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Effects of freeze-thaw cycles on High Arctic soil bacterial communities
Journal Pre-proof Effects of freeze-thaw cycles on High Arctic soil bacterial communities P.P.J. Lim, D.A. Pearce, P. Convey, L.S. Lee, K.G. Chan, G.Y.A. Tan
PII:
S1873-9652(19)30131-8
DOI:
https://doi.org/10.1016/j.polar.2019.100487
Reference:
POLAR 100487
To appear in:
Polar Science
Received Date: 15 July 2018 Revised Date:
28 August 2019
Accepted Date: 12 November 2019
Please cite this article as: Lim, P.P.J., Pearce, D.A., Convey, P., Lee, L.S., Chan, K.G., Tan, G.Y.A., Effects of freeze-thaw cycles on High Arctic soil bacterial communities, Polar Science, https:// doi.org/10.1016/j.polar.2019.100487. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
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Title Page
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Effects of freeze-thaw cycles on High Arctic soil bacterial communities
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Lim, P.P.J.1, Pearce, D.A.2, Convey, P.3, Lee, L.S.1, Chan, K.G.1* and Tan, G.Y.A.1,4*
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Lumpur, Malaysia
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University at Newcastle, Newcastle uponTyne NE1 8ST, United Kingdom
Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 Kuala
Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria
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United Kingdom
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Lumpur, Malaysia
British Antarctic Survey, NERC, High Cross, Madingley Road, Cambridge CB3 OET,
National Antarctic Research Centre, IPS Building, University of Malaya, 50603 Kuala
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*Corresponding author email: [email protected], [email protected]
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Abstract
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The projected increase in freeze-thaw frequency associated with warmer temperatures in
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the High Arctic could affect the dynamics of soil bacterial communities. We report here the
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effects of freeze-thaw (FT) cycles on High Arctic bacterial communities of soil samples
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collected from three sites with different depths of snow cover. Analysis of 16S rRNA gene
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amplicon sequences showed that bacterial diversity in soil sampled under high snow cover
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were significantly different from those under low snow cover and those with no snow cover,
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and showed little change in community diversity after nine consecutive FT cycles.
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Conversely, bacterial diversity in soil samples under low and with no snow cover decreased
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after the simulated FT cycles. It is therefore likely that reduced snow cover will influence soil
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bacterial community structure through an increased frequency of freeze-thaw cycling.
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Keywords: Freeze-thaw, high throughput sequencing, Svalbard, soil, bacteria.
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1. Introduction
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The North polar region is expected to continue warming more quickly than other regions on
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Earth at lower latitudes, a phenomenon known as Polar amplification (IPCC 2014; Symon et
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al. 2005). According to the NOAA Global Climate Report for March 2018, the anomaly of
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Northern Hemisphere land temperature was +1.75°C, which was higher than that of the
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global land temperature (+0.83°C). Although vast areas of the High Arctic region are
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permanently frozen and the temperature of the soil is ameliorated by an insulating snow pack.
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Snow cover data sampled from Svalbard indicated a general and statistically significant
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increase of snow depths with terrain elevation (Möller and Möller, 2019). Warmer winters on
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Svalbard may reduce snow accumulation leading to a decrease in snow depth. Due to the
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reduced or absent snowpack, Arctic soils may become more vulnerable to temperature
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fluctuations and to more frequent freeze-thaw (FT) cycling (Henry, 2008; Førland et al. 2011).
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Indeed, changes in the FT cycles have already been shown to have substantial effects on
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Arctic soil microbial communities and associated nutrient cycling functions (Kumar et al.
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2013; Larsen et al. 2002; Sawicka et al. 2009).
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Snow cover is known to control ground thermal regime in cold regions and is an excellent
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shield from temperature fluctuation. Greater insulation is provided with increasing snow
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depth and hence the underlying soil is subjected to reduced variation in soil temperature and
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cycle rate (Lütz 2011; Pauli et al. 2013; Tan et al. 2014; Zhang 2005).
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Different approaches have been carried out in previous studies to assess the effects of FT
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cycles on microbial survival and adaptations to FT cycles using culture-dependent and
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molecular methods (Stres et al. 2010; Visnivetskaya et al. 2007; Walker et al. 2006; Wilson et
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al. 2012; Yergeau and Kowalcuk 2008). However, cultivated isolates provided limited
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information (encompassing < 1% of the diversity) about the total microbial community
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(Douterelo et al., 2014). Mannisto et al. (2009) examined the effects of long term (up to 60
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days) simulated FT events on bacterial community using terminal restriction fragment length
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polymorphism profiles
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significant changes in predominant phyla as compared with the control groups. Zinger et al.
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(2009) also reported that microbial community compositions were different at two extremes
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of the snow cover gradient, with strong spatial and temporal correlation between snow
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dynamics and microbial community.
coupled with clone analysis and showed that there were no
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This study is based on a laboratory-based microcosm study of FT cycles (simulating natural
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freeze-thaw events) on soils sampled from below different snow depths. We presented here
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data on soil bacterial diversity obtained using a high-throughput 16S rRNA gene amplicon
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sequencing approach, with the underlying hypothesis that FT cycles will exert different
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effects on bacterial diversity in soils under different snow depth cover.
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2. Materials and methods
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2.1. Site Description and Sample Collection
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The sampling location was a snow bank opposite The University Centre in Svalbard,
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Longyearbyen, Norway (78° 14.874’N, 15° 24.313’E). Soils were sampled in triplicate on 25
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July 2015 from sites with three different summer snow depths: i) no snow cover (N) with
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ambient FT cycles, ii) low snow cover (L; snow likely to melt completely hence higher FT
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frequency) and iii) high snow cover (H; snow likely to persist hence lower FT frequency).
