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Convergent evolution has been extensively reported in cases of humans[1], woody plants[2], cereal crops[3], and Orchidaceae plants[4]. Many morphological and physiological traits have been undergoing strong convergent selection, especially in plants. Most convergent traits are not linked to core metabolism[5], but photosynthesis is one of the exceptions[6]. Biochemical pathways involved in the capture of atmospheric carbon is more variable than sequestration of light energy. Thus, investigation of genetic and epigenetic regulatory mechanisms canalizing the convergent evolution in photosynthetic pathways, such as non-coding RNAs and DNA methylation is critical for plants' growth and development during long-term local environment adaptation.
In recent years, long non-coding RNAs (lncRNAs) was regarded as one of the important regulators of gene expression of multiple biological processes[7−9]. Several studies have shown that lncRNAs involved in the photosynthesis process. For example, one Arabidopsis lncRNA HIDDEN TREASURE 1 (HID1) acts through PHYTOCHROMEINTERACTING FACTOR 3 (PIF3), which promotes photomorphogenesis in continuous red light[10]. In the anthocyanin-associated coloration pathway, lncRNA MdLNC610 upregulate the expression level of MdACO1 by increasing the ethylene production and anthocyanin levels under high-light treatment[11]. Moreover, lncRNAs evolve rapidly and are poorly conserved among distantly related species[12,13]. LncRNA evolutionary analysis provides great benefit to the understanding of the functions of lncRNAs and the evolution of regulatory networks. The function of ancient lncRNAs may regulate embryonic development and conserved lncRNAs in lncRNA families probably function in many fundamental processes[14]. However, how lncRNAs play roles of adaptative evolution in lineage plants, and to what extent can lncRNAs carry similar functions in photosynthetic pathways in plants, remains largely unknown.
The function of lncRNAs in regulating gene expression can be affected by genetic and epigenetic variation[15−17]. DNA methylation is one of the vital epigenetic modifications that is widespread in the genome of eukaryotes[18]. DNA methylation is heritable during the change of development or affected by environmental conditions. Their adaptive variation may directly evolve through adaptive responses to a changing environment or arise from adaptive genetic variation. Environment-induced epigenetic variation may be limited and restricted to certain regions of the genome during the inheritance[19]. The epiallele near the functional gene in the maintainenance of chloroplast structures participate in the regulation of many genes associated with photosynthesis processes[20]. Over long timescales, genetic variations affect DNA methylation pattern, and associated with segregating structural variants or with mutations in methyltransferase genes[21]. LncRNAs could guide DNA methylation and silence target genes, investigation of the DNA methylation status would enhance the understanding of the regulatory roles of lncRNAs[22]. The expression level of lncRNA was also tightly linked with DNA methylation during the plants development and adaptation to the environment[23]. Yet, more investigations are still needed to unearth the epigenetic variation underlying photosynthetic pathways to response to local adaptation and its lncRNA relevance in the related species.
In this study, transcriptome analysis was performed to systematically identify lncRNAs and characterize their expression patterns in two unrelated Populus species, P. tomentosa and P. simonii. The convergent emergence or loss of photosynthetic phenotypes may facilitate adaptation to ecologically similar environments. The regulatory roles of the lncRNAs were investigated by co-expression between lncRNAs and their target genes enriched in photosynthetic pathways. Based on the evolutionary analysis of lncRNAs of nine diverse plant species including P. tomentosa, P. simonii, Populus trichocarpa, Salix purpurea, Arabidopsis thaliana, Glycine max, Oryza sativa, Zea mays, and Physcomitrella patens, we identified rapid evolvement even between closely related plants. Also, the potential DNA methylation and adaptive DNA sequences that can be subject to evolutionary divergence as methylation variation correlated with SNP variation. The transcription of lncRNAs target genes is fine-tuned through epigenetic modifications. As a result, photosynthetic genes are representative of adaptive evolution governed by the joint and complementary actions of lncRNAs and epigenetic processes.
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Ten P. tomentosa accessions and ten P. simonii accessions were collected from their natural population clonal garden in Guan Xian County, Shandong Province, China (36°10′ N, 114°35′ E). The sampling accessions were selected from the Southern (S), Northwestern (NW), and Northeastern (NE) geographical regions according to their natural distribution[24,25]. In 2019, tree seedlings were planted with three replicates in the same location using the root segment technique.
The measurement for photosynthetic traits were taken on a Li-COR 6400XT portable photosynthesis system (Lincoln, NE, USA). The leaf chamber conditions were: light intensity 1,000 μmol·m−2·s−1 PAR and flow 400 μmol·s−1. Only mature leaves of each plant were measured. All sampling was measured on clear, sunny days between 09:00 and 11:00 in June, 2019. The measurement was performed using three replications per individual. We measured net photosynthetic rate (Pn, µmol·m−2·s−1), conductance to H2O (Cond, mol·m−2·s−1), intercellular CO2 concentration (Ci, µmol·mol−1), and transpiration rate (Trmmol, µmol·m−2·s−1). Water use efficiency (WUE) was determined by photosynthetic rate over transpiration[26]. All measured leaves for each individual were collected, frozen in liquid nitrogen, and stored at −80 °C until use.
