Optimization of probiotic therapeutics using machine learning in an artificial human gastrointestinal tract

Advancements in Lactiplantibacillus plantarum: probiotic characteristics, gene editing technologies and applications

The exploration of microorganisms in fermented products has become a pivotal area of scientific research, primarily due to their widespread availability and profound potential to improve human health. Among these, Lactiplantibacillus plantarum (formerly known as Lactobacillus plantarum) stands out as a versatile lactic acid bacterium, prevalent across diverse ecological niches. Its appeal extends beyond its well-documented probiotic benefits to include the remarkable plasticity of its genome, which has captivated both scientific and industrial stakeholders. Despite this interest, substantial challenges persist in fully understanding and harnessing the potential of L. plantarum. This review aims to illuminate the probiotic attributes of L. plantarum, consolidate current advancements in gene editing technologies, and explore the multifaceted applications of both wild-type and genetically engineered strains.

The role of the microbiota-gut-brain axis and artificial intelligence in cognitive health of pediatric obstructive sleep apnea: A narrative review

Pediatric obstructive sleep apnea (OSA) is a prevalent sleep-related breathing disorder associated with significant neurocognitive and behavioral impairments. Recent studies have highlighted the role of gut microbiota and the microbiota-gut-brain axis (MGBA) in influencing cognitive health in children with OSA. This narrative review aims to summarize current knowledge on the relationship between gut microbiota, MGBA, and cognitive function in pediatric OSA. It also explores the potential of artificial intelligence and machine learning in advancing this field and identifying novel therapeutic strategies. Pediatric OSA is associated with gut dysbiosis, reduced microbial diversity, and metabolic disruptions. MGBA mechanisms, such as endocrine, immune, and neural pathways, link gut microbiota to cognitive outcomes. Artificial intelligence and machine learning methodologies offer promising tools to uncover microbial markers and mechanisms associated with cognitive deficits in OSA. Future research should focus on validating these findings through clinical trials and developing personalized therapeutic approaches targeting the gut microbiota.

Machine learning-enhanced assessment of potential probiotics from healthy calves for the treatment of neonatal calf diarrhea

Neonatal calf diarrhea (NCD) remains a significant contributor to calf mortality within the first 3 weeks of life, prompting widespread antibiotic use with associated concerns about antimicrobial resistance and disruption of the calf gut microbiota. Recent research exploring NCD treatments targeting gut microbiota dysbiosis has highlighted probiotic supplementation as a promising and safe strategy for gut homeostasis. However, varying treatment outcomes across studies suggest the need for efficient treatment options. In this study, we evaluated the potential of probiotics Limosilactobacillus reuteri, formally known as Lactobacillus reuteri, isolated from healthy neonatal calves to treat NCD. Through in silico whole genome analysis and in vitro assays, we identified nine L. reuteri strains, which were then administered to calves with NCD. Calves treated with L. reuteri strains shed healthy feces and demonstrated restored gut microbiota and normal animal behavior. Leveraging a machine learning model, we evaluated microbiota profiles and identified bacterial taxa associated with calf gut health that were elevated by L. reuteri administration. These findings represent a crucial advancement towards sustainable antibiotic alternatives for managing NCD, contributing significantly to global efforts in mitigating antimicrobial resistance and promoting overall animal health and welfare.

Predicting the immunomodulatory activity of probiotic lactic acid bacteria using supervised machine learning in a Cornu aspersum snail model

In the process of screening for probiotic strains, there are no clearly established bacterial phenotypic markers which could be used for the prediction of their in vivo mechanism of action. In this work, we demonstrate for the first time that Machine Learning (ML) methods can be used for accurately predicting the in vivo immunomodulatory activity of probiotic strains based on their cell surface phenotypic features using a snail host-microbe interaction model. A broad range of snail gut presumptive probiotics, including 240 new lactic acid bacterial strains (Lactobacillus, Leuconostoc, Lactococcus, and Enterococcus), were isolated and characterized based on their capacity to withstand snails' gastrointestinal defense barriers, such as the pedal mucus, gastric mucus, gastric juices, and acidic pH, in association with their cell surface hydrophobicity, autoaggregation, and biofilm formation ability. The implemented ML pipeline predicted with high accuracy (88 %) strains with a strong capacity to enhance chemotaxis and phagocytic activity of snails' hemolymph cells, while also revealed bacterial autoaggregation and cell surface hydrophobicity as the most important parameters that significantly affect host immune responses. The results show that ML approaches may be useful to derive a predictive understanding of host-probiotic interactions, while also highlighted the use of snails as an efficient animal model for screening presumptive probiotic strains in the light of their interaction with cellular innate immune responses.

