Abstract
Background
Obesity is a medical problem with an increased risk for other metabolic
disorders like diabetes, heart problem, arthritis, etc. Leptin is an
adipose tissue-derived hormone responsible for food intake, energy
expenditure, etc., and leptin resistance is one of the significant
causes of obesity. Excess leptin secretion by poor diet habits and
impaired hypothalamic leptin signaling leads to LR. Melatonin a sleep
hormone; also possess antioxidant and anti-inflammatory properties. The
melatonin can attenuate the complications of obesity by regulating its
targets towards LR induced obesity.
Aim
The aim of this study includes molecular pathway and network analysis
by using a systems pharmacology approach to identify a potential
therapeutic mechanism of melatonin on leptin resistance-induced
obesity.
Methods
The bioinformatic methods are used to find therapeutic targets of
melatonin in the treatment of leptin resistance-induced obesity. It
includes target gene identification using public databases, Gene
ontology, and KEGG pathway enrichment by ‘ClusterProfiler’ using the R
language, network analysis by Cytoscape, and molecular Docking by
Autodock.
Results
We obtained the common top 33 potential therapeutic targets of
melatonin and LR-induced obesity from the total melatonin targets 254
and common LR obesity targets 212 using the data screening method. They
are involved in biological processes related to sleep and obesity,
including the cellular response to external stimulus, chemical stress,
and autophagy. From a total of 180 enriched pathways, we took the top
ten pathways for further analysis, including lipid and atherosclerosis,
endocrine, and AGE-RAGE signaling pathway in diabetic complications.
The top 10 pathways interacted with the common 33 genes and created two
functional modules. Using Cytoscape network analysis, the top ten hub
genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR)
were identified by the MCC algorithm of the CytoHubba plugin. From a
wide range of pathway classes, melatonin can reduce LR-induced obesity
risks by regulating the major six classes. It includes signal
transduction, endocrine system, endocrine and metabolic disease,
environmental adaptation, drug resistance antineoplastic, and
cardiovascular disease.
Conclusion
The pharmacological mechanism of action in this study shows the ten
therapeutic targets of melatonin in LR-induced obesity.
Keywords: melatonin, obesity, leptin resistance, bioinformatic
analysis, network topological analysis
Introduction
According to WHO, obesity is a global health challenge in the current
century. It recognized obesity as an epidemic with increased BMI.
Around 30% of the global population is affected by obesity due to poor
eating habits and sedentary lifestyles ([25]1). A study by Institute
for Health Metrics and Evaluation (IHME) with the Global Burden of
Disease 2019 estimated that the worldwide health loss has been
increased (50%) compared to 1990 (10.4%). The interaction of COVID-19
with rise in chronic illness leads to increased deaths caused by
pandemic ([26]2). The major cause of death particularly 1 in 5 deaths
are related to high blood pressure (11 million). Next leading causes of
deaths are high blood sugar (6.5 million) and high cholesterol (4.4
million) ([27]3). Obesity with high BMI led to major diseases like T2DM
and cardiovascular disease ([28]4). Obesity is an imbalance of food
intake, and energy expenditure leads to adipose tissue enlargement
([29]5). High fat, high carbohydrate consumption, and altered
environmental factors play a significant role in developing obesity,
leading to severe dysfunction of white adipose tissue ([30]6, [31]7).
Many of the co−morbidities of obesity are related to chronic
inflammation and include heart diseases, non-alcoholic fatty liver
disease, steatohepatitis, cancer, T2DM, and neurodegenerative diseases
([32]8). In a study by Boi et al. ([33]9), diet-induced obesity altered
immune and metabolic profiles. In proportion to the amount of stored
fat in adipocytes, our body secretes a hormone called leptin. Leptin is
a peptide hormone that controls food intake and energy expenditure by
sending signals to the brain, especially the hypothalamus ([34]10), to
reduce obesity and its complications. However, significant obese
populations are found to have high circulating leptin levels and are
resistant to leptin treatment and are leptin resistant ([35]11). To
exert its function in the hypothalamus, leptin must cross the
blood-brain barrier (BBB) through some specific transporters. Decreased
leptin sensitivity in obese subjects may be due to reduced BBB
permeability by saturation of its transporters ([36]11). In addition,
blockage of leptin signaling by continuous overstimulation of the
leptin receptor in the hypothalamus leads to leptin resistance
([37]12).
