The strategy for correcting interference from water in Fourier transform infrared spectrum based bacterial typing

Optimization of Naringin Extraction, Synthesis of Dihydrochalcone and Its Effects on Reducing Blood Lipid Levels In Vitro

Response surface methodology (RSM) was used to optimize the extraction process of naringin. The central component design included three parameters of extraction, namely temperature (X1), solid-liquid ratio (X2), and extraction time (X3). The optimum extraction temperature was 67 °C; the ratio of feed to solvent was 54:1 mL/g, and the extraction time was 2.8 h. According to the best extraction conditions, naringin was processed to verify the accuracy of the model. Five parallel experiments were set up, and a yield of 3.248% naringin was obtained, which was equivalent to the predicted yield of 3.256%. Naringin was purified to obtain naringin-refined products using DM101 macroporous adsorption resin. Naringin dihydrochalcone was synthesized following catalytic hydrogenation of purified naringin. The structures of naringin and naringin dihydrochalcone were determined via Fourier infrared spectrometer and nuclear magnetic resonance spectrometry. In vitro determination of the lipid-lowering activity of naringin dihydrochalcone was also conducted. Further focusing on HepG2 cells, a high cholesterol-induced high-fat HepG2 cell model was established. We measured the effects of different concentrations of naringin dihydrochalcone on intracellular lipids in denatured HepG2 cells and further validated the lipid-lowering effect of naringin at the cellular level. The results showed that naringin dihydrochalcone has a potential application in functional foods for lowering blood lipids.

A magnetic nanozyme platform for bacterial colorimetric detection and chemodynamic/photothermal synergistic antibacterial therapy

Rapid, convenient, and sensitive detection of bacteria and development of novel antibacterial materials are conducive to accurate treatment of bacterial infection and reducing the generation of drug-resistant bacteria caused by overuse of antibiotics. A dual-function magnetic nanozyme, Fc-MBL@rGO@Fe3O4, has been constructed with broad-spectrum bacterial affinity and good peroxidase-like activity. Detection signal amplification was realized in the presence of 3,3',5,5'-tetramethylbenzidine (TMB) with a detection limit of 26 CFU/mL. In addition, the excellent photothermal properties of Fc-MBL@rGO@Fe3O4 could realize synergistic chemodynamic/photothermal antibacterial therapy. Furthermore, the good bacterial affinity of Fc-MBL@rGO@Fe3O4 enhances the accurate and rapid attack of hydroxyl radical (·OH) on the bacterial membrane and achieves efficient sterilization (100%) at low concentration (40 µg/mL) and mild temperature (47℃). Notably, Fc-MBL@rGO@Fe3O4 has a broad spectrum of antibacterial activity against Gram-negative, Gram-positive, and drug-resistant bacteria. The magnetic nanoplatform integrating detection-sterilization not only meets the need for highly sensitive and accurate detection in different scenarios, but can realize low power density NIR-II light-responsive chemodynamic/photothermal antibacterial therapy, which has broad application prospects.

Detection limit of FT-IR-based bacterial typing based on optimized sample preparation and typing model

Accurate and efficient bacterial typing methods are crucial to microbiology. Fourier transform infrared (FT-IR) spectroscopy enables highly distinguishable fingerprint identification of closely related bacterial strains by producing highly specific fingerprints of bacteria, which is increasingly being considered as an alternative to genotypic methods, such as pulsed field gel electrophoresis (PFGE) and whole genome sequencing (WGS), for bacterial typing. Compared with genotypic methods, FT-IR has significant advantages of convenient operation, fast speed, and low cost. Fundamental research into the detection limit based on optimized analytical conditions for FT-IR bacterial typing, which can avoid excessive bacterial culture time or sampling volume, is particularly important, especially in clinical practice. However, the corresponding parameters have not been fully investigated. In this study, we developed a simplified and reliable procedure for sample preparation, optimized the data analysis procedure, and evaluated the FT-IR detection limit based on the above conditions. In particular, we combined the film mold and calcium fluoride plate for sample preparation. We evaluated the detection limit (about 108 CFU/mL) after parameter optimization using hierarchical cluster analysis (HCA) and artificial neural network (ANN). The optimization and evaluation of these key fundamentals will better promote future application of FT-IR-based bacterial typing.

