Abstract
Background
Depression is widespread global problem that not only severely impacts
individuals’ physical and mental health but also imposes a heavy
disease burden on nations and societies. The role of inflammation in
the pathogenesis and pathophysiology of depression has received much
attention, but the precise relationship between the two remains
unclear. This study aims to investigate the correlation between
depression and inflammation using a network medicine approach.
Methods
We utilized a degree-preserving approach to identify the large
connected component (LCC) of all depression-related proteins in the
human interactome. The LCC was deemed as the disease module for
depression. To measure the association between depression and other
diseases, we calculated the overlap between these disease protein
modules using the Sab algorithm. A smaller Sab value indicates a
stronger association between diseases. Building on the results of this
analysis, we further explored the correlation between inflammation and
depression by conducting enrichment and pathway analyses of critical
targets. Finally, we used a network proximity approach to calculate
drug-disease proximity to predict the efficacy of drugs for the
treatment of depression. We calculated and ranked the distances between
depression disease modules and 6,100 drugs. The top-ranked drugs were
selected to explore their potential for treating depression based on
the hypothesis that their antidepressant effects are related to
reducing inflammation.
Results
In the human interactome, all depression-related proteins are clustered
into a large connected component (LCC) consisting of 202 proteins and
multiple small subgraphs. This indicates that depression-related
proteins tend to form clusters within the same network. We used the 202
LCC proteins as the key disease module for depression. Next, we
investigated the potential relationships between depression and 299
other diseases. Our analysis identified over 18 diseases that exhibited
significant overlap with the depression module. Where S[AB] = −0.075
for the vascular disease and depressive disorders module,
S[AB] = −0.070 for the gastrointestinal disease and depressive
disorders module, and S[AB] = −0.062 for the endocrine system disease
and depressive disorders module. The distance between them S[AB] < 0
implies that the pathogenesis of depression is likely to be related to
the pathogenesis of its co-morbidities of depression and that potential
therapeutic approaches may be derived from the disease treatment
libraries of these co-morbidities. Further, considering that the
inflammation is ubiquitous in some disease, we calculate the overlap
between the collected inflammation module (236 proteins) and the
depression module (202 proteins), finding that they are closely related
(S[di] = −0.358) in the human protein interaction network. After
enrichment and pathway analysis of key genes, we identified the HIF-1
signaling pathway, PI3K-Akt signaling pathway, Th17 cell
differentiation, hepatitis B, and inflammatory bowel disease as key to
the inflammatory response in depression. Finally, we calculated the
Z-score to determine the proximity of 6,100 drugs to the depression
disease module. Among the top three drugs identified by drug-disease
proximity analysis were Perphenazine, Clomipramine, and Amitriptyline,
all of which had a greater number of targets in the network associated
with the depression disease module. Notably, these drugs have been
shown to exert both anti-inflammatory and antidepressant effects,
suggesting that they may modulate depression through an
anti-inflammatory mechanism. These findings demonstrate a correlation
between depression and inflammation at the network medicine level,
which has important implications for future elucidation of the etiology
of depression and improved treatment outcomes.
Conclusion
Neuroimmune signaling pathways play an important role in the
pathogenesis of depression, and many classes of antidepressants
exhibiting anti-inflammatory properties. The pathogenesis of depression
is closely related to inflammation.
Keywords: depression, inflammation, network medicine, medical
treatments, gene
1. Introduction
Depression is a severe mental system disorder that afflicts
approximately 300 million people worldwide, and depression is
characterized by high prevalence, high disability, and high suicide
rates ([33]1). Depression imposes a heavy burden of illness on
individuals and society, and it has become the leading cause of mental
health-related disease burden worldwide ([34]2). There are few
clinically available medications for depression, and the low cure rate,
poor compliance, and high relapse rate have caused great distress to
patients, their families, and society ([35]3). Antidepressant drugs are
commonly employed in clinical settings as a primary means of treating
depression. However, despite this approach, only about one-third of
patients ultimately exhibit positive responses to these interventions.
