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The inflammatory cytokines: molecular biomarkers for major depressive disorder?

Cytokines are pleotropic cell signaling proteins that, in addition to their role as inflammatory mediators, also affect neurotransmitter systems, brain functionality and mood. Here we explore the potential utility of cytokine biomarkers for major depressive disorder. Specifically, we explore how genetic, transcriptomic and proteomic information relating to the cytokines might act as biomarkers, aiding clinical diagnosis and treatment selection processes. We advise future studies to investigate whether cytokine biomarkers might differentiate major depressive disorder patients from other patient groups with overlapping clinical characteristics. Furthermore, we invite future pharmacogenetic studies to investigate whether early antidepressant-induced changes to cytokine mRNA or protein levels precede behavioral changes and act as longer-term predictors of clinical antidepressant response.

Charlotte Martin1, Katherine E Tansey1, Leonard C Schalkwyk1 & Timothy R Powell*,1 MRC Social, Genetic & Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, King’s College London, PO 80, Denmark Hill, London, SE5 8AF, UK *Author for correspondence: Tel.: +44 207 848 0856 Fax: +44 207 848 0866 [email protected] 1

Keywords: antidepressants • biomarkers • cytokines • genetic • major depressive disorder • proteomic • transcriptomic

Inflammatory cytokines are cell signaling proteins that act in the periphery to coor­ dinate inflammatory and immunological responses to infection [1]. However, a grow­ ing body of evidence suggests that cytokines such as the chemokines, interferons, inter­ leukins and the TNF family have a multi­ tude of pleo­ tropic functions, including a possible role in the pathophysiology of major depressive disorder (MDD) [2]. MDD is a complex heterogeneous disor­ der characterized by low mood, anhedonia, fatigue and cognitive impairment [3]. MDD is a leading cause of morbidity in economi­ cally developed countries [4], and is the larg­ est cause of nonfatal disease burden world­ wide [5]. MDD has a complex biological and environmental etiology, and the precise mechanisms underlying its development are currently unknown. However, studies have repeatedly shown that the proinflammatory cytokine IFN‑a, used in the treatment of hepatitis and cancer, elicits depressive symp­ toms in a large proportion of patients, along with altered neurotransmitter metabolism, neuroendocrine function and responsive­

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ness to antidepressant pharmacotherapy [1]. Patients with MDD who are otherwise healthy have also been found to exhibit increased levels of proinflammatory cytok­ ines and their receptors in peripheral blood, cerebrospinal fluid and within the brain itself [6,7]. These observations strongly imply that endogenous cytokines and their modulation of the innate immune system are involved in the pathophysiology of MDD and may also possess ‘biomarker’ properties that could be of use in the clinical management of this disorder. A biomarker is defined as a “characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes or pharmacologic responses to a therapeutic intervention” [8]. Biomarker information at the genetic, tran­ scriptomic and proteomic biological system levels may provide distinct and clinically use­ ful information relating to MDD. Genotypes are stable, constant features that influence systemic physiological function throughout development and can predispose to certain disease states. Genomic information can

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Review  Martin, Tansey, Schalkwyk & Powell therefore be used as state predictors of long-lasting functional differences in the brain, disease manifesta­ tion and clinical responses to therapies. However, the dynamic and environmentally sensitive nature of cellu­ lar gene expression means that mRNA and proteomic biomarkers may not only function as baseline state pre­ dictors, but could also potentially be used in conjunc­ tion with changes in behavioral traits as a prospective measure of MDD prognosis. At present, the diagnosis of MDD is based on patients meeting a number of qualitative diagnos­ tic criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) [9] and of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision [10], as opposed to an objective result from an empirical test. As the diagnostic criteria for MDD overlap with other disorders, such as bipolar disorder, misdiagnoses are thought to be high. For example, it is estimated that 69% of bipolar disorder patients are initially mis­ diagnosed, with the most frequent misdiagnosis being MDD [11]. Knowledge of the genetic basis of immune dysregulation in the pathogenesis of MDD has given rise to a hypothesis that there may be endogenous inflammatory biomarkers that could be used to help diagnose MDD [12]. Cytokines are already used as biomarkers for assessing the progression and severity of many disease states [13], thus it stands to reason that their involvement in psychopathophysiology may ren­ der them useful biomarkers in the clinical diagnosis of MDD. Although cytokine dysregulation itself is not specific to MDD, recent studies suggest that disor­ der-specific molecular differences within the cytokines might be useful for differentiating MDD patients from nondepressed control subjects and even from bipolar disorder patients [14–16]. Further research in this field has indicated that molecular differences within cytok­ ine genes may also predict the success of antidepressant therapy [14,15,17,18]. Patient responses to specific antide­ pressants are highly idiosyncratic, and there are cur­ rently no means of predicting how an individual will respond [3]. The ‘trial-and-error’ process of treatment selection can lead to a significant time delay between clinical diagnosis and the identification of a success­ ful pharmacological agent. Consequently, in addition to diagnostic biomarkers, there is a pressing need to establish predictive biomarkers for treatment outcome to different pharmacological therapies. There is evidence to show that the administration of antidepressants induces a measurable change in the expression of cytokines [15,19]. It may therefore be possi­ ble to use baseline levels of cytokines to predict respon­ siveness to antidepressants, and to use the changes in cytokine expression as an objective biological measure­

