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Noncoding and coding transcriptome responses of a marine diatom to phosphate fluctuations Maria Helena Cruz de Carvalho1,2, Hai-Xi Sun1, Chris Bowler2 and Nam-Hai Chua1 1
Laboratory of Plant Molecular Biology, Rockefeller University, New York, NY 10065, USA; 2Institut de Biologie de l’Ecole Normale Superieure (IBENS), Centre National de la Recherche
Scientifique (CNRS) UMR 8197 INSERM U1024, 46 Rue d’Ulm, 75005 Paris, France
Summary Author for correspondence: Nam-Hai Chua Tel: +1 212 327 8126 Email:
[email protected] Received: 29 April 2015 Accepted: 27 October 2015
New Phytologist (2016) 210: 497–510 doi: 10.1111/nph.13787
Key words: diatoms, long noncoding intergenic RNAs, Phaeodactylum tricornutum, phosphate, signaling, strandspecific RNA-sequencing (ssRNA-Seq), transcription factors (TFs).
Phosphorus (P) is an essential element to all living cells, yet fluctuations in P concentrations are recurrent in the marine environment. Diatoms are amongst the most successful phytoplankton groups, adapting to and surviving periods of suboptimal conditions and resuming growth as soon as nutrient concentrations permit. A knowledge of the molecular underpinnings of diatom ecological success is, however, still very incomplete. By strand-specific RNA sequencing, we analyzed the global transcriptome changes of the diatom Phaeodactylum tricornutum in response to P fluctuations over a course of 8 d, defining five distinct physiological states. This study reports previously unidentified genes highly responsive to P stress in P. tricornutum. Our data also uncover the complexity of the P. tricornutum P-responsive sensory and signaling system that combines bacterial two-component systems with more complex pathways reminiscent of metazoans. Finally, we identify a multitude of novel long intergenic nonprotein coding RNAs (lincRNAs) specifically responsive to P depletion, suggesting putative regulatory roles in the regulation of P homeostasis. Our work provides additional molecular insights into the resilience of diatoms and their ecological success, and opens up novel routes to address and explore the function and regulatory roles of P. tricornutum lincRNAs in the context of nutrient stress.
Introduction As a fundamental element to all living cells, phosphorus (P) is a building block of nucleic acids and membrane phospholipids. Through its water-soluble and inorganic active forms (phosphate esters and orthophosphate, respectively), P is also involved in cellular energy transfers, metabolic pathways and protein activation. Life in the oceans can be subject to extreme environments, depleted of the essential elements that sustain phytoplankton growth, especially in the open ocean far from land (Morel, 2003). Moreover, in coastal areas, P is often considered to be the potentially limiting element (Bauerfield et al., 1990; Woodward & Owens, 1990). It is currently accepted that P, like nitrogen (N), iron or light, may drive marine microbial evolution and niche adaptation (Dyhrman et al., 2007). Diatoms are amongst the most ecologically successful phytoplankton groups living in the oceans. Their ecological relevance is paramount, sustaining both marine and terrestrial ecosystems by their role in the global carbon cycle, producing each year the same amount of organic carbon as all of the terrestrial rainforests combined (Nelson et al., 1995; Field et al., 1998). The marine environment is subjected to recurrent fluctuations Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
in nutrient levels through the movement of tides and currents. Diatoms have been shown to be poor competitors when phosphate is deficient (Egge, 1998). Yet, these organisms are able to survive under such deprived conditions and, when nutrients are present again in optimal concentrations, such as well-mixed coastal and upwelling regions, they will often re-dominate (Morel, 2003). Therefore, the ecological success of diatoms also depends on their resilience and their capacity to respond and maintain themselves in a state of survival under unfavorable conditions, which enables them to resume active cell division and growth as soon as favorable environmental conditions resume. This capacity is ultimately underpinned by a sensory and regulatory system that detects environmental changes and directly (or indirectly) regulates genetic pathways, allowing diatoms to optimize their metabolism to the changing environment. The availability of diatom genome sequences, combined with high-throughput sequencing techniques, provides a unique opportunity to perform comparative genome-wide transcriptome analysis of the molecular changes underpinning the diatom’s resilience and ecological success under a fluctuating environment. Our experiments were designed to mimic inorganic phosphate (Pi) fluctuations that occur in the marine environment in order New Phytologist (2016) 210: 497–510 497 www.newphytologist.com
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to identify genes responsive to these changes. Physiological and global transcriptomic changes were assessed in the model pennate diatom Phaeodactylum tricornutum during the early and late stages of Pi depletion over the course of 8 d, as well as on Pi resupply. These changes were compared with cultures growing in replete medium.
Materials and Methods Diatom culture conditions Axenic cultures of Phaeodactylum tricornutum (Bohlin) strain CCMP632 were obtained from the Center for the Culture of Marine Phytoplankton (East Boothbay, ME, USA) and maintained under continuous shaking (100 rpm) in 250 ml of filtered (0.22 lM) steam-sterilized artificial sea water (Sigma) supplemented with f/2 nutrients, elements and vitamins (Guillard & Ryther, 1962), with the exception of silica (f/2-Si), in 1-l glass flasks. Cultures were kept at 20°C under cool white fluorescent lights at 100 lmol m2 s1 with a 12-h photoperiod. For the Pi fluctuation studies, equal aliquots of 4-d-old cultures from the same batch culture were inoculated in parallel in 250 ml of fresh f/2-Si medium (control conditions) and in 250 ml of fresh f/2-Si medium without phosphate supplement (Pi depleted), and cultured in the same conditions as described earlier. Culture growth was followed using a hematocytometer (Fisher Scientific, Pittsburgh, PA, USA) and the growth rate was calculated using the natural logarithm of the difference in cell density during the first 4 d of growth. P-replete and P-depleted cultures were all started with the same initial cell densities of c. 4.5 9 105 cells ml1. Pi resupplementation was performed on 4-d-old Pi-depleted cultures. Briefly, c. 40–50 ml of 4-d-old Pidepleted cultures were pelleted by centrifugation for 10 min at 1500 g and resuspended in 250 ml of fresh f/2-Si medium (starting cell densities of c. 2 9 105 cells ml1). Cells (250 ml) were harvested always at midday (6 h of light period) by vacuum filtration (0.22 lM) at different treatment time points, flash frozen in liquid N2 and maintained at 80°C until use. The filtered medium was used to measure the Pi content employing a SensoLyte MG Phosphate Assay Kit (AnaSpec, Fremont, CA, USA) following the manufacturer’s instructions. Experiments were performed using duplicates of Pi-depleted cultures that were harvested in bulk. All the experiments were repeated at least twice.
