Oct 2007 397.09 7,147,571 
After introduction of next-generation methodologies 
 Oct 2008 3.81 342,502 
 Oct 2009 0.78 70,333 
 Oct 2010 0.32 29,092 
 Oct 2011 0.09 7,743 
 Oct 2012 0.07 6,618 
 Oct 2014 0.06 5,731 
 Oct 2015 0.014 1,245 
 Feb 2017 0.011 1,015 
 Feb 2019 0.011 993 
 Feb 2020 0.007 645 
 May 2022 0.006 525 
Month and yearCost (US$) per million basesCost (US$) per human genome
First generation Sanger methodology 
 Sep 2001 5,292.39 95,263,072 
 Sep 2002 3,413.80 70,175,437 
 Oct 2003 2,230.98 40,157,554 
 Oct 2004 1,028.85 18,519,312 
 Oct 2005 766.73 13,801,124 
 Oct 2006 581.92 10,474,556 
 Oct 2007 397.09 7,147,571 
After introduction of next-generation methodologies 
 Oct 2008 3.81 342,502 
 Oct 2009 0.78 70,333 
 Oct 2010 0.32 29,092 
 Oct 2011 0.09 7,743 
 Oct 2012 0.07 6,618 
 Oct 2014 0.06 5,731 
 Oct 2015 0.014 1,245 
 Feb 2017 0.011 1,015 
 Feb 2019 0.011 993 
 Feb 2020 0.007 645 
 May 2022 0.006 525 

The data from 2001 to 2007 represent the costs of DNA sequencing by using the first generation sequencing methodology based on Sanger’s chain-termination chemistry and capillary electrophoresis. From 2008 onward, the data represent the costs of DNA sequencing using next-generation methodologies.

NGS methods are further divided into short-read (read length = 50–500 bp) and long-read (10–>50 kb) technologies (Table 1). The short-read sequencing technologies include the platforms developed by Roche, Illumina/Solexa, Life Technologies, and Ion Torrent companies (Table 1). Roche’s method utilizes pyrosequencing, in which the nucleotide sequence is determined by detection of the released pyrophosphate upon the addition of the nucleotides to the DNA template [45]. The Illumina platform utilizes a sequencing-by-synthesis method based on reversible dye terminators [43]. The Ion Torrent sequencing method detects the release of hydrogen ions during DNA synthesis [46]. The method used by Life Technologies, known as sequencing by oligonucleotide ligation and detection, uses a ligation-based approach by employing reversible terminators to identify the DNA sequence [47]. The common disadvantages of short-read sequencing technologies include (i) small read lengths (≤500 bp), which cause difficulties in de novo assembly; (ii) regions with high/low G + C contents, tandem repeat regions, and interspersed repeat regions are hard to sequence using the short-read platforms; and (iii) de novo genome assemblies lacking entire portions of genomes and missing vital genes, primarily due to fragmentation of bacterial DNA for library preparation [48].

The long-read NGS technologies improve sequencing efficiency through rapid sample preparation and real-time signaling. These technologies have been developed by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies companies (Table 1). The PacBio sequencing platform uses a single-molecule, real-time approach with fluorescent-labeled nucleotides [49], whereas in the Oxford Nanopore sequencing method, a single-stranded DNA molecule passes through a nanopore, leading to changes in electrical current that are measured to determine the DNA sequence [50]. In addition to providing long-read lengths (10–>50 kb), the long-read technologies provide high percentage of consensus accuracy, are free of systematic errors, have a low degree of bias for G + C content, and have the simultaneous capability of epigenetic characterization [51].

WGS data should be processed and analyzed in steps using bioinformatics tools that help in obtaining useful information necessary for clinical applications. The steps in the processing and analysis of WGS data include data cleaning (quality check, trimming, and contamination removal), de novo assembly (constructing continuous stretches of DNA), scaffolding, quality assessment, annotation (identifying functional elements along the sequence of the genome), read mapping (to align the reads on a reference genome), variant calling (identify variants from sequence data), core genome analysis, strain typing (multilocus sequence typing, MLST), antimicrobial resistance (AMR) prediction, phylogenetic analysis, and visualization of trees [23]. A list of the bioinformatics tools used in various studies to process and analyze whole genome sequence data is given in (Table 3). These are just some examples of the available tools; new bioinformatic tools will continue to evolve.