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Soils were collected into sterile 50ml tubes using a sterile spatula. The soil samples were kept
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at -20°C at the university laboratory and subsequently transported frozen to the University of
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Malaya, Kuala Lumpur, and stored at -20°C until further use.
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2.2. Laboratory Freeze-Thaw Experiment
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All nine soil samples (triplicates of N, L and H) were subjected to freeze-thaw treatments
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over nine successive FT cycles (approx. 1g in each microfuge tube). The freezing phase was
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at -1.4°C for 12 hours and followed by thawing at 4.0°C for 12 hours for each FT cycle
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(based on average freezing and thawing temperatures of diurnal FT fluctuations in High
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Arctic Shrub; Convey et al. 2018) carried out in a temperature-regulated water bath (Haake
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SC-100-A25, Thermo Scientific) filled with propylene glycol. All samples, together with
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controls (same group of thawed samples which were not subjected to the ‘freeze’ treatment)
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were extracted before the start of the experiment (baseline), after the first and last (ninth) FT
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treatment. The experimental design and individual sample names are illustrated in
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Supplementary Table 1.
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2.3. DNA extraction, PCR and High Throughput Sequencing
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DNA was extracted from each soil sample using the MoBio PowerSoil DNA extraction kit
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(MOBIO Laboratories, Inc., CA, USA) following the manufacturer’s instructions. The
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quality and quantity of DNA were examined using DropPlate16-D+ Chips on LabChip DS
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(PerkinElmer) for samples. High-quality samples were processed with 16S rRNA
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Metagenomic Sequencing Library Preparation kit according to the manufacturer’s instruction
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(Illumina, USA). The V3 and V4 hypervariable regions of the bacterial 16S rRNA genes
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were amplified following Klindworth et al. (2013) and in accordance with the manufacturer’s
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instructions (Kapa Biosystems, MA, USA). The Illumina adapter and dual-index barcode
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were then incorporated into the initial PCR product via a second round of 8-cycle PCR to
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generate a final library construct consisting of the complete Illumina adapter, dual 8bp
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Nextera-like barcode and the 16S rRNA fragments. The amplified Index-PCR products were
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quantified with the Qubit dsDNA HS Assay Kit (Invitrogen, Belgium) on an EnSpire
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Multimode Plate Reader (PerkinElmer) and normalized to 10nM using Janus® Automated
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Workstation (PerkinElmer). The concentration of the amplifiable libraries was determined
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using Illumina Eco qPCR machine, as described in the KAPA Library Quantification Kits for
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Illumina sequencing platforms (KAPA BioSystems, MA, USA). The pooled library was
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denatured and further diluted to 4.5pM with PhiX control spike-in of 25%, prior to paired-end
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sequencing using MiSeq Reagent Kit (v2) with the read length set at 2 × 250 base pairs (bp).
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2.4. Quality Control of Sequencing Data
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The demultiplexed sequences generated by the Illumina Miseq sequencer were trimmed,
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filtered according to base quality (q = 20), and merged using CLC Genomic Workbench 8.5.
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Unmerged forward reads (q = 20) with a minimum length of 150 bases were retained.
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Chimeric sequences were identified using UCHIME2 based on the Greengenes database
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(Edgar, 2016). The sequences were then subjected to operational taxonomic unit (OTU)
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clustering based on an open reference approach and downstream analysis using QIIME 1.8.0
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software with taxonomic profiling based on the Greengenes Reference Database-Greengenes
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13_8 version. The assembled sequences were normalized via random sub-sampling at 53016
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reads per sample for subsequent diversity analyses.
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2.5. Diversity Analyses
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Diversity analyses for OTU data were carried out based on the distribution and evenness of
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species in an environment at two levels: Alpha Diversity and Beta Diversity. The Shannon
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index was used on alpha diversity since it took into account both richness and evenness of
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species in an environment studied (Shannon, 1948; Haegeman et al., 2013). For beta diversity,
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pairwise similarity of bacterial community composition at the genus level between different
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groups of samples was assessed using Jaccard similarity indices (generated using a software 6
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named JS Euler (unpublished)). In addition, UniFrac distances for both weighted (semi-
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quantitative analysis with relative abundances considered) and unweighted (qualitative with
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only presence or absence of OTUs considered) were computed among all samples to generate
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principal coordinate analysis (PCoA) plots in order to visualise beta diversity of all samples.
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2.6. Statistical Analysis
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Statistical analyses were carried out using IBM SPSS Statistics for Windows version 19.0
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(IBM Corp., USA). At each snow depth, Shannon diversity indices were compared between
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treatment and control groups at three different FT cycles (Baseline, 1st FT cycle, 9th FT
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cycle). Split-plot ANOVA with Tests of Within-Subjects Contrasts was used to examine for
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any significant changes in Shannon index throughout FT treatments. Mauchly’s test of
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sphericity was performed to confirm variances of the differences between all possible pairs
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and also normality tests were carried out to test for normality of data prior to the ANOVA
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test.
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3. Results
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Before the start of the FT experiment (indicated as baseline), the F value of the three groups
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of soil samples (H-high snow cover; L- low snow cover, N-no snow cover) was significant
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(F= 39.33 (df = 2, 6, p < 0.001)). The ANOVA test showed that there were significant
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differences in Shannon index among the three groups of soils. The Bonferroni post hoc
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multiple comparisons test results showed significant differences between (1) H and N
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samples (p = 0.001) and (2) H and L samples (p = 0.001). There was no significant difference
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between the N and L samples (p= 0.95). The mean plots in Figure 1 showed that the mean
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Shannon index of H samples was lower than the mean Shannon index (mean ± SD) of the
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other two groups. In summary, soil samples from under high snow cover showed
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significantly lower Shannon index (9.56 ± 0.15) than those from low snow cover (10.41 ±
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0.12) and those with no snow cover (10.55 ± 0.17).
