Antibiotics within first year are linked to infant gut microbiome disruption and elevated atopic dermatitis risk.

BACKGROUND: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease in both pediatric and adult populations. The development of AD has been linked to antibiotic usage, which causes perturbation of the microbiome and has been associated with abnormal immune system function. However, imbalances in the gut microbiome itself associated with antibiotic usage have been inconsistently linked to AD. OBJECTIVE: This study aimed to elucidate the timing and specific factors mediating the relationship between systemic (oral or intravenous) antibiotic usage and AD. METHODS: We used statistical modelling and differential analysis to link CHILD participants' history of antibiotic usage and early-life gut microbiome alterations to atopic dermatitis. RESULTS: Here we report that systemic antibiotics during the first year of life, as compared to later, are associated with AD risk (adjusted odds ratio (aOR) = 1.81 [95% CI = 1.28 - 2.57], p < 0.001), with an increased number of antibiotic courses corresponding to a dose-response-like increased risk of AD risk (1 course: aOR = 1.67 [95% CI = 1.17 - 2.38]; 2 or more courses: aOR = 2.16 [95% CI = 1.30 - 3.59]). Further, we demonstrate that microbiome alterations associated with both AD and systemic antibiotic usage fully mediate the effect of antibiotic usage on the development of AD (ßindirect = 0.072, p < 0.001). Alterations in the 1-year infant gut microbiome of participants who would later develop AD included increased Tyzzerella nexilis, increased monosaccharide utilization, and parallel decreased Bifidobacterium, Eubacterium spp., and fermentative pathways. CONCLUSION: Our findings indicate that early-life antibiotic usage, especially in the first year of life, modulates key gut microbiome components that may be used as markers to predict and possibly prevent the development of AD.

Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning.

BACKGROUND: Early identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. This study aims to disentangle the relative timing and importance of early markers of asthma. METHODS: Using the CHILD Cohort Study, 132 variables measured in 1754 multi-ethnic children were included in the analysis for asthma prediction. Data up to 4 years of age was used in multiple machine learning models to predict physician-diagnosed asthma at age 5 years. Both predictive performance and variable importance was assessed in these models. RESULTS: Early-life data (≤1 year) has limited predictive ability for physician-diagnosed asthma at age 5 years (area under the precision-recall curve (AUPRC) < 0.35). The earliest reliable prediction of asthma is achieved at age 3 years, (area under the receiver-operator curve (AUROC) > 0.90) and (AUPRC > 0.80). Maternal asthma, antibiotic exposure, and lower respiratory tract infections remained highly predictive throughout childhood. Wheezing status and atopy are the most important predictors of early childhood asthma from among the factors included in this study. CONCLUSIONS: Childhood asthma is predictable from non-biological measurements from the age of 3 years, primarily using parental asthma and patient history of wheezing, atopy, antibiotic exposure, and lower respiratory tract infections. IMPACT: Machine learning models can predict physician-diagnosed asthma in early childhood (AUROC > 0.90 and AUPRC > 0.80) using ≥3 years of non-biological and non-genetic information, whereas prediction with the same patient information available before 1 year of age is challenging. Wheezing, atopy, antibiotic exposure, lower respiratory tract infections, and the child's mother having asthma were the strongest early markers of 5-year asthma diagnosis, suggesting an opportunity for earlier diagnosis and intervention and focused assessment of patients at risk for asthma, with an evolving risk stratification over time.

Elucidating the roles of the mammary and gut microbiomes in breast cancer development.

The mammary microbiome is a newly characterized bacterial niche that might offer biological insight into the development of breast cancer. Together with in-depth analysis of the gut microbiome in breast cancer, current evidence using next-generation sequencing and metabolic profiling suggests compositional and functional shifts in microbial consortia are associated with breast cancer. In this review, we discuss the fundamental studies that have progressed this important area of research, focusing on the roles of both the mammary tissue microbiome and the gut microbiome. From the literature, we identified the following major conclusions, (I) There are unique breast and gut microbial signatures (both compositional and functional) that are associated with breast cancer, (II) breast and gut microbiome compositional and breast functional dysbiosis represent potential early events of breast tumor development, (III) specific breast and gut microbes confer host immune responses that can combat breast tumor development and progression, and (IV) chemotherapies alter the microbiome and thus maintenance of a eubiotic microbiome may be key in breast cancer treatment. As the field expectantly advances, it is necessary for the role of the microbiome to continue to be elucidated using multi-omic approaches and translational animal models in order to improve predictive, preventive, and therapeutic strategies for breast cancer.

Delayed gut microbiota maturation in the first year of life is a hallmark of pediatric allergic disease.