Furthermore, while such drugs do provide some measure of therapeutic
efficacy, their high cost and significant risk for side effects present
obstacles to broader application ([36]4). Therefore, there is an urgent
need to discover new and better treatments. However, the etiology of
depression remains a mystery, which is a severe obstacle to providing
better clinical treatment options for depressed patients. Considerable
human and material resources have been devoted to research on the
etiology and treatment of depression. The role of neuroimmune in the
pathogenesis and pathophysiology of depression has received much
attention. Studies suggest that immune activation and cytokine
production may be associated with depression and that cytokines are
signaling proteins that mediate and regulate immune responses and
inflammation. Many clinical studies have reported a correlation between
depressive symptoms and concentrations of pro-inflammatory factor
cells. Moreover, it has been shown that pharmacological
anti-inflammatory interventions can successfully reduce both depressive
symptoms and pro-inflammatory cytokine concentrations. This suggests a
bidirectional causal relationship likely exists between inflammation
and the onset of depressive symptoms ([37]5). Whether inflammatory
markers can serve as biomarkers of depression still needs to be
investigated in depth. Here, we utilized the ideas and methods of human
proteome-based network medicine analysis in ([38]6) in the disease
genetics and pharmacogenomics to explore the relationship between
depression and inflammation. Compared with the previous researches
about the depression and the inflammation, more relationships were
introduced into our research, including disease-disease relation,
disease-inflammation relation, drug-disease relation,
drug-anti-inflammation relation and so on. Not only interpret the
neuroimmunological mechanism of depression pathogenesis, but also
provide hypotheses and ideas for treating and rationalizing depression.
Overall, our studies have contributed to advancing the in-depth
understanding of the relationship between neuroinflammation and
depression. By utilizing techniques from networked medicine, we have
simplified the complexity of traditional laboratories, improved
research efficiency, and reduced research costs. These advantages have
enabled researchers in the field of depression and inflammation to
discover new treatments and innovative strategies more quickly. We
trust that our study offers promising solutions for further development
in this field.
2. Methods
2.1. Target screening for depression
To get the targets relevant to depression, we searched the MeSH[39]^1
database for medical keywords related to depression. Then we used the
medical keyword in GeneCards[40]^2 and CTD[41]^3 databases
Depression-related risk genes were retrieved. Because the Score value
in the Genecards and CTD databases is an important indicator reflecting
the correlation between the target and the disease, the target more
closely related to depression is found based on the Score greater than
the median as the screening criterion. Then 239 depression targets were
screened to be standardized with the criterion and rule of the
Uniprot[42]^4 database after duplicates were deleted. Finally, we
localized these risk genes to the human protein interaction network and
constructed a disease module for depression. Since different factors or
pathways often contribute to the development of diseases, we utilized
enrichment analysis to categorize the differential genes or substances
based on their functions, in order to establish functional and
phenotypic correlations.
2.2. Disease co-morbidity measurement
The comorbidity of diseases often indicates the existence of shared
molecular mechanisms in their etiology, and studying disease
co-occurrence is crucial for accurate diagnosis, effective treatment,
and better prognosis ([43]7). Therefore, before investigating the
relationship between depression and inflammation, we first explored
diseases that overlap with depression modules in the human protein
interaction network (HPI network). The used HPI network comes from the
literature ([44]6). Our aim was to examine the connection between
depression comorbidities and inflammation by taking into account
existing clinical studies. Here we use a corpus of all 299 diseases
defined by the Medical Subject Headings (MeSH) ontology, collected and
compiled by the author Barabási ([45]8) to explore the overlapping
relationship between these 299 disease proteins and the depression
disease module. We utilized the S[AB] (A is a sub-network of the
depression disease proteins, B is one of other 299 diseases proteins)
metric to evaluate the network-based overlap between the protein set of
different diseases, where S[AB] < 0 signals a network-based overlap
between the depression disease A and the gene associated with disease
B. Next, S[AB] was defined as follows:
[MATH: SAB=〈dAB〉−
〈dAA〉+
〈dBB〉2 :MATH]
Where
[MATH: 〈dAB〉 :MATH]
presents the average of the shortest path distance between nodes in the
depression targets A and nodes in the other disease targets B. The
equation of
[MATH: 〈dAB〉 :MATH]
is:
[MATH: dAB=1||B||
∑j∈B<
/mi>mini
∈Adi,j,
:MATH]
where
[MATH: d(i,j)
:MATH]
is the length value of the shortest path between protein i of
depression disease and protein j of B disease in the human protein
interaction network (HPI network).
[MATH: ||B||
:MATH]
presents the number of proteins in B disease. Similarly, the definition
of
[MATH: 〈dAA〉 :MATH]
is:
[MATH: dAA=1||A||
∑j∈A<
/mi>mini
∈Adi,j.
