Genomic Determinants of Pathogenicity and Antimicrobial Resistance of Nosocomial Acinetobacter baumannii Clinical Isolates of Hospitalized Patients (2019-2021) from a Sentinel Hospital in Hangzhou, China

Comprehensive Genome Analysis of Colistin-Only-Sensitive KPC-2 and NDM1-1-Coproducing Klebsiella pneumoniae ST11 and Acinetobacter baumannii ST2 From a Critically Ill Patient With COVID-19 in Saudi Arabia: Whole Genome Sequencing (WGS) of K. pneumoniae ST11 and A. baumannii ST2

The COVID-19 pandemic has intensified the issue of multidrug-resistant (MDR) infections, particularly in intensive care units (ICUs). This study documents the first known case of coinfection with two extensively drug-resistant (XDR) bacterial isolates in a critically ill patient with COVID-19 in Saudi Arabia. Both XDR isolates were recovered from blood and were resistant to all tested antimicrobial agents except colistin. Whole genome sequencing (WGS) revealed that the K. pneumoniae isolate KP-JZ107 had sequence type 11 (ST11) and core genome MLST (cgMLST 304742), while the A. baumannii isolate AB-JZ67 had ST2 and cgMLST 785. KP-JZ107 was found to possess the virulence plasmid KpVP-type-1, carbapenemase genes bla NDM and bla KPC , and numerous antimicrobial-resistant genes (ARGs). The AB-JZ67 isolate had several biofilm-related genes, including biofilm-associated protein (BAP), csuE, and pgaB, and multiple ARGs, including bla ADC-25, bla OXA-23, and bla OXA-66. Our findings suggest that the coexistence of KP-JZ107 and AB-JZ67 isolates may indicate their widespread presence in ICUs, requiring comprehensive surveillance studies across all hospitals.

A panel of genotypically and phenotypically diverse clinical Acinetobacter baumannii strains for novel antibiotic development

Acinetobacter baumannii is one of the most important pathogens worldwide. The intrinsic and acquired resistance of A. baumannii, coupled with the slow pace of novel antimicrobial drug development, poses an unprecedented and enormous challenge to clinical anti-infective therapy of A. baumannii. Recent studies in the field of pathogenicity, antibiotic resistance, and biofilms of A. baumannii have focused on the model strains, including ATCC 17978, ATCC 19606, and AB5075. However, these model strains represent only a limited portion of the heterogeneity in A. baumannii. Furthermore, variants of these model strains have emerged that show significant diversity not only at the genotypic level but also reflected in differences at the phenotypic levels of capsule, virulence, pathogenicity, and antibiotic resistance. Research on A. baumannii, a key pathogen, would benefit from a standardized approach, which characterizes heterogeneous strains in order to facilitate rapid diagnosis, discovery of new therapeutic targets, and efficacy assessment. Our study provides and describes a standardized, genomically and phenotypically heterogeneous panel of 45 different A. baumannii strains for the research community. In addition, we performed comparative analyses of several phenotypes of this panel. We found that the sequence type 2 (ST2) group showed significantly higher rates of resistance, lower fitness cost for adaptation, and yet less biofilm formation. The Macrocolony type E (MTE, flat center and wavy edge phenotype reported in the literature) group showed a less clear correlation of resistance rates and growth rate, but was observed to produce more biofilms. Our study sheds light on the complex interplay of resistance fitness and biofilm formation within distinct strains, offering insights crucial for combating A. baumannii infection.

IMPORTANCE

Acinetobacter baumannii is globally notorious, and in an effort to combat the spread of such pathogens, several emerging candidate therapies have already surfaced. However, the strains used to test these therapies vary across studies (the sources and numbers of test strains are varied and often very large, with little heterogeneity). The variation complicates the studies. Furthermore, the limited standardized resources of A. baumannii strains have greatly restricted the research on the physiology, pathogenicity, and antibiotic resistance. Therefore, it is crucial for the research community to acquire a standardized and heterogeneous panel of A. baumannii. Our study meticulously selected 45 diverse A. baumannii strains from a total of 2,197 clinical isolates collected from 64 different hospitals across 27 provinces in China, providing a scientific reference for the research community. This assistance will significantly facilitate scientific exchange in academic research.

Genotypic and phenotypic characteristics of Acinetobacter baumannii isolates from the people's hospital of Qingyang City, Gansu province

BACKGROUND

Acinetobacter baumannii (A. baumannii) is a common opportunistic pathogen in hospitals that causes nosocomial infection. In order to understand the phenotypic and genotypic characteristics of A. baumannii isolates, we sequenced and analyzed 62 A. baumannii isolates from a hospital in Gansu province.