RNA isolation, RNA-sequencing and expression analysis
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Total RNAs were isolated from leaves of both P. tomentosa and P. simonii samples using the Plant Qiagen RNeasy kit which were used for RNA-seq (Methods S1). The clean reads of P. tomentosa were mapped to the P. tomentosa reference genome, and the clean reads of P. simonii were mapped to P. trichocarpa reference genome v4.0 (
www.phytozome.net ) using Hisat2 version2.1.0[27] (Supplemental Table S2). FPKM (fragments per kilobase of transcript per million fragments) values were calculated by Cufflinks v2.1.1[28]. The edgeR software package[29] was employed to identify differentially expressed genes (DEGs) between pairs of samples from different geographical regions of P. tomentosa and P. simonii, respectively, with FDR ≤ 0.05 and fold change ≥ 1.Identification of lncRNAs and prediction of their target Protein-coding genes (PCGs)
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In this study, we integrated RNA-seq data sets of ten P. tomentosa and ten P. simonii, respectively. Each transcriptome was assembled separately by StringTie2[30] and merged by gffcompare, while the transcript with FPKM > 0.5, length > 200, coverage > 1 was filtered. Coding Potential Calculator2 (CPC2) software[31], Coding Noncoding Index (CNCI) software[32], and PLEK[33] were used to evaluate the coding potential of the remaining transcripts. All transcripts with CPC2 labeled as 'coding', or CNCI > 0, or PLEK scores > 0 were discarded. Finally, the class code 'u' refers to the long intergenic noncoding RNAs (lincRNAs), class code 'x' refers to long noncoding natural antisense transcripts (lncNAT), class code 'j' refers to the sense transcripts, and class code 'i' refers to the intronic transcripts. Differentially expressed lncRNAs were calculated in the same way as DEGs above. The GC contents of these lncRNAs were calculated with the GEECEE tool in EMBOSS[34].
Homologous transcription of lncRNA between lncRNAs transcripts of P. tomentosa and P. simonii was performed using the BLASTN software. Alignments with E-value < 1e-5, coverage > 50%, identity > 80% were identified as Conserved lncRNA. Otherwise, lncRNAs were denoted as species-specific lncRNAs.
Target PCGs prediction of lncRNAs
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The potential target genes of lncRNAs were predicted via cis and trans analyses. PCGs around lncRNAs within 10 kb upstream or downstream in genome position were pointed as the potential cis-target genes[35,36]. The potential trans-targets in the Populus PCGs database was based on PCGs sequence complementarity and RNA duplex energy predictions. First, protein sequences of lncRNAs target PCGs in P. tomentosa and P. simonii were used as query sequences in BLASTN with E-value < 1e-5 and identity > 80% to identify homologs. Then RNAplex was used to screen lncRNA–PCGs (duplexes RNAplex -E-60) that exhibited complementary base pairing[37].
Network construction of co-expressed transcripts
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The WGCNA 1.70.3 package in R[38] was used to construct the unsigned co-expression network. One-step network construction and module detection method were adopted in both P. tomentosa and P. simonii with the following parameters: the minModuleSize was 100, and the cut height was 0.25. The soft power was 5 and 12 in P. tomentosa and P. simonii networks, respectively. To relate traits to the network, we calculated correlations between module eigengenes and the five photosynthetic traits.
Data source
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FASTA sequences for the lncRNAs from nine plants were downloaded from CANTATAdb2.0 and NCBI. 2,990 lncRNAs of Populus trichocarpa from Ye et al.[39], 2003 lncRNAs from Physcomitrella patens NCBI annotation (
www.ncbi.nlm.nih.gov/genome/?term=Physcomitrella+patens ), 3,270 lncRNAs from Oryza sativa NCBI annotation (www.ncbi.nlm.nih.gov/genome/?term=Oryza+sativa ), 5,355 lncRNAs from Zea mays NCBI annotation (www.ncbi.nlm.nih.gov/genome/?term=Zea+mays ), the 4,070 predicted lncRNAs of Salix purpurea from CANTATdb 2.0 database, 3,365 lncRNAs from Glycine max NCBI annotation (www.ncbi.nlm.nih.gov/genome/?term=Glycine+max ), and 3,480 lncRNAs from Arabidopsis thaliana NCBI annotation (www.ncbi.nlm.nih.gov/genome/?term=Arabidopsis+thaliana ).Phylogenetic tree construction and inference of the birth, death, and age of lncRNA families
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To gain insight into lncRNA evolution in plants, nine plants were used for comparisons. First, the phylogenetic tree was obtained via OrthoFinder. Single-copy lncRNAs were aligned using nucleotide sequence by MAFFT, and a species tree was built using IQTREE with the default parameters. r8s was performed to establish an ultrametric tree (chronogram) using species tree rooted with Physcomitrella patens. The birth, death, age, and ancestral contents of lncRNA families were assessed via COUNT software[40] using Dollo-Parsimony with default settings.
Whole-genome bisulfite sequencing analysis
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Total DNA of ten P. tomentosa accessions and ten P. simonii was extracted from the collected leaves. DNA extraction was performed using a DNase Plant Mini Kit (Qiagen China, Shanghai, China) for whole-genome bisulfite sequencing analysis. The libraries were sequenced on the Illumina HiSeq 4000, and the sequencing reads were filtered using Trimmomatic[6]. Paired-end reads of P. tomentosa and P. simonii genomes were aligned to P. tomentosa and P. trichocarpa V4.0 genome respectively using Bismark (version 0.16.1)[41] with the default parameters. Methylation cytosine sites with less than five methylated reads were removed. Integrative Genomics Viewer software[42] was used to visualize the DNA methylation Dataset. MethylKit were used to identified Differentially methylated regions (DMRs)[43] (Methods S2).
