Transcriptome analysis of the red marine alga Gracilaria vermiculophylla grown under different light intensities

Transcriptome analysis of the red marine alga Gracilaria vermiculophylla grown under different light intensities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcriptome analysis of the red marine alga Gracilaria vermiculophylla grown under different light intensities Takuya Kandori, Hiroyuki Mizuta, Toshiki Uji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6616872/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Light plays a vital role in seaweed growth by influencing photosynthesis and signaling metabolic processes. Seaweeds are subjected to various dynamic light conditions because of environmental factors such as weather and tidal changes. Despite the knowledge of the effects of light intensity on red algal growth, the gene expression patterns regulating growth under fluctuating light conditions remain unclear. After investigating whether varying light intensity affects growth rates in the economically important red alga Gracilaria vermiculohylla , a comprehensive gene expression analysis using RNA-Seq was performed to determine whether these differences were due to changes in the transcriptome. Under high-light conditions, G. vermiculophylla showed upregulation of nitrogen and carbon metabolism genes such as nitrate reductase and carbonic anhydrase crucial for growth and biomass production. This study provides insights into the molecular mechanisms underlying the growth responses of red marine algae to varying light intensities. The identified genes and their functions could be used to guide genetic engineering approaches to enhance red algal productivity in aquaculture. Gracilaria growth light intensity macroalgae red algae Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Light is the primary factor that affects seaweed growth directly by regulating photosynthesis and signaling metabolic processes. Seaweeds grow under diverse and dynamic light regimes in their natural environments. Factors such as clouds, water transparency, tidal ebb, and flood considerably influence the amount of light reaching seaweed growing sites (Hurd et al. 2014 ). Thus, seaweeds require a highly sensitive regulatory system to adapt to different light intensities to balance photosynthetic light responses and downstream metabolic constraints for their growth. Rhodophyta (red algae) is a distinct phototrophic eukaryotic lineage containing approximately 7,000 species (Guiry and Guiry 2015 ), which include numerous economically important species. The bangiophytes (members of the class Bangiophyceae), one of the multicellular red algae groups, are major lucrative marine crops, such as the food “nori,” including Pyropia and Porphyra (laver). Pyropia and Porphyra are widely cultivated in East Asian countries to supply a billion-dollar (US) annual market (Blouin et al. 2011 ). Florideophytes form the second group of multicellular red algae and include economically important agarophytes and carrageenophytes, which have a variety of industrial uses owing to their unique cell wall composition containing polysaccharides, such as agar and carrageenan (Cardozo et al. 2007 ). Among florideophytes, several species in the order Gracilariales are used as a source of agar and other compounds (Armisen 1995 ). A thorough understanding of the lifecycle of red algae has enabled stable seedling, leading to a considerable expansion of the aquaculture industry. After the elucidation of their life cycle, the knowledge of environmental factors affecting the growth of commercial red algae has been accumulating in the laboratory culture. For example, the optimal intensity for growth has been studied in Gracilaria species (Dawes et al. 1999 ; Yokoya et al. 1999 ; Raikar et al. 2001 ). Despite the knowledge of the effect of light intensity on the growth, gene expression patterns controlling growth under fluctuating light conditions remain poorly understood. To understand the relationship between light intensity and growth at the molecular level, we performed a comprehensive gene expression analysis using RNA-Seq in this study after examining whether different light intensities affect growth rates in the economically important red alga Gracilaria vermiculophylla . The gene expression patterns in response to various light intensities provide a molecular basis for their environmental adaptation. Meanwhile, understanding the potential regulators of these adaptations might help develop novel varieties with increased algal productivity. Materials and Methods Algal materials and treatments Tetrasporophytes of G. vermiculophylla previously collected in Shinori-cho, Hakodate (Hokkaido Prefecture, Japan) were used as materials and cultivated in the laboratory. Gracilaria vermiculophylla was cultured at 15°C in sterile vitamin-free Provasoli’s enriched seawater (PES; Provasoli 1968 ) under a 12-h light/12-h dark photoperiod at a light intensity of 80 µmol photons m − 2 s − 1 . The culture medium for G. vermiculophylla was renewed weekly. Effect of light intensity on algal growth The growth rate of G. vermiculophylla was examined under low light (LL) treatment with light levels of 80 µmol photons m − 2 s − 1 and the high-light (HL) treatment with 230 µmol photons m − 2 s − 1 . The growth rate was calculated as the percentage of length increase per day using the following equation: growth rate (% day − 1 ) = [100(BL t − BL 0 )/BL 0 ]/ t , where BL 0 = initial blade length, BL t = blade length measured on day t of culture, and t = culture time in days. RNA preparation and sequencing Total RNA from algal samples was extracted using an RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) in liquid nitrogen using a mortar and pestle, following the manufacturer’s instructions. The extracted RNA was purified using a TURBO DNA-free kit (Invitrogen/Life Technologies, Carlsbad, CA) to obtain DNA-free RNA. The quantity and integrity of RNA samples were assessed using a NanoDrop™ 2000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA), a Qubit™ 4 Fluorometer (Thermo Fisher Scientific, Waltham), and an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). A total of six libraries of complementary DNA (2 conditions: LL, HL × 3 replicates) for G. vermiculophylla were constructed and subsequently sequenced using an Illumina NovaSeq 6000 instrument at Rhelixa Inc. RNA sequencing analysis The obtained reads were trimmed for low-quality reads and adapter sequences using fastp (Chen et al. 2018 ). RNA-Seq data of G. vermiculophylla were de novo assembled using Trinity (Grabherr et al. 2011 ). The clean reads of G. vermiculophylla were de novo assembled using Trinity and quantified using salmon (Patro et al. 2017 ). The TPM was calculated using RSEM (Li et al. 2011). DEGs were then identified using edgeR considering the FDR significance score 1 (Robinson et al. 2010 ). Functional annotation and Gene Ontology enrichment analysis Gene annotation was performed according to the following methods: The gene coding regions of the amino acid sequence were inferred from the contig assembled in Trinity using Transdecoder. The sequences of the obtained gene coding regions were functionally annotated by EnTAP (Hart et al. 2020 ). To assess the biological significance of the DEGs, we conducted Gene Ontology enrichment analyses. GO terms were assigned to all genes using eggNOG-mapper v2 online ( http://eggnog-mapper.embl.de/ ) (Huerta-Cepas et al. 2019 ; Cantalapiedra et al. 2021 ) with the default parameters except that the min_hit_e-value was set to 0.05. The topGO (Alexa and Rahnenfuhrer 2023 ) R package was used for GO enrichment analysis, and GO terms with P < 0.05 were considered significantly enriched in the DEGs. Quantitative PCR Quantitative PCR (qPCR) analysis was performed as described by Uji et al. ( 2020 ), with minor modifications. First-strand cDNA was synthesized from 0.5 µg of total RNA using the PrimeScript II 1st strand cDNA Synthesis Kit (TaKaRa Bio, Shiga, Japan). The cDNA was diluted 10-fold for qPCR analysis, and 1.0 µL of the diluted cDNA was used as a template in a 20-µL reaction volume using KOD SYBR® qPCR Mix (TOYOBO, Osaka, Japan) following the manufacturer’s instructions. Realtime PCR was performed with a LightCycler® 480 System (Roche Diagnostics, Basel, Switzerland) under the following conditions: 2 min at 98°C followed by 40 cycles of 10 s at 98°C, 10 s at 55°C and 30 s at 68°C. The mRNA levels were calculated using the 2 −△△Ct method and normalized to the levels of the 18S ribosomal RNA gene. The relative expression level was calculated as the ratio of the mRNA level to the transcription level at LL. All experiments were performed in triplicate. Table S1 lists all primers used in this study. Statistical analysis Data are expressed as the means ± standard deviation and analyzed using Steel’s test. For all analyses, p < 0.05 was considered statistically significant. Results and Discussion Effect of light intensity on the growth of G. vermiculophylla After 3 days of incubation at different light levels, differences in growth rates were observed as follows: 3.8% in the LL treatment and 11.5% in the HL treatment (Fig. 1 ). After 10 days of incubation at different light levels, differences in growth rates were observed as follows: 6.9% for LL treatment and 25.2% for HL treatment. Identification of DEGs under different light intensities in G. vermiculophylla We used RNA-Seq to examine transcripts in the thalli of G. vermiculophylla cultivated in LL and HL for 3 days to identify candidate genes affecting the growth rate. Approximately 119 million reads were obtained from the deep sequencing with 19.8 million reads per library for six samples (LL, HL × 3 replicates). The mapping rates obtained from the six libraries from LL and HL were 70.6% and 73.2%, respectively (Table 1 ). Table 1 Summary of transcriptome analysis in G. vermiculophylla under different light intensity Sample name Raw reads Clean reads GC content (%) Mapping rate (%) Gv_ LL1 18,255,934 17,610,812 54.8 70.3 Gv_ LL2 20,732,664 19,974,806 55.2 69.4 Gv_ LL3 22,638,554 21,851,456 54.6 72.3 Gv_ HL1 19,258,422 18,623,806 55.0 70.9 Gv_ HL2 20,121,462 19,478,900 54.2 73.6 Gv_ HL3 22,257,046 21,558,358 53.8 75.2 A total of 593 DEGs were obtained between LL and HL, including 56 upregulated and 537 downregulated genes under different light intensities (Supplementary Table 2, 3). Four DEGs were chosen for qPCR analysis to validate the RNA-Seq findings. As shown in Fig. 2 , the tested genes had similar expression levels in RNA-Seq and qPCR analyses, indicating that the expression dataset was reliable. To determine the biological processes of DEGs under different light intensities, GO analysis was performed (Figs. 3 and 4 ). In the upregulated DEGs, nucleolus organization mitotic chromosome condensation, and sister chromatid cohesion were enriched groups within the biological process category. Within the cellular component category, DEGs were prominently enriched in single-stranded DNA binding, ATP-dependent activity acting on DNA, and protein heterodimerization activity. In the molecular function category, DEGs were classified into condensed nuclear chromosome, chromosome, centromeric region, and nuclear speck. In the downregulated DEGs, endoplasmic reticulum organization, negative regulation of protein ubiquitin, and protein exit from endoplasmic reticulum were enriched groups within the biological process category. Within the cellular component category, DEGs were prominently enriched in peptidase regulator activity, ubiquitin protein ligase binding, and structural constituent of ribosome. In the molecular function category, DEGs were classified into cytosolic large ribosomal subunit, proteasome complex, and chromatin. The upregulated DEGs at HL conditions, included the genes associated with nitrogen and carbon metabolisms, such as nitrate reductase, ammonium transporter, and carbonic anhydrase (Table 2 ). Nitrogen is one of the macronutrients essential for algal growth and development, and thus is often a major limiting factor for productivity (Kumar and Bera 2020 ). Nitrogen is a constituent of key cell molecules, including amino acids, nucleic acids, chlorophyll, and ATP and is also the pivotal regulator of numerous biological processes, including carbon metabolism, amino acid metabolism, and protein synthesis (Foyer et al. 2003 ; Nunes-Nesi et al. 2010 ). Table 2 The list of upregulated genes in G. vermiculophylla under different light intensity Contig ID Functional categories Description Fold Change DN2981_c0_g1_i1 Nitrogen metabolism Nitrate reductase 7.46 DN19587_c0_g1_i2 Polysaccharide catabolism Beta-glucanase 6.50 DN1297_c0_g3_i1 Signal transduction Plexin 5.83 DN19_c0_g1_i2 Nitrogen metabolism Ammonium transporter 4.51 DN619_c0_g3_i1 Methyl transfer SAM-dependent methyltransferase 3.60 DN33786_c0_g1_i1 Proteasome Ubiquitin-like protein 3.56 DN55795_c0_g1_i1 Carbon metabolism Carbonic anhydrase 3.31 DN68_c0_g1_i1 ATP regeneration Phosphate translocator 3.24 DN64501_c0_g1_i1 Transfer of sugar moiety Glycosyl transferase family 3.17 DN21276_c0_g1_i1 Transcription factor Transcription factor MYB44 2.81 DN17557_c0_g1_i1 Oxidized base repair 8-oxoguanine deaminase 2.48 DN63904_c0_g1_i1 Membrane channels Aquaporin 2.43 DN305_c0_g1_i7 Sugar epimerase enzyme NAD-dependent epimerase 2.31 Nitrate reductase is a key nitrogen reduction and assimilation enzyme. It catalyzes the reduction of nitrate (NO 3 − ) to nitrite (NO 2 − ), which is then reduced to ammonia (NH 4+ ) by nitrite reductase before being assimilated into the amino acids and the nitrogen compounds of the cell (Campbell 1999 ). Nitrate reductase catalyzes the initial phase of nitrate assimilation in all organisms, which appears to be a rate-limiting mechanism in acquiring nitrogen in most cases. Light regulation of nitrate reductase activity has been reported in Gracilaria tenuistipitata (Lopes et al. 2002 ) and Gracilaria chilensis (Chow et al. 2004 ). High light-induced expression of nitrate reductase genes in Gracilaria species is responsible for the activation of nitrate assimilation, coupled with the activation of photosynthesis and the maintenance of balanced levels of carbohydrates and amino acids. In macroalgae, NH 4+ accumulating in cells, either through uptake from seawater via ammonium transporters (AMTs) or through reduction of NO 3 − , may be directed into the glutamine synthetase/glutamate synthase (GS/GOGAT) cycle (Fan et al. 2020 ; Kakinuma et al. 2017 ; Shahar et al. 2020 ). Because NH 4+ requires less energy for uptake and assimilation than NO 3 − , NH 4+ is the preferred nitrogen source for uptake in certain macroalgae (Wang et al. 2014 ). On the other hand, excessive NH 4+ concentrations are toxic and inhibit algal growth (Wang et al. 2023 ). AMTs in mesophyll cells of plants might