Abstract

   (1) Background: Diabetic cardiomyopathy (DCM) is a unique form of
   cardiomyopathy that develops as a consequence of diabetes and
   significantly contributes to heart failure in patients. Esaxerenone, a
   selective non-steroidal mineralocorticoid receptor antagonist, has
   demonstrated potential in reducing the incidence of cardiovascular and
   renal events in individuals with chronic kidney and diabetes disease.
   However, the exact protective effects of esaxerenone in the context of
   DCM are still unclear. (2) Methods: The DCM model was successfully
   induced in mice by administering streptozotocin (55 mg/kg per day) for
   five consecutive days. After being fed a normal diet for 16 weeks,
   echocardiography was performed to confirm the successful establishment
   of the DCM model. Subsequent sequencing and gene expression analysis
   revealed significant differences in gene expression in the DCM group.
   These differentially expressed genes were identified as potential
   targets for DCM. By utilizing the Swiss Target Prediction platform, we
   employed predictive analysis to identify the potential targets of
   esaxerenone. A protein–protein-interaction (PPI) network was
   constructed using the common targets of esaxerenone and DCM. Enrichment
   analysis was conducted using Metascape. (3) Results: Compared to the
   control, the diabetic group exhibited impaired cardiac function and
   myocardial fibrosis. There was a total of 36 common targets, with 5 key
   targets. Enrichment analysis revealed that the chemokine and PI3K-Akt
   signaling pathway was considered a crucial pathway. A target-pathway
   network was established, from which seven key targets were identified.
   All key targets exhibited good binding characteristics when interacting
   with esaxerenone. (4) Conclusion: The findings of this study suggest
   that esaxerenone exhibits a favorable therapeutic effect on DCM,
   primarily by modulating the chemokine and PI3K-Akt signaling pathway.

   Keywords: diabetic cardiomyopathy, esaxerenone, pharmacological
   mechanism, therapy, network pharmacology

1. Introduction

   According to statistics, diabetes patients have a significantly
   elevated risk of developing cardiovascular disease in comparison to the
   general population. Among diabetes patients, 65% eventually die from
   cardiovascular disease. Diabetic cardiomyopathy, characterized by
   myocardial fibrosis, poses a threat to patients with diabetes
   [[36]1,[37]2]. However, there is currently no effective treatment for
   diabetic cardiomyopathy (DCM), making it crucial to study its
   pathogenesis and identify key targets for the prevention and delay of
   disease progression.

   The mineralocorticoid receptor (MR) is a nuclear receptor activated by
   steroid hormones. A substantial amount of clinical evidence suggests
   that blocking MRs is an important therapeutic option for heart failure
   and chronic heart diseases, including complications associated with
   diabetes [[38]3]. Although there are currently two steroidal MR
   antagonists, spironolactone and eplerenone, available in clinical
   practice, their use has not been accepted due to the side effects they
   can cause. Spironolactone can lead to menstrual irregularities, while
   eplerenone can cause hyperkalemia. As a result, their clinical use is
   not yet widely adopted [[39]4].

   Esaxerenone, which is a new non-steroidal MR antagonist, improves
   endothelial dysfunction caused by diabetes by promoting eNOS
   phosphorylation in the DCM group [[40]5,[41]6]. Several clinical
   studies have reported that esaxerenone has significant antihypertensive
   and cardiorenal protective effects compared with spironolactone and
   eplerenone [[42]7,[43]8]. Pharmacological studies have shown that due
   to its inverted side-chain structure, esaxerenone has an affinity for
   MRs that is over 1000 times higher than other NR3C nuclear receptors.
   Additionally, the binding site of esaxerenone is larger and penetrates
   deeper into the protein core, making its inhibitory effect more potent
   and longer-lasting compared with that of spironolactone and eplerenone
   [[44]9,[45]10]. However, there is still limited research on the impact
   of esaxerenone on patients with DCM.

   In our study, we aimed to establish the DCM model and utilize
   sequencing and network-pharmacology methods to identify key targets.
   Additionally, enrichment analysis was conducted to determine the main
   pathways through which these proteins act in the treatment of DCM with
   esaxerenone. The aim of this research is to provide treatment evidence
   for clinical studies.