Ghanemi et al. reported that regeneration of the body is known as the
maintenance of a healthy body by normal growth and development.
Exercise, diet, and sleep are vital factors for body regeneration
([38]13). Sleep deprivation is one of the major risk factors for
developing obesity ([39]14), and evidence shows that chronic sleep
deprivation leads to body weight gain ([40]15). Serotonin, melatonin
and histamine are the major indoleamines that play an important role in
health and disease. A similar bioinformatic study by Liu et al,
revealed that the serotonergic neurons are involved in the etiology and
therapy genetics of anxiety disorders ([41]16). Serotonin is the
precursor for melatonin production and is involved in satiety and
feeding behaviors. It also regulates insulin in pancreatic β cells and
leptin from adipocytes. At the hypothalamic level melatonin interacts
particularly with 5-HT[6] receptors in association with α-MSH, orexin
and leptin (peripheral satiety signal) ([42]17).
The pineal gland secretes the sleep hormone melatonin in the night and
regulates the circadian rhythm and body temperature based on 24 h
light/dark cycles ([43]18). Melatonin is an effective antioxidant and
possesses free radical scavenging and anti-inflammatory activities. A
bioinformatic study by Yang et al. explained the possible
pharmacological mechanisms in treating diminished ovarian reserve by
melatonin ([44]19). Melatonin activates antioxidant enzymes at the mRNA
level and increases anti-inflammatory activity and immune function
([45]20). It reduces the risk of oxidative stress and cancer and is
also responsible for modulating the neuroendocrine reproductive axis
([46]21, [47]22). In addition, melatonin plays an essential role in
maintaining the proper functioning of energy metabolism, including
lipid and carbohydrate metabolism ([48]23). Supplementation of
melatonin in several experimental models decreased adipose tissue
levels and the body weight ([49]24). Supplementation of melatonin in
experimental young animals with intact melatonin secretion reduced
long-term body weight gain. The anti-obesogenic effect of melatonin is
observed with diet-induced obesity ([50]7).
Melatonin contributes to the regular expression and leptin secretion
pattern ([51]25), and the lack of melatonin signaling is responsible
for the development of leptin resistance ([52]26). Melatonin
supplementation for 12 weeks in middle-aged rats reduced
intra-abdominal adiposity, body weight gain, and plasma leptin level
([53]27). Treatment of melatonin in diet-induced obese mice reported
reduced leptin mRNA expression and inflammatory cytokines in white
adipose tissue ([54]28). Administration of melatonin to a pineal gland
removed animal improved leptin sensitivity and activated leptin
signaling pathway ([55]29).
Our previous report ([56]30) explained the existing knowledge gap and
future directions for LR-induced obesity by treatment with melatonin.
The role of the regulatory pathway and signaling mechanism will give a
new perspective on melatonin treatment for obesity. Hence, this
revealed the therapeutic mechanism of melatonin by potential predictive
targets of LR-induced obesity using a systematic network
pharmacological -based analysis. [57]Figure 1 shows the work outline of
the study.
Figure 1.
[58]Figure 1
[59]Open in a new tab
Research Work outline.
Materials and Methods
Finding Melatonin Targets
The Simplified Molecular-Input Line-Entry System (SMILES) ([60]31) and
melatonin 3D structure were obtained from the PubChem database
([61]https://pubchem.ncbi.nlm.nih.gov/). The possible targets for
melatonin were spotted using the PharmMapper Server
([62]http://lilab-ecust.cn/pharmmapper/index.html) ([63]32), and SMILES
information was used for the SwissTargetPrediction database
([64]http://swisstargetprediction.ch/) ([65]33). The DrugBank database
([66]https://go.drugbank.com/) ([67]34) and Comparative Toxicogenomics
Database ([68]https://ctdbase.org/) ([69]35) identifies the drug-gene
and chemical-gene interactions. After removing duplicates, the
collected target names and IDs were amended using the UniProt database
([70]https://www.uniprot.org/).
Targets of Leptin Resistance and Obesity
The targets of obesity and leptin resistance were separately retrieved
from the databases GeneCards ([71]https://www.genecards.org/) with the
threshold of relevance score set at a minimum of 10, DisGeNET Score was
set at a minimum of 0.1 ([72]https://www.disgenet.org/) ([73]36) and
NCBI Gene ([74]https://www.ncbi.nlm.nih.gov/gene) with the keywords
leptin resistance and obesity. Duplicates were removed with the aid of
Venn Diagram tool
([75]https://bioinformatics.psb.ugent.be/webtools/Venn/); common
intersecting genes of leptin resistance and obesity were obtained
([76]37). We found 212 common targets and used them for further
analysis.
