Transcriptional response of Emiliania huxleyi under changing nutrient environments in the North Pacific Subtropical Gyre

Different photosynthetic responses of haploid and diploid Emiliania huxleyi (Prymnesiophyceae) to high light and ultraviolet radiation

Solar radiation varies quantitatively and qualitatively while penetrating through the seawater column and thus is one of the most important environmental factors shaping the vertical distribution pattern of phytoplankton. The haploid and diploid life-cycle phases of coccolithophores might have different vertical distribution preferences. Therefore, the two phases respond differently to high solar photosynthetically active radiation (PAR, 400-700 nm) and ultraviolet radiation (UVR, 280-400 nm). To test this, the haploid and diploid Emiliania huxleyi were exposed to oversaturating irradiance. In the presence of PAR alone, the effective quantum yield was reduced by 10% more due to the higher damage rate of photosystem II in haploid cells than in diploid cells. The addition of UVR resulted in further inhibition of the quantum yield for both haploid and diploid cells in the first 25 min, partly because of the increased damage of photosystem II. Intriguingly, this UVR-induced inhibition of the haploid cells completely recovered half an hour later. This recovery was confirmed by the comparable maximum quantum yields, maximum relative electron transport rates and yields of the haploid cells treated with PAR and PAR + UVR. Our data indicated that photosynthesis of the haploid phase was more sensitive to high visible light than the diploid phase but resistant to UVR-induced inhibition, reflecting the ecological niches to which this species adapts.

Nitrogen and Iron Availability Drive Metabolic Remodeling and Natural Selection of Diverse Phytoplankton during Experimental Upwelling

Nearly half of carbon fixation and primary production originates from marine phytoplankton, and much of it occurs in episodic blooms in upwelling regimes. Here, we simulated blooms limited by nitrogen and iron by incubating Monterey Bay surface waters with subnutricline waters and inorganic nutrients and measured the whole-community transcriptomic response during mid- and late-bloom conditions. Cell counts revealed that centric and pennate diatoms (largely Pseudo-nitzschia and Chaetoceros spp.) were the major blooming taxa, but dinoflagellates, prasinophytes, and prymnesiophytes also increased. Viral mRNA significantly increased in late bloom and likely played a role in the bloom's demise. We observed conserved shifts in the genetic similarity of phytoplankton populations to cultivated strains, indicating adaptive population-level changes in community composition. Additionally, the density of single nucleotide variants (SNVs) declined in late-bloom samples for most taxa, indicating a loss of intraspecific diversity as a result of competition and a selective sweep of adaptive alleles. We noted differences between mid- and late-bloom metabolism and differential regulation of light-harvesting complexes (LHCs) under nutrient stress. While most LHCs are diminished under nutrient stress, we showed that diverse taxa upregulated specialized, energy-dissipating LHCs in low iron. We also suggest the relative expression of NRT2 compared to the expression of GSII as a marker of cellular nitrogen status and the relative expression of iron starvation-induced protein genes (ISIP1, ISIP2, and ISIP3) compared to the expression of the thiamine biosynthesis gene (thiC) as a marker of iron status in natural diatom communities. IMPORTANCE Iron and nitrogen are the nutrients that most commonly limit phytoplankton growth in the world's oceans. The utilization of these resources by phytoplankton sets the biomass available to marine systems and is of particular interest in high-nutrient, low-chlorophyll (HNLC) coastal fisheries. Previous research has described the biogeography of phytoplankton in HNLC regions and the transcriptional responses of representative taxa to nutrient limitation. However, the differential transcriptional responses of whole phytoplankton communities to iron and nitrogen limitation has not been previously described, nor has the selective pressure that these competitive bloom environments exert on major players. In addition to describing changes in the physiology of diverse phytoplankton, we suggest practical indicators of cellular nitrogen and iron status for future monitoring.

Bioinformatic Prediction and Characterization of Proteins in Porphyra dentata by Shotgun Proteomics

Porphyra dentata is an edible red seaweed with high nutritional value. It is widely cultivated and consumed in East Asia and has vast economic benefits. Studies have found that P. dentata is rich in bioactive substances and is a potential natural resource. In this study, label-free shotgun proteomics was first applied to identify and characterize different harvest proteins in P. dentata. A total of 13,046 different peptides were identified and 419 co-expression target proteins were characterized. Bioinformatics was used to study protein characteristics, functional expression, and interaction of two important functional annotations, amino acid, and carbohydrate metabolism. Potential bioactive peptides, protein structure, and potential ligand conformations were predicted, and the results suggest that bioactive peptides may be utilized as high-quality active fermentation substances and potential targets for drug production. Our research integrated the global protein database, the first time bioinformatic analysis of the P. dentata proteome during different harvest periods, improves the information database construction and provides a framework for future research based on a comprehensive understanding.

