Exploring Lactic Acid Bacteria and Metabolite-Target Interactions in Undernutrition Prevention: A Network Pharmacology and Molecular Docking Approach
Sari
ABSTRACT
Undernutrition is a major global issue, particularly in children, leading to stunting, wasting, and compromised immune function. Disruption of gut microbiota is a key factor in undernutrition, making probiotics, especially Lactic Acid Bacteria (LAB), a potential solution for improving nutritional status. This study explores the role of LAB and their metabolites in preventing undernutrition using network pharmacology and molecular docking approaches to identify potential molecular targets and related pathways. Network pharmacology tools like TargetNet, SEA, and SwissTargetPrediction were used to predict gene targets influenced by LAB metabolites. Cytoscape was used to build protein-protein interaction (PPI) networks, and molecular docking simulations evaluated the binding of LAB metabolites to key proteins associated with undernutrition. A total of 603 potential genes were identified, including human serum albumin (ALB), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α). Pathway analysis linked these proteins to immune response, nutrient absorption, and metabolic regulation. Molecular docking confirmed stable interactions with LAB metabolites. LAB and their metabolites show promise in managing undernutrition by modulating gut health and supporting nutrient absorption, providing a basis for future clinical applications.
Keywords: Undernutrition, Lactic Acid Bacteria (LAB), Probiotics, Network Pharmacology, Molecular Docking
Teks Lengkap:
Download Artikel (English)Referensi
Ahmadi-Khorram, M., Hatami, A., Asghari, P., Jafarzadeh Esfehani, A., Afshari, A., Javdan, F., & Nematy, M. (2025). Probiotics mitigate stress and inflammation in malnourished adults via gut microbiota modulation: A randomized controlled trial. Frontiers in Nutrition, 12, 1615607. https://doi.org/10.3389/fnut.2025.1615607
Arredondo-Hernandez, R., Siebe, C., Castillo-Rojas, G., Ponce De León, S., & López-Vidal, Y. (2022). The synergistic interaction of systemic inflammation, dysbiosis and antimicrobial resistance promotes growth restriction in children with acute severe malnutrition: An emphasis on Escherichia coli. Frontiers in Antibiotics, 1, 1001717. https://doi.org/10.3389/frabi.2022.1001717
Arwansyah, A., Lewa, A. F., Muliani, M., Warnasih, S., Mustopa, A. Z., & Arif, A. R. (2023). Molecular Recognition of Moringa oleifera Active Compounds for Stunted Growth Prevention Using Network Pharmacology and Molecular Modeling Approach. ACS Omega, 8(46), 44121–44138. https://doi.org/10.1021/acsomega.3c06379
Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., Fridman, W.-H., Pagès, F., Trajanoski, Z., & Galon, J. (2009). ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics, 25(8), 1091–1093. https://doi.org/10.1093/bioinformatics/btp101
Carvalho, M. C. D. C., Ribeiro, S. A., De Sousa, L. S., Lima, A. Â. M., & Maciel, B. L. L. (2025). Undernutrition and Intestinal Infections in Children: A Narrative Review. Nutrients, 17(9), 1479. https://doi.org/10.3390/nu17091479
Da Fonseca, S. T. O., Alves, C. C., Dias, C. T., & Mendes-da-Silva, C. (2025). Probiotics and undernourishment impact on brain 5-Hydroxytryptamine system and neurotrophin BDNF in rats: Risk of depression and anxiety? Nutrition, 132, 112680. https://doi.org/10.1016/j.nut.2024.112680
Daina, A., Michielin, O., & Zoete, V. (2019). SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 47(W1), W357–W364. https://doi.org/10.1093/nar/gkz382
Gatya, M., Fibri, D. L. N., Utami, T., Suroto, D. A., & Rahayu, E. S. (2022). Gut Microbiota Composition in Undernourished Children Associated with Diet and Sociodemographic Factors: A Case–Control Study in Indonesia. Microorganisms, 10(9), 1748. https://doi.org/10.3390/microorganisms10091748
Ge, S. X., Jung, D., & Yao, R. (2020). ShinyGO: A graphical gene-set enrichment tool for animals and plants. Bioinformatics (Oxford, England), 36(8), 2628–2629. https://doi.org/10.1093/bioinformatics/btz931
Gfeller, D., Michielin, O., & Zoete, V. (2013). Shaping the interaction landscape of bioactive molecules. Bioinformatics, 29(23), 3073–3079. https://doi.org/10.1093/bioinformatics/btt540
Hill, C., Guarner, F., Reid, G., Gibson, G. R., Merenstein, D. J., Pot, B., Morelli, L., Canani, R. B., Flint, H. J., Salminen, S., Calder, P. C., & Sanders, M. E. (2014). The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic. Nature Reviews Gastroenterology & Hepatology, 11(8), 506–514. https://doi.org/10.1038/nrgastro.2014.66
Ji, J., Jin, W., Liu, S.-J., Jiao, Z., & Li, X. (2023). Probiotics, prebiotics, and postbiotics in health and disease. MedComm, 4(6), e420. https://doi.org/10.1002/mco2.420
Kambale, R. M., Ntagazibwa, J. N., Kasengi, J. B., Zigashane, A. B., Francisca, I. N., Mashukano, B. N., Amani Ngaboyeka, G., Bahizire, E., Zech, F., Bindels, L. B., & Van Der Linden, D. (2023). Probiotics for children with uncomplicated severe acute malnutrition (PruSAM study): A randomized controlled trial in the Democratic Republic of Congo. The American Journal of Clinical Nutrition, 117(5), 976–984. https://doi.org/10.1016/j.ajcnut.2023.01.019
Karim, Md. R., Morshed, Md. N., Iqbal, S., Mohammad, S., Mathiyalagan, R., Yang, D. C., Kim, Y. J., Song, J. H., & Yang, D. U. (2023). A Network Pharmacology and Molecular-Docking-Based Approach to Identify the Probable Targets of Short-Chain Fatty-Acid-Producing Microbial Metabolites against Kidney Cancer and Inflammation. Biomolecules, 13(11), 1678. https://doi.org/10.3390/biom13111678
Kementerian Kesehatan Republik Indonesia. (2023). Survei Kesehatan Indonesia (SKI) 2023 Dalam Angka.
