Microbial Profiling of Appendicitis Patients

Quality filtering was performed on the raw reads to obtain high-quality clean reads. According to Cutadapt (v1.9.1) [18 (link)] (http://cutadapt.readthedocs.io/en/stable/), the reads were compared with the GOLD reference database (http://drive5.com/uchime/uchime_download.html) with the UCHIME algorithm (http://www.drive5.com/usearch/manual/uchime_algo.html) to detect and remove chimaeric sequences to obtain clean reads [19 (link), 20 (link)].
Sequence analysis was performed with UPARSE software (Uparse v7.0.1001) (http://drive5.com/uparse/) [21 (link)]. Sequences with ≥ 97% similarity were assigned to the same operational taxonomic units (OTUs). Representative sequences for each OTU were screened for further annotation. For each representative sequence, the SSU rRNA [22 (link)] database of Silva (http://www.arb-silva.de/) [23 (link)] was used based on the Mothur algorithm to annotate taxonomic information (set threshold from 0.8 to 1). For determination of the phylogenetic relationships of different OTUs and the difference in the dominant species in different samples (groups), multiple sequence alignments were conducted using MUSCLE (http://www.drive5.com/muscle/) Software (v3.8.31) [24 (link)]. OTUs abundance information was normalized using a standard sequence number corresponding to the sample with the fewest sequences. Subsequent analyses of alpha diversity and beta diversity were all performed based on these output normalized data.
Data are expressed as the mean ± standard error of the mean. Alpha diversity was applied to analyse the complexity of species diversity for a sample through 2 indices, observed species and Chao1 indices. Both of these indices in our samples were calculated with QIIME (Version 1.7.0). The Wilcox test in the agricolae package of R software (Version 2.15.3) was used to analyse the between-group difference in alpha diversity. Beta diversity was applied with Permutational multivariate analysis of variance (Adonis) analysis and the nonmetric multidimensional scaling (NMDS) analysis. NMDS analysis was based on Bray–Curtis dissimilarity and performed by the vegan software package of R software. The correlation between microbiome taxa and rosuvastatin effectiveness was assessed using linear discriminant analysis (LDA) effect size (LEfSe) at various taxonomic ranks [25 (link)]. An LDA score greater than 4.0 was defined as significant by default. LEfSe data were analysed using R software, and analysis of variance (ANOVA) was used to identify the relative abundance differences between groups. Tukey’s test was applied to perform post hoc tests, with P < 0.05 considered a significant difference. PICRUSt2 was performed using the OmicStudio Analysis (https://www.omicstudio.cn/analysis/) to predict the functional profiles of intestinal microbiome. T-test was used for analysing the OTU abundance from the same gut segment between the two groups OmicStudio tools (https://www.omicstudio.cn/tool) was utilized for statistical analyses and visualization of the identified pathways. R software was used for permutational multivariate analysis of variance (Adonis) to analyse the between-group differences in beta diversity. Group comparisons of histological scores were statistically analysed using independent-samples t-tests (SPSS 19.0). Statistical significance was accepted at P < 0.05. Twenty-five appendicitis-associated taxa reported previously (Table 1), such as Actinobacteria, Proteobacteria, and Fusobacteria, were analysed from our samples with/without dysbiosis [13 (link), 26 (link)–29 (link)].Table 1

Appendicitis-associated taxa reported in previous studies

Phylum	Genus	Species
Firmicutes Bacteroidetes Actinobacteria Proteobacteria Fusobacteria	Streptococcus Gemella Bacteroides Faecalibacterium Proteus Fusobacterium Rhizobium Porphyromonas Mogibacterium Prevotella Bilophila Dialister Anaerofilum Bergeyella Peptostreptococcus Fusibacter Parvimonas	Escherichia coli Bacteroides fragilis Porphyromonas endodontalis