Table 3.

Bioinformatics tools for the analysis of whole genome sequences [23]

Steps in WGS analysisBioinformatics tools
Quality check Fact QC, Fastp, NGS QC toolkit, Nullarbor, Gen2Ep, INNuca 
Trimming Fastp, Trimmomatic, TrimGalore, NGS QC toolkit, Nullarbor, Gen2Ep, INNuca 
Contamination removal Kraken, DeconSeq, ConFindr, Nullarbor, Gen2Ep, INNuca 
De novo assembly SPAdes, Velvet, ABySS, CLC Genomics Workbench, Gen2Ep, INNuca 
Scaffolding Sanger pipeline, Gen2Epi 
Assembly quality assessment QUAST, Gen2Ep, INNuca 
Annotation of assembled genomes Prokka, Prodigal, NCBI prokaryotic genome annotation pipeline, SnpEFF, Sanger pipeline, Nullarbor, Gen2Epi 
Read mapping Bowtie, BWA/BWA-mem, Stampy, SMALT, Snippy, Nullarbor, Gen2Ep 
Variant calling GATK, Freebayes, Samtools, VCFtools, Snippy, Snp-sites 
Core genome analysis Roary, ParSNP 
Strain typing pubMLST, Mlst, SRST, Pathogenwatch, Nullarbor, Gen2Ep, INNuca 
AMR prediction NG-STAR, ARIBA, ResFinder, PointFinder, ABRicate, ARG-ANNOT, RAST, PATRIC, CARD, LacED, Pathogenwatch, Nullarbor, Gen2Ep 
Phylogenic analysis RAxML, PhyML FastTree, RapidNJ, MEGA, SplitsTree, IQ-TREE, Pathogenwatch 
Visualization of trees Figtree, iTOL, Phyloviz, Ggtree, Pathogenwatch 
Steps in WGS analysisBioinformatics tools
Quality check Fact QC, Fastp, NGS QC toolkit, Nullarbor, Gen2Ep, INNuca 
Trimming Fastp, Trimmomatic, TrimGalore, NGS QC toolkit, Nullarbor, Gen2Ep, INNuca 
Contamination removal Kraken, DeconSeq, ConFindr, Nullarbor, Gen2Ep, INNuca 
De novo assembly SPAdes, Velvet, ABySS, CLC Genomics Workbench, Gen2Ep, INNuca 
Scaffolding Sanger pipeline, Gen2Epi 
Assembly quality assessment QUAST, Gen2Ep, INNuca 
Annotation of assembled genomes Prokka, Prodigal, NCBI prokaryotic genome annotation pipeline, SnpEFF, Sanger pipeline, Nullarbor, Gen2Epi 
Read mapping Bowtie, BWA/BWA-mem, Stampy, SMALT, Snippy, Nullarbor, Gen2Ep 
Variant calling GATK, Freebayes, Samtools, VCFtools, Snippy, Snp-sites 
Core genome analysis Roary, ParSNP 
Strain typing pubMLST, Mlst, SRST, Pathogenwatch, Nullarbor, Gen2Ep, INNuca 
AMR prediction NG-STAR, ARIBA, ResFinder, PointFinder, ABRicate, ARG-ANNOT, RAST, PATRIC, CARD, LacED, Pathogenwatch, Nullarbor, Gen2Ep 
Phylogenic analysis RAxML, PhyML FastTree, RapidNJ, MEGA, SplitsTree, IQ-TREE, Pathogenwatch 
Visualization of trees Figtree, iTOL, Phyloviz, Ggtree, Pathogenwatch 

Early studies of WGS using NGS technologies primarily focused on the genomic characterization of the bacterial pathogens, for example, genome size, GC content, total genes, protein-coding genes, pseudogenes, genes with assigned functions, hypothetical proteins, tRNA, and mRNA, etc. [52]. Comparisons of the genomes of pathogenic Francisella strains with the genomes of non-pathogenic Francisella tularensis subspecies novicida U112 identified genes specific to the human pathogenic strains and revealed pseudogenes that previously were unidentified [53]. The WGS of 19 Salmonella Typhi isolates and the comparative analysis of genomes with previously sequenced isolates showed little evidence of purifying selection, antigenic variation, or recombination between isolates [54]. In clinical strains of Pseudomonas aeruginosa isolated from patients with fibrosis, about 1.5 Mb of inserted sequences were identified, including 743 kb containing 615 open reading frames that were absent in the already published P. aeruginosa genomes. Furthermore, six rearrangement breakpoints and 220 kb of deleted sequences were also identified [55]. WGS of DNA of pooled Escherichia coli O157:H7 (STEC O157) from human clinical cases and cattle identified polymorphism that characterized genetic diversity within STEC O157 strains [56].

