Identification of hypermucoviscous Klebsiella pneumoniae K1, K2, K54 and K57 capsular serotypes by Raman spectroscopy

Antimicrobial resistance poses a significant challenge in modern medicine, affecting public health. Klebsiella pneumoniae infections compound this issue due to their broad range of infections and the emergence of multiple antibiotic resistance mechanisms. Efficient detection of its capsular serotypes is crucial for immediate patient treatment, epidemiological tracking and outbreak containment. Current methods have limitations that can delay interventions and increase the risk of morbidity and mortality. Raman spectroscopy is a promising alternative to identify capsular serotypes in hypermucoviscous K. pneumoniae isolates. It provides rapid and in situ measurements with minimal sample preparation. Moreover, its combination with machine learning tools demonstrates high accuracy and reproducibility. This study analyzed the viability of combining Raman spectroscopy with one-dimensional convolutional neural networks (1-D CNN) to classify four capsular serotypes of hypermucoviscous K. pneumoniae: K1, K2, K54 and K57. Our approach involved identifying the most relevant Raman features for classification to prevent overfitting in the training models. Simplifying the dataset to essential information maintains accuracy and reduces computational costs and training time. Capsular serotypes were classified with 96 % accuracy using less than 30 Raman features out of 2400 contained in each spectrum. To validate our methodology, we expanded the dataset to include both hypermucoviscous and non-mucoid isolates and distinguished between them. This resulted in an accuracy rate of 94 %. The results obtained have significant potential for practical healthcare applications, especially for enabling the prompt prescription of the appropriate antibiotic treatment against infections.

Deep learning analysis for rapid detection and classification of household plastics based on Raman spectroscopy

The overuse of plastics releases large amounts of microplastics. These tiny and complex pollutants may cause immeasurable damage to human social life. Raman spectroscopy detection technology is widely used in the detection, identification and analysis of microplastics due to its advantages of fast speed, high sensitivity and non-destructive. In this work, we first recorded the Raman spectra of eight common plastics in daily life. By adjusting parameters such as laser wavelength, laser power, and acquisition time, the Raman data under different acquisition conditions were diversified, and the corresponding Raman spectra were obtained, and a database of eight household plastics was established. Combined with deep learning algorithms, an accurate, fast and simple classification and identification method for 8 types of plastics is established. Firstly, the acquired spectral data were preprocessed for baseline correction and noise reduction, Then, four machine learning algorithms, linear discriminant analysis (LDA), decision tree, support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN), are used to classify and identify the preprocessed data. The results showed that the classification accuracy of the three machine learning models for the Raman spectra of standard plastic samples were 84%, 93% and 93% respectively. The 1D-CNN model has an accuracy rate of up to 97% for Raman spectroscopy. Our study shows that the combination of Raman spectroscopy detection techniques and deep learning algorithms is a very valuable approach for microplastic classification and identification.

Biospectroscopic fingerprinting phytotoxicity towards environmental monitoring for food security and contaminated site remediation

Human activities have resulted in severe environmental pollution since the industrial revolution. Phytotoxicity-based environmental monitoring is well known due to its sedentary nature, abundance, and sensitivity to environmental changes, which are essential preconditions to avoiding potential environmental and ecological risks. However, conventional morphological and physiological methods for phytotoxicity assessment mainly focus on descriptive determination rather than mechanism analysis and face challenges of labour and time-consumption, lack of standardized protocol and difficulties in data interpretation. Molecular-based tests could reveal the toxicity mechanisms but fail in real-time and in-situ monitoring because of their endpoint manner and destructive operation in collecting cellular components. Herein, we systematically propose and lay out a biospectroscopic tool (e.g., infrared and Raman spectroscopy) coupled with multivariate data analysis as a relatively non-destructive and high-throughput approach to quantitatively measure phytotoxicity levels and qualitatively profile phytotoxicity mechanisms by classifying spectral fingerprints of biomolecules in plant tissues in response to environmental stresses. With established databases and multivariate analysis, this biospectroscopic fingerprinting approach allows ultrafast, in situ and on-site diagnosis of phytotoxicity. Overall, the proposed protocol and validation of biospectroscopic fingerprinting phytotoxicity can distinguish the representative biomarkers and interrogate the relevant mechanisms to quantify the stresses of interest, e.g., environmental pollutants. This state-of-the-art concept and design broaden the knowledge of phytotoxicity assessment, advance novel implementations of phytotoxicity assay, and offer vast potential for long-term field phytotoxicity monitoring trials in situ.

