Light-dependent microbial metabolisms drive carbon fluxes on glacier surfaces


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SUPPLEMENTARY INFORMATION
Light-dependent microbial metabolisms drive carbon fluxes on glacier surfaces

Andrea Franzetti1*, Ilario Tagliaferri1, Isabella Gandolfi1, Giuseppina Bestetti1, Umberto Minora2$, Christoph Mayer3, Roberto S. Azzoni2, Guglielmina Diolaiuti2, Claudio Smiraglia2, Roberto Ambrosini1
1 Dept. of Earth and Environmental Sciences (DISAT) - University of Milano-Bicocca, Milano, ITALY

2 “A. Desio” Dept. of Earth Sciences, Università degli Studi di Milano, Milano, ITALY

3 Bavarian Academy of Sciences and Humanities, Munich, GERMANY
*Corresponding author:

Andrea Franzetti - Dept. of Earth and Environmental Sciences (DISAT) - University of Milano-Bicocca, Milano, Piazza della Scienza 1, 20126 Milano – ITALY Phone +39 02 64482927 - Email: andrea.franzetti@unimib.it
$ Current affiliation: Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Milano, Italy

METHODS

Sampling and metagenome sequencing

Cryoconite was collected from cryoconite holes during the ablation season (mid-June to September) 2013. The sampling sites were located on the central ablation tongue of the Forni Glacier (Italy, about 46.398° N, 10.585° E) from 2670 to 2700 m a.s.l. and on the Eastern flow in the upper part of Baltoro Glacier (Pakistan, about 35.687° N, 76.647° E) from 5020 to 5040 m a.s.l. On Forni Glacier samples were taken during three visits conducted on July 10th, August 28th, and September 25th. Samples on Baltoro glacier were collected from June 30th to July 1st 2013. Total bacterial DNA was extracted from samples using the FastDNA Spin for Soil kit (MP Biomedicals, Solon, OH, USA) according to the manufacturer’s instructions. WGS was performed on 6 samples from Baltoro and 6 from Forni by HiSeq Illumina (Illumina, Inc., San Diego, CA, USA) using a 100 bp x 2 paired-end protocol on one lane. The paired-end reads were quality-trimmed (minimum length: 80 bp; minimum average quality score: 30) using Sickle (https://github.com/najoshi/sickle).

Bioinformatics procedures

Filtered reads from both glaciers were co-assembled using IDBA-UD (Peng et al., 2012). IDBA-UD iterated the value of kmer from 40 to 99 (with a step of 5). Predicted genes were inferred from contigs with Prodigal (Hyatt et al., 2010) and aligned with Diamond (blastx) against nr protein database (Buchfink et al., 2014). Alignment files were elaborated using MEGAN V5.10.3 (Huson et al., 2011). Kyoto Encyclopaedia of Genes and Genomes (KEGG) numbers were used to estimate the representation of metabolic pathways in the metagenomes. Statistics of sequence reads, assembly results and predicted genes are reported in Table S1 and Table S2. Lowest Common Ancestor (LCA) algorithm was applied to infer taxonomic affiliation of predicted genes using MEGAN default parameters. Hierarchical taxonomic data were visualised with Krona (Ondov et al., 2011). Average per-base coverage of predicted genes was calculated using filtered reads with bowtie2 (Langmead and Salzberg, 2012), SAMtools (Li et al., 2009) and bedtools (Quinlan and Hall, 2010). To normalize the different sequencing depth across the samples, sum of gene coverages was normalized to 1,000,000 for each sample. The normalized coverages of single-copy ubiquitous gene recA ranged from 261 to 337 across the samples. Contigs were binned in putative reconstructed genomes using Maxbin (Wu et al., 2014), and then annotated using Prokka (Seemann, 2014). Taxonomy of putative reconstructed genomes was inferred with Phylosift (Darling et al., 2014). Polaromonas reconstructed genomes were aligned with Polaromonas JS666 complete genome (Mattes et al., 2008) and graphically represented using BRIG (Alikhan et al., 2011).

