Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients

Significant reduction of the culturing time required for bacterial identification and antibiotic susceptibility determination by infrared spectroscopy

Rapid testing of bacteria for antibiotic susceptibility is essential for effective treatment and curbing the emergence of multidrug-resistant bacteria. The misuse of antibiotics, coupled with the time-consuming classical testing methods, intensifies the threat of antibiotic resistance, a major global health concern. In this study, employing infrared spectroscopy-based machine learning techniques, we significantly shortened the time required for susceptibility testing to 10 hours, a significant improvement from the 24 hours in our previous studies as well as the conventional methods that typically take at least 48 hours. This remarkable reduction in turnaround time (from 48 hours to 10 hours), achieved by minimizing the culturing period, offers a game-changing advantage for clinical applications. Our study involves a dataset comprising 400 bacterial samples (200 E. coli, 100 Klebsiella pneumoniae, and 100 Pseudomonas aeruginosa) with an impressive 96% accuracy in the taxonomic classification at the species level and up to 82% accuracy in bacterial susceptibility to various antibiotics.

Machine learning algorithms in microbial classification: a comparative analysis

This research paper presents an overview of contemporary machine learning methodologies and their utilization in the domain of healthcare and the prevention of infectious diseases, specifically focusing on the classification and identification of bacterial species. As deep learning techniques have gained prominence in the healthcare sector, a diverse array of architectural models has emerged. Through a comprehensive review of pertinent literature, multiple studies employing machine learning algorithms in the context of microbial diagnosis and classification are examined. Each investigation entails a tabulated presentation of data, encompassing details about the training and validation datasets, specifications of the machine learning and deep learning techniques employed, as well as the evaluation metrics utilized to gauge algorithmic performance. Notably, Convolutional Neural Networks have been the predominant selection for image classification tasks by machine learning practitioners over the last decade. This preference stems from their ability to autonomously extract pertinent and distinguishing features with minimal human intervention. A range of CNN architectures have been developed and effectively applied in the realm of image classification. However, addressing the considerable data requirements of deep learning, recent advancements encompass the application of pre-trained models using transfer learning for the identification of microbial entities. This method involves repurposing the knowledge gleaned from solving alternate image classification challenges to accurately classify microbial images. Consequently, the necessity for extensive and varied training data is significantly mitigated. This study undertakes a comparative assessment of various popular pre-trained CNN architectures for the classification of bacteria. The dataset employed is composed of approximately 660 images, representing 33 bacterial species. To enhance dataset diversity, data augmentation is implemented, followed by evaluation on multiple models including AlexNet, VGGNet, Inception networks, Residual Networks, and Densely Connected Convolutional Networks. The results indicate that the DenseNet-121 architecture yields the optimal performance, achieving a peak accuracy of 99.08%, precision of 99.06%, recall of 99.00%, and an F1-score of 98.99%. By demonstrating the proficiency of the DenseNet-121 model on a comparatively modest dataset, this study underscores the viability of transfer learning in the healthcare sector for precise and efficient microbial identification. These findings contribute to the ongoing endeavors aimed at harnessing machine learning techniques to enhance healthcare methodologies and bolster infectious disease prevention practices.

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.

Catalytic degradation of methylene blue by biosynthesized Au nanoparticles on titanium dioxide (Au@TiO2)

The degradation of methylene blue is a critical procedure in its wastewater remediation and thus has inspired wide catalysis research with semiconductors such as titanium dioxide (TiO₂) and rare metals such as gold (Au). In this study, we report bacterial cells assisting biosynthesis for Au@TiO₂ as an efficient catalyst for the catalytic degradation of methylene blue. Multiple complementary characterization for bio-Au_x@TiO₂ evidenced the evenly distributed Au nanoparticles (NPs) on the bio-TiO₂ layers. Meanwhile, bio-Au₂@TiO₂ displayed the superior catalytic activity in the degradation of methylene blue with the highest kinetics constant (k_app) value of 0.195 min^-1. In addition, bio-Au₂@TiO₂ keeps stable catalytic activity for up to 10 cycles. The origin of the catalytic activity was investigated by the hydroxyl radical fluorescence quantitative analysis and optical band gap analysis. In the bio-Au₂@TiO₂ catalytic system, Au NPs decreased the band gap energy of TiO₂ and enabled the generation of abundant photogeneration hydroxyl radicals, resulting in an enhanced photocatalytic activity. Our microbial synthesized bio-TiO₂ and bio-Au_x@TiO₂ study would be useful for developing green synthesis catalyst technology.

An Intelligent Strategy with All-Atom Molecular Dynamics Simulations for the Design of Lipopeptides against Multidrug-Resistant Pseudomonas aeruginosa

Multidrug-resistant Gram-negative bacteria seriously threaten modern medicine due to the lack of efficacious therapeutic options. Their outer membrane (OM) is an essential protective fortress to exclude many antibiotics. Unfortunately, current structural biology methods are not able to resolve the membrane structure and it is difficult to examine the specific interaction between the OM and small molecules. These limitations hinder mechanistic understanding of antibiotic penetration through the OM and antibiotic discovery. Here, we developed biologically relevant OM models by quantitatively determining membrane lipidomics of Pseudomonas aeruginosa and elucidated how lipopolysaccharide modifications and OM vesicles mediated resistance to polymyxins. Supported by chemical biology and pharmacological assays, our multiscale molecular dynamics simulations provide an intelligent platform to quantify the membrane-penetrating thermodynamics of peptides and predict their antimicrobial activity. Through experimental validations with our in-house polymyxin analogue library, our computational strategy may have significant potential in accelerating the discovery of lipopeptides against bacterial "superbugs".

