Banca de QUALIFICAÇÃO: BIANCA CRISTIANE FERREIRA SANTIAGO

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : BIANCA CRISTIANE FERREIRA SANTIAGO
DATE: 29/07/2025
TIME: 09:00
LOCAL: ICe
TITLE:

Metagenomic analysis in investigating the role of gut microbiota composition in patients with Crohn’s disease and ulcerative colitis

KEY WORDS:

Shotgun Metagenomics, Inflammatory Bowel Disease, Crohn’s Disease, Ulcerative Colitis, Machine Learning.

Metagenômica de Shotgun, Doença Infalamtória Intestinal, Doença de Crohn, Colite Ulcerativa, Aprendizado de Máquina.

PAGES: 21
BIG AREA: Ciências Biológicas
AREA: Biologia Geral
SUMMARY:

It is widely accepted that the human body contains more bacterial cells than human cells. New sequencing techniques make it possible to investigate the role of the microbiota in health, especially in Inflammatory Bowel Diseases (IBD) such as Crohn’s disease (CD) and Ulcerative Colitis (UC), which are characterized by symptoms such as abdominal pain, diarrhea, weight loss, anemia, and fatigue. CD is a transmural inflammation of the gastrointestinal tract, whereas UC affects the colonic mucosa. The diagnosis of IBD is often delayed, and most IBD patients present with chronic dysbiosis. This study aims to identify differences and patterns of the microbiota in IBD patients and healthy individuals using metagenomic analysis and machine learning. This may help to identify disease subgroups and eliminate confounding factors. Sequencing data from the iHMP Project are analyzed using the Euryale and HUMAnN pipelines for taxonomic classification and functional annotation. Initial results of the taxonomic classification (abundance, diversity, richness, and interaction) showed higher diversity (Shannon index) in UC, followed by the control group (nonIBD), and lowest in CD. The Wilcoxon test indicated greater statistical significance between CD and nonIBD. No significant difference was observed between UC and nonIBD. This suggests that UC preserves microbial diversity, whereas CD is associated with a marked reduction. Future applications of machine learning (Random Forest) will seek patterns and associations between the microbiota and IBD, identifying taxonomic and functional changes. This may provide insights into the development and progression of CD and UC, contributing to understanding underlying mechanisms, identifying therapeutic targets, and potential biomarkers for diagnosis and treatment.

COMMITTEE MEMBERS:
Interno - 1880243 - DANIEL CARLOS FERREIRA LANZA
Presidente - 1507794 - RODRIGO JULIANI SIQUEIRA DALMOLIN
Interno - 3063244 - TETSU SAKAMOTO