In the early years of WGS using NGS technologies, sequence data were obtained from bacterial isolates to identify the species of pathogens [57]. Later, the technology was used to identify the species, strains, and genotypes of bacterial pathogens directly from clinical specimens like vaginal swabs and fecal samples [58‒60]. Hasman et al. [61] analyzed urine samples from 35 patients with suspected urinary tract infections using conventional microbiology, WGS of isolated bacteria, and direct sequencing on pellets from the urine samples. The results showed that pathogenic bacteria were grown in pure cultures from 17 samples. WGS improved the identification of the cultivated bacteria, and almost complete agreement was observed between phenotypic and predicted antimicrobial susceptibilities. Complete agreement was also observed between species identification, multilocus sequence typing, and phylogenetic relationships for E. coli and Enterococcus faecalis isolates when the results of WGS of cultured isolates and urine samples were directly compared. Sequencing directly from the urine enabled bacterial identification in polymicrobial samples, and additional putative pathogenic strains were detected in five culture-negative samples [61]. The authors concluded that direct WGS on clinical samples can provide clinically relevant information and drastically reduce diagnostic times.

Rebelo et al. [62] performed a point-prevalence study of all bacterial isolates (n = 2,009) processed in Denmark in just 1 day in January 2018. They compared the results of species identification using the classical and standard methods of MALDI-TOF with bioinformatics analysis of WGS data. The results showed concordance of species and genus identification by both methods in 95.7% and 99.7% isolates, respectively. Based on the results, they concluded that WGS technologies have the potential for routine diagnostic applications in clinical settings [62].

In a point-of-care study, Morsil et al. [63] performed real-time WGS using DNA isolated from cerebrospinal fluids (CSF, n = 52) from patients suspected of meningitis for the identification, typing, and susceptibility profiling of pathogens responsible for community-acquired meningitis. They compared WGS results with routine real-time multiplex PCR (RT-PCR) assays and concluded that WGS was competitive in terms of time with the routine multiplex RT-PCR assays in point-of-care laboratories. In addition to pathogen detection, WGS provided additional information on bacterial profiling and genotypes [63].

The identification of pathogenic bacterial strains and genotypes using WGS is now well established for applications in clinical bacteriology laboratories [29, 64‒66]. Several web servers have been developed for the identification and genotyping of pathogenic bacteria [67‒69]. Specifically, Tsai et al. [68] have described an online server, PathoBacTyper (http://halst.nhri.org.tw/PathoBacTyper/), for identification and genotyping of more than 400 pathogenic bacteria based on whole genome single-nucleotide polymorphism (wgSNP) analysis. Similarly, Peng et al. [69] have reported the development of an automated online platform (https://pubmlst.org/organisms/pasteurella-multocida/multi-host) to determine sequence types (STs)/strains of Pasteurella multocida (a zoonotic bacterial pathogen) from multiple hosts using whole genome sequence data.

Antimicrobial agents have been widely used to treat bacterial infections since the 1940s [70]. The antimicrobial agents could be bactericidal or bacteriostatic by inhibiting the synthesis of cell walls, nucleic acids, and proteins, inhibition of metabolic pathways, and bacterial membrane function [71]. AMR occurs when microbes change over time and do not respond to antimicrobial agents, thus making infections harder to treat, leading to the increased risk of disease spread, severe illness, and death [71]. Over the past few decades, AMR has become a serious public health problem due to the global spread of multidrug-resistant (MDR) bacterial species and strains. The World Health Organization (WHO) has declared that AMR is one of the top 10 global public health threats facing humanity [72].

