*2.6. qRT-PCR Analysis of the Candidate PG and PME Genes in Various Organs of Grapevine*

*2.6. qRT-PCR Analysis of the Candidate PG and PME Genes in Various Organs of Grapevine*  To validate previous findings based on RNA-seq data, cis-element predications and gene duplication analysis, we randomly selected 32 genes for qRT-PCR, which showed either positive or purifying selection. The expression profiling of *PG* and *PME* genes was quantified in various organs, such as root, stem, tendril, inflorescence, berry flesh, berry skin, and leaf of grapevines (Figure 7a and 7b). The results suggest that PG and PME transcripts show distinct expression patterns, intimating that both (*PG* and *PME*) gene families have positive regulatory roles in various physiological processes in grapevine. Moreover, principal component analysis (PCA) analysis was performed to gain deeper insight into their contribution to organ development. PCA analysis of PG transcripts To validate previous findings based on RNA-seq data, cis-element predications and gene duplication analysis, we randomly selected 32 genes for qRT-PCR, which showed either positive or purifying selection. The expression profiling of *PG* and *PME* genes was quantified in various organs, such as root, stem, tendril, inflorescence, berry flesh, berry skin, and leaf of grapevines (Figure 7a,b). The results suggest that PG and PME transcripts show distinct expression patterns, intimating that both (*PG* and *PME*) gene families have positive regulatory roles in various physiological processes in grapevine. Moreover, principal component analysis (PCA) analysis was performed to gain deeper insight into their contribution to organ development. PCA analysis of PG transcripts suggested a variation of 31.26% in PC1, 24.28% in PC2, and 15.36% in PC3, which accounted for 70.90% of the

suggested a variation of 31.26% in PC1, 24.28% in PC2, and 15.36% in PC3, which accounted for 70.90% of the total variation in the first three axes (Figure 7c). Among PGs, *VvPG31* (0.86) and *VvPG5*

total variation in the first three axes (Figure 7c). Among PGs, *VvPG31* (0.86) and *VvPG5* (0.85) had high positive loadings, while *VvPG21* (−0.90) and *VvPG8* (−0.77) had high negative loadings in PC1. Moreover, PCA analysis of PME transcripts suggested 83.02% in first three axes (PC1, PC2, and PC3) (Figure 7d). Among the PME transcripts, *VvPME5* (0.73) and *VvPME14* (0.61) had high positive loadings in PC1, whereas the highest negative loadings were found in *VvPME16* (−0.96), *VvPME36* (−0.82) and *VvPME1* (−0.79) (Table S6). *Int. J. Mol. Sci.* **2019**, *20*, x; doi: www.mdpi.com/journal/ijms (0.85) had high positive loadings, while *VvPG21* (−0.90) and *VvPG8* (−0.77) had high negative loadings in PC1. Moreover, PCA analysis of PME transcripts suggested 83.02% in first three axes (PC1, PC2, and PC3) (Figure 7d). Among the PME transcripts, *VvPME5* (0.73) and *VvPME14* (0.61) had high positive loadings in PC1, whereas the highest negative loadings were found in *VvPME16* (−0.96), *VvPME36* (−0.82) and *VvPME1* (−0.79) (Table S6).

**Figure 7.** Relative expressions of PGs (**A**) and PMEs (**B**) in various organs, including root, stem, tendril, inflorescence, flesh, skins, and leaves and their principal component analysis for PGs (**C**) and **Figure 7.** Relative expressions of PGs (**A**) and PMEs (**B**) in various organs, including root, stem, tendril, inflorescence, flesh, skins, and leaves and their principal component analysis for PGs (**C**) and PMEs (**D**).

