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Carbon fixation by photosynthetic mats along a temperature gradient in a Tengchong hot spring

Citation

Zhang, Yidi (2020), Carbon fixation by photosynthetic mats along a temperature gradient in a Tengchong hot spring, Dryad, Dataset, https://doi.org/10.5061/dryad.r4xgxd28j

Abstract

Geothermal hot springs, widely distributed on Earth, are important geological green-house gas sources which contribute large amounts of CO2 emission to the atmosphere. Exploring microbial carbon fixation in these springs is vital to fully understand the carbon budget in these terrestrial aquatic systems. In this study, carbon fixation rate by photosynthetic mats was determined with a 13C labeling approach along a gradient of temperature (~69 - ~75 oC) in a geothermal pool at Rehai Park in Tengchong, Yunnan Province, China. Light experiments were performed to determine carbon fixation rate by both photosynthesis and chemosynthesis, while dark experiments were performed by chemosynthesis. The results showed that the highest carbon fixation rate (about 4,637 μg C/g TOC/h) was obtained in light at the lowest temperature (~69 oC) in the afternoon (1:00 to 6:00 pm), most of which (96.7%) was contributed by photosynthesis. The lowest carbon fixation rate (138 μg C/g TOC/h) was measured at the highest temperature (~75 oC), where chemosynthesis dominated over photosynthesis in carbon fixation. At these rates, the photosynthetic mats would fix 80% CO2 emission during the daytime. At the end of daytime, the photosynthetic mats respired 34% -70% newly fixed organic carbon in one hour. Interestingly, both the carbon fixation rate and respiration rate decreased exponentially with increasing temperature, which may be ascribed to a temperature dependence of both microbial community composition and enzymatic activity. In summary, our study revealed that biological carbon fixation by photosynthetic mats is highly temperature dependent and significantly affects carbon cycling in hot springs.  

Methods

File of 13C-Raw data.xlsx

To measure the δ13C values of organic carbon, all samples were thawed at room temperature and centrifuged at 10,000 rpm for 15 minutes to collect the solid (biomass and sediment). The samples were acidified with 1 mM HCl overnight to remove inorganic carbon. The acidified samples were washed with deionized water to neutralize pH, and then dried at 50 ºC overnight. The dried solid samples were homogenized and weighted into sealed tin cups. δ13C values were measured twice for each sample with a Picarro iTOC-CRDS isotope analyzer.

File of DRCB.fasta

About 15 ml mat and surface sediment samples were collected for microbial community analysis. Samples were collected from sites DRCB-1, DRCB-2, DRCB-3, DRCB-4, DRCB-5 with sterilized spoons, and transferred into 50 ml polypropylene tubes. Genomic DNA was extracted from 0.5 wet sample using the FastDNA SPIN Kit (MP Biomedical, Solon, OH, USA) according to manufacturer’s protocol (Hou et al., 2013). PCR amplification was conducted with modified primers 515F (5’-GTG YCA GCM GCC GCG GTA A-3’) and 806R (5’-GGA CTA CHV GGG TWT CTA AT-3’) designed to be universal for bacteria and archaea (Caporaso et al., 2011; Caporaso et al., 2012). In order to identify individual samples from the reads, unique 8-bp barcodes were added at the 5′-end of both the forward and reverse primers. PCR products were purified following previously published literature (Hou et al., 2013). All PCR products were pooled together with equal molar amounts. Illumina MiSeq sequencing was performed with the MiSeq Reagent Kit v2 2× 250 bp.

Sequence demultiplexing and quality control were performed with Cutadapt 1.9.1 (Martin, 2011). The sequences that had an average quality score of lower than 27 were removed from subsequent analysis. Operational taxonomic unit (OTU) cluster at 97% sequence identity was determined by using UCLUST algorithm (Edgar, 2010). The first sequence from each OTU was picked as a representative, and taxonomy was assigned to each representative using the ribosome database project (RDP) classifier algorithm (Wang et al., 2007). Sequences that could not be classified with this algorithm were manually searched against the NCBI BLAST database using BlastN to find highly similar hits. 

Funding

National Natural Science Foundation of China (NSFC), Award: No. 91851116