Malus sieversii (Ldb.) Roem. Rhizosphere Microorganism Database
Due to soil nutrient loss, grazing, climate change, and diseases and insect pest outbreaks, the rhizosphere microecological imbalance of wild apple forests has aggravated the degradation and death of wild apples. 16S/18S high-throughput sequencing, PICRUSt2 and BugBase functional prediction, and a redundancy analysis method were used in this study to analyze the rhizosphere bacterial/archaeal and eukaryotic communities of M. sieversii (Ldb.) Roem. in eight regions of the Ili River. The results indicated that the bacterial/archaeal operational taxonomic units (OTUs), Shannon value, and community composition were significantly lower in regions A, E, and F than in other regions, and the abundance of Acidobacteriota, Verrucomicrobiota, Planctomycetota, Chloroflexi, and Methylomirabilota was also very low. The community composition of region B was similar to regions C, D, G, and H, but its OUTs and Shannon value were lower. However, the composition of dominant eukaryotic communities in the eight regions was relatively similar, and the difference was smaller than the relative abundance of bacterial/archaeal communities. The functional prediction results further confirmed the microbial phenotypes and protein functions related to the metabolism of carbohydrates, amino acids, and vitamins in the wild apple rhizosphere. Through redundancy analysis, it was found that the relationship between environmental factors and rhizosphere microorganisms, such as Acidobacteriota, Verrucomicrobiota, Planctomycetota, Chloroflexi, and Methylomirabilota microbial communities with a relatively lower abundance in regions A, B, E, and F, was affected by soil pH, alkaline-hydrolyzable nitrogen, available phosphorus, and potassium, altitude, and relative humidity. The results showed that the number and diversity of rhizosphere bacteria/archaea in the 10-20 cm of wild apples in regions A, B, E, and F were relatively lower, and greatly affected by the external environment.
Steps to reproduce
Microbiome bioinformatics was performed with QIIME 2 with slight modifications according to the official tutorials. Briefly, raw sequence data were demultiplexed using the demux plugin, followed by primers cutting with the cutadapt plugin (Martin, 2011). Sequences were quality-filtered, denoized, merged, and chimera-removed using the DADA2 plugin (Callahan et al., 2016). Species annotation was performed using QIIME 2. For 16S/18S, the annotation database was Silva Database; for ITS, it was Unite Database. To study the phylogenetic relationship of each ASV and the differences in the dominant species among different samples(groups), multiple sequence alignment was performed using QIIME 2.-diversity analysis was used to analyze the complexity of species diversity for a sample through six indices, including Observed- species, Chao1, Shannon, Simpson, ACE, and Good-coverage. All indices the samples were calculated by QIIME 2 and displayed with R. - Diversity analysis was used to evaluate sample differences in species complexity, and -diversity on both weighted and unweighted Unifrac was calculated by QIIME 2. Cluster analysis was preceded by principal component analysis, which was applied to reduce the dimension of the original variables using the statpackage and ggbiplot packages in R version 3.6.2. PCoA was performed to obtain principal coordinates and visualize sample differences in complex multidimensional data. A matrix of weighted or unweighted Unifrac distances among samples obtained previously was transformed into a new set of orthogonal axes, where the maximum variation factor was demonstrated by the first principal coordinate, the second maximum variation factor was demonstrated by the second principal coordinate, and so on. The three-dimensional PCoA results were displayed using QIIME 2 package, whereas the two-dimensional PCoA results were displayed using the ade and ggplot2 packages in R, version 3.6.2. To confirm the differences in the abundance of individual taxonomy or function annotation between the groups, Metastats and STAMP were utilized. LEfSe analysis (LDA score threshold of 4) was used to quantitatively analyze biomarkers within different groups. This method was designed to analyze data in which the number of species or function annotation is much higher than the number of samples and provide biological class explanations to establish statistical significance, biological consistency, and effect-size estimation of predicted biomarkers. To identify differences in microbial communities between two groups, an analysis of variance was performed based on the Bray–Curtis dissimilarity distance matrices.16S/18S high-throughput sequencing (QIIME 2); PICRUSt2 and BugBase functional Prediction; redundancy analysis (R 3.6.2); Soil nutrient data obtained by UV spectrophotometer (UV-1800) and flame spectrophotometer (FSP6620); Survey to obtain sampling point information (GPS; UT333).