Table of Contents

• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

Transcriptome statistics from samples obtained on LMG1411.

Diatom isolates were obtained from the Western Antarctic Peninsula surface waters.

Nine species of diatoms were isolated from the Western Antarctic Peninsula along the PalmerLTER sampling grid in 2013 and 2014. Isolations were performed using an Olympus CKX41 inverted microscope by single cell isolation with a micropipette (Anderson 2005). Diatom species were identified by morphological characterization and 18S rRNA gene (rDNA) sequencing. DNA was extracted with the DNeasy Plant Mini Kit according to the manufacturer’s protocols (Qiagen). Amplification of the nuclear 18S rDNA region was achieved with standard PCR protocols using eukaryotic-specific, universal 18S forward and reverse primers. Primer sequences were obtained from Medlin et al. (1982). The length of the region amplified is approximately 1800 base pairs (bp). Pseudo-nitzschia species are often difficult to identify by their 18S rDNA sequence, therefore, additional support of the taxonomic identification of P. subcurvata was provided through sequencing of the 18S-ITS1-5.8S regions. Amplification of this region was performed with the 18SF-euk and 5.8SR_euk primers of Hubbard et al. (2008). PCR products were purified using either QIAquick PCR Purification Kit (Qiagen) or ExoSAP-IT (Affymetrix) and sequenced by Sanger DNA sequencing (Genewiz). Sequences were edited using Geneious Pro software (http://www.geneious.com, Kearse et al., 2012) and BLASTn sequence homology searches were performed against the NCBI nucleotide non-redundant (nr) database to determine species with a cutoff identity of 98%.

Diatom phylogenetic analysis was performed with Geneious Pro and included 71 additional diatom 18S rDNA sequences from publically available genomes and transcriptomes, including those in the MMETSP database. Diatom sequences were trimmed to the same length and aligned with MUSCLE (Edgar 2004). A phylogenetic tree was created in Mega with the Maximum-likelihood method of tree reconstruction, the Jukes-Cantor genetic distance model (Jukes and Cantor 1969), and 100 bootstrap replicates.

Illumina TruSeq adapters and poly-A tails were trimmed from raw reads using the Fastx_toolkit clipper function. Fastq_quality_filter was used to remove poor quality sequences, such that remaining sequences had a minimum quality score of 20 with a minimum of 80% of bases within a read meeting this quality score requirement. Any remaining raw sequences less than 50 base pairs in length were also removed. Merged files were assembled de novo using Trinity (Grabherr et al. 2011). The resulting assembly was filtered to remove contigs less than 200 bp in length. Trinity-assembled contigs which exhibited sequence overlap were grouped into isogroups which were then used for sequence homology searches (BLASTx E-value ≤ 10-4) against the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases (Kanehisa 2006).

BUSCO (Benchmarking Universal Single-Copy Orthologs) was used to assess the completeness of genomes and transcriptomes based on sets of single copy orthologous groups derived from OrthoDB that are highly conserved within multiple lineages (Felipe et al. 2015). Completed, duplicated and fragmented orthologs were determined by meeting an ‘expected score’ and having aligned sequences within two standard deviations of the BUSCO gene’s length. A second metric of completeness was performed by evaluating conserved pathways, such as the ribosome and spliceosome, using the single-directional best-hit method in the KEGG Automatic Annotation Server (KAAS) (Moriya et al. 2007). Finally contiguity, was calculated at the 0.75 level as according to Martin and Wang (2011) with custom scripts.

For each transcriptome, unassembled sequence reads were aligned to the final Trinity assembly using Bowtie 2 (Langmead 2012). Mapped reads were normalized by the Reads per Kilobase per Million reads method (RPKM) (Mortazavi et al. 2008).

Gene biogeographical distributions - 20 genes of interest were selected in the study to investigate the molecular basis of iron and light limitation in polar diatoms. Reference sequences for each of these genes were obtained from the F. cylindrus and P. tricornutum JGI genome portals and T. pseudonana and T. oceanica NCBI and GenBank repositories. Reference sequences were identified in the transcriptomes by translated nucleotide homology searches (tBLASTn) with an e-value cutoff of <10-5. A reciprocal tBLASTn homology search was performed for each transcriptome against the KEGG GENES database, using the single-directional best-hit method in the KAAS online tool to ensure consistent gene annotations (Moriya et al. 2007).

Subsequently, reference sequences were identified in the MMETSP protein database by BLASTp (e-value <10-5) homology searches among the diatom transcriptomes. The transcriptomes and their associated latitude and longitude were obtained from iMicrobe Data Commons (Project Code CAM_P_0001000) and the National Center for Marine Algae and Microbiota (NCMA). Custom Matlab scripts allowed global biogeographical distribution of key genes of interest to be mapped.

BCO-DMO Data Processing Notes:

-reformatted column names to comply with BCO-DMO standards
-replaced spaces with underscores
-added names to columns that were unnamed

The Southern Ocean surrounding Antarctica is changing rapidly in response to Earth's warming climate. These changes will undoubtedly influence communities of primary producers (the organisms at the base of the food chain, particularly plant-like organisms using sunlight for energy) by altering conditions that influence their growth and composition. Because primary producers such as phytoplankton play an important role in global biogeochemical cycling, it is essential to understand how they will respond to changes in their environment. The growth of phytoplankton in certain regions of the Southern Ocean is constrained by steep gradients in chemical and physical properties that vary in both space and time. Light and iron have been identified as key variables influencing phytoplankton abundance and distribution within Antarctic waters. Microscopic algae known as diatoms are dominant members of the phytoplankton and sea ice communities, accounting for significant proportions of primary production. The overall objective of this project is to identify the molecular bases for the physiological responses of polar diatoms to varying light and iron conditions. The project should provide a means of evaluating the extent these factors regulate diatom growth and influence net community productivity in Antarctic waters. The project will also further the NSF goals of making scientific discoveries available to the general public and of training new generations of scientists. It will facilitate the teaching and learning of polar-related topics by translating the research objectives into readily accessible educational materials for middle-school students. This project will also provide funding to enable a graduate student and several undergraduate students to be trained in the techniques and perspectives of modern biology.

Although numerous studies have investigated how polar diatoms are affected by varying light and iron, the cellular mechanisms leading to their distinct physiological responses remain unknown. Using comparative transcriptomics, the expression patterns of key genes and metabolic pathways in several ecologically important polar diatoms recently isolated from Antarctic waters and grown under varying iron and irradiance conditions will be examined. In addition, molecular indicators for iron and light limitation will be developed within these polar diatoms through the identification of iron- and light-responsive genes -- the expression patterns of which can be used to determine their physiological status. Upon verification in laboratory cultures, these indicators will be utilized by way of metatranscriptomic sequencing to examine iron and light limitation in natural diatom assemblages collected along environmental gradients in Western Antarctic Peninsula waters. In order to fully understand the role phytoplankton play in Southern Ocean biogeochemical cycles, dependable methods that provide a means of elucidating the physiological status of phytoplankton at any given time and location are essential.