GeoChip as a metagenomics tool to analyze the microbial gene diversity along an elevation gradient

Abstract

To examine microbial responses to climate change, we used a microarray-based metagenomics tool named GeoChip 4.0 to profile soil microbial functional genes along four sites/elevations of a Tibetan mountainous grassland. We found that microbial communities differed among four elevations. Soil pH, temperature, NH4+–N and vegetation diversity were four major attributes affecting soil microbial communities. Here we describe in details the experiment design, the data normalization process, soil and vegetation analyses associated with the study published on ISME Journal in 2014 [1], whose raw data have been uploaded to Gene Expression Omnibus (accession number GSM1185243).

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Data in Brief
GeoChip as a metagenomics tool to analyze the microbial gene diversity
along an elevation gradient
Ying Gao
a
, Shiping Wang
b
,DepengXu
a
,HaoYu
c
,LinweiWu
a
, Qiaoyan Lin
d
, Yigang Hu
d,e
, Xiangzhen Li
f
,
Zhili He
c
,YeDeng
c
, Jizhong Zhou
a,c,g
, Yunfeng Yang
a,
⁎
a
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
b
Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100085, China
c
Institute for Environmental Genomics, Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019, USA
d
Key Laboratory of Adaption and Evolution of Plateau Biota,Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
e
Shapotou Desert Experiment and Research Station, Cold and Arid Regions and Environmental & Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
f
Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China
g
Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
abstractarticle info
Article history:
Received 6 May 2014
Received in revised form 3 June 2014
Accepted 3 June 2014
Available online 11 June 2014
Keywords:
Gene diversity
Soil microbial community
GeoChip 4.0
Genomic technology
To examine microbial responses to climate change, we used a microarray-based metagenomics tool named
GeoChip 4.0 to profile soil microbial functional genes along four sites/elevations of a Tibetan mountainous grass-
land. We found that microbial communities differed among four elevations. Soil pH, temperature, NH
4
+
–Nand
vegetation diversity were four major attributes affecting soil microbial communities. Here we describe in details
the experiment design, the data normalization process, soil and vegetation analyses associated with the study
published on ISME Journal in 2014 [1], whose raw data have been uploaded to Gene Expression Omnibus (acces-
sion number GSM1185243).
© 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Direct link to deposited data
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1185243
Experimental design, materials and methods
Description of the sites
This experiment was conducted at an alpine meadow in the Haibei
Alpine Meadow Ecosystem Research Station of Chinese Academy of
Science, which is located in a large valley surrounded by the Qilian
Mountain of the northeast of Qinghai-Tibet Plateau (37
°
37′N, 101
°
12E
′) in Qinghai province. It has a typical highland continental climate
with cold and long winter but cool and short summer. The annual
mean air temperature recorded at the station is −1.7 °C [2]. The day/
night temperature variation is substantial due to strong sun radiation.
The annual mean precipitation is 560 mm and 85% of rainfall is within
the growing season from May to September [3].
The dominant soil type at the station is Mat Cryic Cambisols (a typ-
ical alpine grassland soil) and its pH values are 7.3 and 7.4 at depths of
10 and 20 cm, respectively. Aboveground plant biomass increases
from May to July, reaches the maximum level in late July and early
August, and withers in early October. Over 80% of vegetation species
use C
3
photosynthetic pathway for carbon fixation [3].
This experiment, designed to study the effects of climate changes
with the space-substitutes-time strategy, was set at foursites/elevations
Genomics Data 2 (2014) 132–134
⁎Corresponding author. Tel.: +86 10 62784692; fax: +86 10 62794006.
E-mail address: yangyf@tsinghua.edu.cn (Y. Yang).
Specifications
Organism Uncultured bacterium
Sequencer or array type GeoChip 4.0
Data format Raw data: TXT, normalized data: TXT
Experimental factors Soil samples were collected from four elevations:
3200 m, 3400 m, 3600 m and 3800 m.
Experimental features Profiling microbial functional potentials with a
microarray-based metagenomics tool named
GeoChip 4.0 along an elevation gradient in a
Tibetan grassland.
Consent n/a
Sample source location The Haibei Alpine Meadow Ecosystem Research
Station (37
°
37′N, 101
°
12E′), Qinghai, China,
http://dx.doi.org/10.1016/j.gdata.2014.06.003
2213-5960/© 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Contents lists available at ScienceDirect
Genomics Data
journal homepage: http://www.journals.elsevier.com/genomics-data/