[Bacteria in cystic fibrosis: United they stand - Recent advances in microbiology research through the eyes of ENS students]

Le dossier que nous présentons a été rédigé par les étudiantes et étudiants de Master 1 de Biologie de l’École Normale Supérieure de Lyon à l’issue de l’UE Microbiologie Moléculaire et Structurale (2020-2021).

Le Master de Biologie de l’ENS de Lyon accueille chaque année environ 40 étudiants en M1 et en M2 et propose une formation de haut niveau à la recherche en biosciences. Chaque étudiant y construit son parcours à la carte, en choisissant ses options parmi un large panel de modules, favorisant ainsi une approche pluridisciplinaire des sciences du vivant, et cela en relation étroite avec les laboratoires de recherche du tissu local, national et international.

En participant à diverses activités scientifiques liées aux UE de leur formation, les étudiants préparent également l’obtention du Diplôme de l’ENS de Lyon, qui valide leur scolarité à l’ENS. La rédaction du présent dossier, qui vise à transmettre de façon claire les messages issus d’une sélection d’articles scientifiques publiés récemment dans le domaine de la microbiologie, constitue l’une de ces activités connexes proposées aux étudiants.

A robust metabolomics approach for the evaluation of human embryos from in vitro fertilization

The identification of the most competent embryos for transfer to the uterus constitutes the main challenge of in vitro fertilization (IVF). We established a metabolomic-based approach by applying Fourier transform infrared (FTIR) spectroscopy on 130 samples of 3-day embryo culture supernatants from 26 embryos that implanted and 104 embryos that failed. On examining the internal structure of the data by unsupervised multivariate analysis, we found that the supernatant spectra of nonimplanted embryos constituted a highly heterogeneous group. Whereas ∼40% of these supernatants were spectroscopically indistinguishable from those of successfully implanted embryos, ∼60% exhibited diverse, heterogeneous metabolic fingerprints. This observation proved to be the direct result of pregnancy's multifactorial nature, involving both intrinsic embryonic traits and external characteristics. Our data analysis strategy thus involved one-class modelling techniques employing soft independent modelling of class analogy that identified deviant fingerprints as unsuitable for implantation. From these findings, we could develop a noninvasive Fourier-transform-infrared-spectroscopy-based approach that represents a shift in the fundamental paradigm for data modelling applied in assisted-fertilization technologies.

Infrared Spectroscopy of Blood

The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field.

Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (FTIR) and Artificial Neural Networks Applied to Investigate Quantitative Changes of Selected Soluble Biomarkers, Correlated with H. pylori Infection in Children and Presumable Consequent Delayed Growth

Helicobacter pylori infections causing gastroduodenal disorders are a common medical problem. The aim of this study was to determine the specific motives of infrared spectroscopy (IR) spectra of sera from H. pylori-infected and uninfected children applied to investigate quantitatively-selected soluble biomarkers correlated with H. pylori infection in children and presumable consequent delayed growth. Sera from 41 children infected with H. pylori (Hp(+)) and 43 uninfected (Hp(-)) under the care of the Polish Mother's Hospital in Lodz, Poland, were analyzed. The H. pylori status was confirmed by gastroscopy, ¹³C urea breath testing, and anti-H. pylori IgG antibodies. Infrared spectra were measured using an FTIR/FT-NIR Spectrum 400 spectrometer (PerkinElmer). The IR spectrum was measured in the wavenumber range 3000-750 cm^-1 and subjected to mathematical calculation of the first derivative. Based on the chi-square test, 10 wavenumbers of spectra correlating with H. pylori infection were selected for use in designing an artificial neural network. Ten parts of the IR spectra correlating with H. pylori infection were identified in the W2 and W3 windows associated mainly with proteins and the W4 window related to nucleic acids and hydrocarbons. Artificial neural networks for H. pylori infection were developed based on chemometric data. By mathematical modeling, children were classified towards H. pylori infection in conjunction with elevated levels of selected biomarkers in serum potentially related to growth retardation. The study concludes that IR spectroscopy and artificial neural networks may help to confirm H. pylori-driven growth disorders in children.

Advances in Optical Detection of Human-Associated Pathogenic Bacteria

Bacterial infection is a global burden that results in numerous hospital visits and deaths annually. The rise of multi-drug resistant bacteria has dramatically increased this burden. Therefore, there is a clinical need to detect and identify bacteria rapidly and accurately in their native state or a culture-free environment. Current diagnostic techniques lack speed and effectiveness in detecting bacteria that are culture-negative, as well as options for in vivo detection. The optical detection of bacteria offers the potential to overcome these obstacles by providing various platforms that can detect bacteria rapidly, with minimum sample preparation, and, in some cases, culture-free directly from patient fluids or even in vivo. These modalities include infrared, Raman, and fluorescence spectroscopy, along with optical coherence tomography, interference, polarization, and laser speckle. However, these techniques are not without their own set of limitations. This review summarizes the strengths and weaknesses of utilizing each of these optical tools for rapid bacteria detection and identification.