Fusion data from FT-IR and MALDI-TOF MS result in more accurate classification of specific microbiota

Microbes are usually present as a specific microbiota, and their classification remains a challenge. MALDI-TOF MS is particularly successful in library-based microbial identification at the species level as it analyzes the molecular weight of peptides and ribosomal proteins. FT-IR allows more accurate classification of bacteria at the subspecies level due to the high sensitivity, specificity and repeatability of FT-IR signals from bacteria, which is not achievable with MALDI-TOF MS. Previous studies have shown that more accurate identification results can be obtained by the fusion of FT-IR and MALDI-TOF MS spectral data. Here, we constructed 20 groups of model microbiota samples and used FT-IR, MALDI-TOF MS, and their fusion data to classify them. Hierarchical clustering analysis (HCA) showed that the classification accuracy of FT-IR, MALDI-TOF MS, and the fusion data was 85%, 90%, and 100%, respectively. These results indicate that both FT-IR and MALDI-TOF MS can effectively classify specific microbiota, and the fusion of their spectral data could improve the classification accuracy. The FT-IR and MALDI-TOF MS data fusion strategy may be a promising technology for specific microbiota classification.

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.

Simple and Rapid Discrimination of Methicillin-Resistant Staphylococcus aureus Based on Gram Staining and Machine Vision

Methicillin-resistant Staphylococcus aureus (MRSA) is a clinical threat with high morbidity and mortality. Here, we describe a new simple, rapid identification method for MRSA using oxacillin sodium salt, a cell wall synthesis inhibitor, combined with Gram staining and machine vision (MV) analysis. Gram staining classifies bacteria as positive (purple) or negative (pink) according to the cell wall structure and chemical composition. In the presence of oxacillin, the integrity of the cell wall for methicillin-susceptible S. aureus (MSSA) was destroyed immediately and appeared Gram negative. In contrast, MRSA was relatively stable and appeared Gram positive. This color change can be detected by MV. The feasibility of this method was demonstrated in 150 images of the staining results for 50 clinical S. aureus strains. Based on effective feature extraction and machine learning, the accuracies of the linear linear discriminant analysis (LDA) model and nonlinear artificial neural network (ANN) model for MRSA identification were 96.7% and 97.3%, respectively. Combined with MV analysis, this simple strategy improved the detection efficiency and significantly shortened the time needed to detect antibiotic resistance. The whole process can be completed within 1 h. Unlike the traditional antibiotic susceptibility test, overnight incubation is avoided. This new strategy could be used for other bacteria and represents a new rapid method for detection of clinical antibiotic resistance. IMPORTANCE Oxacillin sodium salt destroys the integrity of the cell wall of MSSA immediately, appearing Gram negative, whereas MRSA is relatively stable and still appears Gram positive. This color change can be detected by microscopic examination and MV analysis. This new strategy has significantly reduced the time to detect resistance. The results show that using oxacillin sodium salt combined with Gram staining and MV analysis is a new, simple and rapid method for identification of MRSA.

Protocol for bacterial typing using Fourier transform infrared spectroscopy

The Fourier transform infrared (FT-IR) signals obtained from bacterial samples are specific and reproducible, making FT-IR an efficient tool for bacterial typing at a subspecies level. However, the typing accuracy could be affected by many factors, including sample preparation and spectral acquisition. Here, we present a unified protocol for bacterial typing based on FT-IR spectroscopy. We describe sample preparation from bacterial culture and FT-IR spectrum collection. We then detail FT-IR spectrum preprocessing and multivariate analysis of spectral data for bacterial typing.

Epigenetic and Drug Response Modulation of Epigalocaten-In-3-Gallate in Staphylococcus aureus with Divergent Resistance Phenotypes

Healthcare-associated methicillin-resistant Staphylococcus aureus infections represent extremely high morbidity and mortality rates worldwide. We aimed to assess the antimicrobial potential and synergistic effect between Epigalocatenin-3-gallate (EGCG) and different antibiotics in S. aureus strains with divergent resistance phenotypes. EGCG exposure effects in epigenetic and drug resistance key modulators were also evaluated. S. aureus strains (n = 32) were isolated from infected patients in a Lisbon hospital. The identification of the S. aureus resistance phenotype was performed through automatized methods. The antibiotic synergistic assay was performed through disk diffusion according to EUCAST guidelines with co-exposure to EGCG (250, 100, 50 and 25 µg/mL). The bacteria’s molecular profile was assessed through FTIR spectroscopy. The transcriptional expression of OrfX, SpdC and WalKR was performed by using qRT-PCR. FTIR-spectroscopy analysis enabled the clear discrimination of MRSA/MSSA strains and the EGCG exposure effect in the bacteria’s molecular profiles. Divergent resistant phenotypes were associated with divergent transcriptional expression of the epigenetic modulator OrfX, particularly in MRSA strains, as well as the key drug response modulators SpdC and WalKR. These results clearly demonstrate that EGCG exposure alters the expression patterns of key epigenetic and drug response genes with associated divergent-resistant profiles, which supports its potential for antimicrobial treatment and/or therapeutic adjuvant against antibiotic-resistant microorganisms.

