Transcriptional Landscape and Splicing Efficiency in Arabidopsis Mitochondria

Abstract

Plant mitochondrial transcription is initiated from multiple promoters without an apparent motif, which precludes their identification in other species based on sequence comparisons. Even though coding regions take up only a small fraction of plant mitochondrial genomes, deep RNAseq studies uncovered that these genomes are fully or nearly fully transcribed with significantly different RNA read depth across the genome. Transcriptomic analysis can be a powerful tool to understand the transcription process in diverse angiosperms, including the identification of potential promoters and co-transcribed genes or to study the efficiency of intron splicing. In this work, we analyzed the transcriptional landscape of the Arabidopsis mitochondrial genome (mtDNA) based on large-scale RNA sequencing data to evaluate the use of RNAseq to study those aspects of the transcription process. We found that about 98% of the Arabidopsis mtDNA is transcribed with highly different RNA read depth, which was elevated in known genes. The location of a sharp increase in RNA read depth upstream of genes matched the experimentally identified promoters. The continuously high RNA read depth across two adjacent genes agreed with the known co-transcribed units in Arabidopsis mitochondria. Most intron-containing genes showed a high splicing efficiency with no differences between cis and trans-spliced introns or between genes with distinct splicing mechanisms. Deep RNAseq analyses of diverse plant species will be valuable to recognize general and lineage-specific characteristics related to the mitochondrial transcription process.

1. Introduction

Mitochondria are essential organelles of eukaryotic organisms that originated from α-proteobacteria and carry their own genetic material and gene expression system. Since the endosymbiotic event, the mitochondrial genome has undergone severe reduction in gene content through the functional transfer of genes to the nucleus and the definitive loss of many of them [1]. Plant mitochondrial genomes exhibit highly variable genome sizes, but even when extraordinarily large (up to 11.7 Mb [2]), the gene content is conserved (usually less than 60 genes). Thus, most of the genome consists of non-coding regions [3,4,5,6,7,8,9,10] with a variable proportion of repeats [11] that may recombine resulting in rearranged genomes [12,13]. Recombination events across repeats may place non-coding regions near regulatory elements leading to the transcription of non-functional DNA [14]. The transcriptional control of plant mitochondrial genomes is relaxed, frequently with more than one promoter per gene and these promoters exhibiting non-canonical motifs [15]. Arabidopsis has been the focus of extensive experimental studies to understand the mitochondrial gene expression process, including intron splicing mechanisms [16,17,18], transcription initiation and termination [14,19] and identification of co-transcribed units [20,21,22]. However, a thorough analysis of the Arabidopsis mitochondrial transcriptional landscape is still lacking.

Over the past decade, several studies on plant mitochondrial transcriptomes have been published focusing mainly on the identification of RNA editing sites, the expression of unidentified ORFs or the extent of transcription across the genome [2,23,24,25,26]. Even though plant mitochondrial genomes consist predominantly of intergenic DNA, deep RNAseq experiments revealed that non-coding regions are fully or almost-fully transcribed [27], but with transcript levels so low over most of the genome as to be considered functionally insignificant [28,29,30,31,32]. Occasionally, non-coding regions show high RNA read depth suggesting a possible role as long non-coding RNAs. These regions are generally not conserved and have not been further characterized [33,34,35].

Furthermore, RNAseq could be valuable to investigate other aspects of mitochondrial transcription, such as identifying transcription boundaries and potential promoters, inferring which genes are co-transcribed or analyzing the efficiency of the splicing process. For example, co-transcription has been presumed when high RNA read depth was observed across two closely located genes in plant mitochondrial genomes [23,29]. However, only a few studies have unequivocally determined co-transcribed units [20,21,22,36], and such evidence is required to validate the inferences based on RNAseq data. In addition, intron splicing efficiency could be easily assessed by comparing the levels of spliced and unspliced RNA species. The splicing process of intron-containing genes in plant mitochondria has been analyzed for the most part in mutants in order to identify the nuclear factors involved [25,37,38,39]. However, the efficiency of the splicing activity has only rarely been studied in wild type plants [26,33] with no comparative analyses among introns, either group I or II, or cis- and trans-spliced introns that co-exist in plant mitochondrial genomes.

In this work, we analyzed the Arabidopsis mitochondrial transcriptional landscape based on large-scale RNAseq data sets to shed light on: (i) the transcription extent of the mitochondrial genome; (ii) the usefulness of the RNAseq profile to infer transcription start sites, 3′ ends and the co-transcription of gene clusters; and (iii) the splicing efficiency of the 23 cis- or trans-spliced introns in the Arabidopsis mitochondrial genome.

