iProbiotics: a machine learning platform for rapid identification of probiotic properties from whole-genome primary sequences

Probiotic and anti-inflammatory properties of Lactiplantibacillus plantarum MKTJ24 isolated from an artisanal fermented fish of North-east India

The study aimed to isolate and characterize lactic acid bacteria from various traditional fermented fish products from North East India, including Xindol, Hentak, and Ngari, which hold significant dietary importance for the indigenous tribes. Additionally, the study sought to examine their untargeted metabolomic profiles. A total of 43 strains of Bacillus, Priestia, Staphylococcus, Pediococcus, and Lactiplantibacillus were isolated, characterized by 16 S rRNA gene and tested for probiotic properties. Five strains passed pH and bile salt tests with strain dependent antimicrobial activity, which exhibited moderate autoaggregation and hydrophobicity properties. Lactiplantibacillus plantarum MKTJ24 exhibited the highest hydrophobicity (42 %), which was further confirmed by adhesion assay in HT-29 cell lines (100 %). Lactiplantibacillus plantarum MKTJ24 treatment in LPS-stimulated HT-29 cells up-regulated expression of mucin genes compared to LPS-treated cells. Treatment of RAW 264.7 cells with Lactiplantibacillus plantarum MKTJ24 decreased LPS-induced reactive oxygen species (ROS) and nitric oxide (NO) productions. Further, genome analysis of Lactiplantibacillus plantarum MKTJ24 revealed the presence of several probiotic markers and immunomodulatory genes. The genome was found to harbor plantaricin operon involved in bacteriocin production. A pangenome analysis using all the publicly available L. plantarum genomes specifically isolated from fermented fish products identified 120 unique genes in Lactiplantibacillus plantarum MKTJ24. Metabolomic analysis indicated dominance of ascorbic acids, pentafluropropionate, cyclopropaneacetic acid, florobenzylamine, and furanone in Xindol. This study suggests that Lactiplantibacillus plantarum MKTJ24 has potential probiotic and immunomodulatory properties that could be used in processing traditional fermented fish products on an industrial scale to improve their quality and enhance functional properties.

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.

Co-occurrence of ST412 Klebsiella pneumoniae isolates with hypermucoviscous and non-mucoviscous phenotypes in a short-term hospitalized patient

Hypermucoviscosity (HMV) is a phenotype that is commonly associated with hypervirulence in Klebsiella pneumoniae. The factors that contribute to the emergence of HMV subpopulations remain unclear. In this study, eight K. pneumoniae strains were recovered from an inpatient who had been hospitalized for 20 days. Three of the isolates exhibited a non-HMV phenotype, which was concomitant with higher biofilm formation than the other five HMV isolates. All eight isolates were highly susceptible to serum killing, albeit HMV strains were remarkably more infective than non-HMV counterparts in a mouse model of infection. Whole genome sequencing (WGS) showed that the eight isolates belonged to the K57-ST412 lineage. Average nucleotide identity (FastANIb) analysis indicated that eight isolates share 99.96% to 99.99% similarity and were confirmed to be the same clone. Through comparative genomics analysis, 12 non-synonymous mutations were found among these isolates, eight of which in the non-HMV variants, including rmpA (c.285delG) and wbaP (c.1305T > A), which are assumed to be associated with the non-HMV phenotype. Mutations in manB (c.1318G > A), dmsB (c.577C > T) and tkt (c.1928C > A) occurred in HMV isolates only. RNA-Seq revealed transcripts of genes involved in energy metabolism, carbohydrate metabolism and membrane transport, including cysP, cydA, narK, tktA, pduQ, aceB, metN, and lsrA, to be significantly dysregulated in the non-HMV strains, suggesting a contribution to HMV phenotype development. This study suggests that co-occurrence of HMV and non-HMV phenotypes in the same clonal population may be mediated by mutational mechanisms as well as by certain genes involved in membrane transport and central metabolism.

