Unexpected conservation of the RNA splicing apparatus in the highly streamlined genome of Galdieria sulphuraria
Genome reduction in intracellular pathogens and endosymbionts is usually compensated by reliance on the host for energy and nutrients. Free-living taxa with reduced genomes must however evolve strategies for generating functional diversity to support their independent lifestyles. An emerging model for the latter case is the Rhodophyta (red algae) that comprises an ecologically widely distributed, species-rich phylum. Red algae have undergone multiple phases of significant genome reduction, including extremophilic unicellular taxa with limited nuclear gene inventories that must cope with hot, highly acidic environments.
Using genomic data from eight red algal lineages, we identified 155 spliceosomal machinery (SM)-associated genes that were putatively present in the red algal common ancestor. This core SM gene set is most highly conserved in Galdieria species (150 SM genes) and underwent differing levels of gene loss in other examined red algae (53-145 SM genes). Surprisingly, the high SM conservation in Galdieria sulphuraria coincides with the enrichment of spliceosomal introns in this species (2 introns/gene) in comparison to other red algae (< 0.34 introns/gene). Spliceosomal introns in G. sulphuraria undergo alternatively splicing, including many that are differentially spliced upon changes in culture temperature.
Our work reveals the unique nature of G. sulphuraria among red algae with respect to the conservation of the spliceosomal machinery and introns. We discuss the possible implications of these findings in the highly streamlined genome of this free-living eukaryote.
KeywordsGenome reduction RNA splicing Intron Rhodophyta
Alternative last exons
Premature termination codons
The study of eukaryote genome evolution has focused primarily on how genomes grow in size and complexity over time (e.g., via genome duplication  and transposable element accumulation  often due to neutral, population level processes) in model organisms such as vertebrates and land plants. In contrast, there is limited information arising from the opposite perspective (i.e., genome reduction), despite its prevalence in many lineages . In addition, knowledge about genome reduction, which has been studied primarily in highly specialized endosymbionts and pathogens  has limited implications for free-living species and the maintenance of their biodiversity. Therefore, understanding the impact of genome reduction in free-living organisms, particularly in eukaryotes that have complex genomes, provides a novel avenue to understand and test the underlying principles of genome evolution.
An emerging model for elucidating the impacts of genome reduction in free-living eukaryotes is the Rhodophyta (red algae). This monophyletic algal lineage comprises an ecologically widely distributed and species-rich phylum (ca. 7000 species) . Analysis of genomic and transcriptomic data have shown that red algae underwent at least two phases of massive genome reduction . The first is in the stem lineage, where about one-quarter of the gene inventory was shed  and the second is in the ancestor of the anciently diverged extremophiles, Cyanidiophytina, such as Cyanidioschyzon merolae  and Galdieria sulphuraria , that thrive in volcanic hot-spring areas [6, 9]. As a consequence of adaptation to their unusual environment, G. sulphuraria (6.5 K nuclear genes) and C. merolae (4.7 K nuclear genes) contain smaller gene inventories than their mesophilic red algal sisters which encode ~ 10 K nuclear genes [10, 11, 12].
Alternative splicing provides a major avenue of post-transcriptional regulation in eukaryotes . Here, using analysis of genomic and RNA-seq data from G. sulphuraria, we show: 1) selective retention of the spliceosomal machinery (SM) in G. sulphuraria, a toolkit that has been greatly reduced in complexity in many of its sister red algal lineages, and 2) the coincidence of high SM retention and intron enrichment in G. sulphuraria that has resulted in extensive alternative splicing (AS) in this species. Given these unique features in G. sulphuraria, we discuss the possible implications of AS in red algal evolution.
Pattern of spliceosome machinery gene loss in red algae
Spliceosome composition and conservation in red algae
Based on human SM protein expression data  (Additional file 5: Table S3), we found that highly expressed SM proteins (79%) are twice as likely to be retained in red algae as those expressed at low levels (43%). Nearly all of the core proteins that directly bind small nucleolar (sn) RNAs to form small nuclear ribonucleoproteins (i.e., Sm, U1, U2, U5, U4/U6 and U5/U4/U6) are conserved in Galdieria species and have the lowest degree of loss in mesophilic red algae (Additional file 5: Table S3). All five snRNAs (U1-2 and U4-6) are found in G. sulphuraria (Additional file 6: Figure S2) and in five other red algal genomes (Additional file 7: Table S4), except for U1 snRNA that was most likely lost in C. merolae  (Additional file 7: Table S4). Whereas we included G. phlegrea to fully capture the SM gene inventory (Fig. 1), this taxon is not included in downstream analyses because of its close phylogenetic relationship to G. sulphuraria and the relatively low quality of intron annotation due to a lack of transcriptome data. In contrast, the remaining auxiliary SM proteins that generally perform peripheral or modulatory functions underwent more frequent loss (Additional file 5: Table S3). These results suggest that the red algal common ancestor contained 155 SM proteins that comprised the complete core of the spliceosomal machinery that was largely maintained in some lineages (such as Galdieria). It is noteworthy that although C. merolae and G. sulphuraria are both extremophiles that inhabit areas surrounding volcanic hot springs, these two species differ dramatically in lifestyle and metabolic capacity  which presumably is reflected by the additional phase of genome reduction in C. merolae (arrow #3 in Fig. 1b) . Whereas the extremely reduced SM does perform RNA-splicing functions in C. merolae, it likely has a highly compromised efficiency given the minimal number of introns (only 27) present in this genome . A highly reduced SM has also been found in several parasitic eukaryotes . These species invariably show a paucity of introns including the kinetoplastid Trypanosoma brucei (13 introns in 8747 genes) , the microsporidian Encephalitozoon cuniculi (7 introns in 1996 genes) , and the diplomonad Giardia lamblia (6 introns in 7364 genes) .
Consistent with a previous study , we found that many red algal SM proteins are distantly related to reference sequences (i.e., human SM proteins) and have extremely long branches in phylogenies (Additional file 8: Figure S3). This pattern of evolution is common in red algal species with reduced SM gene sets such as C. merolae, Bangiophyceae, and Porphyridium, and is largely absent in G. sulphuraria and R. marinus. Using the BLASTp bit score as the metric, we found G. sulphuraria SM proteins to be generally more conserved than their orthologs in other red algal species (except R. marinus) at the primary sequence level (Additional file 8: Figure S3). Similarly, G. sulphuraria also shows the strongest overall sequence conservation among snRNAs, as reflected by their high alignment scores (Additional file 7: Table S4). C. merolae has the least conserved snRNAs (Additional file 7: Table S4). Whereas the fast evolution of SM proteins and snRNAs might reflect a genome-wide feature in C. merolae , the highly derived SM proteins and snRNAs in other red algal species likely resulted from the acquisition of novel functions or relaxed functional constraints. This result suggests that some apparent cases of gene loss in SM gene-poor red algal species (e.g., C. merolae) might instead be explained by high divergence; i.e., beyond sequence similarity-based recognition. In summary, our results demonstrate the conservation of the G. sulphuraria SM with respect to both gene inventory and protein similarity.
Enrichment of introns in the G. sulphuraria genome
Origin of G. sulphuraria introns
To study the origin of G. sulphuraria spliceosomal introns, we compared their positions within homologous genes across six red algal species (Fig. 2b) and between them and five Viridiplantae lineages (Additional file 9: Table S5). Most of the G. sulphuraria intron positions (90%) appear to be lineage-specific (Fig. 2b), likely resulting from recent intron insertions. Based on a self-BLASTn search (e-value cutoff = 1e-5), only 3.6% (478/13,245) of introns share sequence similarity (query coverage ≥0.5) with one or more (up to 9) other introns. This result does not support the idea that the majority of G. sulphuraria introns resulted from recent intron duplications. Regarding intron positions that are shared with Viridiplantae, G. sulphuraria (1199) contains > 4-fold more ancestral introns than do other red algal lineages, such as C. crispus (212) and G. chorda (121) (Fig. 2b). This result suggests that many anciently derived introns were retained in G. sulphuraria and lost in other red algal lineages. Examples include the intron-rich G. sulphuraria gene encoding geranylgeranyl transferase beta-subunit (NCBI GeneID: 17088310). This gene contains six introns, of which five (from the 2nd to the 6th) have conserved positions in Viridiplantae homologs (Fig. 2c and Additional file 11: Figure S5). In contrast, all of these introns underwent losses in mesophilic red algae, resulting in a 3-exon gene in P. yezoensis, single-exon genes in P. purpureum, C. crispus, and G. chorda (Fig. 2c and Additional file 11: Figure S5). When assuming a simple evolutionary scenario (i.e., Dollo parsimony ), about 1700 introns are estimated to have been present in the red algal stem lineage, followed by significant losses in mesophilic red algae and in C. merolae (Additional file 12: Figure S6). These results suggest that red algal introns have a high turnover rate, that is a common feature of many eukaryotes . The relatively large number of introns in G. sulphuraria resulted from both lineage-specific intron gains and retention of ancestral introns. Notably, G. sulphuraria introns are much smaller in size (50 bp, on average) than in other red algal genomes (Additional file 13: Figure S7A). This is consistent with a strong size constraint that has resulted in the compact genome of G. sulphuraria (Additional file 13: Figure S7B) .
Alternative mRNA splicing in G. sulphuraria
Differential intron splicing in G. sulphuraria
To test if AS in G. sulphuraria responds to temperature fluctuations, we searched and identified 212 introns that were (statistically significantly) differentially spliced between the heat and cold conditions (Additional file 15: Table S7). One example is a novel Galdieria-specific gene that is comprised of three exons (Fig. 3c). The splicing of the first intron is largely restricted to the heat condition, whereas the second intron is spliced in both conditions with apparently more extensive intron retention under heat than under cold (Fig. 3c). A second example is the gene encoding the RNA polymerase primary sigma factor (NCBI Gene ID: 17087802), with the first intron being significantly retained (i.e., 1/4~ 1/3 of total transcripts) under the heat condition. The same intron is rarely retained under the cold condition (Fig. 3c). This intron is located within a Sigma70-r3 domain (pfam04539) that is involved in the binding of core RNA polymerase. Retention of this intron leads to stop codons and a truncated Sigma70-r3 domain (Additional file 16: Figure S8). We also found many differentially spliced introns that are not located within any annotated genic region, which likely reflects splicing of non-coding RNA transcripts (Additional file 15: Table S7). Under the same conditions, 178 genes were differentially expressed (> 2.5 fold down- or up-regulated in terms of overall expression abundance) including only two SM genes (Additional file 17: Table S8). Because of splicing variation within a group (i.e., heat) due to differences in treatment (42 °C and 46 °C), our result represents a conservative estimate of the extent of differential splicing between the overall ‘heat’ and ‘cold’ conditions. Although a comprehensive analysis of temperature-dependent gene expression and the functional consequences of differential splicing are not within the scope of this paper, our results suggest that G. sulphuraria is able to respond to temperature changes (and likely other stimuli) using differential mRNA-splicing. These results explain the considerable fluctuations in microenvironments where many of these species live (e.g., non-thermophilic Galdieria soos ). Given the overall reduced SM in red algae, compared to humans and Viridiplantae, the prevalence of intron retention might also have resulted from compromised splicing efficiency in G. sulphuraria. How this possible scenario contributes to overall intron retention in G. sulphuraria is unknown.
We show here that components of the spliceosomal machinery have undergone recent gene losses and accelerated evolution among different red algal lineages. In this context, the high conservation of the SM in Galdieria is counterintuitive, given its relatively more reduced gene inventory resulting from lineage-specific genome reduction (Fig. 1b, arrow #2). In addition, G. sulphuraria contains a relatively large number of introns via ancient intron preservation and novel insertions, in spite of its compact genome size. This unexpected evolutionary trajectory allows alternative mRNA splicing that generates transcriptomic (and likely proteomic) diversity [26, 27] in this lineage for about a quarter of the alternatively spliced transcripts that do not encode premature stop codons (including 4.3% leading to insertion of amino acids and 19.7% resulting in novel peptides due to frame-shifts, e.g., Fig. 3b). The functional impact of alternative splicing on G. sulphuraria biology remains to be investigated.
It is noteworthy that of the 1152 well annotated, alternatively spliced introns we identified in G. sulphuraria, most cases (68.7%) of intron retention lead to a truncated protein (Fig. 3a). Why might this be tolerated? The most likely reason is nonsense-mediated decay (NMD), a process that is widespread in eukaryotes for regulating post-transcriptional gene expression . NMD allows for the targeted degradation of alternatively spliced isoforms that would result in truncated proteins due to the introduction of premature termination codons (PTCs; e.g., due to intron retention), as described previously . NMD is not however completely effective and PTCs persist in the transcript pools of many eukaryotes, suggesting a functional role . Soergel et al.  postulated an evolutionary interaction between AS and NMD, that allows the rise of alternative, beneficial splice forms (i.e., under the umbrella of a well-established surveillance system) that can ultimately be fixed in the population. This scenario may provide an explanation for the extensive AS-derived PTCs we found in the G. sulphuraria RNA-seq data. Several key genes in NMD (i.e., UPF1-3) are present in G. sulphuraria and other red algal species (Additional file 18: Figure S9). Our findings with G. sulphuraria are generally in line with existing data from other systems. In Arabidopsis thaliana, about 13% of intron-containing genes are potentially regulated by AS/NMD . In the unicellular green alga, Chlamydomonas reinhardtii, there are 611 AS events that impact 3% of all genes with intron retention being the most common outcome leading to many PTCs . The AS-derived variants may enhance gene regulation in G. sulphuraria. This idea is consistent with the reduced intergenic regions (i.e., that could encode cis-regulatory elements) that has resulted from genome streamlining (Additional file 13: Figure S7B).
An additional possible explanation for our results comes from a recent study of the yeast UV stress response where genes associated with transcription are regulated by non-coding RNAs derived from alternatively spliced, short transcripts of the same gene (i.e., alternative last exons (ALEs) [33, 34]). In the case of the ASCC3 gene that represses RNA polymerase II transcription after UV irradiation, transcription of the complete gene (i.e., full-length protein) is de-repressed by an ALE derived from the same gene that acts as a non-coding regulatory RNA. Therefore, it is possible that stress pathways (i.e., not UV irradiation) impacted by our heat and cold treatments of G. sulphuraria may lead to the generation of shorter non-coding RNAs via AS that play a role in regulating the stress response. Thermal stress is clearly a major factor in the ecology of G. sulphuraria, therefore AS may produce both novel protein isoforms as well as regulatory RNAs (perhaps like ALEs) that play roles in responding to this stress. More generally, our results suggest that strong constraints that exist on the growth of gene numbers (and functions) due to genome reduction can be ameliorated at the transcriptome level. This insight required the analysis of free-living organisms that have relatively complex genomes (i.e., containing introns) and a history of ancient genome reduction, together with recent lineage-specific gene losses. In this regard, our results underline the utility of free-living taxa such as red algae as models for studying eukaryote genome reduction.
Our results revealed an unexpected aspect of Galdieria genome evolution. Whereas the correlation between SM gene number and spliceosomal intron density within red algae remains to be validated with more genomic data, our findings lead to several hypotheses that can be tested in this unique model to understand genome reduction in free-living organisms.