(Zeiss) with excitation–emission wavelengths of 594 nm for chlorophyll and 488 nm for Nile Red. RNA extraction, library preparation and ssRNA-Seq Total RNA was extracted from P. tricornutum flash-frozen cell pellets using the Trizol method according to the manufacturer’s instructions (Invitrogen). Extracted RNA was treated with TurboDNAse I (Life Technologies AM2238, Carlsbad, CA, USA) according to product instructions and the treated RNA was purified using an RNeasy spin column (Qiagen) with 0.5 volumes of ethanol, washed and then eluted with 50 ll of roomtemperature molecular-grade water (Qiagen). The quality of the purified RNA was assessed using an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). cDNA libraries for ssRNA-Seq were prepared using the Illumina TruSeq Stranded mRNA Sample Preparation Kit following the Low Sample (LS) Protocol guidelines (Illumina, San Diego, CA, USA) from two independent experiments. ssRNA-Seq data assembly and analysis ssRNA-Seq reads were mapped to the P. tricornutum genome (Phatr2) using TopHat (version 2.0.8) (Kim et al., 2013). The mapped reads were assembled using CUFFLINKS (v.2.1.1) (Trapnell et al., 2013) with Phatr2 annotation as the reference (Nordberg et al., 2014). The assembled transcripts of each ssRNA-Seq sample were merged and annotated using CUFFCOMPARE (v.2.1.1) (Trapnell et al., 2013). The expression levels of each gene were then calculated from the fragments per kilobase of exons per million fragments mapped (FPKM) using CUFFDIFF (v.2.1.1) (Trapnell et al., 2013). The Pearson correlation coefficients (PCCs) were calculated between the FPKM of two replicates of each five physiological states corresponding to two independent experiments. As the PCCs were high (> 0.9), we pooled the reads derived from the two replicates in order to increase the depth of the reads for the increased detection of noncoding RNA transcripts. A two-fold variance in FPKM and a P value (Fisher’s exact test) of < 0.05 were used as cutoffs to define differentially expressed genes. The reads from both replicates have been deposited in the Gene Expression Omnibus (GEO) database http://www.ncbi.nlm.nih.gov/geo (accession no. GSE66997). Quantitative real-time reverse transcription PCR
Confocal laser microscopy Cells were sampled for confocal laser microscopy at the same treatment time points as used for strand-specific RNA sequencing (ssRNA-Seq). For the staining of lipids, 2 ll of a freshly prepared Nile Red solution (250 lg ml1 in acetone) were added to a 1-ml diatom cell suspension. After 30 min staining in the dark at room temperature, cells were washed by pelleting for 10 min at 1500 g and then resuspended in 500 ll of sterile seawater (Sigma). Confocal microscopy analysis of the dyed diatom cell suspension was performed using an LSM 780 laser scanning confocal microscope New Phytologist (2016) 210: 497–510 www.newphytologist.com
Total RNA was isolated and purified from P. tricornutum cells as described above. For cDNA synthesis, 500 ng of total RNA was incubated with SuperScript III Reverse Transcriptase (Invitrogen) according to the manufacturer’s instructions. For quantitative reverse transcription polymerase chain reaction (RT-qPCR) analysis, cDNA was amplified using SYBR Premix ExTaq (Takara, Madison, WI, USA) with specific primers (Supporting Information Table S2) picked from random genes from those most highly expressed under Pi depletion. Primers were designed with the PRIMER-BLAST program (http://www.ncbi.nlm.nih.gov/tools/ Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
New Phytologist primer-blast/) defining a PCR amplicon size of < 180 bp and Phaeodactylum tricornutum CCAP 1055/1 (taxid: 556484) as the reference organism to check for primer pair specificity. Quantitative PCR conditions were set as follows: 95°C for 10 s, followed by 40 cycles of 95°C for 5 s and 60°C for 30 s, and a final cycle of 95°C for 10 s and 60°C for 5 s. Data were collected and analyzed by a Bio-Rad CFX96 real-time system (BioRad, Hercules, CA, USA). CDKA and HISTONE 4 mRNA levels were used for normalization (Siaut et al., 2007). Correlation analysis between transcription factors (TFs) and putative target genes To predict the gene targets of selected transiently expressed heat shock factors (HSFs), the PCCs were calculated using FPKMs vs all Pi-responsive protein coding genes as well as noncoding genes. Only the positively correlated gene targets (PCC > 0 and r2 ≥ 0.6) were retained. As HSFs bind to heat shock elements within promoter regions of their target genes (Nover et al., 2001), only targets with the conserved sequence 50 GAAnnTTC30 in their promoter sequences (broadly defined as sequences 3 kb upstream of the transcriptional start site) were considered. The results of the correlation analysis were visualized using Cytoscape (v2.8.3) (Smoot et al., 2011). Identification of long intergenic nonprotein coding RNAs (lincRNAs) and characterization of their genomic features All assembled intergenic transcription units were collected as lincRNA candidates. Candidates with a length of ≥ 200 nucleotides and a predicted open reading frame (ORF) of ≤ 100 amino acids were defined as lincRNAs (Liu et al., 2012). When considering gene models of protein coding genes, the ‘best’ gene models obtained from the Phatr2 annotation were used; for those of lincRNAs, the gene models with maximum intron numbers were used. The lengths of entire unspliced transcripts (including introns) were used to compare transcript length distribution. For ORF predictions, the spliced transcripts were used and sent to GenScan (Burge & Karlin, 1997). To compare the length of stop codon-free sequences, the distances between each stop codon of each gene in three reading frames were calculated, as described previously (Niazi & Valadkhan, 2012). Mfold was used to calculate the free energy of the spliced transcripts (Zuker, 2003). As longer transcripts have lower free energy, to better compare the free energy of lincRNAs and mRNAs from protein coding genes, we classified them into different groups based on sequence length. This was also performed when comparing the free energy with the GC contents of lincRNAs and mRNAs. To find putative cis-regulation of lincRNAs on their neighboring genes, the PCCs were calculated using FPKMs of all the responsive lincRNAs vs mRNAs encoded by their neighboring upstream and downstream genes. Only those Pi-responsive neighboring genes with r2 ≥ 0.6 (positive or negative correlation) were considered as putative gene targets of cis-regulatory lincRNAs. Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
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Results and Discussion Culture growth and transcriptomic responses under Pi fluctuations When P. tricornutum cultures were exposed to Pi depletion in the medium (Fig. S1), their exponential growth ceased rapidly when compared with control cultures (Fig. 1a,b). Control cultures continued to grow for 8 d, reaching a cell density of c. 4.8 9 106 cells ml1, whereas cultures without Pi grew for the first 2 d, peaking at a maximum cell density of c. 1.3 9 106 cells ml1, and then transitioned to stationary growth (Fig. 1a). This was accompanied by lipid accumulation that was detected after 4 d of Pi depletion and further increased after 8 d (Fig. 1c). Neutral lipid accumulation is a common response of microalgae under unfavorable conditions, namely nutrient stress (Fields et al., 2014). When Pi was resupplied to 4-d starved cultures, culture growth recovered to control rates after 4 d and lipid bodies were no longer detected (Figs 1b,c, S2). These observations allowed us to define five distinct physiological states to be used as sampling points for the comparative transcriptomic studies: control 4 d (‘early control’), Pi 4 d (‘early Pi depletion’), control 8 d (‘late control’), Pi 8 d (‘late Pi depletion’) and ‘recovery’ 4 d. ssRNA-Seq data generated from the five physiological states were assembled and mapped (Table S1), yielding 10 083 genes corresponding to 97% of the P. tricornutum annotated protein coding genome (genome.jgi-psf.org/Phatr2). Globally, 6436 protein coding genes were differentially expressed in response to Pi fluctuations when compared with the control, with the relative portion of up- and downregulated genes being approximately the same (Fig. 2a). During early Pi depletion, the number of upregulated genes was slightly lower than those upregulated at late Pi depletion and at recovery (1503, 1958 and 1991, respectively) (Fig. 2a). The number of downregulated genes was the lowest for recovery (1396) and the highest for late Pi stress (2368), with early Pi stress showing the downregulation of 2032 genes (Fig. 2a). The expression of 21 of the most responsive protein coding genes during Pi depletion was further verified by RTqPCR. These included genes coding for Pi transporters, alkaline phosphatases (AlkPases), heat shock proteins, HSFs and several proteins of unknown function (Table S2). A good correlation between transcript abundance was found between ssRNA-Seq data and RT-qPCR (r = 0.8, P ? 0) validating the robustness of ssRNA-Seq data (Figs 2b, S3). The plethoric origin of the diatom P-responsive genes A striking aspect uncovered by the sequencing of the diatom genomes is the diverse origin of their genes. To a large extent, this is the result of their complex evolutionary origin, making diatoms not quite plants nor animals (Armbrust et al., 2004; Bowler et al., 2008). In order to investigate the origins of the Pi differentially expressed genes, we compared their protein sequences with those in the National Center for Biotechnology Information nonredundant (NCBI nr) database using BLASTp. The criteria used to New Phytologist (2016) 210: 497–510 www.newphytologist.com
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define genes that were ‘present’ in one species were those published previously (Armbrust et al., 2004; Bowler et al., 2008): ≥ 30% sequence positives; ≥ 30% alignment coverage of either the query or subject sequences; and BLAST e-value of < 1e-5. The larger fraction of Pi differentially expressed genes (30%) corresponded to proteins common to plants and animals (Eukaryotes), 20% were ‘core’ proteins and 19.4% were exclusively of plant origin (Fig. 2c). Shared proteins between P. tricornutum and Thalassiosira pseudonana constituted < 2% of the differentially expressed genes, and those found exclusively in P. tricornutum accounted for c. 16% (Fig. 2c). The latter are probable ‘de novo’ genes that have evolved since the two diatom lineages diverged c. 90 million yr ago (Bowler et al., 2008). Seventy-seven differentially expressed genes were exclusively found in bacteria and were absent in all other organisms (Fig. 2c). Many of the ‘bacterial’ genes that responded to Pi fluctuation were related to metabolism and redox processes. Interestingly, included in the Pi differentially expressed genes were 123 genes also present in viruses, some of which were simultaneously present in plants (four), in plants and bacteria (19), in animals and bacteria (five) and in all the groups (62), suggesting a possible horizontal viral–host gene exchange. These consisted largely of membrane proteins, including several putative signaling protein kinases and Pi transporters. The presence of Pi transporter genes in several virus genomes New Phytologist (2016) 210: 497–510 www.newphytologist.com
Fig. 1 Phaeodactylum tricornutum responses to inorganic phosphate (Pi) fluctuations. (a) Time course of cultures grown in fully supplemented medium (closed circles) and Pi-depleted medium (open circles); and (b) mean growth rates (natural logarithm, loge) of the first 4 d of culture under fully replete control conditions (C), Pi depletion (Pi) and Pi resupply after 4 d of Pi depletion (+Pi). Data represent SD of two independent experiments (with at least three biological replicates each). Statistically significant differences between treatment and control cultures were assessed by a Student’s t-test: **, P < 0.01; *, P < 0.05. (c) Confocal laser microscopy of diatom cells in the five distinct physiological states: C 4d, 4 d under control conditions; Pi 4d, 4 d Pi depleted; Pi 8d, 8 d Pi depleted; +Pi 4d, 4 d Pi resupply after 4 d Pi depleted; C 8d, 8 d control conditions. Cells were stained with Nile Red (NR) to visualize lipid bodies (green color). Chloroplasts can be visualized by the chlorophyll autofluorescence (red color). Bars, 10 lm. Chl, chlorophyll autoflorescence; DIC, differential interference contrast.