be involved in retrieving NH 3 /NH 4+ derived from photorespiration, which can reduce the efficiency of photosynthetic carbon assimilation (Gazzarrini et al. 1999 ). Photorespiration is a critical process, releasing substantial quantities of ammonium at rates that can exceed 10-fold the rate of primary nitrate assimilation in plants (Keys et al. 1978 ). Thus, the well-regulated homeostasis of internal NH 4+ concentrations via AMT under HL conditions is critical for the health and productivity of G. vermiculophylla . Carbon is also essential for algae to maintain their basic growth and development. Various carbohydrates, such as glucose and organic acids, provide energy and carbon skeletons for NH 4+ absorption to produce amino acids and proteins (Lancien et al. 2000 ). The efficiency of photosynthesis and metabolism is affected by numerous environmental factors, such as CO 2 concentration, nitrogen availability, and light intensity (Morales et al. 2018 ). Because of aquatic environmental constraints on CO 2 supply, photoautotrophs usually need a regulated acquisition system for dissolved inorganic carbon to maintain rapid and efficient photosynthesis (Matsui et al. 2018 ). Macroalgae may use HCO 3 − as a source of inorganic carbon, and their photosynthetic rates in natural seawater are often limited by inorganic carbon sources (Lignell and Pedersen 1989 ; Gao et al. 1993 ). Carbonic anhydrase (CA) activity is usually linked to the ability of algae to use HCO 3 − (Mercado et al. 1998 ). CA is a zinc metalloenzyme that catalyzes CO 2 and HCO 3 − interconversion (Moroney et al. 2001 ). External CA in algae has been proposed to serve two functions: (1) conversion of HCO 3 − to CO 2 , which would enter the cells through the plasma membrane, either by diffusion or via active transport, and (2) conversion of CO 2 from the air to HCO 3 − , which is then actively or passively transported across the plasma membrane (Mercado et al. 1999). Furthermore, internal CA is considered necessary to provide CO 2 from the internal pool of inorganic carbon to Rubisco (Mercado et al. 1998 ). A previous study found that the photorespiratory glycolate pathway in C. reinhardtii is closely involved in inorganic carbon concentrating mechanisms through the induction of CA activity, thus playing a significant role in adapting photosynthesizing algae to low CO 2 concentration (Ramazanov and Cárdenas 1992 ). The upregulation of the CA gene in this study enhanced the availability of inorganic carbon for photosynthesis, resulting in an increased growth rate of G. vermiculophylla . Our previous study indicated the orchestration of the diurnal patterns of genes encoding enzymes involved in nitrogen and carbon assimilation in P. yezoensis (Kominami et al. 2022 ). The RNA-Seq data of G. vermiculophylla obtained in this study also support the coordination and optimal functioning of the metabolic pathways driving nitrogen and carbon assimilation in red algae, which are critical for growth and biomass. In addition to the genes associated with nitrogen and carbon metabolism, our RNA-Seq data showed that aquaporin transcripts increased under HL conditions. Aquaporins are membrane-intrinsic proteins initially defined as water channels in all taxonomic kingdoms and subsequently found to have multiple substrate specificities, such as CO 2 (Groszmann et al. 2017 ). The roles of the CO 2 -permeable aquaporins have been extensively characterized in C3 plants. Aquaporins localized in both the plasma membrane and the chloroplast envelope of these plants have been shown to facilitate CO 2 diffusion from the intercellular airspace to the site of Rubisco in chloroplasts, with an essential function of supplying CO 2 for photosynthetic reactions (Kaldenhoff 2012 ; Uehlein et al. 2008 ). Similarly, plasma membrane-type aquaporins in a marine diatom may regulate membrane permeability to CO 2 to facilitate the desired substrate fluxes (Matsui et al. 2018 ). Aquaporins in red algae are poorly understood. Nonetheless, the expression patterns of P. yezoensis aquaporins have been examined at the transcriptional and translational levels under various abiotic stress conditions, including dehydration, salinity, osmotic pressure, and temperature (Kong et al. 2017 ). In addition to gene expression analysis, further research is required to unravel the substrate specificity and localization of red algal aquaporins. Conclusions We identified the genes whose expression increases when red macroalga show a high-growth rate under high-light conditions. The genes associated with nitrogen and carbon metabolism had a large abundance of transcripts under high-light conditions, indicating that the regulation and optimal functioning of metabolic pathways that control nitrogen and carbon assimilation are critical for growth and biomass in G. vermiculophylla . The use of genetic engineering and genome editing technologies to elucidate the regulation and function of genes involved in carbon/nitrogen metabolisms might provide a roadmap for enhancing red algal productivity. List of abbreviations AMT, Ammonium transporters CA, Carbonic anhydrase DEG, Differentially expressed genes FDR, False discovery rate HL, High light LL, Low light PES, Provasoli’s enriched seawater TPM, Transcripts per kilobase million Declarations Availability of data and materials RNA sequence data were deposited in DDBJ under the BioProject accession number PRJDB16860 (For editors and reviewers: currently we are already submitted but not open to the public.). Funding This work was supported by the Grant-in-Aid [grant number 22K05779 to TU] from the Japan Society for the Promotion of Science and Program on Open Innovation Platform with Enterprise, Research Institute and Academia, Japan Science and Technology Agency (JST-OPERA, JPMJOP1851 to TU). Authors' contributions TU designed the experiments and interpreted the data. TK and TU performed the experiments. TK, TU, and HM wrote the manuscript. All authors have read and approved the final version of the manuscript. Supplementary Information Supplementary data to this article can be found online at @@@@@@. Ethics declarations Competing Interests The authors declare that they have no competing interests. Ethical Approval Not applicable. References Hurd CL, Harrison PJ, Bischof K, Lobban CS. (2014) Light and photosynthesis. In “Seaweed Ecology and Physiology 2nd Edition”. Cambridge University Press. 176-235. Guiry MD, Guiry GM. (2015) AlgaeBase. World-wide Electronic Publication, National University of Ireland, Galway. . Blouin NA, Brodie JA, Grossman AC, Xu P, Brawley SH. (2011) Porphyra : a marine crop shaped by stress. Trends Plant Sci.16(1):29-37. Cardozo KHM, Guaratini T, Barros MP, Falcão VR, Tonon AP, Lopes NP, et al. (2007) Metabolites from algae with economical impact. 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(1999) Carbonic anhydrase activity and use of HCO 3 - in Bostrychia scorpioides (Ceramiales, Rhodophyceae). Eur J Phycol. 34(1):13-9. Ramazanov Z, Cárdenas J. (1992) Involvement of photorespiration and glycolate pathway in carbonic anhydrase induction and inorganic carbon concentration in Chlamydomonas reinhardtii . Physiol Plant. 84(4):502-8. Kominami S, Mizuta H, Uji T. (2022) Transcriptome profiling in the marine red alga Neopyropia yezoensis under light/dark cycle. Mar Biotechnol. 24(2): 393-407. Groszmann M, Osborn HL, Evans JR. (2017) Carbon dioxide and water transport through plant aquaporins. Plant Cell Environ. 