2. Results and Discussion

2.1. The Mitochondrial Genome of Arabidopsis Is Entirely Transcribed with Highly Variable Levels of Read Depth

Even though Arabidopsis has been the focus of numerous studies centered on plant mitochondria [40,41,42,43], a genome-wide analysis of the transcriptional landscape of the mitochondrial genome (mtDNA) has not been reported. In the past decade high-throughput RNAseq analyses provided new insights into the complexity of RNA metabolism in plant mitochondria and indicated that the regulation of mitochondrial RNA processing plays a critical role in plant organellar gene expression [14,20]. Detailed experimental assays were instrumental to identify transcription initiation sites for each mitochondrial gene and to characterize post-transcriptional processes in Arabidopsis mitochondria [14,20,36,42,44]. To gain insight into different aspects of the transcription profile in the Arabidopsis mtDNA, we analyzed publicly available stranded RNAseq data sets from three biological replicates (BioProject PRJEB15579). The data sets consist of total RNA depleted of ribosomal RNA, extracted from a 14-day seedling of wild type Arabidopsis thaliana ecotype Columbia. We analyzed each of the three data sets individually and combined, totaling 617,847,968 paired-end reads. This volume of data is comparable with previous, large-scale RNAseq studies on angiosperm mitochondria [23,33,45]. The pooled RNAseq reads were aligned strand specifically to the corrected sequence of the A. thaliana ecotype Columbia mtDNA, NCBI accession number BK010421 [46]. The Arabidopsis mtDNA is 367,808 bp in length and includes 33 protein, three rRNA and 22 tRNA coding genes [46]. The coding regions represent around 10.4% of the genome (5% when considering both strands), whereas ~90% are intergenic sequences or unidentified ORFs.

About 7 million reads map to either strand of the mtDNA, masking plastid derived regions to avoid mismapping of plastid RNA (Figure S1). Considering both strands, the mean read depth is 861 and the maximum is 194,844. Excluding known genes, the mean read depth of the intergenic regions is 335. We plotted the frequency of the read depth across the two strands of the mtDNA based on 100-bp windows at selected read depth intervals (Figure S2). About 10% of the mtDNA shows a read depth above 1000, 30% above 100 and 43% above 50 reads (Figure S2). A total of 98%, 95.6% and 88.55% of the mtDNA (considering both strands) is covered by at least one, two and five reads, respectively (Figure S2). The regions with poor or zero read depth are dispersed across the genome ranging from 1 to 6050 bp in length (Figure S1). The alignment of a large amount of sequence data from mitochondrial RNA shows that almost the whole mtDNA of Arabidopsis, although at low levels, is transcribed. This genome-wide transcription has also been reported in other species [27,31,33] in which deep RNAseq experiments were analyzed. The amount of RNAseq data may be limiting the extent of the genome with mapped RNA reads, as observed in other species in which non-coding regions were shown to be transcribed but not the full mtDNA [29,31]. We speculate that an even larger RNAseq data set would likely reveal that both strands of the Arabidopsis mtDNA are entirely transcribed, with low read depth regions representing the major fraction. This genome-wide transcription can be explained by the relaxed nature of the plant mitochondrial transcription process, involving multiple and variable start of transcription sites, frequent genome recombination across the multiple repeats that can relocate promoters to non-coding regions and post-transcriptional processing of the transcripts [14,44,47,48].

Given the genome-wide transcription in plant mitochondria, inferring biologically significant transcribed regions is fundamental to the identification of functional ORFs. A recent study of mitochondrial expression based on RNAseq proposed that a significant read depth, well above the background RNA read depth of the mtDNA, along with efficient RNA editing, and accurate intron splicing, constitutes strong evidence of functionality of mitochondrial genes [33]. The pervasive transcription of non-coding regions calls into question the evidence of mitochondrial gene expression based on transcripts recovered by non-quantitative techniques, given that transcripts from any region of the plant mtDNA could be observed. For example, based on total RNA extracted from multiple organs, transcription of the nuclear-derived mitochondrial-encoded orf164 was detected by RT-PCR at lower abundance than other mitochondrial genes [49]. The present transcriptional landscape analysis shows only a basal RNA read depth for orf164 (Figure S1), even lower than the average for the genome. Furthermore, expression of this ORF was not detected in translation analyses [50]. These results indicate the orf164 is not significantly transcribed and it is likely not functional, at least in the phenological stage studied here and by Planchard et al., 2018 [50].

