Genome sequencing and comparative genome analysis of 6 hypervirulent Klebsiella pneumoniae strains isolated in China

Hypervirulent Klebsiella pneumoniae (hvKp): Overview, Epidemiology, and Laboratory Detection

Klebsiella pneumoniae (Kp) is a Gram-negative pathogen responsible for both hospital- and community-acquired infections. Kp is classified into 2 distinct pathotypes: classical K. pneumoniae (cKp) and hypervirulent K. pneumoniae (hvKp). First described in Taiwan in 1986, hvKp are highly pathogenic and characterized by unique phenotypic and genotypic traits. The hypermucoviscous (hmv) phenotype, generally marked by overproduction of the capsule, is often associated with hvKp, although recent studies show that some cKp strains may also have this characteristic. Furthermore, hvKp can cause severe community-acquired infections in healthy people and have been associated with metastatic infections such as liver abscess, meningitis, and endophthalmitis. HvKp are increasingly being reported in hospital-acquired settings, complicating treatment strategies. In particular, while hvKp have historically been antibiotic-susceptible, multidrug-resistant (MDR) strains have emerged and pose a significant public health threat. The combination of high virulence and limited antibiotic options demands further research into virulence mechanisms and rapid identification methods. This review discusses the epidemiology of hvKp and their virulence factors, highlighting the importance of phenotypic and non-phenotypic tests, including next-generation molecular diagnostics, for the early detection of hvKp.

Integrating genomic and molecular data to predict antimicrobial minimum inhibitory concentration in Klebsiella pneumoniae

Minimum inhibitory concentration (MIC) denotes the in vitro benchmark indicating the quantity of antibiotic required to inhibit proliferation of specific bacterial strains. Determining MIC values corresponding to the infecting bacterial strain is paramount for tailoring appropriate antibiotic therapy. In the interim between specimen collection and laboratory-derived MIC outcomes, clinicians frequently resort to empirical therapy informed by retrospective analyses. Here introduces two deep learning approaches, a Convolutional Neural Network (CNN)-based model and an Enformer-based model, integrating genomic data of Klebsiella Pneumoniae and molecular structural data of 20 antibiotics to anticipate the MIC value of the bacterium for each antibiotic under consideration. These models demonstrate enhanced raw accuracy over the existing state-of-the-art model, which rely exclusively on genomic data. The CNN-based model achieves a notable 20% increase in raw accuracy while further mirroring the 1-tier accuracy of the state-of-the-art model. Although the Enformer-based model does not quite reach the performance levels of the CNN-based model, it offers an advantage by eliminating the need for arbitrary data processing steps. This streamlining of the data processing pipeline facilitates fast updates and improves the model interpretability. It is expected that these deep learning paradigms can significantly inform and bolster clinician decision-making during the empirical treatment phase.

Genomic islands and their role in fitness traits of two key sepsis-causing bacterial pathogens

To survive and establish a niche for themselves, bacteria constantly evolve. Toward that, they not only insert point mutations and promote illegitimate recombinations within their genomes but also insert pieces of 'foreign' deoxyribonucleic acid, which are commonly referred to as 'genomic islands' (GEIs). The GEIs come in several forms, structures and types, often providing a fitness advantage to the harboring bacterium. In pathogenic bacteria, some GEIs may enhance virulence, thus altering disease burden, morbidity and mortality. Hence, delineating (i) the GEIs framework, (ii) their encoded functions, (iii) the triggers that help them move, (iv) the mechanisms they exploit to move among bacteria and (v) identification of their natural reservoirs will aid in superior tackling of several bacterial diseases, including sepsis. Given the vast array of comparative genomics data, in this short review, we provide an overview of the GEIs, their types and the compositions therein, especially highlighting GEIs harbored by two important pathogens, viz. Acinetobacter baumannii and Klebsiella pneumoniae, which prominently trigger sepsis in low- and middle-income countries. Our efforts help shed some light on the challenges these pathogens pose when equipped with GEIs. We hope that this review will provoke intense research into understanding GEIs, the cues that drive their mobility across bacteria and the ways and means to prevent their transfer, especially across pathogenic bacteria.

Genomic Analysis of Multidrug-Resistant Hypervirulent (Hypermucoviscous) Klebsiella pneumoniae Strain Lacking the Hypermucoviscous Regulators (rmpA/rmpA2)

Hypervirulent K. pneumoniae (hvKP) strains possess distinct characteristics such as hypermucoviscosity, unique serotypes, and virulence factors associated with high pathogenicity. To better understand the genomic characteristics and virulence profile of the isolated hvKP strain, genomic data were compared to the genomes of the hypervirulent and typical K. pneumoniae strains. The K. pneumoniae strain was isolated from a patient with a recurrent urinary tract infection, and then the string test was used for the detection of the hypermucoviscosity phenotype. Whole-genome sequencing was conducted using Illumina, and bioinformatics analysis was performed for the prediction of the isolate resistome, virulome, and phylogenetic analysis. The isolate was identified as hypermucoviscous, type 2 (K2) capsular polysaccharide, ST14, and multidrug-resistant (MDR), showing resistance to ciprofloxacin, ceftazidime, cefotaxime, trimethoprim-sulfamethoxazole, cephalexin, and nitrofurantoin. The isolate possessed four antimicrobial resistance plasmids (pKPN3-307_type B, pECW602, pMDR, and p3K157) that carried antimicrobial resistance genes (ARGs) (bla_OXA-1,bla_CTX-M-15, sul2, APH(3″)-Ib, APH(6)-Id, and AAC(6′)-Ib-cr6). Moreover, two chromosomally mediated ARGs (fosA6 and SHV-28) were identified. Virulome prediction revealed the presence of 19 fimbrial proteins, one aerobactin (iutA) and two salmochelin (iroE and iroN). Four secretion systems (T6SS-I (13), T6SS-II (9), T6SS-III (12), and Sci-I T6SS (1)) were identified. Interestingly, the isolate lacked the known hypermucoviscous regulators (rmpA/rmpA2) but showed the presence of other RcsAB capsule regulators (rcsA and rcsB). This study documented the presence of a rare MDR hvKP with hypermucoviscous regulators and lacking the common capsule regulators, which needs more focus to highlight their epidemiological role.