Metabolic Fingerprinting with Fourier-Transform Infrared (FTIR) Spectroscopy: Towards a High-Throughput Screening Assay for Antibiotic Discovery and Mechanism-of-Action Elucidation

Diagnosis of systemic lupus erythematosus using cross-modal specific transfer fusion technology based on infrared spectra and metabolomics

BACKGROUND

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease. Currently, the medical diagnosis of SLE mainly relies on the clinical experience of physicians, and there is no universally accepted objective method for diagnosing SLE. Therefore, there is an urgent need to design an intelligent approach to accurately diagnose SLE to assist physicians in formulating appropriate treatment plans. With the rapid development of intelligent medical diagnostic technology, medical data is becoming increasingly multimodal. Multimodal data fusion can provide richer information than single-modal data, and the fusion of multiple modalities can effectively enhance the richness of data features to improve modeling performance.

RESULTS

In this paper, a cross-modal specific transfer fusion technique based on infrared spectra and metabolomics is proposed to effectively integrate infrared spectra and metabolomics by fully exploiting the intrinsic relationships between features across different modalities, thus achieving the diagnosis of SLE. In this research, a Decision Level Fusion module is also proposed to fuse the representations of two specific transfers further, obtaining the final prediction scores. Comprehensive experimental results demonstrate that the proposed method significantly improves the performance of SLE prediction, with accuracy and Area Under Curve (AUC) reaching 94.98 % and 97.13 %, respectively, outperforming existing methods.

SIGNIFICANCE

Our framework effectively integrates infrared spectra and metabolomics to achieve a more accurate prediction of SLE. Our research indicates that prediction methods based on different modalities outperform those using single-modality data. The Cross-modal Specific Transfer Fusion module effectively captures the complex relationships within each single modality and models the complex relationships between different modalities.

Vibrational spectroscopy coupled with machine learning sheds light on the cellular effects induced by rationally designed TLR4 agonists

In this work, we present the potential of Fourier transform infrared (FTIR) microspectroscopy to compare on whole cells, in an unbiased and untargeted way, the capacity of bacterial lipopolysaccharide (LPS) and two rationally designed molecules (FP20 and FP20Rha) to activate molecular circuits of innate immunity. These compounds are important drug hits in the development of vaccine adjuvants and tumor immunotherapeutics. The biological assays indicated that FP20Rha was more potent than FP20 in inducing cytokine production in cells and in stimulating IgG antibody production post-vaccination in mice. Accordingly, the overall significant IR spectral changes induced by the treatment with LPS and FP20Rha were similar, lipids and glycans signals being the most diagnostic, while the effect of the less potent molecule FP20 on cells resulted to be closer to control untreated cells. We propose here the use of FTIR spectroscopy supported by artificial intelligence (AI) to achieve a more holistic understanding of the cell response to new drug candidates while screening them in cells.

Nondestructive Metabolomic Fingerprinting: FTIR, NIR and Raman Spectroscopy in Food Screening

In recent years, there has been renewed interest in the maintenance of food quality and food safety on the basis of metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Nontargeted spectroscopy techniques such as FTIR, NIR and Raman can provide fingerprint information for metabolomic constituents in agricultural products, natural products and foods in a high-throughput, cost-effective and rapid way. In the current review, we tried to explain the capabilities of FTIR, NIR and Raman spectroscopy techniques combined with multivariate analysis for metabolic fingerprinting and profiling. Previous contributions highlighted the considerable potential of these analytical techniques for the detection and quantification of key constituents, such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids and flavonoids in the food matrices. Additionally, promising results were obtained for the identification and characterization of different microorganism species such as fungus, bacterial strains and yeasts using these techniques combined with supervised and unsupervised pattern recognition techniques. In conclusion, this review summarized the cutting-edge applications of FTIR, NIR and Raman spectroscopy techniques equipped with multivariate statistics for food analysis and foodomics in the context of metabolomic fingerprinting and profiling.