40(6):938-61. Kaldenhoff R. (2012) Mechanisms underlying CO 2 diffusion in leaves. Curr Opin Plant Biol. 15(3):276-81. Uehlein N, Otto B, Hanson DT, Fischer M, McDowell N, Kaldenhoff R. (2008) Function of Nicotiana tabacum aquaporins as chloroplast gas pores challenges the concept of membrane CO 2 permeability. Plant Cell. 20(3):648-57. Kong F, Yang J, Li N, Zhao H, Mao Y. (2017) Identification and characterization of PyAQPs from Pyropia yezoensis , which are involved in tolerance to abiotic stress. J Appl Phycol. 29:1695-706. Additional Declarations No competing interests reported. Supplementary Files TableS2.xlsx TableS1.xlsx TableS3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6616872","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455669169,"identity":"ab9dbd41-43ef-421c-8d05-f758d77e4e91","order_by":0,"name":"Takuya Kandori","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Takuya","middleName":"","lastName":"Kandori","suffix":""},{"id":455669170,"identity":"9bb34026-f8b4-48b7-a6ff-745ba7bed44b","order_by":1,"name":"Hiroyuki Mizuta","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Mizuta","suffix":""},{"id":455669171,"identity":"920824a1-dfb4-437e-9e90-ef6c14dea898","order_by":2,"name":"Toshiki Uji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie2RMUvDQBTH/yFwXQ7nlFrzFV7JJA79Kjkc3KTg0qHDTXEpuB5U6lfQpXPgwU0B1xsc6u6QMaCDSSu6eCluQu+33PG4H//33gGBwD+F8vmZRAIBlIh0vCsK73O5U6rsjwqiIsOPcqipKSTNXtd0mq602NYVj+9uEdUNTq79KZJIbUjSSzmYGMeZYcTDJcSNV0nfOuVDUpKLkaxZaQZG7SzK1+E+5Z5kar6Uhzbl/bCiScJ1imP1yBD9KaWYUW7bWZwqhqa6yp44Ks6X5J9loOPNpFnQNDWXNqntxXj9zOyaufVurEXQ/vz+ke5CNu9R4u1v1UWfEggEAsfFJwWuUEsGYVgBAAAAAElFTkSuQmCC","orcid":"","institution":"Hokkaido University","correspondingAuthor":true,"prefix":"","firstName":"Toshiki","middleName":"","lastName":"Uji","suffix":""}],"badges":[],"createdAt":"2025-05-08 05:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6616872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6616872/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82785059,"identity":"abc5067a-91c8-40d2-a70c-c1a5e658778a","added_by":"auto","created_at":"2025-05-15 09:03:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79327,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of light intensity on the growth of \u003cem\u003eG. vermiculophylla\u003c/em\u003e. (a) Algal growth on the 10th day of culture under LL: 80 µmol photons m\u003csup\u003e−2\u003c/sup\u003e\u0026nbsp;s\u003csup\u003e−1\u003c/sup\u003e\u0026nbsp;and (b) HL: 230 µmol photons m\u003csup\u003e−2\u003c/sup\u003e\u0026nbsp;s\u003csup\u003e−1\u003c/sup\u003e. Scale bar = 10 mm. (d) Growth rate of thalli cultured under LL and HL. The presented data are means ± standard deviations of 10 individuals for each condition. Asterisks above the bars indicate significant differences (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6616872/v1/f2efe8ae490e3a6925bb39dc.jpg"},{"id":82785061,"identity":"ddf610f1-d022-477d-b65e-137802bc88c7","added_by":"auto","created_at":"2025-05-15 09:03:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62132,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of gene expression data obtained by RNA-Seq and qPCR in \u003cem\u003eG. vermiculophylla\u003c/em\u003e. qPCR analysis was performed on four selected genes that exhibited differences in expression levels between LL and HL in \u003cem\u003eG. vermiculophylla\u003c/em\u003e (orange boxes). The gene expression levels obtained from the RNA-Seq data are indicated by blue boxes (RNA-Seq). The results are presented as relative expressions compared with those in LL.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6616872/v1/18b3410067aed6e4f1887dc1.jpg"},{"id":82785065,"identity":"fdf4a515-503f-4f8b-8e45-7adb2a51c71d","added_by":"auto","created_at":"2025-05-15 09:03:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68207,"visible":true,"origin":"","legend":"\u003cp\u003eNumbers of enriched GO terms for upregulated DEGs. GO terms are presented for three main categories: biological processes, molecular functions, and cellular components. Each GO term is listed in ascending order by \u003cem\u003ep\u003c/em\u003e-value.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6616872/v1/47d64bfbf0f78770bb284948.jpg"},{"id":82786062,"identity":"c7812b4b-507a-4f4d-a7aa-a8afb2bbb2bd","added_by":"auto","created_at":"2025-05-15 09:11:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69659,"visible":true,"origin":"","legend":"\u003cp\u003eNumbers of enriched GO terms for downregulated DEGs. GO terms are presented for three main categories: biological processes, molecular functions, and cellular components. Each GO term is listed in ascending order by \u003cem\u003ep\u003c/em\u003e-value.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6616872/v1/9c1e21615717bf95f931e516.jpg"},{"id":83522958,"identity":"c1914aca-3d20-4f7b-ae62-52a6c8cf2a63","added_by":"auto","created_at":"2025-05-28 00:47:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1018365,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6616872/v1/dbfa9f08-31d9-4434-8f13-a56b2f1e5c6a.pdf"},{"id":82785064,"identity":"f51628c0-a773-4c6e-ab74-6ff9b908d054","added_by":"auto","created_at":"2025-05-15 09:03:13","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14002,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6616872/v1/49464751ad62be5474d8da08.xlsx"},{"id":82785060,"identity":"a2dcc1bf-64d2-46d9-9678-ebb5f5b6725a","added_by":"auto","created_at":"2025-05-15 09:03:13","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11753,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6616872/v1/6420f8075f27e617943f7ab2.xlsx"},{"id":82786065,"identity":"0ad7d9c1-321d-4ca5-b859-3146bd44b860","added_by":"auto","created_at":"2025-05-15 09:11:14","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":44388,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6616872/v1/8345ae926aecf122cea1963d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome analysis of the red marine alga Gracilaria vermiculophylla grown under different light intensities","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLight is the primary factor that affects seaweed growth directly by regulating photosynthesis and signaling metabolic processes. Seaweeds grow under diverse and dynamic light regimes in their natural environments. Factors such as clouds, water transparency, tidal ebb, and flood considerably influence the amount of light reaching seaweed growing sites (Hurd et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, seaweeds require a highly sensitive regulatory system to adapt to different light intensities to balance photosynthetic light responses and downstream metabolic constraints for their growth.