2. Materials and Methods

2.1. Experimental Animals

   In our study, the mice (male C57BL/6J mice) were housed with a
   temperature range of 21–23 °C and humidity maintained at 40–60%. The
   mice were acclimated for 2 weeks before the experiment. They were
   randomly divided into two groups, with 6 mice in each group. To induce
   the DCM model, the DCM group mice were treated with streptozotocin
   (STZ, Sigma-Aldrich, St Louis, MO, USA) at 55 mg/kg per day for 5 days
   intraperitoneally. STZ was dissolved in citrate buffer, and the control
   group mice received injections of the vehicle (citrate buffer) for 5
   days. One week after the injections, tail vein blood samples were
   randomly collected from the mice, and their glucose levels were
   measured from Roche, Indianapolis, IN. Mice glucose levels equal to or
   higher than 16.7 mmol/L were considered to have successfully developed
   the diabetic model. Following the establishment of the model, both
   groups of mice were continuously fed a normal diet for 16 weeks. The
   mice were anesthetized with 1.5% isoflurane (30 mg/kg) and euthanized.
   The myocardial tissue was collected for subsequent experiments.
   Specimens from the middle part of the left ventricle were fixed in 4%
   paraformaldehyde for histopathological analysis. The remaining
   left-ventricular tissue was stored at −80 °C, and the cardiac apex
   tissue was used for RNA sequencing analysis. The protocol was approved
   by the Experimental Animal Committee of the Third Affiliated Hospital
   of Southern Medical University, Guangzhou, Guangdong, China. A flow
   chart of the animal study is shown in [46]Figure 1.

Figure 1.

   [47]Figure 1
   [48]Open in a new tab

   Scheme of investigating esaxerenone in treatment of diabetic
   cardiomyopathy.

2.2. Echocardiographic Assessment

   After 16 weeks of establishing the diabetes model, mice were sedated
   with 1.5% isoflurane and placed in the supine position, and cardiac
   function was evaluated using the Vevo 2100 system (Fujifilm Visual
   Sonics, Toronto, ON, Canada). M-mode-and-B-mode echocardiography was
   performed to measure various parameters related to cardiac function.

2.3. Histological Staining

   The hearts from two experimental groups, using a 4% paraformaldehyde
   solution, were embedded in paraffin and sectioned. The sections were
   then subjected to staining procedures, including Masson’s staining
   (performed using Sigma-Aldrich, Burlington, MA, USA) and HE staining
   (performed using Sigma-Aldrich, Burlington, MA, USA). By analyzing the
   stained sections using ImageJ1.8.0 (Media Cybernetics Inc., Rockville,
   Maryland, USA), researchers were able to quantify and measure specific
   features of left-ventricular remodeling, providing insights into the
   structural changes occurring in the hearts of the experimental group’s
   RNA sequencing (RNA-seq).

   RNA sequencing was performed on two groups of mouse apical cardiac
   tissues stored at −80 °C. Total RNA was gained using the RNeasy Mini
   Kit (250) manufactured by Qiagen, Hilden, Germany. Three replicates
   from both the control and DCM groups underwent quality control and RNA
   quantification. Strand-specific libraries were prepared and sequenced
   (Illumina NovaSeq 6000, San Diego, CA, USA). The raw sequencing data
   underwent initial processing. The sequences were then aligned to
   reference genes, and differentially expressed genes were identified
   based on criteria including a p-value and a fold change (FC).
   Differentially expressed genes with FPKM < 1 in each group were
   excluded.

2.4. Data Analysis

   The continuous data were reported as mean ± standard error of the mean
   (SEM). Comparison was performed using a one-way analysis of variance
   (ANOVA), followed by a post hoc Bonferroni test in case of significant
   interaction in ANOVA. A p-value < 0.05 was considered statistically
   significant. Statistical analysis was performed using GraphPad Prism
   7.0 software (GraphPad Software Inc., San Diego, CA, USA).

2.5. Network Pharmacology

2.5.1. Potential Targets of Esaxerenone

   The 2D structure of esaxerenone was obtained from PubChem and gained
   the targets of esaxerenone from the Swiss Target Prediction platform
   ([49]http://www.swisstargetprediction.ch/, accessed on 21 August 2023)
   [[50]11]. Targets with a probability greater than 0.1 were filtered out
   to refine the prediction results. All target names were standardized
   according to Uniprot ([51]http://www.uniprot.org/, accessed on 21
   August 2023) [[52]12].

