Developing an Omics-Driven Computational Framework for Next-Generation Microbiome Therapeutics

Authors

  • Amina Abdulhamid Ahmed Department of Microbiology, School of Science and Information Technology, Skyline University Nigeria
  • Ummulkhair Adamu Yusuf Department of Microbiology, School of Science and Information Technology, Skyline University Nigeria
  • Zainab Hussein Fadlallah Department of Microbiology, School of Science and Information Technology, Skyline University Nigeria
  • Musa Ojeba Innocent Department of Microbiology, School of Science and Information Technology, Skyline University Nigeria
  • Mustapha Abdulsalam Department of Microbiology, School of Science and Information Technology, Skyline University Nigeria

DOI:

https://doi.org/10.55006/biolsciences.2024.4408

Keywords:

Microbiome, Omics Technologies, Computational Drug Design, Therapeutics, Research Gaps

Abstract

The human microbiome is critical for health and disease, influencing multiple physiological systems and contributing to a variety of diseases. Advances in omics technologies—including genomes, transcriptomics, proteomics, and metabolomics—have considerably deepened our understanding of the complexity of the microbiome and its interactions with host systems. This study explores the integration of these omics approaches into computational frameworks for the design and discovery of next-generation microbiome therapeutics. Current advancements and case studies highlight the potential of omics-driven methodologies to uncover novel therapeutic targets and enhance the efficacy of microbiome-based interventions. Moreso, critical research gaps in the field are identified, such as the need for more robust data integration techniques and the exploration of unexplored microbial metabolites. Addressing these gaps is essential for advancing microbiome therapeutics and developing personalized medicine strategies. Ultimately, this study underscores the transformative potential of an omics-driven computational framework in revolutionizing the landscape of microbiome-based therapeutics.

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References

1. Ochoa-RepĂĄraz, J., Ramelow, C. C., & Kasper, L. H. (2020). A gut feeling: the importance of the intestinal microbiota in psychiatric disorders. Frontiers in Immunology, 11, 510113.

2. Acevedo-RomĂĄn, A., PagĂĄn-Zayas, N., VelĂĄzquez-Rivera, L. I., Torres-Ventura, A. C., & Godoy-Vitorino, F. (2024). Insights into Gut Dysbiosis: Inflammatory Diseases, Obesity, and Restoration Approaches. International Journal of Molecular Sciences, 25(17), 9715.

3. Quaranta, G., Guarnaccia, A., Fancello, G., Agrillo, C., Iannarelli, F., Sanguinetti, M., & Masucci, L. (2022). Fecal microbiota transplantation and other gut microbiota manipulation strategies. Microorganisms, 10(12), 2424.

4. Abdulsalam, M., & Ila, M. A. (2023). Closing the Gap: Artificial Intelligence Integration for Advancing Chikungunya Virus Studies in Africa. Biological Sciences, 3(4), 493-502.

5. Fadiji, A. E., & Babalola, O. O. (2020). Metagenomics methods for the study of plant-associated microbial communities: a review. Journal of microbiological methods, 170, 105860.

6. Chen, B., Zhang, D., Wang, X., Ma, W., Deng, S., Zhang, P., & Liang, S. (2017). Proteomics progresses in microbial physiology and clinical antimicrobial therapy. European Journal of Clinical Microbiology & Infectious Diseases, 36, 403-413.

7. Blum, H. E. (2017). The human microbiome. Advances in medical sciences, 62(2), 414-420.

8. Innocent, M. O., Mustapha, A., Abdulsalam, M., Livinus, M. U., Samuel, J. O., Elelu, S. A., & Muhammad, A. S. (2024). Soil Microbes and Soil Contamination. In Soil Microbiome in Green Technology Sustainability (pp. 3-35). Cham: Springer Nature Switzerland.

9. Zhu, B., Wang, X., & Li, L. (2010). Human gut microbiome: the second genome of human body. Protein & cell, 1(8), 718-725.

10. Fusco, W., Lorenzo, M. B., Cintoni, M., Porcari, S., Rinninella, E., Kaitsas, F., & Ianiro, G. (2023). Short-chain fatty-acid-producing bacteria: key components of the human gut microbiota. Nutrients, 15(9), 2211.

