Figure 2. (A) The Venn diagram of identified proteins of rat hippocampus proteome, recovered by TPP technique in 3 groups: treated with DMSO, H2O, Celecoxib (20µM). (B) Drug-target database comparison based on celecoxib-targeted proteins within diverse species. In this inset plot, intersections between the databases are illustrated. The horizontal bar plot shows the total number of proteins in each database. The vertical bar plot indicates the number of proteins in each database uniquely and the different sets of the intersections. Drug Bank (DB), Super Target (ST), Drug Central (DC), Probes & Drugs portal (PDP), Drug Target Commons (DTC) are represented in this plot. The inset plot displays the characterized species in the mentioned databases. We scaled the word size by their frequency of corresponding protein targets of celecoxib in each species independently. (C) Rat-specific drug-target databases comparison based on celecoxib-targeted proteins along with the TPP-identified proteins (D) Homology network of TPP-identified proteins and reported targets of celecoxib in drug-target databases. The identified proteins in the present study are shown with a red triangle, and the proteins introduced by the databases are displayed with green circles. The thickness of the edges indicates the identity percentage of protein sequences in this study.
A comprehensive comparison of identified proteins in samples treated with celecoxib and two controls is shown in Fig. 2A. These proteins were soluble at 67°C, following the treatment in 20µM celecoxib, water, and DMSO, respectively, and finally detected by nano-LC-Thermo Q Exactive Plus Orbi-Trap MS. Water control treatment contains only protein samples without any other additional substances, and 351 proteins are detected in this subset. Also, 378 proteins were identified in the DMSO treatment (other negative control). Furthermore, 357 proteins were detected in the drug-treated sample, in which 44 proteins were specific to this subset. Fifteen out of all identified proteins are heat shock proteins (HSP), which indicate the intrinsic structural stability of these proteins across the high temperature [50]. The identified HSPs were shared with other groups, such as Heat shock protein HSP 90-beta and 60 kDa mitochondrial heat shock protein. Thus, we could infer that HSPs are not the particular targets of celecoxib.
We also examined the previously known targets of celecoxib according to five drug-target databases for all species (Fig. 2B) includingRattus norvegicus (rat) in particular (Fig. 2C). Then, we compared the TPP-identified proteins with the known targets of this drug in rats. Out of 242 already identified celecoxib targets for 24 species in all five databases, only 21 proteins are found in rat. Figure 2B-C shows the total number of proteins in each set by the horizontal bar plots. The vertical bar plot indicates the number of proteins in each database uniquely and the different set of the intersections, sorted by the frequency of targets. In this analysis, we selected five well-known drug-target databases, i.e., Drug Bank (DB) [51], Super Target (ST) [52], Probes & Drugs portal (PDP) [53], Chembl [54], and Drug Target Commons (DTC) [55, 56]. The DB database shows five targets for celecoxib of which one was related to the rat. The ST database and PDP suggests 41 and 45 proteins as a target of celecoxib of which three and five are expressed in the rat, respectively. Searching in DTC and Chembl databases, introduced 168 and 203 proteins in 24 species as a Celecoxib target and 17, and 16 of them are specified in the rat, respectively. In total, around 70% of the identified targets are related to human proteins and the proportion of rat-specific proteins is much lower, especially if we consider each database independently. It can imply the lack of complete information in rat species databases, avoiding a more comprehensive celecoxib target profile in rats. It should be considered that most of the introduced protein targets are associated with the COX protein family, and are involved in NSAID related pathways, i.e., inflammatory process, which is the explicit indication of this drug.
As shown in Figure 2B, the intersection of all databases contains only two human proteins, i.e., PDPK1, CA2 and one rat protein, Ptgs2, due to the cross-reference of the resources. Chembl and DTC are the most comprehensive drug target bioactivity resources based on manual curation (more than 1.9 million chemicals and 13,000 protein targets); therefore, it was expected that they have the highest number of intersected proteins for Celecoxib. At the same time the other databases used experimental evidence to explore targets of drugs. Only six proteins have been identified as Celecoxib targets using ST, DB, and PDP so far. On the other hand, the main subject of celecoxib studies is to study the effects of this drug on the heart and circulatory system; hence researchers focused on exploring new off-targets on related organs and tissues. Although Celecoxib can simply pass through the blood-brain barrier (BBB), its impacts on the brain and CNS have not been well described. Here, we focused on a minute part of CNS, i.e., the hippocampus; hence we did not anticipate to observe a high proportion of intersected protein targets with the other databases. However, we found a Ras-related protein Rab-2A as a shared celecoxib targeted protein between TPP-identified proteins and the PDP database. The high amount of expression of Rab-2A in the whole brain has been previously reported [57], which was helpful for our study (Fig. 2-supplementary Figure 2). This protein can be a clue to explain the association of Celecoxib with cancer-related pathways since Rab-2A is a cancer driver gene product, and it plays a role in promoting tumorigenesis [58].
We also investigated the homology of TPP-identified proteins with reported Celecoxib targets to explore structural similarities (Fig. 2D). The overall similarity of amino acid sequences in both protein groups was represented using a protein homology network. In this graph, the thickness of the edges indicates the amino acid identity percentages. There is a 665 and 3138 pairwise similarity with more than 25% and 10% thresholds. Thus, it can be concluded that several of TPP-identified proteins have a close homology with the previously reported celecoxib targeted proteins.
Furthermore, to characterize the related biological functions of the TPP-identified proteins, we implemented gene enrichment analysis using disease and pathway-related resources available in Enrichr (Fig. 3). The enriched annotations in DisGeNet database include muscular stiffness with the lowest adjusted p-value. Neurodegenerative diseases such as Alzheimer’s disease and epilepsy and breast cancer-related annotations are also highly enriched in these proteins. Therefore, it can be a clue for celecoxib to be a potential choice for add-on therapy in these diseases. We also assessed other resources such as MGI, HumanPhen, and PheWeb for exploring enriched phenotypic annotations in the TPP-identified list of 44 proteins. In these databases, terms such as Broad head, increased motor neuron number, Schizophrenia, psychotic disorders, acquired hemolytic anemias, and abnormal thrombopoiesis showed the lowest adjusted p-value. In the perspective of pathway enrichment analysis, mRNA processing, such as cytoplasmic ribosomal proteins and splicing factor Nova regulated synaptic proteins, were also enriched along with cancer-related pathways such as IL-3, PIK3-Akt-mTOR and G protein-mediated signaling pathways which have an importance in cancer, inflammation, and neurodegenerative diseases.