| Congresso Brasileiro de Microbiologia 2023 | Resumo: 1510-2 | ||||
Resumo:Intestinal microbiota plays a crucial role in regulating the host immune response and inflammation. Changes in intestinal microbiota have been a subject of research in various health conditions, including pediatric leukemia, such as acute lymphoblastic leukemia (ALL). These changes may have implications for the overall health of affected individuals. In microbiota studies, the classification of amplicon sequences is typically carried out using specialized software programs such as Qiime2 or Mothur. To classify raw sequences at various taxonomic levels, researchers rely on reference databases. Some of the most used reference databases in microbiota research include Silva, Greengenes, and RDP (Ribosomal Database Project). These databases contain a wealth of curated microbial sequences and taxonomic information that allow researchers to assign taxonomic identities to the sequences obtained from their microbiota samples. This classification process is a crucial step in understanding the composition and diversity of the microbial communities present in different environments or samples. The objective of this study is to emphasize the differences of the intestinal microbiota taxonomy using two different databases (Silva and RDP) in patients with ALL throughout their treatment. In addition, this research aims to demonstrate how characterizing the intestinal microbiota can serve as a valuable biomarker for predicting prognosis in ALL patients. Understanding these differences may provide valuable insights into the disease and its prognosis. We evaluated the intestinal microbiota composition of 10 children diagnosed with ALL by analyzing fecal samples collected at diagnosis (F01) and the final of consolidation phase of the treatment (F02). In addition, seven fecal samples of healthy children (Control group) were collected and analysed. The samples were submitted to DNA extraction using a commercial kit and DNA libraries were prepared. Amplicons of the bacterial 16S rRNA gene were analyzed by high-throughput sequencing to determine the diversity and composition of the microbiota. We used Qiime2 for bioinformatics analysis and good-quality reads were grouped into amplicon sequence variants (ASVs) using the DADA2 algorithm. Bacterial taxonomic classification was done using USEARCH pipeline and we compare SILVA database release 138 and RDP result at specie level. Statistical analysis was performed with STAMP 2.1.3. The results showed 234 taxa identified (mainly genera) using Silva database and 376 taxa identified (mainly specie) using RDP. Significant differences were observed between the two taxonomies, both in relative abundance and taxonomic classification, for example, in the Control group using Silva database the five most abundant taxa were Bifidobacterium, Blautia, Faecalibacterium, Bacteroides and Ruminococcus torques, totaling 49.53% of the microbiota. In the control group using the RDP, the five most abundant taxa were Bifidobacterium longum, Faecalibacterium prausnitzii, Blautia wexlerae, Blautia obeum and Romboutsia timonensis, totaling 39.07% of the microbiota. These results show how the choice of database can influence the results of microbiota studies. Based on the results of the present study, our recommendation is to use RDP in intestinal microbiota studies and preferably to curate the database before analyzing any amplicon dataset. Palavras-chave: Intestinal microbiota, Faecalibacterium prausnitzii, Ribosomal Database Project, Ruminococcus torques, Bifidobacterium longum Agência de fomento:Programa Nacional de Apoio à Atenção Oncológica (PRONON) | |||||