Essential Oils Biofilm Modulation Activity, Chemical and Machine Learning Analysis. Application on Staphylococcus aureus Isolates from Cystic Fibrosis Patients

Essential Oils as Antimicrobials against Acinetobacter baumannii: Experimental and Literature Data to Definite Predictive Quantitative Composition-Activity Relationship Models Using Machine Learning Algorithms

Essential oils (EOs) exhibit a broad spectrum of biological activities; however, their clinical application is hindered by challenges, such as variability in chemical composition and chemical/physical instability. A critical limitation is the lack of chemical consistency across EO samples, which impedes standardization. Despite this, evidence suggests that EOs with differing chemical profiles often display similar (micro)biological activities, raising the possibility of standardizing EOs based on their biological effects rather than their chemical composition. This study explored the relationship between EO chemical composition and antibacterial activity against carbapenem-resistant Acinetobacter baumannii. A dataset comprising 82 EOs with known minimal inhibitory concentration values was compiled using both experimental results and literature data sourced from the AI4EssOil database (https://www.ai4essoil.com). Machine learning classification algorithms including Support Vector Machines, Random Forest, Gradient Boosting, Decision Trees, and K-Nearest Neighbors were employed to generate quantitative composition-activity relationship models. Model performance was assessed using internal and external prediction accuracy metrics with the Matthews correlation coefficient as the primary evaluation metrics. Features importance analysis, based on the Skater methodology, identified key chemical components influencing EO activity. The single chemical components limonene, eucalyptol, alpha-pinene, linalool, beta-caryophyllene, nerol, beta-pinene, neral, and carvacrol were highlighted as critical to biological efficacy. The predictive capacity of the ML models was validated against a test set of freshly extracted and chemically characterized EOs. The models demonstrated a 91% prediction accuracy for new EO samples, and a strong correlation was observed between predicted features importance and experimental inhibitory values for six selected pure compounds (limonene, eucalyptol, alpha-pinene, linalool, carvacrol, and thymol). Additionally, the machine learning approach was extended to cytotoxicity data from 3T3-Swiss fibroblasts for 61 EOs. The analysis revealed the potential to design EOs with both high antibacterial activity and low cytotoxicity through blending or selective enrichment with identified key components. These findings pave the way for biologically standardized EOs, enabling their rational design and optimization for clinical applications.

Chemical Composition and Antibacterial Effect of Clove and Thyme Essential Oils on Growth Inhibition and Biofilm Formation of Arcobacter spp. and Other Bacteria

Background: In recent years, significant resistance of microorganisms to antibiotics has been observed. A biofilm is a structure that significantly aids the survival of the microbial population and also significantly affects its resistance. Methods: Thyme and clove essential oils (EOs) were subjected to chemical analysis using gas chromatography coupled to mass spectrometry (GC-MS) and gas chromatography with a flame ionization detector (GC-FID). Furthermore, the antimicrobial effect of these EOs was tested in both the liquid and vapor phases using the volatilization method. The effect of the EOs on growth parameters was monitored using an RTS-8 bioreactor. However, the effect of the EOs on the biofilm formation of commonly occurring bacteria with pathogenic potential was also monitored, but for less described and yet clinically important strains of Arcobacter spp. Results: In total, 37 and 28 compounds were identified in the thyme and clove EO samples, respectively. The most common were terpenes and also derivatives of phenolic substances. Both EOs exhibited antimicrobial activity in the liquid and/or vapor phase against at least some strains. The determined antimicrobial activity of thyme and clove oil was in the range of 32-1024 µg/mL in the liquid phase and 512-1024 µg/mL in the vapor phase, respectively. The results of the antimicrobial effect are also supported by similar conclusions from monitoring growth curves using the RTS bioreactor. The effect of EOs on biofilm formation differed between strains. Biofilm formation of Pseudomonas aeruginosa was completely suppressed in an environment with a thyme EO concentration of 1024 µg/mL. On the other hand, increased biofilm formation was found, e.g., in an environment of low concentration (1-32 µg/mL). Conclusions: The potential of using natural matrices as antimicrobials or preservatives is evident. The effect of these EOs on biofilm formation, especially Arcobacter strains, is described for the first time.

Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces

The growing threat of antimicrobial-resistant (AMR) pathogens to human health worldwide emphasizes the need for more effective infection control strategies. Bacterial and fungal biofilms pose a major challenge in treating AMR pathogen infections. Biofilms are formed by pathogenic microbes encased in extracellular polymeric substances to confer protection from antimicrobials and the host immune system. Biofilms also promote the growth of antibiotic-resistant mutants and latent persister cells and thus complicate therapeutic approaches. Biofilms are ubiquitous and cause serious health risks due to their ability to colonize various surfaces, including human tissues, medical devices, and food-processing equipment. Detection and characterization of biofilms are crucial for prompt intervention and infection control. To this end, traditional approaches are often effective, yet they fail to identify the microbial species inside biofilms. Recent advances in artificial intelligence (AI) have provided new avenues to improve biofilm identification. Machine-learning algorithms and image-processing techniques have shown promise for the accurate and efficient detection of biofilm-forming microorganisms on biotic and abiotic surfaces. These advancements have the potential to transform biofilm research and clinical practice by allowing faster diagnosis and more tailored therapy. This comprehensive review focuses on the application of AI techniques for the identification of biofilm-forming pathogens in various industries, including healthcare, food safety, and agriculture. The review discusses the existing approaches, challenges, and potential applications of AI in biofilm research, with a particular focus on the role of AI in improving diagnostic capacities and guiding preventative actions. The synthesis of the current knowledge and future directions, as described in this review, will guide future research and development efforts in combating biofilm-associated infections.

The role of Staphylococcus aureus in cystic fibrosis pathogenesis and clinico-microbiological interactions

Cystic fibrosis (CF) is a progressive and inherited disease that affects approximately 70000 individuals all over the world annually. A mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene serves as its defining feature. Bacterial infections have a significant impact on the occurrence and development of CF. In this manuscript, we discuss the role and virulence factors of Staphylococcus aureus as an important human pathogen with the ability to induce respiratory tract infections. Recent studies have reported S. aureus as the first isolated bacteria in CF patients. Methicillin-resistant Staphylococcus aureus (MRSA) pathogens are approximately resistant to all β-lactams. CF patients are colonized by MRSA expressing various virulence factors including toxins, and Staphylococcal Cassette Chromosome mec (SCCmec) types, and have the potential for biofilm formation. Therefore, variations in clinical outcomes will be manifested. SCCmec type II has been reported in CF patients more than in other SCCmec types from different countries. The small-colony variants (SCVs) as specific morphologic subtypes of S. aureus with slow growth and unusual properties can also contribute to persistent and difficult-to-treat infections in CF patients. The pathophysiology of SCVs is complicated and not fully understood. Patients with cystic fibrosis should be aware of the intrinsic risk factors for complex S. aureus infections, including recurring infections, physiological issues, or coinfection with P. aeruginosa.

Bioactive Phyto-Compounds with Antimicrobial Effects and AI: Results of a Desk Research Study

Resistance of microorganisms to antibiotics represents a formidable global challenge, manifesting in intricate public health ramifications including escalated mortality rates and augmented healthcare costs. The current efforts to manage antimicrobial resistance (AMR) are limited mainly to the standard therapeutic approaches. The aim of this study is to present and analyze the role of artificial intelligence (AI) in the search for new phyto-compounds and novel interactions with antimicrobial effects. The ambition of the current research study is to support researchers by providing summarized information and ideas for future research in the battle with AMR. Inevitably, the AI role in healthcare is growing exponentially. The reviewed AI models reveal new data on essential oils (EOs) as potential therapeutic agents. In terms of antibacterial activity, EOs show activity against MDR bacteria, reduce resistance by sensitizing bacteria to the action of antibiotics, and improve therapeutic efficiency when combined with antibiotics. AI models can also serve for the detailed study of other therapeutic applications of EOs such as respiratory diseases, immune diseases, neurodegenerative diseases, and oncological diseases. The last 5 years have seen an increasing application of AI in the search for potential plant sources to control AMR. For the time being, the application of machine-learning (ML) models is greater in the studies of EOs. Future attention of research teams may also be directed toward a more efficient search for plant antimicrobial peptides (PAMPs). Of course, investments in this direction are a necessary preface, but the excitement of new possibilities should not override the role of human intelligence in directing research processes. In this report, tradition meets innovation to address the "silent pandemic" of AMR.