Detection of spliceosomal proteins in red algae
The culture of G. sulphuraria used in this study is the strain with the completed nuclear genome sequence (i.e., 074 W) and was isolated from a site near Reykjavik Island . Using the 215 non-redundant human SM proteins as queries (Additional file 19: Supplementary Methods), we searched the proteomes from eight red algal species and nine Viridiplantae (Fig. 1a and Additional file 1: Table S1) using BLASTp (e-value cutoff = 1e-5). Significant hits that led to a reasonable alignment length (query coverage > 30%) and had the highest hit-query identities were recorded for each query versus each search species. The resulting data were clustered and visualized with the heatmap function in the R language. Genes and taxa were clustered using Euclidean distances between all gene (or taxon) pairs and the complete-linkage clustering method.
To search red algal SM with higher stringencies and examine their origins, we adopted a phylogenetic-based method . We generated a proteome data comprising SM proteins derived from homology-based gene predictions (using human proteins as reference; Additional file 19: Supplementary Methods) and protein models annotated in existing studies (Table S1). To identify G. sulphuraria SM proteins, we searched the G. sulphuraria proteome data with BLASTp (e-value cutoff = 1e-3) using human SM proteins as queries. The top three G. sulphuraria hits according to bit-score (by default) and the top three hits according to query-hit alignment identity were recorded for further validation. To differentiate between orthologous and paralogous relationships between human SM queries and their G. sulphuraria homologs, these proteins were used as queries to search (BLASTp e-value cutoff = 1e-5) against a comprehensive local protein database . The significant hits were recorded for each SM query and the representative sequences were selected with up to 8 sequences for each phylum in the default order sorted by bit-score. A second set of representative sequences was selected after re-sorting the BLASTp hits according to the query-hit sequence identity. The two sets of representative sequences (by bit-score and alignment identity) for all the SM queries (human and G. sulphuraria homologs potentially corresponding to the same SM gene) were then combined, aligned using MUSCLE (v3.8.31)  and trimmed using TrimAl (version 1.2)  in automated mode (−automated1). The phylogenetic tree was constructed using FastTree (version 2.1.7)  under the ‘WAG+CAT’ model with 4 rounds of minimum evolution SPR moves (−psr 4) and exhaustive ML nearest-neighbor interchanges (−mlacc 2, −slownni). Branch support was derived from the Shimodaira-Hasegawa test . We examine the resulting phylogenies manually. A SM gene was regarded to exist in G. sulphuraria, if at least one of the G. sulphuraria gene candidates appeared in the same orthologous group as the human SM gene (see Additional file 2: Figure S1A for an example of this approach). The SM gene was considered to be absent if no G. sulphuraria candidates were found in the orthologous group with the human SM gene (see Additional file 2: Figure S1B for an example of this approach). HGT was inferred when the candidate red algal sequences were nested within multiple sequences from prokaryotic and/or fungal taxa. Following the same procedure as described above, the presence and absence of SM genes were determined in other red algal lineages that are shown in Fig. 1b.
In addition, we used Galdieria SM proteins as references for homology-based gene prediction in genome or transcriptome data from the remaining six red algal species (Fig. 2) (Additional file 19: Supplementary Methods). The resulting SM proteins were incorporated into our comprehensive local protein database described above. Using the G. sulphuraria SM proteins (or G. phlegrea when the G. sulphuraria gene was missing) as queries, we carried out a BLASTp search, sorted the significant hits, selected representative sequences, and aligned and built phylogenetic trees following the procedures described above. The resulting trees were manually inspected to identify additional red algal SM sequences that were monophyletic with the Galdieria queries.
Assessing completeness of the protein data
We used BUSCO (version 3) under the default settings to estimate the overall completeness of protein data (equivalent to genomic coverage) for each red algal species . The ‘Eukaryota sets’ that contained 303 conserved gene families in eukaryotes were used as the reference for this analysis.