\u003c/p\u003e \u003cp\u003eRhodophyta (red algae) is a distinct phototrophic eukaryotic lineage containing approximately 7,000 species (Guiry and Guiry \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which include numerous economically important species. The bangiophytes (members of the class Bangiophyceae), one of the multicellular red algae groups, are major lucrative marine crops, such as the food \u0026ldquo;nori,\u0026rdquo; including \u003cem\u003ePyropia\u003c/em\u003e and \u003cem\u003ePorphyra\u003c/em\u003e (laver). \u003cem\u003ePyropia\u003c/em\u003e and \u003cem\u003ePorphyra\u003c/em\u003e are widely cultivated in East Asian countries to supply a billion-dollar (US) annual market (Blouin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Florideophytes form the second group of multicellular red algae and include economically important agarophytes and carrageenophytes, which have a variety of industrial uses owing to their unique cell wall composition containing polysaccharides, such as agar and carrageenan (Cardozo et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Among florideophytes, several species in the order Gracilariales are used as a source of agar and other compounds (Armisen \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA thorough understanding of the lifecycle of red algae has enabled stable seedling, leading to a considerable expansion of the aquaculture industry. After the elucidation of their life cycle, the knowledge of environmental factors affecting the growth of commercial red algae has been accumulating in the laboratory culture. For example, the optimal intensity for growth has been studied in \u003cem\u003eGracilaria\u003c/em\u003e species (Dawes et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Yokoya et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Raikar et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Despite the knowledge of the effect of light intensity on the growth, gene expression patterns controlling growth under fluctuating light conditions remain poorly understood.\u003c/p\u003e \u003cp\u003eTo understand the relationship between light intensity and growth at the molecular level, we performed a comprehensive gene expression analysis using RNA-Seq in this study after examining whether different light intensities affect growth rates in the economically important red alga \u003cem\u003eGracilaria vermiculophylla\u003c/em\u003e. The gene expression patterns in response to various light intensities provide a molecular basis for their environmental adaptation. Meanwhile, understanding the potential regulators of these adaptations might help develop novel varieties with increased algal productivity.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAlgal materials and treatments\u003c/h2\u003e \u003cp\u003eTetrasporophytes of \u003cem\u003eG. vermiculophylla\u003c/em\u003e previously collected in Shinori-cho, Hakodate (Hokkaido Prefecture, Japan) were used as materials and cultivated in the laboratory. \u003cem\u003eGracilaria vermiculophylla\u003c/em\u003e was cultured at 15\u0026deg;C in sterile vitamin-free Provasoli\u0026rsquo;s enriched seawater (PES; Provasoli \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1968\u003c/span\u003e) under a 12-h light/12-h dark photoperiod at a light intensity of 80 \u0026micro;mol photons m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The culture medium for \u003cem\u003eG. vermiculophylla\u003c/em\u003e was renewed weekly.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffect of light intensity on algal growth\u003c/h3\u003e\n\u003cp\u003eThe growth rate of \u003cem\u003eG. vermiculophylla\u003c/em\u003e was examined under low light (LL) treatment with light levels of 80 \u0026micro;mol photons m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and the high-light (HL) treatment with 230 \u0026micro;mol photons m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The growth rate was calculated as the percentage of length increase per day using the following equation:\u003c/p\u003e \u003cp\u003egrowth rate (% day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) = [100(BL\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e \u0026minus; BL\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e)/BL\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e]/\u003cem\u003et\u003c/em\u003e,\u003c/p\u003e \u003cp\u003ewhere BL\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;initial blade length, BL\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e = blade length measured on day \u003cem\u003et\u003c/em\u003e of culture, and \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;culture time in days.\u003c/p\u003e\n\u003ch3\u003eRNA preparation and sequencing\u003c/h3\u003e\n\u003cp\u003eTotal RNA from algal samples was extracted using an RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) in liquid nitrogen using a mortar and pestle, following the manufacturer\u0026rsquo;s instructions. The extracted RNA was purified using a TURBO DNA-free kit (Invitrogen/Life Technologies, Carlsbad, CA) to obtain DNA-free RNA. The quantity and integrity of RNA samples were assessed using a NanoDrop\u0026trade; 2000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA), a Qubit\u0026trade; 4 Fluorometer (Thermo Fisher Scientific, Waltham), and an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). A total of six libraries of complementary DNA (2 conditions: LL, HL \u0026times; 3 replicates) for \u003cem\u003eG. vermiculophylla\u003c/em\u003e were constructed and subsequently sequenced using an Illumina NovaSeq 6000 instrument at Rhelixa Inc.\u003c/p\u003e\n\u003ch3\u003eRNA sequencing analysis\u003c/h3\u003e\n\u003cp\u003eThe obtained reads were trimmed for low-quality reads and adapter sequences using fastp (Chen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). RNA-Seq data of \u003cem\u003eG. vermiculophylla\u003c/em\u003e were \u003cem\u003ede novo\u003c/em\u003e assembled using Trinity (Grabherr et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The clean reads of \u003cem\u003eG. vermiculophylla\u003c/em\u003e were \u003cem\u003ede novo\u003c/em\u003e assembled using Trinity and quantified using salmon (Patro et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The TPM was calculated using RSEM (Li et al. 2011). DEGs were then identified using edgeR considering the FDR significance score\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e fold change| \u0026gt; 1 (Robinson et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eFunctional annotation and Gene Ontology enrichment analysis\u003c/h3\u003e\n\u003cp\u003eGene annotation was performed according to the following methods: The gene coding regions of the amino acid sequence were inferred from the contig assembled in Trinity using Transdecoder. The sequences of the obtained gene coding regions were functionally annotated by EnTAP (Hart et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo assess the biological significance of the DEGs, we conducted Gene Ontology enrichment analyses. GO terms were assigned to all genes using eggNOG-mapper v2 online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://eggnog-mapper.embl.de/\u003c/span\u003e\u003cspan address=\"http://eggnog-mapper.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Huerta-Cepas et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cantalapiedra et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) with the default parameters except that the min_hit_e-value was set to 0.05. The topGO (Alexa and Rahnenfuhrer \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) R package was used for GO enrichment analysis, and GO terms with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched in the DEGs.