RESULTS

Non-repeated 62 A. baumannii isolates were collected from August 2015 to November 2021. Most isolates (56/62) were resistant to multiple drugs. All the 62 A. baumannii isolates were resistant to aztreonam and contained blaADC-25 gene which exists only on chromosome contigs. The 62 isolates in this study were not clustered in a single clade, but were dispersed among multiple clades in the common genome. Seven sequence types were identified by Multilocus sequence type (MLST) analysis and most isolates (52/62) belonged to ST2. The plasmids were grouped into 11 clusters by MOB-suite.

CONCLUSIONS

This study furthers the understanding of A. baumannii antimicrobial-resistant genotypes, and may aid in prevention and control nosocomial infection caused by drug-resistant A. baumannii.

Recovery and genetic characterization of clinically-relevant ST2 carbapenem-resistant Acinetobacter baumannii isolates from untreated hospital sewage in Zhejiang Province, China

The global transmission of carbapenem-resistant Acinetobacter baumannii (CRAB) poses a significant and grave threat to human health. To investigate the potential relationship between hospital sewage and the transmission of CRAB within healthcare facilities, isolates of Acinetobacter spp. obtained from untreated hospital sewage samples were subjected to antimicrobial susceptibility tests, genome sequencing, and bioinformatic and phylogenetic tree analysis, and that data were matched with those of the clinical isolates. Among the 70 Acinetobacter spp. sewage isolates tested, A. baumannii was the most prevalent and detectable in 5 hospitals, followed by A. nosocomialis and A. gerneri. Worryingly, 57.14 % (40/70) of the isolates were MDR, with 25.71 % (18/70) being resistant to carbapenem. When utilizing the Pasteur scheme, ST2 was the predominant type among these CRAB isolates, with Tn2006 (ΔISAba1-blaOXA-23-ATPase-yeeB-yeeA-ΔISAba1) and Tn2009 (ΔISAba1-blaOXA-23-ATPase-hp-parA-yeeC-hp-yeeB-ΔISAba1) being the key mobile genetic elements that encode carbapenem resistance. Seven A. gerneri isolates which harbored Tn2008 (ISAba1-blaOXA-23 -ATPase) and the blaPER-1 gene were also identified. Besides, an A. soil isolate was found to exhibit high-level of meropenem resistance (MIC ≥128 mg/L) and harbor a blaNDM-1 gene located in a core genetic structure of ISAba125-blaNDM-1-ble-trpF-dsbC-cutA. To investigate the genetic relatedness between isolates recovered from hospital sewage and those collected from ICUs, a phylogenetic tree was constructed for 242 clinical isolates and 9 sewage isolates. The results revealed the presence of two evolutionary clades, each containing isolates from both ICU and sewage water, suggesting that CRAB isolates in untreated sewage water were also the transmission clones or closely related evolutionary isolates recoverable in hospital settings. Findings in this work confirm that hospital sewage is a potential reservoir of CRAB.

Machine learning and feature extraction for rapid antimicrobial resistance prediction of Acinetobacter baumannii from whole-genome sequencing data

Background

Whole-genome sequencing (WGS) has contributed significantly to advancements in machine learning methods for predicting antimicrobial resistance (AMR). However, the comparisons of different methods for AMR prediction without requiring prior knowledge of resistance remains to be conducted.

Methods

We aimed to predict the minimum inhibitory concentrations (MICs) of 13 antimicrobial agents against Acinetobacter baumannii using three machine learning algorithms (random forest, support vector machine, and XGBoost) combined with k-mer features extracted from WGS data.

Results

A cohort of 339 isolates was used for model construction. The average essential agreement and category agreement of the best models exceeded 90.90% (95%CI, 89.03-92.77%) and 95.29% (95%CI, 94.91-95.67%), respectively; the exceptions being levofloxacin, minocycline and imipenem. The very major error rates ranged from 0.0 to 5.71%. We applied feature selection pipelines to extract the top-ranked 11-mers to optimise training time and computing resources. This approach slightly improved the prediction performance and enabled us to obtain prediction results within 10 min. Notably, when employing these top-ranked 11-mers in an independent test dataset (120 isolates), we achieved an average accuracy of 0.96.

Conclusion

Our study is the first to demonstrate that AMR prediction for A. baumannii using machine learning methods based on k-mer features has competitive performance over traditional workflows; hence, sequence-based AMR prediction and its application could be further promoted. The k-mer-based workflow developed in this study demonstrated high recall/sensitivity and specificity, making it a dependable tool for MIC prediction in clinical settings.