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The taxonomic assignments for OTUs from the N samples were classified into 37 phyla, 145
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classes, 260 orders, 425 genera; from L samples: 39 phyla, 133 classes, 247 orders, 548
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genera and from the H samples: 39 phyla, 125 classes, 230 orders and 361 genera. The OTUs
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of triplicate samples in their corresponding group were pooled to assess for compositional
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similarity/dissimilarity between groups. The H samples had lowest number of overlapping
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genera with N samples (276 OTUs), compared to N with L samples (325 OTUs) or H with L
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samples (298 OTUs). The results corresponded with the assessment using Jaccard similarity
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(data not shown).
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All the soil samples shared similar predominant bacterial phyla (Figure 2). The predominant
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phyla
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Planctomycetes,
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Proteobacteria. Apart from this, there were another 4 predominant phyla (OD1, WPS-2,
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Firmicutes and TM7) which appeared in low relative abundance (< 1%) in L and N samples.
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Rarefaction curve plots were performed at two metrics: the number of species and Shannon
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index (Supplementary Fig. 1). The
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approximately 10,000 sequences indicating that standardized sequencing depth at 53,016
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reads was sufficient for subsequent analysis of microbial diversity. Good’s Coverage and
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Shannon indices for all samples are tabulated in Supplementary Table 2.
were
Armatimonadetes, Verrucomicrobia,
Gemmatimonadetes, Bacteroidetes,
Chloroflexi,
Acidobacteria,
Shannon index curve
Cyanobacteria,
Actinobacteria
and
plot reached a plateau at
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After the FT experiments, FT cycles were found to have significant effects on Shannon
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diversity of the N [F (2, 8) = 49.48, p 208 < .001] and L samples [F (2, 8) = 20.49, p < .001],
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but no significant effects were shown of the H samples [F (1.04, 4.15) = 7.28, p = .052]. For
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the N and L samples, Tests of Within-Subjects Contrasts (Table 1) further indicated
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significant decreases (p ≥ 0.05) in Shannon diversity after all FT cycles. The H samples
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demonstrated only a slight significant decrease (p < 0.05) in Shannon diversity from the 1st
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cycle to the 9th cycle.
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Table 1: Results of statistical analysis on Shannon diversity throughout FT cycles for Tests of Within-Subjects Contrasts in Split-Plot ANOVA. Significant values were indicated by *. Baseline vs 1st cycle 1st cycle vs 9th cycle Baseline vs 9th cycle Samples F ratio p-value F ratio p-value F ratio p-value N 40.21 0.003* 8.37 0.040* 179.50 < 0.001* L 25.49 0.007* 15.58 0.017* 24.68 0.008* H 1.22 0.332 7.80 0.049* 6.91 0.058
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The Shannon diversity in the N samples differed significantly between treatment and control
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groups [F (2, 8) = 11.69, p < .004], particularly between 1st cycle and 9th cycle, p< 0.05
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(Table 2). This indicated that diversity of the bacterial community changed throughout FT
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cycles and differed significantly between treatment and control groups. The L and H samples
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however, demonstrated no significant difference between treatment and control groups (Table
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2).
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Table 2: Results of statistical analysis on Shannon diversity between treatment and control groups throughout FT cycles for Tests of Within-Subjects Contrasts in Split-Plot ANOVA. Significant values were indicated by *. Baseline vs 1st cycle 1st cycle vs 9th cycle Baseline vs 9th cycle Samples F ratio p-value F ratio p-value F ratio p-value N 1.35 0.31 14.61 0.019* 22.68 0.009* L 1.79 0.25 0.96 0.38 1.57 0.280 H 0.34 0.59 0.49 0.52 0.37 0.580
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Overall, there were 692 OTUs identified to genus level (436 to 494 distinct genera per sample)
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in the N soil samples (baseline, treatment and control), with predominant genera (> 1% of
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relative abundance) ranging from 4.0 % to 6.4% in individual soil samples. For the L soil
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samples, there were 685 OTUs identified at genus level (406 to 477 distinct genera per
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sample), with predominant genera (> 1% of relative abundance) ranging from 3.7% to 7.2%
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in all individual soil samples. For the H soil samples, there were 656 unique OTUs identified
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at genera level (394 to 452 distinct genera per sample), with predominant genera (> 1% of
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relative abundance) ranging from 4.9% to 7.2% (Figure 2).
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Bacterial community composition among five groups of the H soil samples (baseline, 1st FT,
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9th FT, 1st control, 9th control) demonstrated slightly higher range of Jaccard similarities
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(0.80 to 0.86) compared to those of N (0.79 to 0.84) and L (0.79 to 0.83) soil samples (data
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not shown). Both weighted and un-weighted UniFrac PCoAs showed distinct separations
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between the three groups of soils (Figure 3). There were no distinct separations between
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control and treatment groups in each group of soils.
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4. Discussion
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In this microcosm study, bacterial diversity in soil samples from different depths of snow
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cover exhibited different responses to FT stress. This suggests that there could be different
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susceptibilities in soil bacteria from soils covered by different snow depth towards FT stress.