2.2. Protein-Coding Genes Show Elevated RNA Read Depth

Despite the relaxed transcription process in plant mitochondria, the expression of functional genes is greatly efficient. With very few exceptions, only known coding regions show RNA read depths that are several times greater than those of non-coding regions [29,33,34]. In this study, we found that the RNA read depth of the majority of the protein-coding genes is five-fold higher than the average read depth for the whole genome, and even greater if the average read depth for non-coding regions is considered (Table S1). These results agree with a study of polysomal RNA in which only transcripts from all protein-coding mitochondrial genes of Arabidopsis were associated to ribosomes and none from the intergenic regions [50].

The Arabidopsis mtDNA contains 33 protein-coding genes, including the two non-identical copies of atp6 [51]. To assess the RNA read depth of each protein-coding gene, the three RNAseq data sets were analyzed individually and combined (Table S1). Here, we estimated the number of RNA molecules present in the cell in a precise moment (steady-state) without distinguishing between those that are being transcribed and those that belong to the population of RNA molecules that are being processed or degraded. The RNA read depth of each mitochondrial gene was normalized by the transcript length and by the total number of reads mapped to all genes in the Arabidopsis mtDNA [52]. A frequent unit to express the normalized RNA read depth per gene is RPKM (Reads Per Kilobase of transcript, per Million mapped reads). The RPKM values of the known protein-coding genes differ widely among (Figure 1) and within (Figure S3) genes. This variability in transcription levels has been reported before for Arabidopsis using other approaches as run-on transcription and Northern blots [20] and for other plant mitochondrial genomes based on RNAseq data [23,31,33,34]. The RNA read depth of each of the 33 protein-coding genes is consistent across the three samples (Table S1). In agreement with previous studies in Arabidopsis [20] and other plants [35], atp9 shows the highest read depth (RPKM = 361,785–369,149), while ccmFn1 (RPKM = 529–638) followed by ccmFn2 (RPKM = 998–1421), rps7 (RPKM = 3911–3580) and rps4 (RPKM = 4576–4635) show the lowest read depth across the three samples. The two copies of atp6 show different RNA read depth, with that of atp6–1 being consistently greater than that of atp6-2 in the three data sets. This observation agrees with the increased association of atp6-1 mRNA to ribosomes reported by Planchard et al., 2018 [50]. In addition, and in line with previous studies of Arabidopsis and other angiosperm mitochondria, genes coding for subunits of respiratory complexes showed much higher read depth compared to other mitochondrial protein-coding genes (Table S1, Figure 1).

Given that intron-containing genes are transcribed and further processed to remove introns, the RNA reads derive from unspliced and spliced versions of the transcripts. To evaluate the abundance of RNA reads at both stages, unspliced and spliced reference sequences of these genes were used for RNA alignment. Thus, RNA reads derived entirely from exons map to both references, while those that expand exon-intron or exon-exon junctions map only to the unspliced or spliced versions, respectively. These data are instrumental to calculate the splicing efficiency of each gene (see below). In general, there are more RNA reads derived from spliced than from unspliced transcripts (Figure S3).

Notably, there are differences in the read depth among exons in intron-containing genes of the Arabidopsis mtDNA (Figure S3); e.g., exon 4 of nad5 (nad5e4) shows twice the read depth of exon 2. This variability has also been observed in other species [33,34] and reinforces the fact that the expression of mitochondrial genes in plants is regulated primarily at the post-transcriptional level [53]. In some cases, observations in other Brassicaceae [34] contrast with those in Arabidopsis. For example, the read depth of exon 5 of the gene nad1 (nad1e5) is seventh-fold higher than that of exon 1 in Arabidopsis, while the opposite pattern was observed in Brassica oleracea [34].

By mapping RNAseq reads to the coding regions of each gene, we observed drops in the read depth near the end of ccmC, mtttB and nad6 (Figure S3), as reported previously for Arabidopsis [50]. A drop in the read depth of the genes ccmC and nad6 has also been reported for other angiosperms, such as Silene noctiflora [30], Allium cepa [24], Solanum tuberosum [23] and Lophophytum mirabile [33]. These drops are probably caused by 3′ endonucleolytic processing events signaled by tRNA-like elements embedded in the 3′ ends of these genes [36,54]. In addition, a late increase in read depth is observed inside the coding region of rps4 (Figure S3), which agrees with the location of the promoter identified experimentally [36].

2.3. The RNAseq Transcriptional Landscape Is Useful to Infer the Region of Transcription Start

Given that promoters are not conserved at the sequence level and exhibit non-canonical motifs, and that multiple transcription start sites were reported for a single gene, the identification of transcription initiation sites in plant mitochondrial genomes is challenging [15,36,42,44]. Furthermore, even when cDNAs are studied it is difficult to rule out that the identified initiation sites are processing products [42]. Using CR-RT-PCR [36], RACE and ribonuclease protection analysis [42,44], more than one transcription start sites were reported for most protein-coding genes in the Arabidopsis mtDNA (Table S2). By contrasting with those reports, we evaluated whether the RNAseq transcription profile is useful to recognize the sites of transcription start of protein-coding genes in the Arabidopsis mtDNA. The agreement between RNAseq profile and the experimentally identified promoter regions in the Arabidopsis mtDNA would be a measure of the predictive ability of RNAseq alignment to identify the transcription start sites based on steep slopes in the RNA read depth upstream of each gene.

The combined RNAseq data sets were aligned over each of the 28 protein-coding genes or coding units (i.e., gene regions for those genes with trans-splicing introns or co-transcribed units) and their flanking sequences. Then, the slopes of the RNA read depth in the 2 kb upstream of each gene or coding unit were calculated every 50 bp. Furthermore, we analyzed spliced and unspliced versions for intron-containing genes (Figure S4). We repeated the comparisons with each of the three data sets and obtained similar results (not shown). Those regions with the highest slope values were considered putative transcription initiation sites. We then evaluated whether those putative transcription start sites based on RNAseq fell within a 100 bp window around the 85 transcription start sites identified experimentally [36,42,44] in the Arabidopsis mtDNA (Figure S4, Table S2). As an example, Figure 2 shows that putative transcription start sites in atp1 and cox2 coincide with previously reported promoters (green circles).

Overall, for 25 (89%) genes or co-transcribed units, putative transcription initiation sites match the location of at least one of the transcription start sites experimentally identified (green circles in Figure S4), although for a few genes, there are additional putative transcription start sites that do not match any known promoter, e.g., atp6-2 and nad3-rps12. In two cases (nad2e1e2 and nad9), some of the experimentally identified promoters are located within the transcribed region of an upstream gene (Figure S4). For the gene nad9, for example, four start transcription sites are located within the coding regions of the upstream genes rps3 (pnad9-1730, pnad9-1400, pnad9-1371) and rpl16 (pnad9-1241). In those cases, in which two genes are located in close vicinity in the genome, the transcription profile based on RNAseq data are not helpful to identify all alternative transcription start sites.

In some cases, the RNA read depth increases upstream of the locations of the transcription start sites identified experimentally (Figure S4; blue circles indicate the higher slopes located upstream of the start of transcription reported). These findings can be explained in two ways. In the first case, a yet unidentified promoter is located upstream of the reported transcription start sites, in the region exhibiting a sharp increase in read depth; for example, in ccmFn2 (blue circle in Figure 2). In the second case, the spurious presence of a DNA sequence identical to a reported promoter is located upstream of the experimentally-identified transcription start and generates a premature increase in read depth. For example, there are sequences identical to promoters reported for other genes upstream of the dicistronic unit nad4L-atp4 and of the gene region nad1e1 that could be acting as spurious transcription initiation sites (Figure S4). The large number of promoters and the high recombination rate in plant mitochondria would be responsible for the random location of transcription start sites throughout the mtDNA. A search across both strands of the Arabidopsis mtDNA for sequences identical to the 85 transcription initiation sites described for Arabidopsis found 11 sequences identical to promoters randomly distributed throughout the genome (including the spurious promoters mentioned above).

For many genes multiple promoters have been described [36,42,44]. While in a few cases (e.g., atp1) an increase in read depth coincides with each of the transcription start sites reported (Figure 2), in other cases we only observe a match with a single promoter (e.g., rps4) or cannot evaluate other promoters because they are located within an upstream coding region (e.g., nad9) and RNAseq data is not informative. Previous studies of multiple promoters in maize using RT-PCR revealed that the genomic context influences promoter strength and suggested a role for genomic recombination in the regulation of the gene expression [55]. In addition, alternative promoters that are only active in specific nuclear backgrounds have been reported [56]. The role and strength of different promoters for a single coding region may vary across stages of the life cycle or among tissues. Here, we observed that in seedlings of Arabidopsis one of the promoters seems to be stronger than the alternative ones, leading to greater RNA read depth than the others.

