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. Author manuscript; available in PMC: 2021 Oct 18.
Published in final edited form as: Brain Behav Immun. 2018 Nov 29;77:37–45. doi: 10.1016/j.bbi.2018.11.317

The JAK-STAT1 transcriptional signature in peripheral immune cells reveals alterations related to illness duration and acuity in psychosis

Jennifer K Melbourne 1, Cherise Rosen 1, Benjamin Feiner 1, Yanzhen Pang 1, Rajiv P Sharma 1,2,*
PMCID: PMC8521437  NIHMSID: NIHMS1515567  PMID: 30503835

Abstract

Multiple lines of inquiry demonstrate alterations to immune function in psychosis. Clinically, this is reflected by elevated proinflammatory cytokines in serum, indicating activation of circulating immune cells. Data from isolated cells in clinical populations support the presence of altered activity of pertinent intracellular signaling pathways. Here, we focus on the well-characterized IFN-γ mediated JAK-STAT1 signaling pathway, which is involved in multiple aspects of immunity, including activation of circulating immune cells to a proinflammatory phenotype. By measuring a transcriptional signature characteristic of activation of this pathway, we demonstrate that JAK-STAT1 signature gene expression is suppressed in participants with psychosis who are early in illness and in participants who are hospitalized with an acute exacerbation of psychosis. Furthermore, we find that this expression signature normalizes in participants who have a longer illness duration and chronic, but not acute, psychopathology. This relationship of JAK-STAT1 signature gene expression with clinical characteristics highlights the temporal and contextual complexity of alterations to immune activity in psychosis and provides important insight into the functional state of circulating immune cells. These findings are of particular interest given recent research illustrating the importance of peripherally derived immune cells and the effectors they secrete in mediating neurophysiological processes of relevance for psychiatric illness.

Keywords: Psychosis, schizophrenia, bipolar, immune, inflammation, jak-stat, interferon, pbmc, blood

1. INTRODUCTION

Altered serum cytokine expression is a well-documented phenomenon in psychotic disorders such as schizophrenia and bipolar disorder with psychosis 1. Alongside genome-wide association studies, epidemiological and post-mortem data, these findings have precipitated the formation of multiple theories regarding the potential contribution of immune activation to psychotic illness development and exacerbation 2–4. Conceptually, elevated levels of inflammatory cytokines points to activation of peripheral immune cells, yet a pathophysiological role in psychosis remains to be established. This is partly because until recently the brain was considered to be immune privileged, with minimal interaction of these two systems. However, data are emerging that demonstrate that peripheral immune cells along with the effectors they secrete play important roles in central nervous system (CNS) functions both in health and disease 5,6. For example, manipulation of the peripheral immune system in animal models results in neurobiological and behavioral changes relevant to psychiatric illness 2. Conversely, in clinical subjects pharmacological treatments that target the immune system show early promise for improving symptoms in psychotic disorders 7.

Thus, an understanding of the current activity of immune cells in psychosis and how activity relates to clinical presentation may shed light both on pathophysiology and potential treatment targets. While there is some consensus that proinflammatory cytokines, such as interleukin (IL)-6, TNF-α and interferon (IFN)-γ, are elevated in the serum in psychotic disorders, there are discrepancies amongst studies 1,8,9. This is particularly true for IFN-γ. The inconsistencies are partly a consequence of variability in clinical cohorts. Though it is common practice to compare participants with a psychiatric diagnosis to a control group without examination of the relationship with clinical characteristics, evidence suggests that alterations to certain immune parameters are not static, but rather vary with illness stage 2. Additionally, heterogeneity is also likely due to the unresolved source of serum cytokines. Cytokines may derive from multiple tissue and cellular sources, and assays often lack the sensitivity to adequately quantify expression. If the findings of altered cytokines in psychosis reflect functional alterations to cellular signaling activity, there should be measurable changes at the genomic level. Measuring a molecular signature, here messenger RNA (mRNA) expression, in an isolated tissue type such as peripheral blood mononuclear cells (PBMCs) both resolves this issue of source specificity and allows for inference of intracellular signaling activity. Results from the few studies that have so far used this approach seem to support a proinflammatory signature in psychosis, demonstrating increased expression of genes encoding cytokines and other immune effectors that indicate activation of the NF-κB signaling pathway, an important mediator of proinflammatory cellular activity10–12.

Another critical cell signaling system that regulates proinflammatory activity is the JAK-STAT1 pathway. IFN-γ is a key soluble activator of the JAK-STAT1 pathway, orchestrating the induction of a type 1 immune response, characterized by activation of T-helper 1, natural killer and myeloid cells. Activation of the JAK-STAT1 pathway by IFN-γ induces a transcriptional program involved in a proinflammatory immune response 13 as well as inducing changes to the chromatin landscape that prime the myeloid cell proinflammatory response to NF-κB activating ligands 14 (Figure 1). Altered activity of the JAK-STAT1 pathway has been associated with multiple autoinflammatory and autoimmune disorders, and is being actively investigated as a potential treatment target in these conditions 13. We have recently demonstrated, using a protein assay of activated STAT1 in peripheral blood cells, that a subset of participants with psychosis have alterations to the JAK-STAT1 signaling system 15. In order to further investigate IFN-γ mediated JAK-STAT1 signaling in psychosis, we selected a panel of genes representative of genomic activation of this pathway: the gene for IFN-γ itself (IFNG), and the prototypical IFN-γ response genes CXCL10, IRF1, STAT1, and TLR4. These genes were selected based on previously published transcriptional signatures and STAT1 binding sites 16,17, and our own data demonstrating sensitivity of these genes to IFN-γ in a human monocyte cell line (unpublished observations). In addition to investigating overall differences between individuals with psychosis and control participants, measures of JAK-STAT1 pathway activity were examined in relation to clinical characteristics including illness duration, hospitalization versus outpatient status and measures of psychopathology.