Efficient classification of Escherichia coli and Shigella using FT-IR spectroscopy and multivariate analysis

Accurate and effective discrimination of E. coli and Shigella is an important clinical issue, and there are many limitations in traditional methods of analysis. FT-IR shows great potential in the classification of bacteria with high specificity and low cost. In this study, we evaluated the efficiency of this technique when combined with multivariate analysis for rapid classification of E. coli and Shigella, which is difficult using traditional analytical methods. Machine learning and statistical tools were employed in combination with FT-IR to classify 14 E. coli and 9 Shigella strains. The classification accuracies for select E. coli and Shigella strains from blood agar were 0.7826, 0.8696, and 0.9565 at the genus, species, and strain levels, respectively. In addition, we used the FT-IR data of select strains from three different culture media for cross-validation, yielding an accuracy of 0.3681 at the strain level. These results indicate that the bacterial culture conditions have a significant impact on the FT-IR patterns. Based on this, an improved strategy for training an ensemble classifier model considering bacterial culture factors was constructed, resulting in almost perfect separation with an accuracy of 0.9394 for strain-level classification. These results show the potential of FT-IR combined with multivariate analysis for more reliable bacterial classification.

Protein FT-IR amide bands are beneficial to bacterial typing

Bacterial FT-IR signals are extremely specific and highly reproducible, making FT-IR an efficient tool for bacterial typing at the subspecies level. The polysaccharide and nucleic acid FT-IR regions (1200-900 cm^-1) are recommended as a precise and reproducible pattern for bacterial typing. However, proteins are the major macromolecules present in bacteria, and the FT-IR spectral region of proteins (1800-1300 cm^-1) is conceivably an important factor in bacterial typing. In this study, we investigated the influence of water on bacterial protein amide bands by comparing spectra obtained with and without FT-IR system dehydration. Eight Escherichia coli, ten Klebsiella pneumoniae, and eleven Staphylococcus aureus strains were typed by FT-IR under different conditions in a blinded experimental setup. Hierarchical clustering analysis (HCA) showed that, when protein signals were included (1800-900 cm^-1), the typing accuracies for select E. coli, K. pn and S. aureus strains without system dehydration were 50%, 30% and 18.2%, respectively. However, the accuracies greatly improved to 100%, 90% and 90.9% when the FT-IR system was dehydrated. These results indicate that the FT-IR signals of protein amide bands are beneficial for bacterial typing.

Infrared Spectroscopy in Aqueous Solutions: Capabilities and Challenges

Fourier-transform infrared (FTIR) spectroscopy provides rapid, reliable, quantitative, and qualitative analysis of samples in different aggregation states, i.e., gases, thin films, solids, liquids, etc. However, when analyzing aqueous solutions, particular issues associated with the rather pronounced IR absorption characteristics of water appear to interfere with the solute determination. In this review, Fourier-transform infrared spectroscopic techniques and their analytical capabilities for analyzing aqueous solutions are reviewed, and highlight examples are discussed.

Progress in infrared spectroscopy as an efficient tool for predicting protein secondary structure

Infrared (IR) spectroscopy is a highly sensitive technique that provides complete information on chemical compositions. The IR spectra of proteins or peptides give rise to nine characteristic IR absorption bands. The amide I bands are the most prominent and sensitive vibrational bands and widely used to predict protein secondary structures. The interference of H₂O absorbance is the greatest challenge for IR protein secondary structure prediction. Much effort has been made to reduce/eliminate the interference of H₂O, simplify operation steps, and increase prediction accuracy. Progress in sampling and equipment has rendered the Fourier transform infrared (FTIR) technique suitable for determining the protein secondary structure in broader concentration ranges, greatly simplifying the operating steps. This review highlights the recent progress in sample preparation, data analysis, and equipment development of FTIR in A/T mode, with a focus on recent applications of FTIR spectroscopy in the prediction of protein secondary structure. This review also provides a brief introduction of the progress in ATR-FTIR for predicting protein secondary structure and discusses some combined IR methods, such as AFM-based IR spectroscopy, that are used to analyze protein structural dynamics and protein aggregation.