IMPORTANCE

K. pneumoniae with a hypermucoviscosity (HMV) phenotype is a community-acquired pathogen that is associated with increased invasiveness and pathogenicity, and underlying diseases are the most common comorbid risk factors inducing metastatic complications. HMV was earlier attributed to the overproduction of capsular polysaccharide, and more data point to the possibility of several causes contributing to this bacterial phenotype. Here, we describe a unique event in which the same clonal population showed both HMV and non-HMV characteristics. Studies have demonstrated that this process is influenced by mutational processes and genes related to transport and central metabolism. These findings provide fresh insight into the mechanisms behind co-occurrence of HMV and non-HMV phenotypes in monoclonal populations as well as potentially being critical in developing strategies to control the further spread of HMV K. pneumoniae.

Genomic and Phenotypic Safety Assessment of Probiotic Bacillus coagulans Strain JBI-YZ6.3

Spore-forming Bacillus coagulans has been widely recognized as an important probiotic, which is commonly used in products for human consumption and animal feeds. B. coagulans exhibits beneficial traits from both Bacillus and lactic acid-producing Lactobacillus. The present study evaluated the safety of the newly isolated B. coagulans strain JBI-YZ6.3, using combined genomic and phenotypic analysis approaches. The taxonomic classification based on genome sequence and biochemical tests identified strain JBI-YZ6.3 as B. coagulans. Comprehensive genome-based analyses established JBI-YZ6.3 as a novel strain of B. coagulans. Antibiotic susceptibility testing showed that the strain JBI-YZ6.3 was sensitive to a panel of fourteen antibiotics, and no genes related to antibiotic resistance were found in its genome. The spores of strain JBI-YZ6.3 exhibited tolerance to acid and bile salts, as well as stability at ambient and elevated conditions of temperature and relative humidity. There were no homologs of Bacillus toxin genes identified in the genome of JBI-YZ6.3, and the strain exhibited no cytotoxicity towards Vero cells and human peripheral blood mononuclear cells. In conclusion, findings from this study support the safety of B. coagulans strain JBI-YZ6.3, which can be developed into new probiotic products for preventive and therapeutic benefits in human and animal hosts.

The Prediction of Recombination Hotspot Based on Automated Machine Learning

Meiotic recombination plays a pivotal role in genetic evolution. Genetic variation induced by recombination is a crucial factor in generating biodiversity and a driving force for evolution. At present, the development of recombination hotspot prediction methods has encountered challenges related to insufficient feature extraction and limited generalization capabilities. This paper focused on the research of recombination hotspot prediction methods. We explored deep learning-based recombination hotspot prediction and scrutinized the shortcomings of prevalent models in addressing the challenge of recombination hotspot prediction. To addressing these deficiencies, an automated machine learning approach was utilized to construct recombination hotspot prediction model. The model combined sequence information with physicochemical properties by employing TF-IDF-Kmer and DNA composition components to acquire more effective feature data. Experimental results validate the effectiveness of the feature extraction method and automated machine learning technology used in this study. The final model was validated on three distinct datasets and yielded accuracy rates of 97.14%, 79.71%, and 98.73%, surpassing the current leading models by 2%, 2.56%, and 4%, respectively. In addition, we incorporated tools such as SHAP and AutoGluon to analyze the interpretability of black-box models, delved into the impact of individual features on the results, and investigated the reasons behind misclassification of samples. Finally, an application of recombination hotspot prediction was established to facilitate easy access to necessary information and tools for researchers. The research outcomes of this paper underscore the enormous potential of automated machine learning methods in gene sequence prediction.

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.

