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İstanbul Tıp Fakültesi Klinik Nütrisyon ve Mikrobiyota Araştırma Laboratuvarı Bakteriyel Topluluk Analiz Algoritması

Yıl 2020, Cilt: 3 Sayı: 3, 157 - 167, 05.11.2020

Öz

Amaç:Bu çalışmada, “Bakteriyel Topluluk Analiz Algoritması” oluşturularak 16S ribozomal RNA (rRNA) Amplikon Dizileme (AD) stratejisine dayalı Yeni Nesil Dizileme Teknolojisi kullanılarak gerçekleştirilen bakteriyel topluluk analizlerinden elde edilen verilerin daha verimli kullanılabilmesi amaçlanmıştır. Gereç ve Yöntem:Çalışmamızda, 96 insan bağırsak mikrobiyota örneğinin 16S rRNA genlerinin V3-V4 bölgeleri İllumina MiSeq sistemi kullanılarak çift-sonlu dizileme yöntemiyle dizilenmiştir. Biyoinformatik analizler QIIME 2 açık kaynaklı yazılımı ile gerçekleştirilmiştir. Bulgular:16S rRNA AD koşumundaki 96 örneğin tamamının 16S rRNA V3-V4 bölgeleri başarıyla dizilenmiştir. Çalışmanın sonunda toplam okuma 23.42 M, kümelenme yoğunluğu 883 K/mm2, filtreyi geçen küme yoğunluğu % 92.13, kalite skorları ise > Q30 = % 76,7 olarak tespit edilmiştir. Bu çalışmada elde ettiğimiz veriler ve çalışma sürecinde oluşan tecrübe ve bilgi birikimimiz sonucunda laboratuvarımız tarafından bir Bakteriyel Topluluk Analiz Algoritması oluşturulmuştur. Sonuç:“İstanbul Tıp Fakültesi, Klinik Nütrisyon ve Mikrobiyota Araştırma Laboratuvarı Bakteriyel Topluluk Analiz Algoritması” laboratuvarımızda gerçekleştirdiğimiz çalışmaların aynı standartlarda ve karşılaştırılabilir olması için kullanılacaktır. Bu algoritmanın ülkemizdeki araştırmacılar için bir referans niteliğinde olacağını düşünmekteyiz. Ayrıca farklı merkezlerin bu algoritmayı kullanmaları durumunda elde edilecek veriler laboratuvarımızın verileriyle ve bu protokolü kullanan ülkemizdeki veya Dünyadaki diğer merkezlerin verileriyle karşılaştırılabilir olacaktır. Bu şekilde ülkemizdeki insan bağırsak mikrobiyotası ile ilgili yapılan çalışmaların veriminin ve elde edilen verilerin değerinin arttırılmasına katkıda bulunmayı hedefliyoruz.

