Peer Review #2 of "Whole-genome sequence analyses of Glaesserella parasuis isolates reveals extensive genomic variation and diverse antibiotic resistance determinants (v0.1)"

Background Glaesserella parasuis (G. parasuis) is a respiratory pathogen of swine and the etiological agent of Glässer’s disease. The structural organization of genetic information, antibiotic resistance genes, potential pathogenicity, and evolutionary relationships among global G. parasuis strains remain unclear. The aim of this study was to better understand patterns of genetic variation, antibiotic resistance factors, and virulence mechanisms of this pathogen. Methods The whole-genome sequence of a ST328 isolate from diseased swine in China was determined using Pacbio RS II and Illumina MiSeq platforms and compared with 54 isolates from China sequenced in this study and 39 strains from China and eigtht other countries sequenced by previously. Patterns of genetic variation, antibiotic resistance, and virulence mechanisms were investigated in relation to the phylogeny of the isolates. Electrotransformation experiments were performed to confirm the ability of pYL1—a plasmid observed in ST328—to confer antibiotic resistance. Results The ST328 genome contained a novel Tn6678 transposon harbouring a unique resistance determinant. It also contained a small broad-host-range plasmid pYL1 carrying aac(6’)-Ie-aph(2”)-Ia and blaROB-1; when transferred to Staphylococcus aureus RN4220 by electroporation, this plasmid was highly stable under kanamycin selection. Most (85.13–91.74%) of the genetic variation between G. parasuis isolates was observed in the accessory genomes. Phylogenetic analysis revealed two major subgroups distinguished by country of origin, serotype, and multilocus sequence type (MLST). Novel virulence factors (gigP, malQ, and gmhA) and drug resistance genes (norA, bacA, ksgA, and bcr) in G. parasuis were identified. Resistance determinants (sul2, aph(3”)-Ib, norA, bacA, ksgA, and bcr) were widespread across isolates, regardless of serovar, isolation source, or geographical location. Conclusions Our comparative genomic analysis of worldwide G. parasuis isolates provides valuable insight into the emergence and transmission of G. parasuis in the swine industry. The result suggests the importance of transposon-related and/or plasmid-related gene variations in the evolution of G. parasuis.

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Identification of bacterial antibiotic resistance genes in next-generation sequencing data (review of literature)

Russian Clinical Laboratory Diagnostics ◽

10.51620/0869-2084-2021-66-11-684-688 ◽

2021 ◽

Vol 66 (11) ◽

pp. 684-688

Author(s):

A. V. Chaplin ◽

M. Korzhanova ◽

D. O. Korostin

Keyword(s):

Antibiotic Resistance ◽

Genome Sequence ◽

Protein Sequence ◽

Susceptibility Testing ◽

Sequence Data ◽

Genetic Resistance ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data ◽

Resistance Determinants

The spread of antibiotic-resistant human bacterial pathogens is a serious threat to modern medicine. Antibiotic susceptibility testing is essential for treatment regimens optimization and preventing dissemination of antibiotic resistance. Therefore, development of antibiotic susceptibility testing methods is a priority challenge of laboratory medicine. The aim of this review is to analyze the capabilities of the bioinformatics tools for bacterial whole genome sequence data processing. The PubMed database, Russian scientific electronic library eLIBRARY, information networks of World health organization and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) were used during the analysis. In this review, the platforms for whole genome sequencing, which are suitable for detection of bacterial genetic resistance determinants, are described. The classic step of genetic resistance determinants searching is an alignment between the query nucleotide/protein sequence and the subject (database) nucleotide/protein sequence, which is performed using the nucleotide and protein sequence databases. The most commonly used databases are Resfinder, CARD, Bacterial Antimicrobial Resistance Reference Gene Database. The results of the resistance determinants searching in genome assemblies is more correct in comparison to results of the searching in contigs. The new resistance genes searching bioinformatics tools, such as neural networks and machine learning, are discussed in the review. After critical appraisal of the current antibiotic resistance databases we designed a protocol for predicting antibiotic resistance using whole genome sequence data. The designed protocol can be used as a basis of the algorithm for qualitative and quantitative antimicrobial susceptibility testing based on whole genome sequence data.

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