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ment of treatment success. This is of particular clinical interest if early changes to cytokine mRNA or protein precede behavioral changes relating to early response to antidepressant drugs. In this review we summarize recent literature pertaining to the cytokines at each sys­ tem level (genetic, transcriptomic and proteomic) and demonstrate how they might be used to inform clin­ ical decisions regarding the diagnosis and treatment of MDD. Molecular diagnostic biomarkers for MDD Genetic biomarkers for MDD

No single gene has yet been robustly associated with MDD based on results from genome-wide association studies (GWASs) [20]. However, candidate gene studies suggest that single nucleotide polymorphism (SNP) differences in cytokine genes may mediate susceptibil­ ity to MDD and, as such, future polygenic signatures incorporating this information may have useful diag­ nostic capabilities. SNPs within genes encoding IL‑1b, TNF, IL‑10, IL‑18 and CCL2 have been identified as possible candidates for future genetic biomarkers for MDD, as summarized in Table 1. IL‑1b

IL‑1b is a member of the IL‑1 cytokine family, encoded by the IL1B gene at location 2q14 of chromo­some 2 [21]. IL‑1b is an important proinflammatory cytokine that has a role in coordination of the immune response and regulation of cell proliferation, differentiation and apoptosis [4]. Data from both animal and clinical stud­ ies have consistently implicated IL‑1b in the patho­ physiology of MDD [31–35]. IL1B variants have been associated with decreased function in the amygdala and anterior cingulate cortex [4]. These brain regions are involved in memory, emotional processing and reward mechanisms, and impaired functioning in each of these neural pathways has been implicated in the manifestation of MDD symptoms [36]. Subsequently, differences in the IL1B gene might be involved in MDD pathophysiology. One study identified a biallelic (T/C) polymor­ phism in the promoter region of IL1B at position -511 (rs16944); individuals homozygous for the -511T allelic variant produced significantly higher levels of IL‑1b than -511C homozygotes (as discussed in [35]). These findings have led to investigations into the IL1B SNP at position -511 and whether it might act as a genetic diagnostic biomarker for the development of MDD. However, candidate gene association studies have found no differences between MDD cases and controls at this SNP [21,22], but MDD patients who are homozygous for the ‘low-producer’ -511C allele were found to have a higher level of symptom severity than

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Table 1. Putative genetic biomarkers for the diagnosis of major depressive disorder. Cytokine Gene locus gene IL1B

2q14 of chromosome 2

Candidate gene/SNP

Association with MDD

Ref.

-511C/C ‘low-producer’ allele

Increased MDD symptom severity

[21]

-511C/C ‘low-producer’ allele

Earlier onset of geriatric MDD

[22]

-31T/T and -511C/C alleles

Recurrent MDD

[23]

IL10

1q31–1q32 chromosome 1

-1082A/A ‘low-producer’ allele

Increased prevalence among MDD patients

[24]

IL18

11q22.2–q22.3 chromosome 11

-607G and -137C

Increased risk of MDD after adverse life event

[25]

CCL2

17q11.2–q21.1 chromosome 17

-2518A

Increased risk of MDD with psychosis

[26]

TNF

6p21.1–21.3 chromosome 6

-308A

Increased incidence of MDD in Korean populations

[27]

-308G ‘low-producer’ allele

Increased geriatric MDD in Caucasian populations

-308G/A alleles

No association

[24]

rs76917

Associated with MDD phenotype

[30]

[28,29]

MDD: Major depressive disorder; SNP: Single nucleotide polymorphism.