Neuroinflammation and Schizophrenia: New Therapeutic Strategies through Psychobiotics, Nanotechnology, and Artificial Intelligence (AI)

The prevalence of schizophrenia, affecting approximately 1% of the global population, underscores the urgency for innovative therapeutic strategies. Recent insights into the role of neuroinflammation, the gut-brain axis, and the microbiota in schizophrenia pathogenesis have paved the way for the exploration of psychobiotics as a novel treatment avenue. These interventions, targeting the gut microbiome, offer a promising approach to ameliorating psychiatric symptoms. Furthermore, advancements in artificial intelligence and nanotechnology are set to revolutionize psychobiotic development and application, promising to enhance their production, precision, and effectiveness. This interdisciplinary approach heralds a new era in schizophrenia management, potentially transforming patient outcomes and offering a beacon of hope for those afflicted by this complex disorder.

Characterization of the Effects of a Novel Probiotic on Salmonella Colonization of a Piglet-Derived Intestinal Microbiota Using Improved Bioreactor

The carriage of Salmonella in pigs is a major concern for the agri-food industry and for global healthcare systems. Humans could develop salmonellosis when consuming contaminated pig products. On the other hand, some Salmonella serotypes could cause disease in swine, leading to economic losses on farms. The purpose of the present study was to characterize the anti-Salmonella activity of a novel Bacillus-based probiotic using a bioreactor containing a piglet-derived intestinal microbiota. Two methods of probiotic administration were tested: a single daily and a continuous dose. Salmonella enumeration was performed using selective agar at T24h, T48h, T72h, T96h and T120h. The DNA was extracted from bioreactor samples to perform microbiome profiling by targeted 16S rRNA gene sequencing on Illumina Miseq. The quantification of short-chain fatty acids (SCFAs) was also assessed at T120h. The probiotic decreased Salmonella counts at T96 for the daily dose and at T120 for the continuous one. Both probiotic doses affected the alpha and beta diversity of the piglet-derived microbiota (p < 0.05). A decrease in acetate concentration and an increase in propionate proportion were observed in the continuous condition. In conclusion, the tested Bacillus-based product showed a potential to modulate microbiota and reduce Salmonella colonization in a piglet-derived intestinal microbiota and could therefore be used in vivo.

The Potential of Meta-Proteomics and Artificial Intelligence to Establish the Next Generation of Probiotics for Personalized Healthcare

The symbiosis of probiotic bacteria with humans has rendered various health benefits while providing nutrition and a suitable environment for their survival. However, the probiotics must survive unfavorable gut conditions to exert beneficial effects. The intrinsic resistance of probiotics to survive harsh conditions results from a myriad of proteins. Interaction of microbial proteins with the host is indispensable for modulating the gut microbiome, such as interaction with cell receptors and protective action against pathogens. The complex interplay of proteins should be unraveled by utilizing metaproteomic strategies. The contribution of probiotics to health is now widely accepted. However, due to the inconsistency of generalized probiotics, contemporary research toward precision probiotics has gained momentum for customized treatment. This review explores the application of metaproteomics and AI/ML algorithms in resolving multiomics data analysis and in silico prediction of microbial features for screening specific beneficial probiotic organisms. Implementing these integrative strategies could augment the potential of precision probiotics for personalized healthcare.

