Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance.

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g., siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Furthermore, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N = 16) or cKp (N = 33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression, the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49), respectively. These findings can be used to inform the identification of hvKp.IMPORTANCEHypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all five of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which, in turn, would assist in optimizing patient care.

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance.

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combination of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g. siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Further, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combination of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N=16) or cKp (N=33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥ 4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49) respectively. These findings can be used to inform the identification of hvKp. Importance: Hypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all 5 of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥ 4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which in turn would assist in optimizing patient care.

Identification of an Outbreak Cluster of Extensively Antibiotic-Resistant GC1 Acinetobacter baumannii Isolates in U.S. Military Treatment Facilities.

An outbreak involving an extensively antibiotic-resistant Acinetobacter baumannii strain in three military treatment facilities was identified. Fifty-nine isolates recovered from 30 patients over a 4-year period were found among a large collection of isolates using core genome multilocus sequence typing (MLST). They differed by only 0 to 18 single nucleotide polymorphisms (SNPs) and carried the same resistance determinants except that the aphA6 gene was missing in 25 isolates. They represent a novel sublineage of GC1 lineage 1 that likely originated in Afghanistan. IMPORTANCE A. baumannii is recognized as one of the most important nosocomial pathogens, and carbapenem-resistant strains pose a particularly difficult treatment challenge. Outbreaks linked to this pathogen are reported worldwide, particularly during periods of societal upheaval, such as natural disasters and conflicts. Understanding how this organism enters and establishes itself within the hospital environment is key to interrupting transmission, but few genomic studies have examined these transmissions over a prolonged period. Though historical, this report provides an in-depth analysis of nosocomial transmission of this organism across continents and within and between different hospitals.

A one-year genomic investigation of Escherichia coli epidemiology and nosocomial spread at a large US healthcare network.

BACKGROUND: Extra-intestinal pathogenic Escherichia coli (ExPEC) are a leading cause of bloodstream and urinary tract infections worldwide. Over the last two decades, increased rates of antibiotic resistance in E. coli have been reported, further complicating treatment. Worryingly, specific lineages expressing extended-spectrum ß-lactamases (ESBLs) and fluoroquinolone resistance have proliferated and are now considered a serious threat. Obtaining contemporary information on the epidemiology and prevalence of these circulating lineages is critical for containing their spread globally and within the clinic. METHODS: Whole-genome sequencing (WGS), phylogenetic analysis, and antibiotic susceptibility testing were performed for a complete set of 2075 E. coli clinical isolates collected from 1776 patients at a large tertiary healthcare network in the USA between October 2019 and September 2020. RESULTS: The isolates represented two main phylogenetic groups, B2 and D, with six lineages accounting for 53% of strains: ST-69, ST-73, ST-95, ST-131, ST-127, and ST-1193. Twenty-seven percent of the primary isolates were multidrug resistant (MDR) and 5% carried an ESBL gene. Importantly, 74% of the ESBL-E.coli were co-resistant to fluoroquinolones and mostly belonged to pandemic ST-131 and emerging ST-1193. SNP-based detection of possible outbreaks identified 95 potential transmission clusters totaling 258 isolates (12% of the whole population) from ≥ 2 patients. While the proportion of MDR isolates was enriched in the set of putative transmission isolates compared to sporadic infections (35 vs 27%, p = 0.007), a large fraction (61%) of the predicted outbreaks (including the largest cluster grouping isolates from 12 patients) were caused by the transmission of non-MDR clones. CONCLUSION: By coupling in-depth genomic characterization with a complete sampling of clinical isolates for a full year, this study provides a rare and contemporary survey on the epidemiology and spread of E. coli in a large US healthcare network. While surveillance and infection control efforts often focus on ESBL and MDR lineages, our findings reveal that non-MDR isolates represent a large burden of infections, including those of predicted nosocomial origins. This increased awareness is key for implementing effective WGS-based surveillance as a routine technology for infection control.

IS26-mediated plasmid reshuffling results in convergence of toxin-antitoxin systems but loss of resistance genes in XDR Klebsiella pneumoniae from a chronic infection.

Carbapenem-resistant Enterobacterales pose an urgent threat to human health worldwide. Klebsiella pneumoniae sequence type (ST) 14, initially identified in the Middle East and South-Asia and co-harbouring the carbapenemase genes bla OXA-232 and bla NDM-1, is now emerging globally. One such strain was detected in the USA in 2013 from a patient initially treated in India that also carried armA, a 16S rRNA methyltransferase that confers resistance to all clinically relevant aminoglycosides. Genetic and phenotypic changes were observed in 14 serial isolates collected from this chronically infected patient. The index isolate carried five plasmids, including an IncFIB-IncHI1B (harbouring armA and bla NDM-1), an IncFIA (bla CTX-M-15) and a ColE-like (bla OXA-232), and was extensively resistant to antibiotics. Four years later, a subsequent isolate had accumulated 34 variants, including a loss-of-function mutation in romA, resulting in tigecycline non-susceptibility. Importantly, this isolate now only carried two plasmids, including a large mosaic molecule made of fragments, all harbouring distinct toxin-antitoxin systems, from three of the canonical plasmids. Of the original acquired antibiotic resistance genes, this isolate only retained bla CTX-M-15, and as a result susceptibility to the carbapenems and amikacin was restored. Long-read sequencing of a subset of five representative isolates, collected between 2013 and 2017, allowed for the elucidation of the complex plasmid patterns and revealed the role of IS26-mediated plasmid reshuffling in the evolution of this clone. Such investigations of the mechanisms underlying plasmid stability, together with global and local surveillance programmes, are key to a better understanding of plasmid host range and dissemination.