suggested the occurrence of viral manipulation of the infected host capacity for Pi uptake (Monier et al., 2012). Overall, 80% of the differentially expressed protein coding genes were shared with plants and 50% shared with animals, amongst which a small fraction (< 1%, 64 genes) was absent from plants. These included annotated genes for metabolism, DNA methylation and a putatively secreted AlkPase (49678). Our data strongly support the view that the physiology of the P-starved diatom response results from a combination of genes of diverse origins, probably forming original metabolic pathways, such as the recently dissected urea cycle (Allen et al., 2011), enabling the resilience and success of diatoms in a fluctuating environment. Pi depletion and Pi resupply define distinct physiological and transcriptomic profiles In order to assess the function of the differentially expressed genes under Pi fluctuation, we performed a search on the available gene ontology (GO) terms of the annotated transcripts and analyzed their relative over-representation in the five distinct physiological states. During early Pi depletion, the upregulated protein coding genes that were most significantly over-represented were putative phosphate transporters, revealing an active molecular strategy to increase the efficiency of Pi uptake (Fig. 3a). Other overÓ 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
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Fig. 2 Genome-wide transcriptome analysis of Phaeodactylum tricornutum response to inorganic phosphate (Pi) fluctuations. (a) Venn diagrams representing the up- and downregulated genes in response to progressive Pi depletion and resupply. Cutoff of two-fold difference in fragments per kilobase of exons per million fragments mapped, P < 0.05, when compared with 4 d control cultures. Pi 4d, 4 d Pi depleted; Pi 8d, 8 d Pi depleted; +Pi 4d, 4 d Pi resupply after 4 d Pi depletion. (b) Correlation between strand-specific RNA-sequencing (ssRNA-Seq) and quantitative reverse transcription polymerase chain reaction (RT-qPCR) in the detection of Pi-responsive Phaeodactylum tricornutum genes. The x-axis gives the log2 value of the fold change detected by ssRNA-Seq and the y-axis gives the same value detected by RT-qPCR. Green dots represent differentially expressed (DE) genes detected by both platforms; gray dots represent genes that do not change by more than two-fold in expression under Pi fluctuation. (c) Gene origins of the 6436 Pi DE protein coding genes. Phaeodactylum tricornutum protein sequences were compared with the National Center for Biotechnology Information nonredundant (NCBI nr) database using BLASTp. The criteria used to define genes that were ‘present’ in one species were as follows: ≥ 30% sequence positives; ≥ 30% alignment coverage of either the query or subject sequences; and a BLAST e-value of < 1e-5. Core, proteins present in animals, plants, bacteria; Unique, proteins only found in P. tricornutum; Diatom, proteins common to Thalassiosira pseudonana and P. tricornutum.
represented GO enrichments were for protein coding genes belonging to several metabolic pathways involved in carbohydrate metabolism and catabolism (Fig. 3a). This points to the occurrence of a general shift in carbon metabolism in response to Pi depletion. The regulation of intermediate carbon metabolism has been demonstrated previously to be essential for the growth optimization of N-starved P. tricornutum (Allen et al., 2011; Levitan et al., 2015), which suggests that the same adaptive metabolic processes also operate in response to P starvation. Under late Pi depletion, cells were probably severely P starved because of the prolonged Pi scarcity and possibly the consumption of internal reserves. Other over-represented GO enrichments in the upregulated genes during this stage were ubiquitin cycle-associated terms, and genes encoding components in intracellular signaling cascades and chromatin organization (Fig. 3a). The downregulated genes most significantly over-represented during early Pi Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
depletion encoded proteins involved in the photosynthetic light reactions, and transcripts for these genes declined further during late Pi depletion (Fig. 3b). The over-representation of the downregulated genes relating to photosynthesis also occurred during the late exponential phase when cell population growth was entering into stationary phase (Fig. 1a). Reduction of the photosynthetic machinery by the downregulation of genes involved in the light reactions in photosystem II (PSII) and photosystem I (PSI) is ultimately beneficial to a cell being subjected to a nutritive imbalance by reducing the generation site of reactive oxygen species, and hence oxidative stress. A reduction in photosynthetic activity has been reported previously to occur in P. tricornutum under P scarcity (Yang et al., 2014), as well as under N stress (Yang et al., 2013; Levitan et al., 2015). Our data highlight the existence of a stringent relationship between nutrient supply and the genomic control of photosynthesis. When Pi was resupplied New Phytologist (2016) 210: 497–510 www.newphytologist.com
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Phosphate transport Nucleode-sugar metabolic process Inorganic anion transport Anion transport Glycolysis Glucose catabolic process Monosaccharide catabolic process Hexose catabolic process Alcohol catabolic process Carbohydrate catabolic process Cellular carbohydrate catabolic process Glucose metabolic process Hexose metabolic process Monosaccharide metabolic process Cellular carbohydrate metabolic process Alcohol metabolic process Ubiquin cycle Unsaturated fay acid biosynthec process Unsaturated fay acid metabolic process Phosphoenolpyruvate-dependent sugar phosphotransferase system Regulaon of metabolic process Intracellular signaling cascade DNA replicaon iniaon DNA packaging Chroman assembly or disassembly Chroman assembly Chroman organizaon Protein-DNA complex assembly Nucleosome assembly Nucleosome organizaon Chromosome organizaon Deoxyribonucleode metabolic process Organelle organizaon Regulaon of gene expression Regulaon of macromolecule metabolic process Establishement of localizaon Localizaon Gene expression Transport Nitrogen compound metabolic process Nucleobase, nucleoside, nucleode and nucleic acid metabolic process
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Fig. 3 Gene ontology (GO) enrichment analysis of (a) upregulated and (b) downregulated genes in the transcriptome response of Phaeodactylum tricornutum to inorganic phosphate (Pi) fluctuations. The top 15 (highest odds ratio) enriched GO terms of the biological process category in each sample are shown. The odds ratio was defined as the ratio of the proportion of a GO term in (a) upregulated and (b) downregulated genes to the proportion of this GO term in all diatom genes. The larger the odds ratio, the higher the relative abundance of this GO term compared with background. Multiple-test adjusted P < 0.05 was used to define statistical significance. Pi 4d, 4 d Pi depleted; Pi 8d, 8 d Pi depleted; +Pi 4d, 4 d Pi resupply after 4 d Pi depletion; C 8d, 8 d control conditions.
