scholarly journals Assess The Diversity of Gut Microbiota Among Healthy Adults For Forensic Application

2021 ◽  
Author(s):  
Shuangshuang Wang ◽  
Feng Song ◽  
Haoyu Gu ◽  
Zhilong Shu ◽  
Xiaowen Wei ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Study Groups ◽  
Normal Weight ◽  
Healthy Adults ◽  
Personal Identification ◽  
Female Group ◽  
Microbiota Composition ◽  
Linear Discriminant ◽  
Potential Biomarker ◽  
Gut Microbiota Composition

Abstract Background: Human gut microbiota is individually unique that hints the microbiota in fecal traces left in the crime scene could act as a potential biomarker for forensic personal identification. Next-generation DNA sequencing and bioinformatic analysis of fecal samples are revolutionizing our insights into gut microbial communities. While the formation of the gut microbiota is known to be multifactorial, it is unclear whether these characteristics can be applied to forensic applications. Therefore, the gut microbiota of healthy adults with different traits in Chengdu was investigated in this study.Results: Based on the STAMP analysis of each study group, the difference in gut microbiota composition in male and female subjects was observed. The male group was characterized by taxa in the phylum Proteobacteria, while the female group was described by Synergistetes phylum. The gut bacterial community assembly mechanism was mainly affected by the deterministic process. In addition, gut microbiota composition showed meaningful discrimination in each of the BMI groups. At the phylum level, in male subjects, increased representative phyla were Patescibacteria (p<0.05) in the underweight group and Bacteroidetes (p<0.05) in the normal-weight group, while in the female group, the significantly different phyla were Bacteroidetes, Firmicutes, and Actinobacteria. At the genus level, 44 unique genera were found to be significantly distinct across BMI study groups. By Fisher’s Linear Discriminant Analysis based on 38 of 44 unique genera, 94.4% of original BMI grouped subjects were correctly classified.Conclusion: In conclusion, subjects with different individual characters have specific gut microbiota, and can be discriminated by bioinformatics methods, suggesting it is promising to apply gut microbiota to forensic personal identification.

scholarly journals Effects of cat ownership on the gut microbiota of owners

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253133
Author(s):  
Guankui Du ◽  
Hairong Huang ◽  
Qiwei Zhu ◽  
Li Ying
Keyword(s):  
Gut Microbiota ◽  
Microbial Diversity ◽  
Metabolic Pathways ◽  
Normal Weight ◽  
Female Group ◽  
Microbiota Composition ◽  
Pet Ownership ◽  
Weight Group ◽  
And Function ◽  
Gut Microbiota Composition

Pet ownership is an essential environmental exposure that might influence the health of the owner. This study’s primary objectives were to explore the effects of cat ownership on the gut microbial diversity and composition of owners. Raw data from the American Gut Project were obtained from the SRA database. A total of 214 Caucasian individuals (111 female) with cats and 214 individuals (111 female) without cats were used in the following analysis. OTU number showed significant alteration in the Cat group and Female_cat group, compared with that of the no cat (NC) group and Female_ NC group, respectively. Compared with the NC group, the microbial phylum Proteobacteria was significantly decreased in the Cat group. The microbial families Alcaligenaceae and Pasteurellaceae were significantly reduced, while Enterobacteriaceae and Pseudomonadaceae were significantly increased in the Cat group. Fifty metabolic pathways were predicted to be significantly changed in the Cat group. Twenty-one and 13 metabolic pathways were predicted to be significantly changed in the female_cat and male_cat groups, respectively. Moreover, the microbial phylum Cyanobacteria was significantly decreased, while the families Alcaligenaceae, Pseudomonadaceae and Enterobacteriaceae were significantly changed in the normal weight cat group. In addition, 41 and 7 metabolic pathways were predicted to be significantly changed in the normal-weight cat and overweight cat groups, respectively. Therefore, this study demonstrated that cat ownership could influence owners’ gut microbiota composition and function, especially in the female group and normal-weight group.

Dysbiosis of Fecal Microbiota in Allergic Rhinitis Patients

2020 ◽  
Vol 34 (5) ◽  
pp. 650-660 ◽  
Author(s):  
Xiang Liu ◽  
Jing Tao ◽  
Jing Li ◽  
Xiaolin Cao ◽  
Yong Li ◽  
...  
Keyword(s):  
Allergic Rhinitis ◽  
Statistical Analysis ◽  
Gut Microbiota ◽  
Study Groups ◽  
Fecal Microbiota ◽  
Rrna Gene ◽  
Microbiota Composition ◽  
Linear Discriminant ◽  
Α Diversity ◽  
Gut Microbiota Composition