Biomolecular and bioanalytical applications of infrared spectroscopy - A review

Infrared (IR; or mid-infrared, MIR; 4000-400 cm^-1; 2500-25,000 nm) spectroscopy has become one of the most powerful and versatile tools at the disposal of modern bioscience. Because of its high molecular specificity, applicability to wide variety of samples, rapid measurement and non-invasivity, IR spectroscopy forms a potent approach to elucidate qualitative and quantitative information from various kinds of biological material. For these reasons, it became an established bioanalytical technique with diverse applications. This work aims to be a comprehensive and critical review of the recent accomplishments in the field of biomolecular and bioanalytical IR spectroscopy. That progress is presented on a wider background, with fundamental characteristics, the basic principles of the technique outlined, and its scientific capability directly compared with other methods being used in similar fields (e.g. near-infrared, Raman, fluorescence). The article aims to present a complete examination of the topic, as it touches the background phenomena, instrumentation, spectra processing and data analytical methods, spectra interpretation and related information. To suit this goal, the article includes a tutorial information essential to obtain a thorough perspective of bio-related applications of the reviewed methodologies. The importance of the fundamental factors to the final performance and applicability of IR spectroscopy in various areas of bioscience is explained. This information is interpreted in critical way, with aim to gain deep understanding why IR spectroscopy finds extraordinarily intensive use in this remarkably diverse and dynamic field of research and utility. The major focus is placed on the diversity of the applications in which IR biospectroscopy has been established so far and those onto which it is expanding nowadays. This includes qualitative and quantitative analytical spectroscopy, spectral imaging, medical diagnosis, monitoring of biophysical processes, and studies of physicochemical properties and dynamics of biomolecules. The application potential of IR spectroscopy in light of the current accomplishments and the future prospects is critically evaluated and its significance in the progress of bioscience is comprehensively presented.

Extended multiplicative signal correction for FTIR spectral quality test and pre-processing of infrared imaging data

Spectral quality control is an important step in the analysis of infrared spectral data, however, often neglected in scientific literature. A frequently used quality test that was originally developed for infrared spectra of bacteria is provided by OPUS software from Bruker Optik GmbH. In this study, the OPUS quality test is applied to a large number of spectra of bacteria, yeasts and moulds and hyperspectral images of microorganisms. It is shown that the use of strict thresholds for parameters of the OPUS quality test leads to discarding too many spectra. A strategy for optimizing parameters thresholds of the OPUS quality test is provided and a novel approach for spectral quality testing based on extended multiplicative signal correction (EMSC) is suggested. For all the data sets considered in our study, the EMSC quality test is shown to be the best among different alternatives of OPUS quality test provided.

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

BACKGROUND

Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems.

AIMS

This narrative review aims to explore the current use of ML In clinical microbiology.

SOURCES

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019.

CONTENT

We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n = 71, 73%) but a significant number used data from low- and middle-income countries (n = 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes.

IMPLICATIONS

In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings.

Detection of Antimicrobial Resistance-Related Changes in Biochemical Composition of Staphylococcus aureus by Means of Atomic Force Microscopy-Infrared Spectroscopy

The development of antimicrobial resistance (AMR) resulting from widespread antibiotic usage is occurring at an alarming pace, much faster than our understanding of the mechanisms behind resistance. Knowledge about resistance-related phenotypic and genotypic changes is critical for the development of new drugs. Here, we identify changes in the chemical composition of Staphylococcus aureus associated with the development of resistance to last resort drugs, vancomycin and daptomycin, using a novel, single cell, nanoscale technique, atomic force microscopy-infrared spectroscopy (AFM-IR), combined with chemometric analysis. We utilized paired clinical isolates, with the parent (susceptible) strain isolated prior to treatment and the daughter (resistant) strain obtained from the same patient after drug admission and clinical failure. We observed an increase in the amount of nonintracellular carbohydrates, indicating thickening or changes in the packing of the cell wall, as well as changes in the phospholipid content in relation to vancomycin resistance and daptomycin nonsusceptibility, respectively.

Is Infrared Spectroscopy Ready for the Clinic?

Fourier transform-infrared spectroscopy (FT-IR) represents an attractive molecular diagnostic modality for translation to the clinic, where comprehensive chemical profiling of biological samples may revolutionize a myriad of pathways in clinical settings. Principally, FT-IR provides a rapid, cost-effective platform to obtain a molecular fingerprint of clinical samples based on vibrational transitions of chemical bonds upon interaction with infrared light. To date, considerable research activities have demonstrated competitive to superior performance of FT-IR strategies in comparison to conventional techniques, with particular promise for earlier, accessible disease diagnostics, thereby improving patient outcomes. However, amidst the changing healthcare landscape in times of aging populations and increased prevalence of cancer and chronic disease, routine adoption of FT-IR within clinical laboratories has remained elusive. Hence, this perspective shall outline the significant clinical potential of FT-IR diagnostics and subsequently address current barriers to translation from the perspective of all stakeholders, in the context of biofluid, histopathology, cytology, microbiology, and biomarker discovery frameworks. Thereafter, future perspectives of FT-IR for healthcare will be discussed, with consideration of recent technological advances that may facilitate future clinical translation.

The Pseudomonas aeruginosa biofilm matrix and cells are drastically impacted by gas discharge plasma treatment: A comprehensive model explaining plasma-mediated biofilm eradication

Biofilms are microbial communities encased in a protective matrix composed of exopolymeric substances including exopolysaccharides, proteins, lipids, and extracellular DNA. Biofilms cause undesirable effects such as biofouling, equipment damage, prostheses colonization, and disease. Biofilms are also more resilient than free-living cells to regular decontamination methods and therefore, alternative methods are needed to eradicate them. The use of non-thermal atmospheric pressure plasmas is a good alternative as plasmas contain reactive species, free radicals, and UV photons well-known for their decontamination potential against free microorganisms. Pseudomonas aeruginosa biofilms colonize catheters, indwelling devices, and prostheses. Plasma effects on cell viability have been previously documented for P. aeruginosa biofilms. Nonetheless, the effect of plasma on the biofilm matrix has received less attention and there is little evidence regarding the changes the matrix undergoes. The aim of this work was to study the effect plasma exerts mostly on the P. aeruginosa biofilm matrix and to expand the existing knowledge about its effect on sessile cells in order to achieve a better understanding of the mechanism/s underlying plasma-mediated biofilm inactivation. We report a reduction in the amount of the biofilm matrix, the loss of its tridimensional structure, and morphological changes in sessile cells at long exposure times. We show chemical and structural changes on the biofilm matrix (mostly on carbohydrates and eDNA) and cells (mostly on proteins and lipids) that are more profound with longer plasma exposure times. We also demonstrate the presence of lipid oxidation products confirming cell membrane lipid peroxidation as plasma exposure time increases. To our knowledge this is the first report providing detailed evidence of the variety of chemical and structural changes that occur mostly on the biofilm matrix and sessile cells as a consequence of the plasma treatment. Based on our results, we propose a comprehensive model explaining plasma-mediated biofilm inactivation.