to the medium, the over-represented upregulated genes coded for proteins involved in unsaturated fatty acid biosynthetic/ metabolic processes and DNA replication initiation and DNA packaging (Fig. 3a); these proteins are involved in the resumption of active cell growth under recovery (Fig. 1c). Furthermore, other over-represented upregulated genes during recovery included those putatively involved in epigenetic modifications, such as chromatin and nucleosome assembly, DNA packaging and chromosome assembly (Fig. 3a), suggesting a new acclimated epigenomic state anticipating repeated Pi-depleted conditions. Optimization of P scavenging under Pi depletion Common strategies used by microorganisms and plants to optimize Pi scavenging in response to Pi scarcity include an increase in the number of Pi transporters and/or replacement of lowaffinity transporters with higher affinity ones, and the production of phosphatases to utilize organic P sources from the environment (Clark et al., 1998; Riegman et al., 2000; Vance et al., 2003). In a recent report, five of the six annotated Pi transporter genes in P. tricornutum were upregulated after 48 h of Pi depletion (Yang et al., 2014). Manual curation and analysis of conserved domains enabled us to identify several new putative Pi New Phytologist (2016) 210: 497–510 www.newphytologist.com
transporter genes in P. tricornutum, with the number increasing to 24 (Fig. 4a). This is approximately the same number of Pi transporters as annotated in Arabidopsis thaliana (The Arabidopsis Information Ressource, TAIR, database), whereas Chlamydomonas reinhardtii has 14 annotated putative Pi transporters (Grossman & Aksoy, 2015). Twelve of the Pi transporter genes in P. tricornutum were highly upregulated under Pi depletion, with 11 being dramatically downregulated on Pi resupply (Fig. 4a), revealing a tight regulation of their expression level by environmental Pi concentration. These results suggest that diatoms are equipped with a highly responsive Pi transporter system programmed to operate only when Pi is scarce. Five of the highly induced Pi transporters have been annotated as Na+/Pi cotransporters, a type of transporter that occurs in phytoplankton, yeast and vertebrates, but which is lacking in plants. Interestingly, the five Na+/Pi co-transporters in P. tricornutum share more sequence homology to mammalian renal Pi transporters than to yeast, bacterial or green algae (Chlamydomonas) Na+/Pi cotransporters. Also supporting the notion of optimized Pi uptake under Pi depletion was the very high induction of five genes coding for AlkPases, with three expressed > 1000-fold under late Pi stress (Fig. 4a). Marine bacteria have been shown to have a large Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
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Fig. 4 Heat maps of log2 fold expression changes under inorganic phosphate (Pi) fluctuation of putative (a) Pi transporters and alkaline phosphatase (AlkPase) coding genes and (b) membrane lipid metabolism and polyphosphate metabolism (PolyP) coding genes in Phaeodactylum tricornutum. Numbers correspond to Joint Genome Institute (JGI) protein identifiers. Pi 4d, 4 d Pi depleted; Pi 8d, 8 d Pi depleted; +Pi 4d, 4 d Pi resupply after 4 d Pi depletion; C 8d, 8 d control conditions.
fraction of AlkPases secreted under Pi-deficient conditions (Luo et al., 2009). In P. tricornutum, one of the AlkPases (49678) was predicted to be secreted. This AlkPase has been biochemically characterized previously (termed PtAPase) and has been shown to be released to the medium in response to Pi depletion (Lin et al., 2013). In T. pseudonana, several AlkPases have been shown to be upregulated on Pi depletion in both the transcriptome and proteome (Dyhrman et al., 2012). Furthermore, the authors also showed that AlkPase activity triggered by Pi depletion was mainly surface associated with some intracellular location (Dyhrman et al., 2012). This, together with our data, suggests a similar location of activity of P. tricornutum AlkPases. Maintenance of P homeostasis in diatoms has common and distinct characteristics compared with higher plants The ability to economize cellular Pi demands by the replacement of membrane phospholipids by sulfolipids is known to occur in plants (Vance et al., 2003), in Chlamydomonas (Grossman & Aksoy, 2015) and in phytoplankton (Van Mooy et al., 2009; Martin et al., 2011; Dyhrman et al., 2012; Abida et al., 2015). Under prolonged Pi-depleted conditions (13 d), a total disappearance of phospholipids, with a concomitant increase in diacylglyceryl-hydroxymethyl-N,N,N-trimethyl-b-alanine (DGTA), sulfoquinovosyldiacylglycerol (SQDG) and digalactosyldiacylglycerol (DGDG), has been reported recently in P. tricornutum (Abida et al., 2015). Membrane phospholipids in P-starved Arabidopsis plants have been shown to be cleaved by phospholipases C (Nakamura et al., 2005; Gaude et al., 2008) and D (CruzRamırez et al., 2006). In this work, two phosphatidylinositolspecific phospholipases C (PI-PLC) were specifically upregulated Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
on early Pi depletion, and three others were upregulated during late Pi stress (Fig. 4b). Two genes encoding phospholipases D were also upregulated throughout Pi depletion (Fig. 4b). This supports the occurrence of membrane phospholipid degradation under Pi depletion, similar to that which occurs in Arabidopsis. It also shows that the process of membrane phospholipid degradation under Pi depletion is regulated at the transcriptional level. Different from the observations in Arabidopsis, the polar lipid that increases most strongly in P. tricornutum membranes under Pi stress is DGTA, a betaine glycerolipid (Abida et al., 2015). This is corroborated by our transcriptome data, where a putative DGTA enzyme (ARF4, 42872) was expressed > 30-fold under early Pi depletion and > 80-fold under late Pi depletion (Fig. 4b). Two genes coding for sulfolipid synthases (SQD2) were also highly induced during early and late Pi depletion (up to 13-fold expression increase) and, to a lesser extent, a gene coding for a monogalactosyldiacylglycerol synthase (MGD2) (Fig. 4b). These data corroborate, at the transcriptional level, previous findings (Yang et al., 2014; Abida et al., 2015). They also confirm that membrane remodeling events in P. tricornutum are tightly regulated at the transcriptional level and are related directly to P scarcity. Neutral lipid accumulation is a common response of microalgae under unfavorable conditions, namely nutrient stress (Fields et al., 2014), and, in the present study, P. tricornutum cells accumulated large lipid bodies after 8 d of Pi depletion (Fig. 2c). We suggest that the lipids resulting from the remodeling of membranes were shifted in the form of triacylglycerol (TAG) to the nascent lipid bodies. In Dunaliella salina, fatty acids typically found in chloroplast membrane galactolipids have been detected in storage lipids (Cho & Thompson, 1986). Membrane New Phytologist (2016) 210: 497–510 www.newphytologist.com
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phospholipids can enter the TAG metabolic pathway by direct conversion to diacylglycerol (DAG) by phospholipid diacylglycerol acyltransferase (PDAT)-catalyzed transesterification. The gene encoding PDAT, an acyl-CoA-independent pathway enzyme for the production of TAGs, initially characterized in yeast (Dahlqvist et al., 2000; Oelkers et al., 2000), has an ortholog in A. thaliana (Stahl et al., 2004) and in C. reinhardtii (Merchant et al., 2012). An ortholog of PDAT is also present in P. tricornutum (8860), being specifically upregulated during early and late Pi stress (Fig. 4b), further supporting a role for membrane phospholipid recycling to the TAG pathway during Pi depletion. A recent report has shown that N depletion in P. tricornutum leads to the remodeling of membranes, and that the PDAT gene ortholog is concomitantly upregulated, supporting its involvement in the movement of membrane phospholipids to the TAG pathway (Yang et al., 2013). Polyphosphate (PolyP), the most important phosphate reserve used on Pi depletion in yeast, is synthesized through the vacuolar transporter chaperone (VTC) complex (Ogawa et al., 2000). Unlike plants, diatoms have VTC orthologs, and these proteins have been shown to increase Pi allocation to PolyP in response to N deficiency (Perry, 1976) and Pi deficiency (Dyhrman et al., 2012). Genes encoding VTCs appeared to be regulated, at least partially, at the transcriptional level, as two genes coding for putative VTC proteins were significantly upregulated in response to Pi deficiency in T. pseudonana (Dyhrman et al., 2012). Four genes encoding proteins with a VTC domain (IPR018966) were detected in P. tricornutum, with two being highly upregulated in response to Pi depletion and the other two in response to Pi resupply (Fig. 4b), suggesting distinct roles in PolyP metabolism in response to Pi concentration. Long noncoding transcripts specifically induced in response to Pi depletion The P starvation response in plants involves the induction of several RNA transcripts with no protein coding potential, which have been described as having a key role in the regulation of P homeostasis. These noncoding RNAs include several precursors of micro-RNAs (miRNAs), such as miR399 and miR827, involved in the regulation of signaling pathways and Pi transport under Pi depletion (Bari et al., 2006; Hsieh et al., 2009). Although the function of miRNAs in diatoms remains to be verified (Lopez-Gomollon et al., 2014; Rogato et al., 2014), gene silencing has been shown to occur (De Riso et al., 2009). In the present work, two annotated miRNA precursors (pti-MIR5472 and pti-MIR5471; Huang et al., 2011) were significantly upregulated in response to Pi stress in P. tricornutum (Table S3), although functional studies are needed to examine the relevance of these transcripts in the diatom Pi response. Pi starvation in plants also involves longer nonprotein coding transcripts, such as INDUCED BY PHOSPHATE STARVATION 1 (IPS1). This transcript has a small ORF, but was later found to be active as an RNA molecule, ‘target mimicking’ miR399 (Franco-Zorrilla et al., 2007). IPS1 is recognized by miR399 as a target; however, as a result of mismatches in the New Phytologist (2016) 210: 497–510 www.newphytologist.com
