The ecological analysis of the soil microbial community in the world has revealed that desert microorganisms are phylogenetically and functionally different from other biological communities and that diversity of function related to nutrient circulation is relatively low. In order to properly assess the impact of climate change on land productivity, it is necessary to determine the resistance of these low diversity microorganisms and deepen their understanding of the potential response to the dry environment. Soil microbial analysis requires analysis across the gradient of dryness. In this study, we utilize such extreme condition present in the Atacama Desert, located in northern part of Chile, to identify the deciding factors that explain the changes in soil microbial community. Soil samples were taken from two transects (Yungay, Baquedano) crossing the Atacama desert from the east to the west. Plot coverage, geochemical measurements, soil relative humidity and temperature are recorded at each point.
・Metadata from obtained samples
Only 10% of the actual data will be used here.
・Full Length Sequence OTUs (99%) from Greengenes
Soil samples were collected from two parallel west-east zones across the Atacama desert, from the Pacific near Antofagasta to the slope of the Andean western part near the Argentine border (Figure 1). From an area of 1,000 to 2,000 m in altitude with no vegetation for a few million years(rainfall less than 5 mm) to the dry area located on the west slope of Andean with vegetation with precipitation of 36 to 115 mm It is extending to the east, we can see that the transect is extending to the east. For the location, total of 22 points (12 points in Yungay (YUN; Antofagasta to Paso de Socompa) and 10 points in Baquedano (BAQ; Baquedano to Paso Jama) were chosen for sampling. We sampled three soil microbial communities (amplicon analysis of 16S) at each point.
Figure 1. Locations for sampling (Julia W. Neilson et al. 2017)
Separating sequence data.
Based on the sample barcode described in the metadata, we will distribute the sequence data for each sample. Here, we can obtain information such as how many reads for each sample were obtained, and the quality of the array (Figure 2A, B, Figure 3).
In the obtained table, array/feature information (OTU in QIIME 1) after quality control of each sample are described below.
Figure 2A. Quality scatter plot on Forward array
Figure 2B. Quality scatter plot on Reverse array
Figure 3. Reads per Sample
Figure 4. Sequence Feature Information
・Performing composition analysis of species
We used Naïve Bayes classifier to assign seeds to array data. Here Greengenes 13.8 with 99% OTUs was used. We also visualized the relative abundance of microorganisms as a bar chart. We then sorted the sample classification level by “Taxnonomic Level” and sample by metadata category “Sort Samples By”.
When samples were classified by mean relative humidity of the soil, archaebacteria were not found in the most dry places (Figure 5A). In addition, bacteria such as Acidobacteria, Proteobacteria, Verrucomicrobia, Nitrospirae, and Elusimicrobia showed a decreasing trend when relative humidity also dropped in the soil (Figure 5B).
Figure 5A. Bar graph showing the relative abundance in the bacterial community (at Kingdom level)
Figure 5B. Bar graph showing relative abundance of bacterial community (Phylum level)
Analyze α diversity and β diversity
· Shannon Diversity Index Number of OTUs
· Phylogenetic diversity of Faith
· Evenness (a measure of community uniformity)
Calculates by default.
· Jaccard distance (qualitative measure of community dissimilarity)/Bray-Curtis distance (a quantitative measure of community dissimilarities)
· Unweighted UniFrac distance (Qualitative measure of community dissimilarity incorporating systematic relationships between features)
UniFrac distance (quantitative measure of community dissimilarity incorporating the phylogenetic relation between features)
There were no specific bacterial communities among the samples obtained with the two transects of Yungay and Baquedano, and the bacterial community between the two transects was similar (Fig. 6, 7).
Drawing the rarefaction curve based on the diversity of the microbial community (Faith’s phylogenetic diversity index) the analysis revealed that the diversity of the bacterial community declined with the decrease in the average soil relative humidity (Figure 8). Suggesting that the effect of increased dryness on soil microorganisms is amplified in more dry ecosystems.
Figure 6. Scatter plot of PCoA at unweighted UniFrac distance
Figure 7. Box plot at unweighted UniFrac distance
Figure 8. Rare faction curve classified by mean soil relative humidity
- Neilson JW, Califf K, Cardona C, Copeland A, van Treuren W, Josephson KL, Knight R, Gilbert JA, Quade J, Caporaso JG, Maier RM. Significant Impacts of Increasing Aridity on the Arid Soil Microbiome. mSystems. 2017 May 30;2(3). pii: e00195-16. doi: 10.1128/mSystems.00195-16. eCollection 2017 May-Jun. PubMed PMID: 28593197; PubMed Central PMCID: PMC5451488.
- “Atacama soil microbiome” tutorial
- Fierer N, Leff JW, Adams BJ, Nielsen UN, Bates ST, Lauber CL, Owens S, Gilbert JA, Wall DH, Caporaso JG. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc Natl Acad Sci U S A. 2012 Dec 26;109(52):21390-5. doi: 10.1073/pnas.1215210110. Epub 2012 Dec 10. PubMed PMID: 23236140; PubMed Central PMCID: PMC3535587.
- 微生物群集解析ツール QIIME 2 を使う．Part1.（インストール，ファイルインポート編）
- 微生物群集解析ツール QIIME 2 を使う．Part2.（QC，FeatureTable生成編）