Evaluation of Chemical Changes in Laboratory-Induced Colistin-Resistant Klebsiella pneumoniae

Rapid Identification of Pathogens Causing Bloodstream Infections by Raman Spectroscopy and Raman Tweezers

The search for the "Holy Grail" in clinical diagnostic microbiology-a reliable, accurate, low-cost, real-time, easy-to-use method-has brought up several methods with the potential to meet these criteria. One is Raman spectroscopy, an optical, nondestructive method based on the inelastic scattering of monochromatic light. The current study focuses on the possible use of Raman spectroscopy for identifying microbes causing severe, often life-threatening bloodstream infections. We included 305 microbial strains of 28 species acting as causative agents of bloodstream infections. Raman spectroscopy identified the strains from grown colonies, with 2.8% and 7% incorrectly identified strains using the support vector machine algorithm based on centered and uncentred principal-component analyses, respectively. We combined Raman spectroscopy with optical tweezers to speed up the process and captured and analyzed microbes directly from spiked human serum. The pilot study suggests that it is possible to capture individual microbial cells from human serum and characterize them by Raman spectroscopy with notable differences among different species. IMPORTANCE Bloodstream infections are among the most common causes of hospitalizations and are often life-threatening. To establish an effective therapy for a patient, the timely identification of the causative agent and characterization of its antimicrobial susceptibility and resistance profiles are essential. Therefore, our multidisciplinary team of microbiologists and physicists presents a method that reliably, rapidly, and inexpensively identifies pathogens causing bloodstream infections-Raman spectroscopy. We believe that it might become a valuable diagnostic tool in the future. Combined with optical trapping, it offers a new approach where the microorganisms are individually trapped in a noncontact way by optical tweezers and investigated by Raman spectroscopy directly in a liquid sample. Together with the automatic processing of measured Raman spectra and comparison with a database of microorganisms, it makes the whole identification process almost real time.

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.

Unraveling the Secrets of Colistin Resistance with Label-Free Raman Spectroscopy

The rise in number of infections from multidrug-resistant (MDR) Gram-negative microbes has led to an increase in the use of a variety of ‘polymyxins’ such as colistin. Even though colistin is known to cause minor nephro- and neuro-toxicity, it is still considered as last resort antibiotic for treating MDR infections. In this study, we have applied Raman spectroscopy to understand the differences among colistin sensitive and resistant bacterial strains at community level. We have successfully generated colistin resistant clones and verified the presence of resistance-causing MCR-1 plasmid. A unique spectral profile associated with specific drug concentration has been obtained. Successful delineation between resistant and sensitive cells has also been achieved via principal component analysis. Overall findings support the prospective utility of Raman spectroscopy in identifying anti-microbial resistance.

A Polysaccharide Biosynthesis Locus in Vibrio parahaemolyticus Important for Biofilm Formation Has Homologs Widely Distributed in Aquatic Bacteria Mainly from Gammaproteobacteria

Vibrio parahaemolyticus is a seafood-borne pathogen that poses a great threat to public health worldwide. It is found in either a planktonic cell or a biofilm form in the natural environment. The cps locus has been the only extensively studied polysaccharide biosynthesis gene cluster involved in biofilm formation for this bacterium. In this study, we found that an additional polysaccharide biosynthesis locus, scv, is also necessary for biofilm maturation. The scv locus is composed of two operons, and a loss of their expression leads to a defective biofilm phenotype. The transcription of the scv locus is under the control of a sigma 54-dependent response regulator, ScvE. In contrast, the quorum-sensing regulator AphA stimulates the expression of the cps locus and the scvABCD operon found in the scv locus. Bioinformatic analyses demonstrated that scv loci are divergent and widely distributed among 28 genera, including 26 belonging to the Gammaproteobacteria and 2 within the Alphaproteobacteria. We also determined that all scv locus-positive species are water-dwelling. Some strains of Aeromonas, Aliivibrio salmonicida, Pseudomonas anguilliseptica, Vibrio breoganii, and Vibrio scophthalmi probably acquired scv loci through insertion sequences and/or integrase-mediated horizontal gene transfer. Gene duplication and fusion were also detected in some scv homologs. Together, our results suggest that the genome of V. parahaemolyticus harbors two distinct polysaccharide biosynthesis loci, which may play a role in fine-tuning biofilm development, and that scv loci likely evolved by horizontal gene transfer, gene loss, gene duplication, and fragment fusion. IMPORTANCE Polysaccharides are the major component of biofilms, which provide survival advantages for bacteria in aquatic environments. The seafood-borne pathogen V. parahaemolyticus possesses a functionally uncharacterized polysaccharide biosynthesis locus, scv. We demonstrated that the scv locus is important for biofilm maturation and that scv expression is positively regulated by ScvE. Strains from 148 aquatic bacterial species possess scv homolog loci. These bacterial species belong to 28 genera, most of which belong to the Gammaproteobacteria class. The evolution and diversification of scv loci are likely driven by horizontal gene transfer, gene loss, gene duplication, and fragment fusion. Our results provide new insights into the function and evolution of this widespread polysaccharide biosynthesis locus.