Accounting for the health risk of probiotics

Probiotics have long been associated with a myriad of health benefits, so much so that their adverse effects whether mild or severe, are often neglected or overshadowed by the enormous volume of articles describing their beneficial effects in the current literature. Recent evidence has demonstrated several health risks of probiotics that warrant serious reconsideration of their applications and further investigations. This review aims to highlight studies that report on how probiotics might cause opportunistic systemic and local infections, detrimental immunological effects, metabolic disturbance, allergic reactions, and facilitating the spread of antimicrobial resistance. To offer a recent account of the literature, articles within the last five years were prioritized. The narration of these evidence was based on the nature of the studies in the following order of preference: clinical studies or human samples, in vivo or animal models, in situ, in vitro and/or in silico. We hope that this review will inform consumers, food scientists, and medical practitioners, on the health risks, while also encouraging research that will focus on and clarify the adverse effects of probiotics.

Promiscuous, persistent and problematic: insights into current enterococcal genomics to guide therapeutic strategy

Vancomycin-resistant enterococci (VRE) are major opportunistic pathogens and the causative agents of serious diseases, such as urinary tract infections and endocarditis. VRE strains mainly include species of Enterococcus faecium and E. faecalis which can colonise the gastrointestinal tract (GIT) of patients and, following growth and persistence in the gut, can transfer to blood resulting in systemic dissemination in the body. Advancements in genomics have revealed that hospital-associated VRE strains are characterised by increased numbers of mobile genetic elements, higher numbers of antibiotic resistance genes and often lack active CRISPR-Cas systems. Additionally, comparative genomics have increased our understanding of dissemination routes among patients and healthcare workers. Since the efficiency of currently available antibiotics is rapidly declining, new measures to control infection and dissemination of these persistent pathogens are urgently needed. These approaches include combinatory administration of antibiotics, strengthening colonisation resistance of the gut microbiota to reduce VRE proliferation through commensals or probiotic bacteria, or switching to non-antibiotic bacterial killers, such as bacteriophages or bacteriocins. In this review, we discuss the current knowledge of the genomics of VRE isolates and state-of-the-art therapeutic advances against VRE infections.

metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model

Beneficial bacteria remain largely unexplored. Lacking systematic methods, understanding probiotic community traits becomes challenging, leading to various conclusions about their probiotic effects among different publications. We developed language model-based metaProbiotics to rapidly detect probiotic bins from metagenomes, demonstrating superior performance in simulated benchmark datasets. Testing on gut metagenomes from probiotic-treated individuals, it revealed the probioticity of intervention strains-derived bins and other probiotic-associated bins beyond the training data, such as a plasmid-like bin. Analyses of these bins revealed various probiotic mechanisms and bai operon as probiotic Ruminococcaceae's potential marker. In different health-disease cohorts, these bins were more common in healthy individuals, signifying their probiotic role, but relevant health predictions based on the abundance profiles of these bins faced cross-disease challenges. To better understand the heterogeneous nature of probiotics, we used metaProbiotics to construct a comprehensive probiotic genome set from global gut metagenomic data. Module analysis of this set shows that diseased individuals often lack certain probiotic gene modules, with significant variation of the missing modules across different diseases. Additionally, different gene modules on the same probiotic have heterogeneous effects on various diseases. We thus believe that gene function integrity of the probiotic community is more crucial in maintaining gut homeostasis than merely increasing specific gene abundance, and adding probiotics indiscriminately might not boost health. We expect that the innovative language model-based metaProbiotics tool will promote novel probiotic discovery using large-scale metagenomic data and facilitate systematic research on bacterial probiotic effects. The metaProbiotics program can be freely downloaded at https://github.com/zhenchengfang/metaProbiotics.

