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  • hhu-plant-biochemistry/Wrobel-2023-CastorBeanEndospermProteome
  • ceplas/Wrobel-2023-CastorBeanEndospermProteome
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# 2.8 Computational MS data analysis
For peptide and protein identification the acquired MS spectra were analyzed using the MaxQuant version 1.3.0.5 (MPI for Biochemistry, Planegg, Germany) with default parameters (Cox and Mann, 2008). Quantification was performed using the unlabeled quantification option of MaxQuant. The identified spectra were matched against the Ricinus proteome using the peptide search engine Andromeda (Cox et al., 2011). Only proteins containing at least two unique peptides and a minimum of three valid values in at least one group were quantified. A full list of all identified peptides from the proteome experiment is presented in Supplemental Table S1.
All identified Ricinus proteins were analyzed by bidirectional BLAST against the Arabidopsis proteome (Altschul et al., 1990). Organelle distribution within the collected fractions was assayed using a set of marker proteins. Proteins were assigned as organelle markers if the experimental localization of their Arabidopsis homologues in the SUBA 5.0 database (Hooper et al., 2017; Hooper et al., 2022) corresponded with their sequence-based localization prediction in Ricinus. We predicted protein localization to peroxisomes, mitochondria, and plastids manually and using the publicly available tools PPero, PredPlantPTS1, and TargetP (Emanuelsson et al., 2000; Reumann et al., 2012; Wang et al., 2015).
- Cox, J., and Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372. doi: 10.1038/nbt.1511
- Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V., and Mann, M. (2011). Andromeda: A peptide search engine integrated into the maxQuant environment. J. Proteome Res. 10, 1794–1805. doi: 10.1021/pr101065j
- Altschul, S. F., Gish, W., Miller, W., Myers, E. W., and Lipman, D. J. (1990). Basic local alignment search tool. J. Mol. Biol. 215, 403–410. doi: 10.1016/S0022-2836(05) 80360-2
- Hooper, C. M., Castleden, I. R., Tanz, S. K., Aryamanseh, N., and Millar, A. H. (2017). SUBA4: the interactive data analysis centre for Arabidopsis subcellular protein locations. Nucleic Acids Res. 45, D1064–D1074. doi: 10.1093/nar/gkw1041
- Hooper, C. M., Castleden, I., Tanz, S. K., Grasso, S. V., Aryamanesh, N., and Millar, A. H. (2022). Subcellular Localisation database for Arabidopsis proteins version 5 (The University of Western Australia). doi: 10.26182/8dht-4017
- Emanuelsson, O., Nielsen, H., Brunak, S., and von Heijne, G. (2000). Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol. 300, 1005–1016. doi: 10.1006/jmbi.2000.3903
- Reumann, S., Buchwald, D., and Lingner, T. (2012). PredPlantPTS1: A web server for the prediction of plant peroxisomal proteins. Front. Plant Sci. 3. doi: 10.3389/ fpls.2012.00194
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![fpls-14-1182105-g005.jpg](dataset\fpls-14-1182105-g005.jpg)
**Figure 5**
Principal Component Analysis of the Ricinus proteome of the fractions obtained from the sucrose density centrifugation, representing the total proteins (T1 to T4) and the enriched membrane proteins (M1 to M4). In this PCA plot each point represents the identified proteome of one biological experiment.
![fpls-14-1182105-g006.jpg](dataset\fpls-14-1182105-g006.jpg)
**Figure 6**
Distribution profile of compartment-specific marker proteins in the density-gradient fractions containing the total proteins (T1 to T4) and membrane proteins (M1 to M4). (A) peroxisomes; (B) plastid; (C) mitochondria; (D) ER; (E) nucleus; (F) Golgi; (G) vacuole; (H) cytosol. Relative LFQ represents the sum of Label Free Quantification (LFQ) values for all proteins belonging to an organelle relative to its maximal value.