In the past, studies to determine AMR were conducted using classical methods of culture isolation and the ability of the cultured organisms to survive at different concentrations of antibiotics, thereby determining the minimum inhibitory concentrations. Generally, culture-based microbiology techniques allow for the characterization of isolates with respect to susceptibility/resistance to various antibiotics, but they are limited by speed and scale [73]. Hence, the characterization of many bacterial isolates can become a time-consuming and costly exercise. In recent years, diagnostic microbiological studies have combined traditional methods and the analysis of whole genome sequence data using various bioinformatics tools (Table 4) [74]. The application of bioinformatics tools is also making it possible to identify novel AMR genes (AMRGs) [75].

Table 4.

Bioinformatics tools for the identification of AMRGs in bacteria using WGS data [66]

Bioinformatics toolDescription and website
AMRFinderPlus Identifies AMR genes and resistance-associated point mutations in protein and/or assembled nucleotide sequences https://www.github.com/ncbi/amr/wiki 
ARGDIT An integrated non-redundant AMRG database with structured annotation https://github.com/phglab/ARGDIT 
ARGminer A web platform for the crowdsourcing-based curation of antibiotic resistance genes https://bench.cs.vt.edu/argminer/#/home 
CARD/RGI A bioinformatics database for resistance genes, their products, and associated phenotypes https://card.mcmaster.ca 
BacARscan Rapid monitoring, characterization and surveillance of all bacterial AMRGs https://github.com/mkubiophysics/BacARscan 
ResFinderFG v2.0 A database of antibiotic resistance genes obtained by functional metagenomics. https://cge.food.dtu.dk/services/ResFinderFG/ 
PointFinder A database for detection of AMR associated with chromosomal point mutations in bacterial pathogens 
ARGs-OAP v3.0 A database for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes 
Bioinformatics toolDescription and website
AMRFinderPlus Identifies AMR genes and resistance-associated point mutations in protein and/or assembled nucleotide sequences https://www.github.com/ncbi/amr/wiki 
ARGDIT An integrated non-redundant AMRG database with structured annotation https://github.com/phglab/ARGDIT 
ARGminer A web platform for the crowdsourcing-based curation of antibiotic resistance genes https://bench.cs.vt.edu/argminer/#/home 
CARD/RGI A bioinformatics database for resistance genes, their products, and associated phenotypes https://card.mcmaster.ca 
BacARscan Rapid monitoring, characterization and surveillance of all bacterial AMRGs https://github.com/mkubiophysics/BacARscan 
ResFinderFG v2.0 A database of antibiotic resistance genes obtained by functional metagenomics. https://cge.food.dtu.dk/services/ResFinderFG/ 
PointFinder A database for detection of AMR associated with chromosomal point mutations in bacterial pathogens 
ARGs-OAP v3.0 A database for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes 

Many studies to identify novel AMR genes by using WGS have been published (reviewed in [76]). Grad et al. [77] used WGS analysis of Neisseria gonorrhoeae and identified a new penA allele in mosaic and mtrR mutations in mosaic conferring resistance to cefixime and azithromycin, respectively. Marques and co-workers in their study performed WGS with 17 Helicobacter pylori clinical isolates from pediatric patients and reported single and combined mutations located in the 23S rRNA gene (A2142C and A2143G), which are associated with resistance to the antibiotic clarithromycin [78]. In addition, WGS was used by Zhu et al. [79] to study the possible horizontal transfer of florfenicol resistance (floR) gene-related sequences in Proteus strains; this study showed that the Proteus cibarius G11 strain harbored two copies of the floR gene, one on the chromosome and the other on a plasmid (pG11-152). In another plasmid (pG11-51), the presence of the chloramphenicol-florfenicol resistance (cfr) gene was observed, flanked by two IS26. With this bundle, the authors demonstrated the importance of mobile genetic elements in the replication of the floR gene and in the horizontal transfer of the resistance gene. Along the same lines, Wu et al. [80] sequenced the genome of MDR Staphylococcus lentus strain H29 and found 11 genes conferring resistance to this microorganism, with one copy encoded on the plasmid and the other on the chromosome. Furthermore, Zhang et al. [81] identified eight genes related to antibiotic resistance in tetracycline resistant Arthrobacter nicotianae; three of them were located non-plasmidically and had mobile features.