SARS-CoV-2 infection threatening intestinal health: A review of potential mechanisms and treatment strategies

The outbreak of the COVID-19 pandemic has brought great problems to mankind, including economic recession and poor health. COVID-19 patients are frequently reported with gastrointestinal symptoms such as diarrhea and vomiting in clinical diagnosis. Maintaining intestinal health is the key guarantee to maintain the normal function of multiple organs, otherwise it will be a disaster. Therefore, the purpose of this review was deeply understanded the potential mechanism of SARS-CoV-2 infection threatening intestinal health and put forward reasonable treatment strategies. Combined with the existing researches, we summarized the mechanism of SARS-CoV-2 infection threatening intestinal health, including intestinal microbiome disruption, intestinal barrier dysfunction, intestinal oxidative stress and intestinal cytokine storm. These adverse intestinal events may affect other organs through the circulatory system or aggravate the course of the disease. Typically, intestinal disadvantage may promote the progression of SARS-CoV-2 through the gut-lung axis and increase the disease degree of COVID-19 patients. In view of the lack of specific drugs to inhibit SARS-CoV-2 replication, the current review described new strategies of probiotics, prebiotics, postbiotics and nutrients to combat SARS-CoV-2 infection and maintain intestinal health. To provide new insights for the prevention and treatment of gastrointestinal symptoms and pneumonia in patients with COVID-19.

'Multi-omics' data integration: applications in probiotics studies

The concept of probiotics is witnessing increasing attention due to its benefits in influencing the host microbiome and the modulation of host immunity through the strengthening of the gut barrier and stimulation of antibodies. These benefits, combined with the need for improved nutraceuticals, have resulted in the extensive characterization of probiotics leading to an outburst of data generated using several 'omics' technologies. The recent development in system biology approaches to microbial science is paving the way for integrating data generated from different omics techniques for understanding the flow of molecular information from one 'omics' level to the other with clear information on regulatory features and phenotypes. The limitations and tendencies of a 'single omics' application to ignore the influence of other molecular processes justify the need for 'multi-omics' application in probiotics selections and understanding its action on the host. Different omics techniques, including genomics, transcriptomics, proteomics, metabolomics and lipidomics, used for studying probiotics and their influence on the host and the microbiome are discussed in this review. Furthermore, the rationale for 'multi-omics' and multi-omics data integration platforms supporting probiotics and microbiome analyses was also elucidated. This review showed that multi-omics application is useful in selecting probiotics and understanding their functions on the host microbiome. Hence, recommend a multi-omics approach for holistically understanding probiotics and the microbiome.

A Microbial-Based Approach to Mental Health: The Potential of Probiotics in the Treatment of Depression

Probiotics are currently the subject of intensive research pursuits and also represent a multi-billion-dollar global industry given their vast potential to improve human health. In addition, mental health represents a key domain of healthcare, which currently has limited, adverse-effect prone treatment options, and probiotics may hold the potential to be a novel, customizable treatment for depression. Clinical depression is a common, potentially debilitating condition that may be amenable to a precision psychiatry-based approach utilizing probiotics. Although our understanding has not yet reached a sufficient level, this could be a therapeutic approach that can be tailored for specific individuals with their own unique set of characteristics and health issues. Scientifically, the use of probiotics as a treatment for depression has a valid basis rooted in the microbiota-gut-brain axis (MGBA) mechanisms, which play a role in the pathophysiology of depression. In theory, probiotics appear to be ideal as adjunct therapeutics for major depressive disorder (MDD) and as stand-alone therapeutics for mild MDD and may potentially revolutionize the treatment of depressive disorders. Although there is a wide range of probiotics and an almost limitless range of therapeutic combinations, this review aims to narrow the focus to the most widely commercialized and studied strains, namely Lactobacillus and Bifidobacterium, and to bring together the arguments for their usage in patients with major depressive disorder (MDD). Clinicians, scientists, and industrialists are critical stakeholders in exploring this groundbreaking concept.