We conclude that, if there is no overlap in the transcription bubbles of two genes or co-transcribed units, it is possible to infer the regions of transcription start sites based on RNAseq mapping, although with limited accuracy and precision. Identifying the transcription start sites in plant mitochondrial genomes may help to uncover the signals recognized by the transcription machinery and to improve our understanding of the transcription process. Given that promoters are not conserved at the sequence level, even within the same genome or in homologous genes across species [6], DNA sequence comparisons are not useful to infer transcription start sites. Thus, the patterns of RNAseq read depth may serve as a valuable start point for predicting the region of transcription start.

Noticeably, mature protein-coding mRNAs tend to have heterogeneous 5′ ends but generally well-defined 3′ ends [36]. The 3′ termini have been identified experimentally in Arabidopsis mitochondrial genes or gene regions [36,42,44]. We evaluated whether the transcription profile based on RNAseq is informative to determine the 3′ termini for the Arabidopsis protein-coding genes. Of the 27 drops in the RNA read depth observed after the stop codon of a gene or coding unit, 24 (88%) matched a 3′ termini reported (Figure S4). Those cases in which the described 3′ termini do not coincide with a drop in the read depth are mainly explained by the fact that the 3′ ends are located near or within the transcribed region of a downstream gene (e.g., rps3-rpl16, rps4; Figure S4) preventing the identification of a drop of the read depth. Read depth drops downstream of the gene regions nad1e1, nad2e1e2 and nad5e1e2 (Figure S4) could reflect the limits of the trans-splicing introns.

2.4. RNAseq Mapping Predicts Co-Transcription except When Genes Are Too Closely Located

The close spatial arrangement of two or more genes on the same DNA strand of plant mitochondria raises the possibility that these genes could be co-transcribed [3]. A continuously elevated RNA read depth across two genes in plant mitochondrial genomes has been interpreted as evidence of co-transcription [23,29] but this type of evidence was not validated. Here, we evaluate the reliability of the RNAseq transcription profile of Arabidopsis mtDNA as a predictor of co-transcribed genes by comparing those predictions with experimentally confirmed co-transcribed units based on cDNA analyses [20], Northern blots [21] or CR-RT-PCR [36].

Co-transcription of protein-coding genes has been experimentally shown for five units in the Arabidopsis mtDNA: rps3-rpl16, nad4L-atp4, nad3-rps12, rpl5-ψrps14-cob and rpl2-mttB [21,36]. The first three of those are conserved in angiosperms [57] and all five are found in the Brassicaceae. In addition, co-transcription has been proposed for other units because they were found in the same cDNA, i.e., rps3-rpl16-nad9, rps4-nad2e1e2 [21,22,43]. The identification of transcription initiation sites for each gene within a co-transcribed unit demonstrates that both forms of transcription, either individual or co-transcription, are possible in some cases, such as in rpl2-mttb [36], rps3-rpl16-nad9 [36,42] and rpl5-ψrps14-cob. Thus, the mitochondrial genetic system exhibits enough flexibility to tolerate changes in gene order and the disruption of some gene clusters [57].

In this case, 11 pairs of genes that are located at a maximum distance of 2 kb in the Arabidopsis mtDNA could be potentially co-transcribed: rps3-rpl16, rpl16-nad9, nad9-nad5e4e5, rps4-nad2e12, rpl5-ψrps14-cob; rpl2-mttb, nad1e4e5-nad5e1e2, atp8-nad7, nad3-rps12, nad5e3-nad4L and nad4L-atp4. However, those cases in which two genes are located very close to each other (i.e., genes within the pairs nad3-rps12 and nad1e4e5-nad5e1e2 are separated by less than 150 bp) or even with overlapping coding regions (rps3-rpl16) cannot be evaluated using RNAseq data as the overlapping elevated RNA read depth of each gene precludes distinguishing independent versus co-transcription; thus, these gene pairs were not further analyzed. We aligned all RNA reads of the three data sets combined on the remaining eight pairs of genes. Three of the eight gene pairs exhibit a continuous high read depth, rpl16-nad9; rpl5-ψrps14-cob and nad4L-atp4, while the rest show a drop between the genes of a gene pair (Figure 3 and Figure S5).