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Soil samples before 9x FT treatments (+4.0°C to –1.4°C for 12 hours each) were found to
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have the highest diversity as indicated by Shannon index. This suggested that freeze-thaw
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(FT) events in soils may potentially select bacterial taxa which can withstand freeze-thaw
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stress and eliminate those that can’t (Sharma et al. 2006; Walker et al. 2006). Viable bacteria
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would likely utilize the liberated nutrients from the lysed cells (Schimel and Clein, 1996;
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Pesaro et al., 2003; Grogan et al., 2004) and grow in abundance leading to an increase in the
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evenness of community structure which eventually led to higher soil bacterial diversity. In
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addition, the bacterial diversity of baseline soil samples N and L were significantly higher
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compared to the FT treated or control soils suggesting a negative effect of FT on bacterial
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diversity of soils with low or no snow cover. This finding was in line with previous FT
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studies (Larsen et al., 2002; Schimel & Clein, 1996; Stres et al., 2010; Wilson et al., 2012)
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which found significant decreases in soil microbial abundance, diversity or activities with
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increasing FT treatments.
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Previous work had shown that the effect of FT cycles on soil microbial communities could
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range from mild to significant (Edwards et al., 2007; Ren et al., 2018; Sawicka et al., 2010;
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Walker et al., 2006). Soil samples from low and no snow cover were found to have a
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significant decline in alpha diversity with increasing FT cycles. Bacterial diversity of soils
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from under high snow cover was relatively stable and not significantly affected by FT; indeed
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only a slight decrease in the Shannon index was observed after nine multiple cycles of FT.
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Bacterial diversity in soil samples from low snow cover did not show similar signs of
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recovery compared to those of samples with no snow cover after nine FT cycles. Decreased
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snow cover advanced the timings of soil freezing and melting, and thus increased frequency
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of FT cycles and daily soil temperature variations (Hardy et al., 2001; Groffman et al., 2011;
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Tan et al., 2014). Thus, it is possible that increasing FT cycles will have unfavourable effects
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on soil bacteria which have yet to adapt to the dynamics freezing and melting. It would be
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interesting to assess the bacterial diversity in the snowmelt using the samples from low snow
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cover, because there would be some degree of effects from the dynamics of bacteria in
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snowpack (Hell et al., 2013).
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Nevertheless, this study showed that there could be different susceptibilities in soil bacteria
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from soils covered by different snow depth towards FT stress. However, due to the limited
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sampling size, further investigations are needed in future although similar snow habitats were
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known to harbour large variation of microbial diversity (Hauptmann, 2014) and the duration
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and depth of snow cover had dramatic impacts on Arctic and alpine ecosystem structure and
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functioning (Edwards et al., 2007; Fisk et al., 1998; Hell et al., 2013; Welker et al., 2000).
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Acknowledgements
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The authors thank S.J. George from University of Tromsø and UNIS for assistance in soil
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sampling. The authors gratefully acknowledge grants from the Malaysian Ministry of Science
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Technology and Innovation (FP0712E012 subproject 1) and University of Malaya (RP002E-
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13SUS and RP026C-18SUS).
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Conflict of interest
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The authors declare no conflict of interest.
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Tan, B., Wu, F.Z., Yang, W.Q., He, X.H., 2014. Snow removal alters soil microbial biomass and enzyme activity in a Tibetan alpine forest. Appl. Soil Ecol. 76, 34-41.
354
Vishnivetskaya, T.A., Siletzky, R., Jefferies, N., Tiedje, J.M., Kathariou, S., 2007. Effect of
355
low temperature and culture media on the growth and freeze-thawing tolerance of
356
Exiguobacterium strains. Cryobiology 54, 234-240.
357 358
Walker, V.K., Palmer, G.R., Voordouw, G., 2006. Freeze-thaw tolerance and clues to the winter survival of a soil community. Appl. Environ. Microbiol. 72, 1784-1792.
359
Welker, J.M., Fahnestock, J.T. and Jones, M.H., 2000. Annual CO2 flux in dry and moist
360
arctic tundra: field responses to increases in summer temperatures and winter snow depth.
361
Clim. Change 44, 139-150.
362
Whittaker, R.H., 1972. Evolution and measurement of species diversity. Taxon, 213-251.
363
Wilson, S.L., Grogan, P., Walker, V.K., 2012. Prospecting for ice association:
364
characterization of freeze–thaw selected enrichment cultures from latitudinally distant
365
soils. Can. J. Microbiol. 58, 402-412.
366
Yergeau, E., Kowalchuk, G.A., 2008. Responses of Antarctic soil microbial communities and
367
associated functions to temperature and freeze–thaw cycle frequency. Environ. Microbiol.
368
10, 2223-2235.
369 370 371 372
Zhang, T., 2005. Influence of the seasonal snow cover on the ground thermal regime: An overview. Rev. Geophys. 43, RG4002. Zinger, L., Shahnavaz, B., Baptist, F., Geremia, R.A., Choler, P., 2009. Microbial diversity in alpine tundra soils correlates with snow cover dynamics. ISME J. 3, 850-859.
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373
Figure Legends
374
Figure 1. Bacterial diversity (mean Shannon index) of soil samples before FT treatment
375
(baseline), after the first cycle and ninth FT cycles.
376 377
Figure 2. Mean relative abundances of co-occurring bacterial genera which were
378
predominant (>1%) in samples N (top), L (middle) and H (bottom).
379 380
Figure 3. Principal coordinate analysis of bacterial communities for all 45 samples based on
381
unweighted (A) and weighted (B) UniFrac distance metrics.
