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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.
Maintaining oral health for a hundred years and more? - An analysis of microbial and salivary factors in a cohort of centenarians
Published in Journal of Oral Microbiology, 2022
Caroline Sekundo, Eva Langowski, Diana Wolff, Sébastien Boutin, Cornelia Frese
16S data were analyzed using R 3.1.4 with the R package dada2 as previously described [42]. Descriptive indices as alpha-diversity (Shannon index), richness (numbers of ASVs observed), evenness (Pielou index), and dominance (Berger-Parker index) were calculated using the package microbiome. The impact of clinical parameters and sources of sampling on these indices was analyzed using the Mann-Whitney-Wilcoxon test for categorical data and the spearman correlation for ranked, scored or measured clinical data. Beta-diversity was assessed by calculating weighted Unifrac distances. Principal coordinate analysis (PCoA) was used to illustrate clustering of samples. PERMANOVA was performed to assess the statistical significance of differences between the two samples (plaque and saliva) and the impact of the different clinical parameters on the microbial structure. Differential abundance between groups was evaluated using Deseq2 [47]. p values are purely descriptive and regarded considerable if ≤ 0.05.
Quantitative insights into effects of intrapartum antibiotics and birth mode on infant gut microbiota in relation to well-being during the first year of life
Published in Gut Microbes, 2022
Roosa Jokela, Katri Korpela, Ching Jian, Evgenia Dikareva, Anne Nikkonen, Terhi Saisto, Kirsi Skogberg, Willem M. de Vos, Kaija-Leena Kolho, Anne Salonen
The statistical analysis was conducted in R version 3.6.3 with the package mare,61 with tools from packages vegan,66MASS,67 and nlme.68 The effects of birth mode and IP antibiotics on the abundances of the bacterial taxa were analyzed using, primarily, negative binomial, secondarily, Poisson, or tertiarily, quasi-Poisson models, depending on the data distribution and model fit, using the VD group without antibiotics as the reference group. If the fitted model failed to fulfill model assumptions (primarily heteroscedasticity of the residuals), generalized least-squares models were used. Only the genera observed in >30% of the samples were analyzed individually. To account for multiple comparisons, only results with false discovery rate (FDR) values <0.1 were considered significant. All models were adjusted for infant probiotic intake at the time of sampling (none, Lactobacillus spp., Bifidobacterium spp. or both, or Saccharomyces spp.), feeding type, including breastfeeding status (none, partial, exclusive) and weeks since the introduction of solids, and the sample treatment history, including possible PCR modifications and sequencing run ID. Principal coordinate analysis was done by log-transforming the data and calculating Pearson correlation-based distances between samples. Analysis was done using the capscale function in the R package vegan. The significance of the group differences at each time point in the first two principal coordinates was analyzed using ANOVA.
Gut microbiota modulate radiotherapy-associated antitumor immune responses against hepatocellular carcinoma Via STING signaling
Published in Gut Microbes, 2022
Zongjuan Li, Yang Zhang, Weifeng Hong, Biao Wang, Yixing Chen, Ping Yang, Jian Zhou, Jia Fan, Zhaochong Zeng, Shisuo Du
We performed a prospective longitudinal trial in 24 HCC patients who had received RT. The baseline demographic and clinical characteristics of the patients are shown in Table 1. We recruited 46 healthy individuals as controls (6 from the Zhongshan cohort and 40 from public data [PRJNA736821]). The gut microbiome was characterized by 16S rRNA sequencing. The rarefaction curve showed that OTU richness in each sample approached saturation (Figure S1a–c). As estimated by the Chao (Figure S1a and d), Shannon (Figure S1b and e), and Simpson indices (Figure S1c and f), gut microbe diversity was significantly lower in non-responders (NRs) than in healthy controls and responders (according to the best clinical responses determined by RECIST1.1). The observed OTUs in the R group were comparable to those of the healthy controls (Figure S1). A principal coordinate analysis (PcoA) was used to illustrate the microbiome space of different samples, the healthy control, R, and NR groups were distributed in three distinct clusters, representing significant differences in gut microbiome polymorphisms (Figure 1a). Compared to the NR group samples, the distribution of the R group samples was closer to that observed for the healthy control group samples; this indicates greater similarities in the microbial community between these two groups. These findings imply that serious dysbiosis in the gut microbiome of HCC patients is related to responsiveness to RT.