Applications of Fourier Transform-Infrared spectroscopy in microbial cell biology and environmental microbiology: advances, challenges, and future perspectives

Microorganisms play pivotal roles in shaping ecosystems and biogeochemical cycles. Their intricate interactions involve complex biochemical processes. Fourier Transform-Infrared (FT-IR) spectroscopy is a powerful tool for monitoring these interactions, revealing microorganism composition and responses to the environment. This review explores the diversity of applications of FT-IR spectroscopy within the field of microbiology, highlighting its specific utility in microbial cell biology and environmental microbiology. It emphasizes key applications such as microbial identification, process monitoring, cell wall analysis, biofilm examination, stress response assessment, and environmental interaction investigation, showcasing the crucial role of FT-IR in advancing our understanding of microbial systems. Furthermore, we address challenges including sample complexity, data interpretation nuances, and the need for integration with complementary techniques. Future prospects for FT-IR in environmental microbiology include a wide range of transformative applications and advancements. These include the development of comprehensive and standardized FT-IR libraries for precise microbial identification, the integration of advanced analytical techniques, the adoption of high-throughput and single-cell analysis, real-time environmental monitoring using portable FT-IR systems and the incorporation of FT-IR data into ecological modeling for predictive insights into microbial responses to environmental changes. These innovative avenues promise to significantly advance our understanding of microorganisms and their complex interactions within various ecosystems.

Sensing of Antibiotic-Bacteria Interactions

Sensing of antibiotic-bacteria interactions is an important area of research that has gained significant attention in recent years. Antibiotic resistance is a major public health concern, and it is essential to develop new strategies for detecting and monitoring bacterial responses to antibiotics in order to maintain effective antibiotic development and antibacterial treatment. This review summarizes recent advances in sensing strategies for antibiotic-bacteria interactions, which are divided into two main parts: studies on the mechanism of action for sensitive bacteria and interrogation of the defense mechanisms for resistant ones. In conclusion, this review provides an overview of the present research landscape concerning antibiotic-bacteria interactions, emphasizing the potential for method adaptation and the integration of machine learning techniques in data analysis, which could potentially lead to a transformative impact on mechanistic studies within the field.

Label-free analysis of biofilm phenotypes by infrared micro- and correlation spectroscopy

The methodology development for deeply describing the complex biofilm phenotypes is an urgent demand for understanding their basic biology and the central clinic relevance. Here, we developed an infrared microspectroscopy-based method for the quantitative evaluation and description of biofilm phenotypic characteristics by calculating the spectral similarity of the infrared data. Using this approach, we revealed the phenotypic variation during the biofilm formation process and biofilm heterogeneity between two E. coli strains. Two-dimensional correlation spectroscopy was further combined to deeply investigate the biochemical component evolution sequences during E. coli biofilm formation and revealed the first-order of the polysaccharide molecules change, expanding new opportunities for infrared microspectroscopy in revealing molecule evolution in the biofilm formation. This novel development offers a label-free optical toolkit for the bioanalytical analysis of biofilm phenotypes but also paves the way for screening the drugs to modulate the structure and ecology of biofilm microbiome.

Rapid Biomarker-Based Diagnosis of Fibromyalgia Syndrome and Related Rheumatologic Disorders by Portable FT-IR Spectroscopic Techniques

Fibromyalgia syndrome (FM), one of the most common illnesses that cause chronic widespread pain, continues to present significant diagnostic challenges. The objective of this study was to develop a rapid vibrational biomarker-based method for diagnosing fibromyalgia syndrome and related rheumatologic disorders (systemic lupus erythematosus (SLE), osteoarthritis (OA) and rheumatoid arthritis (RA)) through portable FT-IR techniques. Bloodspot samples were collected from patients diagnosed with FM (n = 122) and related rheumatologic disorders (n = 70), including SLE (n = 17), RA (n = 43), and OA (n = 10), and stored in conventional protein saver bloodspot cards. The blood samples were prepared by four different methods (blood aliquots, protein-precipitated extraction, and non-washed and water-washed semi-permeable membrane filtration extractions), and spectral data were collected with a portable FT-IR spectrometer. Pattern recognition analysis, OPLS-DA, was able to identify the signature profile and classify the spectra into corresponding classes (Rcv > 0.93) with excellent sensitivity and specificity. Peptide backbones and aromatic amino acids were predominant for the differentiation and might serve as candidate biomarkers for syndromes such as FM. This research evaluated the feasibility of portable FT-IR combined with chemometrics as an accurate and high-throughput tool for distinct spectral signatures of biomarkers related to the human syndrome (FM), which could allow for real-time and in-clinic diagnostics of FM.

