A large number of variants of Corynebacterium diphtheriae causing diphtheria outbreaks in Indonesia, 2010–2017

Abstract

Background Several methods exist for the molecular typing of diphtheria-causing bacteria, including core genome multilocus sequence typing (cgMLST). This study aims to provide an overview of the sequence types, virulence factors, and molecular resistances of Corynebacterium diphtheriae (C. Diphtheriae) in different regions of Indonesia between 2010–2017 to predict diseases transmission patterns and evaluate control measures . Methods A total of 89 archived isolates of C. diphtheriae collected between 2010 and 2017 from 10 provinces in Indonesia were used as test samples. DNA extraction and whole genome sequencing (using the QIAamp DNA Minikit and Illumina MiSeq, respectively) followed standard protocols. Bioinformatic analysis was performed using a pipeline that included ‘diphtOscan’. Results Seventy out of eighty-nine samples qualified for analysis. Fifteen sequence types (STs) were identified: ST377 (24.3%), ST534 (44.3%) and others 31.4%. Approximately 50% of isolates predicted as Gravis biovar, based on spuA gene presence; two isolates were predicted as Belfanti due to narG gene absence. The Gravis biovar includes isolates with ST105, ST123, and ST534. The cgMLST analysis revealed 14 sublineages corresponding to sequence types; we also identified 33 genetic clusters (GC) and 20 clonal groups among the analyzed samples. Notable GC such as GC1197, GC1199, and GC9 were found across provinces and islands. Isolates with ST141/GC75 shared similarity with Malaysian isolate; those with ST377/GC9 showed resemblance to Indian or European Union counterparts. The antibiotic resistance genes (ARGs) including cmx, tetW and tetO, sul1, aad, aph-Ib , aph-Ia , aph-Id, dfrA, were identified in most isolates. In contrast, msr(D), mef(A), which encode erythromycin resistance via ermX, were found in only two isolates. One isolate harbored pbp2, responsible for penicillin resistance. Notably, almost all ST377 isolates exhibited aminoglycoside resistance; conversely, the single non-ST377 isolate showed this trait as well. Conclusion This study demonstrates that Indonesia’s 2010–2017 diphtheria outbreaks were caused by multiple strains, with ST534 and ST377 being the most prevalent. GC similarity suggests a link or disease transmission between various outbreaks across different regions /islands/countries and periods. ARGs were predominantly associated with phenicol, tetracycline, and sulfonamides resistances; only a few isolates exhibited ARGs linked to penicillin and erythromycin. Our analysis revealed correlations between STs and biovar or resistance profiles. We recommend to implement whole genome sequencing (WGS) for diphtheria surveillance in Indonesia.