PPI Network Construction and Identification of Hub Genes
The STRING (Search Tool for the Retrieval of Interacting Genes)
database aids to anticipate protein-protein interactions. STRING
database version 11. 5 was used to predict the PPI network of gene
lists with a high confidence interaction score of 0.700 ([77]38). The
predicted PPI network was visualized by Cytoscape (v3.9.0) and using
Maximal Clique Centrality (MCC) topological analysis, the foremost 10
core genes were recognized by Cytoscape plugin CytoHubba ([78]39). For
network construction, we used Cytoscape software (1) melatonin with its
targets, (2) LR with Obesity and its PPI interaction, (3) PPI
interaction of common targets of melatonin and LR with Obesity, (4)
functional modules of top 10 network using clustermaker2 Glay
(Community Cluster) algorithm, and (5) subnetworks of therapeutic
targets enriched in different pathway classes ([79]19).
The GO Enrichment and KEGG Pathway Analysis
The ClusterProfiler package (A universal enrichment tool for
interpreting omics data) of the R program (4.1.2) used for the Gene
Ontology and KEGG pathway enrichment analysis ([80]40) of the common 33
targets of melatonin and LR with Obesity. The GO enrichment analysis
was done by enrichGO and KEGG pathway enrichment analysis was done by
enrichKEGG options of ClusterProfiler. plots were developed using
ggplot2 to create GO (Chord diagram), enrichKEGG (Dot plot & Sankey
plot) and pathway class graph. We used KEGG pathway database
([81]https://www.kegg.jp/) for the pathway class analysis of the top 33
targets.
Molecular Docking
Protein Preparation for Docking
The RCSB Protein Data Bank ([82]https://www.rcsb.org/) provided the
target proteins 3D crystal structures in PDB format. The CASTp –
Computed Atlas of Surface Topography of proteins
([83]http://sts.bioe.uic.edu/castp/index.html?4jii) database used to
predict the active site amino acids and binding pocket details of
target proteins. We used Autodock tools (1.5.7) to add charges and
remove water from the protein molecule, and the PDBQT format of
proteins was prepared ([84]41).
Ligand Preparation for Docking
The 3D SDF structure of melatonin downloaded from PubChem database was
converted into PDB format with the help of Open Babel software (3.1.1)
([85]42). The.pdbqt format of melatonin was constructed using Autodock
tools (1.5.7)
Docking and Visualization
Docking of the target protein with melatonin and its binding affinity
was calculated using Autodock (4.2) software. DS visualizer software
used to visualize the binding of melatonin with target proteins with
best binding pose.
Results
Melatonin Target Network
We retrieved 254 melatonin targets from SwissTargetPrediction,
PharmMapper, DrugBank, and CTD (Comparative Toxicogenomics Database).
Using Cytoscape 3.9.0, duplicates were removed, and we constructed the
melatonin target network. The intersecting targets among databases are
colored differently ([86] Figure 2 ).
Figure 2.
[87]Figure 2
[88]Open in a new tab
Target genes of melatonin. All known targets of melatonin are in green
nodes. The Blue nodes -intersection of Swiss target
prediction/CTD/DrugBank. Purple nodes - Swiss target prediction/Pharm
Mapper. Yellow nodes - Swiss target prediction/CTD. Gray nodes -
DrugBank/Swiss target prediction or CTD. Pink nodes – DrugBank.
LR-Induced Obesity Targets and PPI Network
We separately collected the obesity and leptin resistance targets from
databases like DisGeNET, GeneCards, and NCBI Gene databases. We
obtained a total of 212 common intersecting targets of LR and Obesity.
These were the key targets for developing LR-induced obesity. Using
analyze network option of Cytoscape in string interaction network [by
an above-average of Degree Centrality (96.69811321), Betweenness
Centrality (0.003931137), and Closeness Centrality (0.556192402)], a
total of 48 significant targets common for LR and Obesity was obtained
([89] Figure 3 ).
Figure 3.
