Raman Metabolomics of Candida auris Clades: Profiling and Barcode Identification

Genetic microevolution of clinical Candida auris with reduced Amphotericin B sensitivity in China

The global rate of Amphotericin B (AmB) resistance in Candida auris has surpassed 12%. However, there is limited data on available clinical treatments and microevolutionary analyses concerning reduced AmB sensitivity. In this study, we collected 18 C. auris isolates from five patients between 2019 and 2022. We employed clinical data mining, genomic, and transcriptomic analyses to identify genetic evolutionary features linked to reduced AmB sensitivity in these isolates during clinical treatment. We identified six isolates with a minimum inhibitory concentration (MIC) of AmB below 0.5 µg/mL (AmB0.5) and 12 isolates with an AmB-MIC of 1 µg/mL (AmB1) or ≥ 2 µg/mL (AmB2). All five patients received 24-hour AmB (5 mg/L) bladder irrigation treatment. Evolutionary analyses revealed an ERG3 (c923t) mutation in AmB1 C. auris. Additionally, AmB2 C. auris was found to contain a t2831c mutation in the RAD2 gene. In the AmB1 group, membrane lipid-related gene expression (ERG1, ERG2, ERG13, and ERG24) was upregulated, while in the AmB2 group, expression of DNA-related genes (e.g. DNA2 and PRI1) was up-regulated. In a series of C.auris strains with reduced susceptibility to AmB, five key genes were identified: two upregulated (IFF9 and PGA6) and three downregulated (HGT7, HGT13,and PRI32). In this study, we demonstrate the microevolution of reduced AmB sensitivity in vivo and further elucidate the relationship between reduced AmB sensitivity and low-concentration AmB bladder irrigation. These findings offer new insights into potential antifungal drug targets and clinical markers for the "super fungus", C. auris.

Identification of hypermucoviscous Klebsiella pneumoniae strains via untargeted surface-enhanced Raman spectroscopy

Klebsiella pneumoniae is one of the most common causes of hospital-acquired infections, especially due to the emergence of the hypervirulent K. pneumoniae (hvKp) strains. Multiple methods have been developed to discriminate hvKp strains from classical K. pneumoniae (cKp) strains, such as the presence of candidate genes (e.g., peg-344, iroB, and iucA), high level of siderophore production, hypermucoviscosity phenotype, etc. Although the string test is commonly used to confirm the hypermucoviscosity of K. pneumoniae strains, it is a method lacking rigidity and accuracy. Surface-enhanced Raman spectroscopy (SERS) coupled with machine learning algorithms has been widely used in discriminating bacterial pathogens with different phenotypes. However, the technique has not be applied to identify hypermucoviscous K. pneumoniae (hmvKp) strains. In this study, we isolated a set of K. pneumoniae strains from clinical samples, among which hmvKp strains (N = 10) and cKP strains (N = 10) were randomly selected to collect SESR spectra. Eight machine learning algorithms were recruited for model construction and spectral prediction in this study, among which support vector machine (SVM) outperforms all other algorithms with the highest prediction accuracy of hmvKp strains (5-fold cross validation = 99.07%). Taken together, this pilot study confirms that SERS, combined with machine learning algorithms, can accurately identify hmvKp strains, which can facilitate the fast recognition of hvKP strains when combined with relevant methods and biomarkers in clinical settings in the near future.

Uniqueness of Candida auris cell wall in morphogenesis, virulence, resistance, and immune evasion

Candida auris has drawn global attention due to its alarming multidrug resistance and the emergence of pan resistant strains. C. auris poses a significant risk in nosocomial candidemia especially among immunocompromised patients. C. auris showed unique virulence characteristics associated with cell wall including cell polymorphism, adaptation, endurance on inanimate surfaces, tolerance to external conditions, and immune evasion. Notably, it possesses a distinctive cell wall composition, with an outer mannan layer shielding the inner 1,3-β glucan from immune recognition, thereby enabling immune evasion and drug resistance. This review aimed to comprehend the association between unique characteristics of C. auris's cell wall and virulence, resistance mechanisms, and immune evasion. This is particularly relevant since the fungal cell wall has no human homology, providing a potential therapeutic target. Understanding the complex interactions between the cell wall and the host immune system is essential for devising effective treatment strategies, such as the use of repurposed medications, novel therapeutic agents, and immunotherapy like monoclonal antibodies. This therapeutic targeting strategy of C. auris holds promise for effective eradication of this resilient pathogen.

Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms

Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.

Multi-Omics Profiling of Candida albicans Grown on Solid Versus Liquid Media

Candida albicans is a common pathogenic fungus that presents a challenge to healthcare facilities. It can switch between a yeast cell form that diffuses through the bloodstream to colonize internal organs and a filamentous form that penetrates host mucosa. Understanding the pathogen's strategies for environmental adaptation and, ultimately, survival, is crucial. As a complementary study, herein, a multi-omics analysis was performed using high-resolution timsTOF MS to compare the proteomes and metabolomes of Wild Type (WT) Candida albicans (strain DK318) grown on agar plates versus liquid media. Proteomic analysis revealed a total of 1793 proteins and 15,013 peptides. Out of the 1403 identified proteins, 313 proteins were significantly differentially abundant with a p-value < 0.05. Of these, 156 and 157 proteins were significantly increased in liquid and solid media, respectively. Metabolomics analysis identified 192 metabolites in total. The majority (42/48) of the significantly altered metabolites (p-value 0.05 FDR, FC 1.5), mainly amino acids, were significantly higher in solid media, while only 2 metabolites were significantly higher in liquid media. The combined multi-omics analysis provides insight into adaptative morphological changes supporting Candida albicans' life cycle and identifies crucial virulence factors during biofilm formation and bloodstream infection.

Raman Fingerprints of SARS-CoV-2 Omicron Subvariants: Molecular Roots of Virological Characteristics and Evolutionary Directions

The latest RNA genomic mutation of SARS-CoV-2 virus, termed the Omicron variant, has generated a stream of highly contagious and antibody-resistant strains, which in turn led to classifying Omicron as a variant of concern. We systematically collected Raman spectra from six Omicron subvariants available in Japan (i.e., BA.1.18, BA.2, BA.4, BA.5, XE, and BA.2.75) and applied machine-learning algorithms to decrypt their structural characteristics at the molecular scale. Unique Raman fingerprints of sulfur-containing amino acid rotamers, RNA purines and pyrimidines, tyrosine phenol ring configurations, and secondary protein structures clearly differentiated the six Omicron subvariants. These spectral characteristics, which were linked to infectiousness, transmissibility, and propensity for immune evasion, revealed evolutionary motifs to be compared with the outputs of genomic studies. The availability of a Raman "metabolomic snapshot", which was then translated into a barcode to enable a prompt subvariant identification, opened the way to rationalize in real-time SARS-CoV-2 activity and variability. As a proof of concept, we applied the Raman barcode procedure to a nasal swab sample retrieved from a SARS-CoV-2 patient and identified its Omicron subvariant by coupling a commercially available magnetic bead technology with our newly developed Raman analyses.

Raman Multi-Omic Snapshot and Statistical Validation of Structural Differences between Herpes Simplex Type I and Epstein-Barr Viruses

Raman spectroscopy was applied to study the structural differences between herpes simplex virus Type I (HSV-1) and Epstein-Barr virus (EBV). Raman spectra were first collected with statistical validity on clusters of the respective virions and analyzed according to principal component analysis (PCA). Then, average spectra were computed and a machine-learning approach applied to deconvolute them into sub-band components in order to perform comparative analyses. The Raman results revealed marked structural differences between the two viral strains, which could mainly be traced back to the massive presence of carbohydrates in the glycoproteins of EBV virions. Clear differences could also be recorded for selected tyrosine and tryptophan Raman bands sensitive to pH at the virion/environment interface. According to the observed spectral differences, Raman signatures of known biomolecules were interpreted to link structural differences with the viral functions of the two strains. The present study confirms the unique ability of Raman spectroscopy for answering structural questions at the molecular level in virology and, despite the structural complexity of viral structures, its capacity to readily and reliably differentiate between different virus types and strains.