Isolation of intact bacteria from blood by selective cell lysis in a microfluidic porous silica monolith

Rapid and efficient isolation of bacteria from complex biological matrices is necessary for effective pathogen identification in emerging single-cell diagnostics. Here, we demonstrate the isolation of intact and viable bacteria from whole blood through the selective lysis of blood cells during flow through a porous silica monolith. Efficient mechanical hemolysis is achieved while providing passage of intact and viable bacteria through the monoliths, allowing size-based isolation of bacteria to be performed following selective lysis. A process for synthesizing large quantities of discrete capillary-bound monolith elements and millimeter-scale monolith bricks is described, together with the seamless integration of individual monoliths into microfluidic chips. The impact of monolith morphology, geometry, and flow conditions on cell lysis is explored, and flow regimes are identified wherein robust selective blood cell lysis and intact bacteria passage are achieved for multiple gram-negative and gram-positive bacteria. The technique is shown to enable rapid sample preparation and bacteria analysis by single-cell Raman spectrometry. The selective lysis technique presents a unique sample preparation step supporting rapid and culture-free analysis of bacteria for the point of care.

Reagent-Free Identification of Clinical Yeasts by Use of Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy

Invasive fungal infections by opportunistic yeasts have increased concomitantly with the growth of an immunocompromised patient population. Misidentification of yeasts can lead to inappropriate antifungal treatment and complications. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy is a promising method for rapid and accurate identification of microorganisms. ATR-FTIR spectroscopy is a standalone, inexpensive, reagent-free technique that provides results within minutes after initial culture. In this study, a comprehensive spectral reference database of 65 clinically relevant yeast species was constructed and tested prospectively on spectra recorded (from colonies taken from culture plates) for 318 routine yeasts isolated from various body fluids and specimens received from 38 microbiology laboratories over a 4-month period in our clinical laboratory. ATR-FTIR spectroscopy attained comparable identification performance with matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). In a preliminary validation of the ATR-FTIR method, correct identification rates of 100% and 95.6% at the genus and species levels, respectively, were achieved, with 3.5% unidentified and 0.9% misidentified. By expanding the number of spectra in the spectral reference database for species for which isolates could not be identified or had been misidentified, we were able to improve identification at the species level to 99.7%. Thus, ATR-FTIR spectroscopy provides a new standalone method that can rival MALDI-TOF MS for the accurate identification of a broad range of medically important yeasts. The simplicity of the ATR-FTIR spectroscopy workflow favors its use in clinical laboratories for timely and low-cost identification of life-threatening yeast strains for appropriate treatment.

Detection and Identification of Bacillus cereus, Bacillus cytotoxicus, Bacillus thuringiensis, Bacillus mycoides and Bacillus weihenstephanensis via Machine Learning Based FTIR Spectroscopy

The Bacillus cereus group comprises genetical closely related species with variable toxigenic characteristics. However, detection and differentiation of the B. cereus group species in routine diagnostics can be difficult, expensive and laborious since current species designation is linked to specific phenotypic characteristic or the presence of species-specific genes. Especially the differentiation of Bacillus cereus and Bacillus thuringiensis, the identification of psychrotolerant Bacillus mycoides and Bacillus weihenstephanensis, as well as the identification of emetic B. cereus and Bacillus cytotoxicus, which are both producing highly potent toxins, is of high importance in food microbiology. Thus, we investigated the use of a machine learning approach, based on artificial neural network (ANN) assisted Fourier transform infrared (FTIR) spectroscopy, for discrimination of B. cereus group members. The deep learning tool box of Matlab was employed to construct a one-level ANN, allowing the discrimination of the aforementioned B. cereus group members. This model resulted in 100% correct identification for the training set and 99.5% correct identification overall. The established ANN was applied to investigate the composition of B. cereus group members in soil, as a natural habitat of B. cereus, and in food samples originating from foodborne outbreaks. These analyses revealed a high complexity of B. cereus group populations, not only in soil samples but also in the samples from the foodborne outbreaks, highlighting the importance of taking multiple isolates from samples implicated in food poisonings. Notable, in contrast to the soil samples, no bacteria belonging to the psychrotolerant B. cereus group members were detected in the food samples linked to foodborne outbreaks, while the overall abundancy of B. thuringiensis did not significantly differ between the sample categories. None of the isolates was classified as B. cytotoxicus, fostering the hypothesis that the latter species is linked to very specific ecological niches. Overall, our work shows that machine learning assisted (FTIR) spectroscopy is suitable for identification of B. cereus group members in routine diagnostics and outbreak investigations. In addition, it is a promising tool to explore the natural habitats of B. cereus group, such as soil.

Pulse UV light effect on microbial biomolecules and organic pollutants degradation in aqueous solutions

This study present assessed the effect of UV pulsed light (PL) on microbial and organic pollutants using two spiral lamps were used, i.e., PL1 and PL2 lamps, with wavelength cut-offs of 190 and 240 nm, respectively. Overall, our study demonstrated that pulsed UV light impacts several microbial biomolecules and degrades polycyclic aromatic hydrocarbons (PAHs) in aqueous solution. In microbial inactivation by PL2, temporary changes of bacterial cellular components, specifically proteins, were observed, but the compositional changes of bacteria that were exposed to PL1 were permanent due to ozonolysis. PL1 irradiation caused greater deactivation of the bacteria than PL2 irradiation due to the generation of ozone. The higher efficacy of PL1 in terms of membrane disruption, reduction of respiration rate, and reduction of growth rate was due to the production of ozone during the irradiation period. The bacteria that were irradiated with both PL lamps regrew due to photoreactivation, such as an enzymatic DNA-repair mechanism. The PAH degradation kinetics indicate that higher molecular weights degraded faster than those with lower molecular weights. For both lamps, the degradation of naphthalene and fluorene was first order, whereas second order for pyrene and anthracene. Any effect of ozonolysis on the PAH degradation rates was not apparent, which indicated that photolysis was the primary degradation pathway. PAH solutions treated with both pulsed UV lamps did not result in a toxicity effect on the bacteria.