16S rRNA gene fragment sequencing, sequence processing and data analysis (data of Forni from Franzetti et al., submitted)

Quality of extracted DNA was evaluated electrophoretically before PCR amplification. We used a dual PCR amplification protocol to amplify the V5-V6 hypervariable regions of the 16S rRNA gene and to prepare the multiplexed libraries. The first PCR was performed in 3 × 75 µL volume reactions with GoTaq® Green Master Mix (Promega Corporation, Madison, WI) and 1 µM of each primer. The primers targeting the region of interest were added with Illumina adapters at 5’ position. We used the 783F and 1046R primers (Huber et al., 2007; Wang & Qian, 2009) with the following cycling conditions: initial denaturation at 98 °C for 30 s; 20 cycles at 98 °C for 10 s, 47 °C for 30 s, and 72 °C for 5 s and a final extension at 72 °C for 2 min. The second PCR was performed in 3 × 50 µL volume reactions by using 23 µL of the purified amplicons (Wizard® SV Gel and PCR Clean-up System, Promega Corporation, Madison, WI) from the first step as template and 0.2 µM of each primer, which contained regions complementary to the Illumina adapters and standard Nextera indexes (Illumina, Inc., San Diego, CA). The cycling conditions of the second PCR were: initial denaturation at 98 °C for 30 s; 15 cycles at 98 °C for 10 s, 62 °C for 30 s, and 72 °C for 6 s and a final extension at 72 °C for 2 min.

After the amplification, DNA quality was evaluated a second time spectrophotometrically and DNA was quantified using Qubit® (Life Technologies, Carlsbad, CA).

Genes were then sequenced by MiSeq Illumina (Illumina, Inc., San Diego, CA) with a 250 bp × 2 paired-end protocol at Parco Tecnologico Padano (Lodi, Italy) and at Science for Life Sequencing facility (Stockholm, Sweden).

Reads from sequencing were demultiplexed according to the indices. We then used the Uparse pipeline for the following elaborations (Edgar 2013). First, we merged forward and reverse reads with perfect overlapping and filtered according to default quality parameters. Second, we removed suspected chimeras and singletons sequences (i.e. sequences appearing only once in the whole data set). Third, we defined OTUs on the whole data set by clustering the sequences at > 97% of similarity and defined a representative sequence for each cluster. Fourth we chose a subset of 10000 random sequences from each sample and estimated the abundance of each OTU by mapping the sequences of each sample against the representative sequence of each OTU at 97% of similarity. Up to this step, the bioinformatics analyses applied to the data were identical to that used in Franzetti et al. (submitted). The final step, however, differed in that in Franzetti et al. (submitted) the taxonomic classification of the OTU representative sequences was obtained by RDP classifier (Wang et al. 2007), while in this case we used the LCA algorithm applied to predicted genes using MEGAN default parameters. The latter procedure was preferred because is consistent with those used for the taxonomic affiliation of functional genes. Hence, despite the original reads obtained from the Illumina were the same, bacterial community composition and OTU abundance in the cryoconite holes investigated in this paper may slightly differ from those reported in Franzetti et al. (submitted). In addition, Franzetti et al. (submitted) only investigated cryoconite from Forni Glacier, and not from Baltoro Glacier.

