Consequences of light spectra for pigment composition and gene expression in the cryptophyte Rhodomonas salina.

Algae with a more diverse suite of pigments can, in principle, exploit a broader swath of the light spectrum through chromatic acclimation, the ability to maximize light capture via plasticity of pigment composition. We grew Rhodomonas salina in wide-spectrum, red, green, and blue environments and measured how pigment composition differed. We also measured expression of key light-capture and photosynthesis-related genes and performed a transcriptome-wide expression analysis. We observed the highest concentration of phycoerythrin in green light, consistent with chromatic acclimation. Other pigments showed trends inconsistent with chromatic acclimation, possibly due to feedback loops among pigments or high-energy light acclimation. Expression of some photosynthesis-related genes was sensitive to spectrum, although expression of most was not. The phycoerythrin α-subunit was expressed two-orders of magnitude greater than the ß-subunit even though the peptides are needed in an equimolar ratio. Expression of genes related to chlorophyll-binding and phycoerythrin concentration were correlated, indicating a potential synthesis relationship. Pigment concentrations and expression of related genes were generally uncorrelated, implying post-transcriptional regulation of pigments. Overall, most differentially expressed genes were not related to photosynthesis; thus, examining associations between light spectrum and other organismal functions, including sexual reproduction and glycolysis, may be important.

De novo transcriptome assembly of the green alga Ankistrodesmus falcatus.

Ankistrodesmus falcatus is a globally distributed freshwater chlorophyte that is a candidate for biofuel production, is used to study the effects of toxins on aquatic communities, and is used as food in zooplankton research. Each of these research fields is transitioning to genomic tools. We created a reference transcriptome for of A. falcatus using NextGen sequencing and de novo assembly methods including Trinity, Velvet-Oases, and EvidentialGene. The assembled transcriptome has a total of 17,997 contigs, an N50 value of 2,462, and a GC content of 64.8%. BUSCO analysis recovered 83.3% of total chlorophyte BUSCOs and 82.5% of the eukaryotic BUSCOs. A portion (7.9%) of these supposedly single-copy genes were found to have transcriptionally active, distinct duplicates. We annotated the assembly using the dammit annotation pipeline, resulting in putative functional annotation for 68.89% of the assembly. Using available rbcL sequences from 16 strains (10 species) of Ankistrodesmus, we constructed a neighbor-joining phylogeny to illustrate genetic distances of our A. falcatus strain to other members of the genus. This assembly will be valuable for researchers seeking to identify Ankistrodesmus sequences in metatranscriptomic and metagenomic field studies and in experiments where separating expression responses of zooplankton and their algal food sources through bioinformatics is important.

Simple Comparative Analyses of Differentially Expressed Gene Lists May Overestimate Gene Overlap.

Comparing the overlap between sets of differentially expressed genes (DEGs) within or between transcriptome studies is regularly used to infer similarities between biological processes. Significant overlap between two sets of DEGs is usually determined by a simple test. The number of potentially overlapping genes is compared to the number of genes that actually occur in both lists, treating every gene as equal. However, gene expression is controlled by transcription factors that bind to a variable number of transcription factor binding sites, leading to variation among genes in general variability of their expression. Neglecting this variability could therefore lead to inflated estimates of significant overlap between DEG lists. With computer simulations, we demonstrate that such biases arise from variation in the control of gene expression. Significant overlap commonly arises between two lists of DEGs that are randomly generated, assuming that the control of gene expression is variable among genes but consistent between corresponding experiments. More overlap is observed when transcription factors are specific to their binding sites and when the number of genes is considerably higher than the number of different transcription factors. In contrast, overlap between two DEG lists is always lower than expected when the genetic architecture of expression is independent between the two experiments. Thus, the current methods for determining significant overlap between DEGs are potentially confounding biologically meaningful overlap with overlap that arises due to variability in control of expression among genes, and more sophisticated approaches are needed.