3.1 Behavioral results
3.1.1. Effect of dimethyl fumarate in the reversal of CUMS-induced changes in open field test parameters
There were no significant differences in the number of crossings between the groups studied, showing that the model and the treatment with DMF and FLU did not alter the locomotor activity of the animals (Figure 1A).
The analysis of the number of rearings (Figure 1B) in the open field test by two-way ANOVA revealed a significant interaction between the factors “CUMS model” and “treatment” [F (3, 63) = 5.012, P=0.0035]. In this regard, we observed a significant increase in the number of rearings in CUMS+VEH group compared to CONT+VEH (P < 0.001). Additionally, CUMS+FLU and CUMS+DMF100 groups presented a significant reduction in this parameter when compared to CUMS+VEH group (P < 0.01).
In the evaluation of the number of groomings (Figure 1C), we observed a significant interaction between the factors [F (3, 64) = 5.667, P=0.0017]. CUMS+VEH group showed an increase in this parameter compared to the CONT+VEH (P <0.05). DMF treatment at both doses reversed this increase (P <0.05).
3.1.2 Effects of dimethyl fumarate in the reversal of CUMS-induced increase in the immobility time in the forced swimming test
Two-way ANOVA analysis demonstrated a significant interaction [F (3, 66) = 11.30, P<0.0001] between factors with significant effect of “CUMS model” [F (1, 66) = 4.104, P=0.0468] and “treatment” [F (3, 66) = 10.93, P<0.0001]. In Tukey’s test, a significant increase in immobility duration was observed in the group CUMS + VEH (P < 0.001) when compared to CONT+VEH. The treatment with DMF (50 and 100) and FLU was able to reverse the increase in immobility time (P< 0.001), as illustrated in Fig. 2.
3.1.3 Effects of dimethyl fumarate in the reversal of CUMS-induced decrease in the sucrose preference index in the Sucrose Preference Test
In addition to the immobility time, the CUMS modified another parameter indicative of depressive-like behavior, the sucrose preference index, which was significantly reduced when compared to the control (p <0.01), indicating anecdotal behavior. The CUMS + DMF50 and CUMS + DMF100 groups led to an increase in this index in relation to the CUMS + VEH group (p <0.001 and p <0.05, respectively), as shown in Figure 3. The interaction between the factors was significant F (3, 64) = 7,628, p = 0.0002.
3.1.4. Effect of dimethyl fumarate in the reversal of CUMS-induced memory alteration in novel object recognition test
In the evaluation of the recognition index, we observed a significant interaction between the factors [F (3, 66) = 13.01, P<0.0001]. CUMS + VEH group showed a decrease in this parameter compared to the CONT+VEH (P <0.001). The treatment with DMF at both doses (P<0.001) and FLU (P<0.01) was able to reverse this alteration caused by CUMS (Figure 4).
3.2 Neurochemical results
3.2.1 Effect of dimethyl fumarate in the reversal of CUMS-induced changes in astrocytic activation marker in hippocampus.
In CA1 area, GFAP expression was reduced in the CUMS group compared to the control (p <0.05) and treatment with DMF50 was able to reverse this reduction (p <0.05), as seen in Figure 5A [F (2, 10 ) = 8.16, p = 0.0079].
In CA3 [F (2, 9) = 12.89, p = 0.0023] and DG [F (2, 9) = 4.93, p <0.0001] areas, the GFAP expression was reduced by CUMS compared to the control (CA3: p <0.05 and DG: p <0.001), however the treatment with DMF50 did not interfere in this parameter, as seen in Figures 5B and 5C, respectively.
3.2.2 Effect of dimethyl fumarate in the reversal of CUMS-induced changes in microglial activation marker in hippocampus.
In the three studied areas CA1 (Figure 6A), CA3 (Figure 6B) and DG (Figure 6C), the expression of Iba1 was increased in the CUMS group in relation to the CONT+VEH (p <0.001) and treatment with DMF50 was able to reverse this increase (p <0.001). CA1: F (2, 13) = 25.89, p <0.0001, CA3: F (2, 13) = 31.58, p <0.0001 and DG: F (2, 11) = 76.90, p <0.0001.