Anti-Biofilm Activity of Oleacein and Oleocanthal from Extra-Virgin Olive Oil toward Pseudomonas aeruginosa

New antimicrobial molecules effective against Pseudomonas aeruginosa, known as an antibiotic-resistant "high-priority pathogen", are urgently required because of its ability to develop biofilms related to healthcare-acquired infections. In this study, for the first time, the anti-biofilm and anti-virulence activities of a polyphenolic extract of extra-virgin olive oil as well as purified oleocanthal and oleacein, toward P. aeruginosa clinical isolates were investigated. The main result of our study was the anti-virulence activity of the mixture of oleacein and oleocanthal toward multidrug-resistant and intermediately resistant strains of P. aeruginosa isolated from patients with ventilator-associated pneumonia or surgical site infection. Specifically, the mixture of oleacein (2.5 mM)/oleocanthal (2.5 mM) significantly inhibited biofilm formation, alginate and pyocyanin production, and motility in both P. aeruginosa strains (p < 0.05); scanning electron microscopy analysis further evidenced its ability to inhibit bacterial cell adhesion as well as the production of the extracellular matrix. In conclusion, our results suggest the potential application of the oleacein/oleocanthal mixture in the management of healthcare-associated P. aeruginosa infections, particularly in the era of increasing antimicrobial resistance.

Screening of the activity of sixty essential oils against plasmodium early mosquito stages in vitro and machine learning analysis reveals new putative inhibitors of malaria parasites

Malaria, an infectious disease with a tremendous impact on human health is caused by Plasmodium parasites, and transmitted by Anopheles mosquitoes. New approaches to control the disease involve transmission blocking strategies aiming to target the parasite in the mosquito. Here, we investigated the putative inhibitory activity of essential oils and their components on the early mosquito stages of the parasite. We employed an in vitro assay of gametocyte-to-ookinete development of the rodent model parasite Plasmodium berghei combined with high content screening. 60 essential oils with known composition were tested. The results revealed that fifteen EOs had inhibitory activity. Furthermore, a machine learning approach was used to identify the putative inhibitory components. Five of the most important chemical components indicated by the machine learning-based models were actually confirmed by the experimental approach. This combined approach was used for the first time to identify the potential transmission blocking activity of essential oils and single components at the zygote and ookinete stages.

In Vitro Antioxidant and In Vivo Antigenotoxic Features of a Series of 61 Essential Oils and Quantitative Composition-Activity Relationships Modeled through Machine Learning Algorithms

The antioxidant activity of essential oils (EOs) is an important and frequently studied property, yet it is not sufficiently understood in terms of the contribution of EOs mixtures' constituents and biological properties. In this study, a series of 61 commercial EOs were first evaluated as antioxidants in vitro, following as closely as possible the cellular pathways of reactive oxygen species (ROS) generation. Hence, EOs were assessed for the ability either to chelate metal ions, thus interfering with ROS generation within the respiratory chain, or to neutralize 2,2-diphenyl-1-picrylhydrazyl (DPPH•) and lipid peroxide radicals (LOO•), thereby halting lipid peroxidation, as well as to neutralize 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid cation radicals (ABTS•+) and hydroxyl radicals (OH•), thereby preventing the ROS species from damaging DNA nucleotides. Showing noteworthy potencies to neutralize all of the radicals at the ng/mL level, the active EOs were also characterized as protectors of DNA double strands from damage induced by peroxyl radicals (ROO•), emerging from 2,2'-azobis-2-methyl-propanimidamide (AAPH) as a source, and OH•, indicating some genome protectivity and antigenotoxicity effectiveness in vitro. The chemical compositions of the EOs associated with the obtained activities were then analyzed by means of machine learning (ML) classification algorithms to generate quantitative composition-activity relationships (QCARs) models (models published in the AI4EssOil database available online). The QCARs models enabled us to highlight the key features (EOSs' chemical compounds) for exerting the redox potencies and to define the partial dependencies of the features, viz. percentages in the mixture required to exert a given potency. The ML-based models explained either the positive or negative contribution of the most important chemical components: limonene, linalool, carvacrol, eucalyptol, α-pinene, thymol, caryophyllene, p-cymene, eugenol, and chrysanthone. Finally, the most potent EOs in vitro, Ylang-ylang (Cananga odorata (Lam.)) and Ceylon cinnamon peel (Cinnamomum verum J. Presl), were promptly administered in vivo to evaluate the rescue ability against redox damage caused by CCl4, thereby verifying their antioxidant and antigenotoxic properties either in the liver or in the kidney.

