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.