2.5.2. Construction of PPI Network

   We utilized an online tool called Interactive Venn to generate a Venn
   diagram, where the overlapping regions represent the typical targets of
   esaxerenone and DCM [[53]13]. The identified targets were then uploaded
   to the STRING 11.0 platform ([54]http://string-db.org/, accessed on 25
   August 2023), and a protein–protein-interaction (PPI) network was
   generated. This PPI network provided a comprehensive view of the
   potential protein interactions and functional associations among the
   identified targets [[55]14].

2.5.3. Enrichment Analysis

   The Metascape platform was used to perform enrichment analysis
   ([56]https://metascape.org/, accessed on 15 September 2023) [[57]15].
   The top-20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (p <
   0.05) and top-10 Gene Ontology (GO) items were selected for analysis.
   The results were further processed using EHBIO for visualization.

2.5.4. Construction of Target-Pathway Network

   We uploaded the results of enrichment analysis to Cystoscopes 3.7.2
   [[58]16] to identify the core targets for intervention in DCM.
   Cystoscopes 3.7.2 revealed the interactions between components,
   targets, and pathways, providing insights into potential therapeutic
   targets.

2.5.5. Molecular Docking Verification

   The 2D structure information of esaxerenone was gained from the PubChem
   database [[59]17]. The candidate targets were found on the PDB database
   to download their 3D structures [[60]18]. Autodock Vina was employed
   for saving pdbqt-format files, which are compatible with molecular
   docking software [[61]19]. The molecular docking verification was
   visualized using PyMOL 2.4.025 and Discovery Studio 2019 [[62]20]. A
   flow chart of the network-pharmacology study is shown in [63]Figure 1.

3. Results

3.1. Myocardial Injury in DCM Mice

   Normal and diabetic-cardiomyopathy mice were used to evaluate cardiac
   function ([64]Figure 2A). Through echocardiography ([65]Figure 2B), the
   contraction and relaxation functions of the mouse heart were evaluated.
   The DCM group showed notable impairment in ventricular contraction
   function compared to the control group, manifested by a decrease in
   both fractional shortening (FS%) and ejection fraction (EF%).
   Additionally, the DCM group showed poorer stroke volumes (SV),
   end-systolic left-ventricular internal diameter (LVESD), and
   end-systolic left-ventricular volume (LVESV), indicating the successful
   establishment of the DCM mouse model ([66]Figure 2C).

Figure 2.

   [67]Figure 2
   [68]Open in a new tab

   Echocardiographic variables and cardiac fibrosis in mice. (A)
   Experimental animals. (B) The quantitative results of echocardiography.
   (C) Results are shown as the mean ± SEM (n = 6). Significance: **** p <
   0.0001. (D) HE staining in 2 groups. (E) Masson’s trichrome staining in
   2 groups. (F) Measurements of fibrotic area in cardiac cross sections
   in 2 groups of mice.

   Tissue samples were collected from the mice and subjected to HE
   staining and Masson’s staining. [69]Figure 2D demonstrates that the
   cardiomyocytes in the DCM group exhibited significant sparsity and
   hypertrophy, while the myocardial fibers appeared fragmented and
   disorganized. Additionally, [70]Figure 2E,F reveal that collagen fiber
   deposition was enhanced in the DCM group (p < 0.0001) compared to the
   control group, as observed through Masson’s trichrome staining.

3.2. Candidate Targets of Esaxerenone and DCM

   In the RNA sequencing analysis of root tip tissue in the DCM group, a
   total of 24,197 genes were detected, among which 375 genes showed
   differential expression, with 74 upregulated genes and 301
   downregulated genes ([71]Figure 3A). Through screening of literature
   and online databases, a total of 104 candidate genes for esaxerenone
   were identified. A total of 36 common genes were identified between
   esaxerenone and DCM ([72]Figure 3B). Then, a heatmap was generated
   using these common genes ([73]Figure 3C).

Figure 3.