Molecular mechanisms of antimicrobial resistance in Acinetobacter baumannii, with a special focus on its epidemiology in Lebanon

Acinetobacter baumannii is an opportunistic bacterium involved in several types of infection with high mortality and morbidity, especially in intensive care units. Treatment of these infections remains a challenge due to the worldwide emergence of broad-spectrum resistance to many antibiotics. Following the implementation of molecular techniques to study A. baumannii outbreaks, it has been shown that they are mainly caused by specific clones such as international clones I, II and III. The present work aims to review the available data on the mechanisms underlying antimicrobial resistance in A. baumannii, with a special focus on the molecular epidemiology of this species in Lebanon.

Detection of mycoplasma in contaminated mammalian cell culture using FTIR microspectroscopy

Mycoplasma contamination represents a significant problem to the culture of mammalian cells used for research as it can cause disastrous effects on eukaryotic cells by altering cellular parameters leading to unreliable experimental results. Mycoplasma cells are very small bacteria therefore they cannot be detected by visual inspection using a visible light microscope and, thus, can remain unnoticed in the cell cultures for long periods. The detection techniques used nowadays to reveal mycoplasma contamination are time consuming and expensive with each having significant drawbacks. The ideal detection should be simple to perform with minimal preparation time, rapid, inexpensive, and sensitive. To our knowledge, for the first time, we employed Fourier transform infrared (FTIR) microspectroscopy to investigate whether we can differentiate between control cells and the same cells which have been infected with mycoplasmas during the culturing process. Chemometric methods such as HCA and PCA were used for the data analysis in order to detect spectral differences between control and intentionally infected cells, and spectral markers were revealed even at low contamination level. The preliminary results showed that FTIR has the potential to be used in the future as a reliable complementary detection technique for mycoplasma-infected cells. Graphical abstract FTIR microspectroscopy is able to differentiate between mycoplasma infected cells (LC for low contamination and HC for high contamination) and control non-infected cells (CN).

Hierarchical classification of microorganisms based on high-dimensional phenotypic data

The classification of microorganisms by high-dimensional phenotyping methods such as FTIR spectroscopy is often a complicated process due to the complexity of microbial phylogenetic taxonomy. A hierarchical structure developed for such data can often facilitate the classification analysis. The hierarchical tree structure can either be imposed to a given set of phenotypic data by integrating the phylogenetic taxonomic structure or set up by revealing the inherent clusters in the phenotypic data. In this study, we wanted to compare different approaches to hierarchical classification of microorganisms based on high-dimensional phenotypic data. A set of 19 different species of molds (filamentous fungi) obtained from the mycological strain collection of the Norwegian Veterinary Institute (Oslo, Norway) is used for the study. Hierarchical cluster analysis is performed for setting up the classification trees. Classification algorithms such as artificial neural networks (ANN), partial least-squared discriminant analysis and random forest (RF) are used and compared. The 2 methods ANN and RF outperformed all the other approaches even though they did not utilize predefined hierarchical structure. To our knowledge, the RF approach is used here for the first time to classify microorganisms by FTIR spectroscopy.

Fourier Transform Infrared Spectroscopy as a Tool in Analysis of Proteus mirabilis Endotoxins

Fourier transform infrared spectroscopy (FT-IR) was used to scan whole bacterial cells as well as lipopolysaccharides (LPSs, endotoxins) isolated from them. Proteus mirabilis cells, with chemically defined LPSs, served as a model for the ATR FT-IR method. The paper focuses on three steps of infrared spectroscopy: (1) sample preparation, (2) IR scanning, and (3) multivariate analysis of IR data (principal component analysis, PCA).

Applications of mid-infrared spectroscopy in the clinical laboratory setting

Fourier transform mid-infrared (MIR-FTIR) spectroscopy is a nondestructive, label-free, highly sensitive and specific technique that provides complete information on the chemical composition of biological samples. The technique both can offer fundamental structural information and serve as a quantitative analysis tool. Therefore, it has many potential applications in different fields of clinical laboratory science. Although considerable technological progress has been made to promote biomedical applications of this powerful analytical technique, most clinical laboratory analyses are based on spectroscopic measurements in the visible or ultraviolet (UV) spectrum and the potential role of FTIR spectroscopy still remains unexplored. In this review, we present some general principles of FTIR spectroscopy as a useful method to study molecules in specimens by MIR radiation together with a short overview of methods to interpret spectral data. We aim at illustrating the wide range of potential applications of the proposed technique in the clinical laboratory setting with a focus on its advantages and limitations and discussing the future directions. The reviewed applications of MIR spectroscopy include (1) quantification of clinical parameters in body fluids, (2) diagnosis and monitoring of cancer and other diseases by analysis of body fluids, cells, and tissues, (3) classification of clinically relevant microorganisms, and (4) analysis of kidney stones, nails, and faecal fat.