:MATH]
2.3. Network-based enrichment and pathway analysis
We performed enrichment and pathway analysis of the screened vital
targets based on a network medicine approach to decipher the
inflammatory mechanisms of depression. We constructed the depression
disease module based on the human protein interaction network and used
the algorithm of network proximity ([46]8) to explore the relationship
between the disease module and inflammation critical targets in the
network. The screened essential genes were then enriched to analyze the
possible pathway of the neuroimmune mechanism of depression occurrence
based on the important relevant disease ontology and pathways in the
enrichment results. The functional annotation of the screened key
targets could be obtained based on the String database.[47]^5 The
screened key genes were further used to analyze the biological
processes, molecular functions, cellular components, and signaling
pathways. We expect to discover the new role of inflammatory pathways
in the pathogenesis of depression. Essential cytokines may be
identified to serve as biological markers for the diagnosis of
depression. It will be helpful to provide new reference ideas and a
basis for future clinical diagnosis of depression, and effectively
predict the effect of antidepressant drug treatment. The better
treatment plan promptly can be selected for the depressed patient based
on his individualized characteristics.
2.4. Drug-disease proximity
Network-based can be used as a valid tool to explore the link between
different diseases and existed drugs and measure the effect of drug
treatment ([48]9). Therefore, similar to the measurement in ([49]6,
[50]9), drug-disease proximity of the top-ranked drugs were used to
analyze their action mechanisms, which could reveal the
anti-inflammatory effects of antidepressants in the clinic as well as
the potential antidepressants from the existed drugs. Since the
DrugBank database provided the drug-target information and drug
indication information ([51]10), we employ the network proximity
approach ([52]6, [53]9) to conduct the quantitative analysis between
6,100 drugs targets collected by ([54]6) and the depression disease
module. In this method, the Z-value metric is used to evaluate the
proximity between a disease and a drug. The process of obtaining the
Z-value first calculates the mean value of all the closet path lengths
between drug targets and diseases to get the distance d[c].
Specifically, given the set of depression proteins set A and the set of
drug targets T, we calculate the distance
[MATH:
dc(A
,T)
:MATH]
by integrating the shortest path length between nodes s and t in the
network, as follows:
[MATH:
dcA,T=
1||T||∑t∈T<
/mi>mina
∈Ada,t :MATH]
However, the set sizes of various drugs are different, which leads to
bias and unfair comparison, if we only use d[c]. as the proximity
without other processes. Therefore, we created a reference distance
distribution corresponding to the expected distances between two
randomly selected groups of proteins matching the size and the degrees
of the original depression proteins and drug targets in the network.
The reference distance distribution was generated by calculating the
distance between these randomly selected groups, a procedure repeated
100 times. The mean
[MATH:
μdc
(A,T) :MATH]
and S.D.
[MATH:
δdc
(A,T) :MATH]
of the reference distribution were used to convert an observed distance
to a normalized distance, defining the proximity measure:
[MATH: Z(A,T)=
dc(A,T)−
μdc(
A,T)δdc(A,T) :MATH]
Obviously, the smaller the Z-value, the better the efficacy of the drug
and the higher the ranking of the drug. To assess their expected
efficacy against depression, we performed a ranking of these drugs and
conducted the common analysis. We found that most of the FDA-approved
antidepressants were ranked highly, presenting that calculating the
drug-disease proximity (Z-value) has practical significance. More
interestingly, we found that these top-ranked drugs often have
anti-inflammatory effects in clinical treatment of corresponding
diseases. This implies that anti-inflammatory strategies can alleviate
depressive symptoms to some extent. In addition, some drugs with
similar disease modules as depression rank high on the list. We
speculate that they may have a positive effect on the treatment of
depression, although more clinical trials are needed to determine the
results.
3. Results
The clinical diagnosis of depression is not only based on the patient’s
clinical manifestations but also includes scale evaluation and relevant
physical and chemical inspection ([55]11). However, for some patients
with atypical symptoms of depression, specific diagnostic indicators
may be lacking ([56]12). Therefore, it is important to accurately
identify the pathogenesis of depression and to clarify the etiology of
depression for both differential diagnosis and therapeutic care of
depressed patients. A large body of clinical evidence suggests that
neuroimmune plays an important role in the pathogenesis of depression
and that the interaction of the central nervous system and inflammatory
pathways are relevant to its development ([57]13, [58]14). In the
medical field, the concept of inflammation is frequently used but its
clinical meaning can be ambiguous and dependent on the specific
clinical context ([59]15). One commonly employed strategy in drug
development is “repurposing”—the process by which a known drug or
treatment is applied to a new disease manifestation of interest. This
approach enables rapid localization of potential therapeutic targets,
substantially reducing clinical exploration time.