Correlation analysis between Raman spectral signature and transcriptomic features of carbapenem-resistant Klebsiella pneumoniae

The Raman microspectroscopy technology has been successfully applied to evaluate the molecular composition of living cells for identifying cell types and states, but the rationale behind it was not well investigated. In this study, we acquired single-cell Raman spectra (SCRS) of three Klebsiella pneumoniae (K. pneumoniae) strains with different Carbapenem resistant mechanisms and analyzed them with machine learning algorithm. Two carbapenem resistant Klebsiella pneumoniae (CRKP) strains can be successfully distinguished from susceptible strain and CRKP with KPC or IMP carbapenemases can be classified with an overall accuracy achieving 100 %. Furthermore, we performed a correlation analysis between transcriptome and Raman spectra, and found that Raman shifts such as 752 and 1039 cm-1 highly correlated with drug resistance genes expression and could be regarded as Raman biomarkers for CRKP with different mechanisms. The findings of the study provide a theoretical basis for identifying the relationship between Raman spectra and transcriptome of bacteria, as well as a novel method for rapid identification of CRKP and their carbapenemases types.

Synergistic/antagonistic toxicity characterization and source-apportionment of heavy metals and organophosphorus pesticides by the biospectroscopy-bioreporter-coupling approach

Many anthropogenic chemicals are manufactured and eventually enter the surrounding environment, threatening food security and human health. Considering the additive or synergistic effects of pollutant mixtures, there is an expanding need for rapid, cost-effective and field-portable screening methods in environmental monitoring. This study used a recently developed biospectroscopy-bioreporter-coupling (BBC) approach to investigate the binary toxicity of Ag(I), Cr(VI) and four organophosphorus pesticides (dichlorvos, parathion, omethoate and monocrotophos). Ag(I) and Cr(VI) altered the toxicity mechanisms of pesticides, explained by the synergistic or antagonistic effect of Ag/Cr-induced cytotoxicity and pesticide-induced genotoxicity. The discriminating Raman spectral peaks associated with organophosphorus pesticides were 1585 and 1682 cm-1, but 750, 1004, 1306 and 1131 cm-1 were found in heavy metal and pesticide mixtures. More spectral alterations were related to pesticides rather than Ag(I) or Cr(VI), hinting at the dominant toxicity mechanisms of pesticides in mixtures. Ag(I) supplement significantly increased the levels of reactive oxygen species induced by organophosphorus pesticides, attributing to the increased permeability of cell membrane and entrance of toxic substances into the cells by the oligodynamic actions. This study lends deeper insights into the interactions between microbes and pollutant mixtures, offering clues to assess the cocktail effects of multiple pollutants comprehensively.

Detection of extended-spectrum β-lactamase-producing bacteria isolated directly from urine by infrared spectroscopy and machine learning

Resistant bacteria have become one of the leading health threats in the last decades. Extended-spectrum β-lactamase (ESBL) producing bacteria, including Escherichia (E.) coli and Klebsiella (K.) pneumoniae (the most frequent ones), are a significant class out of all resistant infecting bacteria. Due to the widespread and ongoing development of ESBL-producing (ESBL⁺) resistant bacteria, many routinely used antibiotics are no longer effective against them. However, an early and reliable ESBL⁺ bacteria detection method will improve the efficiency of treatment and limit their spread. In this work, we investigated the capability of infrared (IR) spectroscopy based machine learning tools [principal component analysis (PCA) and Random Forest (RF) classifier] for the rapid detection of ESBL⁺ bacteria isolated directly from patients' urine. For that, we examined 1881 E. coli samples (416 ESBL⁺ and 1465 ESBL^-) and 609 K. pneumoniae samples (237 ESBL⁺ and 372 ESBL^-). All samples were isolated directly from the urine of midstream patients. This study revealed that within 40 min of receiving the patient urine it is possible to determine the infecting bacterium as E. coli or K. pneumoniae with 95% success rate while it was possible to determine the ESBL⁺E. coli and ESBL⁺K. pneumoniae with 83% and 78% accuracy rates, respectively.