SNP genotype calling and positional association analysis with DNA methylation
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A total of ten accessions of P. tomentosa and P. simonii were sequenced on the Illumina GA II platform with an average depth of 15-fold genome coverage. The clean data were collected by removing low-quality reads (< 10% of nucleotides with quality < Q20). The paired-end data were aligned to the P. tomentosa and P. trichocarpa V4.0 reference genome using Bowtie 2 software with default parameters[44]. Samtools and Genome Analysis Toolkit (GATK) were used to perform single nucleotide polymorphisms (SNPs) calling. Low-quality SNPs that missing data ≥ 20% were filtered. 12,651,394 and 4,996,309 high-quality SNPs were retained for further analysis. To determine the relationship between DMRs and SNPs, we computed a one-sided permutation test between each pair of DMRs and SNPs within 2 kb upstream and downstream of each DMR. A DMR was determined to correlate with SNPs when there are at least three SNPs significant correlates with this DMR (one-sided permutation p-value < 0.01).
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The shape of leaves were associated with photosynthetic abilities of plants and probably contribute to photosynthetic differences of different species[45−47]. P. tomentosa and P. simonii accessions displayed considerable variation in size and shape of leaves (Fig. 1a). As there was considerable macroscopic variation in leaf characteristics, we next investigated whether differences observed in features affected photosynthetic performance. Interestingly, there were statistically significant differences in net photosynthetic rate (P = 2.18 × 10−4), conductance to H2O (P = 1.77 × 10−3), and intercellular CO2 concentration (P = 4.0 × 10−3) among two Populus. For all accessions, photosynthetic traits varied greatly especially in P. tomentosa, with coefficients of variation (CV) values ranging from 9.48% (Conductance to H2O) to 38.79% (Water use efficiency). Additionally, all five phenotypic traits showed significant differentiation among the geographical regions of both P. tomentosa and P. simonii (P < 0.05, post-test by LSD) (Supplemental Table S1). For example, the net photosynthetic rate of accessions from the Southern region in both Populus was significantly higher than those from the Northern regions (Fig. 1b). This showed that photosynthetic variation between accessions from geographical regions in P. tomentosa and P. simonii may be partly due to the underlying selective pressure in their environments[48]. Therefore, differences in transcripts expression and regulatory networks are critical to determining interspecific and intraspecific phenotypic variation.
Figure 1.
Morphological and photosynthetic variation of Populus tomentosa and Populus simonii from different geographical regions. (a) Leaf size and shape of P. tomentosa and P. simonii from the Southern geographical region (S), the Northwestern geographical region, and the Northeastern geographical region (NE). Numbers represent accession number in its geographical region. Scale bar, 5 cm. (b) Photosynthetic traits of P. tomentosa (blue) and P. simonii (orange) from three geographical regions. Photosynthetic traits include net photosynthetic rate (Pn), conductance to H2O (Cond), intercellular CO2 (Ci), transpiration rate (Tr), and water use efficiency (WUE). Data represent means ± SE. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Genome-wide identification, characterization, and expression profile of lncRNAs in P. tomentosa and P. simonii accessions
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To obtain a comprehensive profile of lncRNAs in different Populus species, we assembled transcriptome using the strand-specific RNA-seq data from ten P. tomentosa accessions and ten P. simonii accessions. In total, we identified a total of 1,600 and 1,013 high-confidence lncRNAs in P. tomentosa and P. simonii, respectively (Data S1, S2). Four classes of lncRNAs were identified, and the majority of them were long intergenic noncoding RNAs (lincRNAs) and long noncod natural antisense transcripts (lncNATs) in both species (Fig. 2a). We then investigated the characters and expression profile of these lncRNAs between two species. The lncRNAs are unevenly distributed across the 19 chromosomes of both Populus species, and there was no difference in GC content between P. tomentosa (37.13%) and P. simonii (37.35%) for lncRNA (Fig. 2b). According to genomic locations, the lncRNAs of P. tomentosa range in length from 230 to 13,266 nucleotides(nt), with a median length of 2,009 nt that is significantly shorter than the median length (2,255 nt) of P. simonii (Fig. 2c). On average, the lncRNAs of P. simonii contain a significantly fewer exons than the P. tomentosa lncRNAs. As expected, most of the lncRNAs comprised fewer exons (> 50% consist of one exon) than PCGs (Fig. 2d).
Figure 2.
Identification and characterization of lncRNAs in two Populus species. (a) Percentage distribution of different classifications of the total lncRNAs in Populus tomentosa and Populus simonii. (b) The GC content of lncRNAs and protein-coding genes (PCGs) in P. tomentosa and P. simonii. (c) Length density distributions of lncRNAs and PCGs. The x-axis indicates the log10-transformed sequence length and the y-axis indicates the density value. (d) Percentage distribution of exon numbers for PCGs and lncRNAs.
The evolution and expression of lncRNAs with intraspecific variation between Populus species
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To evaluate lncRNA differences between Populus species and intraspecific variation, we analyzed the lncRNAs expression of each accession of both Populus species. The expression level of the lncRNAs from both species was lower than for the PCGs[28] (Fig. 3a). The overall expression levels of lncRNAs in P. simonii were lower than that of P. tomentosa. We next identified the differentially expressed lncRNAs and PCGs between geographical regions in both species (Data S3−S6). Intriguingly, differentially expressed lncRNAs from P. tomentosa (41.25%−45.06%) and P. simonii (10.86%−17.47%) occupied a large proportion in their total lncRNAs, but PCGs had a lower expression variation ratio among geographical regions (P. tomentosa, 8.18%−12.20%; P. simonii, 3.88%−7.36%) (Fig. 3b).
Figure 3.
