Early-Life Human Microbiota Associated With Childhood Allergy Promotes the T Helper 17 Axis in Mice

Abstract

The intestinal microbiota influences immune maturation during childhood, and is implicated in early-life allergy development. However, to directly study intestinal microbes and gut immune responses in infants is difficult. To investigate how different types of early-life gut microbiota affect immune development, we collected fecal samples from children with different allergic heredity (AH) and inoculated germ-free mice. Immune responses and microbiota composition were evaluated in the offspring of these mice. Microbial composition in the small intestine, the cecum and the colon were determined by 16S rRNA sequencing. The intestinal microbiota differed markedly between the groups of mice, but only exposure to microbiota associated with AH and known future allergy in children resulted in a T helper 17 (Th17)-signature, both systemically and in the gut mucosa in the mouse offspring. These Th17 responses could be signs of a particular microbiota and a shift in immune development, ultimately resulting in an increased risk of allergy.

Defective IgA response to atypical intestinal commensals in IL-21 receptor deficiency reshapes immune cell homeostasis and mucosal immunity

“…The lack of breast milk IgA may predispose these non-breastfed neonates to enhanced pathogenic T cell responses to oral antigens in the presence of particular atypical commensal microbiota. Interestingly, infant microbiota associated with allergies has been reported to promote Th17 responses in the intestine of germ-free mice 53 .…”

Establishing or Exaggerating Causality for the Gut Microbiome: Lessons from Human Microbiota-Associated Rodents

“…An updated literature search was conducted in October 2019 to include studies that had been published since the initial searches, and the same search strategy was used. In total, 38 studies were included in this review for analysis that assessed the role of the gut microbiome in the etiology of a number of disease states, pathologies, or physiological changes: allergies (Feehley et al, 2019;Petursdottir et al, 2017); C. difficile-associated diarrhea (Battaglioli et al, 2018); colorectal cancer (Baxter et al, 2014;Tomkovich et al, 2019;Wong et al, 2017); mental disorders (Fujii et al, 2019;Kelly et al, 2016;Zheng et al, 2016Zheng et al, , 2019; HIV ; undernutrition (Kau et al, 2015;Smith et al, 2013;Wagner et al, 2016); IBD and irritable bowel syndrome (Britton et al, 2019;Chen et al, 2018;De Palma et al, 2017;Ge et al, 2017;Nagao-Kitamoto et al, 2016;Natividad et al, 2015;Torres et al, 2020;Touw et al, 2017); multiple sclerosis and Parkinson's disease (Berer et al, 2017;Cekanaviciute et al, 2017;Sampson et al, 2016); autism spectrum disorder (Sharon et al, 2019); arthritis and rheumatoid arthritis (Maeda et al, 2016;Stoll et al, 2019); frailty (Fielding et al, 2019); pregnancy-induced physiological changes (Koren et al, 2012); and obesity and its associated pathologies (i.e., metabolic syndrome, hypertension, and non-alcoholic fatty liver disease; Chiu et al, 2017;Goodrich et al, 2014;Kovatcheva-Datchary et al, 2015;Li et al, 2017;Liu et al, 2019;…”

‘Statistical Irreproducibility’ Does Not Improve with Larger Sample Size: How to Quantify and Address Disease Data Multimodality in Human and Animal Research

“…After examining the statistical content of 38 high-quality studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ] assessed in a recent systematic review [ 51 ], herein, we found that scientists who increased N, concurrently reduced the number of mice/donor (MxD), indicating that statistically, scientists replace the disease variance in mice by the disease variance in humans in their hGM-FMT studies. Furthermore, supporting our previous report [ 8 , 52 ], studies lacked proper statistics methods to control for animal density, and most importantly with respect to data modality, we found that none of the studies considered data multimodality/violin plots.…”