As mentioned above, the use of WGS enables the rapid identification of AMR-related genes, in addition to providing greater discriminatory ability regarding the genomic epidemiology and AMR determinants of different strains. In this regard, many studies targeting the epidemiology of microorganisms exhibiting resistance genes are being reported in the literature. For example, Boiko et al. [82] characterized AMR determinants in 150 strains of N. gonorrhoeae that spread in Ukraine between the years 2013 and 2018 and found isolates resistant to ciprofloxacin, tetracycline, and benzylpenicillin. The results of phylogenomic analysis highlighted six major groups, the majority of which were associated with the MDR gonococcal lineage. The presence of GyrA S91F and ParC S87R mutations was associated with resistance to ciprofloxacin; on the other hand, mutations in rpsJ V57M and tetM were revealed to be resistant to tetracyclines; the penA-34.001 mosaic with penicillin; the presence of mtrR, PorB1b, and G101D genes; and PBP1 L421Pla mutations with resistance to β-lactamases [82].

In line with the above studies, Rokney et al. [83] also used WGS to study the occurrence and genetic basis of AMR in 263 Campylobacter jejuni isolates and compared the results with those obtained by phenotypic testing. The most prevalent resistance-related genes found were cmeABC (related to efflux pumps), tet(O) (tetracycline resistance gene), aadE (streptomycin resistance gene), and a quinolone resistance-point mutation, gyrA T861. They also detected 12 genes conferring resistance to β-lactams in 241 isolates, with blaOXA-580, blaOXA-461, and blaOXA-193 being the most common. There was an excellent correlation (98.8%) between WGS-based genotypic prediction and phenotypic resistance [83].

Colistin is considered the last-resort antibiotic for the treatment of infections caused by MDR and carbapenem-resistant Gram-negative bacteria, but colistin resistance has been emerging globally [84]. In a study to analyze the molecular epidemiology and mechanisms of colistin resistance using WGS, 24 multidrug- and colistin-resistant clinical isolates (14 K. pneumoniae, one E. aerogenes, one E. cloacae, and eight A. baumannii) were obtained from four hospitals in Croatia [84]. The study reported that 12 strains of K. pneumoniae were resistant to colistin and other drugs, and blaOXA-48 (carbapenem resistance gene most prevalent in Croatia and other places in Europe) was present in 63% of the isolates. All A. baumannii isolates possessed OXA-23-type oxacillin hydrolyzing carbapenemases, and five were pandrug-resistant. Furthermore, previously reported mutations associated with colistin resistance were also identified (PmrB, PhoP, PhoQ, and MgrB). In the global phylogenetic analysis, the DNA mutations causing the MgrB protein mutations were mainly present in the lineages comprising the colistin-resistant strains, and the second most prevalent mutation (K3X) was also detected in the strains [84].

A high prevalence of vancomycin resistance has been reported in Enterococcus faecium in the last decade [85]. To study the phylogenetic relationship and genetic characteristics of E. faecium isolates, WGS was used as a screening tool in the surveillance of 1,025 bloodstream-infection-associated E. faecium isolates collected across Australia from 2015 to 2017 [85]. WGS analysis identified three distinct clusters of isolates with additional subgroups. One cluster harbored mainly non-CC (clonal complex), while others were dominant for the vanA and vanB operons. In addition, different dominant subclusters were observed in each region of Australia. The authors concluded that their results may help place future surveillance data into a broader perspective that includes the detection of novel E. faecium strains in Australia and the dissemination and evolution of each strain [85].

