Mycobacterium chelonae is an environmental, non-tuberculous mycobacterial species, capable of causing infections in humans. Biofilm formation is a key strategy used by M. chelonae in colonising niches in the environment and in the host. We studied a water-air interface (pellicle) biofilm of M. chelonae using a wide array of approaches to outline the molecular structure and composition of the biofilm. Scanning electron micrographs showed that M. chelonae biofilms produced an extracellular matrix. Using a combination of biochemical analysis, Raman spectroscopy, and fluorescence microscopy, we showed the matrix to consist of proteins, carbohydrates, lipids and eDNA. Glucose was the predominant sugar present in the biofilm matrix, and its relative abundance decreased in late (established) biofilms. RNA-seq analysis of the biofilms showed upregulation of genes involved in redox metabolism. Additionally, genes involved in mycolic acid, other lipid and glyoxylate metabolism were also upregulated in the early biofilms.

The rapid identification of antibiotic resistant bacteria is important for public health. In the environment, bacteria are exposed to subinhibitory antibiotic concentrations which has implications in the generation of multi-drug resistant strains. To better understand these issues, Raman spectroscopy was employed coupled with partial least squares-discriminant analysis to profile Escherichia coli strains treated with sub-inhibitory concentrations of antibiotics. Clear differences were observed between cells treated with bacteriostatic (tetracycline and rifampicin) and bactericidal (ampicillin, ciprofloxacin, and ceftriaxone) antibiotics for 6 hr: First, atomic force microscopy revealed that bactericidal antibiotics cause extensive cell elongation whereas short filaments are observed with bacteriostatic antibiotics. Second, Raman spectral analysis revealed that bactericidal antibiotics lower nucleic acid to protein (I 812 /I 830) and nucleic acid to lipid ratios (I 1483 / I 1452) whereas the opposite is seen with bacteriostatic antibiotics. Third, the protein to lipid ratio (I 2936 /I 2885 and I 2936 /I 2850) is a Raman stress signature common to both the classes. These signatures were validated using two mutants, Δlon and ΔacrB, that exhibit relatively high and low resistance towards antibiotics, respectively. In addition, these spectral markers correlated with the emergence of phenotypic antibiotic resistance. Overall, this study demonstrates the efficacy of Raman spectroscopy to identify resistance in bacteria to sub-lethal concentrations of antibiotics.

The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.

There has been a steep rise in the emergence of antibiotic-resistant bacteria in the past few years. A timely diagnosis can help in initiating appropriate antibiotic therapy. However, conventional techniques for diagnosing antibiotic resistance are time-consuming and labor-intensive. Therefore, we investigated the potential of Raman spectroscopy as a rapid surveillance technology for tracking the emergence of antibiotic resistance. In this study, we used Raman spectroscopy to differentiate clinical isolates of antibiotic-resistant and -sensitive bacteria of Escherichia coli, Acinetobacter baumannii, and Enterobacter species. The spectra were collected with or without exposure to various antibiotics (ciprofloxacin, gentamicin, meropenem, and nitrofurantoin), each having a distinct mechanism of action. Ciprofloxacin- and meropenem-treated sensitive strains showed a decrease in the intensity of Raman bands associated with DNA (667, 724, 785, 1378, 1480, and 1575 cm–1) and proteins (640 and 1662 cm–1), coupled with an increase in the intensity of lipid bands (891, 960, and 1445 cm–1). Gentamicin- and nitrofurantoin-treated sensitive strains showed an increase in the intensity of nucleic acid bands (668, 724, 780, 810, 1378, 1480, and 1575 cm–1) while a decrease in the intensity of protein bands (640, 1003, 1606, and 1662 cm–1) and the lipid band (1445 cm–1). The Raman spectral changes observed in the antibiotic-resistant strains were opposite to that of antibiotic-sensitive strains. The Raman spectral data correlated well with the antimicrobial susceptibility test results. The Raman spectral dataset was used for partial least-squares (PLS) analysis to validate the biomarkers obtained from the univariate analysis. Overall, this study showcases the potential of Raman spectroscopy for detecting antibiotic-resistant and -sensitive bacteria.

The bacteria, Clostridium difficile the main pathogen associated with nosocomial infections is a gram positive, anaerobic and forms spores. The host and pathogen reaction results in a wide array of manifestation of the disease which ranges at one end as asymptomatic carriage to the other end as severe; toxic megacolon and thus making the treatment with antibiotic a challenge. To add upon the Clostridium difficile shows antibiotic resistance making its eradication almost impossible. This antibiotic resistance of Clostridium difficile has made it a global challenge over the decades, making it impossible for us to come to a conventional antibiotic to treat infections by Clostridium difficile. Several new strains of Clostridium difficile have emerged following the unscrupulous use of antimicrobial agents and subsequent transfer of resistance-causing genes between virulent strains the subsequent generations. The resistance to antibiotics is still rampant and not in control, rather it is on rise as the bacteria has greater access to new host due to its mobility and very infectious nature. With the use of extended antimicrobial therapy, an acute danger of creating and spreading new resistant and multi-drug resistant strains always looms. Clostridium difficile, during its life cycle, produces spores, which give the organism the capacity to resist extremes of change in the environment. In a country such as India, with a high population density and thus forming a high potential platform for the spread of multidrug-resistant strains of Clostridium difficile isusually addressed. An understanding of the emergence of antibiotic resistance in Clostridium difficile, the mechanisms by which the Clostridium difficile acquires it and sensitivity to antibiotics becomes very important. Therefore, we present a review of the antibiotic resistance seen in Clostridium difficile. RT027 has emerged as the most virulent strain all the strain of Clostridium difficile. Drug resistance is via multiple pathways, including erm, cfr, tet, and rpo B genes, as well as Gyr A and Gyr B. Of these, tet is transferable to non-resistant strains. Thus, indiscriminate use of antibiotics has created multiple antibiotic resistant strains of Clostridium difficile. Judicious and planned antibiotic treatment is advisable.