New Phytologist recognition sequence, their binding does not lead to miR399assisted cleavage and remains stable, which results in the finetuning of Pi uptake (Franco-Zorrilla et al., 2007). We have thoroughly analyzed the P. tricornutum noncoding transcriptome and have identified 1510 putative lincRNAs (Table S4), 202 of which were specifically upregulated in response to Pi depletion and downregulated when Pi was resupplied to the medium. The 1510 lincRNA sequences identified in this study were compared with the 10 402 mRNAs obtained from Phatr2. Phaeodactylum tricornutum lincRNAs are significantly shorter than mRNAs with c. 90% at < 1000 nucleotides (Fig. 5a,b). Most of these diatom lincRNAs (> 70%) lack ORFs with coding potential in any of the three possible reading frames (Fig. 5c). Nonetheless, approximately 28% of the identified lincRNAs have the presence of a very short putative ORF (< 100 amino acids) (Fig. 5c,d), which could potentially be translated into a short peptide. The majority of the P. tricornutum lincRNAs are intronless (90%) with only a small fraction containing a single intron (Fig. 5e) of similar size to the introns found in mRNAs (Fig. 5f). The exons of lincRNAs are shorter than those of protein coding transcripts (Fig. S4a,c) and the distance between stop codons and the longest stop codon-free sequences are shorter in diatom lincRNAs than in protein coding transcripts (Fig. S4d,e). LincRNAs also have lower GC content and lower free energy compared with mRNAs (Figs S4f, S5, S6). Interestingly, all of these genomic features have also been detected in functional human lincRNAs (Niazi & Valadkhan, 2012), suggesting conserved features of these molecules across kingdoms. To search for lincRNAs with a similar function to that of IPS1 in higher plants, we analyzed whether the P. tricornutum Piresponsive lincRNAs had a predicted target mimicry function (Wu et al., 2013). No target mimicry function could be predicted in any of the Pi-responsive lincRNAs identified in this work. Using recently publically available RNA-Seq reads (accession no. SRP040703), we sought to investigate the expression changes of the Pi-responsive lincRNAs under 48 h N depletion (Levitan et al., 2015). Amongst the top 20 upregulated lincRNAs in Pi, only two were significantly upregulated under N (two-fold difference and P < 0.05) (Fig. 6a), revealing very specific responses of the noncoding transcriptome to stress conditions. The fact that these diatom lincRNAs were specifically expressed in response to Pi depletion (Figs 6a, S3) suggests that at least some of these transcripts could have regulatory roles in P. tricornutum. LincRNAs have been shown to associate with chromatin remodeling complexes and to affect gene expression by cis- and trans-action (De Lucia & Dean, 2011; Nagano & Fraser, 2011; Guttman & Rinn, 2012; Bonasio & Shiekhattar, 2014; Liu et al., 2015). We investigated the coexpression correlation between the Pi-responsive lincRNA genes and their neighboring genes to explore putative cis-regulatory functions of the lincRNAs. Several potential gene targets have been identified as having a correlation (positive or negative) to lincRNAs (Fig. 6b; Table S5). These results can be used to further investigate the function of diatom lincRNAs in the context of Pi depletion, but also in the context of other abiotic stresses that have putative common responsive molecular modules, such as N stress. Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
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Diatom TFs as coordinators of the Pi transcriptomic response The dynamic nature of the diatom transcriptomic response to Pi fluctuation is ultimately controlled by a network of TFs, whose activity changes in response to the varying signals, thereby coordinating the activation of target genes. We have detected the presence of 275 genes coding for putative TFs in P. tricornutum, which include all the previously annotated TFs (Rayko et al., 2010) and several other newly identified putative TFs (Table S6). These TFs have been manually curated for the presence of DNA binding domains using INTERPRO (http://www.ebi.ac.uk/Tools/ InterProScan/), although we cannot completely exclude the presence of false positives. In response to Pi fluctuation, 62.5% of the Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
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Fig. 5 Genomic structure of Phaeodactylum tricornutum long intergenic nonprotein coding RNAs (lincRNAs). (a, b) Length distribution in nucleotides (nt) of unspliced lincRNAs and mRNAs; (c) number of predicted open reading frames (ORFs) and (d) length of putative ORFs in amino acids (aa) considering the three reading frames of lincRNAs and mRNAs; (e) intron number and (f) length of introns in nucleotides (nt) in lincRNAs and mRNAs.
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genes for putative TFs were differentially expressed, revealing a very dynamic response. Approximately 32% of the upregulated TFs throughout Pi depletion belong to the HSF family (Fig. 7a). When Pi was resupplied to the medium, there was a switch between the abundance of TF families, with the ‘Myeloblastosis’ family (MYB) being over-represented (Fig. 7a). These results support a role for HSFs in the present stress response, which is in agreement with the function of this class of TFs in higher plants and mammals ( Akerfelt et al., 2010). However, as in Arabidopsis (Nover et al., 2001), the significance of the abundance of the HSF family in diatoms remains to be resolved. In Chlamydomonas and in vascular plants, the key regulators of the Pi response are TFs (PSR1 and PHR1, respectively) of the MYB family with a coiled-coil domain (MYB-CC) (Wykoff et al., New Phytologist (2016) 210: 497–510 www.newphytologist.com
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1999; Rubio et al., 2001). In diatoms, there are 34 annotated MYB family TFs, but none is MYB-CC with the characteristic LHEQLE conserved motif, suggesting that this domain was acquired after the separation of green and red algae from the common ancestor. Amongst the TF genes upregulated during early Pi depletion, genes for two HSFs (HSF3.1b and HSF4.7a) returned to their control levels during late Pi depletion (Fig. 7b). This was further confirmed by RT-qPCR (Fig. S7), suggesting a role for these TFs in the coordination of an early response-specific gene network. Assuming that these TFs will bind to the conserved heat shock element (HSE) present in the upstream promoter region of putative target genes (Nover et al., 2001; Akerfelt et al., 2010), we generated a transcription activation network between these TFs and the significantly coexpressed genes (Fig. 7c). Several signaling/sensing genes and other genes coding for other HSF and MYB TFs were present in this early responsive network, as well as several lincRNA genes (Table S7). This gene regulatory model can be used to design new studies to further dissect the relationship between the two transiently expressed TFs and the coexpressed genes, to deepen our understanding of the molecular networks underlying diatom early physiological adaptations to P depletion. Sensing and signaling of environmental Pi fluctuations involves a singular mix-and-match of genes and pathways Transmembrane or cell surface receptors in unicellular organisms, such as diatoms, are likely to play primordial roles in sensing environmental changes, and the resulting signals are then New Phytologist (2016) 210: 497–510 www.newphytologist.com
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Fig. 6 Long intergenic nonprotein coding RNAs (lincRNAs) involved in Phaeodactylum tricornutum responses to inorganic phosphate (Pi) depletion. (a) Heat map of the top 20 upregulated lincRNAs (P < 0.05) under early Pi depletion (4 d, this work) and early N depletion (N) (2 d, from Levitan et al., 2015; RNA sequencing (RNA-Seq) reads obtained from accession no. SRP040703). C 8d, 8 d control conditions; Pi 4d, 4 d Pi depleted; Pi 8d, 8 d Pi depleted; +Pi 4d, 4 d Pi resupply after 4 d Pi depletion; U, significantly upregulated under N (more than two-fold, P < 0.05); D, significantly downregulated under N (more than two-fold, P < 0.05). (b) Putative cisregulatory protein coding targets of Piresponsive lincRNAs (Supporting Information Table S5). Neighboring upstream and/or downstream putative cis-targets were identified according to positive or negative expression correlation (r2 ≥ 0.6) to lincRNAs.