Toxicity Characterization of Environment-Related Pollutants Using a Biospectroscopy-Bioreporter-Coupling Approach: Potential for Real-World Toxicity Determination and Source Apportionment of Multiple Pollutants

Exposure to environmental pollutants occurs ubiquitously and poses many risks to human health and the ecosystem. Although many analytical methods have been developed to assess such jeopardies, the circumstances applying these means are restricted to linking the toxicities to compositions in the pollutant mixtures. The present study proposes a novel analytical approach, namely, biospectroscopy-bioreporter-coupling (BBC), to quantify and apportion the toxicities of metal ions and organic pollutants. Using a toxicity bioreporter ADPWH_recA and Raman spectroscopy, both bioluminescent signals and spectral alterations had similar dosage- and time-response behavior to the toxic compounds, validating the possibility of coupling these two methods from practical aspects. Raman spectral alterations successfully distinguished the biomarkers for different toxicity mechanisms of individual pollutants, such as ring breathing mode of DNA/RNA bases (1373 cm^-1) by Cr, reactive oxygen species-induced peaks of proteins (1243 cm^-1), collagen (813 cm^-1), and lipids (1255 cm^-1) by most metal ions, and indicative fingerprints of organic toxins. The support vector machine model had a satisfactory performance in distinguishing and apportioning toxicities of individual toxins from all input data, achieving a sensitivity of 88.54% and a specificity of 97.80%. This work set a preliminary database for Raman spectral alterations of whole-cell bioreporter response to multiple pollutants. It proved the state-of-the-art concept that the BBC approach is feasible to rapidly quantify and precisely apportion toxicities of numerous pollutant mixtures.

Herbicides 2,4-Dichlorophenoxy Acetic Acid and Glyphosate Induce Distinct Biochemical Changes in E. coli during Phenotypic Antibiotic Resistance: A Raman Spectroscopic Study

Antibiotic resistance is a major global health concern. The increased use of herbicides may lead to multiple antibiotic resistance in bacteria. Conventional techniques for diagnosing antibiotic resistance are laborious, time-intensive, expensive, and lack information about antibiotic susceptibility. On the other hand, Raman spectroscopy is a rapid, label-free, noninvasive alternative to traditional techniques to detect antibiotic resistance. In this study, two popular herbicides 2,4-dichlorophenoxy acetic acid (2,4-D) and N-(phosphonomethyl)glycine (glyphosate) were used to study their effects on the emergence of antibiotic resistance. The Escherichia coli wild-type (WT) MG1655 strain and two isogenic mutants, Δlon and ΔacrB, were used together with Raman spectroscopy. The WT E. coli is sensitive to antibiotics, but exposure to both herbicides induces antibiotic resistance. Using an excitation wavelength of 785 nm, the intensity ratios (e.g., I₇₄₀/I₇₈₅, I₇₄₀/I₁₀₀₃, I₁₄₈₀/I₁₄₄₅, I₂₉₃₄/I₂₈₆₈, and I₂₉₃₄/I₂₈₄₅) were identified as biomarkers to study the induction of antibiotic resistance in bacteria but not NaCl-mediated stress. Using an excitation wavelength of 633 nm, the peak intensity at 740 cm^-1 assigned to cytochrome bd decreases under antibiotic stress but increases upon exposure to both herbicides and antibiotics, indicating the development of resistance. Thus, this study can be applied to monitor antibiotic resistance using Raman spectroscopy.

In-vitro toxicity assessment of Eucalyptus robusta Smith extracts via whole-cell bioreporter

Eucalyptus is widely planted in China for wood industries, and there are increasing concerns about its ecotoxicity in the environment. This study explored the in-vitro toxicity of Eucalyptus extracts by assessing the impacts of water-soluble and dimethylsulfoxide (DMSO)-soluble fractions via a whole-cell bioreporter, Acinetobacter baylyi ADPWH_recA. Compounds identified in Eucalyptus extracts included one tannin, two phenolic acids, four terpenoids, four glycosides, and five flavonoids. The leaf extracts contained more biological-active components than barks and roots. Genotoxicity induced by Eucalyptus extracts was mainly associated with water extracts (e.g., flavonoids, phenolic acids) instead of DMSO extracts. The significant cytotoxicity was explained by programmed cell death (PCD), suggested by the results of propidium iodide (PI) and 2',7'-dichlorofluorescein-diacetate (DCFH-DA) assays. Generally, water-soluble fractions contributed more toxicities than DMSO-soluble fractions, particularly at high concentrations. A robust linear regression was built between the compromised toxicity and PCD index (Compromised toxicity = -2.192 × PCD index + 2.219; R² = 0.8886), suggesting a PCD-dependent compromised toxicity which was greatly underestimated. Our results implied non-neglectable ecotoxicological risks of Eucalyptus extracts, hinting at the possible magnified ecological impacts of its large-scale plantation and the potential adverse outcomes to the surrounding ecosystems.