Unsweetened and sucrose-sweetened black and green tea modifies the architecture of in vitro oral biofilms.

OBJECTIVE: It is unclear whether tea infusions with or without sucrose supplementation alter oral biofilm development, so we evaluated the effect of unsweetened and sucrose-sweetened black and green tea infusions on in vitro saliva-derived biofilms. DESIGN: Biofilms were developed from human saliva for 20 h in cell-free 25% human saliva within static glass-bottom microplates. During biofilm development, biofilms were treated with either (i) unsweetened black tea, (ii) unsweetened green tea, (iii) 10% sucrose-sweetened black tea, (iv) 10% sucrose-sweetened green tea (v) deionized water (negative control), or (vi) 10% sucrose (positive control). Biofilms were incubated at 37 °C in 5% CO2. After 20 h of development, biofilms were imaged using a CLSM, and biofilm architecture and viability were evaluated. RESULTS: All the tea infusions reduced biofilm biomass and altered some other biofilm architectural outcomes (e.g., biofilm surface area) compared to the control groups. Statistically significant differences in biofilm biomass, number of objects, surface area, and convex-hull porosity were observed between biofilms treated with green and black tea. The addition of sugar to tea did not significantly modify the ability of tea to alter biofilm architecture. Only the treatment of biofilms with unsweetened black tea significantly reduced bacterial viability. CONCLUSIONS: While both teas reduced biofilm biomass and altered biofilm architecture, black tea had an enhanced effect that may relate to this tea's observed antimicrobial activity. The addition of sucrose to tea infusions did not appear to reduce the impact of either tea in modifying oral biofilm architecture.

In vitro model systems for exploring oral biofilms: From single-species populations to complex multi-species communities.

Numerous in vitro biofilm model systems are available to study oral biofilms. Over the past several decades, increased understanding of oral biology and advances in technology have facilitated more accurate simulation of intraoral conditions and have allowed for the increased generalizability of in vitro oral biofilm studies. The integration of contemporary systems with confocal microscopy and 16S rRNA community profiling has enhanced the capabilities of in vitro biofilm model systems to quantify biofilm architecture and analyse microbial community composition. In this review, we describe several model systems relevant to modern in vitro oral biofilm studies: the constant depth film fermenter, Sorbarod perfusion system, drip-flow reactor, modified Robbins device, flowcells and microfluidic systems. We highlight how combining these systems with confocal microscopy and community composition analysis tools aids exploration of oral biofilm development under different conditions and in response to antimicrobial/anti-biofilm agents. The review closes with a discussion of future directions for the field of in vitro oral biofilm imaging and analysis.

Introducing BAIT (Biofilm Architecture Inference Tool): a software program to evaluate the architecture of oral multi-species biofilms.

Biofilm model systems are used to study biofilm growth and predict the effects of anti-biofilm interventions within the human oral cavity. Many in vitro biofilm model systems use a confocal laser scanning microscope (CLSM) in conjunction with image analysis tools to study biofilms. The aim of this study was to evaluate an in-house developed image analysis software program that we call BAIT (Biofilm Architecture Inference Tool) to quantify the architecture of oral multi-species biofilms following anti-biofilm interventions using a microfluidic biofilm system. Differences in architecture were compared between untreated biofilms and those treated with water (negative control), sodium gluconate ('placebo') or stannous fluoride (SnF2). The microfluidic system was inoculated with pooled human saliva and biofilms were developed over 22 h in filter-sterilized 25 % pooled human saliva. During this period, biofilms were treated with water, sodium gluconate, or SnF2 (1000, 3439 or 10 000 p.p.m. Sn2+) 8 and 18 h post-inoculation. After 22 h of growth, biofilms were stained with LIVE/DEAD stain, and imaged by CLSM. BAIT was used to calculate biofilm biovolume, total number of objects, surface area, fluffiness, connectivity, convex hull porosity and viability. Image analysis showed oral biofilm architecture was significantly altered by 3439 and 10 000 p.p.m. Sn2+ treatment regimens, resulting in decreased biovolume, surface area, number of objects and connectivity, while fluffiness increased (P<0.01). In conclusion, BAIT was shown to be able to measure the changes in biofilm architecture and detects possible antimicrobial and anti-biofilm effects of candidate agents.

A Sensitive Thresholding Method for Confocal Laser Scanning Microscope Image Stacks of Microbial Biofilms.

