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

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.

Anahtar Kelimeler:

16S rRNA, bağırsak mikrobiyomu, yeni nesil dizileme

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

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.

Keywords:

16S rRNA, gut microbiota, next-generation sequencing,

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