[90]Figure 3
[91]Open in a new tab
PPI network of LR-induced obesity. The node color red to blue
represents the descending order of degree values.
PPI Network of Common Potential Melatonin and LR-Induced Obesity Targets
With the help of the Venn Diagram tool
([92]https://bioinformatics.psb.ugent.be/webtools/Venn/), the 33
potential targets common for melatonin and LR-induced obesity were
obtained ([93] Figure 4A ). The network was constructed using Cytoscape
and MCC score using the cytoHubba plugin the top 10 hub targets (TP53,
AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were obtained
([94] Figure 4B ). These ten targets might play an essential role in
the melatonin treatment for LR-induced Obesity.
Figure 4.
[95]Figure 4
[96]Open in a new tab
PPI network and Venn diagram of melatonin remedial targets in LR
induced obesity. (A) The common targets of melatonin and LR induced
obesity are depicted in the Venn diagram intersection. (B) PPI network
of common remedial targets. The node sizes from large to small and
color from red to cyan indicate the degree values in descending order.
GO Analysis
Using the ClusterProfiler package of R language 4.1.2, the common 33
targets were analyzed, and we selected the top 10 GO terms based on
p-value and number of counts. As shown in [97]Figure 5A , we visualized
the GO results using the ggplot2 package of R. The top 5 biological
processes (BP) were selected based on p-value and number of counts and
plotted using the R Chord Diagram ([98] Figure 5B ). The top 5 enriched
BP were directly involved with melatonin treatment in LR-induced
Obesity; the impact of cellular response to chemical stress, external
stimulus, autophagy, negative regulation of phosphate metabolic
process, regulation of small molecule metabolic process. Nine out of
ten hub genes were enriched in top 5 BP (TP53, AKT1, MAPK3, PTGS2, TNF,
IL6, MAPK1, IL1B, MTOR).
Figure 5.
[99]Figure 5
[100]Open in a new tab
GO enrichment analysis and top five biological processes. (A) GO
enrichment analysis. Top 10 significantly enriched terms of each part.
BP, biological process; CC, cellular component; MF, molecular function.
(B) The top 5/10 enriched biological processes.
KEGG Pathway Enrichment Analysis
EnrichKEGG pathway analysis for common 33 targets were done using
ClusterProfiler R 4.1.2. We obtained a total of 180 pathways (p-value
<0.05), took the top 10 pathways, and performed the dot-plot ([101]
Figure 6A ). The genes enriched in individual pathways were constructed
([102] Figure 6B ). The top 10 pathways were decomposed using the
Cytoscape Glay (community cluster) algorithm of clustermaker2 to
understand the mechanism of melatonin in treating LR-induced Obesity.
The top 10 pathways were divided into two functional modules ([103]
Figure 6C ). Module 1 consists of five pathways, including lipid and
atherosclerosis (hsa05417), Yersinia infection (hsa05135), signaling
pathway of C-type lectin receptor (hsa04625), Chagas disease
(hsa05142), signaling pathway (AGE-RAGE) in diabetic complications
(hsa04933). Module 2 contained five pathways, including endocrine
resistance (hsa01522), HIF-1 signaling pathway (hsa04066), Prostate
cancer (hsa05215), EGFR tyrosine kinase inhibitor resistance
(hsa01521), Breast cancer (hsa05224). The significantly enriched lipid
and atherosclerosis (hsa05417) pathway and endocrine resistance
(hsa01522) pathway were further analyzed by the KEGG pathway ([104]
Figure 7 and [105]Table 1 ). A total of 180 pathways were classified in
different biological systems using the KEGG database. The related
melatonin and LR-induced obesity-related six pathways were further
divided into six subnetworks ([106] Figures 8A, B ).
Figure 6.
[107]Figure 6
[108]Open in a new tab
The KEGG pathway enrichment analysis of the common 33 therapeutic
targets. (A) Top 10 significantly enriched pathways. The dot sizes from
large to small and colors from red to blue indicate count of genes and
p-value in descending order. (B) Genes involved in the top 10 enriched
pathways. (C) Community cluster module analysis of the target pathway
network. Blue nodes are the pathways, and pink nodes are the genes
involved in each module.
Figure 7.
[109]Figure 7
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Distribution of potential targets on vital pathways: The nodes in red
represent the hub genes; nodes in yellow represent common 33 target
genes and nodes in green represents the other targets of pathways [(A)
Lipid and atherosclerosis and (B) endocrine resistance].
