The programming effects of nutrition‐induced catch‐up growth on gut microbiota and metabolic diseases in adult mice

Zheng, Jia; Xiao, Xinhua; Zhang, Qian; Yu, Miao; Xu, Jianping; Qi, Cuijuan; Wang, Tong

doi:10.1002/mbo3.328

Cited by 19 publications

(10 citation statements)

References 40 publications

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“…A loss of microbial diversity in the intestine is also shown in several human intestinal and extraintestinal disorders, including inflammatory bowel disease (IBD), colorectal cancer (CRC), chronic liver diseases, type 2 diabetes, and asthma (Tilg et al, 2018 ). In the present study, the core gut microbiome at the phylum level in uninfected and infected mice was dominated by Firmicutes, Bacteroidetes and Proteobacteria, which are commonly found in the mouse model (Zheng et al, 2016a , b ; Deng et al, 2018 ; Kim et al, 2019 ). Significant differences were found before and after infection.…”

Section: Discussionmentioning

confidence: 50%

Alterations of the Mice Gut Microbiome via Schistosoma japonicum Ova-Induced Granuloma

Zhao

Yang

et al. 2019

Front. Microbiol.

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Schistosomiasis, also called bilharziasis, is a neglected tropical disease induced by Schistosoma spp. that causes hundreds of millions of infections. Although Schistosoma ova-induced granulomas commonly cause inflammation, hyperplasia, ulceration, micro abscess formation, and polyposis, the role of the egg granuloma on the gut microbiome remains unclear. To explore the role, gut microbial communities in mice infected with Schistosoma japonicum were surveyed. Female C57BL/6 and BALB/c mice were exposed to cercariae of S. japonicum for 45 and 65 days and then sacrificed. Intestinal contents and feces were collected, DNA was extracted, and high-throughput 16S rRNA gene-based pyrosequencing was used to provide a comparative analysis of gut microbial diversity. The intestinal mucosal tissues were also examined. Histopathologic analysis demonstrated that the basic structure of the colonic mucosa was damaged by ova-induced granuloma. Regarding the gut microbiome, 2,578,303 good-quality sequences were studied and assigned to 25,278 Operational Taxonomic Units (OTUs) at a threshold of 97% similarity. The average number of OTUs for C57BL/6 and BALB/c were 545 and 530, respectively. At the phylum level, intestinal microbial communities were dominated by Firmicutes, Bacteroidetes, Proteobacteria, and Verrucomicrobia. Infection with S. japonicum modified bacterial richness in the fecal associated microbiota. Exposure significantly modified bacterial community composition among different groups. At the phylogenetic levels, LEfSe analysis revealed that several bacterial taxa were significantly associated with the S. japonicum-infected mice. The present results suggest that egg granulomas in the intestine influence differentiation of the gut microbial community under pathophysiological conditions. This result suggests that intestinal microbiome-based strategies should be considered for early diagnosis, clinical treatment, and prognosis evaluation of schistosomiasis.

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Section: Discussionmentioning

confidence: 50%

Alterations of the Mice Gut Microbiome via Schistosoma japonicum Ova-Induced Granuloma

Zhao

Yang

et al. 2019

Front. Microbiol.

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“…The amplicons were extracted and purified from a 1% agarose gel in accordance with the manufacturer’s instructions using a AxyPrep DNA Gel Extraction Kit (Axygen Biosciences company, Union City’s, CA, USA) and using Quanti Fluor™-ST (Promega, USA) for quantification based on the previous describe methods (Zheng et al 2016 ). Uqimolar purified amplicons were combined and paired-end sequenced according to standard protocols (2 × 250) of the Illumina MiSeq platform (Yan et al 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Lachnospiraceae shift in the microbial community of mice faecal sample effects on water immersion restraint stress

Wang

Yang

et al. 2017

AMB Expr

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AbstractsStress, including both psychological and physical stimulation, can cause changes in the microbiota and mucosal function of the gastrointestinal system. There are few research studies available about the faecal microbiota changes after stress, such as water immersion restraint stress (WIRS). Therefore, in this study, we focused on analysing the composition changes of faecal microbiota in WIRS mice. The WIRS model, in which Blab/c mice were immersed in 21 ± 2 °C water for 4 h each day for 14 days, was established. Behavioural changes, the serum levels of corticosterone, IFN-γ and IL-17 and gastric mucosal injury were also assessed. Ten faecal microbiota samples were detected by Illumina Miseq sequencing of the 16S rRNA genes from 367205 characterised sequences. Finally, we find significant differences in the faecal microbiota composition between the control and the WIRS groups. There was an obvious increase in Lachnospiraceae in the WIRS mice (p = 0.0286, p < 0.05), which is associated with human diseases, such as ulcerative colitis, Crohn’s and celiac disease. Our research indicates that stress changes in the faecal microbiota. These results suggest that observing shifts of the intestinal microbiota is a promising method to explore the mechanism of the stress associated with gastrointestinal diseases and to provide us with a better understanding of the relationship between the microbiota and disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s13568-017-0383-4) contains supplementary material, which is available to authorized users.

