Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era
Abstract
1. Introduction
2. Current Knowledge of Probiotics
3. Exploring Probiotic Characteristics via AI
3.1. Omics Technologies
3.2. Mechanism of Action
3.2.1. Role of AI on Microbiota Modulation
3.2.2. Role of AI on Metabolite Production
3.2.3. Role of AI to Understand Immune Modulation
3.2.4. Models and Algorithms in AI-Assisted Probiotic Research
4. Precision of Health Effects
4.1. Intestinal Health
4.2. Anticancerogenic Activities
4.3. Antiaging Roles
4.4. Cardiovascular Health
4.5. Type 2 Diabetes
4.6. Other Roles
5. Technological Perspectives with AI
6. Boosting Approach with AI to Increase the Capacity of Probiotics
7. Conclusions and Research Outlook
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Metabolite | Type | Microorganism | Biological Activity/Key Outcomes | Reference |
---|---|---|---|---|
Exopolysaccharides | α-glucan | Pediococcus acidilactici NCDC 252 | Anticancer (human colon cancer cell line) | [62] |
Heteropolysaccharide | Lactiplantibacillus paraplantarum NCCP 962 | Cholesterol-lowering | [59] | |
Heteropolysaccharide | Limosilactobacillus fermentum NCDC400 | Cholesterol-lowering | [63] | |
- | Lactiplantibacillus plantarum MI01 | Anticholesterol | [64] | |
- | Lactobacillus delbrueckii ssp. bulgaricus DSM 20081 | Antioxidant, antitumor, periodontal regeneration | [65] | |
Negatively charged acidic | Lactiplantibacillus plantarum SN35N | Antiviral | [56] | |
Galactoglucan and levan | Lactococcus lactis F-mou | Antimicrobial | [57] | |
Glucomannan | Lactiplantibacillus plantarum BR2 | Antidiabetic, cholesterol-lowering, and antioxidant | [66] | |
Vitamin | Vitamin B | Bifidobacteria spp. | Secretion pyridoxine (B6): 0.988–26.060 mg/L | [67] |
Lactic acid bacteria spp. | Secretion pyridoxine (B6): 1.100–11.400 mg/L Secretion pantothenic acid (B3): 3.966–138.600 mg/L Secretion thiamine (B1): 14.720–19.540 mg/L | |||
Lactiplantibacillus plantarum HY7715 | Secretion riboflavin (B2): 34.5 ± 2.41 mg/L | [68] | ||
Vitamin B2 and B9 | Leuconostoc mesenteroides subsp. mesenteroides, Lactiplantibacillus plantarum, Lacticaseibacillus rhamnosus | About 1.7–32-fold increase in quinoa sourdough | [69] | |
Riboflavin (B2) | Lactiplantibacillus plantarum M5MA1-B2 | About 2.5-fold increase in oat kefir | [70] | |
Short-chain fatty acids | Butyrate | Lacticaseibacillus paracasei SD1 and Lacticaseibacillus rhamnosus SD11 | Anticancer and anti-inflammation | [71] |
Clostridium butyricum | ||||
Postbiotic metabolites | Organic acids, acetoin, 2,3- butanediol | Leuconostoc pseudomesenteroides Y6 | Anticancer | [72] |
- | Lactobacillus plantarum | Anticancer | [73] | |
Organic acids | Lactiplantibacillus plantarum, Lacticaseibacillus rhamnosus, Lactobacillus gasseri | Antifungal | [74] | |
Organic acids, volatile organic compounds, polyphenols | Lacticaseibacillus rhamnosus | Antiaflatoxigenic | [74] | |
3-phenyl-1,2,4-benzotriazine | Lactiplantibacillus plantarum | Anticancer | [75] |
Omics Approach | Data | Evaluation Output | Application Area | AI Tools | Reference |
---|---|---|---|---|---|
Genomics | Whole sequence, DNA sequences, annotated genes | Identification of functional genes, prediction of probiotic traits | Strain selection, probiotic characterization | Machine learning, deep learning, natural language processing | [13,116] |
Transcriptomics | Raw RNA reads, single-cell RNA-seq data, mRNA expression levels, etc. | Gene expression profiling under stress, prediction of stress responses | Strain selection, probiotic characterization | Machine learning and clustering algorithms (such as hierarchical clustering or K-means) | [9] |
Metatranscriptomics | Total RNA expression profiles in microbiota, RNA-seq data | Functional activities of probiotics within microbiota, host interaction | Strain selection, probiotic characterization, personalized diet, formulation | Machine learning, deep learning | [116] |
Proteomics | Proteins, peptide sequences | Protein structure-function prediction, adaptation analysis | Viability, strain selection, functional food design, disease diagnostics, drug development | Machine learning, deep learning, natural language processing | [13,116] |
Metaproteomics | Collective protein profiles, functional markers | Functional protein markers linked to probiotic activity | Health biomarker identification, survival | Machine learning, deep learning | [117] |
Lipidomics | Lipid composition, lipid profiles | Membrane lipid profiling, adaptation indicators, host interaction | Strain selection, probiotic characterization | Machine learning, deep learning, natural language processing | [13,116] |