In Silico Safety Assessment of Bacillus Isolated from Polish Bee Pollen and Bee Bread as Novel Probiotic Candidates

Bacillus species isolated from Polish bee pollen (BP) and bee bread (BB) were characterized for in silico probiotic and safety attributes. A probiogenomics approach was used, and in-depth genomic analysis was performed using a wide array of bioinformatics tools to investigate the presence of virulence and antibiotic resistance properties, mobile genetic elements, and secondary metabolites. Functional annotation and Carbohydrate-Active enZYmes (CAZYme) profiling revealed the presence of genes and a repertoire of probiotics properties promoting enzymes. The isolates BB10.1, BP20.15 (isolated from bee bread), and PY2.3 (isolated from bee pollen) genome mining revealed the presence of several genes encoding acid, heat, cold, and other stress tolerance mechanisms, adhesion proteins required to survive and colonize harsh gastrointestinal environments, enzymes involved in the metabolism of dietary molecules, antioxidant activity, and genes associated with the synthesis of vitamins. In addition, genes responsible for the production of biogenic amines (BAs) and D-/L-lactate, hemolytic activity, and other toxic compounds were also analyzed. Pan-genome analyses were performed with 180 Bacillus subtilis and 204 Bacillus velezensis genomes to mine for any novel genes present in the genomes of our isolates. Moreover, all three isolates also consisted of gene clusters encoding secondary metabolites.

In silico prospection of Lactobacillus acidophilus strains with potential probiotic activity

Lactic acid bacteria (LAB) are fermentative microorganisms and perform different roles in biotechnological processes, mainly in the food and pharmaceutical industries. Among the LAB, Lactobacillus acidophilus is a species that deserves to be highlighted for being used both in prophylaxis and in the treatment of pathologies. Most of the metabolites produced by this species are linked to the inhibition of pathogens. In this study, we utilized a pangenomic and metabolic annotation analysis using Roary and BlastKOALA, ML-based probiotic activity prediction with iProbiotic and whole-genome similarity using ANI to identify strains of L. acidophilus with potential probiotic activity. According to the results in BlastKOALA and iProbiotics, L. acidophilus NCTC 13721 had the greatest potential among the 64 strains tested, both in terms of its ability to be a Lactobacillus spp. probiotic, when in the amount of genes involved in the metabolism of organic acids and quorum sensing. In addition, DSM 20079 proved to be promising for prospecting new probiotic Lactobacillus from BlastKOALA analyses, as they presented similar results in the number of genes involved in the production of lactic acid, acetic acid, hydrogen peroxide, except for quorum sensing where the NCTC 13721 strain had 14 more genes. L. acidophilus NCTC 13721 and L. acidophilus La-5 strains showed greater ability to be Lactobacillus spp. probiotic capacity, showing 84.8% and 51.9% capacity in the iProbiotics tool, respectively. When analyzed in ANI, none of the evaluated strains showed genomic similarity with NCTC 13721. In contrast, the DSM 20079 strain showed genomic similarity with all evaluated strains except NCTC 13721. Furthermore, eight strains with characteristics with approximately 100% genomic similarity to La-5 were listed: S20_1, LA-5, FSI4, APC2845, LA-G80-111, DS1_1A, LA1, and BCRC 14065. Therefore, according to the findings in iProbiotics and BlastKoala, among the 64 strains evaluated, NCTC 13721 is the most promising strain to be used for future in vitro studies.

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.

ProbioMinServer: an integrated platform for assessing the safety and functional properties of potential probiotic strains

Motivation

ProbioMinServer is a platform designed to help researchers access information on probiotics regarding a wide variety of characteristics, such as safety (e.g. antimicrobial resistance, virulence, pathogenic, plasmid, and prophage genes) and functionality (e.g. functional classes, carbohydrate-active enzyme, and metabolite gene cluster profile). Because probiotics are functional foods, their safety and functionality are a crucial part of health care. Genomics has become a crucial methodology for investigating the safety and functionality of probiotics in food and feed. This shift is primarily attributed to the growing affordability of next-generation sequencing technologies. However, no integrated platform is available for simultaneously evaluating probiotic strain safety, investigating probiotic functionality, and identifying known phylogenetically related strains.

Results

Thus, we constructed a new platform, ProbioMinServer, which incorporates these functions. ProbioMinServer accepts whole-genome sequence files in the FASTA format. If the query genome belongs to the 25 common probiotic species collected in our database, the server performs a database search and analyzes the core-genome multilocus sequence typing. Front-end applications were implemented in JavaScript with a bootstrap framework, and back-end programs were implemented using PHP, Perl, and Python. ProbioMinServer can help researchers quickly and easily retrieve information on the safety and functionality of various probiotics.

