Discrimination of Staphylococcus aureus Strains from Coagulase-Negative Staphylococci and Other Pathogens by Fourier Transform Infrared Spectroscopy

Perspectives of FTIR as Promising Tool for Pathogen Diagnosis, Sanitary and Welfare Monitoring in Animal Experimentation Models: A Review Based on Pertinent Literature

Currently, there is a wide application in the literature of the use of the Fourier Transform Infrared Spectroscopy (FTIR) technique. This basic tool has also proven to be efficient for detecting molecules associated with hosts and pathogens in infections, as well as other molecules present in humans and animals' biological samples. However, there is a crisis in science data reproducibility. This crisis can also be observed in data from experimental animal models (EAMs). When it comes to rodents, a major challenge is to carry out sanitary monitoring, which is currently expensive and requires a large volume of biological samples, generating ethical, legal, and psychological conflicts for professionals and researchers. We carried out a survey of data from the relevant literature on the use of this technique in different diagnostic protocols and combined the data with the aim of presenting the technique as a promising tool for use in EAM. Since FTIR can detect molecules associated with different diseases and has advantages such as the low volume of samples required, low cost, sustainability, and provides diagnostic tests with high specificity and sensitivity, we believe that the technique is highly promising for the sanitary and stress and the detection of molecules of interest of infectious or non-infectious origin.

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800-700 cm-1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.

Detection of Staphylococcus Isolates and Their Antimicrobial Resistance Profiles and Virulence Genes from Subclinical Mastitis Cattle Milk Using MALDI-TOF MS, PCR and Sequencing in Free State Province, South Africa

Staphylococcus species are amongst the bacteria that cause bovine mastitis worldwide, whereby they produce a wide range of protein toxins, virulence factors, and antimicrobial-resistant properties which are enhancing the pathogenicity of these organisms. This study aimed to detect Staphylococcus spp. from the milk of cattle with subclinical mastitis using MALDI-TOF MS and 16S rRNA PCR as well as screening for antimicrobial resistance (AMR) and virulence genes. Our results uncovered that from 166 sampled cows, only 33.13% had subclinical mastitis after initial screening, while the quarter-level prevalence was 54%. Of the 50 cultured bacterial isolates, MALDI-TOF MS and 16S rRNA PCR assay and sequencing identified S. aureus as the dominant bacteria by 76%. Furthermore, an AMR susceptibility test showed that 86% of the isolates were resistant to penicillin, followed by ciprofloxacin (80%) and cefoxitin (52%). Antimicrobial resistance and virulence genes showed that 16% of the isolates carried the mecA gene, while 52% of the isolates carried the Lg G-binding region gene, followed by coa (42%), spa (40%), hla (38%), and hlb (38%), whereas sea and bap genes were detected in 10% and 2% of the isolates, respectively. The occurrence of virulence factors and antimicrobial resistance profiles highlights the need for appropriate strategies to control the spread of these pathogens.

Multi-resistant diarrheagenic Escherichia coli identified by FTIR and machine learning: a feasible strategy to improve the group classification

The identification of multidrug-resistant strains from E. coli species responsible for diarrhea in calves still faces many laboratory limitations and is necessary for adequately monitoring the microorganism spread and control. Then, there is a need to develop a screening tool for bacterial strain identification in microbiology laboratories, which must show easy implementation, fast response, and accurate results. The use of FTIR spectroscopy to identify microorganisms has been successfully demonstrated in the literature, including many bacterial strains; here, we explored the FTIR potential for multi-resistant E. coli identification. First, we applied principal component analysis to observe the group formation tendency; the first results showed no clustering tendency with a messy sample score distribution; then, we improved these results by adequately selecting the main principal components which most contribute to group separation. Finally, using machine learning algorithms, a predicting model showed 75% overall accuracy, demonstrating the method's viability as a screaming test for microorganism identification.

