Diversification of light capture ability was accompanied by the evolution of phycobiliproteins in cryptophyte algae.

Evolutionary biologists have long sought to identify phenotypic traits whose evolution enhances an organism's performance in its environment. Diversification of traits related to resource acquisition can occur owing to spatial or temporal resource heterogeneity. We examined the ability to capture light in the Cryptophyta, a phylum of single-celled eukaryotic algae with diverse photosynthetic pigments, to better understand how acquisition of an abiotic resource may be associated with diversification. Cryptophytes originated through secondary endosymbiosis between an unknown eukaryotic host and a red algal symbiont. This merger resulted in distinctive pigment-protein complexes, the cryptophyte phycobiliproteins, which are the products of genes from both ancestors. These novel complexes may have facilitated diversification across environments where the spectrum of light available for photosynthesis varies widely. We measured light capture and pigments under controlled conditions in a phenotypically and phylogenetically diverse collection of cryptophytes. Using phylogenetic comparative methods, we found that phycobiliprotein characteristics were evolutionarily associated with diversification of light capture in cryptophytes, while non-phycobiliprotein pigments were not. Furthermore, phycobiliproteins were evolutionarily labile with repeated transitions and reversals. Thus, the endosymbiotic origin of cryptophyte phycobiliproteins provided an evolutionary spark that drove diversification of light capture, the resource that is the foundation of photosynthesis.

Single-Cell and Bulk Fluorescence Excitation Signatures of Seven Phytoplankton Species During Nitrogen Depletion and Resupply.

Phytoplankton play a vital role as primary producers in aquatic ecosystems. One common approach to classifying phytoplankton is fluorescence excitation spectroscopy, which leverages the variation in types and concentrations of pigments among different phytoplankton taxonomic groups. Here, we used a fluorescence imaging photometer to measure excitation ratios ("signatures") of single cells and bulk cultures of seven differently pigmented phytoplankton species as they progressed from nitrogen N-replete to N-depleted conditions. Our objective was to determine whether N depletion alters the fluorescence excitation signature of each species and, if so, how quickly they recover when N (as nitrate) was resupplied, because these factors affect our ability to classify the species correctly. Of the seven species studied, only Proteomonas sulcata, a marine cryptophyte, showed measurable changes in single-cell fluorescence excitation ratios and bulk fluorescence excitation spectra. These changes were likely due to decreases in the cellular concentration of phycoerythrin, a N-rich pigment, as N became scarce. Within 3 h of resupply of N, fluorescence signatures began returning to pre-depletion values and were indistinguishable from N-replete cells by 80 h after resupply. These data suggest that our classification approach is robust for non-PE containing phytoplankton. PE-containing phytoplankton might exhibit systematic changes in their signatures depending on their level of N depletion, but this could be detected and the phytoplankton re-classified following a few hours of incubation in N replete conditions.

Light capture and pigment diversity in marine and freshwater cryptophytes.

Phenotypic traits associated with light capture and phylogenetic relationships were characterized in 34 strains of diversely pigmented marine and freshwater cryptophytes. Nuclear SSU and partial LSU rDNA sequence data from 33 of these strains plus an additional 66 strains produced a concatenated rooted maximum likelihood tree that classified the strains into 7 distinct clades. Molecular and phenotypic data together support: (i) the reclassification of Cryptomonas irregularis NIES 698 to the genus Rhodomonas, (ii) revision of phycobiliprotein (PBP) diversity within the genus Hemiselmis to include cryptophyte phycocyanin (Cr-PC) 569, (iii) the inclusion of previously unidentified strain CCMP 2293 into the genus Falcomonas, even though it contains cryptophyte phycoerythrin 545 (Cr-PE 545), and (iv) the inclusion of previously unidentified strain CCMP 3175, which contains Cr-PE 545, in a clade with PC-containing Chroomonas species. A discriminant analysis-based model of group membership correctly predicted 70.6% of the clades using three traits: PBP concentration · cell-1 , the wavelength of PBP maximal absorption, and habitat. Non-PBP pigments (alloxanthin, chl-a, chl-c2 , α-carotene) did not contribute significantly to group classification, indicating the potential plasticity of these pigments and the evolutionary conservation of the PBPs. Pigment data showed evidence of trade-offs in investments in PBPs vs. chlorophylls (a +c2 ).

Fluorescence Excitation Spectroscopy for Phytoplankton Species Classification Using an All-Pairs Method: Characterization of a System with Unexpectedly Low Rank.

An all-pairs method is used to analyze phytoplankton fluorescence excitation spectra. An initial set of nine phytoplankton species is analyzed in pairwise fashion to select two optical filter sets, and then the two filter sets are used to explore variations among a total of 31 species in a single-cell fluorescence imaging photometer. Results are presented in terms of pair analyses; we report that 411 of the 465 possible pairings of the larger group of 31 species can be distinguished using the initial nine-species-based selection of optical filters. A bootstrap analysis based on the larger data set shows that the distribution of possible pair separation results based on a randomly selected nine-species initial calibration set is strongly peaked in the 410-415 pair separation range, consistent with our experimental result. Further, the result for filter selection using all 31 species is also 411 pair separations; The set of phytoplankton fluorescence excitation spectra is intuitively high in rank due to the number and variety of pigments that contribute to the spectrum. However, the results in this report are consistent with an effective rank as determined by a variety of heuristic and statistical methods in the range of 2-3. These results are reviewed in consideration of how consistent the filter selections are from model to model for the data presented here. We discuss the common observation that rank is generally found to be relatively low even in many seemingly complex circumstances, so that it may be productive to assume a low rank from the beginning. If a low-rank hypothesis is valid, then relatively few samples are needed to explore an experimental space. Under very restricted circumstances for uniformly distributed samples, the minimum number for an initial analysis might be as low as 8-11 random samples for 1-3 factors.