Metabolomics | Metabolite concentrations, metabolic fingerprints | Metabolite profiles (e.g., SCFAs, vitamins), prediction of health effects | Strain selection, functional food design, personalized diet | Machine learning, deep learning | [13,118] |
Metagenomics | Microbial abundance tables (OTUs, taxa profiles) | Microbial abundance shifts | Strain selection, microbiota composition optimization, functional food design, functional food design | Machine learning, deep learning (such as Meta-Signer, DeepMicro, mAML, PaPrBaG, MicrobiomeAnalystR, mothur, QIIME, BiomMiner, Scikit-learn, and MIPMLP) | [119] |
Key Findings | AI Technology | Significance | Reference |
---|---|---|---|
PCA used to visualize excipients’ chemical structure impact. The model started with 6 excipients, predicted effects on 111 ones. After 3 rounds of AML, achieved 67.7% accuracy, identifying 3/4 tested excipients. | Active ML to predict the effects of pharmaceutical excipients on Lacticaseibacillus paracasei. | Demonstrates ML’s power in pharmaceutical formulation even with small datasets. | [14] |
144 LAB strains were evaluated for low pH, bile salt resistance, and antimicrobial activity. Best strains identified: Lacticaseibacillus paracasei S23, Lactiplantibacillus plantarum S57 & S70, Lacticaseibacillus casei S81. Decision Tree algorithm validated biofilm production’s role in gut colonization. | Applied ML algorithms: Information Gain Ratio, Information Gain, PCA, Gini Index, Chi-square, Deviance, Rule-Based Learning, Uncertainty, Correlation, and Relaxation. | ML methods successfully identified top LAB strains with >99% accuracy. | [43] |
AI used to identify beneficial microbial species, link them to diseases, and develop personalized probiotic formulations. NGPs could be key to personalized medicine. | AI-driven data analytics for large-scale gut microbiota studies across geographic regions. | AI-microbiome integration could revolutionize clinical applications and personalized probiotics. | [140] |
91 calf microbiome samples (74 healthy, 17 diarrheic used to train random forest model. Limosilactobacillus reuteri administration restored gut microbiota in diarrheic calves. Model identified health-associated bacteria. | Whole-genome sequencing of 22 Limosilactobacillus reuteri strains; random forest model to analyze gut microbiome profiles. | Probiotic treatment confirmed effective in restoring gut health; ML model validated findings. | [141] |
Key Findings | AI Technology | Database Used | Applications | Reference |
---|---|---|---|---|
Applied ABIOME model to the optimization of probiotic formulation; used MARS algorithm; reported synergistic interactions in metabolite production. | ML algorithm for probiotic synergism | ABIOME bioreactor, MARS algorithm, probiotic-metabolite interactions | Development of next-generation probiotics, personalized formulations | [104] |
Developed machine learning model (SVM) to predict probiotic potential based on genomic k-mer analysis; achieved 97.77% accuracy. | Support vector machine (SVM) algorithm model | Genomic k-mer analysis, SVM model, incremental feature selection | Probiotic genome classification, food and supplement industry applications | [150] |
Analyzed 89 bacterial genomes; applied multiple ML models; neural networks achieved 95.1% accuracy in probiotic classification. | Multiple ML models (GLM, RF, SVM, NN) are used to predict the ability to classify bacteria | NCBI GenBank data | Probiotic identification for food and health industries | [151] |
Used ICA to analyze Limosilactobacillus reuteri transcriptional regulation; identified 35 iModulons; discovered bistable regulatory mechanisms. | ML model | RNA-seq datasets, independent component analysis (ICA) | Optimization of probiotic properties, microbial food production strategies | [9] |
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Asar, R.; Erenler, S.; Devecioglu, D.; Ispirli, H.; Karbancioglu-Guler, F.; Ozturk, H.I.; Dertli, E. Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era. Fermentation 2025, 11, 259. https://doi.org/10.3390/fermentation11050259
Asar R, Erenler S, Devecioglu D, Ispirli H, Karbancioglu-Guler F, Ozturk HI, Dertli E. Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era. Fermentation. 2025; 11(5):259. https://doi.org/10.3390/fermentation11050259
Chicago/Turabian StyleAsar, Remziye, Sinem Erenler, Dilara Devecioglu, Humeyra Ispirli, Funda Karbancioglu-Guler, Hale Inci Ozturk, and Enes Dertli. 2025. "Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era" Fermentation 11, no. 5: 259. https://doi.org/10.3390/fermentation11050259
APA StyleAsar, R., Erenler, S., Devecioglu, D., Ispirli, H., Karbancioglu-Guler, F., Ozturk, H. I., & Dertli, E. (2025). Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era. Fermentation, 11(5), 259. https://doi.org/10.3390/fermentation11050259