Reduction in the cost and time required for WGS has enabled researchers to develop WGS-based antimicrobial susceptibility/resistance testing using machine-learning computational methods [86, 87]. Machine-learning methods can effectively predict the imipenem resistance feature in K. pneumoniae and provide resistance sequence profiles for predicting resistance phenotypes and exploring potential resistance mechanisms [88]. By combining WGS of 1,113 pre- and post-treatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7,365 wound infections, Stracy et al. [89] reported that treatment-induced emergence of AMR could be predicted and minimized at the individual-patient level. Jiang et al. [90] have proposed a hierarchical attentive neural network (HANN) model that helps identify drug resistance-related genes and variants. The use of HANN for determining resistance in M. tuberculosis to isoniazid, rifampicin, ethambutol, and pyrazinamide led to optimal sensitivity and specificity. Moreover, the model helped identify drug resistance-related genes and variants consistent with those confirmed by previous studies [90]. Babatunde et al. [91] blasted the genes present on the ResFinder database against the WGS of five bacterial isolates, i.e., C. jejuni, E. coli, K. pneumoniae, Salmonella enterica, and P. aeruginosa, and the best-matching genes were automatically generated by the system to enable antibiogram profile prediction for each species and strain. They concluded that the in-silico method of AMR detection provides an easy way for interpretation and reproducibility of results, hence reduced the cost and time required [91]. In another study, Sherry et al. [92] developed an International Organization for Standardization (ISO)-certified bioinformatics tool, arbitAMR, for WGS-based bacterial AMR gene detection and antibiogram prediction, providing customized reports. They validated abritAMR by using 1,500 different bacteria and 415 resistance alleles and found that the platform displayed 99.9% accuracy, 97.9% sensitivity, and 100% specificity. They also compared genomic predictions of phenotype for 864 Salmonella spp. against agar dilution results and reported 98.9% accuracy. The abritAMR platform and validation datasets are freely available to help bacteriological laboratories anywhere in the world harness the power of AMR genomics in professional practice [92].

Healthcare-associated infections (HAIs) are an important cause of global morbidity and mortality [93]. HAIs have an immense impact on developing as well as highly developed countries of the world. About two million people suffer from HAIs in the USA annually, and more than 98,000 die. It is estimated that the direct cost of HAIs to hospitals is up to US$ 45 billion [94]. WGS of pathogenic bacterial genomes and subsequent bioinformatics analyses have increased the speed and efficiency applying genomics to analyze infection outbreaks for public health surveillance and infection control interventions [95]. This approach has been used on several taxa, such as mycobacteria, and infections corresponding to various modes of transmission, which include sexually transmitted diseases (STDs) and food- and water-borne diseases (FWDs). Furthermore, major healthcare-associated pathogenic bacteria such as Clostridium difficile, methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci, and carbapenemase-producing K. pneumoniae have been the target of several studies to track bacterial pathogens from local and global perspectives [96].

WGS using NGS technology was undertaken to investigate potential outbreaks of MRSA and C. difficile in hospital settings. Twenty-six MRSA and 15 C. difficile isolates were obtained from potential outbreaks associated with three UK hospitals. All the isolates were successfully sequenced and analyzed within 5 days of culture. The MRSA clusters were identified as outbreaks, with most sequences in each cluster indistinguishable and all within three single-nucleotide variants. However, the C. difficile clusters were shown to be genetically distinct. A reconstruction applying rapid sequencing in C. difficile surveillance provided early outbreak detection and identified previously undetected probable community transmission [97]. The findings provide an example that rapid and precise WGS using NGS could transform identification of transmission of healthcare-associated infection and hence improve hospital infection control and patient outcomes in routine clinical practices.

Analyses of pathogen genomes by WGS have provided unprecedented opportunities for understanding the epidemiology of healthcare-associated infection, leading to the control and prevention of infections [98]. It has identified heterogeneity within pathogen species, with some subtypes transmitting and persisting in hospitals better than others [98]. Furthermore, WGS has identified sources of infection in healthcare-associated outbreaks of foodborne pathogens, Candida auris and Mycobacterium chimera, as well as individual patients or groups responsible for transmission of infection [98].

The application of WGS has several challenges with respect to technical and analytical expertise to process and analyze the samples. Studies such as EuSCAPE and the CCRE survey of the European Antimicrobial Resistance Genes Surveillance Network (EURGen-Net: https://www.ecdc.europa.eu/en/about-us/who-we-work/disease-and-laboratory-networks/EURGen-net) and the EU-funded research project COMPARE (https://www.compare-europe.eu/) have been successful in establishing and building capacity for WGS-based surveillance. Similarly, the Global Microbial Identifier (GMI) initiative (https://www.globalmicrobialidentifier.org/) has also been founded to facilitate the approach. Similarly, Higgs et al. [99] have developed a standardized genomic method for identifying putative vancomycin-resistant E. faecium (VREfm) transmission links. The authors suggested that this standardized genomic framework for inferring VREfm transmission can be the basis for global deployment of VREfm genomics into routine outbreak detections and investigations [99].

