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Bioactive peptides are short amino acid chains possessing biological activity and exerting physiological effects relevant to human health. Despite their therapeutic value, their identification remains a major problem, as it mainly relies on time-consuming in vitro tests. While bioinformatic tools for the identification of bioactive peptides are available, they are focused on specific functional classes and have not been systematically tested on realistic settings. To tackle this problem, bioactive peptide sequences and functions were here gathered from a variety of databases to generate a unified collection of bioactive peptides from microbial fermentation. This collection was organized into nine functional classes including some previously studied and some unexplored such as immunomodulatory, opioid and cardiovascular peptides. Upon assessing their sequence properties, four alternative encoding methods were tested in combination with a multitude of machine learning algorithms, from basic classifiers like logistic regression to advanced algorithms like BERT. Tests on a total of 171 models showed that, while some functions are intrinsically easier to detect, no single combination of classifiers and encoders worked universally well for all classes. For this reason, we unified all the best individual models for each class and generated CICERON (Classification of bIoaCtive pEptides fRom micrObial fermeNtation), a classification tool for the functional classification of peptides. State-of-the-art classifiers were found to underperform on our realistic benchmark dataset compared to the models included in CICERON. Altogether, our work provides a tool for real-world peptide classification and can serve as a benchmark for future model development.

BackgroundColorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC.ResultsThe proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state.DiscussionThese findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies.

The presented study protocol outlines a comprehensive investigation into the interplay among the human microbiota, volatilome, and disease biomarkers, with a specific focus on Behçet’s disease (BD) using methods based on explainable artificial intelligence. The protocol is structured in three phases. During the initial three-month clinical study, participants will be divided into control and experimental groups. The experimental groups will receive a soluble fiber-based dietary supplement alongside standard therapy. Data collection will encompass oral and fecal microbiota, breath samples, clinical characteristics, laboratory parameters, and dietary habits. The subsequent biological data analysis will involve gas chromatography, mass spectrometry, and metagenetic analysis to examine the volatilome and microbiota composition of salivary and fecal samples. Additionally, chemical characterization of breath samples will be performed. The third phase introduces Explainable Artificial Intelligence (XAI) for the analysis of the collected data. This novel approach aims to evaluate eubiosis and dysbiosis conditions, identify markers associated with BD, dietary habits, and the supplement. Primary objectives include establishing correlations between microbiota, volatilome, phenotypic BD characteristics, and identifying patient groups with shared features. The study aims to identify taxonomic units and metabolic markers predicting clinical outcomes, assess the supplement’s impact, and investigate the relationship between dietary habits and patient outcomes. This protocol contributes to understanding the microbiome’s role in health and disease and pioneers an XAI-driven approach for personalized BD management. With 70 recruited BD patients, XAI algorithms will analyze multi-modal clinical data, potentially revolutionizing BD management and paving the way for improved patient outcomes.

This study explored the impact of starter-assisted fermentation on apple blossoms to enhance their potential as a source of antioxidant and antifungal molecules. Fructobacillus fructosus PL22 and Wickerhamomyces anomalus GY1 were chosen as starters owing to their origin and promising ability to modify plant secondary metabolites. An initial assessment through microbiological and physicochemical analyses showed superior outcomes for starter-assisted fermentation compared to the spontaneous process. Enzymatic hydrolysis of proteins, primarily controlled by starters, orchestrated the generation of new low-molecular-weight peptides. W. anomalus GY1 also induced modifications in the phenolic profile, generating a diverse array of bioactive metabolites. These metabolic changes, particularly the release of potentially bioactive peptides, were associated with significant antioxidant activity and marked antifungal efficacy against three common mold species. Our results shed light on the potential of microbial starters to valorize agricultural wastes and convert them into a valuable resource for industry.