11. Kogut, M. H., Lee, A., & Santin, E. (2020). Microbiome and pathogen interaction with the immune system. Poultry science, 99(4), 1906-1913.

12. Skonieczna-Ć»ydecka, K., Marlicz, W., Misera, A., Koulaouzidis, A., & Ɓoniewski, I. (2018). Microbiome—the missing link in the gut-brain axis: focus on its role in gastrointestinal and mental health. Journal of clinical medicine, 7(12), 521.

13. Das, B., & Nair, G. B. (2019). Homeostasis and dysbiosis of the gut microbiome in health and disease. Journal of biosciences, 44, 1-8.

14. GĂ©rard, P. (2016). Gut microbiota and obesity. Cellular and molecular life sciences, 73(1), 147-162.

15. Fadiji, A. E., & Babalola, O. O. (2020). Metagenomics methods for the study of plant-associated microbial communities: a review. Journal of microbiological methods, 170, 105860.

16. Tedersoo, L., Albertsen, M., Anslan, S., & Callahan, B. (2021). Perspectives and benefits of high-throughput long-read sequencing in microbial ecology. Applied and environmental microbiology, 87(17), e00626-21.

17. Sudhagar, A., Kumar, G., & El-Matbouli, M. (2018). Transcriptome analysis based on RNA-Seq in understanding pathogenic mechanisms of diseases and the immune system of fish: a comprehensive review. International journal of molecular sciences, 19(1), 245.

18. Abdulsalam, M., Salihu, A. T., Usman, H. Y., & Usman, M. Y. (2024). Protein Biosynthesis in Microorganisms: Mechanisms, Regulation, and Biotechnological Applications. World Journal of Advanced Research and Reviews, 21(1), 869-881.

19. Bhosle, A., Wang, Y., Franzosa, E. A., & Huttenhower, C. (2022). Progress and opportunities in microbial community metabolomics. Current Opinion in Microbiology, 70, 102195.

20. Chetty, A., & Blekhman, R. (2024). Multi-omic approaches for host-microbiome data integration. Gut Microbes, 16(1), 2297860.

21. Abdulsalam, M., Hamisu, A. A., Ahmad, A. M., Wakili, F. B., Rabiu, I. B., Burodo, R. A., & Iliyas, N. A. (2024). Synergies at the Intersection: Exploring the Role of Bioinformatics in Enhancing Physiotherapy and Nursing Practice. Biological Sciences, 4(3), 712-724.

22. Decherchi, S., & Cavalli, A. (2020). Thermodynamics and kinetics of drug-target binding by molecular simulation. Chemical Reviews, 120(23), 12788-12833.

23. Nantasenamat, C., Isarankura-Na-Ayudhya, C., & Prachayasittikul, V. (2010). Advances in computational methods to predict the biological activity of compounds. Expert opinion on drug discovery, 5(7), 633-654.

24. Abdulsalam, M. (2024). Exploring the Therapeutic Promise of Cationic Antibacterial Peptides. Biological Sciences, 4(3), 692-700.

25. Musa, I. O., Isibor, P. O., Samuel, J. O., Mustapha, A., Mustapha, A., Akande, S., & Adeniji, H. (2024). Introduction to Nanotoxicology. In Environmental Nanotoxicology: Combatting the Minute Contaminants (pp. 1-22). Cham: Springer Nature Switzerland.

26. Saheed, Y. K., Balogun, B. F., Odunayo, B. J., & Abdulsalam, M. (2023). Microarray gene expression data classification via Wilcoxon sign rank sum and novel Grey Wolf optimized ensemble learning models. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(6), 3575-3587.

27. D’Urso, F., & Broccolo, F. (2024). Applications of Artificial Intelligence in Microbiome Analysis and Probiotic Interventions— An Overview and Perspective Based on the Current State of the Art. Applied Sciences, 14(19), 8627.

28. Wörheide, M. A., Krumsiek, J., KastenmĂŒller, G., & Arnold, M. (2021). Multi-omics integration in biomedical research–A metabolomics-centric review. Analytica chimica acta, 1141, 144-162.

29. Manzoni, C., Kia, D. A., Vandrovcova, J., Hardy, J., Wood, N. W., Lewis, P. A., & Ferrari, R. (2018). Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Briefings in bioinformatics, 19(2), 286-302.