Sublethally injured microorganisms in food processing and preservation: Quantification, formation, detection, resuscitation and adaption

Sublethally injured state has been recognized as a survival strategy for microorganisms suffering from stressful environments. Injured cells fail to grow on selective media but can normally grow on nonselective media. Numerous microorganism species can form sublethal injury in various food matrices during processing and preservation with different techniques. Injury rate was commonly used to evaluate sublethal injury, but mathematical models for the quantification and interpretation of sublethally injured microbial cells still require further study. Injured cells can repair themselves and regain viability on selective media under favorable conditions when stress is removed. Conventional culture methods might underestimate microbial counts or present a false negative result due to the presence of injured cells. Although the structural and functional components may be affected, the injured cells pose a great threat to food safety. This work comprehensively reviewed the quantification, formation, detection, resuscitation and adaption of sublethally injured microbial cells. Food processing techniques, microbial species, strains and food matrix all significantly affect the formation of sublethally injured cells. Culture-based methods, molecular biological methods, fluorescent staining and infrared spectroscopy have been developed to detect the injured cells. Cell membrane is often repaired first during resuscitation of injured cells, meanwhile, temperature, pH, media and additives remarkably influence the resuscitation. The adaption of injured cells negatively affects the microbial inactivation during food processing.

Growth Kinetics Monitoring of Gram-Negative Pathogenic Microbes Using Raman Spectroscopy

Optical density based measurements are routinely performed to monitor the growth of microbes. These measurements solely depend upon the number of cells and do not provide any information about the changes in the biochemical milieu or biological status. An objective information about these parameters is essential for evaluation of novel therapies and for maximizing the metabolite production. In the present study, we have applied Raman spectroscopy to monitor growth kinetics of three different pathogenic Gram-negative microbes Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii. Spectral measurements were performed under 532 nm excitation with 5 seconds of exposure time. Spectral features suggest temporal changes in the "peptide" and "nucleic acid" content of cells under different growth stages. Using principal component analysis (PCA), successful discrimination between growth phases was also achieved. Overall, the findings are supportive of the prospective adoption of Raman based approaches for monitoring microbial growth.

A new strategy for canine visceral leishmaniasis diagnosis based on FTIR spectroscopy and machine learning

Visceral leishmaniasis is a neglected disease caused by protozoan parasites of the genus Leishmania. The successful control of the disease depends on its accurate and early diagnosis, which is usually made by combining clinical symptoms with laboratory tests such as serological, parasitological, and molecular tests. However, early diagnosis based on serological tests may exhibit low accuracy due to lack of specificity caused by cross-reactivities with other pathogens, and sensitivity issues related, among other reasons, to disease stage, leading to misdiagnosis. In this study was investigated the use of mid-infrared spectroscopy and multivariate analysis to perform a fast, accurate, and easy canine visceral leishmaniasis diagnosis. Canine blood sera of 20 noninfected, 20 Leishmania infantum, and eight Trypanosoma evansi infected dogs were studied. The data demonstrate that principal component analysis with machine learning algorithms achieved an overall accuracy above 85% in the diagnosis.

Fast label-free identification of bacteria by synchronous fluorescence of amino acids

Fast identification of pathogenic bacteria is an essential need for patient's diagnostic in hospitals and environmental monitoring of water and air quality. Bacterial cells consist of a very high amount of biological molecules whose content changes in response to different environmental conditions. The similarity between the molecular compositions of different bacterial cells limits the possibility to find unique markers to enable differentiation among species. Although many biological molecules in the cells absorb at the UV-Vis region, only a few of them can be detected in whole cells by their intrinsic fluorescence. Among these molecules are the amino acids phenylalanine, tyrosine, and tryptophan. In this work, we develop a rapid method for bacterial identification by synchronous fluorescence. We show that we can quantify the concentration for the 3 amino acids without any significant interference from other fluorophores in the cells and that we can differentiate among 6 pathogenic bacterial species by using the concentrations of their amino acids as a bacterial fingerprint. Fluorescent amino acids exist in all living cells. Therefore, this method has the potential to be applicative for the rapid identification of cells from all kinds of organisms.