Meteorological data

Meteorological conditions on Forni Glacier were recorded by an Automatic Weather Station (named AWS1 Forni) in operation from 2005 on the Forni Glacier melting surface. The AWS1 Forni is located on the ablation tongue (2670 m. a.s.l. 46°23’56” N, 10°35’25” E) 400 meters far from the sampling site. Meteorological conditions on Baltoro Glacier were recorded by two Automatic Weather Stations. Data on temperature and radiation were recorded by the AWS Concordia located on the glacier melting surface (4700 m a.s.l, 35°44'39"N 76°30'49"E), 13 km far from the sampling site. Precipitations were recorded by the AWS Urdukas, operating on a lateral moraine of the glacier (3926 m a.s.l. 35°43'04" N, 76°17'10" E), 30 km far from the sampling site since precipitation data were not available at Concordia. Data from a third AWS in Askole (3029 m a.s.l.; 35°40'50" N, 75°48'55" E), 70 km far from the sampling site were used for modelling temperature, radiation and precipitation on days when these data were missing from the other stations (from 29th of June to 8th of July) due to technical problems. In particular, the daily mean air temperature was modelled by applying a constant lapse rate of –0.0075 °C m–1 (Mihalcea et al 2006); the daily mean incoming solar radiation was estimated based on the data gathered at Askole as previously reported (Mihalcea and others, 2008).

Chemical/physical parameters and microbial activities

On the days of sampling we estimated oxygen consumption rates on the cryoconite holes at light and dark conditions by the light and dark bottle technique (Telling et al. 2010). Specifically, we placed 1-2 g layer of cryoconite in two 50 mL Falcon™ tubes filled with water from the same cryoconite hole and measured oxygen content by a portable oximeter/pH meter (HACH LANGE HQ40D, Loveland, CO) immediately after the preparation of the bottle and after 24 hours of incubation into the cryoconite hole. We referred the oxygen balance to the dry weight of the cryoconite in the tube, which was measured for Forni sample and estimated on the field for Baltoro ones (Telling et al. 2010). We also measured organic matter content of cryoconite with the loss-on-ignition method by heating the samples at 400 °C overnight (ASTM, 2000). On the field, we also measured oxygen concentration in the pool, pH and temperature at each hole prior to sample collection, with the same portable oximeter/pH meter as above, and measured maximum depth by a ruler (precision 1 mm). We also took a picture of the hole together with a reference ruler to estimate hole area by an automatic method of cryoconite holes delimitation through ImageJ software (Hodson et al., 2010).

ENVIRONMENTAL AND METEOROLOGICAL DATA

Figure S1 shows the daily temperature (low, mean and high), the mean daily shortwave radiation (W/m2) and the daily rainfall (mm) in the July-September 2013 on both Forni Glacier and in May-August 2013 on Baltoro Glacier (meteorological data were not available for Baltoro in September), i.e. during the ablation season on both glaciers. In the analysed timeframe, on Forni Glacier temperature ranged from -4.7 °C of 17th September to 17.3 °C of 3rd August. Mean temperature were similar in July and August (7.2 °C in July and 6.9 °C in August); in September the mean temperature decreased to 4.0 °C. The incoming solar radiation (SWin) decreased on the study site during the season: in July the mean SWin was 239 W m-2, decreasing to 196 W m-2 in August and 134 W m-2 in September. Overall, rainfalls were 46 mm in July, 128 mm in August, 72 mm in September. Finally, the dominant wind blew from SE, hence down the glacier, a typical behaviour of katabatic-type flow.

On Baltoro Glacier, the temperature ranged from -17.5 °C on 2th May to 17.8 °C on 6th July. July was the warmest month (5.4 °C), May the coldest (-5.3 °C). The mean daily incoming solar radiation on Baltoro Glacier exceeded 700 W m-2, more than twice of the highest daily mean SWin value observed on Forni Glacier (314 W m-2 on 2th of August). The precipitations were scarce, in May no rainfall occurred. The wettest month was August with a total of 59.8 mm accumulated.

Table S1. Statistics of the obtained assembly and annotation

Assembly




Annotation

Number of contigs

844,647




Number of predicted genes

1,639,421

Total Bases

1,078,797,576




Number of KEGG categories

7,580

Min Length (bp)

200







Median Length (bp)

616







Mean Length (bp)

1,277







Max Length (bp)

386,386







N50

85,113







N50 length

2,236







N90

546,373







N90 length

465






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