Abstract
To examine soil microbial functional gene diversity and causative factors in tropical rainforests, we used a microarray-based metagenomic tool named GeoChip 5.0 to profile it. We found that high microbial functional gene diversity and different soil microbial metabolic potential for biogeochemical processes were considered to exist in tropical rainforest. Soil available nitrogen was the most associated with soil microbial functional gene structure. Here, we mainly describe the experiment design, the data processing, and soil biogeochemical analyses attached to the study in details, which could be published on BMC microbiology Journal in 2015, whose raw data have been deposited in NCBI's Gene Expression Omnibus (accession number GSE69171).
Keywords: Soil microbial community, Functional gene diversity, GeoChip 5.0
| Specifications | |
|---|---|
| Organism/cell line/tissue | Uncultured bacterium |
| Sex | N/A |
| Sequencer or array type | GeoChip 5.0 |
| Data format | Raw data: txt; normalized data: txt |
| Experimental factors | Soil samples were collected from three tropical rainforest sites. |
| Experimental features | Exploring soil microbial functional gene diversity and causative factors based on the functional gene microarray GeoChip 5.0 in tropical rainforests |
| Consent | N/A |
| Sample source location | Jianfengling National Reserve (18°23′–18°52′N, 108°36′–109°05′E), Hainan, China |
1. Direct link to deposited data
2. Experimental design, materials and methods
2.1. Description of the study sites
The study sites are located on Jianfengling Forest Area (18°23′–18°52′N, 108°36′–109°05′E) in the southwestern of Hainan Island, which is one of primary tropical rainforests preserved. The climate is characterized by tropical monsoons, and the mean altitude is about 936 m. The mean annual temperature is about 24.5 °C, with an annual rainfall of 1600–2600 mm [1]. The dominant forest types include mossy forest, tropical montane rainforest, tropical evergreen monsoon forest, and semi-deciduous monsoon forest. The soil type of study sites is lateritic red earth, and their pH values are about 4.5 at the depth of 10 cm. This area is seriously influenced by nitrogen deposition.
To explore the soil microbial functional gene diversity and causative factors, we chose three forest sites (JFL-1, JFL-2, and JFL-3) in this study in March 2012, which have different plant dominant species.
Total of 24 plots were set in three sampling sites. In each site, eight of 20 m × 20 m plots were carried out with roughly 20 m interval between adjacent sampling plots. All soil samples were randomly collected at a depth of 10 cm by multiple sampling methods to ensure homogeneity. Through a 2 mm sieve, removing roots, stones, pebbles and gravels, soil was combined into a mixed sample. Then soil samples were stored at 4 °C for soil attribute analysis, and at − 80 °C for DNA analysis, respectively.
2.2. DNA extraction
Soil DNA extraction followed the manufacturer's instructions using a MoBioPowerSoil DNA isolation kit (MoBio Laboratories, Carlsbad, CA, USA). The extracted DNA was purified by a Genomic DNA Clean & Concentrator™ kit (Zymo, CA, USA) and assessed by UV absorbance ratios of 260 nm/280 nm and 260 nm/230 nm. Then DNA concentrations were quantified by a PicoGreen [2] using a FLUOstar Optima (BMG Labtech, Jena, Germany).
2.3. GeoChip 5.0 experiment
GeoChip 5.0 was manufactured by Agilent (Agilent Technologies Inc., Santa Clara, CA) in the 8 × 60 K format. 600 ng of purified soil DNA of each sample was labeled with the fluorescent dye Cy-3 (GE Healthcare, CA, USA) using a random priming method as described previously [3] purified using a QIAquick Purification kit (Qiagen, CA, USA), and dried in a SpeedVac (Thermo Savant, NY, USA) into a powder. Subsequently, the labeled DNA was resuspended into 27.5 μl of DNase/RNase-free distilled water, and mixed completely with 42 μl of hybridization solution, containing 1 × Acgh blocking, 1 × HI-RPM hybridization buffer, 10 pM universal standard DNA, 0.05 μg/μl Cot-1 DNA, and 10% formamide (final concentrations). After these, the solution was denatured at 95 °C for 3 min, and then incubated at 37 °C for 30 min, then hybridized with GeoChip 5.0 arrays (60 K). GeoChip hybridization was preceded at 67 °C in Agilent hybridization oven for 24 h. After hybridization, the slides were washed using Agilent Wash Buffers at room temperature. Then the arrays were scanned at 633 nm by a laser power of 100% and a photomultiplier tube gain of 75% with a NimbleGen MS200 Microarray Scanner (Roche NimbleGen, Inc., Madison, WI, USA). The images data were extracted by following Agilent Feature Extraction program.