Availability and implementation

The platform is available at https://probiomindb.imst.nsysu.edu.tw.

Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a rapidly developing and sometimes lethal pulmonary disease. Accurately predicting COVID-19 mortality will facilitate optimal patient treatment and medical resource deployment, but the clinical practice still needs to address it. Both complete blood counts and cytokine levels were observed to be modified by COVID-19 infection. This study aimed to use inexpensive and easily accessible complete blood counts to build an accurate COVID-19 mortality prediction model. The cytokine fluctuations reflect the inflammatory storm induced by COVID-19, but their levels are not as commonly accessible as complete blood counts. Therefore, this study explored the possibility of predicting cytokine levels based on complete blood counts.

METHODS

We used complete blood counts to predict cytokine levels. The predictive model includes an autoencoder, principal component analysis, and linear regression models. We used classifiers such as support vector machine and feature selection models such as adaptive boost to predict the mortality of COVID-19 patients.

RESULTS

Complete blood counts and original cytokine levels reached the COVID-19 mortality classification area under the curve (AUC) values of 0.9678 and 0.9111, respectively, and the cytokine levels predicted by the feature set alone reached the classification AUC value of 0.9844. The predicted cytokine levels were more significantly associated with COVID-19 mortality than the original values.

CONCLUSIONS

Integrating the predicted cytokine levels and complete blood counts improved a COVID-19 mortality prediction model using complete blood counts only. Both the cytokine level prediction models and the COVID-19 mortality prediction models are publicly available at http://www.healthinformaticslab.org/supp/resources.php .

Whole genome sequencing and the lignocellulose degradation potential of Bacillus subtilis RLI2019 isolated from the intestine of termites

BACKGROUND

Lignocellulosic biomass is the most abundant and renewable terrestrial raw material for conversion into bioproducts and biofuels. However, the low utilization efficiency of lignocellulose causes environmental pollution and resource waste, which limits the large-scale application of bioconversion. The degradation of lignocellulose by microorganisms is an efficient and cost-effective way to overcome the challenge of utilizing plant biomass resources. This work aimed to screen valuable cellulolytic bacteria, explore its molecular mechanism from genomic insights, and investigate the ability of the strain to biodegrade wheat straw.

RESULTS

Bacillus subtilis (B. subtilis) RLI2019 was isolated from the intestine of Reticulitermes labralis. The strain showed comprehensive enzyme activities related to lignocellulose degradation, which were estimated as 4.06, 1.97, 4.12, 0.74, and 17.61 U/mL for endoglucanase, β-glucosidase, PASC enzyme, filter paper enzyme, and xylanase, respectively. Whole genome sequencing was performed to better understand the genetic mechanism of cellulose degradation. The genome size of B. subtilis RLI2019 was 4,195,306 bp with an average GC content of 43.54%, and the sequence characteristics illustrated an extremely high probability (99.41%) as a probiotic. The genome contained 4,381 protein coding genes with an average GC content of 44.20%, of which 145 genes were classified into six carbohydrate-active enzyme (CAZyme) families and 57 subfamilies. Eight cellulose metabolism enzyme-related genes and nine hemicellulose metabolism enzyme-related genes were annotated by the CAZyme database. The starch and sucrose metabolic pathway (ko00500) was the most enriched with 46 genes in carbohydrate metabolism. B. subtilis RLI2019 was co-cultured with wheat straw for 7 days of fermentation, the contents of neutral detergent fiber, acid detergent fiber, hemicellulose, and lignin were significantly reduced by 5.8%, 10.3%, 1.0%, and 4.7%, respectively. Moreover, the wheat straw substrate exhibited 664.9 μg/mL of reducing sugars, 1.22 U/mL and 6.68 U/mL of endoglucanase and xylanase activities, respectively. Furthermore, the fiber structures were effectively disrupted, and the cellulose crystallinity was significantly reduced from 40.2% to 36.9%.