Destekleyen Kurum

İ.Ü. Bilimsel Araştırmalar Proje Birimi

Proje Numarası

25863

Kaynakça

  • 1. Glendinning L, Free A. Supra-organismal interactions in the human intestine. Front Cell Infect Microbiol 2014;4(47):1-4.
  • 2. Kramer P, Bressan P. Humans as Superorganisms:How Microbes, Viruses, Imprinted Genes, and Other Selfish Entities Shape Our Behavior. Perspect Psychol Sci 2015;10(4):464–81.
  • 3. D’Argenio V, Salvatore F. The role of the gut microbiome in the healthy adult status. Clin Chim Acta 2015;451:97–102.
  • 4. Conrad R, Vlassov AV. The Human Microbiota:Composition, Functions, and Therapeutic Potential. Med Sci Rev 2015;2:92-103. 5. Sankar SA, Lagier JC, Pontarotti P, Raoult D, Fournier P. E. The human gut microbiome, a taxonomic conundrum. Syst Appl Microbiol 2015;38(4):276-86.
  • 6. Mandal RS, Saha S, Das S. Metagenomic surveys of gut microbiota. Genom Proteom Bioinform 2015;13:148–58.
  • 7. Carding S, Verbeke K, Vipond DT, Corfe, B. M, Owen LJ. Dysbiosis of the gut microbiota in disease. Microb Ecol Health Dis 2015;26:26191.
  • 8. Alkasir R, Li J, Li X, Jin M, Zhu B. Human gut microbiota:the links with dementia development. Protein Cell 2017;8:90–102.
  • 9. Baothman OA, Zamzami MA, Taher I, Abubaker J, Abu-Farha M. The role of gut microbiota in the development of obesity and diabetes. Lipids Health Dis 2016;15:108.
  • 10. Walker AW, Lawley TD. Therapeutic modulation of intestinal dysbiosis. Pharmacol Res 2013;69(1):75–86.
  • 11. Almeida C, Oliveira R, Soares R, Barata, P. Influence of gut microbiota dysbiosis on brain function:a systematic review. Porto Biomed J 2020;5:2.
  • 12. Pace NR. A molecular view of microbial diversity and the biosphere. Science 1997;276:734–40.
  • 13. Santos A, Aerle RV, Barrientos L, Martinez- Urtaza J. Computational methods for 16S metabarcoding studies using Nanopore sequencing data. Computational and Structural Biotechnology Journal 2020;18:296–305.
  • 14. Malla MA, Dubey A, Kumar A, Yadav S, Hashem A, Abd_Allah EF. Exploring the Human Microbiome:The Potential Future Role of Next Generation Sequencing in Disease Diagnosis and Treatment. Front Immunol 2018;9:2868.
  • 15. Nygaard AB, Tunsjø HS, Meisal R, Charnock C. A preliminary study on the potential of Nanopore MinION and Illumina MiSeq 16S rRNA gene sequencing to characterize building-dust microbiomes. Scientific Reports 2020;10:3209.
  • 16. 16S Metagenomic Sequencing Library Preparation. Part # 15044223 Rev. B. Available from:https://support.illumina. com/documents/documentation/chemistry_ documentation/16s/16s-metagenomic-libraryprep- guide-15044223-b.pdf
  • 17. Klindworth A, Pruesse E, Schweer T, Peplles J, Quast C, Horn M, Glöckner FO. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 2013;7:41(1).
  • 18. Hang J, Desai V, Zavaljevski N, Yang Y, Lin X, Satya RV, et al. 16S rRNA gene pyrosequencing of reference and clinical samples and investigation of the temperature stability of microbiome profiles. Microbiome 2014;2:31.
  • 19. Wesolowska-Andersen A, Bahl MI, CarvalhO V, Kristiansen K, Sicheritz-Pontén T, Gupta R, Licht TR. Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome 2014;2:19.
  • 20. Wu GD, Lewis JD, Hoffmann C, Chen Y, Knight R, Bittinger K. Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags. BMC Microbiology 2010;10:206.
  • 21. Hart ML, Meyer A, Johnson PJ, Ericsson AC. Comparative Evaluation of DNA Extraction Methods from Feces of Multiple Host Species for Downstream Next-Generation Sequencing. PLoS One 2015;10:e0143334.
  • 22. Smith B, Li N, Andersen A. S, Slotved H. C, Krogfelt K. A. Optimising Bacterial DNA Extraction from Faecal Samples:Comparison of Three Methods. The Open Microbiology Journal. 2011;5:14-7.
  • 23. Kennedy NA, Walker AW, Berry SH, Duncan SH, Farquarson FM, Louis P, et al. The Impact of Different DNA Extraction Kits and Laboratories upon the Assessment of Human Gut Microbiota Composition by 16S rRNA Gene Sequencing. PLoS One 2014;9(2):e88982.
  • 24. Gatew H, Tarekegn GM. Next-generation sequencing platforms for latest livestock reference genome assemblies. Afr J Biotechnol 2018;17(39):1232-40.
  • 25. Sinclair L, Osman O. A, Bertilsson S, Eiler A. Microbial community composition and diversity

İstanbul University, İstanbul Faculty of Medicine, Clinical Nutrition and Microbiota Research Laboratory’s Bacterial Community Analysis Algorithm

Yıl 2020, Cilt: 3 Sayı: 3, 157 - 167, 05.11.2020

Öz

Objective:This study aims to more efficiently use the data obtained from a bacterial community analysis performed using next-generation sequencing based on the 16S ribosomal RNA (rRNA) amplicon sequencing (AS) strategy by creating a bacterial community analysis algorithm. Materials and Methods:The V3–V4 16S rRNA hypervariable regions of 96 human gut microbiota samples were sequenced using the Illumina MiSeq system by the paired-end sequencing method. Bioinformatics analysis was performed by the QIIME 2 open-source software. Results:The V3–V4 16S rRNA hypervariable regions of 96 gut samples were sequenced successfully using 16S rRNA AS. At the end of the study, the total reading was 23.42 M, the cluster density was 883 K/mm2, the cluster density passing the filter was 92.13%, and the quality scores were >Q30 = 76.7%. Our laboratory created a bacterial community analysis algorithm from the data we have obtained in this study and based on our experience and knowledge during the study process. Conclusion: Our bacterial community analysis algorithm will be used to carry out similar studies in our laboratory within the same, comparable standards. We believe that this algorithm will serve as a reference for researchers in Turkey. In addition, if different research centers will use this algorithm, their data will be comparable to those of our laboratory and other centers in our country and the world using the same protocol. In this way, we can help increase the efficiency of studies and the value of the data obtained from the human gut microbiota.