Kim, S.-K., Guevarra, R. B., Kim, Y.-T., Kwon, J., Kim, H., Cho, J. H., Kim, H. B., & Lee, J.-H. (2019). Role of Probiotics in Human Gut Microbiome-Associated Diseases. Journal of Microbiology and Biotechnology, 29(9), 1335–1340. https://doi.org/10.4014/jmb.1906.06064
Lengelé, L., Bruyère, O., Beaudart, C., Reginster, J.-Y., & Locquet, M. (2021). Impact of Malnutrition Status on Muscle Parameter Changes over a 5-Year Follow-Up of Community-Dwelling Older Adults from the SarcoPhAge Cohort. Nutrients, 13(2), 407. https://doi.org/10.3390/nu13020407
Li, L., Yang, L., Yang, L., He, C., He, Y., Chen, L., Dong, Q., Zhang, H., Chen, S., & Li, P. (2023). Network pharmacology: A bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine. Chinese Medicine, 18(1), 146. https://doi.org/10.1186/s13020-023-00853-2
Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z., & Zhang, B. (2019). WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Research, 47(W1), W199–W205. https://doi.org/10.1093/nar/gkz401
Markowiak-Kopeć, P., & Śliżewska, K. (2020). The Effect of Probiotics on the Production of Short-Chain Fatty Acids by Human Intestinal Microbiome. Nutrients, 12(4), 1107. https://doi.org/10.3390/nu12041107
Oliveros, J.C. (2024). Venny. An Interactive Tool for Comparing Lists with Venn’s Diagrams [Computer software]. https://bioinfogp.cnb.csic.es/tools/venny/
Paiandeh, M., Maghalian, M., Mohammad-Alizadeh-Charandabi, S., & Mirghafourvand, M. (2024). The effect of probiotic, prebiotic, and synbiotic supplements on anthropometric measures and respiratory infections in malnourished children: A systematic review and meta-analysis of randomized controlled trials. BMC Pediatrics, 24(1), 702. https://doi.org/10.1186/s12887-024-05179-y
Park, H., Kim, S.-H., & Lee, K.-A. (2025). Protective effects of Lactobacillus plantarum strain against protein malnutrition-induced muscle atrophy and bone loss in juvenile mice. PloS One, 20(1), e0317197. https://doi.org/10.1371/journal.pone.0317197
Spaggiari, L., Pedretti, N., Ricchi, F., Pinetti, D., Campisciano, G., De Seta, F., Comar, M., Kenno, S., Ardizzoni, A., & Pericolini, E. (2024). An Untargeted Metabolomic Analysis of Lacticaseibacillus (L.) rhamnosus, Lactobacillus (L.) acidophilus, Lactiplantibacillus (L.) plantarum and Limosilactobacillus (L.) reuteri Reveals an Upregulated Production of Inosine from L. rhamnosus. Microorganisms, 12(4), 662. https://doi.org/10.3390/microorganisms12040662
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., & Mering, C. von. (2019). STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613. https://doi.org/10.1093/nar/gky1131
The, H. C., Florez De Sessions, P., Jie, S., Pham Thanh, D., Thompson, C. N., Nguyen Ngoc Minh, C., Chu, C. W., Tran, T.-A., Thomson, N. R., Thwaites, G. E., Rabaa, M. A., Hibberd, M., & Baker, S. (2018). Assessing gut microbiota perturbations during the early phase of infectious diarrhea in Vietnamese children. Gut Microbes, 9(1), 38–54. https://doi.org/10.1080/19490976.2017.1361093
Thomsen, R., & Christensen, M. H. (2006). MolDock: A New Technique for High-Accuracy Molecular Docking. Journal of Medicinal Chemistry, 49(11), 3315–3321. https://doi.org/10.1021/jm051197e
World Health Organization. (2024). Fact sheets—Malnutrition. https://www.who.int/news-room/fact-sheets/detail/malnutrition
Yao, Z.-J., Dong, J., Che, Y.-J., Zhu, M.-F., Wen, M., Wang, N.-N., Wang, S., Lu, A.-P., & Cao, D.-S. (2016). TargetNet: A web service for predicting potential drug–target interaction profiling via multi-target SAR models. Journal of Computer-Aided Molecular Design, 30(5), 413–424. https://doi.org/10.1007/s10822-016-9915-2
Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications, 10(1), 1523. https://doi.org/10.1038/s41467-019-09234-6
DOI: https://doi.org/10.33024/mahesa.v5i11.23016
Refbacks
- Saat ini tidak ada refbacks.
Publisher: Universitas Malahayati Lampung

Semua artikel dapat digunakan dibawah lisensi Creative Commons Attribution-ShareAlike 4.0 International License
kostenlose besucherzähler


Panduan Penulisan