CONCLUSIONS

The complex diversity of CAZyme composition mainly contributed to the strong cellulolytic attribute of B. subtilis RLI2019. These findings suggest that B. subtilis RLI2019 has favorable potential for biodegradation applications, thus it can be regarded as a promising candidate bacterium for lignocellulosic biomass degradation.

Genome Analysis of Potential Probiotic Levilactobacillus brevis AcCh91 Isolated from Indian Home-Made Fermented Milk Product (Chhurpi)

Consumption of naturally fermented milk (NFM) products is the dietary culture in India. The mountainous people of Arunachal Pradesh in India prepare the assorted artisanal home-made NFM products from cow and yak milk. Previously, we isolated and identified 76 strains of lactic acid bacteria (LAB) from NFM products of Arunachal Pradesh, viz. mar, chhurpi and churkam. We hypothesized that some of these LAB strains may possess probiotic potentials; hence, we investigated the probiotic potentials of these strains. On the basis of in vitro and genetic screening for probiotic attributes including haemolytic ability, 20 LAB strains were selected out of 76 strains, for further analysis. Using in silico analysis, viz. multivariate heatmap and PCA (principal component analysis) biplot, Levilactobacillus brevis AcCh91 was selected as the most promising probiotic strain, which was further characterized by the whole-genome analysis. Lev. brevis AcCh91 showed the highest survival rate of 93.38% in low pH and 86.68 ± 2.69% in low bile and the highest hydrophobicity average of 86.34 ± 5.53%. This strain also showed auto-aggregation and co-aggregation with antimicrobial properties against the pathogens, showed ability to produce beta-galactosidase and cholesterol reduction property and, most importantly, produced GABA, an important psychobiotic element. Genomic analysis of Lev. brevis AcCh91 showed the presence of genes corresponding to GABA, vitamins, amino acids, cholesterol reduction, immunomodulation, bioactive peptides and antioxidant activity. The absence of antimicrobial-resistant genes and virulence factors was observed. Hence, genome analysis supports the probiotic potentials of Lev. brevis AcCh91, which may be further investigated to understand its health-promoting properties.

A systematic review on the flavor of soy-based fermented foods: Core fermentation microbiome, multisensory flavor substances, key enzymes, and metabolic pathways

The characteristic flavor of fermented foods has an important impact on the purchasing decisions of consumers, and its production mechanisms are a concern for scientists worldwide. The perception of food flavor is a complex process involving olfaction, taste, vision, and oral touch, with various senses contributing to specific properties of the flavor. Soy-based fermented products are popular because of their unique flavors, especially in Asian countries, where they occupy an important place in the dietary structure. Microorganisms, known as the souls of fermented foods, can influence the sensory properties of soy-based fermented foods through various metabolic pathways, and are closely related to the formation of multisensory properties. Therefore, this review systematically summarizes the core microbiome and its interactions that play an active role in representative soy-based fermented foods, such as fermented soymilk, soy sauce, soybean paste, sufu, and douchi. The mechanism of action of the core microbial community on multisensory flavor quality is revealed here. Revealing the fermentation core microbiome and related enzymes provides important guidance for the development of flavor-enhancement strategies and related genetically engineered bacteria.