Classification and prediction of Klebsiella pneumoniae strains with different MLST allelic profiles via SERS spectral analysis

The Gram-negative non-motile Klebsiella pneuomoniae is currently a major cause of hospital-acquired (HA) and community-acquired (CA) infections, leading to great public health concern globally, while rapid identification and accurate tracing of the pathogenic bacterium is essential in facilitating monitoring and controlling of K. pneumoniae outbreak and dissemination. Multi-locus sequence typing (MLST) is a commonly used typing approach with low cost that is able to distinguish bacterial isolates based on the allelic profiles of several housekeeping genes, despite low resolution and labor intensity of the method. Core-genome MLST scheme (cgMLST) is recently proposed to sub-type and monitor outbreaks of bacterial strains with high resolution and reliability, which uses hundreds or thousands of genes conserved in all or most members of the species. However, the method is complex and requires whole genome sequencing of bacterial strains with high costs. Therefore, it is urgently needed to develop novel methods with high resolution and low cost for bacterial typing. Surface enhanced Raman spectroscopy (SERS) is a rapid, sensitive and cheap method for bacterial identification. Previous studies confirmed that classification and prediction of bacterial strains via SERS spectral analysis correlated well with MLST typing results. However, there is currently no similar comparative analysis in K. pneumoniae strains. In this pilot study, 16 K. pneumoniae strains with different sequencing typings (STs) were selected and a phylogenetic tree was constructed based on core genome analysis. SERS spectra (N = 45/each strain) were generated for all the K. pneumoniae strains, which were then comparatively classified and predicted via six representative machine learning (ML) algorithms. According to the results, SERS technique coupled with the ML algorithm support vector machine (SVM) could achieve the highest accuracy (5-Fold Cross Validation = 100%) in terms of differentiating and predicting all the K. pneumoniae strains that were consistent to corresponding MLSTs. In sum, we show in this pilot study that the SERS-SVM based method is able to accurately predict K. pneumoniae MLST types, which has the application potential in clinical settings for tracing dissemination and controlling outbreak of K. pneumoniae in hospitals and communities with low costs and high rapidity.

New Insights in Phenothiazinium-Mediated Photodynamic Inactivation of Candida Auris

In recent years, Candida auris has emerged as a hazardous hospital-acquired pathogen. Its resistance to antifungal treatments makes it challenging, requiring new approaches to manage it effectively. Herein, we aimed to assess the impact of photodynamic inactivation mediated by methylene blue (MB-PDI) or 1,9-dimethyl MB (DMMB-PDI) combined with a red LED against C. auris. To evaluate the photoinactivation of yeasts, we quantified colony-forming units and monitored ROS production. To gain some insights into the differences between MB and DMMB, we assessed lipid peroxidation (LPO) and mitochondrial membrane potential (ΔΨm). After, we verified the effectiveness of DMMB against biofilms by measuring metabolic activity and biomass, and the structures were analyzed through scanning electron microscopy and optical coherence tomography. We also evaluated the cytotoxicity in mammalian cells. DMMB-PDI successfully eradicated C. auris yeasts at 3 μM regardless of the light dose. In contrast, MB (100 μM) killed cells only when exposed to the highest dose of light. DMMB-PDI promoted higher ROS, LPO and ΔΨm levels than those of MB. Furthermore, DMMB-PDI was able to inhibit biofilm formation and destroy mature biofilms, with no observed toxicity in fibroblasts. We conclude that DMMB-PDI holds great potential to combat the global threat posed by C. auris.

In Situ Raman Analysis of Biofilm Exopolysaccharides Formed in Streptococcus mutans and Streptococcus sanguinis Commensal Cultures

This study probed in vitro the mechanisms of competition/coexistence between Streptococcus sanguinis (known for being correlated with health in the oral cavity) and Streptococcus mutans (responsible for aciduric oral environment and formation of caries) by means of quantitative Raman spectroscopy and imaging. In situ Raman assessments of live bacterial culture/coculture focusing on biofilm exopolysaccharides supported the hypothesis that both species engaged in antagonistic interactions. Experiments of simultaneous colonization always resulted in coexistence, but they also revealed fundamental alterations of the biofilm with respect to their water-insoluble glucan structure. Raman spectra (collected at fixed time but different bacterial ratios) showed clear changes in chemical bonds in glucans, which pointed to an action by Streptococcus sanguinis to discontinue the impermeability of the biofilm constructed by Streptococcus mutans. The concurrent effects of glycosidic bond cleavage in water-insoluble α - 1,3-glucan and oxidation at various sites in glucans' molecular chains supported the hypothesis that secretion of oxygen radicals was the main "chemical weapon" used by Streptococcus sanguinis in coculture.

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings.