## 2 Results
### Validation of DEGs Genes with qRT-PCR Analysis  
To validate the transcriptomics data, qRT-PCR was performed on nine randomly selected genes (Figure 8A). qRT-PCR is generally more sensitive than RNA-Seq, especially for genes with low expression levels. The selected genes are involved in various aspects of fruit metabolism, including cell wall modification, ethylene biosynthesis and signaling, flavonoid biosynthesis, phenylpropanol metabolism, and ripening-related processes. Several genes exhibited strong agreement between the two methods. For example, _HCT_ (_Solyc03g117600_), _CHS2_ (_Solyc05g053550_), _non-specific lipid transfer_ (_Solyc10g075070.2_), _Expansin_ (_Solyc06g051800.3_), and _histidine decarboxylase_ (_Solyc08g066250.3_) showed similar fold change directions and magnitudes in both the qRT-PCR and transcriptomics data, across both M7_5 and M29_2 lines (Figure 8A). Figure 8: RT-qPCR validation of the DEGs and a quantitative assessment of the concordance between the RT-qPCR and transcriptomics data. **(A)** Fold change of mRNA expression of randomly selected genes _HCT1_, _PE_, _ACS2_, _WAT1(DUF6)_-related, _non-specific lipid transfer_ (_NSLT_), _Expansin_, _histidine decarboxylase_ (_HD_), _ETR1_, and _CHS2_. The genes were analyzed in fruit from two independent _lII4_ ZFN lines (M7_5 and M29_2) relative to the wild-type (WT) fruit. The expression levels were normalized to the reference gene _S. lycopeneium EF1-\(\alpha\)_ (_Elongation factor 1-\(\alpha\)_, _Solyc06g005060_)._ Data are presented as the mean \(\pm\) SD from three biological replicates (\(n=3\)). Concordance between the two methods was assessed using Pearson’s correlation coefficient (r) and R\({}^{2}\) values. **(B)** Analysis of the M7_5 samples revealed a significant positive correlation (r = 0.78, R\({}^{2}=0.62\), \(p=0.0202\)). **(C)** The M29_2 samples exhibited a more substantial and highly significant correlation (r = 0.88, R\({}^{2}=0.78\), \(p<0.01\)). Significance levels are indicated by asterisks as follows: * \(p<0.05\) and *** \(p<0.001\)**. For assessment of the reliability and accuracy of the transcriptomic findings, the qPCR data were transformed to log2(2\({}^{-\Delta\Delta\)CT) values for a direct comparison with the log2-transformed transcriptomic fold changes. The concordance between the two methods was quantitatively assessed using [PERSON]'s correlation and R\({}^{2}\) analysis (Figure 8B,C). The quantitative assessment revealed high concordance between the two techniques across both mutant lines. In M7_5, a strong positive linear correlation (r = 0.78, \(p\) = 0.0202) was observed, with an R\({}^{2}\) value of 0.62, indicating that the transcriptomics data explained 62% of the variance in the qPCR measurements (Figure 8B). An even higher degree of concordance was found in M29_2, with an exceptionally strong positive correlation (r = 0.88, \(p\) < 0.01) and an R\({}^{2}\) of 0.78, demonstrating that the transcriptomics accounted for 78% of the variance in the qPCR results (Figure 8C). Beyond these quantitative correlations, individual gene expression patterns were also compared. Some genes exhibited agreement in only one of the two mutant lines, as they had contrasting expressions. For instance, _PE (Solyc03g083730)_ provided data for the M29_2 line. The significant upregulation observed in M29_2 aligns well with the transcriptomics data. This suggests that _PE_, involved in cell wall modification, is significantly increased in the M29_2 mutant line. The qRT-PCR results for _ETR1 (Solyc12g011330)_ only provide data for the M29_2 line, where it shows a decrease in expression, aligning with the transcriptomics data. Similarly, _WAT1(DUF6)-related (Solyc1 lg012930.2)_ aligned well in M7_5 but was low for M29_2 in the transcriptomics dataset. _ACS2 (Solyc0 lg05080.3)_ showed a significant discrepancy between the two methods. While the qPCR indicated minimal changes in expression in both conditions, the transcriptomics data suggested a strong downregulation in M29_2.