Table 1.
The KEGG pathway results.
Pathway class Pathway ID Pathway Count Total genes p-value
Immune system hsa04625 C-type lectin receptor signaling pathway 12 151
7.00E-15
Drug resistance: antineoplastic hsa01522 Endocrine resistance 12 159
1.31E-14
Signal transduction hsa04066 HIF-1 signaling pathway 12 182 6.73E-14
Cancer: specific types hsa05215 Prostate cancer 11 148 2.40E-13
Cardiovascular disease hsa05417 Lipid and atherosclerosis 14 346
2.94E-13
Infectious disease: parasitic hsa05142 Chagas disease 11 163 6.99E-13
Endocrine and metabolic disease hsa04933 AGE-RAGE signaling pathway in
diabetic complications 11 166 8.54E-13
Drug resistance: antineoplastic hsa01521 EGFR tyrosine kinase inhibitor
resistance 10 119 1.01E-12
Infectious disease: bacterial hsa05135 Yersinia infection 11 210
1.12E-11
Cancer: specific types hsa05224 Breast cancer 11 217 1.61E-11
[111]Open in a new tab
The top 10 enriched KEGG pathway classes of common 33 genes.
Figure 8.
[112]Figure 8
[113]Open in a new tab
The KEGG pathway class analysis. (A) The KEGG pathway class analysis of
top 33 targets. (B, 1-6) Subnetworks of major pathway classes.
Molecular Docking
For molecular docking analysis with melatonin, ten hub genes were
chosen. Using CastP, we predicted the active site parameters of each
target. Conventionally the target binding with lower affinity means
stronger binding ability with melatonin. The strongly bound targets
with melatonin were chosen for analysis.
The proteins that were strongly bound to melatonin might alleviate the
LR-induced obesity complications. [114]Figure 9 depicts the binding of
core targets to melatonin that possess a strong binding affinity ([115]
Table 2 ).
Figure 9.
Figure 9
[116]Open in a new tab
Molecular Docking results of melatonin with top 10 target proteins. (A)
Binding of melatonin with IL-6. (B) Binding of melatonin with TP53. (C)
Binding of melatonin with ERBB2. (D) Binding of melatonin with IL1B.
(E) Binding of melatonin with MAPK3. (F) Binding of melatonin with
MTOR. (G) Binding of melatonin with AKT1. (H) Binding of melatonin with
PTGS2. (I) Binding of melatonin with TNF. (J) Binding of melatonin with
MAPK1.
Table 2.
Top 10 hub protein target list with docking results.
MCC Rank Name PDB ID Target Name Affinity (kcal/mol)
1 TP53 1GZH Cellular tumor antigen p53 -5.7
2 AKT1 3MV5 RAC-alpha serine/threonine-protein kinase -7.4
3 MAPK3 2ZOQ Mitogen-activated protein kinase 3 -6.4
4 PTGS2 5IKT Prostaglandin G/H synthase 2 -7.4
5 TNF 5M2J Tumor necrosis factor -6.0
6 IL6 1ALU Interleukin-6 -4.4
7 MAPK1 5NHV Mitogen-activated protein kinase 1 -6.7
8 ERBB2 1S78 Receptor tyrosine-protein kinase erbB-2 -7.3
9 IL1B 2KH2 Interleukin-1 beta -6.6
10 MTOR 3JBZ Serine/threonine-protein kinase mTOR -6.2
[117]Open in a new tab
The top 10 hub proteins with its MCC ranking and their binding affinity
with melatonin.
Discussion
Maintaining proper body weight and BMI is essential for everyday living
with normal immune function. It is necessary for preventing type 2
diabetes, cardiovascular diseases, arthritis, including some cancers
([118]43). Currently, the incidence of obesity and its related diseases
have increased worldwide, including childhood obesity ([119]44). The
anti-obesity effect of melatonin and sleep has been proved, but the
exact therapeutic targets of leptin resistance-induced obesity are yet
to be studied. Hence, for the first time, we used the in-silico network
pharmacological analysis to identify the mechanism of action of
melatonin against LR-induced Obesity. This will become the preliminary
data for reducing LR-induced Obesity by melatonin for further clinical
trials. We found ten genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1,
ERBB2, IL1B, MTOR) that might play an important role in reducing
LR-induced Obesity ([120] Table 1 ). Molecular docking results also
revealed that the hub target genes bound to melatonin with high
affinity, particularly AKT1, PTGS2, and ERBB2, can be used to treat
LR-induced obesity by melatonin ([121] Figures 9A-J and [122]Table 2 ).
