NCBI accessions of the harmful alga Heterosigma akashiwo (CCMP2393) grown under a range of CO2 concentrations from 200-1000 ppm

Data Type: experimental
Version: 1
Version Date: 2018-10-11

» Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2 (P-ExpEv)

» Science, Engineering and Education for Sustainability NSF-Wide Investment (SEES): Ocean Acidification (formerly CRI-OA) (SEES-OA)
Dyhrman, Sonya T.Lamont-Doherty Earth Observatory (LDEO)Principal Investigator
Morris, James JeffreyUniversity of Alabama at Birmingham (UA/Birmingham)Co-Principal Investigator
Hennon, GwennLamont-Doherty Earth Observatory (LDEO)Scientist
Copley, NancyWoods Hole Oceanographic Institution (WHOI BCO-DMO)BCO-DMO Data Manager

This dataset includes metadata associated with NCBI BioProject PRJNA377729 "Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2" PRJNA377729: The alga Heterosigma akashiwo was grown at CO2 levels from about 200 to 1000 ppm and then the DNA and RNA were sequenced.


Temporal Extent: 2017-06-21 - 2017-07-13

Dataset Description

This dataset includes metadata associated with NCBI BioProject PRJNA377729 "Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2" PRJNA377729 The alga Heterosigma akashiwo was grown at CO2 levels from about 200 to 1000 ppm and then the DNA and RNA were sequenced.

Methods & Sampling

Uni-algal, non-axenic cultures of Heterosigma akashiwo (CCMP2393) were grown in L1 medium (without silicate) made with a Long Island Sound seawater base collected from Avery Point, CT, USA (salinity 32) at 18°C with a 14:10 (light:dark) cycle with an irradiance of approximately 100 µmol m-2 s-1 . Cells were acclimated in exponential growth phase to different carbonate chemistries in 1.2 L of L1 media in 2.5-L polycarbonate bottles. To control the carbonate chemistry of the water, the headspace of each bottle was purged continuously with a custom gas mixture of ~21% oxygen, ~79% nitrogen and either 200, 400, 600, 800 or 1000 ppmv CO2 (TechAir, NY).

At the point of harvest, 150 mL (~6 x 106 cells) were filtered on to 5 µm pore size, 25 mm polycarbonate filter and flash frozen in liquid nitrogen. Genetic material from samples was extracted with the RNeasy Mini kit (Qiagen, Valencia, CA) and DNA was removed on-column using the RNase-free DNase Set (Qiagen), yielding total RNA. Total RNA extracts of the triplicate cultures were quantified on a 2100 Bioanalyzer (Agilent, Santa Clara, CA). Libraries were prepared using poly-A pull down with the TruSeq Stranded mRNA Library Prep kit (Illumina, San Diego, CA). Library preparation, barcoding, and sequencing from each library was performed by the JP Sulzberger Columbia University Genome Center (New York, NY).

Sequence reads were de-multiplexed and trimmed to remove sequencing barcodes. Reads were aligned using Bowtie2 (Langmead and Salzberg 2012) to the MMETSP consensus contigs for Heterosigma akashiwo CCMP2393 (

Significant differences between physiological parameters by CO2 treatment were assessed with analysis of variance (ANOVA) and Tukey’s honestly significant differences test (aov and TukeyHSD, stats, R). Differential expression of genes in any CO2 treatment compared to modern was determined using the general linear model (GLM) exact test (edgeR, R). Briefly, the read counts were normalized by trimmed mean of M-values (TMM) using the function calcNormFactors, tagwise dispersions were calculated with the function estimateGLMTagwiseDisp, a GLM was fit using glmFit, and log2 fold change (logFC) for each treatment was calculated relative to average expression at modern CO2. P-values from likelihood ratio tests were corrected for multiple testing using the false discovery method (fdr).