Antibiotic discovery in the artificial intelligence era

As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.

Antimicrobial evaluation of the Cynara cardunculus extract in Helicobacter pylori cells using mid-infrared spectroscopy and chemometric methods

AIMS

The treatment effectiveness of gastric diseases caused by the bacteria Helicobacter pylori is failing due to high resistance to some antibiotics. Consequently, it is urgent to develop an accurate methodology to screen new antimicrobial agents.

METHODS AND RESULTS

A preliminary assay, using both therapeutic-based antibiotics (clarithromycin and metronidazole), was conducted to optimize experimental conditions in terms of the sensibility of the Fourier Transform Mid-Infrared spectroscopy (MIR-FTIR) associated with chemometric methods. Principal component analysis was applied to understand how the Cynara extract concentration acts differentially against H. pylori bacteria. The partial least squares model, characterized by R² = 0.98, and root mean square error cross-validation, 0.011, was developed for the spectral regions (3600 - 2500 cm^-1 , and 2000 - 698 cm^-1 ).

CONCLUSIONS

MIR-FTIR spectroscopy associated with chemometric methods can be considered a suitable approach to discover and analyze the promissory antimicrobial agents based on the biomolecular changes observed according to the Cynara extract.

SIGNIFICANCE AND IMPACT OF THE STUDY

MIR-FTIR spectroscopy and chemometric methods allowed to register the biomolecular changes due to the potential antimicrobial drugs at reduced concentrations comparatively to the conventional assay based on an agar-dilution method, being considered a useful approach to develop a platform to discover new bioactive molecules, allowing to reduce time and costs related to the exploratory step.

Elucidating the Mechanisms of Action of Antimicrobial Agents

Despite the ever-growing antibiotic resistance crisis, the rate at which new antimicrobials are being discovered and approved for human use has rapidly declined over the past 75 years. A barrier for advancing newly identified antibiotics beyond discovery is elucidating their mechanism(s) of action.

Using least angular regression to model the antibacterial potential of metronidazole complexes

Imidazole has anti-inflammatory, antituberculotic, antimicrobial, antimycotic, antiviral, and antitumor properties in the human body, to name a few. Metronidazole [1-(2-Hydroxyethyl)-2-methyl-5-nitroimidazole] is a widely used antiprotozoan and antibacterial medication. Using fourier transform infrared spectroscopy, the current study models the antibacterial activity of already synthesised Metronidazole (MTZ) complexes (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MTZ-Benz$$\end{document}MTZ-Benz, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MTZ-Benz-Cu$$\end{document}MTZ-Benz-Cu, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MTZ-Benz-Cu-Cl_2CHCOOH$$\end{document}MTZ-Benz-Cu-Cl2CHCOOH, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MTZ$$\end{document}MTZ, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MTZ-Cu$$\end{document}MTZ-Cu, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MTZ-Cu-Cl_2CHCOOH$$\end{document}MTZ-Cu-Cl2CHCOOH, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MTZ-Benz-Ag$$\end{document}MTZ-Benz-Ag, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MTZ-Benz-Ag-Cl_2CHCOOH$$\end{document}MTZ-Benz-Ag-Cl2CHCOOH, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MTZ-Ag$$\end{document}MTZ-Ag and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MTZ-Ag-Cl_2CHCOOH$$\end{document}MTZ-Ag-Cl2CHCOOH) against E. coli, B. bronceptica, S. epidermidis, B. pumilus and S. aureus. To characterise the Metronidazole complexes for antibacterial activity against 05 microbes, the least angular regression and least absolute shrinkage selection operators were used. Asymmetric Least Squares was used to correct the spectrum baseline. Least angular regression outperforms cross-validated root mean square error in the fitted models. Using Least angular regression, influential wavelengths that explain the variation in antibacterial activity of Metronidazole complexes were identified and mapped against functional groups.

