Type 1 diabetes induces cognitive dysfunction in rats associated with alterations of the gut microbiome and metabolomes in serum and hippocampus

Gao, Hongchang; Jiang, Qiaoying; Ji, Hui; Ning, Jie; Li, Chen; Zheng, Hong

doi:10.1016/j.bbadis.2019.165541

Cited by 79 publications

(62 citation statements)

References 79 publications

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“…Romboutsia is commonly associated with the intestinal tract of mammalian species and has only recently been identified in poultry [17,39,52,53]. Both Turicibacter and Romboutsia have recently been implicated in metabolic alterations in the serum and hippocampus of rats, suggesting these genera may be important in the regulation of host energy metabolism through host-microbial cross-talk via regulation of neurotransmitter levels [54]. The thin mucosal layer and prevalent innervation via the enteric nervous system make the small intestine an ideal location for host-microbiota interactions that impact hormone levels, metabolism, and other important physiological processes [55,56].…”

Section: Discussionmentioning

confidence: 99%

Eggshell and environmental bacteria contribute to the intestinal microbiota of growing chickens

Maki

Bobeck

Sylte

et al. 2020

J Animal Sci Biotechnol

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Background: The initial intestinal microbiota acquired from different sources has profound impacts on animal health and productivity. In modern poultry production practices, the source(s) of the establishing microbes and their overall contribution during development of gastrointestinal tract communities are still unclear. Using fertilized eggs from two independent sources, we assessed the impact of eggshell-and environmental-associated microbial communities on the successional processes and bacterial community structure throughout the intestinal tract of chickens for up to 6 weeks post-hatch. Results: Culturing and sequencing techniques identified a viable, highly diverse population of anaerobic bacteria on the eggshell. The jejunal, ileal, and cecal microbial communities for the egg-only, environment-only, and conventionally raised birds generally displayed similar successional patterns characterized by increasing community richness and evenness over time, with strains of Enterococcus, Romboutsia, and unclassified Lachnospiraceae abundant for all three input groups in both trials. Bacterial community structures differed significantly based on trial and microbiota input with the exception of the egg-exposed and conventional birds in the jejunum at week 1 and the ileum at week 6. Cecal community structures were different based on trial and microbiota input source, and cecal short-chain fatty acid profiles at week 6 highlighted functional differences as well. Conclusion: We identified distinct intestinal microbial communities and differing cecal short-chain fatty acid profiles between birds exposed to the microbiota associated with either the eggshell or environment, and those of conventionally hatched birds. Our data suggest the eggshell plays an appreciable role in the development of the chicken intestinal microbiota, especially in the jejunum and ileum where the community structure of the eggshellonly birds was similar to the structure of conventionally hatched birds. Our data identify a complex interplay between the eggshell and environmental microbiota during establishment and succession within the chicken gut. Further studies should explore the ability of eggshell-and environment-derived microbes to shape the dynamics of succession and how these communities can be targeted through interventions to promote gut health and mitigate food-borne pathogen colonization in poultry.

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Section: Discussionmentioning

confidence: 99%

Eggshell and environmental bacteria contribute to the intestinal microbiota of growing chickens

Maki

Bobeck

Sylte

et al. 2020

J Animal Sci Biotechnol

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“…Lachnospiraceae NK4A136 group were previously reported in several animal studies with various conclusions. Zhao et al (2017) considered Lachnospiraceae NK4A136 group to be intestinal probiotics showing a negative correlation to intestinal inflammation in rats; while Zheng et al (2018) suggested that the Lachnospiraceae NK4A136 group was an indicator of gut dysbiosis, higher relative abundances of Lachnospiraceae NK4A136 group were found in rats with advanced-stage type 1 diabetes (Gao et al, 2019).…”

Section: Alterations In Gut Microbial Composition and Functional Annomentioning

confidence: 99%

Alterations in Gut Microbiota of Gestational Diabetes Patients During the First Trimester of Pregnancy

You

Huang

et al. 2020

Front. Cell. Infect. Microbiol.