Data Processing Description

BCO-DMO Processing Notes:
- added conventional header with dataset name, PI name, version date
- modified parameter names to conform with BCO-DMO naming conventions
- reformatted date from DD-Mmm-YYYY to yyyy-mm-dd
- changed entries of 'not applicable' to 'nd'

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Data Files

(Comma Separated Values (.csv), 6.46 KB)
Primary data file for dataset ID 747872

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Related Publications

Hennon, G. M., Williamson, O. M., Limón, M. D. H., Haley, S. T., & Dyhrman, S. T. (2019). Non-linear Physiology and Gene Expression Responses of Harmful Alga Heterosigma akashiwo to Rising CO2. Protist, 170(1), 38-51. Protist Supplemental Table 2

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Related Datasets

Lamont-Doherty Earth Observatory, Columbia University (2017). Phytoplankton, Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2. 2017/03. NCBI:BioProject: PRJNA377729.[Internet]. Bethesda, MD: National Library of Medicine (US), National Center for Biotechnology Information; Available from:

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sample_nameA unique name for the sample unitless
sample_titleTitle of the sample unitless
bioproject_accessionThe accession number of the BioProject(s) to which the BioSample belongs. unitless
organismThe most descriptive organism name for this sample unitless
strainThe microbial or eukaryotic strain name unitless
isolateIdentification or description of the specific individual from which this sample was obtained unitless
hostThe natural (as opposed to laboratory) host to the organism from which the sample was obtained. unitless
isolation_sourceDescribes the physical - environmental and/or local geographical source of the biological sample from which the sample was derived. unitless
collection_dateDate of sampling formatted as yyyy-mm-dd unitless
geo_loc_nameGeographical origin of the sample unitless
sample_typeSample type unitless
biomaterial_providerName and address of the lab or PI or a culture collection identifier unitless
collected_byName of persons or institute who collected the sample unitless
depthSample collection depth meters
env_biomeDescriptor of the broad ecological context of a sample. unitless
genotypeObserved genotype unitless
lat_lonlatitude and longitude of sample colllection decimal degrees
passage_historyNumber of passages and passage method unitless
samp_sizeAmount or size of sample that was collected unitless
temp_CTemperature of the sample at time of sampling degrees Celsius
light_level_umol_m2_sLight level micromol photons m-2 s-1
light_dark_hrduration of light and dark cycles hours
MediaType of growth medium used unitless
CO2_ppmCO2 concentration parts per million
AlkalinityAlkalinity of sample micromol per kilogram (umol/kg)
pHThe measure of the acidity or basicity of an aqueous solution unitless; pH scale

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Dataset-specific Instrument Name
Illumina Hi-seq 2500 paired-end sequencing (PE100) with TruSeq RNA sample Prep Kit (Illumina, San Diego, CA)
Generic Instrument Name
Automated DNA Sequencer
Dataset-specific Description
Used to prepare the mRNA libraries. Samples were barcoded for multiplex sequencing and run on in a single lane by the Columbia University Genome Center (CUGC) (New York, NY).
Generic Instrument Description
General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step.

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Project Information

Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2 (P-ExpEv)

Coverage: Experiment housed in laboratories at Michigan State University

Note: This project is also affiliated with the NSF BEACON Center for the Study of Evolution in Action.

Project Description from NSF Award:
Human activities are driving up atmospheric carbon dioxide concentrations at an unprecedented rate, perturbing the ocean's carbonate buffering system, lowering oceanic pH, and changing the concentration and composition of dissolved inorganic carbon. Recent studies have shown that this ocean acidification has many short-term effects on phytoplankton, including changes in carbon fixation among others. These physiological changes could have profound effects on phytoplankton metabolism and community structure, with concomitant effects on Earth's carbon cycle and, hence, global climate. However, extrapolation of present understanding to the field are complicated by the possibility that natural populations might evolve in response to their changing environments, leading to different outcomes than those predicted from short-term studies. Indeed, evolution experiments demonstrate that microbes are often able to rapidly adapt to changes in the environment, and that beneficial mutations are capable of sweeping large populations on time scales relevant to predictions of environmental dynamics in the coming decades. This project addresses two major areas of uncertainty for phytoplankton populations with the following questions:
1) What adaptive mutations to elevated CO2 are easily accessible to extant species, how often do they arise, and how large are their effects on fitness?
2) How will physical and ecological interactions affect the expansion of those mutations into standing populations?

