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Characterization of Proteome Variation During Modern Maize Breeding

Lu-Guang Jiang, Bo Li, Sheng-Xue Liu, Hong-Wei Wang, Cui-Ping Li, Shu-Hui Song, Mary Beatty, Gina Zastrow-Hayes, Xiao-Hong Yang, Feng Qin  Correspondence email and Yan He  Correspondence email
Molecular & Cellular Proteomics February 1, 2019, First published on November 8, 2018, 18 (2) 263-276; https://doi.org/10.1074/mcp.RA118.001021

Article Figures & Data

Figures

  • Figure1
  • Fig. 1.
    Fig. 1.

    Proteome quality control. A, Distribution plot of log2-transformed proteome ratios (individual inbred line versus B73) for all the 98 samples. B, Heat-map of log2-transformed proteome ratios (individual inbred line versus B73) for all the 98 samples. C, Distribution plot of the correlation coefficients between pairs of non-replicates with a mean correlation coefficient of 0.13 (black dashed line). We then calculated the correlation coefficient between the seven replicate lines (mean value of 0.74, red dashed line). We carried out 100,000 permutations to judge whether the replicate line correlations were signiicantly higher than non-replicates. (p < 1 × 10−5). D, The Spearman's correlation of two replicates in a representative inbred line CIMBL157. Each point denoted a protein.

  • Fig. 2.
    Fig. 2.

    Comparison of protein and mRNA networks. A, WGCNA derived coexpression dendrograms and corresponding modules (colored boxes) for protein network (upper). We imposed the module definitions from protein onto the mRNA network (lower). B, Heat maps depicting normalized protein expression levels for all genes (rows) in all the samples (columns; purple labels are NSS lines, green are SS and red represents TST) for red (left) and purple (right) protein modules. C, The global structure of the protein WGCNA network using the top 100 gene-gene interactions in each module. Modules were colored based on the WGCNA module default name, and representative enriched GO categories were used for the annotation of each network. D, Module overlaps between protein and mRNA networks. Dots corresponded to modules from the protein (ball) and mRNA (rectangle) networks. The dots colors were assigned according to modules derived from WGCNA. Line widths were scaled based on the significance of overlap between corresponding modules. Red lines indicate significant mRNA-protein network preservation. Position of the dots and length of the lines were arbitrary to aid visualization. E, Zsummary statistics of module preservation. Each dot represents a module, labeled by WGCNA module default name. The dashed blue and red lines indicate the thresholds Zsummary = 2 and Zsummary = 10, respectively. Zsummary statistic (In general, Zsummary > 10 means highly preserved, Zsummary in between 2 and 10 is weak to moderate preservation, Zsummary < 2 indicates no preservation. F, Median rank of module preservation. Each dot represents a module, labeled by WGCNA module default name. In general, modules with lowest rank are highly preserved.

  • Fig. 3.
    Fig. 3.

    Association of proteomic or transcriptomic subtypes with genomic subpopulation. The gray lines indicate the missing lines in proteomic or transcriptomic subtype analysis.

  • Fig. 4.
    Fig. 4.

    The interdependencies between protein levels, RNA expression and genomic subpopulation. A, Six different colorings of the trained SOM illustrated the relative mean levels of protein and mRNA in the three subpopulations for each neuron. B, Neurons were grouped using affinity propagation clustering (65). Shared coloring between nodes specifies membership to the same cluster. For each cluster, the mean rank of protein levels and mRNA levels in NSS, SS and TST subpopulations was shown for the representative neuron of the cluster. C, In three out of five clusters, significantly enriched gene ontology (GO) terms were identified (permutation-based corrected p < 0.05) (48). The bars were colored according to the cluster colors shown in Fig. 4B.

  • Fig. 5.
    Fig. 5.