of 3200, 3400, 3600 and 3800 m in May 2006. The spatial distances be-
tween adjacent sites are 6.2 km (3200–3400 m), 4.2 km (3400–3600 m)
and 1.3 km (3600–3800 m), respectively.
The sites have typical vegetation and soil attributes for their respec-
tive elevations. The alpine meadow plant community at 3200 m is
largely dominated by Kobresia humilis,Elymus nutans,Stipa aliena,
Potentilla anserine and Thalictrum alpinum. The plant community at
3400 m is primarily dominated by Potentilla fruticosa shrub meadow
and grass species of K. humilis,E. nutans and Festuca ovina.Theplant
community at 3600 m site is dominated by K. humilis,Potentilla nivea,
Thalictrum alpinum,Carex atrofusca,Poa crymophila and P. fruticosa.
At the 3800 site, the plant community is dominated by K. humilis,
P. crymophila,Androsace mariae,Polygonum macrophyllum and
Kobresia pygmaea. Due to the short growth period, aboveground
plant biomass has low primary production and diversity.
Three 1.0 × 1.0 × 0.3 m
3
plots were fenced at each elevation/site to
prevent disturbance. The distance between two adjacent plots was
roughly 0.6 m. In August 2009, soil at a depth of 0–20 cm was collected
from all plots. Briefly, soil samples were collected randomly at five loca-
tions of every plot to ensure homogeneity. Then soil cores were mixed
thoroughly on a clean tray. After materials such as roots, stones, pebbles
and gravels were removed, soil was combined into a composite sample.
Soil was sieved through a 2 mm sieve and stored at 4 °C for soil attribute
measurements or −80 °C until DNA extraction. All tools were sterilized
with 70% alcohol.
DNA extraction
Soil metagenomic DNA was extracted using a FastDNA spin kit for
soil (MP Biomedical, Carlsbad, CA, USA) following the manufacturer's
instructions and precipitated with 100% ethanol and 0.3 M NaOAc.
DNA purity was assessed by UV absorbance ratios of A260/A280
(N1.8) and A260/A230 (N1.7), and DNA concentrations were measured
with a PicoGreen method [4].
GeoChip 4.0 experiment
The labeling and hybridization of soil DNA were conducted as
previously prescribed [5].Atotalof2μg extracted DNA was mixed
with 20 μlrandomprimers,containing2.5μl deoxynucleoside triphos-
phate (dNTP) (5 mM dATP/dGTP/dCTP, 2.5 mM dTTP), 1 μl Cy5 dUTP
(Amersham, Piscataway, NJ) and 80 U of the large Klenow fragment
(Invitrogen, Carlsbad, CA). Then DNA mixture was treated at 99.9 °C
for 5 min and chilled immediately to denature DNA, followed by addi-
tion of 2.5 μl of water and incubation at 37 °C for 3 h. Finally, the mixture
was heated at 95 °C for 3 min to terminate DNA labeling.
Labeled DNA was purified using the QIA quick purification kit
(Qiagen, Valencia, CA, USA) following manufacturer's instructions and
measured by NanoDrop ND-1000 spectrophotometer to assess label-
ing efficiency. DNA was then dried in the SpeedVac (ThermoSavant,
Milford, MA, USA) at 45 °C for 45 min.
DNA hybridization
Labeled DNA was dissolved in 50 μl hybridization buffer (40%
formamide, 25% SSC, 5 μg of unlabeled herring sperm DNA [Promega,
Madison, WI], and 0.1% SDS) and 2 μl universal standard DNA
(0.2 pmol μl
−1
) labeled with fluorescent dye Cy5. The samples were
then mixed by vortexing, incubated at 95 °C for 5 min, and maintained
at 50 °C until hybridization. Microarrays were scanned by a NimbleGen
MS 200 Microarray Scanner (Roche NimbleGen, Madison, WI) for
approximately 16 h at 42 °C. Then scanned images were quantified by
NimbleScan software as previously described [6].
Raw data processing
Data of signal intensities were uploaded to the laboratory's Microar-
ray Data Manager System (http://ieg.ou.edu/microarray/)[1,5,6].Then
we processed them in the following steps: (i) spots of poor quality
were removed, which were flagged as 1 or 3 by ImaGene (Arrayit, Sun-
nyvale, CA, USA) or with a signal to noise ratio of less than 2.0; (ii) the
relative abundance of each sample was calculated by dividing the total
intensity of the microarray, then multiplying by a constant and applying
natural logarithm transformation; and (iii) probes detected in only one
out of three replicates were removed to improve data quality.
Statistical analysis