Allergic diseases affect millions of people worldwide. An increase in their prevalence has been associated with alterations in the gut microbiome, i.e., the microorganisms and their genes within the gastrointestinal tract. Maturation of the infant immune system and gut microbiota occur in parallel; thus, the conformation of the microbiome may determine if tolerant immune programming arises within the infant. Here we show, using deeply phenotyped participants in the CHILD birth cohort (n = 1115), that there are early-life influences and microbiome features which are uniformly associated with four distinct allergic diagnoses at 5 years: atopic dermatitis (AD, n = 367), asthma (As, n = 165), food allergy (FA, n = 136), and allergic rhinitis (AR, n = 187). In a subset with shotgun metagenomic and metabolomic profiling (n = 589), we discover that impaired 1-year microbiota maturation may be universal to pediatric allergies (AD p = 0.000014; As p = 0.0073; FA p = 0.00083; and AR p = 0.0021). Extending this, we find a core set of functional and metabolic imbalances characterized by compromised mucous integrity, elevated oxidative activity, decreased secondary fermentation, and elevated trace amines, to be a significant mediator between microbiota maturation at age 1 year and allergic diagnoses at age 5 years (ßindirect = -2.28; p = 0.0020). Microbiota maturation thus provides a focal point to identify deviations from normative development to predict and prevent allergic disease.

Breastfeeding enrichment of B. longum subsp. infantis mitigates the effect of antibiotics on the microbiota and childhood asthma risk.

BACKGROUND: Early antibiotic exposure is linked to persistent disruption of the infant gut microbiome and subsequent elevated pediatric asthma risk. Breastfeeding acts as a primary modulator of the gut microbiome during early life, but its effect on asthma development has remained unclear. METHODS: We harnessed the CHILD cohort to interrogate the influence of breastfeeding on antibiotic-associated asthma risk in a subset of children (n = 2,521). We then profiled the infant microbiomes in a subset of these children (n = 1,338) using shotgun metagenomic sequencing and compared human milk oligosaccharide and fatty acid composition from paired maternal human milk samples for 561 of these infants. FINDINGS: Children who took antibiotics without breastfeeding had 3-fold higher asthma odds, whereas there was no such association in children who received antibiotics while breastfeeding. This benefit was associated with widespread "re-balancing" of taxonomic and functional components of the infant microbiome. Functional changes associated with asthma protection were linked to enriched Bifidobacterium longum subsp. infantis colonization. Network analysis identified a selection of fucosylated human milk oligosaccharides in paired maternal samples that were positively associated with B. infantis and these broader functional changes. CONCLUSIONS: Our data suggest that breastfeeding and antibiotics have opposing effects on the infant microbiome and that breastfeeding enrichment of B. infantis is associated with reduced antibiotic-associated asthma risk. FUNDING: This work was supported in part by the Canadian Institutes of Health Research; the Allergy, Genes and Environment Network of Centres of Excellence; Genome Canada; and Genome British Columbia.

Composition and Functional Potential of the Human Mammary Microbiota Prior to and Following Breast Tumor Diagnosis.

Microbiota studies have reported changes in the microbial composition of the breast upon cancer development. However, results are inconsistent and limited to the later phases of cancer development (after diagnosis). We analyzed and compared the resident bacterial taxa of histologically normal breast tissue (healthy, H, n = 49) with those of tissues donated prior to (prediagnostic, PD, n = 15) and after (adjacent normal, AN, n = 49, and tumor, T, n = 46) breast cancer diagnosis (n total = 159). DNA was isolated from tissue samples and submitted for Illumina MiSeq paired-end sequencing of the V3-V4 region of the 16S gene. To infer bacterial function in breast cancer, we predicted the functional bacteriome from the 16S sequencing data using PICRUSt2. Bacterial compositional analysis revealed an intermediary taxonomic signature in the PD tissue relative to that of the H tissue, represented by shifts in Bacillaceae, Burkholderiaceae, Corynebacteriaceae, Streptococcaceae, and Staphylococcaceae. This compositional signature was enhanced in the AN and T tissues. We also identified significant metabolic reprogramming of the microbiota of the PD, AN, and T tissue compared with the H tissue. Further, preliminary correlation analysis between host transcriptome profiling and microbial taxa and genes in H and PD tissues identified altered associations between the human host and mammary microbiota in PD tissue compared with H tissue. These findings suggest that compositional shifts in bacterial abundance and metabolic reprogramming of the breast tissue microbiota are early events in breast cancer development that are potentially linked with cancer susceptibility. IMPORTANCE The goal of this study was to determine the role of resident breast tissue bacteria in breast cancer development. We analyzed breast tissue bacteria in healthy breast tissue and breast tissue donated prior to (precancerous) and after (postcancerous) breast cancer diagnosis. Compared to healthy tissue, the precancerous and postcancerous breast tissues demonstrated differences in the amounts of breast tissue bacteria. In addition, breast tissue bacteria exhibit different functions in pre-cancerous and post-cancerous breast tissues relative to healthy tissue. These differences in function are further emphasized by altered associations of the breast tissue bacteria with gene expression in the human host prior to cancer development. Collectively, these analyses identified shifts in bacterial abundance and metabolic function (dysbiosis) prior to breast tumor diagnosis. This dysbiosis may serve as a therapeutic target in breast cancer prevention.