transmitted to intracellular regulatory pathways, enabling the cell to respond, regulate and adjust its metabolism to maintain cellular homeostasis. We detected 659 genes putatively encoding sensing and signaling functions (Table S8), 42 of which were predicted to be membrane localized and therefore to have a putative receptor/sensing function. These receptors constitute a collection of several representatives of the signaling pathways typically described in metazoans (G protein-coupled receptors (GPCRs), serine/threonine (Ser/Thr) receptor kinases and/or tyrosine (Tyr) receptor kinases) and bacteria (histidine kinase receptors). Of the total putative sensing/signaling genes, 65.5% were differentially expressed under Pi fluctuation. Amongst the membrane-localized putative protein kinases were five histidine kinase (HK) domain-containing proteins, the gene of one of which, with an extracellular PAS domain (a protein domain functioning as a signal sensor), was upregulated in response to Pi stress (45485). Membrane HKs are major sensors of environmental changes in bacteria, regulating the activity of a second component, through a phosphorelay between the C-terminal HK domain and the aspartate residue of the response regulator (Laub & Goulian, 2007). Several annotated genes for Tyr and/ or Ser/Thr kinases were also upregulated in response to Pi depletion, with four such kinases being putatively membrane located. Genes encoding GPCR signaling pathway components have been documented to exist in diatoms (Port et al., 2013). GPCR proteins, found in most eukaryotic organisms, are cell surface receptors that play a major role in signal transduction, perception and response to the environment (Fredriksson & Schi€oth, 2005). Four genes coding for GPCRs of the rhodopsin/class A family and two of the glutamate/class C were significantly Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
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Fig. 7 Transcription factors (TFs) responsive to inorganic phosphate (Pi) depletion (more than two-fold differentially expressed, P < 0.05) in Phaeodactylum tricornutum. (a) Pie charts of the different classes of TFs upregulated under early Pi depletion (Pi 4d), late Pi depletion (Pi 8d) and resupply (+Pi 4d). (b) Heat maps of the top 10 most upregulated TFs during Pi depletion. C 8d corresponds to control cultures during late exponential growth in full medium. (c) Inferred transcription activation pathways under early Pi depletion of the two transiently expressed heat shock factors (HSFs) (Supporting Information Table S7): 3.1b, HSF3.1b (45560); 4.7a, HSF4.7a (1650). Blue dots correspond to protein coding genes, red dots correspond to long intergenic nonprotein coding RNAs (lincRNAs).
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upregulated during P starvation. Other signaling genes encoding proteins downstream of these receptors were found to be upregulated by Pi depletion, including genes for several adenylate cyclase and Ras small GTPase. These results suggest that the signaling pathways may be fully activated and presumably functional in response to P scarcity in P. tricornutum. Conclusion Genome-wide transcriptome-enabled studies have proven useful in providing mechanistic insights into the complexity of phytoplankton responses and adaptations to recurrent environmental challenges (Mock et al., 2008; Shrestha et al., 2012; Thamatrakoln et al., 2012; Ashworth et al., 2013; Levitan et al., 2015). Our data show that diatoms have a multitude of processes and pathways enabling survival and resilience under P scarcity that combine features possibly derived from different sources during their evolutionary history. These processes are involved not only in the optimization of P scavenging, but also in reducing cellular P demand by the internal recycling of P sources. Globally, these strategies seem to be general processes used by diatoms when confronting nutrient limitations in the marine medium, as similar strategies were detected in response to the depletion of iron (Allen et al., 2008), cobalamin (Bertrand et al., 2012) and N (Levitan et al., 2015). The apparent dynamic, ongoing, putative horizontal transfer of critical genes between marine Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
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bacteria, viruses and this diatom (i.e. phosphate transporters, membrane signaling receptors, metabolism- and redox-related genes) probably complements and enhances its molecular capacity to successfully respond and adapt to fluctuating Pi environments, hence contributing to its ecological success in the global oceans. Perhaps the most striking feature revealed by this work was the identification of a multitude of lincRNAs specifically expressed under Pi depletion. LincRNAs are ubiquitous molecules which, in other model systems (yeast, animals and plants), are beginning to attract considerable attention because of their important regulatory roles during development and stress (De Lucia & Dean, 2011; Nagano & Fraser, 2011; Guttman & Rinn, 2012; Bonasio & Shiekhattar, 2014; Liu et al., 2015). These findings provide a glimpse into the complexity of the molecular regulatory program in diatoms, and open up additional routes to explore the function and evolutionary significance of the nonprotein coding transcriptome in these ecologically successful organisms.
Acknowledgements We thank Connie Zhao and Bin Zhang (Genomics Resource Center, Rockefeller University), as well as Kaye Thomas, Pablo Ariel and Tao Tong (Bio-imaging Resource Center, Rockefeller University), for technical support. We also thank Jun Liu and Huan Wang for help with the bioinformatic analysis, and the New Phytologist (2016) 210: 497–510 www.newphytologist.com
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four anonymous reviewers for their constructive comments. M.H.C.C. and part of this work were supported by the Seventh Research Program of the European Union FP7/2007-2013 under Marie Curie Grant PIOF-GA-301466.
Author contributions M.H.C.C., H-X.S. and N-H.C. planned and designed the research; M.H.C.C. and H-X.S. performed the research; M.H.C.C., H-X.S. and N-H.C. analyzed the data; M.H.C.C., H-X.S., C.B. and N-H.C. wrote the manuscript.
References Abida H, Dolch L-J, Mei C, Villanova V, Conte M, Block MA, Finazzi G, Bastien O, Tirichine L, Bowler C et al. 2015. Membrane glycerolipid remodeling triggered by nitrogen and phosphorus starvation in Phaeodactylum tricornutum. Plant Physiology 167: 118–136. Akerfelt M, Morimoto RI, Sistonen L. 2010. Heat shock factors: integrators of cell stress, development and lifespan. Nature Reviews Molecular Cell Biology 11: 545–555. Allen AE, Dupont CL, Obornık M, Horak A, Nunes-Nesi A, McCrow JP, Zheng H, Johnson DA, Hu H, Fernie AR et al. 2011. Evolution and metabolic significance of the urea cycle in photosynthetic diatoms. Nature 473: 203–207. Allen AE, Laroche J, Maheswari U, Lommer M, Schauer N, Lopez PJ, Finazzi G, Fernie AR, Bowler C. 2008. Whole-cell response of the pennate diatom Phaeodactylum tricornutum to iron starvation. Proceedings of the National Academy of Sciences, USA 105: 10438–10443. Armbrust EV, Berges JA, Bowler C, Green BR, Martinez D, Putnam NH, Zhou S, Allen AE, Apt KE, Bechner M et al. 2004. The genome of the diatom Thalassiosira pseudonana: ecology, evolution, and metabolism. Science 306: 79– 86. Ashworth J, Coesel S, Lee A, Armbrust EV, Orellana MV, Baliga NS. 2013. Genome-wide diel growth state transitions in the diatom Thalassiosira pseudonana. Proceedings of the National Academy of Sciences, USA 110: 7518– 7523. Bari R, Datt Pant B, Stitt M, Scheible W-R. 2006. PHO2, microRNA399, and PHR1 define a phosphate-signaling pathway in plants. Plant Physiology 141: 988–999. Bauerfield E, Hickle W, Niermann U, Westerhagen V. 1990. Phytoplankton biomass and potential nutrient limitation of phytoplankton development in the southeastern North Sea in spring 1985 and 1986. Netherlands Journal of Sea Research 25: 131–142. Bertrand EM, Allen AE, Dupont CL, Norden-Krichmar TM, Bai J, Valas RE, Saito MA. 2012. Influence of cobalamin scarcity on diatom molecular physiology and identification of a cobalamin acquisition protein. Proceedings of the National Academy of Sciences, USA 109: E1762–E1771. Bonasio R, Shiekhattar R. 2014. Regulation of transcription by long noncoding RNAs. Annual Review of Genetics 48: 433–455. Bowler C, Allen AE, Badger JH, Grimwood J, Jabbari K, Kuo A, Maheswari U, Martens C, Maumus F, Otillar RP et al. 2008. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature 456: 239–244. Burge C, Karlin S. 1997. Prediction of complete gene structures in human genomic DNA. Journal of Molecular Biology 268: 78–94. Cho SH, Thompson GA. 1986. Properties of a fatty acyl hydrolase preferentially attacking monogalactosyldiacylglycerols in Dunaliella salina chloroplasts. Biochimica et Biophysica Acta (BBA) 878: 353–359. Clark LL, Ingall ED, Benner R. 1998. Marine phosphorus is selectively remineralized. Nature 393: 426. Cruz-Ramırez A, Oropeza-Aburto A, Razo-Herna ndez F, Ramırez-Cha vez E, Herrera-Estrella L. 2006. Phospholipase DZ2 plays an important role in extraplastidic galactolipid biosynthesis and phosphate recycling in Arabidopsis roots. Proceedings of the National Academy of Sciences, USA 103: 6765–6770.