30. Collin, C. B., Gebhardt, T., Golebiewski, M., Karaderi, T., Hillemanns, M., Khan, F. M., & Kuepfer, L. (2022). Computational models for clinical applications in personalized medicine—guidelines and recommendations for data integration and model validation. Journal of personalized medicine, 12(2), 166.

31. Khanna, S. (2021). Microbiota restoration for recurrent Clostridioides difficile: Getting one step closer every day!. Journal of Internal Medicine, 290(2), 294-309.

32. Lee, J. W. J., Plichta, D., Hogstrom, L., Borren, N. Z., Lau, H., Gregory, S. M., & Ananthakrishnan, A. N. (2021). Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease. Cell host & microbe, 29(8), 1294-1304.

33. Yang, J., Qin, S., & Zhang, H. (2022). Precise strategies for selecting probiotic bacteria in treatment of intestinal bacterial dysfunctional diseases. Frontiers in Immunology, 13, 1034727.

34. Al-Fakhrany, O. M., & Elekhnawy, E. (2024). Next-generation probiotics: the upcoming biotherapeutics. Molecular Biology Reports, 51(1), 505.

35. Abdulsalam, M., Hamisu, A. A., Ahmad, A. M., Wakili, F. B., Annu, F. S., & Garba, M. M. (2024). Microbiome Dynamics and Strategic Management Strategies: Exploring Synergies between Integrative Medicine Modalities and Microbial Balance. Biological Sciences, 4(3), 725-735.

36. Tiamiyu, B. B., Lateef, A. A., Ajadi, I., Adeyemi, S. B., Owolabi, P. O., Sagaya, A., & Mustapha, O. T. (2024). Bioclimatic Modeling of Current Geographic Distribution and Future Range Shifts of Selected Edible Mushrooms in Nigeria. Sahel Journal of Life Sciences FUDMA, 2(1), 51-61.

37. Gulliver, E. L., Young, R. B., Chonwerawong, M., D'Adamo, G. L., Thomason, T., Widdop, J. T., & Forster, S. C. (2022). The future of microbiome‐based therapeutics. Alimentary Pharmacology & Therapeutics, 56(2), 192-208.

38. Saheed, Y. K., Salau-Ibrahim, T. T., Abdulsalam, M., Adeniji, I. A., & Balogun, B. F. (2024). Modified bi-directional long short-term memory and hyperparameter tuning of supervised machine learning models for cardiovascular heart disease prediction in mobile cloud environment. Biomedical Signal Processing and Control, 94, 106319.

39. Abdulsalam, M., Ubah, F. Z. Y., Ahmed, H. U., Tafida, U. A., & Nasir, A. W. (2024). Deciphering the Genetic Code: Mechanisms, Evolution, and Implications for Biotechnology. World Journal of Advanced Research and Reviews, 21(1), 858-868.

40. Abdulsalam, M., Natour, S., Rabiu, I. B., & Abdulazeez, M. (2024). Microbial Magic: Decoding the Impact of Hypoxia on Exercise Physiology through Microbiota Dynamics. Biological Sciences, 4(3), 736-745.

41. Abdulsalam, M., Abdulrazaq, I., Tiamiyu, B. B., & Salam, O. L. (2023). Comparative Antimicrobial Properties of a Consortium of Nauclea latifolia Sm. and Ocimum gratissium L. Extracts with Their CuO. Biological Sciences, 3(1), 386-393.

42. Beam, A., Clinger, E., & Hao, L. (2021). Effect of diet and dietary components on the composition of the gut microbiota. Nutrients, 13(8), 2795.

43. Livinus, M. U., Bala, S. Z., Abdulsalam, M., Innocent, M. O., Hassan, M., Elelu, S. A., & Kini, P. (2024). Role of Microbes in Soil Food Webs and Vegetation Development. In Soil Microbiome in Green Technology Sustainability (pp. 107-132). Cham: Springer Nature Switzerland.

44. Musa, I. O., Samuel, J. O., Adams, M., Abdulsalam, M., Nathaniel, V., Maude, A. M., & Tiamiyu, A. G. T. (2024). Soil Erosion, Mineral Depletion and Regeneration. In Prospects for Soil Regeneration and Its Impact on Environmental Protection (pp. 159-172). Cham: Springer Nature Switzerland.