Understanding the antimicrobial activity of selected disinfectants against methicillin-resistant Staphylococcus aureus (MRSA)

Disinfectants and biocidal products have been widely used to combat Methicillin-resistant Staphylococcus aureus (MRSA) infections in homes and healthcare environments. Although disruption of cytoplasmic membrane integrity has been documented as the main bactericidal effect of biocides, little is known about the biochemical alterations induced by these chemical agents. In this study, we used Fourier transform infrared (FT-IR) spectroscopy and chemometric tools as an alternative non-destructive technique to determine the bactericidal effects of commonly used disinfectants against MRSA USA-300. FTIR spectroscopy permits a detailed characterization of bacterial reactivity, allowing an understanding of the fundamental mechanism of action involved in the interaction between bacteria and disinfectants. The disinfectants studied were ethanol 70% (N = 5), isopropanol (N = 5), sodium hypochlorite (N = 5), triclosan (N = 5) and triclocarban (N = 5). Results showed less than 5% colony forming units growth of MRSA treated with triclocarban and no growth in the other groups. Nearly 70,000 mid-infrared spectra from the five treatments and the two control (untreated; N = 4) groups of MRSA (bacteria grown in TSB and incubated at 37°C (Control I) / at ambient temperature (Control II), for 24h) were pre-processed and analyzed using principal component analysis followed by linear discriminant analysis (PCA-LDA). Clustering of strains of MRSA belonging to five treatments and the discrimination between each treatment and two control groups in MRSA (untreated) were investigated. PCA-LDA discriminatory frequencies suggested that ethanol-treated spectra are the most similar to isopropanol-treated spectra biochemically. Also reported here are the biochemical alterations in the structure of proteins, lipid membranes, and phosphate groups of MRSA produced by sodium hypochlorite, triclosan, and triclocarban treatments. These findings provide mechanistic information involved in the interaction between MRSA strains and hygiene products; thereby demonstrating the potential of spectroscopic analysis as an objective, robust, and label-free tool for evaluating the macromolecular changes involved in disinfectant-treated MRSA.

Using Infrared Spectroscopy and Multivariate Analysis to Detect Antibiotics' Resistant Escherichia coli Bacteria

Bacterial pathogens are one of the primary causes of human morbidity worldwide. Historically, antibiotics have been highly effective against most bacterial pathogens; however, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global health-care problem. Early and rapid determination of bacterial susceptibility to antibiotics has become essential in many clinical settings and, sometimes, can save lives. Currently classical procedures require at least 48 h for determining bacterial susceptibility, which can constitute a life-threatening delay for effective treatment. Infrared (IR) microscopy is a rapid and inexpensive technique, which has been used successfully for the detection and identification of various biological samples; nonetheless, its true potential in routine clinical diagnosis has not yet been established. In this study, we evaluated the potential of this technique for rapid identification of bacterial susceptibility to specific antibiotics based on the IR spectra of the bacteria. IR spectroscopy was conducted on bacterial colonies, obtained after 24 h culture from patients' samples. An IR microscope was utilized, and a computational classification method was developed to analyze the IR spectra by novel pattern-recognition and statistical tools, to determine E. coli susceptibility within a few minutes to different antibiotics, gentamicin, ceftazidime, nitrofurantoin, nalidixic acid, ofloxacin. Our results show that it was possible to classify the tested bacteria into sensitive and resistant types, with success rates as high as 85% for a number of examined antibiotics. These promising results open the potential of this technique for faster determination of bacterial susceptibility to certain antibiotics.

Optical Biosensing of Bacteria and Bacterial Communities

Bacterial sensing is important for understanding the numerous roles bacteria play in nature and in technology, understanding and managing bacterial populations, detecting pathogenic bacterial infections, and preventing the outbreak of illness. Current analytical challenges in bacterial sensing center on the dilemma of rapidly acquiring quantitative information about bacteria with high detection efficiency, sensitivity, and specificity, while operating within a reasonable budget and optimizing the use of ancillary tools, such as multivariate statistics. This review starts from a general description of bacterial sensing methods and challenges, and then focuses on bacterial characterization using optical methods including Raman spectroscopy and imaging, infrared spectroscopy, fluorescence spectroscopy and imaging, and plasmonics, including both extended and localized surface plasmon resonance spectroscopy. The advantages and drawbacks of each method in relation to the others are discussed, as are their applications. A particularly promising direction in bacterial sensing lies in combining multiple approaches to achieve multiplex analysis, and examples where this has been achieved are highlighted.

Chemometric analysis of attenuated total reflectance infrared spectra of Proteus mirabilis strains with defined structures of LPS

Proteus spp. strains are some of the most important pathogens associated with complicated urinary tract infections and bacteremia affecting patients with immunodeficiency and long-term urinary catheterization. For epidemiological purposes, various molecular typing methods have been developed for this pathogen. However, these methods are labor intensive and time consuming. We evaluated a new method of differentiation between strains. A collection of Proteus spp. strains was analyzed by attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy in the mid-infrared region. ATR FT-IR spectroscopy used in conjunction with a diamond ATR accessory directly produced the biochemical profile of the surface chemistry of bacteria. We conclude that a combination of ATR FT-IR spectroscopy and mathematical modeling provides a fast and reliable alternative for discrimination between Proteus isolates, contributing to epidemiological research.

Diversity, community structure, and bioremediation potential of mercury-resistant marine bacteria of estuarine and coastal environments of Odisha, India

Both point and non-point sources increase the pollution status of mercury and increase the population of mercury-resistant marine bacteria (MRMB). They can be targeted as the indicator organism to access marine mercury pollution, besides utilization in bioremediation. Thus, sediment and water samples were collected for 2 years (2010-2012) along Odisha coast of Bay of Bengal, India. Mercury content of the study sites varied from 0.47 to 0.99 ppb irrespective of the seasons of sampling. A strong positive correlation was observed between mercury content and MRMB population (P < 0.05) suggesting the utilization of these bacteria to assess the level of mercury pollution in the marine environment. Seventy-eight percent of the MRMB isolates were under the phylum Firmicutes, and 36 and 31% of them could resist mercury by mer operon-mediated volatilization and mercury biosorption, respectively. In addition, most of the isolates could resist a number of antibiotics and toxic metals. All the MRMB isolates possess the potential of growth and survival at cardinal pH (4-8), temperature (25-37 °C), and salinity (5-35 psu). Enterobacteria repetitive intergenic consensus (ERIC) and repetitive element palindromic PCR (REP-PCR) produced fingerprints corroborating the results of 16S rRNA gene sequencing. Fourier transform infrared (FTIR) spectral analysis also revealed strain-level speciation and phylogenetic relationships.