To search for diseases that overlap with depression in the network, we
first mapped the depression disease module (consisting of 239 host
protein targets) onto a human protein interaction network comprising
18,508 proteins and 332,749 physical interactions ([60]Figure 1). The
nodes in the network represent genes or their corresponding gene
products, the edges refer to the connection relationship between nodes
and nodes, and the network constituted by continuous phases between
nodes in the network is the maximum connected module. We analyzed the
distribution of overlapping relationships between the depressive
disorder module and 299 other disorders, as shown in [61]Figure 2. We
found that among the relationships between disease modules and 299
disease-related proteins, depression and cardiovascular diseases,
gastrointestinal diseases, and endocrine diseases have significant
overlapping relationships, such as coronary heart disease, inflammatory
bowel disease, and diabetes mellitus, which often appear together with
depression as comorbidities ([62]Table 1). These findings have been
verified in numerous clinical studies demonstrating that patients with
cardiovascular disease are more likely to experience depression than
the general population, and that depressed patients have a higher risk
of developing cardiovascular disease and mortality rate ([63]16).
Inflammation plays a crucial role in the development and progression of
cardiovascular disease, with inflammation acting as a trigger for the
early stages of the atherosclerotic process. Patients with increased
inflammatory cytokines are at an elevated risk of developing
cardiovascular diseases (CVD). Additionally, numerous studies indicate
that depression often co-occurs with gastrointestinal diseases such as
functional dyspepsia (FD), inflammatory bowel disease (IBD), gastritis,
gastric ulcer, and acute enteritis, with inflammatory cytokines
exerting an important pathogenic role in the development of these
disorders ([64]17). Similarly, a growing number of studies have
demonstrated the correlation between depression and metabolic diseases.
Depression often co-morbid with multiple metabolic diseases, including
obesity and diabetes, and the underlying mechanisms of both
pathogeneses involve chronic inflammation, which strongly correlates
with disease severity. In summary, by exploring the overlap of
depression modules with other disease proteins, this approach reveals
the comorbid features of depression to some extent. By comparing these
common comorbidities, we can speculate that they share similar
pathogenesis. Thus, inflammation is likely to be their shared
pathogenesis. This also implies that potential treatments for
depression are likely to be derived from disease-specific treatments.
Figure 1.
Figure 1
[65]Open in a new tab
Depression disease module. Depression-targeted proteins are not
randomly distributed in the human interactome but form a large
connectivity component (LCC) consisting of 202 proteins and multiple
small subgraphs.
Figure 2.
Figure 2
[66]Open in a new tab
Distribution of the network overlap measure S[AB] between 299 diseases
and depression targets. S[AB] values represent the network-based
overlap between depression targets A and the genes associated with each
disease B.
Table 1.
The top 10 where depression targets overlap with 299 other disease
networks.
Number Disease S[AB]
1 Vascular diseases −0.075
2 Gastrointestinal diseases −0.070
3 Cardiovascular diseases −0.067
4 Digestive system diseases −0.063
5 Endocrine system diseases −0.062
6 Neurologic manifestations −0.050
7 Intestinal diseases −0.045
8 Neoplasms by site −0.028
9 Bone diseases −0.027
10 Heart diseases −0.024
[67]Open in a new tab
Distance ranking of diseases that overlap with the depression disease
module, where the smaller the S[AB], the larger the overlap between the
two and the higher the ranking.
3.1. Analysis of key genes and pathways
On top of the established disease modules, using this network overlap
approach, we further explored the relationship between depression and
inflammation. We screened immune-related genes and their products in
peripheral blood and calculated the network relationship between them
and the disease module. We found that the distance between them in the
network S[d] = −0.358, i.e., the network formed by inflammation-related
factors was included in the depression disease module, and the protein
interaction network with 90 common targets was obtained by STRING
database, with a total of 347 edges and a mean degree value of 7.71.