    Genetic loci associated with protein expression levels. A, Overlap between genes with cis-pQTLs and cis-eQTLs. Red hues indicated 267 genes with cis-pQTLs, and the blue shading lines represented those genes simultaneously with cis-eQTLs. Blue hues indicated 434 genes with cis-eQTLs, and the red shading represented genes simultaneously with cis-pQTLs. B, Density plot of Pearson's correlations for each gene's protein and mRNA levels in four classes: with both pQTL and eQTL (green), with pQTL,but no eQTL (red), with eQTL, but no pQTL (blue) and no both pQTL and eQTL (black). C, Pearson's correlation between RPL10 mRNA and protein expression levels. Protein and mRNA levels showed a poor correlation (r = −0.24, p = 0.02). D, Identification of cis-pQTLs for RPL10 protein. The p value and genomic coordinates for each protein/cis-SNP association test was plotted in the Manhattan plot. SNPs with significance threshold (Benjamini-Hochberg adjusted p < 0.05) were highlighted with a bigger dot size. The arrow indicates the location of the RPL10 gene with a significant cis-pQTL. E, Overview of RPL10 protein level and SNP genotype association. The bottom plot was the fine mapping of cis-pQTL for the RPL10 protein. Each dot represents a tested SNP. The arrow depicts the chromosome location and transcription direction of the RPL10 gene. There were several highly significant SNPs in the RPL10 gene region. The exact locations of these SNPs in the RPL10 gene region were illustrated in the top plot. The most significant SNP was chr6_9612267 and located at the fourth exon of RPL10. F, The bar plots showed the mean of RPL10 protein level of each chr6_9612267 genotype in 11 representative inbred lines, and the date were collected from iTRAQ (upper) and MRM (lower). Error bars denoted standard error of the mean.

Tables

  • Table I Characterization and preservation of protein and mRNA network modules.
    Protein modulemRNA moduleOverlap numberOverlap p valuePreservation Z-score
    p-blackm-black77 (77)10.95
    p-bluem-blue111 (69)7.32 × 10−65.3
    p-brownm-brown126 (84)5.93 × 10−66.8
    p-greenm-green120 (60)1.13 × 10−147.7
    p-magentam-magenta77(55)0.121.8
    p-pinkm-pink70 (41)2.55 × 10−42.1
    p-purplem-purple45 (22)2.96 × 10−40.6
    p-redm-red27 (33)1−0.54
    p-turquoisem-turquoise108 (108)10.8
    p-yellowm-yellow117 (68)3.04 × 10−83.9

    • For each protein network, significance of overlap with the corresponding mRNA module was presented in column 3–4. The expected number of overlapping genes was presented in parentheses. p values were adjusted for multiple comparisons. Column 5 measured module preservation.

  • Table II Association of proteomic and mRNA subtypes with genomic features.
    Fisher's exact testSubtype PASubtype PBSubtype TASubtype TB
    NSS4.87 × 10−7*4.87 × 10−7$5.55 × 10−25.55 × 10−2
    SS2.42 × 10−3*2.42 × 10−4$1.86 × 10−11.86 × 10−1
    TST1.06 × 10−11$1.06 × 10−11*5.60 × 10−35.60 × 10−3
    Temperate1.06 × 10−11*1.06 × 10−11$5.60 × 10−35.60 × 10−3

    • ↵$ indicates significantly more events in one cluster compared to the other one (p < 0.05);

    • ↵* indicates significantly less events in one cluster compared to the other one (p < 0.05).

  • Table III Number of cis-QTLs identified at the protein and mRNA levels.
    MeasurementNo. of linesSignificanceCis-genesCis-QTLs
    Protein levels981.98 × 10−4267281
    mRNA levels843.47 × 10−4434461
  • Table IV Enrichment of genomic annotations among pQTLs and eQTLs.
    AnnotationpQTLBackgroundp valueeQTLBackgroundp value
    CDS913,0438.77 × 10−432374,6741.24 × 10−167
    5′UTR114645.09 × 10−5518903.29 × 10−35
    3′UTR331,6661.62 × 10−10852,6644.96 × 10−39
    Intronic86171.95 × 10−2337672.49 × 10−19
    Extragenic13846,359133074,1421

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Characterization of Proteome Variation During Modern Maize Breeding
Lu-Guang Jiang, Bo Li, Sheng-Xue Liu, Hong-Wei Wang, Cui-Ping Li, Shu-Hui Song, Mary Beatty, Gina Zastrow-Hayes, Xiao-Hong Yang, Feng Qin, Yan He
Molecular & Cellular Proteomics February 1, 2019, First published on November 8, 2018, 18 (2) 263-276; DOI: 10.1074/mcp.RA118.001021
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