High-resolution subtyping of Staphylococcus aureus strains by means of Fourier-transform infrared spectroscopy

Staphylococcus aureus causes a variety of serious illnesses in humans and animals. Subtyping of S. aureus isolates plays a crucial role in epidemiological investigations. Metabolic fingerprinting by Fourier-transform infrared (FTIR) spectroscopy is commonly used to identify microbes at species as well as subspecies level. In this study, we aimed to assess the suitability of FTIR spectroscopy as a tool for S. aureus subtyping. To this end, we compared the subtyping performance of FTIR spectroscopy to other subtyping methods such as pulsed field gel electrophoresis (PFGE) and spa typing in a blinded experimental setup and investigated the ability of FTIR spectroscopy for identifying S. aureus clonal complexes (CC). A total of 70 S. aureus strains from human, animal, and food sources were selected, for which clonal complexes and a unique virulence and resistance gene pattern had been determined by DNA microarray analysis. FTIR spectral analysis resulted in high discriminatory power similar as obtained by spa typing and PFGE. High directional concordance was found between FTIR spectroscopy based subtypes and capsular polysaccharide expression detected by FTIR spectroscopy and the cap specific locus, reflecting strain specific expression of capsular polysaccharides and/or other surface glycopolymers, such as wall teichoic acid, peptidoglycane, and lipoteichoic acid. Supervised chemometrics showed only limited possibilities for differentiation of S. aureus CC by FTIR spectroscopy with the exception of CC45 and CC705. In conclusion, FTIR spectroscopy represents a valuable tool for S. aureus subtyping, which complements current molecular and proteomic strain typing.

Direct determination of phosphatase activity from physiological substrates in cells

A direct and continuous approach to determine simultaneously protein and phosphate concentrations in cells and kinetics of phosphate release from physiological substrates by cells without any labeling has been developed. Among the enzymes having a phosphatase activity, tissue non-specific alkaline phosphatase (TNAP) performs indispensable, multiple functions in humans. It is expressed in numerous tissues with high levels detected in bones, liver and neurons. It is absolutely required for bone mineralization and also necessary for neurotransmitter synthesis. We provided the proof of concept that infrared spectroscopy is a reliable assay to determine a phosphatase activity in the osteoblasts. For the first time, an overall specific phosphatase activity in cells was determined in a single step by measuring simultaneously protein and substrate concentrations. We found specific activities in osteoblast like cells amounting to 116 ± 13 nmol min^-1 mg^-1 for PPi, to 56 ± 11 nmol min^-1 mg^-1 for AMP, to 79 ± 23 nmol min^-1 mg^-1 for beta-glycerophosphate and to 73 ± 15 nmol min^-1 mg^-1 for 1-alpha-D glucose phosphate. The assay was also effective to monitor phosphatase activity in primary osteoblasts and in matrix vesicles. The use of levamisole – a TNAP inhibitor- served to demonstrate that a part of the phosphatase activity originated from this enzyme. An IC₅₀ value of 1.16 ± 0.03 mM was obtained for the inhibition of phosphatase activity of levamisole in osteoblast like cells. The infrared assay could be extended to determine any type of phosphatase activity in other cells. It may serve as a metabolomic tool to monitor an overall phosphatase activity including acid phosphatases or other related enzymes.

Identification of new bacterial and fungal pathogens on surveillance bronchoscopy prior to sinus surgery in patients with cystic fibrosis

BACKGROUND

Flexible fiberoptic bronchoscopy was performed prior to functional endoscopic sinus surgery (FESS) while under general anesthesia to collect bronchoalveolar lavage fluid (BALF) for lower respiratory tract cultures in patients with cystic fibrosis (CF).

METHODS

A retrospective chart review was performed on all CF patients who underwent combined FESS and bronchoscopy between January 2009 and October 2010. Along with demographic data, bacterial, fungal, and acid fast bacillus culture data from BALF was collected and compared to oropharyngeal swab and sputum cultures obtained over the year prior to FESS and bronchoscopy.

RESULTS

A total of 77 patients were enrolled with mean age 12.5 ± SD 6.5 (range 2-29) years. Mean FEV1 was 86% ±18.4 (range 33-128) % of predicted. Patients averaged 6.5 (range 1-13) sputum or OP cultures in the year prior to FESS. BALF cultures identified a new bacterial pathogen in 19% (n=15) of patients, which altered antibiotic regimen immediately in two patients and sub-acutely in five patients. BALF cultures identified a new fungal pathogen in 42% (n=32) of patients, which resulted in the addition of antifungal therapy in eight patients. BALF cultures did not identify previously undetected AFB culture positive patients. No significant differences were found between patients with and without new discoveries of bacterial or fungal pathogens with regards to key clinical demographic data, lung function parameters, healthcare utilization, or need for antibiotics over the year prior to FESS. There was no relationship between the total number of respiratory cultures obtained in the year prior to bronchoscopy and the identification of new bacterial or fungal pathogens.

CONCLUSIONS

Surveillance BALF cultures obtained prior to FESS identified bacterial and fungal pathogens not previously detected by sputum or OP swab cultures in a cohort of CF patients with chronic sinus disease. Moreover, the identification of these new pathogens altered clinical management in a small number of patients.