Effect of Different Soil Treatments on Production and Chemical Composition of Essential Oils Extracted from Foeniculum vulgare Mill., Origanum vulgare L. and Thymus vulgaris L

The aim of the study was to investigate how essential oil production and associated chemical composition and related biological activity could be influenced by different cultivation treatments and distillation methods. Foeniculum vulgare Mill. (fennel), Origanum vulgare L. (oregano) and Thymus vulgaris L. (thyme) were cultivated in absence of any fertilizer (control) and in presence of three different fertilizers: a chemical one with augmented mineral phosphorus and potassium, a second added with hydrolyzed organic substance and mineral phosphorus and potassium (organic-mineral) and a third one treated with a high content of organic nitrogen of protein origin (organic). The plants were subjected to steam distillation using two modalities, recycled and continuous, to obtain 32 essential oil samples. Chemical composition analysis was performed using gas chromatography-mass spectrometry; in vitro antimicrobial activity was evaluated using a broth microdilution method. In general, the recycled distillation method appeared to have a slightly higher yield than the continuous method. The "mineral" and "organic-mineral" treatments resulted in a higher yield compared to the "organic" or "control" treatments, and this was particularly evident in the recycled method. The "control" plants had a lower yield of essential oils. Anethole (13.9-59.5%) and estragole (13.4-52.2%) were the main constituents of the fennel oils; p-cymene and its derivatives carvacrol and thymol were the main constituents of the oregano and thyme samples. The antimicrobial activity of the thyme oils on Staphylococcus aureus ranged from 0.31 to 0.16% (v/v); a lower effect of the oregano samples and no activity of the fennel samples were observed. The essential oils failed to inhibit the growth of Pseudomonas aeruginosa strains.

In Vivo Antiphytoviral and Aphid Repellency Activity of Essential Oils and Hydrosols from Mentha suaveolens and Foeniculum vulgare to Control Zucchini Yellow Mosaic Virus and Its Vector Aphis gossypii

In recent years, natural compounds have gained attention in many fields due to their wide-range biological activity. In particular, essential oils and their associated hydrosols are being screened to control plant pests, exerting antiviral, antimycotic and antiparasitic actions. They are more quickly and cheaply produced and are generally considered safer for the environment and non-target organisms than conventional pesticides. In this study, we report the evaluation of the biological activity of two essential oils and their corresponding hydrosols obtained from Mentha suaveolens and Foeniculum vulgare in the control of zucchini yellow mosaic virus and its vector, Aphis gossypii, in Cucurbita pepo plants. The control of the virus was ascertained with treatments applied either concurrently with or after virus infection; choice tests were performed to verify repellency activity against the aphid vector. The results indicated that treatments could decrease virus titer as measured using real-time RT-PCR, while the experiments on the vector showed that the compounds effectively repelled aphids. The extracts were also chemically characterized using gas chromatography-mass spectrometry. Mentha suaveolens and Foeniculum vulgare hydrosol extracts mainly comprised fenchone and decanenitrile, respectively, while essential oils analysis returned a more complex composition, as expected.

Biofilm Formation by Staphylococcus aureus in the Specific Context of Cystic Fibrosis

Staphylococcus aureus is a major human pathogen whose characteristics support its success in various clinical settings including Cystic Fibrosis (CF). In CF, S. aureus is indeed the most commonly identified opportunistic pathogen in children and the overall population. S. aureus colonization/infection, either by methicillin-susceptible or methicillin-resistant strains, will become chronic in about one third of CF patients. The persistence of S. aureus in CF patients' lungs, despite various eradication strategies, is favored by several traits in both host and pathogen. Among the latter, living in biofilm is a highly protective way to survive despite deleterious environmental conditions, and is a common characteristic shared by the main pathogens identified in CF. This is why CF has earned the status of a biofilm-associated disease for several years now. Biofilm formation by S. aureus, and the molecular mechanisms governing and regulating it, have been extensively studied but have received less attention in the specific context of CF lungs. Here, we review the current knowledge on S. aureus biofilm in this very context, i.e., the importance, study methods, molecular data published on mono- and multi-species biofilm and anti-biofilm strategies. This focus on studies including clinical isolates from CF patients shows that they are still under-represented in the literature compared with studies based on reference strains, and underlines the need for such studies. Indeed, CF clinical strains display specific characteristics that may not be extrapolated from results obtained on laboratory strains.