Integrating reduced amino acid composition into PSSM for improving copper ion-binding protein prediction

Copper ion-binding proteins play an essential role in metabolic processes and are critical factors in many diseases, such as breast cancer, lung cancer, and Menkes disease. Many algorithms have been developed for predicting metal ion classification and binding sites, but none have been applied to copper ion-binding proteins. In this study, we developed a copper ion-bound protein classifier, RPCIBP, which integrating the reduced amino acid composition into position-specific score matrix (PSSM). The reduced amino acid composition filters out a large number of useless evolutionary features, improving the operational efficiency and predictive ability of the model (feature dimension from 2900 to 200, ACC from 83 % to 85.1 %). Compared with the basic model using only three sequence feature extraction methods (ACC in training set between 73.8 %-86.2 %, ACC in test set between 69.3 %-87.5 %), the model integrating the evolutionary features of the reduced amino acid composition showed higher accuracy and robustness (ACC in training set between 83.1 %-90.8 %, ACC in test set between 79.1 %-91.9 %). Best copper ion-binding protein classifiers filtered by feature selection progress were deployed in a user-friendly web server (http://bioinfor.imu.edu.cn/RPCIBP). RPCIBP can accurately predict copper ion-binding proteins, which is convenient for further structural and functional studies, and conducive to mechanism exploration and target drug development.

Roles of adenine methylation in the physiology of Lacticaseibacillus paracasei

Lacticaseibacillus paracasei is an economically important bacterial species, used in the food industry and as a probiotic. Here, we investigate the roles of N6-methyladenine (6mA) modification in L. paracasei using multi-omics and high-throughput chromosome conformation capture (Hi-C) analyses. The distribution of 6mA-modified sites varies across the genomes of 28 strains, and appears to be enriched near genes involved in carbohydrate metabolism. A pglX mutant, defective in 6mA modification, shows transcriptomic alterations but only modest changes in growth and genomic spatial organization.

Emerging pre-clinical safety assessments for potential probiotic strains: a review

Probiotics are amply studied and applied dietary supplements of greater consumer acceptance. Nevertheless, the emerging evidence on probiotics-mediated potential risks, especially among immunocompromised individuals, necessitates careful and in-depth safety studies. The traditional probiotic safety evaluation methods investigate targeted phenotypic traits, such as virulence factors and antibiotic resistance. However, the rapid innovation in omics technologies has offered an impactful means to ultimately sequence and unknot safety-related genes or their gene products at preliminary levels. Further validating the genome features using an array of phenotypic tests would provide an absolute realization of gene expression dynamics. For safety studies in animal models, the in vivo toxicity evaluation guidelines of chemicals proposed by the Organization for Economic Co-operation and Development (OECD) have been meticulously adopted in probiotic research. Future research should also focus on coupling genome-scale safety analysis and establishing a link to its transcriptome, proteome, or metabolome for a fine selection of safe probiotic strains. Considering the studies published over the years, it can be inferred that the safety of probiotics is strain-host-dose-specific. Taken together, an amalgamation of in silico, in vitro, and in vivo approaches are necessary for a fine scale selection of risk-free probiotic strain for use in human applications.

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.

Effect of Yeast Saccharomyces cerevisiae as a Probiotic on Diet Digestibility, Fermentative Metabolites, and Composition and Functional Potential of the Fecal Microbiota of Dogs Submitted to an Abrupt Dietary Change

The aim was to evaluate the effects of yeast probiotic on diet digestibility, fermentative metabolites, and fecal microbiota of dogs submitted to dietary change. Sixteen dogs were divided into two groups of eight dogs each: control, without, and with probiotic, receiving 0.12 g/dog/day of live Saccharomyces cerevisiae yeast. The dogs were fed a lower protein and fiber diet for 21 days and then changed to a higher protein and fiber diet until day 49. Yeast supplementation did not statistically influence diet digestibility. The probiotic group had a lower fecal concentration of total biogenic amines (days 21 and 49), ammonia (day 23), and aromatic compounds and a higher fecal concentration of butyrate (p < 0.05). The probiotic group showed a lower dysbiosis index, a higher abundance (p < 0.05) of Bifidobacterium (days 35 and 49) and Turicibacter, and a lower abundance of Lactobacillus and E. coli (p < 0.05). Beta diversity demonstrated a clear differentiation in the gut microbiota between the control and probiotic groups on day 49. The control group showed upregulation in genes related to virulence factors, antibiotic resistance, and osmotic stress. The results indicated that the live yeast evaluated can have beneficial effects on intestinal functionality of dogs.

