Food allergies are abnormal immune reactions to otherwise harmless food antigens, influenced by multiple factors, including the oral microbiota. During my PhD, I contributed to studies investigating the oral microbiota in food-allergic children, with a particular focus on the Candidate Phyla Radiation (CPR), an elusive bacterial group increasingly implicated in health and disease. We assessed available protocols for quantifying CPR in human salivary samples and performed in silico analyses to identify alternative genetic markers, highlighting RecA as a strong candidate. Building on this, we explored its potential for bacterial community profiling, developing a machine learning algorithm that accurately infers taxonomy from RecA sequences across bacterial diversity. This algorithm was implemented in a software that provides ultra-fast, accurate taxonomic profiling from raw shotgun metagenomic reads. In parallel, we analyzed the oral microbiota of allergic children using absolute rather than relative quantification, revealing patterns of microbial susceptibility and resilience to immune pressure. We also studied children who achieved antigen tolerance, observing microbial shifts that partially restored a healthy-like composition. Finally, I contributed to a study clarifying diagnostic markers for food-protein–induced allergic proctocolitis.

DECIPHERING THE ORAL MICROBIOTA IN FOOD ALLERGY: INSIGHTS INTO THE CANDIDATE PHYLA RADIATION AND NOVEL METHODOLOGICAL APPROACHES

STERZI, LODOVICO
2026

Abstract

Food allergies are abnormal immune reactions to otherwise harmless food antigens, influenced by multiple factors, including the oral microbiota. During my PhD, I contributed to studies investigating the oral microbiota in food-allergic children, with a particular focus on the Candidate Phyla Radiation (CPR), an elusive bacterial group increasingly implicated in health and disease. We assessed available protocols for quantifying CPR in human salivary samples and performed in silico analyses to identify alternative genetic markers, highlighting RecA as a strong candidate. Building on this, we explored its potential for bacterial community profiling, developing a machine learning algorithm that accurately infers taxonomy from RecA sequences across bacterial diversity. This algorithm was implemented in a software that provides ultra-fast, accurate taxonomic profiling from raw shotgun metagenomic reads. In parallel, we analyzed the oral microbiota of allergic children using absolute rather than relative quantification, revealing patterns of microbial susceptibility and resilience to immune pressure. We also studied children who achieved antigen tolerance, observing microbial shifts that partially restored a healthy-like composition. Finally, I contributed to a study clarifying diagnostic markers for food-protein–induced allergic proctocolitis.
26-gen-2026
Inglese
D'AURIA, ENZA CARMINA
CHELI, FEDERICA
Università degli Studi di Milano
229
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/355567
Il codice NBN di questa tesi è URN:NBN:IT:UNIMI-355567