Prediction of whole-cell transcriptional response with machine learning

MOTIVATION

Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations.

RESULTS

The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment.

AVAILABILITY AND IMPLEMENTATION

The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Representative Diatom and Coccolithophore Species Exhibit Divergent Responses throughout Simulated Upwelling Cycles

Wind-driven upwelling followed by relaxation results in cycles of cold nutrient-rich water fueling intense phytoplankton blooms followed by nutrient depletion, bloom decline, and sinking of cells. Surviving cells at depth can then be vertically transported back to the surface with upwelled waters to seed another bloom. As a result of these cycles, phytoplankton communities in upwelling regions are transported through a wide range of light and nutrient conditions. Diatoms appear to be well suited for these cycles, but their responses to them remain understudied. To investigate the bases for diatoms’ ecological success in upwelling environments, we employed laboratory simulations of a complete upwelling cycle with a common diatom, Chaetoceros decipiens, and coccolithophore, Emiliania huxleyi. We show that while both organisms exhibited physiological and transcriptomic plasticity, the diatom displayed a distinct response enabling it to rapidly shift-up growth rates and nitrate assimilation when returned to light and available nutrients following dark nutrient-deplete conditions. As observed in natural diatom communities, C. decipiens highly expresses before upwelling, or frontloads, key transcriptional and nitrate assimilation genes, coordinating its rapid response to upwelling conditions. Low-iron simulations showed that C. decipiens is capable of maintaining this response when iron is limiting to growth, whereas E. huxleyi is not. Differential expression between iron treatments further revealed specific genes used by each organism under low iron availability. Overall, these results highlight the responses of two dominant phytoplankton groups to upwelling cycles, providing insight into the mechanisms fueling diatom blooms during upwelling events.

IMPORTANCE Coastal upwelling regions are among the most biologically productive ecosystems. During upwelling events, nutrient-rich water is delivered from depth resulting in intense phytoplankton blooms typically dominated by diatoms. Along with nutrients, phytoplankton may also be transported from depth to seed these blooms then return to depth as upwelling subsides creating a cycle with varied conditions. To investigate diatoms’ success in upwelling regions, we compare the responses of a common diatom and coccolithophore throughout simulated upwelling cycles under iron-replete and iron-limiting conditions. The diatom exhibited a distinct rapid response to upwelling irrespective of iron status, whereas the coccolithophore’s response was either delayed or suppressed depending on iron availability. Concurrently, the diatom highly expresses, or frontloads, nitrate assimilation genes prior to upwelling, potentially enabling this rapid response. These results provide insight into the molecular mechanisms underlying diatom blooms and ecological success in upwelling regions.

Effects of multiple drivers of ocean global change on the physiology and functional gene expression of the coccolithophore Emiliania huxleyi

Ongoing ocean global change due to anthropogenic activities is causing multiple chemical and physical seawater properties to change simultaneously, which may affect the physiology of marine phytoplankton. The coccolithophore Emiliania huxleyi is a model species often employed in the study of the marine carbon cycle. The effect of ocean acidification (OA) on coccolithophore calcification has been extensively studied; however, physiological responses to multiple environmental drivers are still largely unknown. Here we examined two-way and multiple driver effects of OA and other key environmental drivers-nitrate, phosphate, irradiance, and temperature-on the growth, photosynthetic, and calcification rates, and the elemental composition of E. huxleyi. In addition, changes in functional gene expression were examined to understand the molecular mechanisms underpinning the physiological responses. The single driver manipulation experiments suggest decreased nitrate supply being the most important driver regulating E. huxleyi physiology, by significantly reducing the growth, photosynthetic, and calcification rates. In addition, the interaction of OA and decreased nitrate supply (projected for year 2100) had more negative synergistic effects on E. huxleyi physiology than all other two-way factorial manipulations, suggesting a linkage between the single dominant driver (nitrate) effects and interactive effects with other drivers. Simultaneous manipulation of all five environmental drivers to the conditions of the projected year 2100 had the largest negative effects on most of the physiological metrics. Furthermore, functional genes associated with inorganic carbon acquisition (RubisCO, AEL1, and δCA) and calcification (CAX3, AEL1, PATP, and NhaA2) were most downregulated by the multiple driver manipulation, revealing linkages between responses of functional gene expression and associated physiological metrics. These findings together indicate that for more holistic projections of coccolithophore responses to future ocean global change, it is necessary to understand the relative importance of environmental drivers both individually (i.e., mechanistic understanding) and interactively (i.e., cumulative effect) on coccolithophore physiology.