Iron physiology and metabolism of model phytoplankton taxa in the South Atlantic Bight

Table of contents

Project Background

rawData

Output

R scripts

conda_envs

Bash scripts

Jupyter notebooks for expression analysis

Project Background

This project paired physiology and transcriptomics to study the impact of iron availablility on phytoplankton isolates from the South Atlantic Bight (SAB). The SAB is a wide continental shelf ecosystem in the south east US that hosts diverse, productive phytoplankton communities. Iron availability decreases with distance from the shore, and physiochemical properties seperates the SAB into distinct shelf zones. The outer shelf is flanked by the Gulf Stream which regularly introduces nutrient rich North Atlantic Deep Water to the photic zone with latitudinal fluctuations in its position. These intrusions stimulate phytoplankton blooms that can grow into iron limitation because the retention time of SAB shelf water is ~9-11 weeks.

A C. closterium and coccolithophores (G. oceanica and G. huxleyi) were isolated from the inner and outer shelf zone, respectively. Isolates were grown in steady state high and low iron treatments, with an addition 24 hr iron amendment treatment with half of the low iron treatment volume. The cultures were harvested at early exponention growth, after which physiological parameters were measured and RNA was extracted.

This repository contains data and code used in the analysis of this project.

rawData

All of the raw data collected for physiology is contained in rawData.

physio_exp.csv:

This file contains most of the physiological data. It contains the growth rate, Chl a concentration, cell size, cell count, pH of media before and after the experiment, as well as the maximum potential quantum yeild of PSII (Fv/Fm), the reoxidation time of the first quinone acceptor ($\tau Q_a$ ), and the functional absorption cross-section of PSII ($\sigma_{PSII}$) which were extracted from the FIRe output files. The full output from the FIRe is the fire_exp.csv file; these are results from dark-adapted samples. Results from the Actinic Light Source (ALS) run on the fire are in the als_exp.csv file; this contains the non-photochemical quenching (NPQ) data used in the physiology figure.

rawData/histData

Maintainence cultures were kept in small volumes during the year prior to the experiment. Growth rate and FIRe measurements were also taken during this time and are found in the subfolder histData. Growth rates are in histGrowth.csv, FIRe output from dark-adapted samples such as Fv/Fm are in histFire.csv, and FIRe output using the ALS, such as NPQ, are in histALS.csv. These data were used in the physiology figure to make the dashed lines in the bar graphs and plotted alone as a SFig2_histPhysio.

The sequences used in the transcriptomic analysis can be found at link to ncbi, and the coassemblies can be found at link to zenodo.

output

The output folder contains summarized physiological data and descriptive data from sequencing and assembly used to make the tables in the figures folder, using the makeTables.R script.

r_scripts

physio_fig.R :

This file will clean up data, summarize, and run statistical tests on each physiology parameter, eventually creating the physiology figure.

• Input: physiology data and NPQ from the FIRe
• Output: Physiology figure and raw csv files used to make tables

sourmash.R:

This script will create a heatmap showing the Jaccard similarity coefficients between all samples in a clustered by similarity. The correlations were calculated using the sourmash.sh bash script.

functions.R:

Functions defined that are repeatidly used; these include loading/installing packages, statistical tests, or mutating dataframes.

conda_envs

All conda environments used durring analysis. Bash scripts list conda environment used.

bash_scripts

All bash scripts used to assemble a hybrid De Novo assembly for each organism, quantify translated genes, and functionally annotate the genes.

Transcriptome assembly:

1. Initial quality assement with FastQC using fastqc.sh
2. Trim adapter sequences with Trimmomatic using trimmomatic.sh
3. Remove ribosomeal RNA (rRNA) with RiboDetector using ribodetector.sh
   • Although polyadenalated ends were selected for during sequencing to reduce rRNA, this was performed as an additional quality control step.
4. Quality check with FastQC and MultiQC using fastqc.sh and multiqc.sh
5. Reduce sequence redundancy with CD-HIT using cd-hit.sh
6. Check for potential contamination with sourmash using sig.sh then gather.sh
7. Assembly a hybrid De Novo transcriptome for each organism with rnaSPAdes using assemble.sh
8. Assess transcriptome quality with rnaQUAST using quast.sh
9. Check transcriptome completeness with BUSCO using busco.sh