-511T carriers, evidenced by higher baseline Hamilton Depression Rating (HAM‑D) scores and increased early insomnia [21]. The homozygous -511C genotype has also been observed to reduce the age of onset of geriatric depression by 7 years when compared with heterozygotes or homozygous T carriers [22]. A second SNP with potential biomarker proper­ ties has also been identified in the promoter region of the IL1B gene at position -31(T/C) (rs1143627). A genotypic combination of homozygosity for the -31T allele and -511C was associated with recurrent MDD, whereas heterozygosity at both sites was more frequent among controls [23]. -31(T/C) is located in a transcrip­ tion factor-binding domain (part of the ‘TATA box’) within IL1B [37], and so may directly affect transcrip­ tion and protein expression of the IL‑1b cytokine [4]. These findings suggest that the combination of -511 and -31 in IL1B may be used to predict the presence of MDD, and -511 may also be used to predict severity. IL‑10

The anti-inflammatory properties of IL‑10 are well described, with its main role being that of prevent­ ing damage to the host during infection [38]. IL‑10 is produced by and suppresses the action of T cells dur­ ing inflammation [38]. The gene encoding IL‑10 maps to the 1q31–1q32 region on chromosome 1 and it is highly polymorphic, with the biallelic polymorphisms at positions -1082 (G/A) (rs1800896), -819 (T/C) (rs1800871) and -592 (A/C) (rs1800872) being asso­

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ciated with the transcription and production of IL‑10 [39]. In light of the established link between immu­ noinflammatory pathways and MDD, it has been hypothesised that MDD patients may be carriers of a ‘low-producer’ polymorphism of the IL10 gene. The A/A genotype of the polymorphism at -1082 has sub­ sequently been identified as a ‘low-producer’ allele, and the distribution of this genotype has indeed been found to be significantly more prevalent among MDD patients than controls [24]. IL‑18

IL‑18 is part of the IL‑1 superfamily and is involved in the proinflammatory response by means of inhibit­ ing production of IL‑10 and inducing the expression of IFN‑g and TNF (see below) [40]. Polymorphisms have been identified at positions -607(G/T) (rs1946518) and -137(C/G) (rs187238) in the promoter region of IL18. The major -607G and -137C alleles have shown to significantly increase the risk of developing MDD in individuals who have experienced previous adverse life events [25]. CCL2

CCL2 is involved in cell recruitment and chemo­ taxis during inflammation [41]. The CCL2 gene is located on chromosome 17 between loci 17q11.2 and q21.1 [42]. Two studies have focused on the role of the -2518(A/G) (rs1024611) polymorphism in MDD to reveal that individuals carrying the A allele have

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Review  Martin, Tansey, Schalkwyk & Powell an increased risk of developing MDD and psychotic symptomatology [26,43]. TNF

The systemic proinflammatory actions of TNF and its role in the pathogenesis of many disease states are well characterized [44]. The TNF gene is located within the class III coding region of the major histo­compatibility complex on the small arm of chromosome 6 (6p21.1–21.3) [24]. Multiple studies have examined the role of SNPs in TNF and MDD with variable results. A polymorphism at position -308(G/A) in the pro­ moter region and its association with MDD has been studied in different population groups. In a Korean population, the A allele was significantly associated with MDD [27]. In contrast to this finding, Cerri and colleagues also studied the -308G/A polymorphism in a Caucasian population, and found that the ‘low-­ producer’ G allele was significantly linked to geriatric MDD [28,29]. Conflicting with all of the above stud­ ies, no association was found between MDD and either forms of the -308G/A gene in a small sample of 32 MDD patients and 363 controls [24]. Another poly­ morphism (rs76917) was identified as part of a candi­ date gene association study of 1738 MDD patients and 1802 controls (the largest of all the TNF studies cited in this review). This study confirmed that rs76917 was the only SNP with a significant association with MDD after within-gene multiple testing correction [30]. How­ ever, the authors did note that this result may be a false positive due to the high number of other genes that were tested as part of this candidate study. Although there is currently mixed evidence as to whether TNF genotypes can be used to predict the risk of developing MDD, the results reviewed here are sug­ gestive of a relationship between TNF poly­morphisms and the development of MDD. TNF levels are known to be related to the expression and functionality of the serotonin transporter, a key protein implicated in both the pathophysiology and treatment of MDD [45,46]. Thus, it is reasonable to hypothesize that functional polymorphisms in the TNF gene may interact via this mechanism to predispose to MDD. Consequently, TNF genotype could have a role in determining susceptibil­ ity to MDD, but further studies with larger population groups are warranted to determine exactly what that role may be. mRNA biomarkers for MDD