Biofilms are surface-attached microbial communities whose architecture can be captured with confocal microscopy. Manual or automatic thresholding of acquired images is often needed to help distinguish biofilm biomass from background noise. However, manual thresholding is subjective and current automatic thresholding methods can lead to loss of meaningful data. Here, we describe an automatic thresholding method designed for confocal fluorescent signal, termed the biovolume elasticity method (BEM). We evaluated BEM using confocal image stacks of oral biofilms grown in pooled human saliva. Image stacks were thresholded manually and automatically with three different methods; Otsu, iterative selection (IS), and BEM. Effects on biovolume, surface area, and number of objects detected indicated that the BEM was the least aggressive at removing signal, and provided the greatest visual and quantitative acuity of single cells. Thus, thresholding with BEM offers a sensitive, automatic, and tunable method to maintain biofilm architectural properties for subsequent analysis.

Coaggregation occurs between microorganisms isolated from different environments.

Coaggregation, the specific recognition and adherence of different microbial species, is thought to enhance biofilm formation. To date, no studies have focused on the ability of microorganisms isolated from a broad range of environments to coaggregate with each other and it is unclear whether coaggregation promotes the transmission of microorganisms between environmental niches. We aimed to evaluate the coaggregation ability of 29 bacteria and one fungus, isolated from a range of different environments, and to characterize the cell-surface polymers that mediate coaggregation between selected pairs. Strains were categorized as belonging to one of the four microbial archetypes: aquatic, broad environment, human opportunistic pathogen or human oral. A total of 23 of the 30 strains (77%) coaggregated with at least one other and 21/30 (70%) coaggregated with strains belonging to other archetypes. Nasopharyngeal bacteria belonging to the human opportunistic pathogen archetype showed the least number of coaggregations, and five Haemophilus influenzae strains did not coaggregate. Protease and sugar treatments indicated that coaggregation between strains of different archetypes was often mediated by lectin-saccharide interactions (9 of 15 evaluated pairs). In conclusion, coaggregation can occur between taxonomically disparate species isolated from discrete environments. We propose that these organisms be labeled as 'cross-environment coaggregating organisms'. The ability to coaggregate may aid species to colonize non-indigenous biofilms.

Association of blaOXA-23 and bap with the persistence of Acinetobacter baumannii within a major healthcare system.

OBJECTIVES: Acinetobacter baumannii is an emerging opportunistic nosocomial pathogen. Two factors that may enhance persistence in healthcare settings are antimicrobial resistance and biofilm-forming ability. The aim of this work was to determine whether A. baumannii isolates that persist in healthcare settings (endemic), can be differentiated from sporadic isolates based upon their ability to resist antibiotics and their biofilm-forming capability. METHODS: Two hundred and ninety A. baumannii isolates were isolated over 17 months in the Detroit Medical Center (DMC). The isolates were genotyped using repetitive extragenic palindromic-PCR (REP-PCR). REP-types appearing greater than 10 times during active surveillance were considered endemic. The in vitro biofilm-forming ability and antibiotic resistance profile of each isolate were evaluated. Isolates were tested for the presence of two genetic markers-one implicated in biofilm formation (bap) and the other in antibiotic resistance (blaOXA-23). RESULTS: Of the 290 isolates evaluated, 84% carried bap and 36% carried blaOXA-23 . Five unique REP-PCR banding-types were detected >10 times (endemic) and constituted 58% of the 290 isolates. These five endemic REP-PCR types were 5.1 times more likely than sporadic isolates to carry both bap and blaOXA-23 . Furthermore, endemic isolates were resistant to 3 more antibiotic classes, on average, than sporadic isolates and four of the five endemic REP-PCR types formed denser biofilms in vitro than sporadic isolates. CONCLUSIONS: Endemic A. baumannii isolates are more likely than sporadic isolates to possess factors that increase virulence and enhance survival within a large healthcare system.

Use of a high-throughput in vitro microfluidic system to develop oral multi-species biofilms.

There are few high-throughput in vitro systems which facilitate the development of multi-species biofilms that contain numerous species commonly detected within in vivo oral biofilms. Furthermore, a system that uses natural human saliva as the nutrient source, instead of artificial media, is particularly desirable in order to support the expression of cellular and biofilm-specific properties that mimic the in vivo communities. We describe a method for the development of multi-species oral biofilms that are comparable, with respect to species composition, to supragingival dental plaque, under conditions similar to the human oral cavity. Specifically, this methods article will describe how a commercially available microfluidic system can be adapted to facilitate the development of multi-species oral biofilms derived from and grown within pooled saliva. Furthermore, a description of how the system can be used in conjunction with a confocal laser scanning microscope to generate 3-D biofilm reconstructions for architectural and viability analyses will be presented. Given the broad diversity of microorganisms that grow within biofilms in the microfluidic system (including Streptococcus, Neisseria, Veillonella, Gemella, and Porphyromonas), a protocol will also be presented describing how to harvest the biofilm cells for further subculture or DNA extraction and analysis. The limits of both the microfluidic biofilm system and the current state-of-the-art data analyses will be addressed. Ultimately, it is envisioned that this article will provide a baseline technique that will improve the study of oral biofilms and aid in the development of additional technologies that can be integrated with the microfluidic platform.