Details of Top 10 Melatonin Targets in Treating Obesity
TP53 is the “guardian of the genome” and a major tumor suppressor gene,
and in many types of cancer, major somatic mutations occur in TP53. The
significance of p53 in heart disease, obesity, T2DM etc. has been
demonstrated ([123]45). Depending on the type of adipocytes, the roles
of p53 on adipogenesis differ. Regulation by P53 has a negative impact
on the differentiation of brown adipocytes to white adipocytes in the
in vitro study. TP53 reduces diet-induced and gene related obesity in
mouse models and homo sapiens ([124]46). Supplementation of melatonin
protects liver function during obesity and diabetes ([125]47).
Melatonin reduces the severity of non-alcoholic fatty liver disease
(NAFLD). The TP53/p53 gene plays a vital role in regulating glycogen
and lipid metabolism. The studies associated with genome disclosed that
TP53 plays an essential role in obesity and type 2 diabetes ([126]48).
AKT1, otherwise known as RAC-alpha serine/threonine-protein kinase. In
vivo studies depicted AKT signaling is involved in brown adipose tissue
development. It may regulate adipocyte cell size through pathways like
TAG synthesis, lipid uptake, lipolysis, and thermogenesis. Faria et al.
([127]49) demonstrated that melatonin acts locally in the hypothalamus
through a mechanism dependent on MT1/MT2 receptors; and activates the
phosphatidylinositol 3-kinase/insulin-stimulated RAC-α
serine/threonine-protein kinase (PI3K/AKT) pathway. Melatonin
stimulates the AKT phosphorylation in the hypothalamus (45% higher than
in control, P < 0.05). Melatonin increases glycogen synthesis in mouse
liver and reduces glucose production by activating GSK3B via
insulin-stimulated phosphatidylinositol 3-kinase (PI3K)–AKT signaling
([128]50).
The MTOR (mechanistic target of rapamycin) acts as a key regulator of
metabolism, including energy homeostasis and the nutritional status of
the cell. Altered signaling from growth factors, cytokines, and
hormones are through MTOR and are involved in obesity and insulin
resistance ([129]51). The MTOR signaling is involved in adipogenesis,
and the treatment of rapamycin inhibited both the proliferation and
differentiation of human adipocytes ([130]52). Melatonin treatment
concomitantly reduced MTOR phosphorylation in the cells treated with
H[2]O[2] ([131]53).
MAPK1 is known as ERK2, and MAPK3 is known as ERK1, and these are the 2
MAPKs that play an essential role in a wide variety of cellular
processes. MAPK1 up-regulates the expression of vital regulators
involved in adipogenesis such as CCAAT-enhancer-binding proteins a, b,
and d, and peroxisome proliferator-activated receptor g (PPARg) at the
beginning stage of adipogenesis ([132]54, [133]55). Deregulation of the
MAPK pathway was observed during obesity and is involved in insulin
resistance ([134]56). Melatonin is tightly linked to the PKA and ERK1/2
pathways. Melatonin inhibits proliferation and promotes differentiation
of porcine intramuscular preadipocytes. It promotes lipid degradation
in intramuscular adipocytes by activating ERK1/2 and PKA ([135]57).
Melatonin increases the expression of solute carrier family 39-member
1, activates MAPK/ERK pathways, increases phosphorylation of ERK at
1/2/5 levels and notably inhibits ROS production; and increases the
uptake of zinc ([136]58).
The cytokines involved in systemic inflammation and acute-phase
reactions are TNFα and IL-6. Elevated serum cytokine levels,
particularly IL-6 and TNFα, were observed in obese subjects ([137]59,
[138]60). The obesity patients with increased BMI had abnormal
circulation of inflammatory cytokines and are strongly associated in
developing severe respiratory failure in COVID-19 ([139]61, [140]62).
Leptin is a cytokine belonging to the family of proinflammatory
cytokines and structurally similar to IL-6. Production of leptin can be
induced by other inflammatory mediators like TNFα ([141]63). Adipocytes
secrete both IL-6 and TNF-α, and their concentration correlates with
the percentage and distribution of fat tissue in the body ([142]64).