Proje Numarası

25863

Kaynakça

  • 1. Glendinning L, Free A. Supra-organismal interactions in the human intestine. Front Cell Infect Microbiol 2014;4(47):1-4.
  • 2. Kramer P, Bressan P. Humans as Superorganisms:How Microbes, Viruses, Imprinted Genes, and Other Selfish Entities Shape Our Behavior. Perspect Psychol Sci 2015;10(4):464–81.
  • 3. D’Argenio V, Salvatore F. The role of the gut microbiome in the healthy adult status. Clin Chim Acta 2015;451:97–102.
  • 4. Conrad R, Vlassov AV. The Human Microbiota:Composition, Functions, and Therapeutic Potential. Med Sci Rev 2015;2:92-103. 5. Sankar SA, Lagier JC, Pontarotti P, Raoult D, Fournier P. E. The human gut microbiome, a taxonomic conundrum. Syst Appl Microbiol 2015;38(4):276-86.
  • 6. Mandal RS, Saha S, Das S. Metagenomic surveys of gut microbiota. Genom Proteom Bioinform 2015;13:148–58.
  • 7. Carding S, Verbeke K, Vipond DT, Corfe, B. M, Owen LJ. Dysbiosis of the gut microbiota in disease. Microb Ecol Health Dis 2015;26:26191.
  • 8. Alkasir R, Li J, Li X, Jin M, Zhu B. Human gut microbiota:the links with dementia development. Protein Cell 2017;8:90–102.
  • 9. Baothman OA, Zamzami MA, Taher I, Abubaker J, Abu-Farha M. The role of gut microbiota in the development of obesity and diabetes. Lipids Health Dis 2016;15:108.
  • 10. Walker AW, Lawley TD. Therapeutic modulation of intestinal dysbiosis. Pharmacol Res 2013;69(1):75–86.
  • 11. Almeida C, Oliveira R, Soares R, Barata, P. Influence of gut microbiota dysbiosis on brain function:a systematic review. Porto Biomed J 2020;5:2.
  • 12. Pace NR. A molecular view of microbial diversity and the biosphere. Science 1997;276:734–40.
  • 13. Santos A, Aerle RV, Barrientos L, Martinez- Urtaza J. Computational methods for 16S metabarcoding studies using Nanopore sequencing data. Computational and Structural Biotechnology Journal 2020;18:296–305.
  • 14. Malla MA, Dubey A, Kumar A, Yadav S, Hashem A, Abd_Allah EF. Exploring the Human Microbiome:The Potential Future Role of Next Generation Sequencing in Disease Diagnosis and Treatment. Front Immunol 2018;9:2868.
  • 15. Nygaard AB, Tunsjø HS, Meisal R, Charnock C. A preliminary study on the potential of Nanopore MinION and Illumina MiSeq 16S rRNA gene sequencing to characterize building-dust microbiomes. Scientific Reports 2020;10:3209.
  • 16. 16S Metagenomic Sequencing Library Preparation. Part # 15044223 Rev. B. Available from:https://support.illumina. com/documents/documentation/chemistry_ documentation/16s/16s-metagenomic-libraryprep- guide-15044223-b.pdf
  • 17. Klindworth A, Pruesse E, Schweer T, Peplles J, Quast C, Horn M, Glöckner FO. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 2013;7:41(1).
  • 18. Hang J, Desai V, Zavaljevski N, Yang Y, Lin X, Satya RV, et al. 16S rRNA gene pyrosequencing of reference and clinical samples and investigation of the temperature stability of microbiome profiles. Microbiome 2014;2:31.
  • 19. Wesolowska-Andersen A, Bahl MI, CarvalhO V, Kristiansen K, Sicheritz-Pontén T, Gupta R, Licht TR. Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome 2014;2:19.
  • 20. Wu GD, Lewis JD, Hoffmann C, Chen Y, Knight R, Bittinger K. Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags. BMC Microbiology 2010;10:206.
  • 21. Hart ML, Meyer A, Johnson PJ, Ericsson AC. Comparative Evaluation of DNA Extraction Methods from Feces of Multiple Host Species for Downstream Next-Generation Sequencing. PLoS One 2015;10:e0143334.
  • 22. Smith B, Li N, Andersen A. S, Slotved H. C, Krogfelt K. A. Optimising Bacterial DNA Extraction from Faecal Samples:Comparison of Three Methods. The Open Microbiology Journal. 2011;5:14-7.
  • 23. Kennedy NA, Walker AW, Berry SH, Duncan SH, Farquarson FM, Louis P, et al. The Impact of Different DNA Extraction Kits and Laboratories upon the Assessment of Human Gut Microbiota Composition by 16S rRNA Gene Sequencing. PLoS One 2014;9(2):e88982.
  • 24. Gatew H, Tarekegn GM. Next-generation sequencing platforms for latest livestock reference genome assemblies. Afr J Biotechnol 2018;17(39):1232-40.
  • 25. Sinclair L, Osman O. A, Bertilsson S, Eiler A. Microbial community composition and diversity
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Klinik Tıp Bilimleri
Bölüm Research Article
Yazarlar

Dilek Sever Kaya 0000-0001-9155-935X

Bülent Saka 0000-0001-5404-5579

Proje Numarası 25863
Yayımlanma Tarihi 5 Kasım 2020
Gönderilme Tarihi 18 Eylül 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 3 Sayı: 3

Kaynak Göster

MLA Sever Kaya, Dilek ve Bülent Saka. “İstanbul Tıp Fakültesi Klinik Nütrisyon Ve Mikrobiyota Araştırma Laboratuvarı Bakteriyel Topluluk Analiz Algoritması”. Sağlık Bilimlerinde İleri Araştırmalar Dergisi, c. 3, sy. 3, 2020, ss. 157-6.