## Supplementary material
Source for supplementary material: https://www.frontiersin.org/articles/10.3389/fpls.2023.1182105/full#supplementary-material
## "Table 3.xlsx"
SUPPLEMENTARY TABLE 3
Deconvolution-based assignment of the MS-identified proteins from etiolated castor bean seedlings to their subcellular localization. Linear regression (LR), quadratic programming (QP), support vector regression (SVR), and non-negative matrix factorization (NMF) were applied to assign 2258 identified endosperm proteins to a specific cell compartment. The assignment of a subcellular compartment was made when at least three of the four deconvolution approaches resulted in a matching localization (consensus).
## "Table 4.xlsx"
SUPPLEMENTARY TABLE 4
List of proteins found in peroxisomes, mitochondria, and plastids isolated from castor bean etiolated endosperm in this study. For functional annotation of proteins and their assignment to biological processes or metabolic pathways, gene ontology analysis was performed by Uniprot.
## "Image 1.jpg"
SUPPLEMENTARY FIGURE 1
Classification of the identified castor bean endosperm proteins from a soluble and membrane fraction enriched with peroxisomes (A), mitochondria (B), and plastids (C). The classification is based on UniProt database. Numbers in percent refer to the number of proteins that have been assigned to a certain metabolic pathway relative to the total proteins of all metabolic pathways of the respective organelle.
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assays/2_9-StatisticalAnalysis-ProteomeDeconvolution/dataset/Image 1.jpg

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# 2.9 Statistical analysis and proteome deconvolution
Unless stated otherwise all data analyses were performed in R using the statistical package e1071 for support vector regression (http://www.R-project.org), ggplot2 for visualization (Wickham, 2009; Wickham, 2016; https://ggplot2.tidyverse.org) and NFM for Nonnegative Matrix factorization (Gaujoux and Seoighe, 2010; http://cran.r-project.org). To assign MS-identified proteins to a certain organellar fraction, several deconvolution approaches have been applied. Unsupervised deconvolution was performed by Nonnegative matrix factorization (NMF). We determined the rank of factorization using the Brunet- and Lee-Seung-algorithms for ranks from 2 to 10 with 50 repetitions (Gaujoux and Seoighe, 2010). Both algorithms were further constrained to sum to one in a sample, so that its value can be interpreted as the relative proportion of a fraction in a sample. We used random as well as double singular value decomposition for seeding (Boutsidis and Gallopoulos, 2008). The final factorization was performed 500 times with random seeding and a rank of 5. We determined the fractions specific for an organelle by scaling the coefficients per protein and clustered them via k-means with Euclidean distance. An enrichment of organelle-specific proteins in a cluster was determined via Fisher’s Exact Test and proteins specific for an organelle were used for classification. Proteins were assigned either to peroxisomes, plastids, mitochondria, and other organelles based on the fraction with the highest coefficient.
For supervised deconvolution we used the distribution of marker proteins normalized to its maximum in all samples. Quadratic programming as well as Support vector regression were constrained to yield positive values. Nu-type SVR was performed with a linear kernel and a set of Nu values (0.25; 0.5; 0.75; 1.0). To test the quality of prediction, deconvolution was repeated 500 times splitting the dataset into a training set containing 70% of the dataset and a testing set using the remaining 30% of the data. Proteins were associated to the organelle with the highest coefficient and consensus classification was achieved by simple majority vote of all four algorithms.
- Gaujoux, R., and Seoighe, C. (2010). A flexible R package for nonnegative matrix factorization. BMC Bioinf. 11, 367. doi: 10.1186/1471-2105-11-367
- Boutsidis, C., and Gallopoulos, E. (2008). SVD based initialization: A head start for nonnegative matrix factorization. Pattern recognition 41, 1350–1362. doi: 10.1016/ j.patcog.2007.09.010
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## Ricinus-communis-seeds
![fpls-14-1182105-g001.jpg](resources/fpls-14-1182105-g001.jpg)
**Figure 1**
Endosperm morphology in seeds and germinating seedling of R. communis. Photographs were taken from the whole plant (upper panel) and two cotyledons embedded in the endosperm (lower panel) from mature seeds (dry), 24h-imbibed seeds (imb) and from 1- to 7-day old dark-grown castor bean seedlings (1-7d). Scale bar = 1 cm.
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