Protein subcellular location prediction of the 18 proteins influenced by CHA in nectarine fruit according to PSORT (http://wolfpsort.org).

(a)SNP no corresponds to numbers in row 2 of Supplementary table S4: Annotation of SNPs. More information about the SNP can be found in this table.(b)Subcellular localization was predicted for proteins were no localization was determined experimentally using PSORTb vs 3.0 (http://www.psort.org/psortb/). Cyt: cytoplasm, im: inner membrane, per: periplasm, om: outer membrane, es: extracellular space, uk: unknown, -: not determined.(c)Information of regulation by bvg was retrieved from Cummings et al. [56], Streefland et al. [78] and de Gouw et al. in preparation. Act: bvg-activated, rep: bvg-repressed.(d)Information about domains, active sites and conserved positions was derived from SMART (http://smart.embl-heidelberg.de) and Conserved Domain Database (http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml).

Information about size and chromosome location (Chr) were obtained from the JEC21 genome database hosted at TIGR [71]. Motif prediction was based on information of the JEC21 genome database and the motif scan tool as described previously (http://myhits.isb-sib.ch/cgi-bin/motif_scan) [114]. Subcellular localization was predicted using the WoLF PSORT program as described previously (http://wolfpsort.org/) [115].

aAbbreviation of cellular role categories of theoretical (http://www.ncbi.nlm.gov/COG/).bAbbreviation of cellular location. Protein cellular location was annotated by PSORTb V. 2.0 (http://www.psort.org/). C: Cytoplasmic, P: Periplasmic, U: Unknown, OM: OuterMembrane, CM: CytoplasmicMembrane.cProteins upshifted in the BMΔvirB mutant are marked with “+”, and those downshifted with “−”; unique protein spots in BM are marked with “Y”, and in BMΔvirB with “T”.

PSORT II Computer prediction of putative NLSs in mouse NBEA.

aSignal peptides and signal peptide type of each protein were predicted with software SignalP 4.0 or LipoP 1.0, which are available online (http://www.cbs.dtu.dk/services/SignalP-4.0 and http://www.cbs.dtu.dk/services/LipoP-1.0. Accessed 30 March 2013).bThe subcellular localization of each protein was predicted using PSORTb 3.0.2 or SOSUI-GramN, which is available online (http://www.psort.org/psortb/index.htmlandhttp://bp.nuap.nagoya-u.ac.jp/sosui/sosuigramn/sosuigramn_submit.html. Accessed 22 January 2013).SpI, signal peptide (signal peptidase I); EC, extracellular; OM, outer membrane; PP: periplasmic; C, cytoplasmic.

File S1 contains all scripts and code used for the analysis of the Brachypodium cold-acclimated PM proteome. File S2 contains the stress response network meta-analysis for the CA2 dataset of the Brachypodium PM proteome. File S3 contains the total raw Brachypodium PM proteome dataset. File S4 contains the significantly increased Brachypodium PM protein dataset. File S5 contains the significantly decreased Brachypodium PM protein dataset. File S6 contains the CA2 decreased annotated Brachypodium PM protein dataset. File S7 contains the CA6 decreased annotated Brachypodium PM protein dataset. File S8 contains the CA2 increased annotated Brachypodium PM protein dataset. File S9 contains the CA6 increased annotated Brachypodium PM protein dataset. File S10 contains the interactive network for the stress response meta-analysis of Brachypodium CA2 proteins. File S11 contains the interactive network for the CA2 predicted protein-protein interactions. File S12 contains the interactive network for the CA6 predicted protein-protein interactions. File S13 contains the CA2 preliminary Brachypodium PM dataset.Protein descriptions were manually predicted using UniProt (UniProt Consortium), RIKEN Brachypodium FLcDNA database (Mochida et al. 2013), BLAST, and through literature searches. PM localizations were predicted using UniProt, TMHMM Server (version 2.0) for transmembrane helices (Krogh et al. 2001), GPS-lipid for N-myristoylation/-palmitylation sites, DeepLoc-1.0 (http://www.cbs.dtu.dk/services/DeepLoc-1.0/) (Almagro Armenteros et al. 2019), BUSCA (http://busca.biocomp.unibo.it/) (Savojardo et al. 2018), WolF PSORT (https://wolfpsort.hgc.jp/) (Horton et al. 2007), and known localization of orthologous plant proteins. Localization to other compartments was predicted using Uniprot (UniProt Consortium) and SignalP 5.0 (http://www.cbs.dtu.dk/services/SignalP/) (Almagro Armenteros et al. 2019) for localization to the extracellular space, mitochondria, and chloroplasts. Proteins were classified based on the functional categories as described by Bevan et al. (1998) and Miki et al. (2019). A list of protein accession identifications for all significantly increased and decreased proteins obtained by MS were assembled and used as inputs for STRING (version 11.0) to predict protein-protein interactions (Franceschini et al. 2016; Szklarczyk et al. 2019) for CA2 and CA6 timepoints. A predicted network was prepared and exported to Cytoscape (version 3.8.1) for further modification. Additional protein metadata was input into Cytoscape including corresponding log2 fold-change values which were assigned to node fill mapping. To construct a stress response meta-analysis network, individual protein accession identifications were subjected to literature searches (performed to 1/1/2021) and annotated according to their protein descriptions and involvement in stress response pathways (File S2). Proteins with no reported involvement in stress responses were omitted. The dataset was then input into Cytoscape with and log2 fold-changes were again selected as node fill mapping as described previously. All networks were centred in the plot area and exported as Scalable Vector Graphics (SVG) files where further modification was performed and legends added in Inkscape (version 0.92.2). Interactive versions of each network were additionally exported as full webpages for viewing in any modern web browser as HTML files with all metadata.