Towards the sustainable discovery and development of new antibiotics

An ever-increasing demand for novel antimicrobials to treat life-threatening infections caused by the global spread of multidrug-resistant bacterial pathogens stands in stark contrast to the current level of investment in their development, particularly in the fields of natural-product-derived and synthetic small molecules. New agents displaying innovative chemistry and modes of action are desperately needed worldwide to tackle the public health menace posed by antimicrobial resistance. Here, our consortium presents a strategic blueprint to substantially improve our ability to discover and develop new antibiotics. We propose both short-term and long-term solutions to overcome the most urgent limitations in the various sectors of research and funding, aiming to bridge the gap between academic, industrial and political stakeholders, and to unite interdisciplinary expertise in order to efficiently fuel the translational pipeline for the benefit of future generations.

Fast identification of off-target liabilities in early antibiotic discovery with Fourier-transform infrared spectroscopy

Structural modifications of known antibiotic scaffolds have kept the upper hand on resistance, but we are on the verge of not having antibiotics for many common infections. Mechanism-based discovery assays reveal novelty, exclude off-target liabilities, and guide lead optimization. For that, we developed a fast and automatable protocol using high-throughput Fourier-transform infrared spectroscopy (FTIRS). Metabolic fingerprints of Staphylococcus aureus and Escherichia coli exposed to 35 compounds, dissolved in dimethyl sulfoxide (DMSO) or water, were acquired. Our data analysis pipeline identified biomarkers of off-target effects, optimized spectral preprocessing, and identified the top-performing machine learning algorithms for off-target liabilities and mechanism of action (MOA) identification. Spectral bands with known biochemical associations more often yielded more significant biomarkers of off-target liabilities when bacteria were exposed to compounds dissolved in water than DMSO. Highly discriminative models distinguished compounds with predominant off-target effects from antibiotics with well-defined MOA (AUROC > 0.87, AUPR > 0.79, F1 > 0.81), and from the latter predicted their MOA (AUROC > 0.88, AUPR > 0.70, F1 > 0.70). The compound solvent did not affect predictive models. FTIRS is fast, simple, inexpensive, automatable, and highly effective at predicting MOA and off-target liabilities. As such, FTIRS mechanism-based screening assays can be applied for hit discovery and to guide lead optimization during the early stages of antibiotic discovery.

Enzyme-based sensing on nanohybrid film coated over FTO electrode for highly sensitive detection of antibiotics

Cefepime and Meropenem are the new class of antibiotics, which are particularly used as last potent defender or the antibiotics of the last resort against multi-resistant bacterial species. In this paper, an impedance-based electrochemical biosensor was fabricated for identifying antibiotics of last resort in the forensic samples including gastric lavage and other body fluids. The sensor was developed using platinum nanoparticles (PtNPs) and electrodeposited zinc oxide- zinc hexacyanoferrate hybrid film (ZnO/ZnHCF) on the surface of a fluorine-doped glass electrode (FTO). Further, penicillinase was immobilized onto the modified electrode using penicillinase enzyme. The developed biosensor exhibits a good analytical response for the detection of antibiotics evaluated using electrochemistry studies. The linear response of the fabricated electrode was observed from 0.1 to 750 µM and the electrode limit of detection (LOD) was observed as 0.1 µM. The sensor confirms good accuracy, is highly selective, and sensitive for the target. While storing the modified electrode at 4 °C, the stability of biosensor was evaluated for 45 days, and activity loss of 30-40% was observed. The highly sensitive interface of Penicillinase@CHIT/PtNP-ZnO/ZnHCF/FTO electrode shows a promising future in forensic studies.

A new method to predict genotoxic effects based on serum molecular profile

It is critical to develop new methods to assess genotoxic effects in human biomonitoring since the conventional methods are usually laborious, time-consuming, and expensive. It is aimed to evaluate if the analysis of a drop of serum by Fourier Transform Infrared spectroscopy, allow to assess genotoxic effects in occupational exposure to cytostatic drugs in hospital professionals, as obtained by the lymphocyte cytokinesis-block micronucleus assay. It was considered peripheral blood from hospital professionals exposed to cytostatic drugs (n = 22) and from a non-exposed group (n = 36). It was observed that workers occupationally exposed presented a higher number of micronuclei (p < 0.05) in lymphocytes, in relation to the non-exposed group. The serum Fourier Transform Infrared spectra from exposed workers presented diverse different peaks (p < 0.01) in relation to the non-exposed group. The hierarchical cluster analysis of serum spectra separated serum samples of the exposed group from the non-exposed group with 61% sensitivity and 88% specificity. A support vector machine model of serum spectra enables to predict exposure with high accuracy (0.91), precision (0.89), sensitivity (0.86), F1 score (0.87) and AUC (0.96). Therefore, Fourier Transform Infrared spectroscopic analysis of a drop of serum enabled to predict in a rapid and simple mode the genotoxic effects of cytostatic drugs. The method presents therefore potential for high-dimension screening of exposure of genotoxic substances, due to its simplicity and rapid setup mode.