Biofilm Microenvironment-Mediated MoS2 Nanoplatform with Its Photothermal/Photodynamic Synergistic Antibacterial Molecular Mechanism and Wound Healing Study

Drug-resistant bacterial infections pose a serious threat to human public health. Biofilm formation is one of the main factors contributing to the development of bacterial resistance, characterized by a hypoxic and microacidic microenvironment. Traditional antibiotic treatments have been ineffective against multidrug-resistant (MDR) bacteria. Novel monotherapies have had little success. On the basis of the photothermal effect, molybdenum disulfide (MoS₂) nanoparticles were used to link quaternized polyethylenimine (QPEI), dihydroporphyrin e6 (Ce6), and Panax notoginseng saponins (PNS) in a zeolitic imidazolate framework-8 (ZIF-8). A multifunctional nanoplatform (MQCP@ZIF-8) was constructed with dual response to pH and near-infrared light (NIR), which resulted in synergistic photothermal and photodynamic antibacterial effects. The nanoplatform exhibited a photothermal conversion efficiency of 56%. It inhibited MDR Escherichia coli (E. coli) and MDR Staphylococcus aureus (S. aureus) by more than 95% and effectively promoted wound healing in mice infected with MDR S. aureus. The nanoplatform induced the death of MDR bacteria by promoting biofilm ablation, disrupting bacterial cell membranes and intracellular DNA, and interfering with intracellular material and energy metabolism. In this study, a multifunctional nanoplatform with good antibacterial effect was developed. The molecular mechanisms of MDR bacteria were also elucidated for possible clinical application.

Strategies for the Identification and Assessment of Bacterial Strains with Specific Probiotic Traits

Early in the 1900s, it was proposed that health could be improved and senility delayed by manipulating gut microbiota with the host-friendly bacteria found in yogurt. Later, in 1990, the medical community reconsidered this idea and today probiotics represent a developed area of research with a billion-dollar global industry. As a result, in recent decades, increased attention has been paid to the isolation and characterization of novel probiotic bacteria from fermented foods and dairy products. Most of the identified probiotic strains belong to the lactic acid bacteria group and the genus Bifidobacterium. However, current molecular-based knowledge has allowed the identification and culture of obligatory anaerobic commensal bacteria from the human gut, such as Akkermansia spp. and Faecalibacterium spp., among other human symbionts. We are aware that the identification of new strains of these species does not guarantee their probiotic effects and that each effect must be proved through in vitro and in vivo preclinical studies before clinical trials (before even considering it as a probiotic strain). In most cases, the identification and characterization of new probiotic strain candidates may lack the appropriate set of in vitro experiments allowing the next assessment steps. Here, we address some innovative strategies reported in the literature as alternatives to classical characterization: (i) identification of alternatives using whole-metagenome shotgun sequencing, metabolomics, and multi-omics analysis; and (ii) probiotic characterization based on molecular effectors and/or traits to target specific diseases (i.e., inflammatory bowel diseases, colorectal cancer, allergies, among others).

CnnPOGTP: a novel CNN-based predictor for identifying the optimal growth temperatures of prokaryotes using only genomic k-mers distribution

SUMMARY

Temperature is very important for the growth of microorganisms. Appropriate temperature conditions can improve the possibility for isolation of currently uncultured microorganisms. The development of metagenomic binning technology had dramatically increased the availability of genomic information of prokaryotes, providing convenience to infer the optimal growth temperature (OGT). Here, we proposed CnnPOGTP, a predictor for optimal growth temperatures of prokaryotes based on deep learning method using only k-mers distribution derived from genomic sequence. This method was annotation free, and the predicted OGT could be obtained by simply providing the genome sequence to the CnnPOGTP website.

AVAILABILITY AND IMPLEMENTATION

http://www.orgene.net/CnnPOGTP.