Hypermutation in Burkholderia cepacia complex is mediated by DNA mismatch repair inactivation and is highly prevalent in cystic fibrosis chronic respiratory infection

The Burkholderia cepacia complex (Bcc) represents an important group of pathogens involved in long-term lung infection in cystic fibrosis (CF) patients. A positive selection of hypermutators, linked to antimicrobial resistance development, has been previously reported for Pseudomonas aeruginosa in this chronic infection setting. Hypermutability, however, has not yet been systematically evaluated in Bcc species. A total of 125 well characterized Bcc isolates recovered from 48 CF patients, 10 non-CF patients and 15 environmental samples were analyzed. In order to determine the prevalence of mutators their spontaneous mutation rates to rifampicin resistance were determined. In addition, the genetic basis of the mutator phenotypes was investigated by sequencing the mutS and mutL genes, the main components of the mismatch repair system (MRS). The overall prevalence of hypermutators in the collection analyzed was 13.6%, with highest occurrence (40.7%) among the chronically infected CF patients, belonging mainly to B. cenocepacia, B. multivorans, B. cepacia, and B. contaminans -the most frequently recovered Bcc species from CF patients worldwide. Thirteen (76.5%) of the hypermutators were defective in mutS and/or mutL. Finally, searching for a possible association between antimicrobial resistance and hypermutability, the resistance-profiles to 17 antimicrobial agents was evaluated. High antimicrobial resistance rates were documented for all the Bcc species recovered from CF patients, but, except for ciprofloxacin, a significant association with hypermutation was not detected. In conclusion, in the present study we demonstrate for the first time that, MRS-deficient Bcc species mutators are highly prevalent and positively selected in CF chronic lung infections. Hypermutation therefore, might be playing a key role in increasing bacterial adaptability to the CF-airway environment, facilitating the persistence of chronic lung infections.

Identification and discrimination of bacteria using Fourier transform infrared spectroscopy

Bacterial spectra were obtained in the wavenumber range of 4000-600 cm(-1) using FTIR spectroscopy. FTIR spectral patterns were analyzed and matched with 16S-rRNA signatures of bacterial strains OS1 and OS2, isolated from oil sludge. Specific spectral bands obtained from OS1 (FJ226761), reference strain Bacillus flexus (ATCC 49095), OS2 (FJ215874) and reference strain Stenotrophomonas maltophilia (ATCC 19861) respectively, suggested that OS1 and ATCC 49095 were closely related whereas OS2 was different. The bands probably represent groups of proteins and lipids of specific bacteria. Separate peaks found in B. flexus were similar to those of OS1. The S. maltophilia (ATCC 19861) and OS2 exhibited a similar peak at 3272 cm(-1). Amide bands (I, II and III) exhibited that OS1 and B. flexus were closely related, but were different from OS2. In the fingerprint region, peak at 1096 cm(-1) and 1360 cm(-1) exhibited the specific fingerprints of OS2 and reference strain S. maltophilia (ATCC 19861), respectively. The specific fingerprint signature was found at 1339 cm(-1) for OS1 and at 1382 cm(-1) for B. flexus ATCC 49095, allowing these two strains of B. flexus to be differentiated. This spectral signature originated from phospholipid and RNA components of the cell. Principle components analysis (PCA) of spectral regions exhibited with distinct sample clusters between Bacillus flexus (ATCC 49095), S. maltophilia (ATCC 19861), OS1 and OS2 in amide and fingerprint region.

High-fat diet alters gut microbiota physiology in mice

The intestinal microbiota is known to regulate host energy homeostasis and can be influenced by high-calorie diets. However, changes affecting the ecosystem at the functional level are still not well characterized. We measured shifts in cecal bacterial communities in mice fed a carbohydrate or high-fat (HF) diet for 12 weeks at the level of the following: (i) diversity and taxa distribution by high-throughput 16S ribosomal RNA gene sequencing; (ii) bulk and single-cell chemical composition by Fourier-transform infrared- (FT-IR) and Raman micro-spectroscopy and (iii) metaproteome and metabolome via high-resolution mass spectrometry. High-fat diet caused shifts in the diversity of dominant gut bacteria and altered the proportion of Ruminococcaceae (decrease) and Rikenellaceae (increase). FT-IR spectroscopy revealed that the impact of the diet on cecal chemical fingerprints is greater than the impact of microbiota composition. Diet-driven changes in biochemical fingerprints of members of the Bacteroidales and Lachnospiraceae were also observed at the level of single cells, indicating that there were distinct differences in cellular composition of dominant phylotypes under different diets. Metaproteome and metabolome analyses based on the occurrence of 1760 bacterial proteins and 86 annotated metabolites revealed distinct HF diet-specific profiles. Alteration of hormonal and anti-microbial networks, bile acid and bilirubin metabolism and shifts towards amino acid and simple sugars metabolism were observed. We conclude that a HF diet markedly affects the gut bacterial ecosystem at the functional level.

Identification of microorganisms by FTIR spectroscopy: perspectives and limitations of the method

Fourier transform infrared (FTIR) spectroscopy was introduced in 1991 as a technique to identify and classify microbes. Since then, it has gained growing interest and has undergone a remarkable evolution. Highly sophisticated spectrometers have been developed, enabling a high sample throughput. Today, the generation of high-quality data in a short time and the application of the technique for rapid and reliable identification of microbes to the species level are well documented. What makes FTIR spectroscopy even more attractive is the fact that spectral information can also be exploited for strain typing purposes, which is particularly important for epidemiological analyses and some technological applications. Accordingly, in recent years, FTIR spectroscopy has been increasingly used for typing and classifying microorganisms below the species level. The intention of this review is to give an overview over current knowledge and strategies of using FTIR spectroscopy for species identification and to describe different approaches for strain typing.