Terpenes Combinations Inhibit Biofilm Formation in Staphyloccocus aureus by Interfering with Initial Adhesion

The biofilm is a conglomerate of cells surrounded by an extracellular matrix, which contributes to the persistence of infections. The difficulty in removing the biofilm drives the research for new therapeutic options. In this work, the effect of terpenes (−)-trans-Caryophyllene, (S)-cis-Verbenol, (S)-(−)-Limonene, (R)-(+)-Limonene, and Linalool was evaluated, individually and in combinations on bacterial growth, by assay with resazurin; the formation of biofilm, by assay with violet crystal; and the expression of associated genes, by real-time PCR, in two clinical isolates of Staphyloccocus aureus, ST30-t019 and ST5-t311, responsible for more than 90% of pediatric infections by this pathogen in Paraguay. All combinations of terpenes can inhibit biofilm formation in more than 50% without affecting bacterial growth. The most effective combination was (−)-trans-Caryophyllene and Linalool at a 500 μg/mL concentration for each, with an inhibition percentage of 88%. This combination decreased the expression levels of the sdrD, spa, agr, and hld genes associated with the initial cell adhesion stage and quorum sensing. At the same time, it increased the expression levels of the cap5B and cap5C genes related to the production of capsular polysaccharides. The combinations of compounds tested are promising alternatives to inhibit biofilm formation in S. aureus.

Essential Oils Biofilm Modulation Activity and Machine Learning Analysis on Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients

The opportunistic pathogen Pseudomonas aeruginosa is often involved in airway infections of cystic fibrosis (CF) patients. It persists in the hostile CF lung environment, inducing chronic infections due to the production of several virulence factors. In this regard, the ability to form a biofilm plays a pivotal role in CF airway colonization by P. aeruginosa. Bacterial virulence mitigation and bacterial cell adhesion hampering and/or biofilm reduced formation could represent a major target for the development of new therapeutic treatments for infection control. Essential oils (EOs) are being considered as a potential alternative in clinical settings for the prevention, treatment, and control of infections sustained by microbial biofilms. EOs are complex mixtures of different classes of organic compounds, usually used for the treatment of upper respiratory tract infections in traditional medicine. Recently, a wide series of EOs were investigated for their ability to modulate biofilm production by different pathogens comprising S. aureus, S. epidermidis, and P. aeruginosa strains. Machine learning (ML) algorithms were applied to develop classification models in order to suggest a possible antibiofilm action for each chemical component of the studied EOs. In the present study, we assessed the biofilm growth modulation exerted by 61 commercial EOs on a selected number of P. aeruginosa strains isolated from CF patients. Furthermore, ML has been used to shed light on the EO chemical components likely responsible for the positive or negative modulation of bacterial biofilm formation.

Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites

Essential oils (EOs) are a mixture of chemical compounds with a long history of use in food, cosmetics, perfumes, agricultural and pharmaceuticals industries. The main object of this study was to find chemical patterns between 45 EOs and antiprotozoal activity (antiplasmodial, antileishmanial and antitrypanosomal), using different machine learning algorithms. In the analyses, 45 samples of EOs were included, using unsupervised Self-Organizing Maps (SOM) and supervised Random Forest (RF) methodologies. In the generated map, the hit rate was higher than 70% and the results demonstrate that it is possible find chemical patterns using a supervised and unsupervised machine learning approach. A total of 20 compounds were identified (19 are terpenes and one sulfur-containing compound), which was compared with literature reports. These models can be used to investigate and screen for bioactivity of EOs that have antiprotozoal activity more effectively and with less time and financial cost.