2.4. Raw data processing
The microarray data were preprocessed by the microarray analysis pipeline on IEG website (http://ieg.ou.edu/microarray/) as previously described [3]. The main steps were in the following steps: (i) removing the spots of poor quality, which was a signal to noise ratio of less than 2.0; (ii) the relative abundance of each soil sample was calculated by dividing the total intensity of the detected probes, then multiplying a constant and taking the natural logarithm transformation; (iii) the detected probes in only two out of eight samples from the same sampling sites were removed.
2.5. Statistical analysis
A one-way analysis of variance was used to analyze the statistical differences at a significance level of P < 0.05. The Shannon index and Simpson index were used to characterize the soil microbial community diversity. The normalized signal intensity of each gene category was used to represent the gene relative abundance [4]. Detrended correspondence analysis was used to evaluate the differences of sampling sites. Canonical correspondence analysis was used to examine the linkages between soil microbial communities and environmental factors. The multivariate regression tree was used to determine the important factors in influencing the soil microbial functional gene diversity. All the data analyses were performed in the Vegan package (v. 1.15-1) in R (v. 2.9.1).
3. Discussion
In this study, we described the functional gene diversity and metabolic potential of soil microbial community in the tropical rainforests of JFL based on GeoChip 5.0 technology. GeoChip 5.0 is comprised of more than 57,000 oligonucleotide probes, covering over 144,000 gene sequences from 393 functional gene families involved in carbon, nitrogen, sulfur, phosphorus cycling and others.[5] We found that soil available nitrogen and plant diversity were significantly correlated with soil microbial functional gene structure. Our results indicated that the metabolic potential for soil microbial community could acclimatize to acid tropical rainforest soils.
Conflict of interest
We declare that there is no conflict of interest on our work published in this paper.
Acknowledgements
This research was supported by the public welfare project of the National Scientific Research Institution (CAFRIFEEP201101, CAFYBB2011004), China, and the National Biological Specimens and Resources Sharing Platform in Nature Reserve (2005DKA21404), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB15010302), and also supported by Graduate Student Research Innovation Project in Hunan Province (CX2014B095).
References
- 1.Zou F.-S., Chen G.-Z., Yang Q.-F. Bird species richness along an elevational gradient in a forest at Jianfengling, Hainan Island, China. Zool. Stud. 2012;51:362–371. [Google Scholar]
- 2.Ahn S.J., Costa J., Emanuel J.R. PicoGreen quantitation of DNA: effective evaluation of samples pre-or psost-PCR. Nucleic Acids Res. 1996;24(13):2623–2625. doi: 10.1093/nar/24.13.2623. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Tu Q., Yu H., He Z. GeoChip 4: a functional gene-array-based high-throughput environmental technology for microbial community analysis. Mol. Ecol. Resour. 2014;14(5):914–928. doi: 10.1111/1755-0998.12239. [DOI] [PubMed] [Google Scholar]
- 4.Bai S., Li J., He Z. GeoChip-based analysis of the functional gene diversity and metabolic potential of soil microbial communities of mangroves. Appl. Microbiol. Biotechnol. 2013;97(15):7035–7048. doi: 10.1007/s00253-012-4496-z. [DOI] [PubMed] [Google Scholar]
- 5.Wang C., Wang X., Liu D. Aridity threshold in controlling ecosystem nitrogen cycling in arid and semi-arid grasslands. Nat. Commun. 2014;5 doi: 10.1038/ncomms5799. [DOI] [PubMed] [Google Scholar]