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.

Biospectroscopy and chemometrics as an analytical tool for comparing the antibacterial mechanism of silver nanoparticles with popular antibiotics against Escherichia coli

Despite the fact that silver nanoparticles (AgNPs) have been widely studied in medical and correlated fields, details on their mechanisms are yet to be fully understood. Herein we present the first study on the combination of infrared spectroscopy and chemometrics as an analytical tool to investigate the mechanism of action of AgNPs against Escherichia coli by comparison with popular and commercially available antibiotics. The rationale behind this study is that the selected antibiotics act on bacteria in specific and distinct manners (DNA, cell membrane, mitochondria, etc.). Hence, via multivariate analysis we were able to compare the spectra of bacteria treated with the antibiotics and AgNPs to determine the main target of the latter. Spectral comparison, exploratory analysis, clustering and classification based on infrared spectra were carried out for E. coli samples in the absence and presence (treated) of four widely known antibiotics (ampicillin, ciprofloxacin, gentamicin and sulfadiazine) as well as RA-AgNPs and ERA-AgNPs. Chemometrics models indicated an interesting similarity between infrared spectra from E. coli treated with sulfadiazine and AgNPs, in which vibrational modes associated to phosphate groups were found to be the most representative. This result suggests that both AgNPs and sulfadiazine affects DNA structural features and availability, but not necessarily through the same mechanism. This biospectroscopy-based approach opens an interesting possibility for the understanding over the mechanism of antibacterial activity of AgNPs.

Recent advances in the application of magnetic Fe3O4 nanomaterials for the removal of emerging contaminants

Emerging contaminants (ECs) are widely distributed and potentially hazardous to human health and the ecological system. However, traditional wastewater treatment techniques are not sufficient to remove ECs. Magnetic nanomaterials are made of ferromagnetic or superparamagnetic magnetic elements such as iron and nickel, which can be easily separated from the aqueous solution, making them ideal adsorbents for contaminants in water. This review focused on the synthesis approaches of magnetic Fe₃O₄ nanoparticles (MFNs), as well as surface modification in order to improve their stability and functional diversity. Also, a detailed summary on the state-of-art application of magnetic nanomaterials on the removal of ECs was addressed. Additionally, challenges and future prospective of applying magnetic nanomaterials into real-world cases were discussed, in which the green and simple synthesis and evaluation of the toxic effects of MFNs are still of great challenge. This work summarizes the recent progress of using magnetic nanomaterials as promising and powerful tools in the treatment of ECs-contaminated water, benefiting researchers interested in nanomaterials and environmental studies.

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

This review compares and contrasts MALDI-MS, FT-IR spectroscopy and Raman spectroscopy for whole organism fingerprinting and bacterial typing.

Profiling antibiotic resistance in Escherichia coli strains displaying differential antibiotic susceptibilities using Raman spectroscopy

The rapid identification of antibiotic resistant bacteria is important for public health. In the environment, bacteria are exposed to sub-inhibitory antibiotic concentrations which has implications in the generation of multi-drug resistant strains. To better understand these issues, Raman spectroscopy was employed coupled with partial least squares-discriminant analysis to profile Escherichia coli strains treated with sub-inhibitory concentrations of antibiotics. Clear differences were observed between cells treated with bacteriostatic (tetracycline and rifampicin) and bactericidal (ampicillin, ciprofloxacin, and ceftriaxone) antibiotics for 6 hr: First, atomic force microscopy revealed that bactericidal antibiotics cause extensive cell elongation whereas short filaments are observed with bacteriostatic antibiotics. Second, Raman spectral analysis revealed that bactericidal antibiotics lower nucleic acid to protein (I₈₁₂ /I₈₃₀ ) and nucleic acid to lipid ratios (I₁₄₈₃ /I₁₄₅₂ ) whereas the opposite is seen with bacteriostatic antibiotics. Third, the protein to lipid ratio (I₂₉₃₆ /I₂₈₈₅ and I₂₉₃₆ /I₂₈₅₀ ) is a Raman stress signature common to both the classes. These signatures were validated using two mutants, Δlon and ΔacrB, that exhibit relatively high and low resistance towards antibiotics, respectively. In addition, these spectral markers correlated with the emergence of phenotypic antibiotic resistance. Overall, this study demonstrates the efficacy of Raman spectroscopy to identify resistance in bacteria to sub-lethal concentrations of antibiotics.