Anti-Virulence Properties of Coridothymus capitatus Essential Oil against Pseudomonas aeruginosa Clinical Isolates from Cystic Fibrosis Patients

Pseudomonas aeruginosa is an opportunistic pathogen responsible for nosocomial infections, and is often involved in airway infections of cystic fibrosis (CF) patients. P. aeruginosa virulence is related to its ability to form biofilm, trigger different types of motilities, and produce toxins (for example, bacterial pigments). In this scenario, essential oils (EOs) have gained notoriety for their role in phenotype modulation, including virulence modulation. Among different EOs previously analyzed, herein we investigated the activity of Coridothymus capitatus EO (CCEO) against specific virulence factors produced by P. aeruginosa isolated from CF patients. CCEO showed inhibition of new biofilm formation and reduction in mature biofilm in about half of the tested strains. On selected strains, SEM analysis provided interesting information regarding CCEO action in a pre-adhesion assay. CCEO treatment showed a dramatic modification of the extracellular matrix (ECM) structure. Our results clearly showed a drastic reduction in pyocyanin production (between 84% and 100%) for all tested strains in the presence of CCEO. Finally, CCEO was also able to strongly affect P. aeruginosa swarming and swimming motility for almost all tested strains. In consideration of the novel results obtained on clinical strains isolated from CF patients, CCEO may be a potential candidate to limit P. aeruginosa virulence.

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition-Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils

Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, the investigation of 61 assayed essential oils is reported focusing on their inhibition activity against Microsporum spp. including development of machine learning models with the aim of highlining the possible chemical components mainly related to the inhibitory potency. The application of machine learning and deep learning techniques for predictive and descriptive purposes have been applied successfully to many fields. Quantitative composition-activity relationships machine learning-based models were developed for the 61 essential oils tested as Microsporum spp. growth modulators. The models were built with in-house python scripts implementing data augmentation with the purpose of having a smoother flow between essential oils' chemical compositions and biological data. High statistical coefficient values (Accuracy, Matthews correlation coefficient and F1 score) were obtained and model inspection permitted to detect possible specific roles related to some components of essential oils' constituents. Robust machine learning models are far more useful tools to reveal data augmentation in comparison with raw data derived models. To the best of the authors knowledge this is the first report using data augmentation to highlight the role of complex mixture components, in particular a first application of these data will be for the development of ingredients in the dermo-cosmetic field investigating microbial species considering the urge for the use of natural preserving and acting antimicrobial agents.

Anti-Virulence Activity of the Cell-Free Supernatant of the Antarctic Bacterium Psychrobacter sp. TAE2020 against Pseudomonas aeruginosa Clinical Isolates from Cystic Fibrosis Patients

Pseudomonas aeruginosa is an opportunistic pathogen often involved in airway infections of cystic fibrosis (CF) patients. Its pathogenicity is related to several virulence factors, such as biofilm formation, motility and production of toxins and proteases. The expression of these virulence factors is controlled by quorum sensing (QS). Thus, QS inhibition is considered a novel strategy for the development of antipathogenic compounds acting on specific bacterial virulence programs without affecting bacterial vitality. In this context, cold-adapted marine bacteria living in polar regions represent an untapped reservoir of biodiversity endowed with an interesting chemical repertoire. In this paper, we investigated the biological activity of a supernatant derived from a novel Antarctic bacterium (SN_TAE2020) against specific virulence factors produced by P. aeruginosa strains isolated from FC patients. Our results clearly show a reduction in pyocyanin and protease production in the presence of SN_TAE2020. Finally, SN_TAE2020 was also able to strongly affect swarming and swimming motility for almost all tested strains. Furthermore, the effect of SN_TAE2020 was investigated on biofilm growth and texture, captured by SEM analysis. In consideration of the novel results obtained on clinical strains, polar bacteria might represent potential candidates for the discovery of new compounds limiting P. aeruginosa virulence in CF patients.

In vitro interactions of Alternaria mycotoxins, an emerging class of food contaminants, with the gut microbiota: a bidirectional relationship

The human gut microbiota plays an important role in the maintenance of human health. Factors able to modify its composition might predispose the host to the development of pathologies. Among the various xenobiotics introduced through the diet, Alternaria mycotoxins are speculated to represent a threat for human health. However, limited data are currently available about the bidirectional relation between gut microbiota and Alternaria mycotoxins. In the present work, we investigated the in vitro effects of different concentrations of a complex extract of Alternaria mycotoxins (CE; containing eleven mycotoxins; e.g. 0.153 µM alternariol and 2.3 µM altersetin, at the maximum CE concentration tested) on human gut bacterial strains, as well as the ability of the latter to metabolize or adsorb these compounds. Results from the minimum inhibitory concentration assay showed the scarce ability of CE to inhibit the growth of the tested strains. However, the growth kinetics of most of the strains were negatively affected by exposure to the various CE concentrations, mainly at the highest dose (50 µg/mL). The CE was also found to antagonize the formation of biofilms, already at concentrations of 0.5 µg/mL. LC-MS/MS data analysis of the mycotoxin concentrations found in bacterial pellets and supernatants after 24 h incubation showed the ability of bacterial strains to adsorb some Alternaria mycotoxins, especially the key toxins alternariol, alternariol monomethyl ether, and altersetin. The tendency of these mycotoxins to accumulate within bacterial pellets, especially in those of Gram-negative strains, was found to be directly related to their lipophilicity.