45. Scher, J. U., Nayak, R. R., Ubeda, C., Turnbaugh, P. J., & Abramson, S. B. (2020). Pharmacomicrobiomics in inflammatory arthritis: gut microbiome as modulator of therapeutic response. Nature Reviews Rheumatology, 16(5), 282-292.

46. Kok, C. R., Rose, D., & Hutkins, R. (2023). Predicting personalized responses to dietary fiber interventions: opportunities for modulation of the gut microbiome to improve health. Annual Review of Food Science and Technology, 14(1), 157-182.

47. Zhang, Y., Thompson, K. N., Branck, T., Yan, Y., Nguyen, L. H., Franzosa, E. A., & Huttenhower, C. (2021). Metatranscriptomics for the human microbiome and microbial community functional profiling. Annual Review of Biomedical Data Science, 4(1), 279-311.

48. Abdulsalam, M., Innocent, M. O., Livinus, M. U., Elelu, S. A., Ibrahim, G. O., Lateefat, S. O., ... & Muhammad, A. S. (2024). Future Research of Soil Microbiomes and Green Technology Innovation for a Better Tomorrow. In Soil Microbiome in Green Technology Sustainability (pp. 569-585). Cham: Springer Nature Switzerland.

49. Musa, I. O., Auwal, A. I., Abdulsalam, M., Livinus, M. U., Abdulhakeem, A. I., Muhammed, A., & Muhammad, A. S. (2024). Microbial Bioprospecting Products of Marine Economy. In Marine Bioprospecting for Sustainable Blue-bioeconomy (pp. 181-204). Cham: Springer Nature Switzerland.

50. Gudivada, V., Apon, A., & Ding, J. (2017). Data quality considerations for big data and machine learning: Going beyond data cleaning and transformations. International Journal on Advances in Software, 10(1), 1-20.

51. Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and biology insights, 14, 1177932219899051.

52. Livinus, M. U., Bala, S. Z., Abdulsalam, M., Innocent, M. O., Hassan, M., & Kini, P. (2024). Natural Occurrences of Soil Dilapidation. In Prospects for Soil Regeneration and Its Impact on Environmental Protection (pp. 205-223). Cham: Springer Nature Switzerland.

53. Abdulsalam, M., Musa, I. O., Livinus, M. U., Elelu, S. A., Ibrahim, G. O., Salami, O. L., & Pal, S. K. (2024). Blue Bioeconomy and Biomedical Innovation. In Marine Bioprospecting for Sustainable Blue-bioeconomy (pp. 143-157). Cham: Springer Nature Switzerland.

54. Muhammed, A., Aminu, B. M., Musa, I. O., Abdulsalam, M., Isma’il, R., Gimba, Y. M., & Moses, E. O. (2024). Blue Economy in Nigeria and the African Continent. In Marine Bioprospecting for Sustainable Blue-bioeconomy (pp. 355-370). Cham: Springer Nature Switzerland.

55. Mogielnicki, M., Swieczkowski, D., Bachorski, W., Zuk, G., Gilis-Malinowska, N., Zarzeka, A., & Jaguszewski, M. (2016). The Food and Drug Administration (FDA) and the European Medicines Agency (EMA) perspective on cardiovascular Polypill: A multidimensional concept. Cardiology Journal, 23(5), 515-517.

Biological Sciences Journal

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Published

19-12-2024
CITATION

How to Cite

Ahmed, A. A., Yusuf, U. A., Fadlallah, Z. H., Innocent, M. O., & Abdulsalam, M. (2024). Developing an Omics-Driven Computational Framework for Next-Generation Microbiome Therapeutics. Biological Sciences, 4(4), 810–821. https://doi.org/10.55006/biolsciences.2024.4408

Issue

Vol. 4 No. 4 (2024): Volume 4, Issue 4 (December)

Section

Review Article

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Copyright (c) 2024 Amina Abdulhamid Ahmed, Ummulkhair Adamu Yusuf, Zainab Hussein Fadlallah, Musa Ojeba Innocent, Mustapha Abdulsalam

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