Detection of snRNAs
We downloaded snRNA alignments for U1 (RF00003), U2 (RF00004), U4 (RF00015), U5 (RF00020) and U6 (RF00026) from the Rfam database . The alignments were calibrated (using cmcalibrate) and then used for snRNA searches in red algal genomes (using cmsearch) using Infernal (v1.1.2) with the default settings .
We downloaded the genome and coding DNA sequences (CDSs) from six red algal species that have high-quality whole genome sequences: G. sulphuraria , C. merolae , P. purpureum , C. crispus , P, yezoensis  and G. chorda (unpublished data), and from five Viridiplantae species (Table S4). The CDSs were mapped to the corresponding genome sequences using BLAT  under the default settings. The non-specific alignments were removed and the positions of introns (in genomes) and exon junctions (in CDSs) were then subtracted from the BLAT output using custom scripts. Most of the introns (94-99%) were flanked by the canonical splicing signal (GT-AG) (Table S4).
To identify G. sulphuraria intron positions that are shared with C. crispus, we searched G. sulphuraria proteins against the C. crispus proteome using BLASTp (e-value cutoff = 1e-10) and retrieved information about the top 10 BLASTp hits. Because the original intron positions along the protein primary sequences are not comparable across sequences due to variable lengths of N′-terminal domains, insertions, and deletions, we built alignments for each query protein and its corresponding C. crispus hit(s) using MUSCLE (v3.8.31) . With gaps being introduced during the alignment procedure, the intron positions in the original sequences (without gaps) were converted into column numbers for each sequence in their respective alignments. A G. sulphuraria intron position was considered as being conserved if it was located at the same column position in an alignment with one or more introns of the same phase in C. crispus. The same method was used to identify intron positions that were shared between any two of the species included in this study (Fig. 2b).
Galdieria sulphuraria cell cultures
Biological replicate cultures of G. sulphuraria 074W were grown separately at 42 °C, constant illumination (90 μE), and constant shaking (160 rpm) in photoautotrophic conditions using 2xGS Medium . The experimental design followed a temperature shift timeline: after two weeks of cultivation at stated conditions the first sampling took place (H-42) and the cultures were swiftly moved to 28 °C. After cold treatment at 28 °C for 48 h, a second sampling was performed (C-28.1). The G. sulphuraria was then switched to 46 °C for 48 h, at the end of which a third sample was retrieved (H-46). It again was followed by a cold treatment at 28 °C for 48 h when a fourth sample was retrieved (C-28.2). Altogether, two time-points from high temperatures (42 °C and 46 °C) and two time-points from cold temperatures (28 °C) were targeted for sampling.
Sequencing of the Galdieria sulphuraria transcriptome
RNA was extracted using Roboklon’s Universal RNA Purification Kit by following the “plant tissue samples” protocol (Roboklon, Berlin - Germany). RNA quality and concentration was assessed using a Nanodrop photospectrometer ND-1000 (Peqlab Biotechnologie GmbH, Erlangen -Germany). The samples were synthesized to be compatible with Illumina HiSeq2000 RNA-seq libraries strictly following the "Illumina TruSeq RNA Sample Prep v2 LS Protocol" (Illumina, San Diego - USA). All reagents were scaled by 2/3 to the volume proposed in the protocol. The quality of all libraries was assessed using the Bioanalyzer (Agilent Technologies, Santa Clara - USA). The libraries were sequenced in paired-end mode (2x100bp) on two lanes with an Illumina HiSeq2000 sequencer at the BMFZ (Biologisch-Medizinisches Forschungszentrum, Düsseldorf, Germany). The resulting RNA-seq data were deposited in the NCBI Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE89169.