The observations on the eight gene pairs generally agree with the experimentally confirmed co-transcribed units and independent transcribed genes reported in previous studies. Similar to rpl5-ψrps14-cob and nad4L-atp4, the continuously elevated read depth across the rps3-rpl16-nad9 cluster (Figure 3) is consistent with the presence of these genes in a single cDNA [43]. However, given that three of the promoters described for nad9 are located within the coding region of rpl16 (see above and Figure S4), a continuously elevated RNA read depth between rpl16 and nad9 is expected even if nad9 is independently transcribed using those promoters located in the upstream coding region. In contrast, a drop in the read depth between the gene pairs rpl2-mttb and rps4-nad2_e1_2 is consistent with the independent transcription reported for rpl2, rps4 and nad2e1_2 [36,42], although there is also evidence for co-transcription of these units [22,36]. Finally, the gene pairs nad9-nad5e4e5, nad5e3-nad4L and atp8-nad7 show a drop in the read depth within the intergenic space in agreement with their independent transcription using different transcription start sites [36,42]. Overall, mapping RNAseq data to the mitochondrial genome is useful to predict if two genes are co-transcribed only when the genes (and their promoters) are located sufficiently apart in the genome to avoid an overlap of the RNA read depths in case of independent transcription.

2.5. Splicing Efficiency of Cis and Trans-Splicing Introns and Those with Different Splicing Mechanisms

Intron splicing is essential for the correct expression of several mitochondrial genes and requires precise coordination with the nucleus given that the splicing machinery is encoded in the nuclear genome [16,58]. The restructuring of angiosperm mitochondrial genomes caused by recombination events can contribute to disparate evolutionary directions of the introns that interrupt coding regions [58]. Two types of intron splicing processes are present in the Arabidopsis mtDNA. Cis-splicing is the intramolecular ligation of exon sequences on the same precursor RNA, and trans-splicing involves the intermolecular ligation of exon sequences from different primary transcripts [3,46]. Due to their degenerate nature and the fact that most organellar introns have lost their cognate maturase factors, both cis and trans-splicing reactions of plant mitochondrial group II introns rely on the action of different catalytic enzymes, most of which are encoded by nuclear loci [16,59].

In Arabidopsis mtDNA, there are 23 group II introns in genes related to respiratory complexes I (nad1, nad2, nad4, nad5 and nad7) and IV (cox2), and in the genes ccmFc, rpl2 and rps3. Most introns undergo cis-splicing, while trans-spliced introns are found in the genes nad1, nad2 and nad5 [58]. Group II introns exhibit a secondary structure with six double-helical domains (dI-dVI). The splicing of classical group II introns involves two sequential transesterifications. In the first, the dVI unpaired adenosine, named ‘bulging A’, is covalently joined to the intron 5′ end resulting in the formation of a lariat shaped intermediate. In the second transesterification the two exons are joined, releasing the intron lariat [60]. This splicing mechanism is known as branching pathway [16]. In an alternative group II intron splicing mechanism (termed hydrolytic pathway) the first step occurs by hydrolysis [61]. The splicing mechanisms of the introns in the Arabidopsis mtDNA have been previously elucidated (

Table 1. Splicing efficiency of introns in the Arabidopsis mtDNA based on the combined RNAseq data sets of the three biological samples.

	Cis or Trans-Splicing a	Splicing Mechanism b	RNA Read Depth			I3′E/EI5′	Splicing Efficiency (SE)
			EI5′ c	I3′E d	EE e
ccmFci	Cis-splicing	Branching pathway	192	356	1149	1.85	0.81
cox2i	Cis-splicing	Branching pathway	129	282	12,433	2.19	0.98
nad1i1	Trans-splicing	Hydrolytic pathway	180	1197	944	6.65	0.58
nad1i2	Cis-splicing	Hydrolytic pathway	151	174	2322	1.15	0.93
nad1i3	Trans-splicing	Both pathways were reported	187	945	3210	5.05	0.85
nad1i4	Cis-splicing	Branching pathway	240	1280	1982	5.33	0.72
nad2i1	Cis-splicing	Hydrolytic pathway	141	437	919	3.10	0.76
nad2i2	Trans-splicing	Branching pathway	54	476	1958	8.81	0.88
nad2i3	Cis-splicing	Branching pathway	59	581	1471	9.85	0.82
nad2i4	Cis-splicing	Branching pathway	389	460	2688	1.18	0.86
nad4i1	Cis-splicing	Both pathways were reported	480	633	2498	1.32	0.82
nad4i2	Cis-splicing	Hydrolytic pathway	144	125	6924	0.87	0.98
nad4i3	Cis-splicing	Branching pathway	163	468	8402	2.87	0.96
nad5i1	Cis-splicing	Branching pathway	278	817	1452	2.94	0.73
nad5i4	Cis-splicing	Branching pathway	189	1031	17,008	5.46	0.97
nad7i1	Cis-splicing	Hydrolytic pathway	75	135	639	1.80	0.86
nad7i2	Cis-splicing	Branching pathway	177	1379	1169	7.79	0.60
nad7i3	Cis-splicing	Branching pathway	104	332	4001	3.19	0.95
nad7i4	Cis-intron	Branching pathway	209	304	2685	1.45	0.91
rpl2i	Cis-splicing	Both pathways were reported	122	685	465	5.61	0.54
rps3i	Cis-splicing	Branching pathway	496	211	734	0.43	0.67

