• 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • Cell Counting Kit by the diversity and sparsity of correlati


    by the Cell Counting Kit and sparsity of correlation in human microbiome data
    2.0 Fluorometer (Invitrogen). The purified amplicons were then pooled sets [36]. SparCC was employed to represent co-abundance and co-
    in equimolar concentrations and the final concentration of the library exclusion networks between OTUs. For SparCC, 1000 bootstrap
    replicates were used to calculate significance values, and considered correlation coefficients greater or b0.2 and −0.2 respectively and p-values b .05. This set of iterative procedures were applied separately to normal, peritumor and tumor data sets to infer the basis correlation values within and/or between paired sampling sites. Visualization of the network was achieved using Cytoscape version 3.4.0.
    2.4. Statistical analysis
    For continuous variables, independent t-test, White's nonparametric t-test, and Mann-Whitney U test were applied. For categorical variables between groups, Pearson chi-square or Fisher's exact test was used, de-pending on assumption validity. For taxon among subgroups, ANOVA test was applied (Tukey-Kramer was used in Post-hoc test, Effect size was Eta-squared). For correlation analyses, Spearman's rank correlation test was used. False-discovery rate (FDR) was calculated according to Benjamini-Hochberg, FDR-corrected p values were denoted as QFDR and was used when performing all untargeted screening analyses of dif-ferent taxa. Statistical analysis was performed using the SPSS V19.0 (SPSS Inc., Chicago, IL) and STAMP V2.1.3 [33]. GraphPad Prism version 6.0 (San Diego, CA) was used for preparation of graphs. All tests of sig-nificance were two sided, and p b .05 or corrected p b .05 was consid-ered statistically significant.
    2.5. Accession number
    The sequence data from this study were deposited in the GenBank Sequence Read Archive with the accession number SRP128749.
    3. Results
    3.1. Altered gastric mucosal microbiota in GC microhabitats
    In the present study, the possible confounders of microbiota analy-ses such as the gender and age of GC patients in each GC stomach micro-habitat, were not significantly different (p N .05). To investigate the gastric microbiota in different stomach microhabitats, we obtained 39,188,435 high-quality reads with an average of 55,507 reads per sam-ple for the microbiota analysis (Table 2). Good's estimator of coverage was nearly 100%, indicating that the identified reads represented the majority of bacterial sequences present in the stomach. Diversity indi-ces, such as Shannon, Simpson and Heip evenness, were significantly decreased in peritumoral microhabitats (Fig. 1a–c), while richness indi-ces, such as ACE, observed species and phylogenetic diversity (PD) whole tree, were also decreased in peritumoral and tumoral microhab-itats (Fig. 1d–f). In normal microhabitats, higher species richness and more low-abundance OTUs were observed (Fig. 1g–h), with more than twice the number of unique OTUs obtained (Fig. 1i). Due to signif-icant inter-individual variation, principal coordinate analysis (PCoA) could not separate the three microhabitats into different clusters (Fig. S1).
    Generally, the gastric microbiota was dominated by Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Acidobacteria, and Fusobacteria in descending order (Fig. 2a), which is distinct from the dominant phyla identified in the gut microbiota [37,38]. The Proteobacteria/Firmicutes ratio was significantly increased in peritumoral microhabitats (6.41 ± 10.63 in normal microhabitat Vs. 
    11.09 ± 17.40 in peritumoral microhabitat, p = .000; 11.09 ± 17.40 in peritumoral microhabitat Vs. 4.66 ± 11.67 in tumoral microhabitat, p = .000; 6.41 ± 10.63 in normal microhabitat Vs. 4.66 ± 11.67 in tu-moral microhabitat, p = .177; Mann-Whitney U test). The top 16 fami-lies and 24 genera of the gastric microbiota are shown in Fig. 2b and c. Notably, the abundant genera, such as Helicobacter, Halomonas and Shewanella, were enriched in the peritumoral microhabitat, while Strep-tococcus, Selenomonas, Fusobacterium, Propionibacterium, and Coryne-bacterium were enriched in the tumoral microhabitat. A heatmap depicting the most abundant genera identified in the gastric microbiota demonstrated correlations between the stomach microhabitats and the abundance of selected genera (Fig. S2). Discriminant analyses using LEfSe showed that 16 bacterial phylotypes were significantly different among the three microhabitats (Fig. 2d–e). More differential bacterial phylotypes were also identified in the stomach microhabitats (Fig. S3). We also observed that the species of HP, Prevotella copri, Prevotella melaninogenica, Streptococcus anginosus, Propionibacterium acnes, Bacil-lus cereus, Bacteroides uniformis, Bacteroides fragilis and Akkermansia muciniphila were significantly different across the three groups (Fig. 2f). In contrast to previous reports, our deep sequencing data indi-cated that HP, P. copri and B. uniformis were significantly decreased, while P. melaninogenica, S. anginosus and P. acnes were increased in the tumoral microhabitat [39].