The cytohubba tool in Cytoscape analyzed the PPI network, and the MCC
method screened the top 10 key targets ([68]Table 2). Their network
relationships are shown in [69]Figure 3. Among them, the
pro-inflammatory factors TNF-α and IL-6 and the anti-inflammatory
factors IL-4 and IL-10 are more central. And a large number of studies
have shown that depressed patients with depression have C-reactive
protein (CRP), prostaglandins, and other arachidonic acid derivatives,
as well as IL-6, IL-1β and TNF-α were significantly increased ([70]18).
In addition, some data suggest that serum cytokine concentrations,
including IL-6, are associated with the severity and duration of
depressive illness and the effectiveness of antidepressant medication
([71]19).
Table 2.
The top 10 key targets were screened by the MCC method.
Rank Name Score
1 IL6 43,964
2 IL10 43,712
3 TNF 42,358
4 IL4 41,906
5 IL1B 41,330
6 IL18 40,442
7 CXCL8 40,440
8 IL1A 40,350
9 CCL2 40,344
10 IL17A 3,120
[72]Open in a new tab
The PPI network was analyzed by cytohubba tool in cytoscape, and the
top 10 key targets were filtered by MCC method to obtain the essential
proteins in the network. Proteins with a high score tend to be critical
proteins.
Figure 3.
Figure 3
[73]Open in a new tab
The network relationship of the first 10 key targets. The 10 key
targets in the figure were screened by the MCC method.
Subsequently, we screened key genes common to both using the STRING
database for functional annotation analysis ([74]Figure 4). We found
that the targets of action between depression and inflammation exert
molecular functions such as regulation of cytokine activity mainly
through cellular responses to chemical stimuli, organic substances,
etc., based on cellular components such as extracellular gaps. The top
20 signaling pathways obtained from KEGG signaling pathway enrichment
analysis are shown in [75]Figure 5. In the pathogenesis of depression,
the HIF-1 signaling pathway, PI3K-Akt signaling pathway, Th17 cell
differentiation, hepatitis B, and inflammatory bowel disease are
crucial in the inflammatory response. The HIF-1 signaling pathway
indicates that there is an interdependent relationship between immune
response and hypoxic response. Tissue hypoxia is a prominent feature of
the inflammatory response. HIF is rapidly stabilized under hypoxia and
is responsible for the activation of adaptive transcriptional
responses, including the upregulation of metabolic factors such as
vascular growth factor. In addition, studies have shown that huperzine
activates the HIF-1α-VEGF signaling pathway in vivo, enhancing neuronal
synaptic plasticity and thus exerting antidepressant effects ([76]20,
[77]21). In a mouse model of depression exposed to lipopolysaccharide
(LPS), pioglitazone with potential anti-inflammatory effects improved
depressive behavior via upregulation of the PI3K/AKT pathway ([78]22).
In clinical practice, various drugs, such as saffron and NGR1, have
been shown to improve depressive behavior through the PI3K/AKT pathway
([79]23, [80]24). Th17 cell differentiation may, to some extent,
reflect the risk of depression in patients. Although it has been
clinically shown that the signature cytokine interleukin 17A (IL-17A)
produced by Th17 cells does not significantly correlate with the
severity of depression, Th17 cells are involved in the gut-brain axis
to mediate the stress response, possibly by promoting
neuroinflammation, microglia, and astrocyte activation thereby neuronal
damage triggering depressive symptoms. Therefore, Th17 cells may be a
promising target for the treatment of depression ([81]25–27). In
conclusion, multiple signaling pathways are intertwined in the
pathogenesis of depression, with the neuroimmune system signaling
pathway playing an important role.
Figure 4.
[82]Figure 4
[83]Open in a new tab
Enrichment analysis.
Figure 5.
[84]Figure 5
[85]Open in a new tab
Pathway analysis.
3.2. Anti-inflammatory effect analysis of antidepressants
Finally, we calculated the distance between 6,100 drugs and the
depression disease module to rank them. The top 20 drug-disease
proximity profiles are shown in [86]Table 3. We found that most of the
FDA-approved antidepressants were ranked highly. As shown in [87]Figure
6, we mapped the network relationships between the first three drugs
and the depressive disorders module. The drugs shown in [88]Figures
6B,[89]C are all FDA-approved antidepressants. We found that they have
more acting relationships with the depressive disorder’s module in the
network, which aligns with our expectations. Even more interesting is
that these drugs usually also have anti-inflammatory effects, which
suggests that depression and inflammation have potential similarities
in terms of pathogenesis. This implies that anti-inflammatory
strategies can alleviate depressive symptoms to some extent. In
addition, drugs that treat other diseases and rank highly, we speculate
that they may have a positive effect on the treatment of depression,
although more clinical trials are needed to determine the results. We
found that most of the FDA-approved drugs with antidepressant effects
ranked high, and our analysis revealed that these drugs could usually
treat not only depression but also diseases closely related to
inflammation, such as cancer, chronic neuropathic pain, and diabetes.