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Background: Dysbiosis of human gut microbiota is associated with a wide range of metabolic disorders, including gestational diabetes mellitus (GDM). Yet whether gut microbiota dysbiosis participates in the etiology of GDM remains largely unknown.Objectives: Our study was initiated to determine whether the alternations in gut microbial composition during early pregnancy linked to the later development of GDM, and explore the feasibility of microbial biomarkers for the early prediction of GDM. Study design:This nested case-control study was based upon an early pregnancy follow-up cohort (ChiCTR1900020652). Gut microbiota profiles of 98 subjects with GDM and 98 matched healthy controls during the early pregnancy (10-15 weeks) were assessed via 16S rRNA gene amplicon sequencing of V4 region. The data set was randomly split into a discovery set and a validation set, the former was used to analyze the differences between GDM cases and controls in gut microbial composition and functional annotation, and to establish an early identification model of GDM, then the performance of the model was verified by the external validation set.Results: Bioinformatic analyses revealed changes to gut microbial composition with significant differences in relative abundance between the groups. Specifically, Eisenbergiella, Tyzzerella 4, and Lachnospiraceae NK4A136 were enriched in the GDM group, whereas Parabacteroides, Megasphaera, Eubacterium eligens group, etc. remained dominant in the controls. Correlation analysis revealed that GDM-enriched genera Eisenbergiella and Tyzzerella 4 were positively correlated with fasting blood glucose levels, while three control-enriched genera (Parabacteroides, Parasutterella, and Ruminococcaceae UCG 002) were the opposite. Further, GDM functional annotation modules revealed enrichment of modules for sphingolipid metabolism, starch and sucrose metabolism, etc., while lysine biosynthesis and nitrogen metabolism were reduced. Finally, five genera and two clinical indices were included in the linear discriminant analysis model for the prediction of GDM; the areas under receiver operating characteristic curves of the training and validation sets were 0.736 (95% confidence interval: 0.663-0.808) and 0.696 (0.575-0.818), respectively. Ma et al. Gut Bacterial Dysbiosis Before GDMConclusions: Gut bacterial dysbiosis in early pregnancy was found to be associated with the later development of GDM, and gut microbiota-targeted biomarkers might be utilized as potential predictors of GDM.

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“…Animal studies indicated that the GM dysbiosis exacerbated the outcome of stroke, while transplantation of fecal microbiota or normalization of GM dysbiosis by antibiotics improved the outcome (Singh et al, 2016;Chen et al, 2019). Moreover, increasing evidence indicated the close correlation between the GM and cognitive impairment in different diseases (Bajaj et al, 2012;Carlson et al, 2018;Gao et al, 2019;Liu et al, 2019). However, the gut microbial characteristics in PSCI patients remain unclear.…”

Section: Introductionmentioning

confidence: 99%

Gut Microbiome Signatures Are Biomarkers for Cognitive Impairment in Patients With Ischemic Stroke

Liu

Gong

Zhang

et al. 2020

Front. Aging Neurosci.

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Post-stroke cognitive impairment (PSCI) is a common neuropsychiatric complication of stroke. Mounting evidence has demonstrated a connection between gut microbiota (GM) and neuropsychiatric disease. Our previous study revealed the changes in the GM in a mouse model of vascular dementia. However, the characteristic GM of PSCI remains unclear. This study aimed to characterize the GM of PSCI and explored the potential of GM as PSCI biomarkers. A total of 93 patients with ischemic stroke were enrolled in this study. The patients were divided into two groups according to their MoCA scores 3 months after stroke onset. Clinical data and biological variables were recorded. GM composition was analyzed using 16S ribosomal RNA sequencing, and the characteristic GM was identified by linear discriminant analysis Effect Size (Lefse). Our results showed that Proteobacteria was highly increased in the PSCI group compared with the post-stroke non-cognitive impairment (PSNCI) group, the similar alterations were also observed at the class, order, family, and genus levels of Proteobacteria. After age adjustments, the abundance of Firmicutes, and its members, including Clostridia, Clostridiales, Lachnospiraceae, and Lachnospiraceae_other, were significantly decreased in the age-matched PSCI group compared with the PSNCI group. Besides, the GM was closely associated with MoCA scores and the risk factors for PSCI, including higher baseline National Institute of Health Stroke Scale score, higher homocysteine (Hcy) level, higher prevalence of stroke recurrence, leukoaraiosis, and brain atrophy. The KEGG results showed the enriched module for folding, sorting and degradation (chaperones and folding catalysts) and the decreased modules related to metabolisms of cofactors and vitamins, amino acid, and lipid in PSCI patients. A significant correlation was observed between PSCI and the abundance of Enterobacteriaceae after adjustments (P = 0.035). Moreover, the receiver operating characteristic (ROC) models based on the characteristic GM and Enterobacteriaceae could distinguish PSCI patients from PSNCI patients [area under the curve (AUC) = 0.840, 0.629, respectively]. Our findings demonstrated that the characteristic GM, especially Enterobacteriaceae, might have the ability to predict PSCI in post-stroke patients, which are expected to be used as clinical biomarkers of PSCI.

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Type 1 diabetes induces cognitive dysfunction in rats associated with alterations of the gut microbiome and metabolomes in serum and hippocampus

Cited by 79 publications

References 79 publications

Eggshell and environmental bacteria contribute to the intestinal microbiota of growing chickens

Eggshell and environmental bacteria contribute to the intestinal microbiota of growing chickens

Alterations in Gut Microbiota of Gestational Diabetes Patients During the First Trimester of Pregnancy

Gut Microbiome Signatures Are Biomarkers for Cognitive Impairment in Patients With Ischemic Stroke

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