Expression profiles and evolution of lncRNAs in Populus tomentosa and Populus simonii. (a) Box plot of expression levels of lncRNAs and protein-coding genes (PCGs) in P. tomentosa and P. simonii. Student t-test was used to calculate the p-value. *** p < 0.001. (b) The numbers of differentially expressed (DE) lncRNAs in P. tomentosa and P. simonii between accessions from the Southern geographical region and the Northwestern geographical region, the Southern geographical region and the Northeastern geographical region, the Northwestern geographical region and the Northeastern geographical region. (c) Phylogenetic tree and number of gene families displaying expansion (green) and contraction (red) among nine plant species. Branch lengths reflect evolutionary divergence times in million years (Myrs) inferred from timetrees. Numbers of gained (+) and lost (−) lncRNA families Myr–1 (in red) are indicated next to each branch. (d) The distribution of chromosomes (outer) and lncRNAs (inner) in P. tomentosa (blue) and P. simonii (red). The green lines in the inner rings show lnRNAs that were homoeologous in two Populus lineages. (e), (f) Scatter plots of Pearson correlation coefficient and p-value between the expressions of the lncRNAs and their target PCGs in P. tomentosa and P. simonii. The lncRNA-mRNA with correlation coefficient ≥ 0.6 (red) or ≤ −0.6 (blue), and p-value ≤ 0.05 are considered positive or negative pairs. For screen visualization, p-value were minus log10 transformed after a constant value (0.001) was added.
LncRNAs are highly diverged at the nucleotide level among plant species but may have high sequence conservation at the intraspecies and interspecies levels. lncRNA orthologous pairs were identified through reciprocal best hits, and they were connected using the single-linkage clustering method to construct lncRNA families. The phylogenetic tree revealed that the evolution of the lncRNAs spaned around 277 Myrs (million years). We identified 1,033 lncRNA families with a total of 3,775 conserved lnRNAs. We then sought to investigate the birth and death rates and the ancestor lncRNA families during the plant evolution. Among these lncRNA families, the number of lncRNA families increased from 12 ancestral families to 35−557 families in all the plant species (Fig. 3c). Notably, terminal branches gained more families than internal branches, particularly in Salicaceae trees. The highest net gain rates in recent terminal branches in Salicaceae trees ranging from 1.65 to 5.02 families Myr−1, indicating a high rate of novel lncRNA families in forest trees. In addition, 482 (47.58%), 669 (22.39%), and 746 (18.33%) lncRNAs of P. tomentosa were found to be conservation in P. simonii, P. trichocarpa, and Salix purpurea, respectively. These results suggested that most lncRNAs were conserved between P. tomentosa and P. simonii despite rapid gene fractionation.
To explore whether lncRNAs contribute to evolutionary pressures on plant photosynthesis, we compared conserved lncRNAs with species-specific lncRNAs in two Populus species. Using the reciprocal align features of BLASTN, there are 3,305 homoeologous lncRNA pairs between P. tomentosa and P. simonii (Fig. 3d; Data S7). We found that 39.75% lncRNA of P. tomentosa had homologous copies in the 46.99% lncRNAs of P. simonii. These results suggested that the vast majority of lncRNAs were species-specific or limited to closely related species.
To identify genes potentially regulated by lncRNAs and the potential effects of lncRNAs, we identified 685 and 452 lncRNA-PCGs pairs in P. tomentosa and P. simonii, respectively. Expression analysis on the lncRNAs-PCGs pairs showed that 81.46% and 80.09% of them have a positive correlation (|rp| ≥ 0.6, P < 0.05) in two Populus species (Fig. 3e, f). On average, the rp between expression of P. tomentosa lncRNAs and their targets PCGs (0.48) was higher than that between adjacent PCGs pairs (0.38), which was similar to lncRNA-random PCG pairs (0.39). These correlations were much stronger than those of PCG-random PCG pairs (0.18) (Supplemental Fig. S1a). Moreover, the ratio of extreme expression correlation (|rp| > 0.8) for lncRNA-PCG pairs (25.84%) was higher than those of lncRNA-random PCG pairs (19.09%) and PCG-random PCG pairs (23.54%) (Supplemental Table S2). Similar results were observed in P. simonii (Supplemental Fig. S1b).
LncRNA participates in the regulation network of photosynthetic variation between P. tomentosa and P. simonii
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Although photosynthesis is one of the basic biochemical reactions of plants, they exhibit dramatic differences in multiple characteristics. Differences in gene expression and regulatory networks are critical for determining photosynthesis traits. We performed a weighted gene co-expression network analysis (WGCNA) on PCGs and lncRNAs. Accordingly, we obtained 16 and 13 distinctly expressed modules in P. tomentosa and P. simonii, respectively (Fig. 4a, b). The modules closely related to photosynthetic traits and GO term were of particular interest in this study. 'Photosynthesis, light reaction' (GO:0019684) and 'photosynthesis' (GO:0015979) GO terms were enriched in the subset of PCGs in MEbrown of P. tomentosa network and MEturquoise of P. simonii network (Fig. 4c; Supplemental Fig. S2; Data S8, S9). In P. tomentosa, the module 'Brown' comprised transcripts that were restrained in Northern regions (Fig. 4d). For seven genes enriched in photosynthetic terms, four hub genes including PtoPPL1, PtoLHCA1, PtoPnsb4, and PtoMPH2 were highlighted in the network due to their high eigengene connectivity (Supplemental Table S3). Among those hubgenes, we noted a cis-regulated and three trans-regulated lncRNAs, which were differentially expressed between distinct geographical regions (Fig 4e). In P. simonii, expression of lncRNAs and PCGs in module 'turquoise' was also highly expressed in the Southern geographical region. Three of the nine photosynthetic-enriched genes were highlighted as high eigengene connectivity, including PsiLHCB7, PsiPSBR, and PsiKUP1 (Supplemental Table S3). Gene PsiKUP1, a photosynthetic protein pathway gene that encodes a high-affinity potassium transporter, exhibited the highest expressed level in the S region as compared with NW and NE regions (Fig 4f). Meanwhile, we demonstrated that Psi_XLOC_022701 in the S region was more than two times higher expression than those accesions in the N region, indicating that Psi_XLOC_022701 may trans-regulate PsiKUP1.