Principal componentanalysis (PCA) was used to measure the overall
functional gene structure. Bray–Curtis distance was used to obtain
dissimilarity matrices in the adonis algorithm of the dissimilarity test
for comparing GeoChip data of four elevations. The similarity test,
Mantel test, Canonical correspondence analysis (CCA) and Variation
partitioning analysis (VPA) were usedto evaluate the linkages between
microbial gene compositions and environmental attributes. In the
similarity test, Euclidean distance was used to calculate the distance
between samples, followed by calculation of Pearson correlation coeffi-
cient. To select attributes in CCA modeling, we used variation inflation
factors (VIF) to examine whether the variance of canonical coefficients
was inflated by the presence of correlations with other attributes. If an
attribute had a variation inflation factor value larger than 20, we
deemed it to depend on other attributes and consequently removed it
from the CCA modeling. Correlation coefficients (r) were calculated
using Pearson's correlation. The normalized total gene abundance for
each functional gene was the average of the total gene abundance
from all the replicates and all data are presented as mean ± s.e. The
least significant difference (LSD) test was used to compare the signifi-
cance of differences in relative abundance among four elevations. All
of the analyses were performed with the Vegan package (v.1.15-1)
using R, version 2.8.1 (R Foundation for Statistical Computing, Vienna,
Austria).
Discussion
Here we describe a dataset of GeoChip 4.0 for profiling functional
potentials of microbial community along four elevations in a grassland
of the Tibetan plateau (Table 1). GeoChip is comprised of approximately
82,000 probes covering 410 functionalgene families related to microbi-
al carbon, nitrogen, sulfur, phosphorus cycling and others [6].We
showed that microbial gene abundances were correlated with green-
house gas emissions. Therefore, it is possible to assess soil biogeochem-
ical cycles based on measurements of microbial gene abundance.
Table 1
Number of detected genes at four elevations.
Gene categories 3200 3400 3600 3800 All elevations
a
Antibiotic resistance 903 1668 1548 1495 1818
Bacteria phage 158 424 358 336 468
Bioleaching 192 389 341 329 431
Carbon cycling 2979 5953 5485 5384 6476
Energy process 258 516 479 471 559
Metal Resistance 2832 5352 4999 4887 5757
Nitrogen 2110 4181 3889 3820 4520
Organic Remediation 5926 10,692 10,333 10,122 11,490
Other category 561 1192 1101 1069 1324
Phosphorus 370 764 687 682 829
Stress 5389 11,010 9828 9616 11,958
Sulfur 773 1724 1591 1553 1894
Virulence 918 1820 1643 1574 1996
Total 23,369 45,685 42,282 41,338 49,520
a
The number of genes detected at any of all four elevations.
133Y. Gao et al. / Genomics Data 2 (2014) 132–134

Furthermore, it can be used to predict the impact of further climate
changes in this region on functional potentials of microbial communities.
Conflict of interest
The authors declare that there is no conflict of interest on any work
published in this paper.
Acknowledgments
We thank Haibei Research Station staff for sampling assistance.
This research was supported by grants to Yunfeng Yang from the
National Science Foundation of China (41171201) and the National
>High Technology Research and Development Program of China
(2012AA061401). To Shiping Wang from the National Basic Research
Program (2010CB833502), to Jizhong Zhou from the United States
Department of Energy, Biological Systems Research on the Role of Micro-
bial Communities in C Cycling Program (DE-SC0004601), and Oklahoma
Bioenergy Center (OBC). The GeoChips and associated computational
pipelines used in this study were supported by ENIGMA-Ecosystems
and Networks Integrated with Genes and Molecular Assemblies through
the Office of Science, Office of Biological and Environmental Research, of
the US Department of Energy under Contract No. DE-AC02-05CH11231
and by the United States Department of Agriculture (Project 2007-
35319-18305) through NSF-USDA Microbial Observatories Program.
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