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New Phytologist Dahlqvist A, Stahl U, Lenman M, Banas A, Lee M, Sandager L, Ronne H, Stymne S. 2000. Phospholipid:diacylglycerol acyltransferase: an enzyme that catalyzes the acyl-CoA-independent formation of triacylglycerol in yeast and plants. Proceedings of the National Academy of Sciences, USA 97: 6487–6492. De Lucia F, Dean C. 2011. Long non-coding RNAs and chromatin regulation. Current Opinion in Plant Biology 14: 168–173. De Riso V, Raniello R, Maumus F, Rogato A, Bowler C, Falciatore A. 2009. Gene silencing in the marine diatom Phaeodactylum tricornutum. Nucleic Acids Research 37: e96. Dyhrman S, Ammerman J, Van Mooy B. 2007. Microbes and the marine phosphorus cycle. Oceanography 20: 110–116. Dyhrman ST, Jenkins BD, Rynearson TA, Saito MA, Mercier ML, Alexander H, Whitney LP, Drzewianowski A, Bulygin VV, Bertrand EM et al. 2012. The transcriptome and proteome of the diatom Thalassiosira pseudonana reveal a diverse phosphorus stress response. PLoS ONE 7: e33768. Egge JK. 1998. Are diatoms poor competitors at low phosphate concentrations? Journal of Marine Systems 16: 191–198. Field C, Behrenfeld M, Randerson J, Falkowski P. 1998. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281: 237–240. Fields MW, Hise A, Lohman EJ, Bell T, Gardner RD, Corredor L, Moll K, Peyton BM, Characklis GW, Gerlach R. 2014. Sources and resources: importance of nutrients, resource allocation, and ecology in microalgal cultivation for lipid accumulation. Applied Microbiology and Biotechnology 98: 4805–4816. Franco-Zorrilla JM, Valli A, Todesco M, Mateos I, Puga MI, Rubio-Somoza I, Leyva A, Weigel D, Garcıa JA, Paz-Ares J. 2007. Target mimicry provides a new mechanism for regulation of microRNA activity. Nature Genetics 39: 1033–1037. Fredriksson R, Schi€oth HB. 2005. The Repertoire of G-protein-coupled receptors in fully sequenced genomes. Molecular Pharmacology 67: 1414–1425. Gaude N, Nakamura Y, Scheible W-R, Ohta H, Doermann P. 2008. Phospholipase C5 (NPC5) is involved in galactolipid accumulation during phosphate limitation in leaves of Arabidopsis. Plant Journal 56: 28–39. Grossman AR, Aksoy M. 2015. Algae in a phosphorus-limited landscape. Annual Plant Reviews 48: 337–374. Guillard RR, Ryther JH. 1962. Studies of marine planktonic diatoms: i. Cyclotella nana hustedt, and Detonula confervacea (cleve) gran. Canadian Journal of Microbiology 8: 229–239. Guttman M, Rinn JL. 2012. Modular regulatory principles of large non-coding RNAs. Nature 482: 339–346. Hsieh L-C, Lin S-I, Shih AC-C, Chen J-W, Lin W-Y, Tseng C-Y, Li W-H, Chiou T-J. 2009. Uncovering small RNA-mediated responses to phosphate deficiency in Arabidopsis by deep sequencing. Plant Physiology 151: 2120– 2132. Huang A, He L, Wang G. 2011. Identification and characterization of microRNAs from Phaeodactylum tricornutum by high-throughput sequencing and bioinformatics analysis. BMC Genomics 12: 337. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. 2013. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology 14: R36. Laub MT, Goulian M. 2007. Specificity in two-component signal transduction pathways. Annual Review of Genetics 41: 121–145. Levitan O, Dinamarca J, Zelzion E, Lun DS, Guerra LT, Kim MK, Kim J, Van Mooy BAS, Bhattacharya D, Falkowski PG. 2015. Remodeling of intermediate metabolism in the diatom Phaeodactylum tricornutum under nitrogen stress. Proceedings of the National Academy of Sciences, USA 112: 412–417. Lin H-Y, Shih C-Y, Liu H-C, Chang J, Chen Y-L, Chen Y-R, Lin H-T, Chang Y-Y, Hsu C-H, Lin H-J. 2013. Identification and characterization of an extracellular alkaline phosphatase in the marine diatom Phaeodactylum tricornutum. Marine Biotechnology 15: 425–436. Liu J, Jung C, Xu J, Wang H, Deng S, Bernad L, Arenas-Huertero C, Chua NH. 2012. Genome-wide analysis uncovers regulation of long intergenic noncoding RNAs in Arabidopsis. The Plant Cell 24: 4333–4345. Liu J, Wang H, Chua N-H. 2015. Long noncoding RNA transcriptome of plants. Plant Biotechnology Journal 13: 319–328.
Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
New Phytologist Lopez-Gomollon S, Beckers M, Rathjen T, Moxon S, Maumus F, Mohorianu I, Moulton V, Dalmay T, Mock T. 2014. Global discovery and characterization of small non-coding RNAs in marine microalgae. BMC Genomics 15: 697. Luo H, Benner R, Long RA, Hu J. 2009. Subcellular localization of marine bacterial alkaline phosphatases. Proceedings of the National Academy of Sciences, USA 106: 21219–21223. Martin P, Van Mooy BAS, Heithoff A, Dyhrman ST. 2011. Phosphorus supply drives rapid turnover of membrane phospholipids in the diatom Thalassiosira pseudonana. The ISME Journal 5: 1057–1060. Merchant SS, Kropat J, Liu B, Shaw J, Warakanont J. 2012. TAG, You’re it! Chlamydomonas as a reference organism for understanding algal triacylglycerol accumulation. Current Opinion in Biotechnology 23: 352–363. Mock T, Samanta MP, Iverson V, Berthiaume C, Robison M, Holtermann K, Durkin C, Bondurant SS, Richmond K, Rodesch M et al. 2008. Wholegenome expression profiling of the marine diatom Thalassiosira pseudonana identifies genes involved in silicon bioprocesses. Proceedings of the National Academy of Sciences, USA 105: 1579–1584. Monier A, Welsh RM, Gentemann C, Weinstock G, Sodergren E, Armbrust EV, Eisen JA, Worden AZ. 2012. Phosphate transporters in marine phytoplankton and their viruses: cross-domain commonalities in viral–host gene exchanges. Environmental Microbiology 14: 162–176. Morel FMM. 2003. The biogeochemical cycles of trace metals in the oceans. Science 300: 944–947. Nagano T, Fraser P. 2011. No-nonsense functions for long noncoding RNAs. Cell 145: 178–181. Nakamura Y, Awai K, Masuda T, Yoshioka Y, Takamiya K, Ohta H. 2005. A novel phosphatidylcholine-hydrolyzing phospholipase C induced by phosphate starvation in Arabidopsis. The Journal of Biological Chemistry 280: 7469–7476. Nelson DM, Treguer P, Brzezinski MA, Leynaert A, Queguiner B. 1995. Production and dissolution of biogenic silica in the ocean – revised global estimates, comparison with regional data and relationship to biogenic sedimentation. Global Biogeochemical Cycles 9: 359–372. Niazi F, Valadkhan S. 2012. Computational analysis of functional long noncoding RNAs reveals lack of peptide-coding capacity and parallels with 3’ UTRs. RNA 18: 825–843. Nordberg H, Cantor M, Dusheyko S, Hua S, Poliakov A, Shabalov I, Smirnova T, Grigoriev IV, Dubchak I. 2014. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Research 42: D26– D31. Nover L, Bjarti K, Kumar MS, Ganguli A, Scharf K-D. 2001. Arabidopsis and the heat stress transcription factor world: how many heat stress transcription factors do we need? Cell Stress and Chaperones 6: 177. Oelkers P, Tinkelenberg A, Erdeniz N, Cromley D, Billheimer JT, Sturley SL. 2000. A lecithin cholesterol acyltransferase-like gene mediates diacylglycerol esterification in yeast. The Journal of Biological Chemistry 275: 15609–15612. Ogawa N, DeRisi J, Brown PO. 2000. New components of a system for phosphate accumulation and polyphosphate metabolism in Saccharomyces cerevisiae revealed by genomic expression analysis. Molecular Biology of the Cell 11: 4309–4321. Perry MJ. 1976. Phosphate utilization by an oceanic diatom in phosphoruslimited chemostat culture and in oligotrophic waters of central North-Pacific. Limnology Oceanography 21: 88–107. Port JA, Parker MS, Kodner RB, Wallace JC, Armbrust EV, Faustman EM. 2013. Identification of G protein-coupled receptor signaling pathway proteins in marine diatoms using comparative genomics. BMC Genomics 14: 503. Rayko E, Maumus F, Maheswari U, Jabbari K, Bowler C. 2010. Transcription factor families inferred from genome sequences of photosynthetic stramenopiles. New Phytologist 188: 52–66. Riegman R, Stolte W, Noordeloos AAM, Slezak D. 2000. Nutrient uptake and alkaline phosphatase (ec 3:1:3:1) activity of Emiliania huxleyi (Prymnesiophyceae) during growth under N and P limitation in continuous cultures. Journal of Phycology 36: 87–96. Rogato A, Richard H, Sarazin A, Voss BR, Navarro SC, Champeimont RL, Navarro L, Carbone A, Hess WR, Falciatore A. 2014. The diversity of small non-coding RNAs in the diatom Phaeodactylum tricornutum. BMC Genomics 15: 1–19. Ó 2015 The Authors New Phytologist Ó 2015 New Phytologist Trust
Research 509 Rubio V, Linhares F, Solano R, Martın AC, Iglesias J, Leyva A, Paz-Ares J. 2001. A conserved MYB transcription factor involved in phosphate starvation signaling both in vascular plants and in unicellular algae. Genes & Development 15: 2122–2133. Shrestha RP, Tesson B, Norden-Krichmar T, Federowicz S, Hildebrand M, Allen AE. 2012. Whole transcriptome analysis of the silicon response of the diatom Thalassiosira pseudonana. BMC Genomics 13: 499. Siaut M, Heijde M, Mangogna M, Montsant A, Coesel S, Allen A, Manfredonia A, Falciatore A, Bowler C. 2007. Molecular toolbox for studying diatom biology in Phaeodactylum tricornutum. Gene 406: 23–35. Smoot ME, Ono K, Ruscheinski J, Wang P-L, Ideker T. 2011. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27: 431–432. Stahl U, Carlsson AS, Lenman M, Dahlqvist A, Huang B, Banas ́ W, Banas ́ A, Stymne S. 2004. Cloning and functional characterization of a phospholipid:diacylglycerol acyltransferase from Arabidopsis. Plant Physiology 135: 1324–1335. Thamatrakoln K, Korenovska O, Niheu AK, Bidle KD. 2012. Whole-genome expression analysis reveals a role for death-related genes in stress acclimation of the diatom Thalassiosira pseudonana. Environmental Microbiology 14: 67–81. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. 2013. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nature Biotechnology 31: 46–53. Van Mooy BAS, Fredricks HF, Pedler BE, Dyhrman ST, Karl DM, Koblızek M, Lomas MW, Mincer TJ, Moore LR, Moutin T et al. 2009. Phytoplankton in the ocean use non-phosphorus lipids in response to phosphorus scarcity. Nature 458: 69–72. Vance CP, Uhde-Stone C, Allan DL. 2003. Phosphorus acquisition and use: critical adaptations by plants for securing a nonrenewable resource. New Phytologist 157: 423–447. Woodward EMS, Owens NJP. 1990. Nutrient depletion studies in offshore North Sea. Netherlands Journal of Sea Research 25: 57–63. Wu H-J, Wang Z-M, Wang M, Wang X-J. 2013. Widespread long noncoding RNAs as endogenous target mimics for microRNAs in plants. Plant Physiology 161: 1875–1884. Wykoff DD, Grossman AR, Weeks DP, Usuda H, Shimogawara K. 1999. Psr1, a nuclear localized protein that regulates phosphorus metabolism in Chlamydomonas. Proceedings of the National Academy of Sciences, USA 96: 15336–15341. Yang Z-K, Niu Y-F, Ma Y-H, Xue J, Zhang M-H, Yang W-D, Liu J-S, Lu S-H, Guan Y, Li H-Y. 2013. Molecular and cellular mechanisms of neutral lipid accumulation in diatom following nitrogen deprivation. Biotechnology for Biofuels 6: 67. Yang Z-K, Zheng J-W, Niu Y-F, Yang W-D, Liu J-S, Li H-Y. 2014. Systemslevel analysis of the metabolic responses of the diatom Phaeodactylum tricornutum to phosphorus stress. Environmental Microbiology 16: 1793–1807. Zuker M. 2003. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Research 31: 3406–3415.
Supporting Information Additional supporting information may be found in the online version of this article. Fig. S1 Phosphate content in the growth medium of Phaeodactylum tricornutum on the sampling points for strandspecific RNA sequencing (ssRNA-Seq). Fig. S2 Growth curves of Phaeodactylum tricornutum cell cultures under different culture conditions. Fig. S3 Validation of strand-specific RNA sequencing (ssRNASeq) expression fold changes by quantitative reverse transcription
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polymerase chain reaction (RT-qPCR) with regard to Phaeodactylum tricornutum randomly selected protein coding and long intergenic nonprotein coding RNA (lincRNA) genes detected as being upregulated during inorganic phosphate (Pi) depletion by RNA-Seq (more than two-fold, P < 0.05). Fig. S4 Sequence analysis of Phaeodactylum tricornutum long intergenic nonprotein coding RNAs (lincRNAs) and microRNAs (mRNAs). Fig. S5 Distribution of predicted folding energies of transcripts of different lengths across long intergenic nonprotein coding RNAs (lincRNAs) and micro-RNAs (mRNAs) in Phaeodactylum tricornutum. Fig. S6 Correlation between the GC content and the predicted folding energies of transcripts in different length categories across long intergenic nonprotein coding RNAs (lincRNAs) and micro-RNAs (mRNAs) in Phaeodactylum tricornutum. Fig. S7 Validation of RNA sequencing (RNA-Seq) data by quantitative reverse transcription polymerase chain reaction (RTqPCR) of the two transiently expressed heat shock factors (HSFs) during inorganic phosphate (Pi) depletion in Phaeodactylum tricornutum. Table S1 Number of reads and mapped reads obtained by strand-specific RNA sequencing (ssRNA-Seq) for the five physiological sampling points of Phaeodactylum tricornutum in response to inorganic phosphate (Pi) fluctuations
Table S2 Primers designed for quantitative reverse transcription polymerase chain reaction (RT-qPCR) analysis in Phaeodactylum tricornutum Table S3 Expression variation under phosphate fluctuations of Phaeodactylum tricornutum micro-RNAs (miRNAs) Table S4 Expression variation under phosphate fluctuations of Phaeodactylum tricornutum long intergenic nonprotein coding RNA (lincRNA) candidates Table S5 Putative cis-targets of phosphate depletion-responsive long intergenic nonprotein coding RNAs (lincRNAs) in Phaeodactylum tricornutum Table S6 Expression variation of Phaeodactylum tricornutum transcription factors under phosphate fluctuations Table S7 Correlation networks between HSF3.1b and HSF4.7a and their putative target genes under early phosphate depletion in Phaeodactylum tricornutum Table S8 Expression variation of Phaeodactylum tricornutum sensing- and signaling-related genes under phosphate fluctuations Please note: Wiley Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.
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