   [74]Figure 3
   [75]Open in a new tab

   Common targets of esaxerenone in the treatment of DCM. (A) The scatter
   plot displays the gene expression differences in 2 groups. The
   horizontal and vertical axes represent the samples from the control and
   DCM groups, respectively. Upregulated genes in the DCM group are shown
   in red, downregulated genes in blue, and insignificant genes in gray.
   (B) The Venn diagram illustrates the overlap between DCM-related
   targets and esaxerenone-related targets. The pink section represents
   DCM-related targets, while the blue section represents
   esaxerenone-related targets. There are 36 common targets in the
   overlapping section. (C) The heat map visualizes the
   differential-gene-expression patterns. Each row represents a
   differential gene, each column represents a mouse sample, and each
   group consists of 3 replicates. (D) The PPI network consists of 32
   target proteins and 106 interacting edges. The node sizes reflect the
   degree values, with larger nodes indicating higher degrees of
   connectivity. (E) The PPI network analysis identified a potential
   protein functional module with the highest score.

3.3. PPI Network

   In order to explore the target genes’ functions and identify
   connections in complex diseases, we submitted the overlapping targets
   to the STRING website to construct a PPI. After removing disconnected
   nodes, the resulting network consisted of 32 nodes and 106 edges. The
   topological parameters were analyzed, and the 32 targets were ranked
   based on their degrees. They were then arranged into concentric
   circles, as shown in [76]Figure 3D. To gain further insights into the
   mechanism of esaxerenone in treating DCM, MCODE was used to perform
   cluster analysis. This analysis identified a potential protein
   functional module with the highest score, as depicted in [77]Figure 3E.
   Proteins within such a module are believed to have closer relationships
   and may interact with each other to perform specific biological
   functions. Therefore, these targets were predicted to be important,
   including PIK3CA, PIK3CB, CDK4, JAK3, CCR2, CCR3, and CXCR3. These
   targets have the potential to play crucial roles in the mechanism of
   action of esaxerenone in the treatment of DCM.

3.4. Enrichment Analysis

   To gain insights into the biological effects of esaxerenone related to
   the treatment of DCM, we conducted GO functional analysis and
   KEGG-pathway enrichment analysis. [78]Figure 4 displays the top-10 GO
   terms and top-20 KEGG pathways, which were selected based on their
   p-values.

Figure 4.

   [79]Figure 4
   [80]Open in a new tab

   Enrichment analysis. (A) GO functional analysis. Biological process
   items in GO analysis enriched for up- and downregulated genes. (B) The
   KEGG-pathway enrichment analysis represents the sizes and color of the
   bubbles, indicating the potential targets associated with each pathway.

   For biological processes, the targets were mainly enriched in the
   chemokine-mediated signaling pathway, phosphatidylinositol-3-phosphate
   biosynthetic process, peptidyl-threonine phosphorylation, and
   amyloid-beta formation. For cellular components, the enrichment
   analysis revealed that the targets were predominantly associated with
   protein kinase activity, kinase activity, and protein tyrosine kinase
   activity. In terms of molecular functions, the targets were primarily
   enriched in receptor complexes, phosphatidylinositol 3-kinase
   complexes, extrinsic components of the membrane, and glutamatergic
   synapses. The KEGG-pathway analysis indicated that the majority of the
   pathways involved were associated with the chemokine signaling pathway,
   PI3K-Akt signaling pathway, neurotrophin signaling pathway, and ErbB
   signaling pathway. Notably, the chemokine signaling pathway exhibited a
   considerable concentration of targets, emphasizing the significance of
   the chemokine-mediated signaling pathway in the context. Additionally,
   biological processes involved the chemokine-mediated signaling pathway
   and phosphatidylinositol-3-phosphate biosynthetic process. The cellular
   component involved phosphatidylinositol kinase activity, and molecular
   functions involved the phosphatidylinositol 3-kinase complex.
   Therefore, we thought that the chemokine signaling pathway and PI3K-Akt
   signaling pathway should be important.