This review examines the techno-functional properties of lactic acid bacteria (LABs) in the food industry, focusing on their potential health benefits. We discuss current findings related to the techno-functionality of LAB, which includes acidification, proteolytic and lipolytic features, and a variety of other biochemical activities. These activities include the production of antimicrobial compounds and the synthesis of exopolysaccharides that improve food safety and consumer sensory experience. LABs are also known for their antioxidant abilities, which help reduce oxidative reactions in foods and improve their functional properties. In addition, LABs’ role as probiotics is known for their promising effects on gut health, immune system modulation, cholesterol control, and general wellbeing. Despite these advantages, several challenges hinder the effective production and use of probiotic LABs, such as maintaining strain viability during storage and transport as well as ensuring their efficacy in the gastrointestinal tract. Our review identifies these critical barriers and suggests avenues for future research.

Extraction-site alveolar remodeling is a major concern due to negative volumetric hard and soft tissue changes that inevitably limit rehabilitation options and diminish esthetic outcomes. Surgical techniques employed to minimize alveolar dimensional changes are not always predictable. Utilizing a socket shield with an immediate surgical implant procedure helps maintain a thin portion of the root in the vestibular area and thus minimizes bone resorption, especially at the coronal cortical aspect. This case series assesses the dimensional changes in peri-alveolar structures via superimposition of the preoperative and 6-month postoperative 3D digital quantification of soft tissue. Fifty patients with 50 sites fulfilled the inclusion criteria. Implant survival was 100%, with no incidence of complications. Tissue changes were as follows: -0.85 mm at the mesial papilla, -0.95 mm at the distal papilla, -0.7 mm at both the vertical and horizontal central margins, -0.21 mm at a distance 4 mm from the margin, and -0.64 mm at the palatal central margin. The buccal contour was clinically convex in all cases. The site with highest frequency of > 1 mm of dimensional loss was the distal papilla (42% of sites), and the site with the lowest frequency was the point 4 mm from the midfacial margin (0% of sites). [ABSTRACT FROM AUTHOR]

Abstract This Position Statement updates the different components of the therapy of obesity (lifestyle intervention, drugs, and surgery) in children and adolescents, previously reported in the consensus position statement on pediatric obesity of the Italian Society of Pediatric Endocrinology and Diabetology and the Italian Society of Pediatrics. Lifestyle intervention is the first step of treatment. In children older than 12 years, pharmacotherapy is the second step, and bariatric surgery is the third one, in selected cases. Novelties are available in the field of the medical treatment of obesity. In particular, new drugs demonstrated their efficacy and safety and have been approved in adolescents. Moreover, several randomized control trials with other drugs are in process and it is likely that some of them will become available in the future. The increase of the portfolio of treatment options for obesity in children and adolescents is promising for a more effective treatment of this disorder.

Excessive sugar consumption in young people, who are the major consumers of sugary drinks, combined with limited physical activity, is an important determinant of obesity. Despite their natural appeal, fruit juices have a similar sugar content to that of sugary drinks and once metabolized, they may induce the same biological response. This study aimed to verify whether fermentation processes can make juice consumption healthier and whether reduced-sugar juices have a specific impact on intestinal function. We designed a tailored fermentation of apple-pear juices with lactic acid bacteria and yeasts, which resulted in a reduction of sugar content (27–66%) and caloric intake, and an increase in mannitol content. The impact of newly developed apple-pear juices on gut microbiome composition and functionality was evaluated in vitro using the Simulator of the Human Intestinal Microbial Ecosystem (SHIME). Promising changes were found in the gut microbiota and its metabolic responses and functionality, targeting pathways related to obesity and weight loss (lipopolysaccharide and secondary metabolite biosynthesis, polycyclic aromatic hydrocarbon degradation, and amino sugar and nucleotide sugar metabolism). Additionally, the fermented apple-pear juices positively modulated the intestinal epithelial features. While the simulation of the study simplifies the complex in vivo conditions, it suggests that low-sugar fermented apple-pear juices can elicit targeted responses in the gut ecosystem, contributing to healthier alternatives to traditional fruit juices.