Figure 4.
Photosynthetic-associated modules in Populus tomentosa and Populus simonii. Statistical analysis of module–trait correlations in (a) P. tomentosa and (b) P. simonii. The rows and columns indicate the modules and traits, respectively. Cells are colored from blue to red according to the Pearson correlation coefficient in parentheses, and the star-marked cells indicate the highest significant association between the trait and its corresponding module. (c) Circos plot shows the enrichment and differentially expressed genes in each ontology of Module Brown in P. tomentosa. From the outer to the inner circle, is gene ontology (GO) id, number of genes and P -value, number of differentially expressed genes, and enrichment factors. (d) Box plot and beewarm plot of expression levels of lncRNAs and protein-coding genes (PCGs) in Module Brown from P. tomentosa and Module turquoise from P. simonii. Student t-test was used to calculate the p-value. *** p < 0.001, ** p < 0.01. (e) Scatter plots show correlation between the expression of PtoMPH2 and net photosynthetic rate, expression of Pto_XLOC_013503 and net photosynthetic rate, and between expression of PtoMPH2 and Pto_XLOC_013503. Pto_XLOC_013503 positively regulate a chloroplast thylakoid lumen protein, PtoMPH2. r, Pearson correlation coefficient; p, significance of the correlation between trait and gene expression. (e) Scatter plots show correlation between the expression of PtoMPH2 and net photosynthetic rate, expression of Pto_XLOC_013503 and net photosynthetic rate, and between expression of PtoMPH2 and Pto_XLOC_013503. r, Pearson correlation coefficient; p, significance of the correlation between trait and gene expression. (f) Scatter plots show correlation between the expression of PtoKUP1 and net photosynthetic rate, expression of Pto_XLOC_022701 and net photosynthetic rate, and between expression of PtoKUP1 and Pto_XLOC_022701. r, Pearson correlation coefficient; P, significance of the correlation between trait and gene expression.
Similar expression patterns of homologous lncRNAs in P. tomentosa and P. simonii
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Conserved lncRNAs across species can provide further information to demonstrate their possible functions and the processes[49,50]. Combining the results of co-expression analysis and the origination of lncRNAs in two poplars, we were able to update the putative interspecies and intraspecies expression of the lncRNAs involved in the photosynthesis pathway (Data S3). For P. tomentosa, Pto_XLOC_026190 was highly expressed in the S region (Fig. 5a) and trans-regulated Ptom.010.01955 (facilitates the assembly of the photosystem II supercomplexes, PtoPPL1) (Fig. 5b). The regional differentiation was potentially similar to the patterns of Psi_XLOC_011671 homologous lncRNA (Fig. 5c), which positively regulated Potri.007G061400 (encoding a light stimulus response gene, PsiNIP2) (Fig. 5d). Moreover, another Pto_XLOC_001831 that contained two homologous lncRNA pairs in P. simonii was predicted to target Ptom.012.00615 (encoding a subunit of the chloroplast NAD(P)H dehydrogenase complex, which involved in PSI cyclic electron transport, PtoPnsB4). We found that PtoPnsB4 was highly expressed in the NE region for P. tomentosa. Additionally, homologous lncRNAs of Pto_XLOC_001831 in P. simonii also targeted two photosynthetic-related genes including PsiPSBR and PsiLHCB7. This can be inferred that Pto_XLOC_001831 showed high similarity with Psi_XLOC_022416 and may have functions in the regulatory network of photosynthesis. Both lncRNAs were highly expressed in the S region compared to the NE region. These phenomena indicated that Pto_XLOC_001831 was an evolutionarily more important lncRNA than Pto_XLOC_026190 in P. tomentosa (Fig. 5e). It also suggested that the regulatory module is highly conserved across different poplar species and may be functionally maintained.
Figure 5.
LncRNAs have positive regulatory roles for photosynthetic genes and working model of Psi_XLOC_022416. (a) Pto-XLOC_026190 and its regulatory gene (b) PtoPPL1. The RNA-seq coverage of the genes was extracted from the leaf transcriptomes of P. tomentosa accessions from the Southern geographical region (S), the Northwestern geographical region (NW), and the Northeastern geographical region (NE), and the numbers in the panel indicate the mapping read counts of junction reads or exonic reads. (c) Psi-XLOC_011671 and its regulatory gene (d) PtoNIP1. The RNA-seq coverage of the genes was extracted from the leaf transcriptomes of P. simonii accessions from the Southern geographical region (S), the Northwestern geographical region (NW), and the Northeastern geographical region (NE), and the numbers in the panel indicate the mapping read counts of junction reads or exonic reads. (e) A proposed working model of Psi_XLOC_022416 in regulating PsiPSBR and PsiLHCB7 expression to modulate Populus photosynthetic variation in different geographical regions.
The differences in DNA methylation between three geographical regions in P. tomentosa and P. simonii
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Many studies have found that DNA methylation level correlated with the expression level of PCGs. The covariation of DNA methylation and other genetic factors causes phenotypic variation during plants' growth and development[51−53]. To investigate the divergence of genomic DNA methylation on photosynthetic variation, we analyzed the difference in methylation patterns associated with photosynthetic variations in P. tomentosa and P. simonii accessions. More than 100 million cytosines were sequenced in each sample, a number sufficient for further analysis (Supplemental Table S4). P. tomentosa displayed an average of 66.31%, 46.85%, 3.75% methylation in CG, CHG, and CHH contexts, respectively. Correspondingly, P. simonii presented a mean level of 40.34%, 32.55%, and 4.81% methylation in CG, CHG, and CHH contexts, respectively. We detected distinct differences in CV among three contexts, with the lowest diversity for CG context and the highest for CHH context (Supplemental Table S5). Accessions from the same geographical regions were often closely correlated, especially in CG context (Supplemental Fig. S3a, S3b). These results indicated that both Populus accessions possessed significant methylation variability and could contribute to trait variation.