Background The gut microbiota plays an important role in shaping the immune system and may be closely connected to the development of allergic diseases. Objective This study aimed to determine the gut microbiota composition in Chinese allergic rhinitis (AR) patients as compared with healthy controls (HCs). Methods We collected stool samples from 93 AR patients and 72 age- and sex-matched HCs. Gut microbiota composition was analyzed using QIIME targeting the 16S rRNA gene. Functional pathways were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States. Statistical analysis was performed using the R program, linear discriminant analysis effect size (LefSe), analysis of QIIME, and statistical analysis of metagenomic profiles, among other tests. Results Compared with HCs, AR patients had significantly lower gut-microbiota α-diversity ( P < .001). The gut microbiota composition significantly differed between the 2 study groups. At the phylum level, the relative abundance of Bacteroidetes was higher while those of Actinobacteria and Proteobacteria were lower in the AR group than in the HC group ( P < .001, q < 0.001). At the genus level, Escherichia-Shigella, Prevotella, and Parabacteroides ( P < .001, q < 0.001) had significantly higher relative abundances in the AR group than in the HC group. LefSe analysis indicated that Escherichia-Shigella, Lachnoclostridium, Parabacteroides, and Dialister were potential biomarkers for AR. In addition, predictive metagenome functional analysis showed that pyruvate, porphyrin, chlorophyll, purine metabolism, and peptidoglycan biosynthesis significantly differed between the AR and HC groups. Conclusion A comparison of the gut microbiota of AR patients and HCs suggested that dysbiosis of the fecal microbiota is involved in the development of AR. The present results may reveal key differences and identify targets for preventive or therapeutic intervention.

scholarly journals Dietary fiber intervention on gut microbiota composition in healthy adults: a systematic review and meta-analysis

2018 ◽  
Vol 107 (6) ◽  
pp. 965-983 ◽  
Author(s):  
Daniel So ◽  
Kevin Whelan ◽  
Megan Rossi ◽  
Mark Morrison ◽  
Gerald Holtmann ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gut Microbiota ◽  
Dietary Fiber ◽  
Meta Analysis ◽  
Healthy Adults ◽  
Microbiota Composition ◽  
Gut Microbiota Composition

Gut microbiota dysbiosis in polycystic ovary syndrome: association with obesity — a preliminary report

2020 ◽  
Vol 98 (11) ◽  
pp. 803-809 ◽  
Author(s):  
Yuanjiao Liang ◽  
Qi Ming ◽  
Jinlan Liang ◽  
Yan Zhang ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Gut Microbiota ◽  
Polycystic Ovary ◽  
Alpha Diversity ◽  
Shannon Index ◽  
Normal Weight ◽  
Microbiota Composition ◽  
Ovary Syndrome ◽  
Gut Microbiota Dysbiosis ◽  
Gut Microbiota Composition

The objective was to explore if and how the microbiota changed in polycystic ovary syndrome (PCOS) women compared with healthy women. Eight obese PCOS (PO group), 10 nonobese PCOS (PN group), and nine healthy normal weight women (control) (C group) were enrolled. Insulin (INS), testosterone (T), follicle-stimulating hormone (FSH), luteinizing hormone (LH), estrogen (E2), and dehydroepiandrosterone (DHEA) were detected with radioimmunoassay. Antimullerian hormone (AMH), fasting glucose, and hemoglobin A1c (HbA1c) were determined by a chemiluminescence immunoassay, glucose oxidase method, and HPLC, respectively. Gut microbiota composition was evaluated by PCR. Alpha diversity was assessed using Chao1 and the Shannon index. PCOS women showed significantly higher T, LH, and LH/FSH and lower FSH levels than the C group (p < 0.05). The AMH level was significantly higher in the PO than in the PN group (p < 0.05). The PO group presented a significantly higher fasting INS level and HMOA-IR scores than the other groups, lower observed SVs and alpha diversity than the C group, higher beta diversity than the PN group (p < 0.05), and decreased abundances of genera (mainly butyrate producers). Regression analysis showed that decreased abundances of several genera were correlated with higher circulating T and impaired glucose metabolism. PCOS is associated with changes in the gut microbiota composition. Obesity has a driving role in the development of dysbiotic gut microbiota in PCOS.

scholarly journals Relationship between Cardiorespiratory Fitness and Relative Gut Microbiota Composition in Healthy Adults

2018 ◽  
Vol 50 (5S) ◽  
pp. 626
Author(s):  
Ryan P. Durk ◽  
Esperanza Castillo ◽  
Leticia Márquez-Magaña ◽  
Gregory J. Grosicki ◽  
Nicole Bolter ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Cardiorespiratory Fitness ◽  
Healthy Adults ◽  
Microbiota Composition ◽  
Gut Microbiota Composition

scholarly journals Effects of Probiotic NVP-1704 on Mental Health and Sleep in Healthy Adults: An 8-Week Randomized, Double-Blind, Placebo-Controlled Trial