3.5. Compound-Pathway Network

   To establish a target-pathway network, we connected potential pathways
   and hub genes based on the results of the KEGG analysis. This network
   provides a visual representation of the relationships between the
   enriched pathways and the genes involved ([81]Figure 5). Among the
   important targets identified through the PPI network, the average
   degree of connectivity for the included targets was 7.36. PIK3CA
   (Degree = 20), PIK3CB (Degree = 20), CDK4 (Degree = 7), and JAK3
   (Degree = 7) were enriched in the PI3K-Akt signaling pathway, and CXCR3
   (Degree = 7), CCR2 (Degree = 7), and CCR3 (Degree = 7) were enhanced in
   the chemokine signaling pathway.

Figure 5.

   [82]Figure 5
   [83]Open in a new tab

   The compound-pathway network has 41 points and 151 lines. The targets
   are represented by pink squares, while the pathways are represented by
   20 purple V-shapes. The sizes of the square nodes are arranged in
   degree values, from large to small.

3.6. Molecular Docking Result Analysis

   In this study, we performed molecular docking to validate the
   interaction between the core genes and fibrosis. It is commonly
   accepted that a lower energy value indicates a more stable conformation
   of the ligand–receptor binding and a higher likelihood of interaction.
   We conducted docking simulations, and the majority of the binding
   complexes showed strong binding affinities, with an average energy of
   −7.14 kcal/mol. The modes of eight binding complexes were displayed in
   [84]Figure 6, including esaxerenone-PI3KCA(7bi4) docking (−8.0
   kcal/mol), esaxerenone-CXCR3(6wzl) docking (−5.9 kcal/mol),
   esaxerenone-PI3KCB(4v0i) docking (−6.7 kcal/mol),
   esaxerenone-CDK4(3g33) docking (−7.5 kcal/mol), esaxerenone-JAK3(6dap)
   docking (−6.3 kcal/mol), esaxerenone-CCR2(5t1a) docking (−6.6
   kcal/mol), and esaxerenone-CCR3(7x9y) docking (−7.7 kcal/mol). Using
   the esaxerenone-PI3KCA docking as an example, it is observed that the
   small-molecule ligand esaxerenone potentially binds to the interface
   pocket created by the interaction of protein amino acid residues
   ([85]Figure 6A(a)). As shown in [86]Figure 6A(b), a hydrogen bond was
   formed between esaxerenone and THRB:471, near the active site of
   PI3KCA. The other essential residues (TYRB:470, GLNB:475, GLNB:475,
   SERB:474, ASNA:467, LYSA:678, HOHA:1296, ASNA:465, HISA:450, TYRB:467,
   PROA:449, PROA:447, TRPA:424, VALA:448, TBPA:446, THRA:678, GLYA:1009,
   ASNA:677, and HISA:676) interacted with esaxerenone through van der
   Waals forces, pi-alkyl interaction, alkyl, carbon–hydrogen bond, and
   water hydrogen bond. For esaxerenone-CXCR3, the essential residues
   (HOHD:349, ARGD142, GLUD:143, HOHD:337, PROD:141, HOHD:303, GLND:199,
   TYRD140, GLUD:105, LYSD:107, SERD:12, THRD:10, PROD:8, and LEUD:11)
   interacted with esaxerenone through van der Waals, water hydrogen bond,
   conventional hydrogen bond, carbon–hydrogen bond, unfavorable
   donor–donor, amide-pi stacked, alkyl, pi-alkyl, and halogen (fluorine).
   For esaxerenone-PI3KCB, the essential residues (LYSN:598, PHEB:595,
   ARGB:601, SERB:594, GLUB:628, CYSB:590, METB:387, CYSB:569, LEUB:488,
   TYRB:591, ASPB:626, and CYSB:627) interacted with esaxerenone through
   van der Waals, conventional hydrogen bond, carbon–hydrogen bond,
   halogen (fluorine), unfavorable donor–donor, pi-sulfur, alkyl, and
   pi-alkyl. For esaxerenone-CDK4, the essential residues (TYRA:170,
   ALAA:21, TYRA:22, ARGA:186, ASPA:145, VALA:190, ARGA:144, LEUA:183,
   GLUA:224, VALA:181, and PROA:178) interacted with esaxerenone through
   van der Waals, conventional hydrogen bond, halogen (fluorine), Pi-Anoon
   alkyl, and pi-alkyl. For esaxerenone-JAK3, the essential residues
   (PROA:896, SERA:860, GLNA:896, LEUA:898, PHEA:868, LEUA:857, GLNA:856,
   ARGA:899, GLUA:819, and GLNA:858 interacted with esaxerenone through
   van der Waals, conventional hydrogen bond, carbon–hydrogen bond,
   halogen (fluorine), unfavorable donor–donor, pi-sulfur, alkyl, and
   pi-alkyl. For esaxerenone-CCR2, the essential residues (PROA:896,
   LEUA:133, ILEA:217, LEUA:213, ILEA:132, PHEA:129, VALA:167, LEUA:209,
   ILEA:163, THRA:164, THRA:160, VALA:159, and PHEA:156 interacted with
   esaxerenone through van der Waals, halogen (fluorine), pi-sigma, alkyl,
   and pi-alkyl. For esaxerenone-CCR3, the essential residues (PROA:896,
   LEUA:133, ILEA:217, LEUA:213, ILEA:132, PHEA:129, VALA:167, LEUA:209,
   ILEA:163, THRA:164, THRA:160, VALA:159, and PHEA:156 interacted with
   esaxerenone through van der Waals, halogen (fluorine), pi-sigma, alkyl,
   and pi-alkyl. Hydrogen bonds and other types of interactions played a
   vital role in facilitating the stable binding of small molecules to the
   active sites of their target proteins. These interactions were crucial
   for maintaining the structural integrity and functional specificity of
   the binding complexes.