3.2.3 Effect of dimethyl fumarate in the reversal of CUMS-induced increase in the concentration of the pro-inflammatory cytokines TNF-α and IL-1β in hippocampus.
CUMS induced an increase in the expression of TNF-α in relation to the control (P<0.001) and the treatments with DMF50 and FLU were able to reverse this increase (P<0.05), as seen in Figure 7A [F (3, 27) = 21.56, P<0.0001].
Likewise, there was an increase in IL-1β in the CUMS group compared to the control (P<0.001), which was reversed by treatments with DMF50 (P<0.001) and FLU (P<0.01), as seen in Figure 7B [F (3, 28) = 24.91, P<0.0001].
3.3 Computation results
3.3.1 Target Prediction Results for MMF
To estimate the most probable targets for MMF protective effects, we submitted the chemical structure of MMF to the SwissTargetPrediction and Similarity Ensemble Approach servers. These servers predict the most probable proteins, based on the similarity of MMF with chemical structures of the ChEMBL 27 (for SEA) and ChEMBL 23 (for SwissTargetPrediction) compounds databases, and rank proteins from the most to the less probable ones (Table S1 and S2).
Based on both SwissTargetPrediction and SEA servers predictions, human HCAR2 was predicted as the most probable target of MMF. HCAR2 had the larger the quantitative probability rate and also presented known actives with similar chemical properties in SwissTargetPrediction server, measured through Tanimoto index and Manhattan distance similarity. For SEA server, HCAR2 was also predicted as one of eight most probable targets (higher P-values) with the highest Tanimoto index (MaxTC).
3.3.2 3D-structure modeling and Molecular Docking of MMF against HCAR2 and Keap1 proteins
The 3D structure of HCAR2 and Keap1 were used to perform the docking calculations using Maestro programs. In case of HCAR2, as there is no 3D structure available in PDB, we firstly built the protein model using the I-TASSER server. This server used a compilation of the top ten templates (4MBS, 4XNW, 4MBS, 5ZBH, 5ZKP, 6IBB, 5ZBH, 6DO1, 4XNV, 4YAY) and generated a model with C-score of -1.71. C-score values vary from -5 to 2, the higher C-score, the greater the model confidence. The model was analyzed on MolProbity server and showed a MolProbity score of 1.34 (a score normalized to be at the same scale as X-ray resolution). The analysis of the Ramachandran plot of the HCAR2 model (Fig. S1) showed that 99.65% of the residues lie in the most favorable regions, which was more than satisfactory, since ideally this value should be greater than 98%. The docking calculations against Keap1 protein, at both domains Kelch and BTB were performed using the 3D structures PDB ID 6HWS, that present Kelch domain and PDB ID 5GIT, that present the BTB domain.
Then, we performed the docking calculations against the predicted targets of MMF, in order to investigate the predicted binding affinities (measured by the docking score), the possible binding modes and the interactions between MMF and the predicted targets. The more negative a docking score (equivalent to binding affinity energy), the more favorable is the ligand-protein binding interaction. We also performed docking of known HCAR2 activator and Keap1 inhibitors, using them as reference, for docking scores and protein-ligand interactions. As the studied compounds present different molecular weights, we calculated the relation between binding affinity (docking score) and number of heavy atoms (Table 2), through the ligand efficiency (LE) metric (Abad-Zapatero 2007). LE normalizes the affinity with respect to number of heavy atoms. Ligand efficiency is calculated by scaling the binding affinity (ΔG) or docking score by the number of non-hydrogen atoms (n), according to the Equation 1:
LE = (1)
The widely accepted LE values for oral drugs/hits is ≥ 0.3 Kcal·mol-1·non-hydrogen atom-1 (Abad-Zapatero 2007; Hopkins et al. 2014).