WGS of clinical C. difficile isolates demonstrated considerable genetic diversity, suggesting diverse reservoirs for C. difficile infection [100]. WGS was also shown to aid in resolving relapses and reinfections in recurrent C. difficile infections and has potential for use as a tool for assessing hospital infection prevention and control performance [100]. K. pneumoniae is a major cause of opportunistic healthcare-associated infections, which are increasingly complicated by the presence of extended-spectrum beta-lactamases (ESBLs) and carbapenem resistance. Gorrie et al. [101] conducted a year-long prospective surveillance study of K. pneumoniae clinical isolates in hospital patients using WGS. The data indicated that K. pneumoniae infections in hospitalized patients largely included opportunistic infections with diverse strains. A molecular epidemiological analysis of MRSA strains isolated from bloodstream-infected patients in a Japanese university hospital was conducted using WGS and single-nucleotide polymorphism (SNP) analysis [102]. The authors concluded that routine monitoring of MRSA using whole genome analysis was an effective way to gain knowledge regarding molecular epidemiology and detect silent nosocomial transmission [102].

Prospective WGS-based surveillance may be the best approach to rapidly identify transmission of MDR bacteria in healthcare settings. Forde et al. [103] collected a total of 2,660 isolates of different bacterial isolates (MRSA, n = 620), vancomycin-resistant enterococci (VRE, n = 433), extended-spectrum beta-lactamase (ESBL, n = 1,309), and carbapenemase-producing Enterobacterales (CPE, n = 239) over a 4-year period from hospitals in Brisbane, Australia. Of the 76 clusters identified, 43 were contained in the 3 target hospitals, suggesting ongoing transmission within those hospitals. The remaining 33 clusters represented possible inter-hospital transmission events or strains circulating in the community. The study concluded that the implementation of routine WGS for MDR pathogens in clinical laboratories is feasible and can enable targeted infection prevention and control interventions [103].

A study has been conducted to model the economic impact of a WGS surveillance system for proactive detections and interventions for nosocomial infections and outbreaks compared to the classical standard of care without WGS [104]. Inputs from national statistics and peer-reviewed articles were used to determine the economic effect of conducting a WGS-led surveillance system addressing the most common nosocomial pathogens in England and the USA. The model for the National Health System (NHS) in England showed that bacterial HAIs cost the NHS about GBP 3 billion. WGS-based surveillance delivery was estimated to cost GBP 61.1 million. Furthermore, it was associated with the prevention of 74,408 HAIs and 1,257 deaths. The net cost saving by using WGS was GBP 478.3 million [104]. The USA model showed that the bacterial HAI care baseline costs were about $18.3 billion. WGS surveillance cost was $169.2 million and resulted in a saving of about $3.2 billion while preventing 169,260 HAIs and 4,862 deaths. The model predicted a return to the hospitals of GBP 7.83 per GBP 1 invested in diagnostic WGS in the UK and US$ 18.74 per $1 in the USA. Thus, modeling a proactive WGS system for HAI pathogens showed that there could be significant improvements in morbidity and mortality as well as achieving significant savings to healthcare facilities [104].

Bacterial whole genome sequence analysis has evolved to become an important tool for understanding the transmission and epidemiology of pathogens in communities. Smith et al. [105] estimated that, using WGS in case of tuberculosis, only a minority of cases (range, 2–31%) had driven the majority (80%) of ongoing TB transmission at the population level. In recent years, WGS and bioinformatic tools have significantly increased the amount of information we can use to study infectious diseases, and therefore, they have improved the precision of epidemiological inferences for pathogenic bacteria. WGS was used to gain insights into the epidemiology of M. bovis in Cameroon. A total of 91 high-quality sequences of M. bovis were analyzed with respect to environmental, demographic, ecological, and cattle movement data to generate inferences using phylodynamic models [106]. The findings suggested that M. bovis in Cameroon was slowly expanding its epidemiological range over time; therefore, endemic stability was unlikely. The authors concluded that the use of WGS as part of surveillance would vastly improve our understanding of disease ecology and control strategies [106].