The production of biopeptides from food waste through microbial fermentation faces challenges arising from the diverse proteolytic abilities of microorganisms and substrate variability, impacting both the quality and yield of generated biopeptides. To address these challenges, preliminary in-silico bioinformatics analyses play a crucial role in evaluating suitable substrates and proteases for the fermentation process. However, existing tools lack comprehensive predictive capabilities for relevant proteases, substrate performance assessment, and final biopeptide family characterization. To overcome these limitations, we developed FEEDS (Food wastE biopEptiDe claSsifier), a novel biopeptide prediction and classification tool. FEEDS predicts biopeptide families based on microbial genome protease profiles and substrate composition during proteolysis. The tool also employs a machine learning approach for functional biopeptide classification. Results from testing on 1000 microbial genomes demonstrate the effectiveness of biopeptide classification, particularly in categorizing peptides derived from substrates like Hordeum vulgare and Vitis vinifera seed storage proteins. In addition to biopeptide classification, our study delves into the distinctive protease profiles of bacteria and yeast genomes. Bacterial genomes exhibited 60 to 100 proteases across 40–55 families. Contrastingly, yeast genomes displayed a more evenly distributed pattern with 150 to 160 protease-encoding genes across 60 to 67 families, surpassing bacterial counts. Remarkably, a substantial portion of yeast proteases (∼66 %) was secreted. Moreover, our integration of a machine learning methodology within the FEEDS pipeline proved highly effective, achieving over 80 % accuracy in predicting the function of peptides derived from seed storage proteins. Notably, longer peptide sequences exceeding 20 amino acids consistently displayed a higher probability of correct assignment compared to shorter counterparts.

Abstract In the current trend where plant‐based foods are preferred over animal‐based foods, pulses represent an alternative source of protein but also of bioactive peptides (BPs). We investigated the pattern of protein hydrolysis during fermentation of red lentils protein isolate (RLPI) with various lactic acid bacteria and yeast strains. Hanseniaspora uvarum SY1 and Fructilactobacillus sanfranciscensis E10 were the most proteolytic microorganisms. H. uvarum SY1 led to the highest antiradical, angiotensin‐converting enzyme‐inhibitory and antifungal activities, as found in low molecular weight water soluble extracts (LMW‐WSE). The 2039 peptide sequences identified by LMW‐WSE were screened using BIOPEP UWM database, and 36 sequences matched with known BPs. Fermentation of RLPI by lactic acid bacteria and yeasts generated 12 peptides undetected in raw RLPI. Besides, H. uvarum SY1 led to the highest abundance (peak areas) of BPs, in particular with antioxidant and ACE‐inhibitory activities. The amino acid sequences LVR and LVL, identified in the fermented RLPI, represent novel findings, as they were detected for the first time in substrates subjected to microbial fermentation. KVI, another BP highly characteristic of RLPI‐SY1, was previously observed only in dried bonito. 44 novel potential BPs, worthy of further characterization, were correlated with antifungal activity.

Abstract Background The goal of this study was to create a multi-strain probiotic gel that would foster a lactobacilli-dominated vaginal microbiota in pregnant women and ensure appropriate eubiosis for the newborn. Nomadic lactobacilli (95 strains), mostly isolated from food sources, were preliminarily screened for functional traits before being characterized for their capability to inhibit the two vaginal pathogens Streptococcus agalactiae and Candida albicans, which may lead to adverse pregnancy-related outcomes. Eight best-performing strains were chosen and furtherly investigated for their ability to produce biofilm. Lastly, the two selected potential probiotic candidates were analyzed in vitro for their ability to reduce the inflammation caused by C. albicans infection on the reconstituted human vaginal epithelium (HVE). Results Lactiplantibacillus plantarum produced both isomers of lactic acid, while Lacticaseibacillus paracasei produced only l-isomer. The production of hydrogen peroxide was strain-dependent, with the highest concentrations found within Lact. paracasei strains. The auto-aggregation capacity and hydrophobicity traits were species-independent. S. agalactiae 88II3 was strongly inhibited both at pH 7.0 and 4.0, whereas the inhibition of C. albicans UNIBZ54 was less frequent. Overall, L. plantarum strains had the highest pathogen inhibition and functional scoring. L. plantarum C5 and POM1, which were selected as potential probiotic candidates also based on their ability to form biofilms, were able to counteract the inflammation process caused by C. albicans infection in the HVE model. Conclusions Our multi-step and cumulative scoring-based approach was proven successful in mining and highlighting the probiotic potential of two nomadic lactobacilli strains (L. plantarum C5 and POM1), being applicable to preserve and improve human vaginal health.

