Network Toksikolojisi Analizlerinde Kullanılan Biyoinformatik Araçlar

Neşe Başak Türkmen

doi:10.37609/akya.4158

Yazarlar

Neşe Başak Türkmen

DOI: https://doi.org/10.37609/akya.4158.c6837

Özet

Bu çalışma, modern toksikoloji araştırmalarında geleneksel, indirgemeci yaklaşımların ötesine geçerek biyolojik sistemleri bir bütün olarak ele alan "Network (Ağ) Toksikolojisi" disiplinini ve bu alanda kullanılan temel biyoinformatik araçları kapsamlı bir şekilde incelemektedir. Günümüzde çevresel ve endüstriyel kimyasallara maruziyetin karmaşık ve çok katmanlı etkilerini anlamak, klasik doz-yanıt ilişkilerinden ziyade protein etkileşim ağları, gen düzenleyici mekanizmalar ve metabolik yolaklar arasındaki dinamik ilişkilerin analiz edilmesini zorunlu kılmaktadır. Çalışma kapsamında, kimyasal-protein etkileşimlerini, gen ontolojilerini ve toksikolojik yolakları haritalandırmak için kullanılan temel veri tabanları ve yazılımlar detaylandırılmaktadır. Bu bağlamda; kimyasal bileşiklerin biyolojik hedeflerini belirleyen PubChem ve CTD (Comparative Toxicogenomics Database) gibi platformların yanı sıra, protein-protein etkileşim ağlarını görselleştiren STRING ve kimyasal-protein ilişkilerini ağ düzleminde sunan STITCH gibi kritik araçların işlevleri ele alınmaktadır. Ayrıca, karmaşık biyolojik ağların analizi ve görselleştirilmesinde standart kabul edilen Cytoscape yazılımının network toksikolojisindeki merkezi rolü vurgulanmaktadır. Biyoinformatik araçların entegrasyonu, toksik maddelerin etki mekanizmalarını (MoA) aydınlatmak, yeni biyobelirteçler keşfetmek ve ilaç güvenliği değerlendirmelerinde daha öngörülebilir modeller oluşturmak açısından stratejik bir öneme sahiptir. Bu bölüm, araştırmacılara toksikolojik verileri sistem biyolojisi perspektifiyle yorumlayabilmeleri için güncel bir metodolojik rehber sunmakta ve in siliko analizlerin modern risk değerlendirme süreçlerindeki dönüştürücü gücünü ortaya koymaktadır.

Referanslar

Vandenberg, L. N. (2022). Low dose effects and nonmonotonic dose responses for endocrine disruptors. In Endocrine disruption and human health (2nd ed.). Elsevier.

Trasande, L., Zoeller, R. T., Hass, U., Kortenkamp, A., Grandjean, P., Myers, J. P., DiGangi, J., Bellanger, M., Hauser, R., Legler, J., Skakkebaek, N. E., & Heindel, J. J. (2015). Estimating burden and disease costs of exposure to endocrine-disrupting chemicals in the European Union. Journal of Clinical Endocrinology & Metabolism, 100(4), 1245–1255

Ahn, C. H., Oh, T. J., Min, S. H., & Cho, Y. M. (2023). Endocrinology and metabolism. Endocrinology and Metabolism, 38(1), 1-9.

Redelmeier, D. A., & Zipursky, J. S. (2023). A dose of reality about dose–response relationships. Journal of General Internal Medicine, 38(16), 3604-3609.

Shankar, V., Mahboob, S., Al-Ghanim, K. A., Ahmed, Z., Al-Mulhm, N., & Govindarajan, M. (2021). A review on microbial degradation of drinks and infectious diseases: A perspective of human well-being and capabilities. Journal of King Saud University-Science, 33(2), 101293.

Zitnik, M., Agrawal, M., & Leskovec, J. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13), i457-i466.

Hartung, T., FitzGerald, R. E., Jennings, P., Mirams, G. R., Peitsch, M. C., Rostami-Hodjegan, A., ... & Woodung, M. (2017). Systems toxicology: Real world applications and opportunities. Chemical Research in Toxicology, 30(4), 870-882.

Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: A network-based approach to human disease. Nature Reviews Genetics, 12(1), 56-68.

Gore, A. C., Chappell, V. A., Fenton, S. E., Flaws, J. A., Nadal, A., Prins, G. S., ... & Zoeller, R. T. (2015). Executive summary to EDC-2: The Endocrine Society's second scientific statement on endocrine-disrupting chemicals. Endocrine Reviews, 36(6), 593-602.

Zoeller, R. T., Brown, T. R., Doan, L. L., Gore, A. C., Skakkebaek, N. E., Soto, A. M., ... & Vom Saal, F. S. (2012). Endocrine-disrupting chemicals and public health protection: A statement of principles from The Endocrine Society. Endocrinology, 153(9), 4097-4110.

European Food Safety Authority (EFSA), Alvarez, F., Arena, M., Auteri, D., Binaglia, M., Castoldi, A. F., & Villamar-Bouza, L. (2023). Conclusion on the peer review of the pesticide risk assessment of the active substance sulfur. EFSA Journal, 21(3), e07805.

Wetherill, Y. B., Akingbemi, B. T., Kanno, J., McLachlan, J. A., Nadal, A., Sonnenschein, C., & Belcher, S. M. (2007). In vitro molecular mechanisms of bisphenol A action. Reproductive Toxicology, 24(2), 178-198

Rochester, J. R. (2013). Bisphenol A and human health: A review of the literature. Reproductive Toxicology, 42, 132-155.