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative PCR\u003c/h2\u003e \u003cp\u003eQuantitative PCR (qPCR) analysis was performed as described by Uji et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with minor modifications. First-strand cDNA was synthesized from 0.5 \u0026micro;g of total RNA using the PrimeScript II 1st strand cDNA Synthesis Kit (TaKaRa Bio, Shiga, Japan). The cDNA was diluted 10-fold for qPCR analysis, and 1.0 \u0026micro;L of the diluted cDNA was used as a template in a 20-\u0026micro;L reaction volume using KOD SYBR\u0026reg; qPCR Mix (TOYOBO, Osaka, Japan) following the manufacturer\u0026rsquo;s instructions. Realtime PCR was performed with a LightCycler\u0026reg; 480 System (Roche Diagnostics, Basel, Switzerland) under the following conditions: 2 min at 98\u0026deg;C followed by 40 cycles of 10 s at 98\u0026deg;C, 10 s at 55\u0026deg;C and 30 s at 68\u0026deg;C. The mRNA levels were calculated using the 2\u003csup\u003e\u0026minus;△△Ct\u003c/sup\u003e method and normalized to the levels of the 18S ribosomal RNA gene. The relative expression level was calculated as the ratio of the mRNA level to the transcription level at LL. All experiments were performed in triplicate. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e lists all primers used in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData are expressed as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and analyzed using Steel\u0026rsquo;s test. For all analyses, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e \u003cb\u003eEffect of light intensity on the growth of\u003c/b\u003e \u003cb\u003eG. vermiculophylla\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAfter 3 days of incubation at different light levels, differences in growth rates were observed as follows: 3.8% in the LL treatment and 11.5% in the HL treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After 10 days of incubation at different light levels, differences in growth rates were observed as follows: 6.9% for LL treatment and 25.2% for HL treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of DEGs under different light intensities in\u003c/b\u003e \u003cb\u003eG. vermiculophylla\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe used RNA-Seq to examine transcripts in the thalli of \u003cem\u003eG. vermiculophylla\u003c/em\u003e cultivated in LL and HL for 3 days to identify candidate genes affecting the growth rate. Approximately 119\u0026nbsp;million reads were obtained from the deep sequencing with 19.8\u0026nbsp;million reads per library for six samples (LL, HL \u0026times; 3 replicates). The mapping rates obtained from the six libraries from LL and HL were 70.6% and 73.2%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of transcriptome analysis in \u003cem\u003eG. vermiculophylla\u003c/em\u003e under different light intensity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClean reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGC content (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMapping rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGv_ LL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,255,934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,610,812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGv_ LL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20,732,664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,974,806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGv_ LL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,638,554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,851,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGv_ HL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19,258,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,623,806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGv_ HL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20,121,462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,478,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGv_ HL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,257,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,558,358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of 593 DEGs were obtained between LL and HL, including 56 upregulated and 537 downregulated genes under different light intensities (Supplementary Table\u0026nbsp;2, 3). Four DEGs were chosen for qPCR analysis to validate the RNA-Seq findings. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the tested genes had similar expression levels in RNA-Seq and qPCR analyses, indicating that the expression dataset was reliable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo determine the biological processes of DEGs under different light intensities, GO analysis was performed (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the upregulated DEGs, nucleolus organization mitotic chromosome condensation, and sister chromatid cohesion were enriched groups within the biological process category. Within the cellular component category, DEGs were prominently enriched in single-stranded DNA binding, ATP-dependent activity acting on DNA, and protein heterodimerization activity. In the molecular function category, DEGs were classified into condensed nuclear chromosome, chromosome, centromeric region, and nuclear speck.\u003c/p\u003e \u003cp\u003eIn the downregulated DEGs, endoplasmic reticulum organization, negative regulation of protein ubiquitin, and protein exit from endoplasmic reticulum were enriched groups within the biological process category. Within the cellular component category, DEGs were prominently enriched in peptidase regulator activity, ubiquitin protein ligase binding, and structural constituent of ribosome. In the molecular function category, DEGs were classified into cytosolic large ribosomal subunit, proteasome complex, and chromatin.\u003c/p\u003e \u003cp\u003eThe upregulated DEGs at HL conditions, included the genes associated with nitrogen and carbon metabolisms, such as nitrate reductase, ammonium transporter, and carbonic anhydrase (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nitrogen is one of the macronutrients essential for algal growth and development, and thus is often a major limiting factor for productivity (Kumar and Bera \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nitrogen is a constituent of key cell molecules, including amino acids, nucleic acids, chlorophyll, and ATP and is also the pivotal regulator of numerous biological processes, including carbon metabolism, amino acid metabolism, and protein synthesis (Foyer et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Nunes-Nesi et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe list of upregulated genes in \u003cem\u003eG. vermiculophylla\u003c/em\u003e under different light intensity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContig ID \u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunctional categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFold Change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN2981_c0_g1_i1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNitrate reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN19587_c0_g1_i2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolysaccharide catabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-glucanase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN1297_c0_g3_i1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignal transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlexin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN19_c0_g1_i2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmmonium transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN619_c0_g3_i1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethyl transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSAM-dependent methyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN33786_c0_g1_i1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProteasome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUbiquitin-like protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN55795_c0_g1_i1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbonic anhydrase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN68_c0_g1_i1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATP regeneration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhosphate translocator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN64501_c0_g1_i1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransfer of sugar moiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlycosyl transferase family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN21276_c0_g1_i1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTranscription factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTranscription factor MYB44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN17557_c0_g1_i1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOxidized base repair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8-oxoguanine deaminase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN63904_c0_g1_i1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMembrane channels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAquaporin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN305_c0_g1_i7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSugar epimerase enzyme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAD-dependent epimerase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNitrate reductase is a key nitrogen reduction and assimilation enzyme. It catalyzes the reduction of nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) to nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), which is then reduced to ammonia (NH\u003csup\u003e4+\u003c/sup\u003e) by nitrite reductase before being assimilated into the amino acids and the nitrogen compounds of the cell (Campbell \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Nitrate reductase catalyzes the initial phase of nitrate assimilation in all organisms, which appears to be a rate-limiting mechanism in acquiring nitrogen in most cases. Light regulation of nitrate reductase activity has been reported in \u003cem\u003eGracilaria tenuistipitata\u003c/em\u003e (Lopes et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and \u003cem\u003eGracilaria chilensis\u003c/em\u003e (Chow et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). High light-induced expression of nitrate reductase genes in \u003cem\u003eGracilaria\u003c/em\u003e species is responsible for the activation of nitrate assimilation, coupled with the activation of photosynthesis and the maintenance of balanced levels of carbohydrates and amino acids.\u003c/p\u003e \u003cp\u003eIn macroalgae, NH\u003csup\u003e4+\u003c/sup\u003e accumulating in cells, either through uptake from seawater via ammonium transporters (AMTs) or through reduction of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, may be directed into the glutamine synthetase/glutamate synthase (GS/GOGAT) cycle (Fan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kakinuma et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shahar et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Because NH\u003csup\u003e4+\u003c/sup\u003e requires less energy for uptake and assimilation than NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csup\u003e4+\u003c/sup\u003e is the preferred nitrogen source for uptake in certain macroalgae (Wang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). On the other hand, excessive NH\u003csup\u003e4+\u003c/sup\u003e concentrations are toxic and inhibit algal growth (Wang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). AMTs in mesophyll cells of plants might be involved in retrieving NH\u003csub\u003e3\u003c/sub\u003e/NH\u003csup\u003e4+\u003c/sup\u003e derived from photorespiration, which can reduce the efficiency of photosynthetic carbon assimilation (Gazzarrini et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Photorespiration is a critical process, releasing substantial quantities of ammonium at rates that can exceed 10-fold the rate of primary nitrate assimilation in plants (Keys et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). Thus, the well-regulated homeostasis of internal NH\u003csup\u003e4+\u003c/sup\u003e concentrations via AMT under HL conditions is critical for the health and productivity of \u003cem\u003eG. vermiculophylla\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eCarbon is also essential for algae to maintain their basic growth and development. Various carbohydrates, such as glucose and organic acids, provide energy and carbon skeletons for NH\u003csup\u003e4+\u003c/sup\u003e absorption to produce amino acids and proteins (Lancien et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The efficiency of photosynthesis and metabolism is affected by numerous environmental factors, such as CO\u003csub\u003e2\u003c/sub\u003e concentration, nitrogen availability, and light intensity (Morales et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Because of aquatic environmental constraints on CO\u003csub\u003e2\u003c/sub\u003e supply, photoautotrophs usually need a regulated acquisition system for dissolved inorganic carbon to maintain rapid and efficient photosynthesis (Matsui et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Macroalgae may use HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e as a source of inorganic carbon, and their photosynthetic rates in natural seawater are often limited by inorganic carbon sources (Lignell and Pedersen \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Carbonic anhydrase (CA) activity is usually linked to the ability of algae to use HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (Mercado et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). CA is a zinc metalloenzyme that catalyzes CO\u003csub\u003e2\u003c/sub\u003e and HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e interconversion (Moroney et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExternal CA in algae has been proposed to serve two functions: (1) conversion of HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e to CO\u003csub\u003e2\u003c/sub\u003e, which would enter the cells through the plasma membrane, either by diffusion or via active transport, and (2) conversion of CO\u003csub\u003e2\u003c/sub\u003e from the air to HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, which is then actively or passively transported across the plasma membrane (Mercado et al. 1999). Furthermore, internal CA is considered necessary to provide CO\u003csub\u003e2\u003c/sub\u003e from the internal pool of inorganic carbon to Rubisco (Mercado et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). A previous study found that the photorespiratory glycolate pathway in \u003cem\u003eC. reinhardtii\u003c/em\u003e is closely involved in inorganic carbon concentrating mechanisms through the induction of CA activity, thus playing a significant role in adapting photosynthesizing algae to low CO\u003csub\u003e2\u003c/sub\u003e concentration (Ramazanov and C\u0026aacute;rdenas \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The upregulation of the CA gene in this study enhanced the availability of inorganic carbon for photosynthesis, resulting in an increased growth rate of \u003cem\u003eG. vermiculophylla\u003c/em\u003e. Our previous study indicated the orchestration of the diurnal patterns of genes encoding enzymes involved in nitrogen and carbon assimilation in \u003cem\u003eP. yezoensis\u003c/em\u003e (Kominami et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The RNA-Seq data of \u003cem\u003eG. vermiculophylla\u003c/em\u003e obtained in this study also support the coordination and optimal functioning of the metabolic pathways driving nitrogen and carbon assimilation in red algae, which are critical for growth and biomass.