Multi-omics analysis reveals the influence of genetic and environmental risk factors on developing gut microbiota in infants at risk of celiac disease

Maureen M Leonard, Hiren Karathia, Meritxell Pujolassos, Jacopo Troisi, Francesco Valitutti, Poorani Subramanian, Stephanie Camhi, Victoria Kenyon, Angelo Colucci, Gloria Serena, Salvatore Cucchiara, Monica Montuori, Basilio Malamisura, Ruggiero Francavilla, Luca Elli, Brian Fanelli, Rita Colwell, Nur Hasan, Ali R Zomorrodi, Alessio Fasano, CD-GEMM Team, Pasqua Piemontese, Angela Calvi, Mariella Baldassarre, Lorenzo Norsa, Chiara Maria Trovato, Celeste Lidia Raguseo, Tiziana Passaro, Paola Roggero, Marco Crocco, Annalisa Morelli, Michela Perrone, Marcello Chieppa, Giovanni Scala, Maria Elena Lionetti, Carlo Catassi, Adelaide Serretiello, Corrado Vecchi, Gemma Castillejo de Villsante, Maureen M Leonard, Hiren Karathia, Meritxell Pujolassos, Jacopo Troisi, Francesco Valitutti, Poorani Subramanian, Stephanie Camhi, Victoria Kenyon, Angelo Colucci, Gloria Serena, Salvatore Cucchiara, Monica Montuori, Basilio Malamisura, Ruggiero Francavilla, Luca Elli, Brian Fanelli, Rita Colwell, Nur Hasan, Ali R Zomorrodi, Alessio Fasano, CD-GEMM Team, Pasqua Piemontese, Angela Calvi, Mariella Baldassarre, Lorenzo Norsa, Chiara Maria Trovato, Celeste Lidia Raguseo, Tiziana Passaro, Paola Roggero, Marco Crocco, Annalisa Morelli, Michela Perrone, Marcello Chieppa, Giovanni Scala, Maria Elena Lionetti, Carlo Catassi, Adelaide Serretiello, Corrado Vecchi, Gemma Castillejo de Villsante

Abstract

Background: Celiac disease (CD) is an autoimmune digestive disorder that occurs in genetically susceptible individuals in response to ingesting gluten, a protein found in wheat, rye, and barley. Research shows that genetic predisposition and exposure to gluten are necessary but not sufficient to trigger the development of CD. This suggests that exposure to other environmental stimuli early in life, e.g., cesarean section delivery and exposure to antibiotics or formula feeding, may also play a key role in CD pathogenesis through yet unknown mechanisms. Here, we use multi-omics analysis to investigate how genetic and early environmental risk factors alter the development of the gut microbiota in infants at risk of CD.

Results: Toward this end, we selected 31 infants from a large-scale prospective birth cohort study of infants with a first-degree relative with CD. We then performed rigorous multivariate association, cross-sectional, and longitudinal analyses using metagenomic and metabolomic data collected at birth, 3 months and 6 months of age to explore the impact of genetic predisposition and environmental risk factors on the gut microbiota composition, function, and metabolome prior to the introduction of trigger (gluten). These analyses revealed several microbial species, functional pathways, and metabolites that are associated with each genetic and environmental risk factor or that are differentially abundant between environmentally exposed and non-exposed infants or between time points. Among our significant findings, we found that cesarean section delivery is associated with a decreased abundance of Bacteroides vulgatus and Bacteroides dorei and of folate biosynthesis pathway and with an increased abundance of hydroxyphenylacetic acid, alterations that are implicated in immune system dysfunction and inflammatory conditions. Additionally, longitudinal analysis revealed that, in infants not exposed to any environmental risk factor, the abundances of Bacteroides uniformis and of metabolite 3-3-hydroxyphenylproprionic acid increase over time, while those for lipoic acid and methane metabolism pathways decrease, patterns that are linked to beneficial immunomodulatory and anti-inflammatory effects.

Conclusions: Overall, our study provides unprecedented insights into major taxonomic and functional shifts in the developing gut microbiota of infants at risk of CD linking genetic and environmental risk factors to detrimental immunomodulatory and inflammatory effects. Video Abstract.

Trial registration: ClinicalTrials.gov NCT02061306.

Keywords: Celiac disease; Microbiota; Multi-omics analysis, gut microbiome.

Conflict of interest statement

AF is a stockholder at Alba Therapeutics, serves as a consultant for Inova Diagnostics and Innovate Biopharmaceuticals, is an advisory board member for Axial Biotherapeutics and Ubiome, and has a speaker agreement with Mead Johnson Nutrition. MML serves as a consultant to HealthMode and Anokion, has a speaker agreement with Takeda Pharmaceuticals, and performs sponsored research with Glutenostics LLC. HK is a former employee, BF is a current employee, PS is a consultant, and RRC and NAH are stockholders at CosmosID Inc. Other authors have declared no competing interests exist.

Figures

Fig. 1
Fig. 1
Schematic representing the sample selection and study design. We selected 31 infants from the CDGEMM study [33] with fecal samples available at enrollment, 3 months, and 4–6 months after birth. The sample underwent metagenomic and metabolomic profiling and was next analyzed to identify associations between genetic and environmental risk factors and inter-subject and intra-subject variations
Fig. 2
Fig. 2
Analysis of associations between genetic and environmental risk factors and microbial species. We used MaAsLin [22], a widely used multivariate statistical framework, to identify statistically significant associations between each genetic and environmental risk factor and microbial species (p value < 0.01), No genetic risk, vaginal delivery, exclusive breastmilk feeding, and no exposure to antibiotics were taken as reference for genetic risk, delivery mode, feeding type, and antibiotic exposure, respectively. Microbial species were clustered based on Euclidean distance. Here, “u_s” denotes and unspecified species
Fig. 3
Fig. 3
Analysis of associations between genetic and environmental risk factors and functional pathways. We used MaAsLin [22] to identify statistically significant associations between each genetic and environmental risk factor and functional pathways (p value < 0.01), Pathways were clustered based on Euclidean distance. Additional File 8 for grouping of these pathways based on KEGG categorizations
Fig. 4
Fig. 4
Analysis of associations between genetic and environmental risk factors and metabolites. We used MaAsLin [22] to identify statistically significant associations between each genetic and environmental risk factor and metabolites (p value < 0.01). Metabolites were clustered based on Euclidean distance
Fig. 5
Fig. 5
Cross-sectional analysis of microbiota features for genetically predisposed infants. a functional pathways (p value < 0.05), and b metabolites that are differentially abundant between environmentally exposed and non-exposed infants according to Mann-Whitney U test (p value < 0.05). Additional File 8 for grouping of pathways based on KEGG categorizations. See Additional File 9 for boxplots showing altered abundances for these pathways and metabolites. Brackets show time points at which a significant difference between the exposed and non-exposed groups was observed
Fig. 6
Fig. 6
Longitudinal analysis of microbiota features for genetically predisposed infants a microbial species, b functional pathways, and c metabolites that are differentially abundant between each pair of time points (enrollment, 3 months, and 4–6 months) according to a paired Wilcoxon (Wilcoxon signed rank) test (p value < 0.05). Here, “Time1” denotes the earlier time point. In this figure, “u_s” denotes and unspecified species. Additional File 8 for grouping of pathways based on KEGG categorizations. See Additional File 9 for boxplots showing altered abundances for these pathways and metabolites

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