^a The trans-splicing introns nad5i2 and nad5i3 were not analyzed because the nad5 exon 3 is too short. ^b Information taken from Gualberto et al., 2015 [18]. ^c Number of reads that support the conformation exon-5′intron. ^d Number of reads that support the conformation 3′intron-exon. ^e Number of reads that support the conformation exon-exon.

) enabling the comparison of the splicing efficiency based on the RNA profile of spliced and unspliced versions of genes with cis or trans-splicing considering the different types of splicing mechanisms.

In the present work, we analyzed the splicing efficiency in the Arabidopsis mtDNA by comparing the read depth at exon-exon and exon-intron junctions (Figure S3). The splicing efficiency (SE) was calculated as the number of RNAseq reads supporting the mature conformation (exon-exon junctions, EE) divided by the sum of RNAseq reads that support exon-exon junctions and the average number of reads that support either of the immature conformations (exon-intron junctions: EI5′ or I3′E). The splicing efficiency of introns in the Arabidopsis mitochondria ranges from 0.54 to 0.98 with a global average of 0.82 (Table 1). The three biological replicates show highly consistent results with averages of 0.81, 0.83 and 0.81 for the three runs ERR1665120, ERR1665119 and ERR1665115, respectively (Table S3). The splicing efficiency is variable among genes (Table 1 and Table S3). The single intron of cox2 and the second intron of nad4 exhibit the highest splicing efficiencies (0.98). In contrast, the rpl2 intron shows low SE in the individual and combined analyses (0.49–0.58). Notoriously, the SE also differs among introns of those genes that contain more than one intron (Table 1 and Table S3). For example, nad1i1 shows SE = 0.58 and nad1i2 SE = 0.93. Thus, both the RNA read depth and the splicing efficiency are variable within and among genes (Table S3).

Comparisons between cis and trans-splicing introns do not reveal significant differences in splicing efficiency. Furthermore, no correlation in SE is observed for the different splicing mechanisms (branching versus hydrolytic pathways, or both). This indicates that the SE is not related to the splicing mechanism and it is not affected by the split of introns that now require trans-splicing. Noticeably, in all but two introns (rps3 and nad4_intron2), the number of reads supporting the unspliced form of the 3′ end of the intron with the downstream exon (I3′E) is higher (up to 10-fold) than that of the 5′ end of the same intron with the upstream exon (EI5′) (Table 1). This is likely the result of the splicing process, either via the branching or hydrolytic pathway, where the junction I3′E remains for a longer period than the junction EI5′, and thus more reads support this conformation [16].

This study serves as a comparative measure of mitochondrial intron splicing in free living angiosperms. In recent years, a large number of angiosperm transcriptomes have been sequenced and await comprehensive studies to shed light on the splicing efficiency of group I and II introns present in the mitochondrial genomes of diverse plants. In addition, the splicing efficiency could be compared across different tissues and different phenological stages of a single species, or even across land plants with different lifestyles, such as free-living and parasitic species. In agreement with the results shown here, an earlier study of the tobacco transcriptome [29] found that the abundance of the exonic steady-state level was higher than that of the introns, but the splicing efficiency was not measured. These observations contrast with those in the holoparasitic plant Lophophytum mirabile in which similar levels of exonic and intronic RNA read depth were noted suggesting a low splicing efficiency [33]. A comparative study of diverse angiosperms would unveil the evolution of intron splicing in mitochondrial genes of plants.