Table 2 - Docking results against selected targets of MMF and ligand efficiency of docked compounds
Target
|
Compound
|
Docking score (Kcal·mol-1)
|
n§
|
LE* (Kcal·mol-1·non-hydrogen atom-1)
|
HCAR2
|
MMF
|
-3.51
|
9
|
0.39
|
nicotinic acid
|
-5.69
|
9
|
0.63
|
Kelch of Keap1
|
MMF
|
-2.12
|
9
|
0.24
|
sulfonyl-amino derivate
|
-3.97
|
76
|
0.09
|
BTB of Keap1
|
MMF
|
-3.05
|
9
|
0.33
|
britanin
|
-3.81
|
39
|
0.09
|
§number of non-hydrogen atoms; *Ligand efficiency
As we can see, MMF presented acceptable LE values (from 0.24 to 0.39 Kcal·mol-1·non-hydrogen atom-1) compared with nicotinic acid, sulfonyl-amino derivate and britanin, suggesting that it was efficient binding to HCAR2 and Kelch and BTB domain of Keap1, respectively. As MMF is a small compound, it could be considered a fragment since its molecular weight (MW) ≤ 300 Da, it can easily bind to different binding sites. Recently, a new crystal structure of mus musculus Keap1 (Kelch) (PDB ID 6LRZ) complexed with three DMF molecules, with good resolution, was released in the Protein Data Bank (Unni et al. 2021). Comparing and superposing this crystallographic structure with the calculated docking of human Kelch-MMF, we observe that MMF and DMF molecules acquire similar orientation and conformation (Figure 8D). The root Mean Square Deviation (RMSD) of ligands was 2.03 Å, indicating that docking of Kelch-MMF resulted in a pose very similar to the experimental Kelch-DMF.
MMF docked into HCAR2 presented a docking score of -3.51 Kcal·mol-1. MMF made hydrophobic interactions with Leu104 residue and salt bridges (in blue) with Arg111, Lys166 and Arg251 (Figure 8A). Docking calculation was also performed between HCAR2 protein and nicotinic acid, a potent HCAR2 activator, which had a docking score of -5.69 Kcal·mol-1. Nicotinic acid made interactions with the Arg111 residue as MMF did (through salt bridge) and in addition it made a Hbond with the Ser179 (data not shown).
Docking calculations at BTB domain of Keap1 showed a docking score of -3.05 Kcal·mol-1 and the main interactions were a covalent bond with the Cys151 residue, hydrogen bonds with the Gly148 residue (in yellow) and salt bridge (in blue) with His129 and Arg135 (Figure 8B). Redocking calculation (process by which a ligand co-crystallized is taken from its original crystal structure and redocked) was performed to validate the docking method and to verify if docking was able to recover a known complex's structure and interactions. Using the structure available in PDB 5GIT, we performed the docking of the britanin inhibitor co-crystallized at its original structure. Britanin presented a docking score of -3.81 Kcal·mol-1, similar to the MMF-BTB value. The ligand made a covalent bond with the Cys151 residue, as well as MMF, and a hydrogen bond with Tyr85. The RMSD was 0.426 Å. The RMSD quantifies and compares the docking pose of the ligand with it co-crystallized pose, values below 2.0 Å indicating similar structures and, thus, docking reliability.
In turn, docking calculations at Kelch domain of Keap1 showed that MMF binds to Kelch binding site with a docking score of -2.12 Kcal·mol-1 and the main interactions were hydrogen bonds with Gln530, Ser555 (in yellow) and structural waters (Figure 8C). The redocking calculation was performed with the PDB 6HWS and the co-crystallized sulfonyl-amino derivate inhibitor. The sulfonyl-amino derivate presented a docking score of -3.97 Kcal·mol-1 and, like MMF, made hydrogen bonds with Gln530, structural waters, hydrogen bonds with Arg415, Ser602, Asn414, and salt bridge with Arg415, as well as π-stacking interactions with the Tyr572 (data not shown). The RMSD calculated for sulfonyl-amino derivate was 1.85Å, in relation to its coordinates at crystal structure, thus validating the docking method.