Co-encapsulation of Lactobacillus plantarum and bioactive compounds extracted from red beet stem (Beta vulgaris L.) by spray dryer

Probiotic bacteria and bioactive compounds obtained from plant origin stand out as ingredients with the potential to increase the healthiness of functional foods, as there is currently a recurrent search for them. Probiotics and bioactive compounds are sensitive to intrinsic and extrinsic factors in the processing and packaging of the finished product. In this sense, the present study aims to evaluate the co-encapsulation by spray dryer (inlet air temperature 120 °C, air flow 40 L / min, pressure of 0.6 MPa and 1.5 mm nozzle diameter) of probiotic bacteria (L.plantarum) and compounds extracted from red beet stems (betalains) in order to verify the interaction between both and achieve better viability and resistance of the encapsulated material. When studying the co-encapsulation of L.plantarum and betalains extracted from beet stems, an unexpected influence was observed with a decrease in probiotic viability in the highest concentration of extract (100 %), on the other hand, the concentration of 50 % was the best enabled and maintained the survival of L.plantarum in conditions of 25 °C (63.06 %), 8 °C (88.80 %) and -18 °C (89.28 %). The viability of the betalains and the probiotic was better preserved in storage at 8 and -18 °C, where the encapsulated stability for 120 days was successfully achieved. Thus, the polyfunctional formulation developed in this study proved to be promising, as it expands the possibilities of application and development of new foods.

Human Health during Space Travel: State-of-the-Art Review

The field of human space travel is in the midst of a dramatic revolution. Upcoming missions are looking to push the boundaries of space travel, with plans to travel for longer distances and durations than ever before. Both the National Aeronautics and Space Administration (NASA) and several commercial space companies (e.g., Blue Origin, SpaceX, Virgin Galactic) have already started the process of preparing for long-distance, long-duration space exploration and currently plan to explore inner solar planets (e.g., Mars) by the 2030s. With the emergence of space tourism, space travel has materialized as a potential new, exciting frontier of business, hospitality, medicine, and technology in the coming years. However, current evidence regarding human health in space is very limited, particularly pertaining to short-term and long-term space travel. This review synthesizes developments across the continuum of space health including prior studies and unpublished data from NASA related to each individual organ system, and medical screening prior to space travel. We categorized the extraterrestrial environment into exogenous (e.g., space radiation and microgravity) and endogenous processes (e.g., alteration of humans' natural circadian rhythm and mental health due to confinement, isolation, immobilization, and lack of social interaction) and their various effects on human health. The aim of this review is to explore the potential health challenges associated with space travel and how they may be overcome in order to enable new paradigms for space health, as well as the use of emerging Artificial Intelligence based (AI) technology to propel future space health research.

Lactic Acid Bacteria Mixture Isolated From Wild Pig Alleviated the Gut Inflammation of Mice Challenged by Escherichia coli

Wild pigs usually showed high tolerance and resistance to several diseases in the wild environment, suggesting that the gut bacteria of wild pigs could be a good source for discovering potential probiotic strains. In our study, wild pig feces were sequenced and showed a higher relative abundance of the genus Lactobacillus (43.61% vs. 2.01%) than that in the domestic pig. A total of 11 lactic acid bacteria (LAB) strains including two L. rhamnosus, six L. mucosae, one L. fermentum, one L. delbrueckii, and one Enterococcus faecalis species were isolated. To investigate the synergistic effects of mixed probiotics strains, the mixture of 11 LAB strains from an intestinal ecology system was orally administrated in mice for 3 weeks, then the mice were challenged with Escherichia coli ATCC 25922 (2 × 10⁹ CFU) and euthanized after challenge. Mice administrated with LAB strains showed higher (p < 0.05) LAB counts in feces and ileum. Moreover, alterations of specific bacterial genera occurred, including the higher (p < 0.05) relative abundance of Butyricicoccus and Clostridium IV and the lower (p < 0.05) abundance of Enterorhabdus in mice fed with mixed LAB strains. Mice challenged with Escherichia coli showed vacuolization of the liver, lower GSH in serum, and lower villus to the crypt proportion and Claudin-3 level in the gut. In contrast, administration of mixed LAB strains attenuated inflammation of the liver and gut, especially the lowered IL-6 and IL-1β levels (p < 0.05) in the gut. Our study highlighted the importance of gut bacterial diversity and the immunomodulation effects of LAB strains mixture from wild pig in gut health.