In contrast to genetic biomarkers, mRNA biomarkers have the additional advantage of capturing how geno­ types are interacting with the epigenetic landscape and transcription factors, thus perhaps depicting a more accurate representation of a specific molecular pheno­

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type as it is occurring in real time [17]. Several candi­ dates including IL1B, IL6, IFN‑ g, TNF, IL4 and MIF have been identified as potential mRNA biomarkers for MDD, as summarized in Table 2. As we are focusing on biomarkers, all studies discussed here refer to studies performed in human subject blood. IL1B, IL6, IFN‑g & TNF mRNA

Adding to the evidence for the role of these cytokines in the etiology of MDD, levels of IL1B, IL6, IFN‑ g and TNF mRNA have shown to be significantly higher in MDD patients relative to controls in a Taiwanese pop­ ulation [47]. Furthermore, these levels were still raised after 3 months of treatment with fluoxetine. A second study also found that MDD patients exhibited higher levels of IL1B, IL6 and TNF mRNA than controls [15]. Raised baseline TNF mRNA was also observed in MDD patients by Savitz and colleagues [48]. Additional research is clearly required; however, these limited data do suggest it would be worthwhile to further investigate whether IL1B, IL6, IFN‑ g and TNF mRNA levels could be used as future biomarkers for MDD. IL4 mRNA

IL4 mRNA was found to be significantly lower in MDD patients than controls in one study [15]. The role of IL‑4 in the endogenous inflammatory response is to directly instruct naive T cells to differentiate into Th2 cells [49]. As MDD has been associated with a predomi­ nantly Th1 immune response [38], this low expression of IL4 in MDD is of interest. MIF mRNA

MIF mRNA was found to be 32% higher in MDD patients than in controls in one study [15]. MIF func­ tions to inhibit the anti-inflammatory effects of gluco­ corticoids, thus promoting a proinflammatory pheno­ type [50]. Although a hypothesis cannot be made on one study alone, MIF has evidenced potential to function as a possible biomarker for MDD and therefore warrants further study. Proteomic biomarkers for MDD

Multiple studies have reported increased levels of pro­ inflammatory cytokines in the peripheral blood of MDD patients [14,51]. These peripheral cytokine titres can pro­ vide proteomic information that may have potential use in MDD diagnostics. Unlike mRNA biomarkers, proteomic biomarkers are also affected by translational processes such as protein folding, and can therefore offer potentially different biomarker information. Further­ more, biomarkers found in peripheral blood offer acces­ sible information regarding current inflammatory status and cytokines are also known to cross the blood–brain

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The inflammatory cytokines: molecular biomarkers for major depressive disorder? 

barrier, with the potential to influence brain biochemis­ try [52]. The most frequently described cytokines which show differences in MDD blood are IL‑6, IL‑1b, TNF, IL‑10 and IL‑13, as summarized in Table 3.

Table 2. Putative mRNA biomarkers in blood for the diagnosis of major depressive disorder. Cytokine mRNA

Baseline mRNA expression in MDD vs controls

IL‑6

IL1B

Increased

[15,47]

IL‑6 has repeatedly been found to be raised in the serum or plasma of MDD patients versus controls [1,2,6,55,56]; a finding that has been supported by four recent meta-analyses [53,54,58,59]. Serum levels of IL‑6 have also been shown to significantly correlate with the severity of MDD symptoms [57]. IL‑6 has, in fact, been cited as one of the most reliable peripheral biomarkers of MDD [2].

IL6

Increased

[15,47]

IFN- g

Increased

[47]

TNF

Increased

[15,4748]

MIF

Increased

[15]

IL4

Reduced

[15]

IL‑1b & TNF

To a lesser extent than IL‑6, elevations in IL‑1b have been described in MDD patients [1,12,33,55,60]. There are mixed data from meta-analyses regarding IL‑1b. A meta-ana­lysis by Howren and colleagues found that IL‑1b levels were associated with MDD [53], whereas the more recent study by Dowlati and colleagues found no significant elevation of IL‑1b in MDD patients [54]. Another strong candidate for a peripheral biomarker of MDD is TNF, which has been shown to be elevated in MDD patients in several studies [6,56,60] and two meta-analyses [59,54]. IL‑10 & IL‑13