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“…The early developmental environment has a lasting memory effect that lasts the whole life, called “metabolic memory,” which has been widely accepted and recognized by the academic community. Since Barker first discovered that people with lower birth weight had higher death rates from ischemic heart disease [51], large number of clinical studies [52–57] and animal experiments [7, 58–63] have demonstrated that adverse early-life exposures, such as nutrient restriction or overnutrition, gestational diabetes and maternal obesity and HFD, significantly increased the risk of developing metabolic diseases in later life. However, the precise underlying mechanism by which deciphers the “metabolic memory” is still not fully understood.…”

Section: Disorders Of Circadian Rhythm Might Be An Important Mechamentioning

confidence: 99%

“…We previously reviewed the role of gut microbiota in the effects of maternal obesity on metabolism of offspring and indicated that gut microbiota might be an essential programing factor for the increased risk of metabolic disorders in later life induced by adverse nutritional environments in early life [85]. Our research [7] and other studies [86–89] have found that the composition and diversity of gut microbiota changed significantly, which was accompanied by metabolic disorders in later life, after exposure to adverse nutritional environments in early life. An animal experiment has shown that alpha diversity was significantly decreased in the gut microbiota of mice from HFD-fed dams compared with the chow diet-fed dams [9].…”

Section: The “Gut Microbiota-circadian Clock Axis” May Be the Key mentioning

confidence: 99%

“…“Metabolic memory” and the Developmental Origins of Health and Disease (DOHaD) hypothesis were subsequently proposed [5]. Our previous research [6, 7] and accumulating evidence [8–10] have indicated that an adverse nutritional environment in the uterus significantly increases the risk of chronic metabolic diseases in adulthood. The biological basis of the relationship between the early-life nutritional environment and adult chronic diseases may be the key to the pathogenesis of T2DM.…”

Section: Introductionmentioning

confidence: 99%

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“Gut Microbiota-Circadian Clock Axis” in Deciphering the Mechanism Linking Early-Life Nutritional Environment and Abnormal Glucose Metabolism

Zhou

Kang

Xiao

et al. 2019

International Journal of Endocrinology

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The prevalence of diabetes mellitus (DM) has been increasing dramatically worldwide, but the pathogenesis is still unknown. A growing amount of evidence suggests that an abnormal developmental environment in early life increases the risk of developing metabolic diseases in adult life, which is referred to as the “metabolic memory” and the Developmental Origins of Health and Disease (DOHaD) hypothesis. The mechanism of “metabolic memory” has become a hot topic in the field of DM worldwide and could be a key to understanding the pathogenesis of DM. In recent years, several large cohort studies have shown that shift workers have a higher risk of developing type 2 diabetes mellitus (T2DM) and worse control of blood glucose levels. Furthermore, a maternal high-fat diet could lead to metabolic disorders and abnormal expression of clock genes and clock-controlled genes in offspring. Thus, disorders of circadian rhythm might play a pivotal role in glucose metabolic disturbances, especially in terms of early adverse nutritional environments and the development of metabolic diseases in later life. In addition, as a peripheral clock, the gut microbiota has its own circadian rhythm that fluctuates with periodic feeding and has been widely recognized for its significant role in metabolism. In light of the important roles of the gut microbiota and circadian clock in metabolic health and their interconnected regulatory relationship, we propose that the “gut microbiota-circadian clock axis” might be a novel and crucial mechanism to decipher “metabolic memory.” The “gut microbiota-circadian clock axis” is expected to facilitate the future development of a novel target for the prevention and intervention of diabetes during the early stage of life.

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The programming effects of nutrition‐induced catch‐up growth on gut microbiota and metabolic diseases in adult mice

Cited by 19 publications

References 40 publications

Alterations of the Mice Gut Microbiome via Schistosoma japonicum Ova-Induced Granuloma

Alterations of the Mice Gut Microbiome via Schistosoma japonicum Ova-Induced Granuloma

Lachnospiraceae shift in the microbial community of mice faecal sample effects on water immersion restraint stress

“Gut Microbiota-Circadian Clock Axis” in Deciphering the Mechanism Linking Early-Life Nutritional Environment and Abnormal Glucose Metabolism

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