Antitumor effect of Melaleuca alternifolia essential oil and its main component terpinen-4-ol in combination with target therapy in melanoma models

Essential oils (EOs) have been recently emerging for their promising biological activities in preventing tumorigenesis or progression of different tumor histotypes, including melanoma. In this study, we investigated the antitumor activity of a panel of EOs in different tumor models. The ability of Melaleuca alternifolia (tea tree oil) and its main component, terpinen-4-ol, to sensitize the target therapy currently used for melanoma treatment was also assessed. Our results demonstrated that EOs differently affect the viability of human cancer cells and led us to select six EOs effective in melanoma and lung cancer cells, without toxic effects in human fibroblasts. When combined with dabrafenib and/or trametinib, Melaleuca alternifolia synergistically reduced the viability of melanoma cells by activating apoptosis. Through machine learning classification modeling, α-terpineol, tepinolene, and terpinen-4-ol, three components of Melaleuca alternifolia, were identified as the most likely relevant components responsible for the EO's antitumor effect. Among them, terpinen-4-ol was recognized as the Melaleuca alternifolia component responsible for its antitumor and proapoptotic activity. Overall, our study holds promise for further analysis of EOs as new anticancer agents and supports the rationale for their use to improve target therapy response in melanoma.

Microscopy Methods for Biofilm Imaging: Focus on SEM and VP-SEM Pros and Cons

Simple Summary

Bacterial biofilms cause infections that are often resistant to antibiotic treatments. Research about the formation and elimination of biofilms cannot be undertaken without detailed imaging techniques. In this review, traditional and cutting-edge microscopy methods to study biofilm structure, ultrastructure, and 3-D architecture, with particular emphasis on conventional scanning electron microscopy and variable pressure scanning electron microscopy, are addressed, with the respective advantages and disadvantages. When ultrastructural characterization of biofilm matrix and its embedded bacterial cells is needed, as in studies on the effects of drug treatments on biofilm, scanning electron microscopy with customized protocols such as the osmium tetroxide (OsO₄), ruthenium red (RR), tannic acid (TA), and ionic liquid (IL) must be preferred over other methods for the following: unparalleled image quality, magnification and resolution, minimal sample loss, and actual sample structure preservation. The first step to make a morphological assessment of the effect of the various pharmacological treatments on clinical biofilms is the production of images that faithfully reflect the structure of the sample. The extraction of quantitative parameters from images, possible using specific software, will allow for the scanning electron microscopy morphological evaluation to no longer be considered as an accessory technique, but a quantitative method to all effects.

Abstract

Several imaging methodologies have been used in biofilm studies, contributing to deepening the knowledge on their structure. This review illustrates the most widely used microscopy techniques in biofilm investigations, focusing on traditional and innovative scanning electron microscopy techniques such as scanning electron microscopy (SEM), variable pressure SEM (VP-SEM), environmental SEM (ESEM), and the more recent ambiental SEM (ASEM), ending with the cutting edge Cryo-SEM and focused ion beam SEM (FIB SEM), highlighting the pros and cons of several methods with particular emphasis on conventional SEM and VP-SEM. As each technique has its own advantages and disadvantages, the choice of the most appropriate method must be done carefully, based on the specific aim of the study. The evaluation of the drug effects on biofilm requires imaging methods that show the most detailed ultrastructural features of the biofilm. In this kind of research, the use of scanning electron microscopy with customized protocols such as osmium tetroxide (OsO₄), ruthenium red (RR), tannic acid (TA) staining, and ionic liquid (IL) treatment is unrivalled for its image quality, magnification, resolution, minimal sample loss, and actual sample structure preservation. The combined use of innovative SEM protocols and 3-D image analysis software will allow for quantitative data from SEM images to be extracted; in this way, data from images of samples that have undergone different antibiofilm treatments can be compared.