Stanojević, M., & Sollner Dolenc, M. (2025). Mechanisms of bisphenol A and its analogs as endocrine disruptors via nuclear receptors and related signaling pathways. Archives of Toxicology, 1-21.

Zhang, G. H., Liu, H., Liu, M. H., Liu, Y. C., Wang, J. Q., Wang, Y., & Zhang, Y. L. (2024). Network toxicology prediction and molecular docking-based strategy to explore the potential toxicity mechanism of Metformin chlorination byproducts in drinking water. Combinatorial Chemistry & High Throughput Screening, 27(1), 101-117.

Sturla, S. J., Boobis, A. R., FitzGerald, R. E., Hoeng, J., Kavlock, R. J., Schirmer, K., & Whelan, M. (2014). Systems toxicology: From basic research to risk assessment. Chemical Research in Toxicology, 27(3), 314-329.

Zhao, X., Zhang, S., Zhang, T., Cao, Y., & Liu, J. (2025). A small-scale data driven and graph neural network based toxicity prediction method of compounds. Computational Biology and Chemistry, 117, 108393

Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1).

Jeong, H., Mason, S. P., Barabási, A. L., & Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature, 411(6833), 41-42.

Erciyes, K. (2023). Graph-theoretical analysis of biological networks: A survey. Computation, 11(10), 188.

Heindel, J. J., Blumberg, B., Cave, M., Machtinger, R., Mantovani, A., Mendez, M. A., & Vom Saal, F. S. (2017). Metabolism disrupting chemicals and metabolic disorders. Reproductive Toxicology, 68, 3-33.

Diamanti-Kandarakis, E., Bourguignon, J. P., Giudice, L. C., Hauser, R., Prins, G. S., Soto, A. M., & Gore, A. C. (2009). Endocrine-disrupting chemicals: An Endocrine Society scientific statement. Endocrine Reviews, 30(4), 293-342.

Zoeller, R. T., Bergman, Å., Becher, G., Bjerregaard, P., Bornman, R., Brandt, I., ... & Wood, R. T. (2014). A path forward in the debate over health impacts of endocrine disrupting chemicals. Environmental Health, 13(1), 1-18.

Tan, J., & Han, D. (2025). Network toxicology and machine learning reveal the toxicological impact of Bisphenol A exposure on osteoarthritis. Medicine, 104(44), e45406.

Nadal, A., Quesada, I., Tudurí, E., Nogueiras, R., & Alonso-Magdalena, P. (2017). Endocrine-disrupting chemicals and the regulation of energy balance. Nature Reviews Endocrinology, 13(9), 536-546.

Bousoumah, R., Leso, V., Iavicoli, I., Huuskonen, P., Viegas, S., Porras, S. P., & Scheepers, P. T. (2021). Biomonitoring of occupational exposure to bisphenol A, bisphenol S and bisphenol F: A systematic review. Science of the Total Environment, 783, 146905.

Mustieles, V., & Fernández, M. F. (2020). Bisphenol A shapes children’s brain and behavior: Towards an integrated neurotoxicity assessment including human data. Environmental Health, 19(1), 66.

Kundakovic, M., & Champagne, F. A. (2011). Epigenetic perspective on the developmental effects of bisphenol A. Brain, Behavior, and Immunity, 25(6), 1084-1093.

Ashauer, R., O’Connor, I., & Escher, B. I. (2017). Toxic mixtures in time-the sequence makes the poison. Environmental Science & Technology, 51(5), 3084-3092

Yang, Q., Liu, H., Pang, G., Mo, Y., Zhou, C., Huang, M., & Zhang, J. (2025). Machine learning prediction model combined with network toxicology analysis identifies potential cardiotoxic components and mechanisms among 741 pesticides. Environment International, 109860.

Judson, R. S., Magpantay, F. M., Chickarmane, V., Haskell, C., Tania, N., Taylor, J., ... & Kavlock, R. J. (2015). Integrated model of chemical perturbations of a biological pathway using 18 in vitro high-throughput screening assays for the estrogen receptor. Toxicological Sciences, 148(1), 137-154.

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., & Bolton, E. E. (2025). PubChem 2025 update. Nucleic Acids Research, 53(D1), D1516-D1525.

Davis, A. P., Wiegers, T. C., Johnson, R. J., Sciaky, D., Wiegers, J., & Mattingly, C. J. (2023). Comparative toxicogenomics database (CTD): Update 2023. Nucleic Acids Research, 51(D1), D1257-D1262.

Szklarczyk, D., Nastou, K., Koutrouli, M., Kirsch, R., Mehryary, F., Hachilif, R., ... & Von Mering, C. (2025). The STRING database in 2025: Protein networks with directionality of regulation. Nucleic Acids Research, 53(D1), D730-D737.

Szklarczyk, D., Santos, A., Von Mering, C., Jensen, L. J., Bork, P., & Kuhn, M. (2016). STITCH 5: Augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Research, 44(D1), D380-D384.

Pham, T. N. H., Nguyen, T. H., Tam, N. M., Y. Vu, T., Pham, N. T., Huy, N. T., & Li, H. (2022). Improving ligand‐ranking of AutoDock Vina by changing the empirical parameters. Journal of Computational Chemistry, 43(3), 160-169.

Safran, M., Rosen, N., Twik, M., BarShir, R., Stein, T. I., Dahary, D., ... & Lancet, D. (2022). The GeneCards suite. In Practical guide to life science databases (pp. 27-56). Springer Nature Singapore.

Milacic, M., Beavers, D., Conley, P., Gong, C., Gillespie, M., Griss, J., & D'Eustachio, P. (2024). The Reactome pathway knowledgebase 2024. Nucleic Acids Research, 52(D1), D672-D678.