Figure 6.

   [87]Figure 6
   [88]Open in a new tab

   Molecular docking. (A) The diagram displays conformations obtained from
   the molecular docking simulation. The 3D models illustrate
   esaxerenone’s molecular structure within the protein’s binding pocket,
   represented as orange sticks. The surrounding amino acid residues are
   depicted in a surface style. The interactions between the esaxerenone
   and the neighboring residues are visually depicted in the 2D diagrams.
   (B) The docking affinity of the column diagram.

4. Discussion

   In individuals with diabetes, DCM stands as a prominent contributor to
   both heart failure and mortality, and currently, there is no recognized
   treatment for it. The development of DCM is influenced by factors such
   as increased secretion in mineralocorticoid hormones due to high blood
   glucose and insulin resistance. The current treatment methods include
   ACE inhibitors/ARBs [[89]21], guanosine monophosphate cyclohydrolase
   stimulators, sodium-glucose co-transporter 2 inhibitors [[90]22], MR
   antagonists, or modulation of T-cells [[91]23,[92]24,[93]25,[94]26].
   These treatments can reduce the incidence and mortality rate.
   Currently, the use of non-steroidal MR antagonists is being evaluated
   for the treatment of heart failure, both as standalone therapy and in
   combination with sodium-glucose co-transporter 2 inhibitors. These
   groundbreaking drugs have the potential to become important therapies
   for various cardiac and renal diseases. Esaxerenone has shown promising
   cardiovascular effects in patients with diabetes. However, additional
   investigation is needed to pinpoint the precise treatment targets and
   mechanisms associated with DCM. In our study, we utilized the
   network-pharmacology method to predict and elucidate the potential
   molecular mechanisms of action of esaxerenone in DCM.