17
Samples with no snow cover (N)
10.8
Mean Shannon Index
10.6 10.4 10.2 Control Treatment
10.0 9.8 9.6 Baseline
Cycle
1st
9th
Samples from low snow cover (L)
10.6
Mean Shannon Index
10.4 10.2 10.0 9.8
Control Treatment
9.6 9.4 9.2 9.0 Baseline
Cycle
1st
9th
Samples from high snow cover (H) 10.0 9.8
Mean Shannon Index
9.6 9.4 9.2 9.0 8.8
Control
8.6
Treatment
8.4 8.2 8.0 7.8 Baseline
1st
Cycle
Figure 1
9th
Figure 2
A. Unweighted
N (baseline) N (control) N (treatment) L (baseline) L (control) B. Weighted
L (treatment) H (baseline) H (control) H (treatment)
Figure 3
PII:
S1873-9652(19)30131-8
DOI:
https://doi.org/10.1016/j.polar.2019.100487
Reference:
POLAR 100487
To appear in:
Polar Science
Received Date: 15 July 2018 Revised Date:
28 August 2019
Accepted Date: 12 November 2019
Please cite this article as: Lim, P.P.J., Pearce, D.A., Convey, P., Lee, L.S., Chan, K.G., Tan, G.Y.A., Effects of freeze-thaw cycles on High Arctic soil bacterial communities, Polar Science, https:// doi.org/10.1016/j.polar.2019.100487. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
1
Title Page
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Effects of freeze-thaw cycles on High Arctic soil bacterial communities
4
Lim, P.P.J.1, Pearce, D.A.2, Convey, P.3, Lee, L.S.1, Chan, K.G.1* and Tan, G.Y.A.1,4*
5 6
1
7
Lumpur, Malaysia
8
2
9
University at Newcastle, Newcastle uponTyne NE1 8ST, United Kingdom
Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603 Kuala
Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria
10
3
11
United Kingdom
12
4
13
Lumpur, Malaysia
British Antarctic Survey, NERC, High Cross, Madingley Road, Cambridge CB3 OET,
National Antarctic Research Centre, IPS Building, University of Malaya, 50603 Kuala
14 15 16
*Corresponding author email: [email protected], [email protected]
17
1
18
Abstract
19
The projected increase in freeze-thaw frequency associated with warmer temperatures in
20
the High Arctic could affect the dynamics of soil bacterial communities. We report here the
21
effects of freeze-thaw (FT) cycles on High Arctic bacterial communities of soil samples
22
collected from three sites with different depths of snow cover. Analysis of 16S rRNA gene
23
amplicon sequences showed that bacterial diversity in soil sampled under high snow cover
24
were significantly different from those under low snow cover and those with no snow cover,
25
and showed little change in community diversity after nine consecutive FT cycles.
26
Conversely, bacterial diversity in soil samples under low and with no snow cover decreased
27
after the simulated FT cycles. It is therefore likely that reduced snow cover will influence soil
28
bacterial community structure through an increased frequency of freeze-thaw cycling.
29
30
Keywords: Freeze-thaw, high throughput sequencing, Svalbard, soil, bacteria.
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2
32
1. Introduction
33
The North polar region is expected to continue warming more quickly than other regions on
34
Earth at lower latitudes, a phenomenon known as Polar amplification (IPCC 2014; Symon et
35
al. 2005). According to the NOAA Global Climate Report for March 2018, the anomaly of
36
Northern Hemisphere land temperature was +1.75°C, which was higher than that of the
37
global land temperature (+0.83°C). Although vast areas of the High Arctic region are
38
permanently frozen and the temperature of the soil is ameliorated by an insulating snow pack.
39
Snow cover data sampled from Svalbard indicated a general and statistically significant
40
increase of snow depths with terrain elevation (Möller and Möller, 2019). Warmer winters on
41
Svalbard may reduce snow accumulation leading to a decrease in snow depth. Due to the
42
reduced or absent snowpack, Arctic soils may become more vulnerable to temperature
43
fluctuations and to more frequent freeze-thaw (FT) cycling (Henry, 2008; Førland et al. 2011).
44
Indeed, changes in the FT cycles have already been shown to have substantial effects on
45
Arctic soil microbial communities and associated nutrient cycling functions (Kumar et al.
46
2013; Larsen et al. 2002; Sawicka et al. 2009).
47 48
Snow cover is known to control ground thermal regime in cold regions and is an excellent
49
shield from temperature fluctuation. Greater insulation is provided with increasing snow
50
depth and hence the underlying soil is subjected to reduced variation in soil temperature and
51
cycle rate (Lütz 2011; Pauli et al. 2013; Tan et al. 2014; Zhang 2005).
52 53
Different approaches have been carried out in previous studies to assess the effects of FT
54
cycles on microbial survival and adaptations to FT cycles using culture-dependent and
55
molecular methods (Stres et al. 2010; Visnivetskaya et al. 2007; Walker et al. 2006; Wilson et
56
al. 2012; Yergeau and Kowalcuk 2008). However, cultivated isolates provided limited
3
57
information (encompassing < 1% of the diversity) about the total microbial community
58
(Douterelo et al., 2014). Mannisto et al. (2009) examined the effects of long term (up to 60
59
days) simulated FT events on bacterial community using terminal restriction fragment length
60
polymorphism profiles
61
significant changes in predominant phyla as compared with the control groups. Zinger et al.
62
(2009) also reported that microbial community compositions were different at two extremes
63
of the snow cover gradient, with strong spatial and temporal correlation between snow
64
dynamics and microbial community.
coupled with clone analysis and showed that there were no
65 66
This study is based on a laboratory-based microcosm study of FT cycles (simulating natural
67
freeze-thaw events) on soils sampled from below different snow depths. We presented here
68
data on soil bacterial diversity obtained using a high-throughput 16S rRNA gene amplicon
69
sequencing approach, with the underlying hypothesis that FT cycles will exert different
70
effects on bacterial diversity in soils under different snow depth cover.