Baseline peripheral levels of IL‑10 and IL‑13 have been demonstrated to be lower in MDD patients compared with controls [24,61]. IL‑13 is linked to the cytokine milieu of the Th2 phenotype; therefore, this result is consistent with the hypothesis that MDD is linked to a predominantly Th1 immune response [38,61]. As a result of the limited number of studies, further research is required on the potential of all of these cytokines as biomarkers for MDD. Molecular biomarkers for predicting & monitoring antidepressant treatment response Genetic biomarkers for treatment response

SNPs within several interleukin genes have been associ­ ated with variable response to antidepressant treatment. Genes encoding IL‑1b, IL‑6 and IL‑11 have been iden­ tified as having a possible effect on the reported clinical efficacy of specific antidepressants and, therefore, may be potential candidates for future genetic biomarkers for the prediction of response to treatment for MDD, as summarized in Table 4. IL1B

The role of IL1B polymorphisms in the diagnosis of MDD has already been discussed, but there is fur­

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Ref.

MDD: Major depressive disorder.

ther evidence to show that they may also be useful in predicting treatment response in MDD patients. For example, homozygous carriers of the -511C allele were found to have less favorable responses to the selective serotonin reuptake inhibitor (SSRI) fluoxetine than the -511(C/T) or (T/T) genotype carriers in a Chinese pop­ ulation [21]. Furthermore, an interesting observation in a study by Yu and colleagues was that the -511C/C patient group was the only group to have no remitters after 4 weeks of treatment with fluoxetine [21]. Carrier of the -511T/T variant have also been found to have a significantly faster and more pronounced response to the SSRI paroxetine than carriers of the -511C/C genotype [62]. By contrast, clinical efficacy to the SSRI mirtazapine was not affected by polymorphisms at this locus [62]. Further IL1B polymorphisms associated with reduced responsiveness to treatment include rs114643 in intron 6 and rs16944 in the promoter region. The G/G genotypes of both of these alleles were found to be significantly associated with nonremission after 6 weeks of antidepressant mono- or poly-therapy with several antidepressant classes, including SSRIs, tricyclic antidepressants (TCAs), noradrenergic and specific serotonergic antidepressants and monoamine Table 3. Putative proteomic biomarkers in blood for the diagnosis of major depressive disorder. Peripheral blood cytokine

Baseline serum level in MDD vs controls

Ref.

IL‑1b

Increased

[53]

No significant difference

[54]

IL‑6

Increased

[1,2,6,53–58]

TNF

Increased

[6,54,56,59,60]

IL‑10

Reduced

[24]

IL‑13

Reduced

[61]

MDD: Major depressive disorder.

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Table 4. Putative genetic biomarkers for predicting antidepressant treatment outcomes. Cytokine Gene locus gene IL1B

2q14 of chromosome 2

Candidate gene/SNP

Treatment outcome

-511C/C ‘low-producer’ allele

Reduced response to SSRI fluoxetine

[21]

-511T/T ‘high-producer’ allele

Faster and better response to SSRI paroxetine

[47]

-511T/C alleles

No effect on SSRI mirtazapine efficacy

[47]

rs114643G/G and rs16944G/G

Nonremission with SSRIs, TCAs, NaSSAs and MAOIs

[4]

rs7801617

Reduced response to escitalopram

[18]

Better treatment outcomes with escitalopram

[18]

IL6

7p21–15 of chromosome 7

IL11

19q13.3–q13.4 of rs1126757/A allele chromosome 19

Ref.

MAOI: Monoamine oxidase inhibitor; NaSSA: Noradrenaline and specific serotonergic antidepressant; SNP: Single nucleotide polymorphism; SSRI: Selective serotonin reuptake inhibitor; TCA: Tricyclic antidepressant.

oxidase inhibitors (MAOIs) [4]. However, further ana­ lysis of the G/G genotype versus AG/AA groups com­ bined resulted in only rs1143643 polymorphisms being associated with nonremission [4]. These findings suggest that it may therefore be possible to use IL1B genotype data to predict which antidepressant medication will offer the most clini­ cal efficacy for each individual suffering from MDD. Although these findings highlight the potential use of genotype biomarkers within IL1B as predictors of antidepressant response, it is impossible to make any firm conclusions without further replication studies in much larger samples. IL6