Obesity and liver steatosis are known as low-grade inflammation. In a
study, melatonin treatment reduced inflammatory cytokines (TNFα and
IL-6) levels in young Zucker diabetic fatty rats ([143]65).
In addition, obesity causes a pro-inflammatory state with the release
of several mediators like TNF- α, IL-6, and IL-1β, promoting tumor
growth ([144]66). Obesity and type 2 diabetes are predominantly related
to non-alcoholic fatty liver disease, including accumulation of hepatic
triglyceride and increased pro-inflammatory cytokine expression such as
IL-1β ([145]67). A study showed that eight weeks of melatonin
supplementation for type 2 diabetes patients significantly reduced the
levels of malondialdehyde and IL-1β compared to the control group
([146]68).
Mitochondrial dysfunction is the major factor for developing various
disorders by increased production of free radical and nitric oxide. It
is reported that the maintenance of mitochondrial homeostasis in
obesity and stomach ulcers can be regulated by melatonin treatment
([147]69). The gut dysbiosis increases the ceramide and
lipopolysaccharide (LPS) levels in the circulation which activates the
inflammatory cytokines like TNFα, IL-6, IL-1β and PTGS2 by nitric oxide
synthase (iNOS) and superoxide
[MATH:
(O2−) :MATH]
in microglia. 14-3-3 protein is essential in maintaining mitochondrial
function ([148]70) via modulating butyrate levels. The ceramides
negatively regulate 14-3-3 and perturbs normal mitochondrial
physiology. Mitochondrial dysfunction plays a major role in gut
permeability. Melatonin including butyrate and orexin can positively
regulate mitochondrial function including reduction of iNOS and
[MATH:
O2−
:MATH]
([149]71).
Increased expression in mRNA levels with specific markers, e.g.,
prostaglandin-endoperoxide synthase 2 (PTGS2), have been found in
HFD-induced Obesity ([150]72). It is reported that the treatment of
melatonin significantly reduces the mRNA expression of PTGS2 ([151]73).
The transmembrane receptor ERBB2 is a tyrosine kinase and belongs to
the family of EGFR ([152]74) and plays a prominent role in breast
cancer. Low levels of ERBB2 are linked to mitochondrial dysfunction,
stress response, particularly in cardiac function, and metabolic
reprogramming ([153]75). The altered mRNA levels of cytokines like
IL-6, TNFα, leptin, adiponectin, C-reactive protein (CRP), and tumor
markers like TP53 and ERBB2 in peripheral blood are associated with
breast cancer and obesity ([154]60). Overexpressed ERBB2 is the primary
therapeutic target for cancer. Many reports explained the anticancer
properties of melatonin in ERBB2, particularly in breast cancer
([155]76).
The Pathways and Functional Modules of Therapeutic Targets of Melatonin
Against LR-Induced Obesity
The etiology and pathways involved in obesity, particularly in leptin
resistance-induced obesity, is yet to be elucidated. This study
revealed the top 33 melatonin and LR-induced obesity targets are
involved in the major biological processes in the cells; such as the
cellular response to chemical stress, impact of external stimuli, role
of autophagy, negative impact of phosphate metabolic process, and
regulation of small molecule metabolic process. From the KEGG pathway
enrichment analysis ([156] Figures 7A, B ), the two major pathways 1.
Lipid and atherosclerosis (hsa05417) and 2. Endocrine resistance
(hsa01522) is enriched with the targets, including the top 10 hub genes
([157] Table 1 ). Obesity and atherosclerosis have similar
pathophysiological characteristics and the lipids critically contribute
to them; that trigger inflammation and initiation of these diseases is
also due to oxidized LDL and free fatty acids ([158]77). Several
endocrine alterations are identified and associated with obesity and
are reversible by weight loss in endocrine resistance. However,
specific endocrine syndromes result in irreversible obesity, like the
thyroid hormone levels altered through the
hypothalamic-pituitary-thyroid axis. The incidence of hypogonadism and
growth hormone deficiencies are associated with abdominal obesity
([159]78).