In other words, these drugs are likely to exert antidepressant effects
by inhibiting inflammatory pathways or reducing levels of inflammatory
factors. These drugs likely exert their antidepressant clinical effects
by exerting anti-inflammatory effects. Thus, the mechanism by which
antidepressants act is likely to be related to anti-inflammation, and
there is a correlation between depression and inflammation. For
example, among the top three drugs, Perphenazine is an antipsychotic
phenothiazine derivative found in clinical studies to treat ear
swelling mediated by 12-o-tetradecanoylphorbol-13-acetate (TPA) and
oxazolone (OXA) and exert anti-inflammatory effects in addition to
psychiatric disorders ([90]28); Clomipramine is a tricyclic
antidepressant that has been clinically found to also reduce
LPS-induced neuroinflammation by partially modulating NLRP3 ([91]29);
Amitriptyline is also a tricyclic antidepressant, and many studies have
found that Amitriptyline, in addition to its antidepressant and
analgesic effects, can exert anti-inflammatory effects in humans and
animal models of acute and chronic inflammation ([92]30, [93]31). In
addition to this many of the top-ranked drugs with antidepressant
effects have been found to have some anti-inflammatory effects in
clinical studies, and together these further assists in validating our
prediction that there is a correlation between depression and
inflammation. Second, by looking at the network action relationship map
of disease-drug key targets, we found that most of the top-ranked drugs
treated depression well and produced more associations with disease
proteins in the network. This suggests that our method can measure the
effect of drugs on the disease to some extent. It also suggests that
drugs that are ranked high but are not currently used clinically as
depression treatment are likely to be potentially effective for the
treatment of depression, i.e., the indications for these drugs are
likely to be similar to depression in terms of pathogenesis. More
clinical trials are needed in the future to test our hypothesis.
Table 3.
Top 20 drug-disease proximity.
Number Drug Z-value Is it an antidepressant Indication
1 Perphenazine −9.633 No Perphenazine is a phenothiazine used to treat
schizophrenia as well as nausea and vomiting.
2 Clomipramine −9.249 Yes Clomipramine is a tricyclic antidepressant
used in the treatment of obsessive–compulsive disorder and disorders
with an obsessive–compulsive component, such as depression,
schizophrenia, and Tourette’s disorder.
3 Amitriptyline −9.236 Yes Amitriptyline is a tricyclic antidepressant
indicated in the treatment of depressive illness, either endogenous or
psychotic, and to relieve depression associated anxiety.
4 Selegiline −9.214 Yes Selegiline is a monoamine oxidase inhibitor
used to treat major depressive disorder and Parkinson’s.
5 Minaprine −8.99 Yes Minaprine is a psychotropic drug that has proved
to be effective in the treatment of various depressive states.
6 Prasugrel −8.744 No Prasugrel is a P2Y12 platelet inhibitor used to
reduce the risk of thrombotic cardiovascular events in unstable angina
or non-ST-elevation myocardial infarction (NSTEMI), and in patients
with STEMI when managed with either primary or delayed PCI.
7 Sertraline −8.698 Yes Sertraline is a selective serotonin reuptake
inhibitor (SSRI) indicated to treat major depressive disorder, social
anxiety disorder, and many other psychiatric conditions.
8 Sorafenib −8.695 No Sorafenib is a kinase inhibitor used to treat
unresectable liver carcinoma, advanced renal carcinoma, and
differentiated thyroid carcinoma.
9 Tranylcypromine −8.631 Yes Tranylcypromine is a monoamine oxidase
inhibitor used to treat major depressive disorder.
10 Troglitazone −8.59 No For the treatment of Type II diabetes
mellitus. It is used alone or in combination with a sulfonylurea,
metformin, or insulin as an adjunct to diet and exercise.
11 Fluphenazine −8.564 No Fluphenazine is a phenothiazine used to treat
patients requiring long-term neuroleptic therapy.