The human IL6 gene is organized into 5 exons and 4 introns on the short arm of chromosome 7 [24]. IL‑6 is a pleiotropic cytokine that plays an important role in the acute-phase response and chronic inflammation [63]. A major SNP in the IL6 gene (rs7801617) has been associated with a poor response to the SSRI escitalo­ pram [18]. IL‑6 has been shown to have the potential to switch the production of the neurotransmitter sero­ tonin to acetylcholine in raphe neurons [64]. This inhi­ bition of serotonin production by IL‑6 could offer a possible explanation as to why IL6 gene variants may affect clinical response to SSRIs. IL11

Situated on chromosome 19, a major SNP in the human IL11 gene was shown to be the best predictive marker of clinical response to escitalopram as part of a GWAS [18]. Carriers of the A allele in rs1126757 (A/A or A/G) had better treatment outcomes than individu­ als homozygous for the G allele, after 12 weeks of treat­ ment with escitalopram [18]. IL‑11 is a close functional homolog of IL‑6, and both of these cytokines are

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implicated in the inhibition of serotonin production from raphe neurons in the brain stem (see above). This inhibition of serotonin production by IL‑6 and IL‑11 could offer a possible explanation as to why variants in these genes may affect clinical response to SSRIs. Studies with larger sample sizes and multiple drugs are required to further establish the clinical implications of these SNPs. mRNA biomarkers for prediction of treatment response

Both epigenetic and transcription factors have been implicated in the mechanism of response to antide­ pressants [65,66]. It is therefore possible that mRNA data collected from peripheral blood could be used to predict whether an individual will respond to spe­ cific antidepressant drugs, and contain information above and beyond that obtained at the level of the gene. If predictive mRNA biomarkers can be identi­ fied, it could give rise to personalized treatment selec­ tion based on baseline levels of mRNA biomarkers [17]. Three putative mRNA biomarkers are discussed below, as summarized in Table 5. IL1B, TNF & MIF mRNA

Patients with MDD were found to have higher baseline levels of IL1B, TNF and MIF mRNA (+33%, +39% and +48%, respectively), which were subsequently predictive of nonresponse to antidepressant treatment with nortriptyline or escitalopram [15]. Additionally, baseline levels of IL1B, TNF and MIF mRNA were found to negatively correlate to changes in MDD sever­ ity after treatment with either nortriptyline or escitalo­ pram, with the best predictive model being generated when all three mRNA levels were included [15]. The result for TNF was replicated in another study, whereby higher levels of TNF mRNA was present in

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Table 5. Putative baseline mRNA biomarkers in blood for predicting antidepressant treatment outcomes. Cytokine mRNA

Baseline mRNA expression in MDD vs controls

Treatment outcome

IL1B

Increased

Reduced response to nortryptiline or escitalopram

[15]

TNF

Increased

Reduced response to nortryptiline or escitalopram

[15]

Increased

Nonresponse to escitalopram

[17]

Increased

Reduced response to nortryptiline or escitalopram

[15]

MIF

Ref.

MDD: Major depressive disorder.

nonresponders both at baseline and after 8 weeks of treatment with escitalopram [17]. In the same study, the mRNA of another protein in the TNF family, LTA (formerly known as TNF‑b), was found to similarly act as a putative biomarker for escitalopram response at baseline and after 8 weeks of treatment [17]. As dis­ cussed, TNF is involved in the expression and func­ tionality of the serotonin transporter, which is the primary pharmacological target of SSRIs [45,46]. More­ over, TNF has also been linked to the induction of the IDO enzyme, which breaks down the precursor to serotonin [2]. Thus, increased expression of TNF may lead to altered expression and function of the serotonin transporter and decreased serotonin synthesis [2]. TNF has also been demonstrated to inhibit hippocampal neurogenesis, which is a further mechanism by which antidepressants are thought to exert their therapeutic effects [67,68]. These findings highlight the potential importance of the TNF family as mRNA biomark­ ers for the prediction of response to antidepressants, particularly SSRIs such as escitalopram. Proteomic biomarkers for prediction of treatment response

Peripheral levels of cytokines have potential use as predictors of outcome to antidepressant treatments, as summarized in Table  6. Patients with increased peripheral inflammatory activity before treatment have been shown to be less responsive to antidepres­ sants [1]. For instance, patients who are nonrespond­ ers to anti­depressants show increased plasma levels of

IL‑6 [6,62,69]. However, perhaps the strongest candidate as a potential biomarker for the prediction of treat­ ment outcome is TNF. Higher baseline TNF levels have been found to predict a poor response to TCAs (amitriptyline) [6] and SSRIs [60,70]. Serum levels were also found to negatively correlate with response [62,70]. mRNA changes associated with treatment