#### PMEs (**D**). **3. Discussion**

**4. Discussion**  Realizing the significant role of PGs and PMEs in various plants observed in past studies, it is essential to systematically investigate the potential functions of these genes in grapevine. For instance, polygalacturonases (PGs) and pectin methylesterases (PMEs) have been hypothesized to play an imperative part in plant life cycles, such as cell separation and expansion, dehiscence, abscission, fruit maturity, and plant shedding [8,21,24]. In particular, PGs are a vital component of pectin disassembly, whereas PMEs play a central role in both remodeling and pectin disassembly [19,32]. Thus far, PGs and PMEs have been identified in various crops species although there is a lack of systemic analysis in grapevines. In this study, we comprehensively carried out various bioinformatics analyses by utilizing the available genomic resources of grapevines. In total, 36 *PG* and 47 *PME* genes were identified in grapevines and compared with *Arabidopsis*. For these genes, we also analyzed physicochemical properties, phylogenetic and collinearity relationships, chromosomal localization, motif and gene structure compositions, and duplication analysis. Additionally, the gene Realizing the significant role of PGs and PMEs in various plants observed in past studies, it is essential to systematically investigate the potential functions of these genes in grapevine. For instance, polygalacturonases (PGs) and pectin methylesterases (PMEs) have been hypothesized to play an imperative part in plant life cycles, such as cell separation and expansion, dehiscence, abscission, fruit maturity, and plant shedding [8,21,24]. In particular, PGs are a vital component of pectin disassembly, whereas PMEs play a central role in both remodeling and pectin disassembly [19,32]. Thus far, PGs and PMEs have been identified in various crops species although there is a lack of systemic analysis in grapevines. In this study, we comprehensively carried out various bioinformatics analyses by utilizing the available genomic resources of grapevines. In total, 36 *PG* and 47 *PME* genes were identified in grapevines and compared with *Arabidopsis*. For these genes, we also analyzed physicochemical properties, phylogenetic and collinearity relationships, chromosomal localization, motif and gene structure compositions, and duplication analysis. Additionally, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) enrichment, cis-regulatory elements,

ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) enrichment, cis-regulatory elements, and expression dynamics among various organs of grapevine have revealed extensive information related to the gene functions and their role in plant development. Subcellular predictions and expression dynamics among various organs of grapevine have revealed extensive information related to the gene functions and their role in plant development. Subcellular predictions for maximum members of genes were largely found in diverse organelles, such as the nucleus, endoplasmic reticulum, cytoplasm, plasma membrane, mitochondria, and others. The physicochemical and protein properties (i.e., protein length (aa), molecular weight (kDa), isoelectric point (PIs), and grand average of hydropathicity (GRAVY)) drastically varied among PGs and PMEs, suggesting their variable role in micro and macro environments [33].

The selection pressure analysis (i.e., purifying, positive, and neutral selection) of gene pairs provides valuable information using the rate of divergence [34]. During evolutionary events, the values of *Ka*/*Ks* ratio that are less than 1.00 signify purifying selection; values equal to 1.00 specify neutral selection; and values greater than 1.00 shows positive selection [35,36]. In this study, we observed a strong selective pressure (purifying selection) among PGs compared to PMEs, which is in disagreement with the previously reported study on *Brassica rapa* [25]. We infer that PGs might duplicate earlier for their need and survival, intimating their diverse and variable functions.

In this study, we also intended *Ka*/*Ks* values among 15 pairs of both PGs and PMEs (i.e., tandem, dispersed, and segmental) using the MEGA7.0 software [37]. Most of the pairs had *Ka*/*Ks* ratios that were smaller than 1.00, inferring the purifying selection. Furthermore, only four and five pairs of PGs and PMEs have values greater than 1.00, signifying positive selection. During evolutionary processes, the higher plant underwent polyploidization events, while segmental duplications are usually responsible for larger functional divergence [38,39]. Hence, studying gene duplication is vital for understanding biological functions and expansion of gene families [30,40], Hence, the results from our study highlight the importance of segmental and dispersed duplications in the two large families of PGs and PMEs in grapevines.

Transcript expression patterns and abundance in particular organs at a given time provide clues for understanding the function of genes [28]. Transcriptional profiling and functional characterization of PGs and PMEs has been reported in several species. For example, the *PG* genes found in various species, including *Glycine max*, *Medicago truncatula*, *Zea mays,* and *O. sativa*, showed higher expression levels [41]. In strawberries, the downregulation of *FaPG1* extended their post-harvest life by reducing fruit softening [15]. Previously, PG gene family transcription patterns were also observed in *Arabidopsis* and *Populus* [24,42]. In addition, the members of the *PME* gene family have been reported as key regulators in plant–microbe interactions, cold acclimation, drought, and salt stress sensitivity [43–47]. Likewise, in grapevines, the members of the *PG* and *PME* gene family regulate the development of numerous organs at varying stages. In this study, 19 diverse tissue-specific expression patterns of *PG* and *PME* genes were examined. The results revealed that most PGs and PMEs were highly expressed in berry, tendril, and inflorescence. However, few PGs and PMEs reveal either more or similar expression patterns, signifying their unifying need and importance in plant development. In addition, qRT-PCR validation of 16 *PG* and *PME* genes revealed their vital role in various organs of grapevines (i.e., root, stem, tendril, inflorescence, flesh, skins, and leaves). Transcriptional profiling of these genes in various tissues may consequently aid the study of new adaptive functions regarding plant developmental processes in grapevine. Notably, among various *PG* and *PME* genes, visible tissue-specific expression patterns were detected, which may be correlated with their expansion, evolution and the complex nature in plant growth and development. However, their underlying molecular and evolutionary mechanisms that lead to the measurable and abrupt structural differences must be extensively investigated in the future.

Finally, GO and KEGG enrichment, and cis-element predictions in the promoter regions of PGs and PMEs revealed their key role in pectin and carbohydrate metabolism, and various stress-related activities in grapevine. Taken together, our results highlight the importance of PGs and PMEs in plants and provides a comprehensive overview of their developmental role in grapevine.

## **4. Materials and Methods**

#### *4.1. Mining of Grapevine PGs and PMEs*

For identification of *PG* and *PME* genes in grapevine (genome version 2.1), we used BioEdit tools to obtain PGs and PMEs from all the reference sequences of *Arabidopsis*. Grapevine and *Arabidopsis* genomic sequences were retrieved from Ensembl (https://plants.ensembl.org/index.html) and TAIR (http://www.arabidopsis.org/). The sequences of other species, including apple, peach, and citrus, were downloaded from Phytozome v12.1.6 (https://phytozome.jgi.doe.gov/pz/portal.html) [48]. For domain composition analysis, we used NCBI-Conserved Domain database (https://www.ncbi.nlm.nih.gov/ Structure/cdd/wrpsb.cgi) and SMART databases (http://smart.embl-heidelberg.de/) [49]. In cases where PGs and PMEs domains were absent, the protein sequences were removed from the study and sequences with errors in length or having <100 aa length were also removed before analysis.

#### *4.2. Phylogenetic Analysis of PGs and PMEs*

The amino acid sequences of PGs and PMEs were aligned using MUSCLE [50] implemented in MEGA 7.0 software [51]. The phylogenetic trees were constructed using the maximum likelihood (ML) method in MEGA 7.0. In order to determine the reliability of the resulting trees, bootstrap values of 1000 replications were performed with the Jones, Taylor, and Thornton amino acid substitution model (JTT model).

#### *4.3. Ratio of Synonymous (Ks) and Non-synonymous (Ka) for duplicated genes*

The *Ka*/*Ks* ratios were calculated for duplicated pairs (i.e., tandem, dispersed, and segmental) using MEGA 7.0 [51]. The *Ka* and *Ks* substitution rates were calculated with the standard genetic code table by the Nei–Gojobori method (Jukes-Cantor model) in MEGA 7.0.

#### *4.4. Gene Structure, Conserved Motifs Analysis, and Physicochemical Parameters of PG and PME Proteins*

The gene structure was illustrated by TBtools software [52] by utilizing the GFF3 file of the grapevine genome. The conserved motif scanning of PG and PME proteins was carried out through local MEME Suite (Version 5.0.5) and was visualized by TBtools software. For this purpose, parameter settings were calibrated as follows: a maximum number of motifs of 10, with a minimum and maximum width of 50 and 100. The other parameters were set at default values [53]. The physicochemical properties of the PG and PME proteins (i.e., molecular weight (MW), isoelectronic points (PIs), aliphatic index and GRAVY values for each gene) were calculated using the ExPASY PROTPARAM tools (http://web.expasy.org/protparam/). The subcellular localization was predicted using the WOLF PSORT (https://wolfpsort.hgc.jp/) website.