*Cellular localization predicted via PSORT (http://psort.ims.u-tokyo.ac.jp) and TargetP (http://www.cbs.dtu.dk/services/TargetP) programs.+Sequence homology (%) predicted via ClustalW program.#F, forward primer; R, reverse primer.

The resulting MS/MS data were processed using Maxquant search engine (v.1.5.2.8). Spectra search was performed against the Soybean genome sequence databases downloaded from the phytozome database (https://phytozome.jgi.doe.gov/pz/portal.html, containing 88,647 unigenes) concatenated with reverse decoy database. FDR of protein identification and PSM identification was set to 1%. To be considered diferentially expressed, proteins were required to exhibit a P value ≤ 0.05 calculated by the software. For protein abundance ratios measured using TMT, we considered a 1.3-fold change and a P value < 0.05 as the thresholds for identifying significant changes. Gene Ontology (GO) annotation proteome was derived from the UniProt-GOA database (www. http://www.ebi.ac.uk/GOA/). Firstly, Converting identified protein ID to UniProt ID and then mapping to GO IDs by protein ID. If some identified proteins were not annotated by UniProt-GOA database, the InterProScan soft would be used to annotated protein’s GO functional based on protein sequence alignment method. Then proteins were classified by Gene Ontology annotationbased on three categories: biological process, cellular component and molecular function. Identified proteins domain functional description were annotated by InterProScan (a sequence analysis application) based on protein sequence alignment method, and the InterPro domain database was used. InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Kyoto Encyclopedia of Genes and Genomes (KEGG)database was used to annotate protein pathway. Firstly, using KEGG online service tools KAAS to annotated protein’s KEGG database description. Then mapping the annotation result on the KEGG pathway database using KEGG online service tools KEGG mapper. We used wolfpsort a subcellular localization predication soft to predict subcellular localization. Wolfpsort an updated version of PSORT/PSORT II for the prediction of eukaryotic sequences. Proteins were classified by GO annotation into three categories: biological process, cellular compartment and molecular function. For each category, a two-tailed Fisher’s exact test was employed to test the enrichment of the differentially expressed protein against all identified proteins. The GO with a corrected p-value < 0.05 is considered significant. Encyclopedia of Genes and Genomes (KEGG) database was used to identify enriched pathways by a two-tailed Fisher’s exact test to test the enrichment of the differentially expressed protein against all identified proteins. The pathway with a corrected p-value < 0.05 was considered significant. These pathways were classified into hierarchical categories according to the KEGG website. For each category proteins, InterPro (a resource that provides functional analysis of protein sequences by classifying them into families and predicting the presence of domains and important sites) database was researched and a two-tailed Fisher’s exact test was employed to test the enrichment of the differentially expressed protein against all identified proteins. Protein domains with a p-value < 0.05 were considered significant. For further hierarchical clustering based on different protein functional classification (such as: GO, Domain, Pathway, Complex). We first collated all the categories obtained after enrichment along with their P values, and then filtered for those categories which were at least enriched in one of the clusters with P value

Secretory factors in animals play an important role in communication between different cells, tissues and organs. Especially, the secretory factors with specific expression in one tissue may reflect important functions and unique status of that tissue in an organism. In this study, we identified potential tissue-specific secretory factors in the fat, muscle, heart, lung, kidney and liver in the mouse by analyzing microarray data from NCBI’s Gene Expression Omnibus (GEO) public repository and searching and predicting their subcellular location in GeneCards and WoLF PSORT, and then confirmed tissue-specific expression of the genes using semi-quantitative PCR reactions. With this approach, we confirmed 11 lung, 7 liver, 2 heart, 1 heart and muscle, 7 kidney and 2 adipose and liver-specific secretory factors. Among these genes, 1 lung-specific gene - CTLA2A (cytotoxic T lymphocyte-associated protein 2 alpha), 3 kidney-specific genes - SERPINA1F (serpin peptidase inhibitor, Clade A, member 1F), WFDC15B (WAP four-disulfide core domain 15B) and DEFB29 (defensin beta 29) and 1 liver-specific gene - MUP19 (major urinary protein 19) have not been reported as secretory factors. These genes were tagged with hemagglutinin at the 3’end and then transiently transfected to HEK293 cells. Through protein detection in cell lysate and media using Western blotting, we verified secretion of the 5 genes and predicted the potential pathways in which they may participate in the specific tissue through data analysis of GEO profiles. In addition, alternative splicing was detected in transcripts of CTLA2A and SERPINA1F and the corresponding proteins were found not to be secreted in cell culture media. Identification of novel secretory factors through the current study provides a new platform to explore novel secretory factors and a general direction for further study of these genes in the future.

aF and R represent the forward and reverse directions on the chromosome, respectively.bWoLF PSORT. N, nucleus; C, chloroplast; c, cytoplasm; V, vacuole; E, endoplasmic reticulum; M, mitochondria; n.a., not available.In total, 30 CrPUB proteins were obtained by BLASTP search using the C. reinhardtii V5.5 proteome database and PUB proteins from Arabidopsis thaliana and Oryza sativa as queries. The 30 CrPUB genes were named based on their chromosome position. The molecular weights and pIs of the 30 CrPUB proteins were predicted using ExPASy. The CrPUB sub-cellular locations were predicted using the WOLF PSORT program.List of the 30 U-box genes identified in C. reinhardtii and their sequence characteristics.