As discussed, the dynamic nature of gene expression suggests that mRNA measures may also be used to monitor phenotypic change. This has given rise to the hypothesis that, in addition to baseline levels of mRNA predicting treatment outcomes, treatment-emergent biomarkers that occur shortly after treatment may predict longer term response. Several potential ‘tar­ gets’ have been identified in response to antidepressant therapy, as summarized in Table 7. IL1B & MIF mRNA

In addition to baseline levels of IL1B and MIF mRNA seemingly being predictive of nonresponsiveness, they are both significantly reduced with antidepressant treatment. The antidepressants nortriptyline and esci­ talopram were found to significantly decrease levels of IL1B and MIF mRNA after 8 weeks of treatment [15]. However, these reductions occurred in both responders and nonresponders; therefore, levels of IL1B mRNA and MIF mRNA do not appear to be associated with clinical improvement of MDD. By contrast, another study only found a trend towards a reduction in IL1B after 3 months of treatment with fluoxetine, and a dif­

Table 6. Putative baseline proteomic biomarkers in blood for predicting antidepressant treatment outcomes. Peripheral blood cytokine

Baseline serum level in MDD vs controls

Treatment outcome

Ref.

IL‑6

Increased

Resistant to treatment

TNF

Increased

Poor response to amitriptyline

Increased

Poor response to SSRIs

[60,70]

Increased

Serum levels negatively correlate with response

[14,70]

[3,14,69] [6]

MDD: Major depressive disorder; SSRI: Selective serotonin reuptake inhibitor.

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Table 7. Putative mRNA biomarkers in blood for the assessment of treatment response. Cytokine mRNA

Baseline mRNA expression Transcriptional changes associated with treatment in MDD vs controls

IL1B

Increased

IL6

Ref.

Significantly reduced by escitalopram and nortriptyline

[15]

–

No change with 8 weeks escitalopram

[17]

Increased

Significantly reduced by escitalopram and nortriptyline

[15]

–

Escitalopram has no effect

[17]

Increased

Fluoxetine has no effect

[47]

IL11

No known difference

Reduced by escitalopram

[17]

ABCF1

No known difference

Increases with escitalopram treatment, most greatly in responders

[19]

MIF

Increased

Significantly reduced by escitalopram and nortriptyline

[15]

–

No change with 8 weeks escitalopram

[17]

MDD: Major depressive disorder.

ferent study found no changes in the expression of IL1B or MIF in response to escitalopram after 8 weeks [19,47]. IL6 mRNA

Although baseline levels do not seem to predict treat­ ment outcomes, successful response to the antidepres­ sants nortriptyline and escitalopram were associated with significant decreases in IL6 mRNA [15]. However, Powell and colleagues found no association between changes in IL6 mRNA and response to escitalopram [16], nor did Tsao and colleagues find an association between IL6 mRNA changes and fluoxetine response [47]. Thus, further studies in larger samples are required.

of ABCF1 mRNA increased in the blood of MDD patients after escitalopram treatment [19]. Expression of ABCF1 was markedly increased after 8 weeks of escitalopram treatment, with the increase being most significant in clinical responders [19]. The expression changes at 8 weeks also predicted clinical responder status 4 weeks later, at 12 weeks [19]. Interestingly, ABCF1 is known to negatively regulate the translation of TNF and IL‑6, both of which have been reported to decrease at the protein level with antidepressant therapy [2,19]. This suggests that ABCF1 is a target of escitalopram, and is a possible mechanism by which it is able to regulate TNF and IL‑6 translational changes associated with antidepressant treatment.

IL11 mRNA

As with IL6 mRNA, baseline levels of IL11 mRNA do not seem to predict treatment outcomes prior to commencing antidepressants [17]. However, success­ ful response to escitalopram was linked to a signifi­ cant reduction in IL11 mRNA [17]. Furthermore, this decrease was found to be driven by the presence of the A allele of the of the SNP rs1126757, suggesting rs1126757 may act as a treatment-emergent expres­ sion quantitative trait locus. Furthermore, based on these findings, a recent study also found that baseline levels of IL11 methylation in blood additionally pre­ dicted antidepressant response, and interacted with rs1126757 to predict response [71]. This lends further support to the possible interaction between antidepres­ sants and baseline molecular factors in causing changes to IL11 mRNA levels and, subsequently, antidepressant response. ABCF1 mRNA