Application of WGS to characterize foodborne pathogens has advanced the understanding of circulating genotypes and evolutionary relationships. Rodrigues et al. [107] used WGS to investigate the genomic epidemiology of C. jejuni, a leading cause of foodborne disease. Among the 214 strains studied, 85 multilocus STs were identified. Epidemiological analyses suggested that travel was a significant contributor for ST diversity of C. jejuni. Variation was also observed in the frequency of lineages over a 4-year period. These findings highlight the importance of geographically specific factors, recombination, and horizontal gene transfer in shaping the population structure of C. jejuni [107].

International travel has been a major way to introduce pathogens such as MRSA into naïve geographic areas. MRSA clonal complex 239 (CC239) and sequence type 239 (ST239) are highly virulent clones that are predominant in Asia. In a study conducted in Denmark by Coppens et al. [108], most MRSA ST239 isolates were recovered from individuals who had traveled to Asia, Africa, and the Middle East. This study highlighted the importance of MRSA surveillance using WGS in persons coming back from international travel to areas where MRSA is endemic. WGS methods have the best discriminative power and are therefore recommended for outbreak investigations [109].

A tuberculosis surveillance protocol has been carried out in Aragon, USA, since 2004 to detect all tuberculosis outbreaks occurring in the community. The largest outbreak was caused by the Mycobacterium tuberculosis Zaragoza (MtZ) strain that caused 242 cases of tuberculosis as of 2020. WGS analysis of the isolates was performed to analyze the outbreak in terms of the molecular characteristics of this strain in relation to its greater transmission. The results showed that the MtZ strain consisted of several SNPs in genes related to virulence, pathogenesis, and survival, as well as other genomic polymorphisms, which may be implicated in its successful transmission among the population [110].

WGS has great potential to enhance the understanding of many aspects of infectious diseases and clinical microbiology. Research into these aspects, including pathogen evolution, epidemiology and virulence determinants, and the development and spread of AMR mechanisms, indirectly influences microbiology and clinical infectious disease practices and has the goal of improving patient care. It is expected that sequencing costs will continue to fall, making WGS technologies more common in clinical bacteriology laboratories. In addition, the automation of bioinformatics methods is also developing rapidly, leading to obtaining results without having expertise in bioinformatics. Routine clinical bacteriology applications can be performed through in-built platforms within the sequencers. These in-built platforms integrate pipelines, leading to automated data processing by the installed software that displays the results graphically [111].

However, there are shortcomings that will slow down the widespread use of WGS in clinical microbiology to apply it as a test to provide the information required in clinical practice. Although sequencing efficiency is rapidly improving, WGS may not be able to take over, in the near future, the currently used methods for bacterial identification and antimicrobial susceptibility testing. Its role may be expanded in public health, reference, and infection control laboratories for the purpose of detailed isolate characterization, outbreak investigation, and detection of infectious agents’ transmission. However, with sequencing becoming more readily available and applied, greater use in diagnostic laboratories for the identification of infectious agents in culture-negative samples may be useful. It should be stressed that the development of user-friendly bioinformatic tools and pipelines will be instrumental in facilitating the widespread use of WGS.

WGS technologies are changing the horizons of diagnostic clinical microbiology and public health laboratories. In these areas, WGS helps in identification of fastidious organisms and the surveillance of potential outbreaks. In addition, WGS is useful in the identification of AMR genes and resistance mechanisms and helps in identifying new genes and unknown mechanisms. WGS of the microorganisms directly from clinical specimens is helping to increase the frequency of detection of pathogens, especially those organisms that cannot be identified by conventional phenotypic and/or genotypic methods. Hence, WGS technologies have the potential to broaden the scope and outreach of clinical microbiology laboratories. However, WGS may not be able to fully replace conventional microbiology laboratory workup in the near future, but the information provided by WGS data can contribute to improvements in patient care and management.

This review article did not require any ethical clearance because no unpublished data related to experiments done on humans or animals are presented.

The author declares no conflict of interest.

The study was supported by grants from the Research Sector (Grants No. RM01/13 and SRUL02/13), Kuwait University, Kuwait.

Abu Salim Mustafa prepared, revised, and finalized the manuscript.

Data supporting the findings of this study are available upon reasonable request.

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