Detection of differentially spliced introns
The G. sulphuraria RNA-seq data were cleaned in paired-end mode using Trimmomatic (v0.36)  to remove contaminated adaptor sequences and low quality regions (SLIDINGWINDOW:6:13). Short reads (< 75 bp) were discarded. The cleaned sequence data were then mapped to G. sulphuraria genome sequences using STAR (v2.5.2a) . The genome index was generated taking into account the small size of G. sulphuraria genome (−-genomeSAindexNbases 11). The reads were mapped to the genome assembly with an allowed maximum intron size (1000 bp) and maximum mate-pair distance (500 bp). Reads that mapped to more than one region were removed and broken pairs were discarded. The mapping results (in SAM format) were then used as input to search for alternatively spliced modules  that were differentially expressed across samples using DiffSplice  under the default setting, with the following modifications. We required a splice junction to be considered if the mean coverage across all samples was > 10× and RNA-splicing at the junction was found in at least four out of the eight different samples. The expression thresholds for exons and introns were specified to be 16× and 8× coverage, respectively. For the test of differential splicing, the minimal value for square root of JSD  was set to be 0.25 with false discovery rate threshold (=0.01). The minimum fold change (2.5) was required to call gene differential expression (down- or up-regulation). Because we aimed to test the existence of differential intron splicing in response to temperature changes (instead of global gene expression change across samples), we regarded the two samples (H-42 and H-46) as biological replicates from high temperature, and the other two samples (C-28.1 and C-28.2) as biological replicates from low temperature. This practice maximized statistical power to detect splicing events that were shared within groups (e.g., high temperature samples including H-42 and H-46) and differed between the groups (high versus low temperature samples). The examples of alternatively spliced modules that showed statistically significant difference between high and low temperatures (Fig. 3c) were visualized using CLC workbench (v8) (http://www.clcbio.com/products/clc-main-workbench/).
HQ is partially supported by a grant from the National Science Foundation of the United States (EF1416785). HSY and DB acknowledge the support from the Collaborative Genome Program for Fostering New Post-Genome Industry (20140428) funded by the Ministry of Oceans and Fisheries of Korea. We gratefully acknowledge the BMFZ (Biologisch-Medizinisches Forschungszentrum, Düsseldorf, Germany) for conducting the RNA sequencing reported here. The funders had no role in study design, data collection, analysis and interpretation, or preparation of the manuscript.
Availability of data and materials
The datasets generated and analysed during the current study are available in the NCBI Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE89169.
HQ and DB designed the study. HSY contributed novel red algal genome data. APW conceived and designed the RNA-seq analysis of G. sulphuraria under temperature stress. AR extracted RNA from G. sulphuraria, prepared RNA-seq libraries, and contributed transcriptome data. HQ and AR performed data analyses. HQ and AR drafted the manuscript. APW, HSY, and DB revised the manuscript with each author making important intellectual contributions. All authors read and approved the final manuscript.
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- 13.Shang X, Cao Y, Ma L. Alternative splicing in plant genes: a means of regulating the environmental fitness of plants. Int J Mol Sci. 2017;18:432.Google Scholar
- 16.Qiu H, Yoon HS, Bhattacharya D. Red algal phylogenomics provides a robust framework for inferring evolution of key metabolic pathways. PLoS Curr. 2016;8. https://doi.org/10.1371/currents.tol.7b037376e6d84a1be34af756a4d90846.
- 18.Barbier G, Oesterhelt C, Larson MD, Halgren RG, Wilkerson C, Garavito RM, et al. Comparative genomics of two closely related unicellular thermo-acidophilic red algae, Galdieria sulphuraria and Cyanidioschyzon merolae, reveals the molecular basis of the metabolic flexibility of Galdieria sulphuraria and significant differences in carbohydrate metabolism of both algae. Plant Physiol. 2005;137:460–74.CrossRefPubMedPubMedCentralGoogle Scholar
- 31.DAW S, Lareau LF, Brenner SE. Regulation of gene expression by coupling of alternative splicing and NMD. Nonsense-Mediat. MRNA decay. Georgetown: Landes Bioscience; 2006. p. 175–96.Google Scholar
- 33.Conconi A, Bell B. Molecular biology: the long and short of a DNA-damage response. Nature. 2017;545:165–6.Google Scholar
- 41.Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinforma. Oxf. Engl. 2013;29:2933–5.Google Scholar
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