   To delve into the core constituents and action mechanism of esaxerenone
   in DCM treatment, a PPI network was established for 36 therapeutic
   targets. Utilizing topology analysis on the PPI network, a total of
   seven key pathogenic genes were identified, namely PIK3CA, PIK3CB,
   CDK4, JAK3, CCR2, CCR3, and CXCR3. Among them, PIK3CA and PIK3CB
   correspond to the genes encoding the alpha subunit of
   phosphatidylinositol 3-kinase. Frequent mutations in PIK3CA and PIK3CB
   have been identified in patients with DCM. These mutations have been
   found to be associated with myocardial fibrosis and cardiac dysfunction
   [[95]27]. Additionally, studies have shown that excessive activation of
   PIK3CA and PIK3CB may lead to increased myocardial cell proliferation
   and inflammation, exacerbating the pathological process of DCM
   [[96]28]. Playing a pivotal role in governing the progression of the
   cell cycle, particularly in cellular proliferation and division, CDK4
   emerges as a vital protein [[97]29]. Excessive activation of CDK4 has
   been implicated in the development of myocardial fibrosis and cardiac
   dysfunction. Research has demonstrated that inhibiting CDK4 can
   attenuate the extent of myocardial fibrosis and improve cardiac
   function in animal models of DCM [[98]30]. JAK3 (Janus kinase 3) is a
   member of the Janus kinase family of proteins that play a crucial role
   in signal transduction pathways involved in inflammation and immune
   responses [[99]31]. Activation of the JAK/STAT pathway has been
   observed in various cardiovascular diseases, including DCM, and is
   associated with inflammation, oxidative stress, and fibrosis, which are
   key pathological processes in DCM [[100]32]. Inhibition of JAK/STAT
   signaling has shown promising results in attenuating cardiac
   dysfunction and fibrosis in animal models of DCM. Although the exact
   role of JAK3 in DCM is not fully elucidated, targeting the JAK/STAT
   pathway, including JAK3, has emerged as a potential therapeutic
   strategy [[101]33]. Both CCR2 and CCR3 are chemokine receptors that
   play a role in the recruitment and activation of immune cells in
   inflammatory conditions [[102]34]. This upregulation is associated with
   increased infiltration in monocytes, macrophages, and eosinophils into
   the cardiac tissue, leading to chronic inflammation.
   CCR2-and-CCR3-mediated inflammatory responses actively contributes to
   the generation of pro-inflammatory cytokines, oxidative stress, and
   fibrosis, all of which represent significant pathological
   characteristics observed in DCM [[103]35]. CXCR3 is another chemokine
   receptor that plays a role in immune cell recruitment and activation.
   Our studies have shown that CXCR3 is upregulated in the hearts of
   diabetic animals and patients with DCM. This upregulation is associated
   with increased infiltration in immune cells, such as T-cells and
   macrophages, into the cardiac tissue [[104]36]. The activation of CXCR3
   leads to the release of pro-inflammatory chemokines, such as CXCL9 and
   CXCL10, which further promote the recruitment of immune cells and
   contribute to the chronic inflammation observed in DCM
   [[105]37,[106]38].

   Among the various pathways analyzed using KEGG-pathway enrichment
   analysis, the chemokine and the PI3K-Akt signaling pathway emerged as
   the most prominent, displaying significant roles in relation to the
   study of interest ([107]Figure 7). Furthermore, by constructing a
   compound-pathway network, it was observed that all seven targets were
   significantly enriched in both the chemokine and the PI3K-Akt signaling
   pathway. This reaffirms the potential efficacy of esaxerenone treatment
   in DCM by highlighting the favorable impact of the chemokine signaling
   pathway and the PI3K-Akt signaling pathway. So far, there is
   considerable clinical evidence to suggest that esaxerenone, as an MR
   inhibitor, can significantly improve the prognosis of patients with
   diabetes [[108]39,[109]40,[110]41]. The chemokine signaling pathway is
   significant in the pathogenesis of DCM. Chemokines are small signaling
   proteins that regulate the migration and activation of immune cells
   [[111]42,[112]43]. In DCM, chronic inflammation and immune cell
   infiltration contribute to the development and progression of cardiac
   dysfunction and fibrosis. The involvement of the PI3K signaling pathway
   in cardiac pathology is well established, and changes in the expression
   and activity of PI3K have a significant influence on the progression of
   diabetic cardiomyopathy [[113]44,[114]45]. Research findings have
   demonstrated that esaxerenone exhibits the potential to decrease the
   occurrence and fatality rate of diabetic nephropathy and cardiovascular
   events, enhance myocardial hypertrophy and interstitial fibrosis, and
   slow down the pathological advancement of left-ventricular systolic
   dysfunction [[115]39,[116]46]. Similarly, in patients with diabetic
   cardiomyopathy, MR inhibitors not only prevent left-ventricular
   remodeling and expansion of fibrotic areas but also protect against
   myocardial injury [[117]46].

Figure 7.

   [118]Figure 7
   [119]Open in a new tab

   Chemokine signaling pathway and PI3K-Akt signaling pathway influenced
   by esaxerenone. The red nodes represent the hub genes; the other nodes
   are the genes of pathway.