\u003c/p\u003e \u003cp\u003eIn addition to the genes associated with nitrogen and carbon metabolism, our RNA-Seq data showed that aquaporin transcripts increased under HL conditions. Aquaporins are membrane-intrinsic proteins initially defined as water channels in all taxonomic kingdoms and subsequently found to have multiple substrate specificities, such as CO\u003csub\u003e2\u003c/sub\u003e (Groszmann et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The roles of the CO\u003csub\u003e2\u003c/sub\u003e-permeable aquaporins have been extensively characterized in C3 plants. Aquaporins localized in both the plasma membrane and the chloroplast envelope of these plants have been shown to facilitate CO\u003csub\u003e2\u003c/sub\u003e diffusion from the intercellular airspace to the site of Rubisco in chloroplasts, with an essential function of supplying CO\u003csub\u003e2\u003c/sub\u003e for photosynthetic reactions (Kaldenhoff \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Uehlein et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Similarly, plasma membrane-type aquaporins in a marine diatom may regulate membrane permeability to CO\u003csub\u003e2\u003c/sub\u003e to facilitate the desired substrate fluxes (Matsui et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Aquaporins in red algae are poorly understood. Nonetheless, the expression patterns of \u003cem\u003eP. yezoensis\u003c/em\u003e aquaporins have been examined at the transcriptional and translational levels under various abiotic stress conditions, including dehydration, salinity, osmotic pressure, and temperature (Kong et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition to gene expression analysis, further research is required to unravel the substrate specificity and localization of red algal aquaporins.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe identified the genes whose expression increases when red macroalga show a high-growth rate under high-light conditions. The genes associated with nitrogen and carbon metabolism had a large abundance of transcripts under high-light conditions, indicating that the regulation and optimal functioning of metabolic pathways that control nitrogen and carbon assimilation are critical for growth and biomass in \u003cem\u003eG. vermiculophylla\u003c/em\u003e. The use of genetic engineering and genome editing technologies to elucidate the regulation and function of genes involved in carbon/nitrogen metabolisms might provide a roadmap for enhancing red algal productivity.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eAMT, Ammonium transporters\u003c/p\u003e\n\u003cp\u003eCA, Carbonic anhydrase\u003c/p\u003e\n\u003cp\u003eDEG, Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eFDR, False discovery rate\u003c/p\u003e\n\u003cp\u003eHL, High light\u003c/p\u003e\n\u003cp\u003eLL, Low light\u003c/p\u003e\n\u003cp\u003ePES, Provasoli\u0026rsquo;s enriched seawater\u003c/p\u003e\n\u003cp\u003eTPM, Transcripts per kilobase million\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA sequence data were deposited in DDBJ under the BioProject accession number PRJDB16860\u0026nbsp;(For editors and reviewers: currently we are already submitted but not open to the public.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Grant-in-Aid [grant number 22K05779 to TU] from the Japan Society for the Promotion of Science and Program on Open Innovation Platform with Enterprise, Research Institute and Academia, Japan Science and Technology Agency (JST-OPERA, JPMJOP1851 to TU).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTU designed the experiments and interpreted the data. TK and TU performed the experiments. TK, TU, and HM wrote the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data to this article can be found online at @@@@@@.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHurd CL, Harrison PJ, Bischof K, Lobban CS. (2014) Light and photosynthesis. In \u0026ldquo;Seaweed Ecology and Physiology 2nd Edition\u0026rdquo;. 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(2008) Function of \u003cem\u003eNicotiana tabacum \u003c/em\u003eaquaporins as chloroplast gas pores challenges the concept of membrane CO\u003csub\u003e2\u003c/sub\u003e permeability. Plant Cell. 20(3):648-57.\u003c/li\u003e\n\u003cli\u003eKong F, Yang J, Li N, Zhao H, Mao Y. (2017) Identification and characterization of PyAQPs from \u003cem\u003ePyropia yezoensis\u003c/em\u003e, which are involved in tolerance to abiotic stress. J Appl Phycol. 29:1695-706.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gracilaria, growth, light intensity, macroalgae, red algae","lastPublishedDoi":"10.21203/rs.3.rs-6616872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6616872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLight plays a vital role in seaweed growth by influencing photosynthesis and signaling metabolic processes. Seaweeds are subjected to various dynamic light conditions because of environmental factors such as weather and tidal changes. Despite the knowledge of the effects of light intensity on red algal growth, the gene expression patterns regulating growth under fluctuating light conditions remain unclear.\u003c/p\u003e \u003cp\u003eAfter investigating whether varying light intensity affects growth rates in the economically important red alga \u003cem\u003eGracilaria vermiculohylla\u003c/em\u003e, a comprehensive gene expression analysis using RNA-Seq was performed to determine whether these differences were due to changes in the transcriptome. Under high-light conditions, \u003cem\u003eG. vermiculophylla\u003c/em\u003e showed upregulation of nitrogen and carbon metabolism genes such as nitrate reductase and carbonic anhydrase crucial for growth and biomass production.\u003c/p\u003e \u003cp\u003eThis study provides insights into the molecular mechanisms underlying the growth responses of red marine algae to varying light intensities. The identified genes and their functions could be used to guide genetic engineering approaches to enhance red algal productivity in aquaculture.\u003c/p\u003e","manuscriptTitle":"Transcriptome analysis of the red marine alga Gracilaria vermiculophylla grown under different light intensities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 09:03:09","doi":"10.21203/rs.3.rs-6616872/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dc1ee0a7-534a-4f9e-a6cd-a5fc52a3af2f","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-28T00:39:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 09:03:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6616872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6616872","identity":"rs-6616872","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}