Technologies for High-Throughput Identification of Antibiotic Mechanism of Action

There are two main strategies for antibiotic discovery: target-based and phenotypic screening. The latter has been much more successful in delivering first-in-class antibiotics, despite the major bottleneck of delayed Mechanism-of-Action (MOA) identification. Although finding new antimicrobial compounds is a very challenging task, identifying their MOA has proven equally challenging. MOA identification is important because it is a great facilitator of lead optimization and improves the chances of commercialization. Moreover, the ability to rapidly detect MOA could enable a shift from an activity-based discovery paradigm towards a mechanism-based approach. This would allow to probe the grey chemical matter, an underexplored source of structural novelty. In this study we review techniques with throughput suitable to screen large libraries and sufficient sensitivity to distinguish MOA. In particular, the techniques used in chemical genetics (e.g., based on overexpression and knockout/knockdown collections), promoter-reporter libraries, transcriptomics (e.g., using microarrays and RNA sequencing), proteomics (e.g., either gel-based or gel-free techniques), metabolomics (e.g., resourcing to nuclear magnetic resonance or mass spectrometry techniques), bacterial cytological profiling, and vibrational spectroscopy (e.g., Fourier-transform infrared or Raman scattering spectroscopy) were discussed. Ultimately, new and reinvigorated phenotypic assays bring renewed hope in the discovery of a new generation of antibiotics.

Simultaneous elucidation of antibiotic mechanism of action and potency with high-throughput Fourier-transform infrared (FTIR) spectroscopy and machine learning

The low rate of discovery and rapid spread of resistant pathogens have made antibiotic discovery a worldwide priority. In cell-based screening, the mechanism of action (MOA) is identified after antimicrobial activity. This increases rediscovery, impairs low potency candidate detection, and does not guide lead optimization. In this study, high-throughput Fourier-transform infrared (FTIR) spectroscopy was used to discriminate the MOA of 14 antibiotics at pathway, class, and individual antibiotic level. For that, the optimal combinations and parametrizations of spectral preprocessing were selected with cross-validated partial least squares discriminant analysis, to which various machine learning algorithms were applied. This coherently resulted in very good accuracies, independently of the algorithms, and at all levels of MOA. Particularly, an ensemble of subspace discriminants predicted the known pathway (98.6%), antibiotic classes (100%), and individual antibiotics (97.8%) with exceptional accuracy, and similar results were obtained for simulated novel MOA. Even at very low concentrations (1 μg/mL) and growth inhibition (15%), over 70% pathway and class accuracy was achieved, suggesting FTIR spectroscopy can probe the grey chemical matter. Prediction of inhibitory effect was also examined, for which a squared exponential Gaussian process regression yielded a root mean square error of 0.33 and a R² of 0.92, indicating that metabolic alterations leading to growth inhibition are intrinsically reflected on FTIR spectra beyond cell density. KEY POINTS: • Antibiotic MOA and potency estimated with high-throughput FTIR spectroscopy • Sub-inhibitory MOA identification suggests ability to explore grey chemical matter • Data analysis optimization improved MOA identification at antibiotic level by 38.

Advances in antibiotic drug discovery: reducing the barriers for antibiotic development

Antibiotic drug discovery has been an essential field of research since the early 1900s, but the threat from infectious bacteria has only increased over the decades because of the emergence of widespread multidrug resistance. In this review, we discuss the recent advances in natural product, computational and medicinal chemistry that have reinvigorated the field of antibiotic drug discovery while giving perspective on how easily, both in cost and in expertise, these methods can be implemented by other researchers with the goal of increasing the number of scientists contributing to this public health crisis.