12 Duloxetine −8.563 Yes Duloxetine is a serotonin norepinephrine
reuptake inhibitor used to treat generalized anxiety disorder,
neuropathic pain, osteoarthritis, and stress incontinence.
13 Efavirenz −8.521 No Efavirenz is a non-nucleoside reverse
transcriptase inhibitor used to treat HIV infection or prevent the
spread of HIV.
14 Trimipramine −8.488 Yes Trimipramine is a tricyclic antidepressant
used to treat depression.
15 Desipramine −8.44 Yes Desipramine is a tricyclic antidepressant used
in the treatment of depression.
16 Meperidine −8.396 No Meperidine is an opioid agonist with analgesic
and sedative properties used to manage severe pain.
17 Lopinavir −8.395 No Lopinavir is an HIV-1 protease inhibitor used in
combination with ritonavir to treat human immunodeficiency virus (HIV)
infection.
18 Esketamine −8.323 Yes Esketamine is a NMDA receptor antagonist used
for treatment-resistant depression.
19 Pipotiazine −8.243 No Pipotiazine is an antipsychotic indicated for
the management of chronic, non agitated schizophrenic patients.
20 Phenobarbital −8.161 No Phenobarbital is long-lasting barbiturate
and anticonvulsant used in the treatment of all types of seizures,
except for absent seizures.
[94]Open in a new tab
The shortest path between the drug and the disease protein is
calculated, and then the Z-value is calculated after eliminating the
bias caused by the different number of targets of different drugs; the
smaller the Z-value, the more effective the drug is.
Figure 6.
[95]Figure 6
[96]Open in a new tab
The key targeted relative maps are visualized between the top-3 drugs
and depression. Where (A) presents the perphenazine-depression key
target interaction map, (B) presents the clomipramine-depression key
target interaction map, and (C) presents the amitriptyline-depression
key target interaction map. The drugs at the top of the list were more
closely associated with targets for depression. The undistorted images
could be found in the [97]supplementary materials.
Also importantly, we hope to reposition drugs by clarifying the
inflammatory mechanisms of depression, i.e., “new use of old drugs.”
Because drug development is often time-consuming, costly, and has a low
success rate, new use of old drugs is a strategy to use old drugs or
drugs in development for indications beyond the original approval and
to expand their scope of application and use, which not only saves a
lot of time and resources but also significantly accelerates the drug
development process. Based on this scenario, we predict that the
top-ranked, i.e., other drugs in the network with a similar distance to
the depression disease module, may be useful for the treatment of
depressed patients, and more reliable evidence will need to be provided
by a large number of clinical trials.
4. Discussion
Diseases often occur not because of a defect in one gene but as a
result of coordinated interactions between different genomes. In this
paper, we examined the co-morbidity of depression based on the human
protein interactome network, using a network analysis approach based on
the depression disease module. We further investigated and predicted
the correlation between depression and inflammation by calculating the
distance between the depression disease module and other disease
proteins, and by combining previous clinical studies to identify
possible shared mechanisms between co-morbidities. A genetic study
found that endothelial dysfunction and inflammation factors were
definitively correlated with depressive symptoms in patients with heart
disease ([98]32). Similarly, the results of a clinical trial study
assessing depression, metabolic syndrome, and inflammatory markers
showed that increased appetite in depressed patients was positively
correlated with CRP and HIF-α ([99]33). The findings of this
immunometabolism form of depression study suggest that we must be aware
of disease co-morbidities in clinical treatment and consider
immunometabolism and other pathways to intervene in depressive
episodes. The important role of neuroinflammation in the pathogenesis
of these co-morbidities suggests that there is likely a common feature
between depression and these disorders, namely neuroimmune mechanisms.
This suggests that we should pay attention to common disease
co-morbidities in clinical practice to improve the treatment outcome.