71 72
2. Materials and methods
73
2.1. Site Description and Sample Collection
74
The sampling location was a snow bank opposite The University Centre in Svalbard,
75
Longyearbyen, Norway (78° 14.874’N, 15° 24.313’E). Soils were sampled in triplicate on 25
76
July 2015 from sites with three different summer snow depths: i) no snow cover (N) with
77
ambient FT cycles, ii) low snow cover (L; snow likely to melt completely hence higher FT
78
frequency) and iii) high snow cover (H; snow likely to persist hence lower FT frequency).
79
Soils were collected into sterile 50ml tubes using a sterile spatula. The soil samples were kept
80
at -20°C at the university laboratory and subsequently transported frozen to the University of
81
Malaya, Kuala Lumpur, and stored at -20°C until further use.
4
82 83
2.2. Laboratory Freeze-Thaw Experiment
84
All nine soil samples (triplicates of N, L and H) were subjected to freeze-thaw treatments
85
over nine successive FT cycles (approx. 1g in each microfuge tube). The freezing phase was
86
at -1.4°C for 12 hours and followed by thawing at 4.0°C for 12 hours for each FT cycle
87
(based on average freezing and thawing temperatures of diurnal FT fluctuations in High
88
Arctic Shrub; Convey et al. 2018) carried out in a temperature-regulated water bath (Haake
89
SC-100-A25, Thermo Scientific) filled with propylene glycol. All samples, together with
90
controls (same group of thawed samples which were not subjected to the ‘freeze’ treatment)
91
were extracted before the start of the experiment (baseline), after the first and last (ninth) FT
92
treatment. The experimental design and individual sample names are illustrated in
93
Supplementary Table 1.
94 95
2.3. DNA extraction, PCR and High Throughput Sequencing
96
DNA was extracted from each soil sample using the MoBio PowerSoil DNA extraction kit
97
(MOBIO Laboratories, Inc., CA, USA) following the manufacturer’s instructions. The
98
quality and quantity of DNA were examined using DropPlate16-D+ Chips on LabChip DS
99
(PerkinElmer) for samples. High-quality samples were processed with 16S rRNA
100
Metagenomic Sequencing Library Preparation kit according to the manufacturer’s instruction
101
(Illumina, USA). The V3 and V4 hypervariable regions of the bacterial 16S rRNA genes
102
were amplified following Klindworth et al. (2013) and in accordance with the manufacturer’s
103
instructions (Kapa Biosystems, MA, USA). The Illumina adapter and dual-index barcode
104
were then incorporated into the initial PCR product via a second round of 8-cycle PCR to
105
generate a final library construct consisting of the complete Illumina adapter, dual 8bp
106
Nextera-like barcode and the 16S rRNA fragments. The amplified Index-PCR products were
5
107
quantified with the Qubit dsDNA HS Assay Kit (Invitrogen, Belgium) on an EnSpire
108
Multimode Plate Reader (PerkinElmer) and normalized to 10nM using Janus® Automated
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Workstation (PerkinElmer). The concentration of the amplifiable libraries was determined
110
using Illumina Eco qPCR machine, as described in the KAPA Library Quantification Kits for
111
Illumina sequencing platforms (KAPA BioSystems, MA, USA). The pooled library was
112
denatured and further diluted to 4.5pM with PhiX control spike-in of 25%, prior to paired-end
113
sequencing using MiSeq Reagent Kit (v2) with the read length set at 2 × 250 base pairs (bp).
114 115
2.4. Quality Control of Sequencing Data
116
The demultiplexed sequences generated by the Illumina Miseq sequencer were trimmed,
117
filtered according to base quality (q = 20), and merged using CLC Genomic Workbench 8.5.
118
Unmerged forward reads (q = 20) with a minimum length of 150 bases were retained.
119
Chimeric sequences were identified using UCHIME2 based on the Greengenes database
120
(Edgar, 2016). The sequences were then subjected to operational taxonomic unit (OTU)
121
clustering based on an open reference approach and downstream analysis using QIIME 1.8.0
122
software with taxonomic profiling based on the Greengenes Reference Database-Greengenes
123
13_8 version. The assembled sequences were normalized via random sub-sampling at 53016
124
reads per sample for subsequent diversity analyses.
125 126
2.5. Diversity Analyses
127
Diversity analyses for OTU data were carried out based on the distribution and evenness of
128
species in an environment at two levels: Alpha Diversity and Beta Diversity. The Shannon
129
index was used on alpha diversity since it took into account both richness and evenness of
130
species in an environment studied (Shannon, 1948; Haegeman et al., 2013). For beta diversity,
131
pairwise similarity of bacterial community composition at the genus level between different
132
groups of samples was assessed using Jaccard similarity indices (generated using a software 6
133
named JS Euler (unpublished)). In addition, UniFrac distances for both weighted (semi-
134
quantitative analysis with relative abundances considered) and unweighted (qualitative with
135
only presence or absence of OTUs considered) were computed among all samples to generate
136
principal coordinate analysis (PCoA) plots in order to visualise beta diversity of all samples.