We identified DMRs to further investigate the differences in DNA methylation between three geographical regions in P. tomentosa and P. simonii. Our analysis identified 51,892, 61,161, and 28,981 DMRs in S vs. NW, S vs.NE, and NW vs. NE in P. tomentosa, respectively (Supplemental Table S6). Hypo-DMRs accounted for 54.82%−82.04% of the total DMRs. Similarly, for P. simonii, 23,840 and 34,400 hyper-DMRs were found in NW and NE regions when compared with the accessions from S region, suggesting that the lower methylation in Southern regions in both P. tomentosa and P. simonii (Fig. 6a). It is noteworthy that, despite DMRs in intergenic regions, 16.05% and 16.65% of DMRs occurred in promoters, suggesting that geographical regions might affect DNA methylation within promoter regions (Supplemental Fig. S4a).
Figure 6.
Geographical variation of DNA methylation and genetic variation affect the function of lncRNA. (a) Count of hyper and hypo differentially methylated regions (DMRs) in P. tomentosa and P. simonii from three geographical regions in three contexts. (b), (c) Integrative Genomics Viewer plots of WGBS tracks in accessions from the Southern geographical region (S), the Northwestern geographical region (NW), and the Northeastern geographical region (NE), as well as RNA-seq tracks over (b) PtoPnsB4 and (c) PtoLHCA1. The position of the DMRs is indicated by the pink shadow area. (d) Circos plot representing interaction DMR-SNP pairs. The circles show the structure of gene PtoPnsB4. Blue, green, purple, white, and yellow arcs represent promoter, untranslated region (UTR), exon, intron, and downstream region, respectively. Interior lines represent the pairwise interactions of DMR-SNP pairwise. Orange, red, and blue lines indicate intra-gene interactions of different DMRs between DMR-SNP pairs. (e) A proposed working model that DNA methylation variation participates in the regulation of Pto_XLOC_001831that modulates Populus convergence evolution of photosynthesis.
Division of Populus photosynthesis involved in genomic DNA methylation patterns
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Based on the presence of lncRNAs and genes with DMRs (designated as differentially methylated genes, DMGs), we identified lncRNA targets that were related to DNA methylation variation. Abundant DMGs (70.00% and 52.40%) were identified in the total lncRNA target genes in P. tomentosa and P. simonii (Supplemental Fig. S4b), including 464 and 255 DEGs differentially expressed between geographical regions (Supplemental Fig. S4c). To further investigate whether the balance between DNA methylation and lncRNAs is responsible for the natural variation of photosynthetic traits, we investigated the expression and the methylation patterns among different geographical regions of two species. Our study found several lncRNA-targeted DMGs that hypomethylated in the promoter regions of southern accessions (Supplemental Table S7).
To explore the genetic variation and DNA methylation that affect the function of lncRNA in regulating its target genes, we further analyzed the relationship between DMRs and SNP in P. tomentosa and P. simonii (Supplemental Table S8; Data S10, S11). We identified 339 strong correlated pairs (P < 0.01, r2 > 0.1) between DMR and SNPs range from 2 to 102 in P. tomentosa (Supplemental Fig. S5a). Among these signals, a large proportion of SNPs 60.5% were identified in flanking regions and intergenic regions, only 24.21% of correlated SNPs were from promoter sequences and 9.47% from exon sequences. In P.simonii, we found one DMR correlated with three SNPs distributed in the intron and promoter (Supplemental Fig. S5b). We detected a regulatory model that showed the function of DNA methylation and genetic variation affecting the effect of lncRNAs on gene expression. For example, Pto_XLOC_001831 was higher expression in S regions compared with NE, but was not differentially expressed in NW region. The promoter regions of its target, PtoPnsB4, showed that the accessions from the NE region had higher methylation levels than the accessions from the S. This hypermethylation in NE accessions inhibited the gene expression in PtoPnsB4 (Fig. 6b). Two CG DMRs in promoter and downstream flanking regions of PtoPnsB4 are strongly associated with one and three SNPs in the exon region (Fig. 6d). Interestingly, SNP-12-7790159 (TT) correlated with hypo-methylated DMR in the promoter region in S and NE geographical regions. In comparison to the accessions from the NW, SNP-12-7790159 caused a missense variant resulted in the substitution of Tyr to His. Missense variant in gene transcript together with promoter DNA methylation inhibited the function of Pto_XLOC_001831 of gene and caused a lower expression level in PtoPnsB4 in accessions from NW regions (Fig. 6e). In addition, the homologue of PtoLHCA1 in P. simonii was hypermethylated in the NE region compared with the S region in CHH context (Fig. 6c). This homologue encodes a component of the light-harvesting complex associated with photosystem I. These results indicated a general coherence of interspecific genetic and epigenetic modification in photosynthetic pathways in two Populus species.
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Increasing numbers of progress have been made in elucidating important roles of plant non-coding RNAs due to their extensive abundance. LncRNA has evolved in multiple molecular mechanisms to survive abiotic stress, such as water stress[54], temperature extremities[55], salinity[39], and heavy metal toxicity[56], etc. This study covered two Populus species, P. tomentosa and P. simonii, representing Populus sections white poplar and Tacamahaca Spach, which have similar natural geographical distribution in China. Compared with PCGs, lncRNAs had fewer exons, shorter mean lengths of exons, and were less abundantly expressed across the conditions in two species (Fig. 2c, d), suggesting a similar characteristic among two Populus species. Furthermore, plants evolved different lncRNAs expression abundance in response to distinct geographical climates. PCGs and lncRNA pairs have shown a significant contribution of lncRNA in a strong regulatory manner on gene expression[49,55].
