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Human Gut Microbiota–Transplanted Gn Pig Models for HRV Infection
Published in Lijuan Yuan, Vaccine Efficacy Evaluation, 2022
To analyze LGG's effect on microbial community structures, we performed principal coordinate analysis (PCoA) on weighted UniFrac distances. The results showed that LGG-treated pig microbiota was distinct from those receiving no LGG (Figure 11.3A), supported by a permutational multivariate analysis (PERMANOVA) with a p-value of 0.005 at 999 permutations (Anderson, 2008). The human microbiota appeared to cluster closer with –LGG pigs. Figure 11.3B showed that the extent to which HRV changed microbiota depended on LGG. The HRV-caused microbiota change, measured by UniFrac distances between +HRV and –HRV pigs, was smaller for LGG-treated pigs than for no-LGG treated pigs (p < 0.001, Figure 11.3B), suggesting an interaction between LGG and HRV on the microbiota structure. Overall, LGG treatment could resist the change of microbial community structures caused by HRV challenge.
Assessing the Microbiome—Current and Future Technologies and Applications
Published in David Perlmutter, The Microbiome and the Brain, 2019
Thomas Gurry, Shrish Budree, Alim Ladha, Bharat Ramakrishna, Zain Kassam
On the other hand, beta diversity is an analysis technique used to compare diversity between samples (Jovel et al. 2016 and Olsen 2016). It is typically used to determine “how different” samples are from each other by effectively measuring the distance between samples because similar samples are “closer” together. This technique can be done with the supervision of phylogenetic data (e.g., UniFrac) or without it (e.g., Bray–Curtis dissimilarity). Once the beta diversity is computed, it is often displayed graphically by reducing the dimensionality of the dataset using either non-metric multidimensional scaling or principal coordinate analysis. These methods are extremely useful for both data visualization and clustering based on the covariates under investigation, but both rely on the assumption that variation in beta diversity can be explained by a few independent factors.
Cross-comparison of microbiota in the oropharynx, hypopharyngeal squamous cell carcinoma and their adjacent tissues through quantitative microbiome profiling
Published in Journal of Oral Microbiology, 2022
Hui-Ching Lau, Yujie Shen, Huiying Huang, Xiaohui Yuan, Mengyou Ji, Hongli Gong, Chi-Yao Hsueh, Liang Zhou
Consistent with a prior study on similar microbiome differences in HNC tumor and paired-normal tissue, our study revalidated the result [31]. AT group did have higher microbiota diversities, which HC did not in alpha-diversity. Intriguingly, two beta-diversities analyses (Bray-Curtis and Weighted UniFrac) yielded separate results. In the Bray-Curtis method, the microbial communities were significantly different, yet there was no difference found when using Weighted UniFrac analysis in QMP. We noticed that such similar proximity levels of taxonomy in AT and HC were because they are neighbored and contain similar biofilm. The Bray-Curtis analysis was used to discriminate the only variation of community in different groups, but Weighted UniFrac has also emphasized the taxonomy similarity in microbial communities. Given the interpretation of Bray-Curtis might be more appropriate in this scenario, their similarity could not be neglected. These subtle differences could only be observed in abundance-based QMP, and are absent in RMP. Combining QMP with RMP, we speculate that the compositions in AT group and HC group were the same, and the difference between them comes from their absolute abundance.
Altered composition of the oral microbiome in integrin beta 6-deficient mouse
Published in Journal of Oral Microbiology, 2022
Osamu Uehara, Jiarui Bi, Deshu Zhuang, Leeni Koivisto, Yoshihiro Abiko, Lari Häkkinen, Hannu Larjava
Metagenomic sequencing data were analyzed using the software package Quantitative Insights into Microbial Ecology 2 (QIIME2 v2020.2) against the 16S rRNA gene sequences that were assigned to the 16S rDNA database (Greengenes v13.8). Analysis of the amplicon sequence data employed the DADA2 pipeline. To evaluate alpha diversity, Simpson, Chao1, Shannon, Goods coverage, observed features and ACE were used. Statistical significance was set at p < 0.05. The sequencing depth was determined to be 91,349 reads from alpha rarefaction. To evaluate beta diversity, three-dimensional Principal Coordinate Analysis (PCoA) was used to visually compare microbial composition across groups in UniFrac scatterplots. Bacterial community differences with each group were analyzed using the weighted UniFrac and unweighted UniFrac. Statistical significance was set at p < 0.05.
Differences in the oral and intestinal microbiotas in pregnant women varying in periodontitis and gestational diabetes mellitus conditions
Published in Journal of Oral Microbiology, 2021
Xin Zhang, Pei Wang, Liangkun Ma, Rongjun Guo, Yongjing Zhang, Peng Wang, Jizhi Zhao, Juntao Liu
A combined weighted UniFrac analysis showed a clear separation between intestinal microbiota and oral microbiota. Although salivary, supragingival and subgingival microbiotas partially overlapped, obvious differences among the three floras were also demonstrated (Figure 1). Significant differences were defined between any two groups from different body sites by AMOVA analysis (p < 0.05, Table S1). When defined by GDM, no significant separation was found between groups with and without GDM in any of the four habitats. When defined by periodontitis, a difference was found between the periodontitis group and the non-periodontitis group in both subgingival samples and supragingival samples. The differences in subgingival samples were significant by AMOVA analysis (p = 0.001, Table S1). Samples with periodontitis were located higher than those without periodontitis. In salivary and intestinal samples, no clear differences were found.