Active Machine learning for formulation of precision probiotics

It is becoming clear that the human gut microbiome is critical to health and well-being, with increasing evidence demonstrating that dysbiosis can promote disease. Increasingly, precision probiotics are being investigated as investigational drug products for restoration of healthy microbiome balance. To reach the distal gut alive where the density of microbiota is highest, oral probiotics should be protected from harsh conditions during transit through the stomach and small intestines. At present, few probiotic formulations are designed with this delivery strategy in mind. This study employs an emerging machine learning (ML) technique, known as active ML, to predict how excipients at pharmaceutically relevant concentrations affect the intestinal proliferation of a common probiotic, Lactobacillus paracasei. Starting with a labelled dataset of just 6 bacteria-excipient interactions, active ML was able to predict the effects of a further 111 excipients using uncertainty sampling. The average certainty of the final model was 67.70% and experimental validation demonstrated that 3/4 excipient-probiotic interactions could be correctly predicted. The model can be used to enable superior probiotic delivery to maximise proliferation in vivo and marks the first use of active ML in microbiome science.

Recent Advances in Research on Polyphenols: Effects on Microbiota, Metabolism, and Health

Polyphenols have attracted huge interest among researchers of various disciplines because of their numerous biological activities, such as antioxidative, antiinflammatory, antiapoptotic, cancer chemopreventive, anticarcinogenic, and antimicrobial properties, and their promising applications in many fields, mainly in the medical, cosmetics, dietary supplement and food industries. In this review, the latest scientific findings in the research on polyphenols interaction with the microbiome and mitochondria, their metabolism and health beneficial effects, their involvement in cognitive diseases and obesity development, as well as some innovations in their analysis, extraction methods, development of cosmetic formulations and functional food are summarized based on the papers presented at the 13th World Congress on Polyphenol Applications. Future implications of polyphenols in disease prevention and their strategic use as prophylactic measures are specifically addressed. Polyphenols may play a key role in our tomorrow´s food and nutrition to prevent many diseases.

Precision Postbiotics and Mental Health: the Management of Post-COVID-19 Complications

The health catastrophe originated by COVID-19 pandemic construed profound impact on a global scale. However, a plethora of research studies corroborated convincing evidence conferring severity of infection of SARS-CoV-2 with the aberrant gut microbiome that strongly speculated its importance for development of novel therapeutic modalities. The intense exploration of probiotics has been envisaged to promote the healthy growth of the host, and restore intestinal microecological balance through various metabolic and physiological processes. The demystifying effect of probiotics cannot be defied, but there exists a strong skepticism related to their safety and efficacy. Therefore, molecular signature of probiotics termed as "postbiotics" are of paramount importance and there is continuous surge of utilizing postbiotics for enhancing health benefits, but little is explicit about their antiviral effects. Therefore, it is worth considering their prospective role in post-COVID regime that pave the way for exploring the pastoral vistas of postbiotics. Based on previous research investigations, the present article advocates prospective role of postbiotics in alleviating the health burden of viral infections, especially SARS-CoV-2. The article also posits current challenges and proposes a futuristic model describing the concept of "precision postbiotics" for effective therapeutic and preventive interventions that can be used for management of this deadly disease.

Predicting drug-microbiome interactions with machine learning

Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is now clear that gastrointestinal microbiota have the metabolic power to promote, inactivate, or even toxify the efficacy of a drug to a level of clinically relevant significance. At the same time, it appears that drug intake has the propensity to alter gut microbiome composition, potentially affecting health and response to other drugs. Since the precise composition of an individual's microbiome is unique, one's drug-microbiome relationship is similarly unique. Thus, in the age of evermore personalised medicine, the ability to predict individuals' drug-microbiome interactions is highly sought. Machine learning (ML) offers a powerful toolkit capable of characterising and predicting drug-microbiota interactions at the individual patient level. ML techniques have the potential to learn the mechanisms operating drug-microbiome activities and measure patients' risk of such occurrences. This review will outline current knowledge at the drug-microbiota interface, and present ML as a technique for examining and forecasting personalised drug-microbiome interactions. When harnessed effectively, ML could alter how the pharmaceutical industry and healthcare professionals consider the drug-microbiome axis in patient care.

A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.