Different types of milk are used in the production of milk kefir, but little information is available on the release of potentially antioxidant exopolysaccharides (EPS). The aim of this study was to investigate whether the microbial dynamics and EPS release are dependent on the milk substrate. In our study, the inoculated microbial consortium was driven differently by each type of milk (cow, ewe, and goat). This was evident in the sugar consumption, organic acid production, free amino release, and EPS production. The amount and the composition of the secreted EPS varied depending on the milk type, with implications for the structure and functional properties of the EPS. The low EPS yield in ewe’s milk was associated with a higher lactic acid production and thus with the use of carbon sources oriented towards energy production. Depending on the milk used as substrate, the EPS showed different monosaccharide and FT-IR profiles, microstructures, and surface morphologies. These differences affected the antiradical properties and reducing power of the EPS. In particular, EPS extracted from cow’s milk had a higher antioxidant activity than other milk types, and the antioxidant activity was concentration dependent.

Increasing awareness of the problems caused by synthetic agrochemicals, such as chemical fertilizers, pesticides, and herbicides, makes it crucial to discover substitute approaches that can guarantee competitive plant production and protect the environment while maintaining the natural balance in agroecosystems. One of the leading alternatives is utilizing rhizobacterial strains named plant growth-promoting rhizobacteria (PGPR). The utilization of PGPR-based biofertilizers for advancement in the sustainability of farming productions has received considerable critical attention all over the world because of their contribution to not only improving plant growth but also inducing biotic and abiotic stress tolerance. This review updates the aforementioned eco-friendly strategy in sustainable agroecosystems and provides new insights into the phytostimulation and bioprotection ability of lactic acid bacteria (LAB), an emerging taxon of PGPR. In this regard, the ability of LAB to synthesize metabolites, including organic acids, phenolic acids and their flavonoid derivatives, phytohormones, and antimicrobial substrates, is presented. The use of LAB provides a bridge between PGPR and environmentally friendly crop productivity, which can lead to sustainable production systems by reducing the use of agrochemicals, improving soil quality, and minimizing environmental pollution. All the beneficial aspects of LAB need to be addressed by future research to plan systematic methodologies for their use and/or to combine the use of PGPR along with other organic or inorganic inputs in sustainable production systems.

Summary The alteration of a eubiosis status in honeybees’ gut microbiota is directly linked to the occurrence of diseases, and likely to the honeybees decline. Since fructophilic lactobacilli were suggested as symbionts for honeybees, we mechanistically investigated their behaviour under the exposure to agrochemicals (Roundup, Mediator and Reldan containing glyphosate, imidacloprid and chlorpyrifos‐methyl as active ingredients respectively) and plant secondary metabolites (nicotine and p‐coumaric acid) ingested by honeybees as part of their diet. The effects of exposure to agrochemicals and plant secondary metabolites were assessed both on planktonic cells and sessile communities of three biofilm‐forming strains of Apilactobacillus kunkeei. We identified the high sensitivity of A. kunkeei planktonic cells to Roundup and Reldan, while cells embedded in mature biofilms had increased resistance to the same agrochemicals. However, agrochemicals still exerted a substantial inhibitory/control effect if the exposure was during the preliminary steps of biofilm formation. The level of susceptibility resulted to be strain‐specific. Exopolysaccharides resulted in the main component of extracellular polymeric matrix (ECM) in biofilm, but the exposure to Roundup caused a change in ECM production and composition. Nicotine and p‐coumaric acid had a growth‐promoting effect in sessile communities, although no effect was found on planktonic growth.