It is long been confusing on the evolutionary conservation of lncRNAs, their high levels of sequence divergence make them hard to study. In stark contrast to PCGs, only a small portion of lncRNA sequences (1.8%−52.6%) is conserved across nine species. LncRNAs in Salicaceae trees lack known orthologs in species outside of monocot plants, indicating poor conservation of lncRNAs[57,58]. We use three closely related Populus species P. tomentosa, P. simonii, and P. tritrocarpa to minimize the effects of genomic sequence divergence. LncRNAs are more frequently gained than lost, and the highest net gain rate was identified in the recent terminal Populus species. These results suggested that lncRNA transcription evolved extremely rapidly between closely related plants. The transience of intergenic lncRNA transcription is mirrored by changes to selective pressures acting on their sequences.
The regulatory network of photosynthetic pattern involving lncRNA and DNA methylation in Populus
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Forest trees experienced photosynthetic divergence as a direct response to landscape processes and heterogeneity of habitat. The threshold of temperature and limitation of precipitation may vary substantially with local environmental conditions, which leads to heterogeneous responses in tree biological adaptation of tree growth. The convergence of leaf photosynthetic characteristics in distinct lineages may contribute to the persistence of species in the adjacent environment in forests or similar geographical environmental constraints. Insights into photosynthesis and plants' geographical distribution provide valuable information to investigate plant–environment interactions during their long historical evolution[59,60]. Molecular genetics studies have shown that lncRNAs involved in the precise control of light-mediated development. MLNC3.2 and MLNC4.6 are predicted as endogenous target mimic for miRNA to regulate the expression of the SPL2-like and SPL33 transcription factors during light-induced anthocyanin biosynthesis and involve photosynthesis[61]. Thus, studying photosynthesis-associated modules would be more informative. The co-expression network analysis in P. tomentosa and P. simonii showed that genes involved in 'photosynthesis, light reaction' and 'photosynthesis' were enriched in MEbrown from P. tomentosa co-expression network and MEturquoise from P. simonii co-expression network, respectively. LncRNA expressions from accessions of the south region involving photosynthetic pathways were higher than accessions from the northern region, suggesting a conserved spatial-induced expression of lncRNAs in plants[62,63]. For MEbrown module in P. tomentosa, we discovered a lncRNA Pto_XLOC_013503 was co-expressed with PtoMPH2, and the expression pattern varies among geographical regions. This indicates that the effect of genes and lncRNAs may differ among geographical regions in photosynthetic efficiency and affect growth acclimation under photo-inhibitory light and fluctuating light environments[64]. We also found that Psi_XLOC_022416 in P. simonii has transcriptional regulatory relationships with PsiLHCB7 which can be strongly expressed when light harvesting is limiting for plant growth[65]. AtLHCB7 is also associated with the threshold of light-saturated photosynthesis rate and irradiance threshold for induction of photoprotective non-photochemical quenching[66]. Intriguingly, functional orthologs were found in homolog pairs Pto_XLOC_001831-Psi_XLOC_022416. These homologs were not found outside three Salicaceae species but were shown to have similar functions in photosynthetic pathways. We note these homologs as 'functional orthologs', which may have similar functions but have a poor ancestral relationship.
Epigenetic variation is tightly linked to environmental and fitness differences, implying its involvement in adaptive evolution[67,68]. In this study, Populus samples were well distinguished into three clusters by DNA methylation which were consistent with the origin of accessions. Interestingly, DNA methylation of accessions from the South was lower than those from the Northern accessions in both Populus species (Fig. 6c). Thus, DNA methylation is involved in the variation of Populus from different geographical regions. In photosynthetic genes, we found that PtoLHCA1, PtoPnsB4, and PsiLHCB7 were all hypomethylated in the Southern region, demonstrating that DNA methylation may act as a regulator in plants' light harvesting process. The differentiated expression patterns of these genes across the three geographic regions (Fig. 6d, e) imply that the transcriptional regulation of photosynthetic may also undergo DNA methylation variation creating P. tomentosa ecotypes.
Characterization of interspecies variation in two Populus species and their evolution
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LncRNA works as a regulator by recruiting DNA methyltransferases or demethylases to regulate the target gene transcription. Some lncRNAs are involved in chromatin modification and RNA-directed DNA methylation (RdDM)[69,70]. Theoretical and empirical data showed that the stress responsiveness to fitness traits is typically an interactive modification process of genetic and epigenetic, in which epigenetic signatures are deeply interwoven with DNA sequence polymorphism[71,72]. Drought stress-dependent flowering vigor in the same altitudinal gradient reinforces SNP–DMC associations in adaptive evolution[73]. Patterns of correlation between promising selected DMRs and nearby SNPs assign causality DMRs associated with the flowering time traits and are consistent with the idea that many DMRs are the result of genetic changes for maize[74,75]. In this study, strong SNP-DMR correlation pairs were found when DMRs were involved in epigenetic variation between geographical regions (Fig. 6d), particularly when the co-varying SNPs were in promoter regions and protein-coding regions. Functional variants of genomic regions may have experienced strong selection pressure responsible for local adaptation within the species' widespread natural distribution[24,76]. We reveal distinct types of regulation between lncRNA modulators and target genes that are operative either in one species or across species[77]. The deregulation of Pto_XLOC_001831 expression in NE was associated with alterations in DNA methylation and genetic variation[78]. A missense variant and hypermethylation in the promoter region participate in the regulation of gene expression. On the contrary, the genetic locus can encode a suppressor program that is enforced by the lncRNA independent of the protein product of the locus despite the modification of DNA methylation[79]. DNA methylation regulates lncRNA expression to determine the dysregulation of the gene. A lncRNA arising from the CEBPA gene locus could compete with DNA methyltransferases, which inhibits CEBPA gene methylation and facilitates CEBPA expression[80]. These results provided valuable candidate allelic genes for regional breeding programs to improve photosynthetic efficiency in Populus.
