Short Communication
Emerging challenges of whole-genome-sequencing–powered epidemiological surveillance of globally distributed clonal groups of bacterial infections, giving Acinetobacter baumannii ST195 as an example

https://doi.org/10.1016/j.ijmm.2019.151339Get rights and content

Abstract

Whole-genome sequencing (WGS) has revolutionized the genotyping of bacterial pathogens and is expected to become the new gold standard for tracing the transmissions of bacterial infectious diseases for public health purposes. However, it is still unexpectedly demanding to employ WGS for global epidemiological surveillance because of the high degree of similarity between the genomes of intercontinental isolates. The aim of this study was to utilize genomically derived bioinformatics analysis to identify globally distributed A. baumannii ST195 lineage and differentiation outbreaks to address this issue. The genomic sequences and their related epidemiological metadata of 2850 A. baumannii isolates were recruited from NCBI Genbank database. Assignment into sequence type (Oxford scheme) and lineage (global clone 2/CC92) were performed. A total of 91 ST195 A. baumannii isolates were subsequently classified to perform the bacterial source tracking analysis by implementing both core genome MLST (cgMLST) and core genome SNP (cgSNP) strategy that were integrated in our recently updated BacWGSTdb 2.0 server. Antibiotic resistance genes were identified using the ResFinder database. The ST195 A. baumannii isolates distributed widely in eight countries and harboured multiple antimicrobial resistance genes simultaneously. In most cases, the bacterial isolates recovered from geographically distant sources may present less genomic sequence similarity, i.e., the phylogenetic relationship between these ST195 isolates worldwide was roughly congruent with their country of isolation. However, a few isolates collected from distant geographic regions were revealed to possess smaller genetic distances (less than 8 loci or 20 SNPs) than the threshold without an observable epidemiological link. Our study highlights the emerging challenges entailed in the WGS-powered epidemiological surveillance of globally distributed clonal groups. Standardization is urgently required before WGS can be routinely applied to infectious diseases outbreak investigations.

Introduction

Acinetobacter baumannii has emerged worldwide as a predominant opportunistic pathogen responsible for nosocomial infections (Wong et al., 2017). With a strong capacity for clonal transmission and acquirement of antimicrobial resistance determinants, in 2017 the World Health Organization (WHO) announced carbapenem-resistant A. baumannii as the top priority pathogen critically requiring research and development of new antibiotics (WHO, 2017). The A. baumannii population primarily responsible for nosocomial infections and multiple-drug resistance largely consists of two globally distributed clones, GC1 and GC2 (Zarrilli et al., 2013). The accurate knowledge of an epidemic A. baumannii clone commonly relies on molecular epidemiological analysis, such as unrevealing the genetic relatedness of bacterial isolates and their antimicrobial resistance genes, which boosting our insights into the global transmissions and developing optimized A. baumannii infection control strategies when associated with a region-specific outbreak (Karah et al., 2012; Maragakis and Perl, 2008; Ruan et al., 2013).

Several molecular typing methods have been established during the last decades, particularly pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST), which are the gold standard and complementary extend to each other in global epidemiological investigations of bacterial pathogens (Maiden et al., 1998; Tenover et al., 1995). However, they lack sufficient resolution for providing strain-specific diagnostics and thus cannot meet contemporary requirements (Maiden et al., 2013). Take A. baumannii GC2, the most important clone made up mainly of multiple-drug resistant isolates, as an example: its predominant lineage ST195 disseminates worldwide, rendering MLST unable to delineate on a global scale more delicate transmission routes (Endo et al., 2012; Kao et al., 2014; Zhou et al., 2015). Recently, many successful cases have demonstrated the power of whole-genome sequencing (WGS) in the epidemiological surveillance of infectious disease outbreaks at various geographic scales and in different temporal scenarios (Gardy and Loman, 2018). Because of its single-base resolution, WGS is expected to become the new authoritative norm for genotyping bacterial pathogens for public health purposes (Croucher and Didelot, 2015; Loman and Pallen, 2015).

The WGS-based approaches rely on either the characterization of core-genome single nucleotide polymorphisms (cgSNP) or core-genome gene-by-gene multilocus sequence typing (cgMLST) approach encompassing a stable set of core genome genes (Mellmann et al., 2016). The cgMLST approach allows immediate comparisons of newly determined genotypes with historical data, enabling continuous surveillance, in contrast to SNP-based approaches that call for recalculation once the data set changes unless a preliminarily defined reference genome is given (Ruan et al., 2019). More importantly, cgMLST can hardly be affected by such genetic events as homologous recombination and the lateral transfer of mobile genetic elements (MGEs), which can result in high density SNPs within a short segment and, thereby, distort the true phylogenetic relationships (Awadalla, 2003). The whole-genome comparisons yielded insights into the evolution of pathogenicity in A. baumannii, uncovering wide diversity in gene content, including variation in antimicrobial resistance determinants (Fitzpatrick et al., 2016; Sahl et al., 2015; Wright et al., 2014). However, genomic studies of intra-clonal variation in A. baumannii have been limited to small or highly localized isolates.

To increase our understanding of the genomic epidemiology of A. baumannii, a comparative genomic analysis of 91 A. baumannii ST195 clinical isolates recovered from eight countries was performed to determine the phylogenetic relationship between a large collections of publicly available global A. baumannii isolates. A few of the isolates collected from very distant geographic regions were revealed to possess smaller genetic distances but without an observable epidemiological link. Our study highlights the emerging challenges entailed in the WGS-powered epidemiological surveillance of globally distributed clonal groups.

Section snippets

Bacterial isolates

A total of 2850 A. baumannii genomes currently available from NCBI GenBank non-redundant database and whole-genome shotgun sequence databases (including both complete and draft genomes) were obtained. The relevant clinical metadata were also retrieved from NCBI BioSample database. The sequence type (ST) of the study isolates was determined by Oxford MLST scheme using the whole genome assemblies and a total of 91 ST195 A. baumannii isolates were classified to share this feature. The detailed

Results

Interrogation of the international genomes for the presence of acquired antimicrobial resistance genes revealed almost identical resistance gene profiles for all isolates: armA gene encoding for the 16S rRNA methylase, aph(3′)-VI-a, aph(6′)-Id for aminoglycoside-3′, 6′-phosphotransferases and strA gene all conferring resistance to aminoglycosides, tet(B) gene encoding for tetracycline resistance, mph(E) and mrs(E) genes, both responsible for macrolide resistance, sul1 and sul2 genes for

Discussion

The advent of WGS technologies has greatly increased the volume of genetic information available for characterizing the relatedness of bacterial isolates at the highest resolution level (Deng et al., 2016). This advantage makes inevitable the replacement of traditional typing techniques with WGS to become the new optimal standard of molecular epidemiology for the future surveillance of either local or global infectious diseases (Ruan et al., 2019). The evidence presented herein offers a

Funding information

This work was supported by the National Natural Science Foundation of China (81401698, 81871696 and 31600108), Zhejiang Province Public Welfare Technology Application Research Project (LGF18H190001), Zhejiang Provincial Natural Science Foundation (LQY18H190001), and Zhejiang Provincial Medical and Health Science and Technology Plan (2020RC066 and 2020KY670).

Declaration of Competing Interest

The authors declare that there are no conflicts of interest.

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