Insights on preparation, structure and activities of Gracilaria lemaneiformis polysaccharide

Gracilaria lemaneiformis is a kind of edible economic red algae, which is rich in polysaccharide, phycobiliprotein, pigments, minerals and other nutrients and functional components. Polysaccharide is one of the main active components of Gracilaria lemaneiformis, which has been reported to present various physiological bioactivities, including regulation of glycolipid metabolism, immune, anti-tumor, anti-inflammatory and other biological activities. This paper aims to provide a brief summary of extraction, purification, structural characteristics, and physiological activities of Gracilaria lemaneiformis polysaccharide (GLP). This article is able to provide theoretical basis for the future research and exploitation of GLP, and improve its potential development to promote the healthy and sustainable processing and high value utilization industry of Gracilaria lemaneiformis.

Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging

This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density (OD) concentration range of 0.001 to 10. Results showed that reflectance FTIR of samples with OD lower than 0.1 did not present an acceptable spectral signal to enable classification. Two modelling strategies were devised to evaluate model performance, transferability and consistency among concentration levels. Modelling strategy 1 involves training the model with half of the sample set, consisting of all concentrations, and applying it to the remaining half. Using this approach, for the STS substrate, the best model was achieved using support vector machine (SVM) classification, providing an accuracy of 96% and Matthews correlation coefficient (MCC) of 0.93 for the independent test set. For the Al substrate, the best SVM model produced an accuracy and MCC of 91% and 0.82, respectively. Furthermore, the aforementioned best model built from one substrate was transferred to predict the bacterial samples deposited on the other substrate. Results revealed an acceptable predictive ability when transferring the STS model to samples on Al (accuracy = 82%). However, the Al model could not be adapted to bacterial samples deposited on STS (accuracy = 57%). For modelling strategy 2, models were developed using one concentration level and tested on the other concentrations for each substrate. Results proved that models built from samples with moderate (1 OD) concentration can be adapted to other concentrations with good model generalization. Prediction maps revealed the heterogeneous distribution of biomolecules due to the coffee ring effect. This work demonstrated the feasibility of applying FTIR to characterise spectroscopic fingerprints of dry bacterial cells on substrates of relevance for food processing.

Releasing bacteria from functional magnetic beads is beneficial to MALDI-TOF MS based identification

Bacterial infections are the key cause of morbidity and mortality worldwide. Matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS)-based bacterial identification has been widely accepted in the clinic. Functional material, such as rabbit immunoglobulin G-modified Fe₃O₄ (IgG@Fe₃O₄) and fragment crystallizable mannose binding lectin-modified Fe₃O₄ (FcMBL@Fe₃O₄), is used to capture bacteria from biological samples for MALDI-TOF MS identification, and the bacteria MS signals are usually obtained by directly smearing enriched bacteria on a MALDI target with MALDI matrix solution. However, the accuracy of identification based on MALDI-TOF MS may be affected by the presence of functional molecules, especially proteins, resulting in errors in the comparison with the standard bacterial spectra in the database. Moreover, the long-term presence of the magnetic beads on the MALDI-TOF target may reduce the instrument service life. In this study, we constructed FcMBL@Fe₃O₄ and used it to capture bacteria from both aqueous solution and bovine blood, and the bacterial identification accuracy based on different target preparation methods was compared. In the presence of Ca²⁺, the similarity scores for bacteria identified with FcMBL@Fe₃O₄ were ~88% and ~82% for Staphylococcus. aureus and Escherichia coli, respectively. In the presence of ethylenediaminetetraacetic acid (EDTA), bacteria separate from FcMBL@Fe₃O₄, resulting in similarity scores of ~96% and ~92% for S. aureus and E. coli, respectively. These results indicate that the functional proteins on the surface of nanoparticles affect the accuracy of identification accuracy based on the MALDI-TOF MS database. Thus, the release of bacteria from the functional material could increase the identification accuracy and be beneficial for maintaining the instrument.