Cell-to-cell communication is essential for the appropriate development and maintenance of homeostatic conditions in the central nervous system. Extracellular vesicles have recently come to the forefront of neuroscience as novel vehicles for the transfer of complex signals between neuronal cells. Extracellular vesicles are membrane-bound carriers packed with proteins, metabolites, and nucleic acids (including DNA, mRNA, and microRNAs) that contain the elements present in the cell they originate from. Since their discovery, extracellular vesicles have been studied extensively and have opened up new understanding of cell–cell communication; they may cross the blood–brain barrier in a bidirectional way from the bloodstream to the brain parenchyma and vice versa, and play a key role in brain–periphery communication in physiology as well as pathology. Neurons and glial cells in the central nervous system release extracellular vesicles to the interstitial fluid of the brain and spinal cord parenchyma. Extracellular vesicles contain proteins, nucleic acids, lipids, carbohydrates, and primary and secondary metabolites. that can be taken up by and modulate the behaviour of neighbouring recipient cells. The functions of extracellular vesicles have been extensively studied in the context of neurodegenerative diseases. The purpose of this review is to analyse the role extracellular vesicles extracellular vesicles in central nervous system cell communication, with particular emphasis on the contribution of extracellular vesicles from different central nervous system cell types in maintaining or altering central nervous system homeostasis.

Summary We proposed a novel phenomic approach to track the effect of short‐term exposures of Lactiplantibacillus plantarum and Leuconostoc pseudomesenteroides to environmental pressure induced by brewers' spent grain (BSG)‐derived saccharides. Water‐soluble BSG‐based medium (WS‐BSG) was chosen as model system. The environmental pressure exerted by WS‐BSG shifted the phenotypes of bacteria in species‐ and strains‐dependent way. The metabolic drift was growth phase‐dependent and likely underlay the diauxic profile of organic acids production by bacteria in response to the low availability of energy sources. Among pentosans, metabolism of arabinose was preferred by L. plantarum and xylose by Leuc. pseudomesenteroides as confirmed by the overexpression of related genes. Bayesian variance analysis showed that phenotype switching towards galactose metabolism suffered the greatest fluctuation in L. plantarum. All lactic acid bacteria strains utilized more intensively sucrose and its plant‐derived isomers. Sucrose‐6‐phosphate activity in Leuc. pseudomesenteroides likely mediated the increased consumption of raffinose. The increased levels of some phenolic compounds suggested the involvement of 6‐phospho‐β‐glucosidases in β‐glucosides degradation. Expression of genes encoding β‐glucoside/cellobiose‐specific EII complexes and phenotyping highlighted an increased metabolism for cellobiose. Our reconstructed metabolic network will improve the understanding of how lactic acid bacteria may transform BSG into suitable food ingredients.

Introduction: Patients with Brugada syndrome (BrS) face an increased risk of ventricular arrhythmias and sudden cardiac death. Implantable cardiac monitors (ICMs) have emerged as effective tools for detecting arrhythmias in BrS. Technological advancements, including temperature sensors and improved subcutaneous electrocardiogram (subECG) signal quality, hold promise for further enhancing their utility in this population. Methods and results: We present a case of a 40‐year‐old man exhibiting a BrS type 2 pattern on 12‐lead ECG, who underwent ICM insertion (BIOMONITOR IIIm, BIOTRONIK) due to drug‐induced BrS type 1 pattern and a history of syncope, with a negative response to programmed ventricular stimulation. The device contains an integrated temperature sensor and can transmit daily vital data, such as mean heart rate and physical activity. Several months later, remote alerts indicated a temperature increase, along with transmitted subECGs suggesting a fever‐induced BrS type 1 pattern. The patient was promptly advised to commence antipyretic therapy. Over the following days, remotely monitored parameters showed decreases in mean temperature, physical activity, and mean heart rate, without further recurrence of abnormal subECGs. Conclusion: ICMs offer valuable insights beyond arrhythmia detection in BrS. Early detection of fever using embedded temperature sensors may improve patient management, while continuous subECG morphological analysis has the potential to enhance risk stratification in BrS patients. [ABSTRACT FROM AUTHOR]