137 138
2.6. Statistical Analysis
139
Statistical analyses were carried out using IBM SPSS Statistics for Windows version 19.0
140
(IBM Corp., USA). At each snow depth, Shannon diversity indices were compared between
141
treatment and control groups at three different FT cycles (Baseline, 1st FT cycle, 9th FT
142
cycle). Split-plot ANOVA with Tests of Within-Subjects Contrasts was used to examine for
143
any significant changes in Shannon index throughout FT treatments. Mauchly’s test of
144
sphericity was performed to confirm variances of the differences between all possible pairs
145
and also normality tests were carried out to test for normality of data prior to the ANOVA
146
test.
147 148
3. Results
149
Before the start of the FT experiment (indicated as baseline), the F value of the three groups
150
of soil samples (H-high snow cover; L- low snow cover, N-no snow cover) was significant
151
(F= 39.33 (df = 2, 6, p < 0.001)). The ANOVA test showed that there were significant
152
differences in Shannon index among the three groups of soils. The Bonferroni post hoc
153
multiple comparisons test results showed significant differences between (1) H and N
154
samples (p = 0.001) and (2) H and L samples (p = 0.001). There was no significant difference
155
between the N and L samples (p= 0.95). The mean plots in Figure 1 showed that the mean
156
Shannon index of H samples was lower than the mean Shannon index (mean ± SD) of the
157
other two groups. In summary, soil samples from under high snow cover showed
7
158
significantly lower Shannon index (9.56 ± 0.15) than those from low snow cover (10.41 ±
159
0.12) and those with no snow cover (10.55 ± 0.17).
160 161
The taxonomic assignments for OTUs from the N samples were classified into 37 phyla, 145
162
classes, 260 orders, 425 genera; from L samples: 39 phyla, 133 classes, 247 orders, 548
163
genera and from the H samples: 39 phyla, 125 classes, 230 orders and 361 genera. The OTUs
164
of triplicate samples in their corresponding group were pooled to assess for compositional
165
similarity/dissimilarity between groups. The H samples had lowest number of overlapping
166
genera with N samples (276 OTUs), compared to N with L samples (325 OTUs) or H with L
167
samples (298 OTUs). The results corresponded with the assessment using Jaccard similarity
168
(data not shown).
169 170
All the soil samples shared similar predominant bacterial phyla (Figure 2). The predominant
171
phyla
172
Planctomycetes,
173
Proteobacteria. Apart from this, there were another 4 predominant phyla (OD1, WPS-2,
174
Firmicutes and TM7) which appeared in low relative abundance (< 1%) in L and N samples.
175
Rarefaction curve plots were performed at two metrics: the number of species and Shannon
176
index (Supplementary Fig. 1). The
177
approximately 10,000 sequences indicating that standardized sequencing depth at 53,016
178
reads was sufficient for subsequent analysis of microbial diversity. Good’s Coverage and
179
Shannon indices for all samples are tabulated in Supplementary Table 2.
were
Armatimonadetes, Verrucomicrobia,
Gemmatimonadetes, Bacteroidetes,
Chloroflexi,
Acidobacteria,
Shannon index curve
Cyanobacteria,
Actinobacteria
and
plot reached a plateau at
180 181
8
182
After the FT experiments, FT cycles were found to have significant effects on Shannon
183
diversity of the N [F (2, 8) = 49.48, p 208 < .001] and L samples [F (2, 8) = 20.49, p < .001],
184
but no significant effects were shown of the H samples [F (1.04, 4.15) = 7.28, p = .052]. For
185
the N and L samples, Tests of Within-Subjects Contrasts (Table 1) further indicated
186
significant decreases (p ≥ 0.05) in Shannon diversity after all FT cycles. The H samples
187
demonstrated only a slight significant decrease (p < 0.05) in Shannon diversity from the 1st
188
cycle to the 9th cycle.
189 190 191
Table 1: Results of statistical analysis on Shannon diversity throughout FT cycles for Tests of Within-Subjects Contrasts in Split-Plot ANOVA. Significant values were indicated by *. Baseline vs 1st cycle 1st cycle vs 9th cycle Baseline vs 9th cycle Samples F ratio p-value F ratio p-value F ratio p-value N 40.21 0.003* 8.37 0.040* 179.50 < 0.001* L 25.49 0.007* 15.58 0.017* 24.68 0.008* H 1.22 0.332 7.80 0.049* 6.91 0.058
192 193
The Shannon diversity in the N samples differed significantly between treatment and control
194
groups [F (2, 8) = 11.69, p < .004], particularly between 1st cycle and 9th cycle, p< 0.05
195
(Table 2). This indicated that diversity of the bacterial community changed throughout FT
196
cycles and differed significantly between treatment and control groups. The L and H samples
197
however, demonstrated no significant difference between treatment and control groups (Table
198
2).
199 200 201 202
Table 2: Results of statistical analysis on Shannon diversity between treatment and control groups throughout FT cycles for Tests of Within-Subjects Contrasts in Split-Plot ANOVA. Significant values were indicated by *. Baseline vs 1st cycle 1st cycle vs 9th cycle Baseline vs 9th cycle Samples F ratio p-value F ratio p-value F ratio p-value N 1.35 0.31 14.61 0.019* 22.68 0.009* L 1.79 0.25 0.96 0.38 1.57 0.280 H 0.34 0.59 0.49 0.52 0.37 0.580
203
9
204
Overall, there were 692 OTUs identified to genus level (436 to 494 distinct genera per sample)
205
in the N soil samples (baseline, treatment and control), with predominant genera (> 1% of
206
relative abundance) ranging from 4.0 % to 6.4% in individual soil samples. For the L soil
207
samples, there were 685 OTUs identified at genus level (406 to 477 distinct genera per
208
sample), with predominant genera (> 1% of relative abundance) ranging from 3.7% to 7.2%
209
in all individual soil samples. For the H soil samples, there were 656 unique OTUs identified
210
at genera level (394 to 452 distinct genera per sample), with predominant genera (> 1% of
211
relative abundance) ranging from 4.9% to 7.2% (Figure 2).