Quantification and functional annotation

1. Translate reads from nucleotide to open reading frames (ORFs) using TransDecoder with transdecoder.sh
2. Quantify gene expression with Salmon using salmon.sh
3. Functional annotation with eggNOG-mapper using eggnog.sh

Additional analysis

sourmash was used to calculate the Jaccard similarity between all samples and between transcriptomes, later used to create the figures comparing similarity between all samples and each transcriptome respectively. This process uses sig.sh and compare.sh.

Lineage differences between diatoms and coccolithophores and among species of each taxa were found using cox1.sh. This script will output the COX1 gene for each isolate, which can later be used to construct the maximum likelihood phylogenetic trees.

ncbi_blast.sh was used to find the iron starvation induced proteins (ISIP) in each transcriptome. Kegg orthology does not include ISIPs although reference sequences are located on the NCBI website. These sequences were later added to the functional annotation output for differential expression analysis.

expression_analysis

Jupyter notebooks for the differential expression analysis, enrichment analysis, and making of heatmaps.

Kegg_annotations.ipynb

This notebook cleans the output of eggNOG-mapper and pulls out just the Kegg Ko annotations from each isolate. To make the counts of ORFs to a function/gene, this notebook creates a table for each isolate mapping the ORF ID to Kegg Ko's. Some ORFs were assigned multiple Ko's, so the final output creates a table with three columns, ORF ID, Kegg Ko, and Ko_iteration; where the ORFs are repeated for each unique assigned Ko, and Ko_iteration iterates from 1 to n unique Ko's assigned. These ORF-to-Ko tables are then used to create a list of all unique Kos assinged across all organism, including the name and symbol for that Ko. Additionally, this notebook uses the Kegg API to create tables for pathways of interest, where the ko, name, and symbol for each pathway is listed in the table.

• Input:
  • eggNOG-mapper annotation files
• Output:
  • ORF-to-Ko table, with ORFs repeated for each unique Ko.
  • ko_def.csv, a table of all unique Kos assigned across organisms with name and symbol for each Ko
  • all_paths.csv a table of the Kegg pathways Photosynthesis, Nitrogen metabolism, and Carbon fixation in photosynthetic organisms, including the ko, name, and symbol for each pathway.

DE_run.ipynb

Differential expression anlysis with DESeq2. This notebook uses ORF counts from Salmon to compare expression of genes among iron treatments for each isolate. The analysis is first done on all ORFs quantified, then using Kegg annotations from eggNOG-mapper. The counts are split between the number of Kegg Ko's an ORF was assigned and summed to each Ko to compare expression at the Kegg Ko level.

• Input:
  • Salmon quant.sf file
  • Kegg notebook output, kegg_def.csv
• Output:
  • DESeq2 results comparing high and low iron, and iron amendment and low iron, with adj-pvalues, standard error, and log fold change of expression for each gene across the comparison.
  • Log2 fold shrinkage of DESeq2 results; these files are named lfc.4.Avl.csv where 4 is the organism and Avl is the comparison.
  • Variance transformed stabilized counts (VST counts); these files are named vsd.4.csv where 4 is the organism.

DE_figs.ipynb

Plotting the differential expression anlysis results. Using the output from Kegg_annotations.ipynb and DE_run.ipynb, this notebook creates the photosynthesis and nitrogen metabolism, all shared ORFs, and carbon metabolism heatmaps located in the figures folder.

clusterProfiler.ipynb

Running a gene set enrichment analysis using the DESeq2 results. This notebook finds the pathways with are statistically over represented among significantly expressed genes and creates the enrichmnet dotplot

Acknowledgements

Bioinformatic processing was performed with resources from the UGA Georgia Advanced Computing Resource Center (GACRC). We acknowledge funding support from UGA Skidaway Institute of Oceanography and National Science Foundation awards NSF-OPP #2240780 and NSF-OPP #2149071 to NRC during the time of writing.