Secondly, we screened critical targets based on the magnitude of target
centrality, including the pro-inflammatory factors TNF-α and IL-6 and
the anti-inflammatory factors IL-4 and IL-10. Numerous clinical studies
have shown that depression is closely related to the concentration of
inflammatory factors. Patients with depression usually have higher
concentrations of pro-inflammatory factors such as TNF-α and IL-6 than
healthy controls. As the disease progresses, their body usually has
lower concentrations of the anti-inflammatory factor IL-10 than healthy
controls ([100]34). Studies have found that IL-6 concentrations are
closely associated with depression and that the pro-inflammatory factor
IL-6 may be involved in the brain inflammatory response through
multiple pathways. Among others, an animal study showed that Toll-like
receptor 4 genes and cytokine receptor genes are expressed in the
choroid plexus (CP) and that stimulation of these receptors by
cytokines induces the synthesis and eventual secretion of IL-6 into the
blood-cerebrospinal fluid (CSF) ([101]35). This, in turn, alters areas
of the brain that regulate mood-related conditions and induces
depressive symptoms. Through enrichment and pathway analysis of
critical targets, we identified essential pathways related to hypoxia,
inflammatory diseases, Etc. Several previous studies demonstrated the
therapeutic effects of hypoxic preconditioning in a rat model of
depression ([102]36). In peripheral blood cells from depressed
patients, studies likewise found that mRNA expression of HIF-1 and its
target genes were mainly associated with depressive symptoms ([103]37).
In recent years, the hypoxic pathway that triggers neurodegenerative
lesions has received attention. The hypoxia-inducible factor (HIF)
plays a vital role in cellular biological processes and adaptation to
cellular stress induced by hypoxic environments and is an essential
transcriptional regulator. Therefore, we hypothesize that the HIF-1
signaling pathway is likely to play an important role in preventing
depression. It also can help us identify and diagnose patients with
atypical depression symptoms at the molecular level.
Finally, we ranked the drugs by finding a suitable algorithm to
calculate the distance between drugs and disease modules after
eliminating errors due to, for example, different numbers of drug
targets. Looking at the top-ranked drugs and combining them with modern
clinical studies, we found that most of the FDA-approved
antidepressants also have anti-inflammatory effects, implying that the
mechanism of action by which these drugs exert their antidepressant
effects is likely to be the anti-inflammatory pathway at the same time.
In an animal study, clomipramine was shown to attenuate
lipopolysaccharide (LPS)-induced depression in a mouse model by
partially modulating NLRP3 inflammatory vesicles ([104]29). Among them,
NLRP3 inflammatory vesicles are present in neurons and glial cells
([105]38), which are involved in many critical innate immune processes,
such as infection and inflammation. That is, NLRP3 inflammatory
vesicles may be important in triggering depression. At the same time,
we hypothesized that drugs with anti-inflammatory effects also have the
potential to exert antidepressant efficacy, and the results of a
systematic evaluation of the efficacy and safety of anti-inflammatory
drugs in patients with major depression showed that anti-inflammatory
drugs not only help to treat depressed patients and exert
antidepressant effects but also are quite safe ([106]39).
In conclusion, it is reasonable to conclude that there is a strong
correlation between depression and inflammation, whether from the
perspective of co-morbidity, signaling pathway analysis, or
antidepressant drug action analysis, which provides a basis for the
inclusion of anti-inflammatory strategies in the treatment of
depression in the future and provides a feasible approach to explore
the inflammatory mechanisms of depression. It also provides some
reference ideas for new applications of old drugs to complex diseases.
This has some applicability in both clinical treatment strategies and
drug development.
There are several limitations to this study. Our study is based on
network predictions, and more clinical practice is needed to explore
the relationship between neuroinflammation and the onset of depression
and its specific mechanisms of action. In addition, we only focused on
the use of the drug without refining the therapeutic effects and
adverse effects. Therefore, further studies are necessary to address
these issues and provide a more comprehensive evaluation of their
efficacy and safety. Despite these limitations, our study provides
preliminary insights into the potential role of anti-inflammatory drugs
as a treatment for depression and merits further investigation.
Data availability statement
The original contributions presented in the study are included in the
article/Supplementary materials, further inquiries can be directed to
the corresponding author.
Author contributions
YZ: conceptualization, funding acquisition, and supervision and project
administration. YZ, HP, and JL: methodology. HP: formal analysis. XH:
resources. YW: suggestion. XH, HP, YZ, and YL: writing—original draft.
XH, HP, and JL: visualization. All authors contributed to the article
and approved the submitted version.
Funding
This study was supported by Scientific and Technological Innovation
Project of China Academy of Chinese Medical Sciences (No. C12021A05042,
No. CI2021A05401), the National Natural Science Foundation of China
(No. 81674101), National Key Technology Support Program (No.
2012BAI25B02), Self-selected subject of China Academy of Chinese
Medical Sciences (No. Z0217).
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