This study will address these questions by coupling experimental evolution with computational modeling of ocean biogeochemical cycles. First, cultured unicellular phytoplankton, representative of major functional groups (e.g. cyanobacteria, diatoms, coccolithophores), will be evolved under simulated year 2100 CO2 concentrations. From these experiments, estimates will be made of a) the rate of beneficial mutations, b) the magnitude of fitness gains conferred by these mutations, and c) secondary phenotypes (i.e., trade-offs) associated with these mutations, assayed using both physiological and genetic approaches. Second, an existing numerical model of the global ocean system will be modified to a) simulate the effects of changing atmospheric CO2 concentrations on ocean chemistry, and b) allow the introduction of CO2-specific adaptive mutants into the extant populations of virtual phytoplankton. The model will be used to explore the ecological and biogeochemical impacts of beneficial mutations in realistic environmental situations (e.g. resource availability, predation, etc.). Initially, the model will be applied to idealized sensitivity studies; then, as experimental results become available, the implications of the specific beneficial mutations observed in our experiments will be explored.

This interdisciplinary study will provide novel, transformative understanding of the extent to which evolutionary processes influence phytoplankton diversity, physiological ecology, and carbon cycling in the near-future ocean. One of many important outcomes will be the development and testing of nearly-neutral genetic markers useful for competition studies in major phytoplankton functional groups, which has applications well beyond the current proposal.

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Program Information

Science, Engineering and Education for Sustainability NSF-Wide Investment (SEES): Ocean Acidification (formerly CRI-OA) (SEES-OA)

Coverage: global

NSF Climate Research Investment (CRI) activities that were initiated in 2010 are now included under Science, Engineering and Education for Sustainability NSF-Wide Investment (SEES). SEES is a portfolio of activities that highlights NSF's unique role in helping society address the challenge(s) of achieving sustainability. Detailed information about the SEES program is available from NSF (

In recognition of the need for basic research concerning the nature, extent and impact of ocean acidification on oceanic environments in the past, present and future, the goal of the SEES: OA program is to understand (a) the chemistry and physical chemistry of ocean acidification; (b) how ocean acidification interacts with processes at the organismal level; and (c) how the earth system history informs our understanding of the effects of ocean acidification on the present day and future ocean.

Solicitations issued under this program:
NSF 10-530, FY 2010-FY2011
NSF 12-500, FY 2012
NSF 12-600, FY 2013
NSF 13-586, FY 2014
NSF 13-586 was the final solicitation that will be released for this program.

PI Meetings:
1st U.S. Ocean Acidification PI Meeting(March 22-24, 2011, Woods Hole, MA)
2nd U.S. Ocean Acidification PI Meeting(Sept. 18-20, 2013, Washington, DC)
3rd U.S. Ocean Acidification PI Meeting (June 9-11, 2015, Woods Hole, MA – Tentative)

NSF media releases for the Ocean Acidification Program:

Press Release 10-186 NSF Awards Grants to Study Effects of Ocean Acidification

Discovery Blue Mussels "Hang On" Along Rocky Shores: For How Long?

Discovery - National Science Foundation (NSF) Discoveries - Trouble in Paradise: Ocean Acidification This Way Comes - US National Science Foundation (NSF)

Press Release 12-179 - National Science Foundation (NSF) News - Ocean Acidification: Finding New Answers Through National Science Foundation Research Grants - US National Science Foundation (NSF)

Press Release 13-102 World Oceans Month Brings Mixed News for Oysters

Press Release 13-108 - National Science Foundation (NSF) News - Natural Underwater Springs Show How Coral Reefs Respond to Ocean Acidification - US National Science Foundation (NSF)

Press Release 13-148 Ocean acidification: Making new discoveries through National Science Foundation research grants

Press Release 13-148 - Video - News - Video - NSF Ocean Sciences Division Director David Conover answers questions about ocean acidification. - US National Science Foundation (NSF)

Press Release 14-010 - National Science Foundation (NSF) News - Palau's coral reefs surprisingly resistant to ocean acidification - US National Science Foundation (NSF)

Press Release 14-116 - National Science Foundation (NSF) News - Ocean Acidification: NSF awards $11.4 million in new grants to study effects on marine ecosystems - US National Science Foundation (NSF)

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Funding SourceAward
NSF Division of Ocean Sciences (NSF OCE)

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