ABCF1 is a translational regulator of inflamma­ tory cytokines [72]. One study found that the levels

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Biomarkers Med. (Epub ahead of print)

Proteomic changes associated with treatment

There is evidence to suggest that proteomic changes in cytokines as a result of treatment may provide an empirical biological measurement of responsiveness to antidepressants as it occurs in real time, as sum­ marized in Table 8. Antidepressant treatment has been associated with a decrease in proinflammatory cyto­ kines and an increase in anti-inflammatory cytokines such as IL‑10 [2,73]. TNF levels in peripheral blood show a significant reduction after TCA and SSRI treatment [6,60]. By contrast, the anti-inflammatory cytokine IL‑10 was shown to increase in response to a variety of antidepressants [74]; further supporting the view that a proinflammatory profile is associated with MDD and a reduction in inflammatory cytokines is associated with an alleviation of MDD symptoms. Conclusion & future perspective This review highlights that cytokines might act as useful biomarkers for MDD status, symptom sever­ ity and the prediction of antidepressant treatment

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The inflammatory cytokines: molecular biomarkers for major depressive disorder? 

response. This is a promising area of research with potential implications for future clinical practice in terms of the diagnosis and management of MDD. However, further work is required to draw any firm conclusions. First of all, the studies detailed here are hetero­ geneous in their design and often utilize small sam­ ple sizes. Therefore, single, larger studies, which col­ lect genetic, transcriptomic and proteomic data, are required to evaluate the utility of cytokine biomarker networks across the different biological system levels. This might allow future researchers to create a multi­ omic signature whereby different biological informa­ tion relating to the cytokines might be combined to explain a clinically significant proportion of the vari­ ance in phenotypes such as MDD severity and anti­ depressant response. It would also allow us to estab­ lish more clearly what information is specific to each molecular system level (e.g., biomarkers only present at the mRNA level) and what information is common across the different biological system levels (e.g., if differences at the mRNA level directly correlate to differences at the protein level). Second, it would be interesting to consider whether other biochemical alterations in the cytokines could differentiate MDD patients from controls, and predict antidepressant response. DNA methylation is increas­ ingly being investigated as a biomarker resource for MDD [75], and very recent evidence from our group has identified the first pharmacoepigenetic predictors of antidepressant response in IL11 [71]. Third, cross-disorder studies should also be inves­ tigated to evaluate whether cytokines could be used

Review

Table 8. Putative proteomic biomarkers in blood for the assessment of treatment response. Peripheral blood Treatment effect on serum cytokine levels IL‑10 TNF

Ref. [73]

Increased Reduced after SSRI and TCA

[6,60]

SSRI: Selective serotonin reuptake inhibitor; TCA: Tricyclic antidepressant.

to differentiate between MDD and commonly misdiagnosed disorders such as bipolar disorder. Fourth, it would be interesting to compare whether differences in the cytokines found in blood reflect differences found in brain. This would require the future collection of both blood and post-mortem brain samples from subjects. Finally, pharmacogenetic studies should investigate the effects of antidepressants on early changes in tran­ scription and protein expression of cytokines, to inves­ tigate whether these can predict longer-term clinical response. Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

Executive summary Background • Cytokines are pleiotropic and as well as acting as inflammatory mediators, they may also be involved in major depressive disorder (MDD) pathophysiology. • Cytokines may possess biomarker properties to aid in the diagnosis of MDD and the prediction of antidepressant response.

Molecular diagnostic biomarkers for MDD • Higher levels of IL‑6 both at the mRNA and proteomic levels might predict MDD status. • Higher TNF protein levels might predict MDD status.

Molecular biomarkers for predicting & monitoring antidepressant treatment response • Both a genetic variant in IL6 (rs7801617) and IL11 (rs1126757) and changes to mRNA levels of these genes in response to antidepressants were found to predict clinical outcome to antidepressants. • At the mRNA and protein levels, TNF was shown to be among the most robust predictors of antidepressant response as well as acting as a potential protein target for antidepressant therapies. • Increases in ABCF1 mRNA levels during antidepressant treatment may underlie these putative translational changes in TNF, as ABCF1 is known to regulate the translation of TNF.

Conclusion & future perspective • Larger studies collecting genetic, transcriptomic and protein data are required. • DNA methylation differences in cytokines should be investigated as a biomarker resource. • Cross-disorder studies should be investigated to evaluate whether cytokines could be used to differentiate between MDD and other disorders such as bipolar disorder.

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