Interrogating cadmium and lead biosorption mechanisms by Simplicillium chinense via infrared spectroscopy

Fungi-associated phytoremediation is an environmentally friendly and cost-efficient approach to remove potential toxic elements (PTEs) from contaminated soils. Many fungal strains have been reported to possess PTE-biosorption behaviour which benefits phytoremediation performance. Nevertheless, most studies are limited in rich or defined medium, far away from the real-world scenarios where nutrients are deficient. Understanding fungal PTE-biosorption performance and influential factors in soil environment can expand their application potential and is urgently needed. This study applied attenuated total reflection Fourier-transform infrared (ATR-FTIR) coupled with phenotypic microarrays to study the biospectral alterations of a fungal strain Simplicillium chinense QD10 and explore the mechanisms of Cd and Pb biosorption. Both Cd and Pb were efficiently adsorbed by S. chinense QD10 cultivated with 48 different carbon sources and the biosorption efficiency achieved >90%. As the first study using spectroscopic tools to analyse PTE-biosorption by fungal cells in a high-throughput manner, our results indicated that spectral biomarkers associated with phosphor-lipids and proteins (1745 cm^-1, 1456 cm^-1 and 1396 cm^-1) were significantly correlated with Cd biosorption, suggesting the cell wall components of S. chinense QD10 as the primary interactive targets. In contrast, there was no any spectral biomarker associated with Pb biosorption. Addtionally, adsorption isotherms evidenced a Langmuir model for Cd biosorption but a Freundlich model for Pb biosorption. Accordingly, Pb and Cd biosorption by S. chinense QD10 followed discriminating mechanisms, specific adsorption on cell membrane for Cd and unspecific extracellular precipitation for Pb. This work lends new insights into the mechanisms of PTE-biosorption via IR spectrochemical tools, which provide more comprehensive clues for biosorption behaviour with a nondestructive and high-throughput manner solving the traditional technical barrier regarding the real-world scenarios.

Rapid Approach for Detection of Antibiotic Resistance in Bacteria Using Vibrational Spectroscopy

Here, we applied vibrational spectroscopy to investigate the drug response following incubation of S. aureus with oxacillin. The main focus of this work was to identify the chemical changes caused by oxacillin over time and to determine the feasibility of the spectroscopic approach to detect antimicrobial resistance. The oxacillin-induced changes in the chemical composition of susceptible bacteria, preceding (and leading to) the inhibition of growth, included an increase in the relative content of nucleic acids, alteration in the α-helical/β-sheet protein ratio, structural changes in carbohydrates (observed via changes in the band at 1035 cm^-1), and significant thickening of the cell wall. These observations enabled a dose-dependent discrimination between susceptible bacteria incubated with and without oxacillin after 120 min. In methicillin resistant strains, no spectral differences were observed between cells, regardless of drug exposure. These results pave the way for a new, rapid spectroscopic approach to detect drug resistance in pathogens, based on their early positive/negative drug response.

Interrogating the Transient Selectivity of Bacterial Chemotaxis-Driven Affinity and Accumulation of Carbonaceous Substances via Raman Microspectroscopy

Carbonaceous substances are fundamental organic nutrients for microbial metabolism and catabolism in natural habitats. Microbial abilities to sense, accumulate, and utilize organic carbonaceous substances in the complex nutrient environment are important for their growth and ecological functions. Bacterial chemotaxis is an effective mechanism for microbial utilization of carbonaceous substances under nutrient depletion conditions. Although bacterial accumulation and utilization to individual carbonaceous substance in long-term cultivation has been well studied, their selective affinity of mixed carbonaceous substances remains to be investigated, primarily because of technical limitations of conventional methods. Herein, we applied Raman microspectroscopy to identify chemotaxis-driven affinity and accumulation of four organic carbonaceous substances (glucose, succinate, acetate, and salicylate) by three bacterial strains (Acinetobacter baylyi, Pseudomonas fluorescence, and Escherichia coli). A. baylyi exhibited strong binding affinity toward glucose and succinate, whereas P. fluorescence and E. coli were preferentially responsive to glucose and acetate. For the first time, bacterial transient selectivity of carbonaceous substances was studied via interrogating Raman spectral alterations. Post-exposure to carbonaceous-substance mixtures, the three bacterial strains showed distinct selective behaviors. Stronger selective affinity enhanced the chemotaxis-related signal transduction in A. baylyi cells, whereas the carbonaceous substance signal transduction in E. coli was decreased by higher selective affinity. In P. fluorescence, there was no specific effect of selective affinity on signal transduction. Our study suggests that Raman microspectroscopy can successfully investigate and distinguish different scenarios of bacterial competitive and transient unitization of organic carbonaceous substances.