Limitations of the Study

   This study solely relied on network pharmacology to predict the
   molecular mechanisms of MR antagonist treatment for DCM, specifically
   involving chemokines and the PI3K-Akt signaling pathway. The main
   limitation of this study is the lack of experimental validation to
   verify the accuracy and reliability of these predictions. While
   previous research has established a clear association between the
   pathogenesis of heart failure and the overexpression of MR and
   aquaporin-1, supported by relevant clinical studies, the mechanism
   through which the benefit of inhibitors of MR is still unclear for DCM
   [[120]47,[121]48]. Additionally, we are aware that the guanosine
   monophosphate (GMP) cyclohydrolase pathway plays a role, but the
   pathway we predicted did not enrich the GMP pathway.

   However, from the perspective of practicing physicians, these research
   findings can still provide some guidance for clinical practice.
   Firstly, esaxerenone can be considered one of the drug choices for
   treating diabetic cardiomyopathy. Clinicians can incorporate
   esaxerenone into treatment regimens and personalize the treatment based
   on individual patient characteristics. Secondly, chemokines and the
   PI3K-Akt signaling pathway may be the target of esaxerenone’s effects.
   Therefore, clinicians can focus on the activity of these signaling
   pathways and related biomarkers when evaluating patients with diabetic
   cardiomyopathy. This can help better understand the patient’s condition
   and provide more accurate evidence for treatment plan formulation.
   Finally, we believe that it is necessary to experimentally determine
   the pathway through which this drug treats diabetic cardiomyopathy, and
   this will be the focus of our next study.

5. Conclusions

   In our study, we utilized a DCM model and employed RNA sequencing and
   network-pharmacology approaches to investigate the molecular mechanisms
   underlying the therapeutic effects of esaxerenone in DCM treatment. By
   analyzing a complex network, we identified key targets and highlighted
   the involvement of the chemokine and PI3K-Akt signaling pathway in
   mediating the therapeutic effects of esaxerenone. Our initial
   predictions also indicated promising interactions between esaxerenone
   and targets within these pathways. These preliminary findings provide
   scientific evidence supporting the potential clinical application of
   esaxerenone in DCM treatment. Further research is warranted to explore
   these findings in more detail.

Abbreviations

   STZ: streptozotocin; DCM: diabetic cardiomyopathy; MR:
   mineralocorticoid receptor; FC: fold change; ANOVA: analysis of
   variance; PPI: protein–protein interaction; LVESV: left-ventricular
   end-systolic volume; SV: stroke volume; LVESD: left-ventricular
   end-systolic dimension; FS: fractional shortening; EF: ejection
   fraction. PIK3CA: phosphatidylinositol-4, 5-bisphosphate 3-kinase
   catalytic subunit alpha; PIK3CB: phosphatidylinositol-4,5-bisphosphate
   3-kinase catalytic subunit beta; CDK4: cyclin-dependent kinase 4; JAK3:
   janus kinase-3; CCR2: C-C chemokine receptor type 2; CCR3: C-C
   chemokine receptor type 3; CXCR3: CXC chemokine receptor 3; GO: Gene
   Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes.

Author Contributions

   Conceptualization, Z.L. and H.Z.; methodology, W.Z.; software, Z.Y.;
   validation, Z.L., H.Z. and W.Z.; formal analysis, J.Y.; investigation,
   Z.L.; resources, Z.L.; data curation, Z.L.; writing—original draft
   preparation, Z.L.; writing—review and editing, Z.L.; visualization,
   S.L.; supervision, W.H.; project administration, W.H.; funding
   acquisition, W.H. All authors have read and agreed to the published
   version of the manuscript.

Institutional Review Board Statement

   The animal study protocol was approved by the Experimental Animal
   Committee of the Third Affiliated Hospital of Southern Medical
   University (protocol code SMUL2021013).

Informed Consent Statement

   Not applicable.

Data Availability Statement

   The data presented in this study are available on request from the
   corresponding author.

Conflicts of Interest

   The authors declare no conflict of interest.

Funding Statement

   National Key R&D Program of China (2022YFB4600600), National Natural
   Science Foundation of China (31972915, 82073747, and 82300018), and
   China Postdoctoral Science Foundation (2021M701620).

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References