Nutrients ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 2660
Author(s):  
Hyuk Joo Lee ◽  
Jung Kyung Hong ◽  
Jeon-Kyung Kim ◽  
Dong-Hyun Kim ◽  
Seok Won Jang ◽  
...  
Keyword(s):  
Mental Health ◽  
Gut Microbiota ◽  
Sleep Quality ◽  
Sleep Disturbances ◽  
Lactobacillus Reuteri ◽  
Healthy Adults ◽  
Rrna Gene ◽  
Microbiota Composition ◽  
Bifidobacterium Adolescentis ◽  
Gut Microbiota Composition

The human gut microbiome is closely linked to mental health and sleep. We aimed to verify the efficacy and safety of probiotic NVP-1704, a mixture of Lactobacillus reuteri NK33 and Bifidobacterium adolescentis NK98, in improving stress, depression, anxiety, and sleep disturbances, along with the measurement of some blood biomarkers. A total of 156 healthy adults with subclinical symptoms of depression, anxiety, and insomnia were retrospectively registered and randomly assigned to receive either NVP-1704 (n = 78) or a placebo (n = 78) for eight weeks. Participants completed the Stress Response Inventory, Beck’s Depression and Anxiety Inventory, Pittsburg Sleep Quality Index, and Insomnia Severity Index at baseline, at four and eight weeks of treatment. Pre- and post-treatment blood tests for biomarkers were conducted. After intervention, gut microbiota composition was quantified by pyrosequencing the bacterial 16S rRNA gene. The NVP-1704 group had a more significant reduction in depressive symptoms at four and eight weeks of treatment, and anxiety symptoms at four weeks compared to the placebo group. Those receiving NVP-1704 also experienced an improvement in sleep quality. NVP-1704 treatment led to a decrease in serum interleukin-6 levels. Furthermore, NVP-1704 increased Bifidobacteriaceae and Lactobacillacea, whereas it decreased Enterobacteriaceae in the gut microbiota composition. Our findings suggest that probiotic NVP-1704 could be beneficial for mental health and sleep.

scholarly journals The First Report of Differences in Gut Microbiota Composition between Obese and Normal Weight Iranian Subjects

2020 ◽  
Vol 24 (3) ◽  
pp. 148-154
Author(s):  
Fateme Ettehad Marvasti ◽  
Arfa Moshiri ◽  
Mina Sadat Taghavi ◽  
Soheil Riazi ◽  
Majid Taati ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Normal Weight ◽  
Microbiota Composition ◽  
First Report ◽  
Gut Microbiota Composition

scholarly journals Associations between untargeted plasma metabolomic signatures and gut microbiota composition in the Milieu Intérieur population of healthy adults

2020 ◽  
pp. 1-11
Author(s):  
Valentin Partula ◽  
Mélanie Deschasaux-Tanguy ◽  
Stanislas Mondot ◽  
Agnès Victor-Bala ◽  
Nadia Bouchemal ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Multiple Testing ◽  
Large Scale ◽  
Linear Models ◽  
Healthy Adults ◽  
Rrna Gene ◽  
Microbiota Composition ◽  
Multiple Testing Correction ◽  
Negatively Associated ◽  
Gut Microbiota Composition

Abstract Host–microbial co-metabolism products are being increasingly recognised to play important roles in physiological processes. However, studies undertaking a comprehensive approach to consider host–microbial metabolic relationships remain scarce. Metabolomic analysis yielding detailed information regarding metabolites found in a given biological compartment holds promise for such an approach. This work aimed to explore the associations between host plasma metabolomic signatures and gut microbiota composition in healthy adults of the Milieu Intérieur study. For 846 subjects, gut microbiota composition was profiled through sequencing of the 16S rRNA gene in stools. Metabolomic signatures were generated through proton NMR analysis of plasma. The associations between metabolomic variables and α- and β-diversity indexes and relative taxa abundances were tested using multi-adjusted partial Spearman correlations, permutational ANOVA and multivariate associations with linear models, respectively. A multiple testing correction was applied (Benjamini–Hochberg, 10 % false discovery rate). Microbial richness was negatively associated with lipid-related signals and positively associated with amino acids, choline, creatinine, glucose and citrate (−0·133 ≤ Spearman’s ρ ≤ 0·126). Specific associations between metabolomic signals and abundances of taxa were detected (twenty-five at the genus level and nineteen at the species level): notably, numerous associations were observed for creatinine (positively associated with eleven species and negatively associated with Faecalibacterium prausnitzii). This large-scale population-based study highlights metabolites associated with gut microbial features and provides new insights into the understanding of complex host–gut microbiota metabolic relationships. In particular, our results support the implication of a ‘gut–kidney axis’. More studies providing a detailed exploration of these complex interactions and their implications for host health are needed.

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