A cross talk between dysbiosis and gut-associated immune system governs the development of inflammatory arthropathies

BACKGROUND

Emerging evidence suggests that dysbiosis, imbalanced gut microbial community, might be a key player in the development of various diseases, including inflammatory arthropathies, such as rheumatoid arthritis, spondyloarthritis (mainly, ankylosing spondylitis and psoriatic arthritis), and osteoarthritis. Yet, the underlying mechanisms and corresponding interactions remain poorly understood.

METHODS

We conducted a critical and extensive literature review to explore the association between dysbiosis and the development of inflammatory arthropathies. We also reviewed the literature to assess the perspectives that ameliorate inflammatory arthropathies by manipulating the microbiota with probiotics, prebiotics or fecal microbiota transplantation.

RESULTS

Some bacterial species (e.g. Prevotella, Citrobacter rodentium, Collinsella aerofaciens, Segmented filamentous bacteria) participate in the creation of the pro-inflammatory immune status, presumably via epitope mimicry, modification of self-antigens, enhanced cell apoptosis mechanisms, and destruction of tight junction proteins and intestinal barrier integrity, all leading to the development and maintainance of inflammatory arthropathies. Whether dysbiosis is an epiphenomenon or is an active driver of these disorders remains unclear, yet, recent observations clearly suggest that dysbiosis precedes and triggers their development implying a causative relationship between dysbiosis and inflammatory arthropathies. The underlying mechanisms include dysbiosis-mediated changes in the functional activity of the intestinal immune cell subsets, such as innate lymphoid cells, mucosa-associated invariant T cells, invariant natural killer T cells, T-follicular helper and T-regulatory cells. In turn, disturbed functionality of the gut-associated immune system is shown to promote the overgrowth of many bacteria, thus establishing a detrimental vicious circle of actively maintaining arthritis.

CONCLUSIONS

Analysis of the data described in the review supports the notion that a close, dynamic and tightly regulated cross talk between dysbiosis and the gut-associated immune system governs the development of inflammatory arthropathies.

Raman spectral signature reflects transcriptomic features of antibiotic resistance in Escherichia coli

To be able to predict antibiotic resistance in bacteria from fast label-free microscopic observations would benefit a broad range of applications in the biological and biomedical fields. Here, we demonstrate the utility of label-free Raman spectroscopy in monitoring the type of resistance and the mode of action of acquired resistance in a bacterial population of Escherichia coli, in the absence of antibiotics. Our findings are reproducible. Moreover, we identified spectral regions that best predicted the modes of action and explored whether the Raman signatures could be linked to the genetic basis of acquired resistance. Spectral peak intensities significantly correlated (False Discovery Rate, p < 0.05) with the gene expression of some genes contributing to antibiotic resistance genes. These results suggest that the acquisition of antibiotic resistance leads to broad metabolic effects reflected through Raman spectral signatures and gene expression changes, hinting at a possible relation between these two layers of complementary information.

Techniques for characterizing the mode of action of antibiotic resistance are crucial for developing new antimicrobial drugs. Arno Germond et al. have used Raman spectroscopy combined with gene expression to investigate large metabolic changes that occur when bacteria acquire antibiotic resistance.

Spectroscopy with computational analysis in virological studies: A decade (2006-2016)

This review presents a retrospective of the studies carried out in the last 10 years (2006–2016) using spectroscopic methods as a research tool in the field of virology. Spectroscopic analyses are sensitive to variations in the biochemical composition of the sample, are non-destructive, fast and require the least sample preparation, making spectroscopic techniques tools of great interest in biological studies. Herein important chemometric algorithms that have been used in virological studies are also evidenced as a good alternative for analyzing the spectra, discrimination and classification of samples. Techniques that have not yet been used in the field of virology are also suggested. This methodology emerges as a new and promising field of research, and may be used in the near future as diagnosis tools for detecting diseases caused by viruses.

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A retrospective study of 2006–2016 using spectroscopic methods as a research tool in the field of virology.

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Chemometric algorithms used in virological studies were evidenced.

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This review emerges as a new and promising field of research in virology.