Collectively, using two Populus species that contain accession from three geographical regions, our results suggest a meaningful functional role for lncRNA and DNA methylation variation in the photosynthetic convergent evolution of Populus. The comparison of geographical regions could inform on the adaptive potential of two closely related Populus species in the evolution process. However, further investigation is required to make conclusive statements concerning the evolutionary basis of DNA methylation with genetic variation. Further investigation of the mechanism underlying the recruitment of DNA methylation through lncRNAs to affect genome-wide patterns of gene regulation is warranted. Therefore, the gene editing technology of CRISPR-Cas9[81] and the methylation editing technology of CRISPR-dCas9[82] will help to determine the trigger for the deep-seated mechanism of naturally occurring lncRNAs and epigenetic variation and may provide a useful source of regulatory variation for tree improvement.
This work was supported by the Major Science and Technology project of Inner Mongolia Autonomous Region (No. 2021ZD0008), Natural Science Foundation of Beijing Municipality (No. 6212021), National Natural Science Foundation of China (No. 31872707), Key research and development project of Zhejiang Province (No. 2021C02054), Fok Ying Tung Education Foundation of China (No. 171020) and the 111 Project (No. B20050).
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The authors declare that they have no conflict of interest.
- Supplemental Table S1 Phenotypic variation among ten accessions from S, NW and NE climate regions in Populus tomentosa and Populus simonii.
- Supplemental Table S2 RNA-sequencing data statistics.
- Supplemental Table S3 Phenotypic variation among ten accessions from S, NW and NE climate regions in Populus tomentosa and Populus simonii.
- Supplemental Table S4 Differentially expressed genes and lncRNAs in photosynthetic modules of Populus tomentosa and Populus simonii.
- Supplemental Table S5 Methylome data bisulfite conversion rate and sequencing statistics.
- Supplemental Table S6 Data description of methylation level for ten Populus tomentosa and Populus simonii accessions.
- Supplemental Table S7 The number of differentially methylated regions in genomic features in Populus tomentosa and Populus simonii.
- Supplemental Table S8 The number of differentially methylated regions in genomic features in Populus tomentosa and Populus simonii.
- Supplemental Table S9 Correlation analysis between differentially methylated region and local SNP in Populus tomentosa and Populus simonii .
- Supplemental Table S10 Interacting DMR and SNP in 2kb.
- Supplemental Fig. S1 Box plot of regulatory roles of lncRNAs in Populus tomentosa and Populus simonii.
- Supplemental Fig. S2 Gene Ontology enrichment analyses of Turquoise Module in Populus simonii.
- Supplemental Fig. S3 Geographical variation characterized by global DNA methylation patterns.
- Supplemental Fig. S4 The distribution of differentially methylated genes in Populus tomentosa and Populus simonii.
- Supplemental Fig. S5 Differentially methylated regions (DMRs) correlated with genetic variation.
- Data S1 The original information of lncRNAs of Populus tomentosa.
- Data S2 The original information of lncRNAs of Populus simonii.
- Data S3 The expression data of differentially expressed lncRNA of Populus tomentosa.
- Data S4 The expression data of differentially expressed gene of Populus tomentosa.
- Data S5 The expression data of differentially expressed lncRNA of Populus simonii.
- Data S6 The expression data of differentially expressed gene ofPopulus simoniif.
- Data S7 Conserved and Species-specific lncRNA in P. tomentosa and P. simonii. .
- Data S8 GO term enrichment analyses of genes in MEbrown module in Populus tomentosa.
- Data S9 GO term enrichment analyses of genes in MEturquoise module in Populus simonii.
- Data S10 Polymorphisms around Differientially methylated regions of photosynthetic hubgenes used for linkage disequilibrium analysis of Populus tomentosa.
- Data S11 Polymorphisms around Differientially methylated regions of photosynthetic hubgenes used for linkage disequilibrium analysis of Populus simonii.
- Method S1 The process of RNA-sequencing.
- Method S2 The process to calculate DMRs.
- Copyright: © 2023 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
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Cite this article
Zhou J, Song F, He Y, Zhang W, Xiao L, et al. 2023. LncRNA evolution and DNA methylation variation participate in photosynthesis pathways of distinct lineages of Populus. Forestry Research 3:3 doi: 10.48130/FR-2023-0003 
| Metric | Data |
| Total transcripts | 174,449 |
| N50 | 2,110 |
| Smallest transcript length (bp) | 200 |
| Largest transcript length (bp) | 19,114 |
| Mean transcript length (bp) | 1,198.69 |
| GC (%) | 41.33 |
| Total assembled bases | 209,110,524 |
| TransRate score | 0.05 |
| BUSCO score (%) | C: 85.38 (S:48.22, D:37.16), F: 10.69, M: 3.93. |
| Values were calculated through the TrinityStats function of Trinity and TransRate software. Completeness analysis was carried out through BUSCO by aligning the transcriptome to the Embryophyte database through BLAST with an E value threshold of 1 × 10−3. Complete (C), single (S), duplicated (D), fragmented (F), missing (M). | |