The human gut microbiota is a complex ecosystem of mutualistic microorganisms that play a critical role in maintaining human health through their individual interactions and with the host. The normal gastrointestinal microbiota plays a specific physiological function in host immunomodulation, nutrient metabolism, vitamin synthesis, xenobiotic and drug metabolism, maintenance of structural and functional integrity of the gut mucosal barrier, and protection against various pathogens. Inflammation is the innate immune response of living tissues to injury and damage caused by infections, physical and chemical trauma, immunological factors, and genetic derangements. Most diseases are associated with an underlying inflammatory process, with inflammation mediated through the contribution of active immune cells. Current strategies to control inflammatory pathways include pharmaceutical drugs, lifestyle, and dietary changes. However, this remains insufficient. Bioactive compounds (BCs) are nutritional constituents found in small quantities in food and plant extracts that provide numerous health benefits beyond their nutritional value. BCs are known for their antioxidant, antimicrobial, anticarcinogenic, anti-metabolic syndrome, and anti-inflammatory properties. Bioactive compounds have been shown to reduce the destructive effect of inflammation on tissues by inhibiting or modulating the effects of inflammatory mediators, offering hope for patients suffering from chronic inflammatory disorders like atherosclerosis, arthritis, inflammatory bowel diseases, and neurodegenerative diseases. The aim of the present review is to summarise the role of natural bioactive compounds in modulating inflammation and protecting human health, for their safety to preserve gut microbiota and improve their physiology and behaviour.

ABSTRACT This study provided a new perspective on the bacterial community succession during sauerkraut fermentation, and on resulting metabolic functions. While culture-dependent methods confirmed the key role of the well-known core microbiome species, metagenomic approach (shotgun) revealed Secundilactobacillus malefermentans as a species of the core microbiome, especially during the last weeks of fermentation. Although the potentiality of S. malefermentans has not yet fully explored, it held core functional genes usually attributed to others lactic acid bacteria driving sauerkraut fermentation. Based on our results it is arguable that S. malefermentans might have a key a role during sauerkraut fermentation carried out at low temperature. Under our experimental conditions, the profile of phenolic compounds changed throughout sauerkraut fermentation. The amount of free phenolics, including free phenolic acids, increased at the beginning of the fermentation, whereas the conversion of phenolic acids into microbial derivatives was consistent during the last part of the sauerkraut fermentation. We pioneered correlating changes in the phenolics profile to changes in the microbiome, although the framework presented is still fragmentary. Annotated genes linked to the phenolic compounds metabolism (VprA and padA) were found in many core species during the whole process. A high metabolic potential for phenolics bioconversion emerged for lactobacilli and Pediococcus spp. through correlation analysis between microbiome composition and phenolics profile. IMPORTANCE Our study was not limited to describe the succession pattern of the microbial community during sauerkraut fermentation, but also revealed how some neglected bacterial players belong to the core species during sauerkrauts processing, especially at low temperature. Such species might have a role as potential starters to optimize the fermentation processes and to obtain sauerkrauts with improved and standardized nutritional and sensory features. Furthermore, our correlations between microbiome composition and phenolics profile might also represent new references for sauerkraut biotechnology, aiming to identify new metabolic drivers of potential sauerkraut functionalities. Finally, sauerkraut ecosystem is a tractable model, although with high level of complexity, and resultant ecological information might be extended to other plant ecosystems.