Further, we analyzed the top 10 KEGG enriched pathways by creating
functional modules using a community cluster algorithm. We found that
two modules are mainly related to melatonin and obesity and are densely
packed ([160] Figure 6C ). Module 1 consists of the Immune system,
Cardiovascular disease, Infectious disease: parasitic, Endocrine and
metabolic disease, and Infectious disease: bacterial. Module 2 includes
Drug resistance: antineoplastic, Signal transduction, Cancer: specific
types, Infectious disease: parasitic, cancer: specific types. The
community analysis revealed that melatonin could reduce the risk factor
of LR-induced obesity by a wide variety of pathways.
Biological Processes and Pathway Classes of Melatonin Target LR-Induced
Obesity
The pathway class analysis found six classes to be important in
reducing the risk of LR-induced obesity by melatonin. We constructed
six sub-networks of pathway classes ([161] Figure 8B ), including nodes
from the common top 33 targets, and an increased number of hub genes
were involved in each pathway class. MTOR, AKT1, MAPK1 & 3, and TNF
genes are present in many networks.
Signal Transduction
Melatonin can downregulate the pathways associated with obesity in
signal transduction. The mTORC2 can uniquely phosphorylate the kinases
like AKT, thereby regulating glucose and lipid metabolism. AKT1 is
involved in the browning of white adipocytes. The mTORC2 phosphorylates
the AKT1 (hydrophobic motif site HM; S473) and activates AKT1
([162]79). Administration of melatonin (10 mg/kg) daily for twelve
weeks decreased the serum alanine transaminase in hepatic steatosis.
Activation of MAPK, JNK, and p38 signaling pathways, pro-inflammatory
cytokine expression, including IL-6 and TNFα, are observed in HFD fed
mice. The signaling pathways linked with cytokine production,
inflammation, and apoptosis are inhibited by melatonin ([163]80).
Environmental Adaptation
Exposure to cold induces adaptive thermogenesis in skeletal muscle and
brown adipose tissue. The cell responds both externally and internally
to these changes ([164]81). The external stimuli such as a change in
light intensity, temperature, humidity, etc., while an internal
stimulation is a change within the cell itself. In liver and adipose
tissue, with obesity, molecular markers of stress and autophagy were
increased ([165]82). Melatonin regulates several signaling pathways,
including the ERK and MAPK pathways which regulate the immune system.
In addition, it can reduce H[2]O[2]-induced oxidative stress by
altering the ERK/AKT/NFkB pathways ([166]83).
Endocrine System & Endocrine and Metabolic Disease
Melatonin regulates different physiological and neuroendocrine
functions in mammals. The role of melatonin in obesity and T2DM is
widely studied. The activation of kinases (PI3K, AKT, and ERK1/2) by
melatonin is potentially relevant in regulating glucose homeostasis
([167]44).
Cardiovascular Disease
Studies have demonstrated that melatonin has significant effects on
hypertension, valvular heart diseases, vascular diseases, and lipid
metabolism. Melatonin can be a therapeutic option for treating
cardiovascular disease. Melatonin inhibits the propagation of smooth
myocytes in the pulmonary arteries and decreases the MAPK1/3 (ERK1/2)
expression and phosphorylation of AKT ([168]20).
Drug Resistance
Intervention of melatonin in the dominant signal transduction pathways,
such as AKT and MAPK, is not dependent on its antioxidant properties.
The pleiotropic effects of melatonin can modulate the drug targets
expression and phosphorylation status, increasing the sensitization to
antineoplastic agents ([169]84, [170]85).
Conclusions
To conclude, melatonin treatment could alleviate the complications of
LR-induced obesity by regulating the top 10 targets (TP53, AKT1, MAPK3,
PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR). These improve the
biological processes (cellular response to chemical stress, external
stimuli, autophagy, negative impact in phosphate metabolism, regulation
of small molecule metabolism and signaling pathways such as (lipid and
atherosclerosis and endocrine resistance). In addition, melatonin can
improve LR-induced obesity by modulating various pathway classes,
signal transduction, endocrine system, endocrine and metabolic disease,
environmental adaptation, drug resistance antineoplastic, and
cardiovascular diseases.
Data Availability Statement
The original contributions presented in the study are included in the
article/[171] Supplementary Material . Further inquiries can be
directed to the corresponding author.
Author Contributions
Vision of the manuscript and editing including revisions made by VN. VS
collected literature, made results and wrote the manuscript. All
authors contributed to the article and approved the submitted version.
Funding
This work is funded by the UGC-BSR Faculty fellowship.
Conflict of Interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s Note
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Acknowledgments