3. Materials and Methods

3.1. RNA Data Sets

To analyze the transcriptional landscape of the mtDNA of Arabidopsis, RNA Illumina reads were downloaded from the NCBI Sequence Read Archive (SRA) under the Bioproject accession PRJEB15579 using the fastq-dump tool in the NCBI SRA Toolkit v2.9.6. The RNAseq data sets analyzed here correspond to the sequencing of total RNA treated with Ribozero extracted from three biological replicates of wild-type A. thaliana ecotype Columbia submitted by University of Dundee (UK). Libraries were prepared using Illumina TruSeq stranded Ribozero plant kit and paired-end sequencing was carried out with Illumina HiSeq 2000. The three RNA sequencing runs analyzed include ERR1665220 (biosample SAMEA4475146), ERR1665219 (biosample SAMEA4475145) and ERR1665215 (biosample SAMEA4475141) of 222,419,248; 213,387,956; and 222,124,790 reads of 101 bp, respectively. The quality of the three runs was assessed using FastQC version 0.10.1. Low-quality sequences and the first 12 nucleotides of each read were trimmed using Trimmomatic [62], leading to 208,850,768; 199,739,120; and 209,258,080 paired-end sequences of 89 bp for runs ERR1665220, ERR1665219 and ERR1665215, respectively. For some analyses, we pooled the three data sets of trimmed reads obtaining a total amount of 617,847,968 paired-end sequences.

After exploring the RNAseq data sets available in public databases, we selected this particular data set based on the following: (i) the extracted RNA was depleted of rRNA instead of poly-A selected, which is relevant for mitochondrial expression studies given that poly-A tracts represent a signal for RNA degradation in plant mitochondria [14,63,64]; (ii) the volume of the three data sets combined is large and comparable to recent large-scale studies on angiosperm mitochondrial expression [23,33]; (iii) the data are of high quality as reported by FastQC; and (iv) they were obtained from a wild-type plant of A. thaliana ecotype Columbia, for which a curated mtDNA is available.

3.2. RNA Read Alignments

The RNA alignments were performed using Bowtie2 [65] with the sensitive, end-to-end mode, the no-discordant, no-mixed and R = 10 options, and the fr and nofw options to enforce strand-specific alignment. To avoid the mismapping of RNA reads derived from chloroplast genes, the five plastid-like regions present in the Arabidopsis mtDNA were masked using Beedtools v2.29.2 [66]. Individual and combined RNAseq data sets were aligned to three reference DNA sequences: (i) both strands of the mtDNA of A. thaliana ecotype Columbia (BK010421); (ii) all intact protein-coding genes annotated in the Arabidopsis mtDNA with these sequences extended by 100 bp at each end to obtain a good read depth at the ends of the genes; (iii) for intron-bearing genes, we also used as reference a spliced version extended by 100 bp at each end. The normalized read depth of each protein-coding gene was calculated as RPKM [52] by diving the product of the number of reads mapped to a gene and the scaling factor (109) by the length of the gene in base pairs and by the total number of reads mapped to all protein-coding genes in Arabidopsis mtDNA.

3.3. Initiation and Termination of Transcription Analysis

For the transcription start site prediction, the slopes of the RNA read depth were calculated in 50 bp windows within the 2 kb region upstream of each gene. For this, the alignments of the combined RNAseq data sets over the unspliced and spliced versions of each gene were analyzed. The location of the experimentally verified transcription start sites and 3′ termini on the Arabidopsis mtDNA was compared to that of the higher slope values (putative transcription start sites) using BEDtools [66] and to the drops in RNA read depth downstream of each gene, respectively.

3.4. Co-Transcription Analysis

Gene pairs located up to 2 kb apart in the Arabidopsis mtDNA were subjected to co-transcription analyses. For this, we examined the alignments of the combined RNAseq data sets over the unspliced and spliced versions of each gene pair to distinguish whether the read depth was continuously elevated or showed a drop in between the gene pair.

3.5. Splicing Efficiency

The splicing efficiency was calculated by applying a method developed for yeast nuclear genes [67] except for the correction factor that they used for polyadenylated samples that was not used here. We analyzed the RNA alignments of the spliced and unspliced versions of the intron-containing genes described above. Considering 30 bp at each side of the splicing site, the number of reads that mapped to the exon-intron junction (EI5’), the intron-exon junction (I3′E) and the exon-exon junction (EE) were recorded. The splicing efficiency was calculated according to the following formula SE = N_EE/(N_Total), where N_Total = N_EE + 0.5 × N_EI5 + 0.5 × N_I3E. One-way Anova (Rstudio) was used to compare the splicing efficiency of introns with different mechanisms of splicing (branching and hydrolytic pathways, or both) or alternative types of splicing (cis and trans-splicing).