212 213
Bacterial community composition among five groups of the H soil samples (baseline, 1st FT,
214
9th FT, 1st control, 9th control) demonstrated slightly higher range of Jaccard similarities
215
(0.80 to 0.86) compared to those of N (0.79 to 0.84) and L (0.79 to 0.83) soil samples (data
216
not shown). Both weighted and un-weighted UniFrac PCoAs showed distinct separations
217
between the three groups of soils (Figure 3). There were no distinct separations between
218
control and treatment groups in each group of soils.
219 220
4. Discussion
221
In this microcosm study, bacterial diversity in soil samples from different depths of snow
222
cover exhibited different responses to FT stress. This suggests that there could be different
223
susceptibilities in soil bacteria from soils covered by different snow depth towards FT stress.
224
Soil samples before 9x FT treatments (+4.0°C to –1.4°C for 12 hours each) were found to
225
have the highest diversity as indicated by Shannon index. This suggested that freeze-thaw
226
(FT) events in soils may potentially select bacterial taxa which can withstand freeze-thaw
227
stress and eliminate those that can’t (Sharma et al. 2006; Walker et al. 2006). Viable bacteria
228
would likely utilize the liberated nutrients from the lysed cells (Schimel and Clein, 1996;
10
229
Pesaro et al., 2003; Grogan et al., 2004) and grow in abundance leading to an increase in the
230
evenness of community structure which eventually led to higher soil bacterial diversity. In
231
addition, the bacterial diversity of baseline soil samples N and L were significantly higher
232
compared to the FT treated or control soils suggesting a negative effect of FT on bacterial
233
diversity of soils with low or no snow cover. This finding was in line with previous FT
234
studies (Larsen et al., 2002; Schimel & Clein, 1996; Stres et al., 2010; Wilson et al., 2012)
235
which found significant decreases in soil microbial abundance, diversity or activities with
236
increasing FT treatments.
237 238
Previous work had shown that the effect of FT cycles on soil microbial communities could
239
range from mild to significant (Edwards et al., 2007; Ren et al., 2018; Sawicka et al., 2010;
240
Walker et al., 2006). Soil samples from low and no snow cover were found to have a
241
significant decline in alpha diversity with increasing FT cycles. Bacterial diversity of soils
242
from under high snow cover was relatively stable and not significantly affected by FT; indeed
243
only a slight decrease in the Shannon index was observed after nine multiple cycles of FT.
244 245
Bacterial diversity in soil samples from low snow cover did not show similar signs of
246
recovery compared to those of samples with no snow cover after nine FT cycles. Decreased
247
snow cover advanced the timings of soil freezing and melting, and thus increased frequency
248
of FT cycles and daily soil temperature variations (Hardy et al., 2001; Groffman et al., 2011;
249
Tan et al., 2014). Thus, it is possible that increasing FT cycles will have unfavourable effects
250
on soil bacteria which have yet to adapt to the dynamics freezing and melting. It would be
251
interesting to assess the bacterial diversity in the snowmelt using the samples from low snow
252
cover, because there would be some degree of effects from the dynamics of bacteria in
253
snowpack (Hell et al., 2013).
11
254 255
Nevertheless, this study showed that there could be different susceptibilities in soil bacteria
256
from soils covered by different snow depth towards FT stress. However, due to the limited
257
sampling size, further investigations are needed in future although similar snow habitats were
258
known to harbour large variation of microbial diversity (Hauptmann, 2014) and the duration
259
and depth of snow cover had dramatic impacts on Arctic and alpine ecosystem structure and
260
functioning (Edwards et al., 2007; Fisk et al., 1998; Hell et al., 2013; Welker et al., 2000).
261 262
Acknowledgements
263
The authors thank S.J. George from University of Tromsø and UNIS for assistance in soil
264
sampling. The authors gratefully acknowledge grants from the Malaysian Ministry of Science
265
Technology and Innovation (FP0712E012 subproject 1) and University of Malaya (RP002E-
266
13SUS and RP026C-18SUS).
267 268
Conflict of interest
269
The authors declare no conflict of interest.
270 271
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Figure Legends
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Figure 1. Bacterial diversity (mean Shannon index) of soil samples before FT treatment
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(baseline), after the first cycle and ninth FT cycles.
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Figure 2. Mean relative abundances of co-occurring bacterial genera which were
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predominant (>1%) in samples N (top), L (middle) and H (bottom).
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Figure 3. Principal coordinate analysis of bacterial communities for all 45 samples based on
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unweighted (A) and weighted (B) UniFrac distance metrics.
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Samples with no snow cover (N)
10.8
Mean Shannon Index
10.6 10.4 10.2 Control Treatment
10.0 9.8 9.6 Baseline
Cycle
1st
9th
Samples from low snow cover (L)
10.6
Mean Shannon Index
10.4 10.2 10.0 9.8
Control Treatment
9.6 9.4 9.2 9.0 Baseline
Cycle
1st
9th
Samples from high snow cover (H) 10.0 9.8
Mean Shannon Index
9.6 9.4 9.2 9.0 8.8
Control
8.6
Treatment
8.4 8.2 8.0 7.8 Baseline
1st
Cycle
Figure 1
9th
Figure 2
A. Unweighted
N (baseline) N (control) N (treatment) L (baseline) L (control) B. Weighted
L (treatment) H (baseline) H (control) H (treatment)
Figure 3