Figure 1.

Figure 1.

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A) IFN-γ activates the JAK-STAT1 signaling pathway by binding to its membrane receptor IFNGR. STAT1 is phosphorylated, forms a dimer and enters the nucleus where it initiates transcription of genes including IRF1, CXCL10, STAT1 and TLR4 as well as numerous other effector genes involved in functions such as antigen presentation, chemotaxis and adhesion. B) In addition to initiation of transcription, activation of the JAK-STAT1 pathway induces chromatin remodeling at proinflammatory gene promoters in myeloid cells such as monocytes and macrophages. C) Now, activation of the NF-κB pathway, for example by the bacterial ligand LPS binding to TLR4, results in a strong induction of transcription at these proinflammatory gene promoters.

2. MATERIALS AND METHODS

2.1. Participants information and clinical measures

The study was approved by the IRB of the University of Illinois, and signed consent was obtained prior to the initiation of study procedures. Inclusion criteria for the study included persons between the ages of 21 and 60 who met DSM-IV diagnostic criteria for schizophrenia, bipolar disorder with psychosis or persons with no history of a psychiatric disorder. Exclusion criteria included treatment with valproic acid, carbamazepine, or clozapine in the previous 30 days, current substance dependence, seizure disorders, and neurological conditions. Physical illnesses, including asthma, cardiac problems, renal disease, GI problems, thyroid problems, immune system problems, pulmonary problems, diabetes, hypertension and hyperlipidemia, were recorded by self-report, alongside psychotropic and non-psychotropic medication use. Consensus diagnoses were determined by both the clinical and research team using the Structured Clinical Interview for DSM-IVTR 18 and available collateral information. All participants were recruited from an urban academic setting and the surrounding community.

Demographic characteristics for the sample were obtained at the study evaluation and are outlined in Table 1. Of the 89 participants with psychosis, 76 (85.39%) persons were prescribed antipsychotic medications and 13 (14.61%) were medication-free at the time of study evaluation. The duration of illness was defined as the number of years between the onset of psychosis and current age per self-report, and ranged from initial illness onset to 51 years. Participants with psychosis were split into 3 groups based on length of illness for further analysis; short: 0–17 years (n=42), medium: 18–34 years (n=24), and long: 35–51 years (n=15). Within the clinical sample, 33 were evaluated in the acute inpatient care unit and 56 participants were evaluated in the outpatient setting. As previously used in published analyses 1, participants were also split into a “hospitalized” group (those who participated in the study shortly after hospitalization for an acute exacerbation of psychosis) and an “outpatient” group (those with current outpatient status and no acute exacerbation of psychosis). The Positive and Negative Syndrome Scale (PANSS) was used as a measure of psychopathology 19. The PANSS consists of 30 items scored along a continuum of severity between one (asymptomatic) to seven (extreme symptom severity), with three subscales that are designed to evaluate positive, negative and general psychiatric symptoms. Internal reliability of all subscales in this study were between 0.92 to 0.95 respectively.

Table 1.

Participant demographics for the control group and all participants with psychosis. M = mean, SD = Standard deviation. Age and duration of illness are given in years. n.s. = not significant

Control Psychosis Group difference
Total (n (%)) 44 (33.08%) 89 (66.92%)
Age (M ± SD) 38.77 ± 12.82 40.88 ± 13.14 t131=.−.88, p=.38 (n.s.)
Sex χ2(1)=2.75, p=.097 (n.s.)
Female (n) 25 (18.80%) 37 (27.82%)
Male (n) 19 (14.29%) 52 (39.10%)
Ethnicity χ2(3)=24.90, p<.001
Caucasian, non-Hispanic (n) 12 (9.02%) 9 (6.77%)
Black, non-Hispanic (n) 18 (13.53%) 67 (50.38%)
Asian or other Pacific Islander (n) 10 (7.52%) 2 (1.50%)
Hispanic (n) 4 (3.01%) 11 (8.27%)
BMI (M ± SD) 28.63 ± 7.96 31.66 ± 8.39 t130=−1.99, 130, p=.05
Antipsychotic Use Yes (n) 0 (0.00%) 76 (57.14%)
No (n) 44 (33.08%) 13 (9.77%)
Illness Duration (M ± SD) NA 19.96 ± 14.42
Illness Status Hospitalized (n) NA 33 (37.08%)
Outpatient (n) NA 56 (62.92%)
Psychopathology
PANSS Scores Positive (M ± SD) 7.68 ± 1.36 24.40 ± 6.66 t131=−16.47, p=<.001
Negative (M ± SD) 7.80 ± 1.84 19.91 ± 7.64 t131=−10.35, p=<.001
General (M ± SD) 20.09 ± 4.50 42.79 ± 10.48 t131=−13.74, p=<.001
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2.2. Sample collection, RNA extraction and quantitative real-time PCR

Sample collection and PBMC isolation were carried out as per previously published protocols 20. PBMC pellets were stored at −80C prior to extraction. RNA extraction, DNase treatment and quantitative real-time PCR were carried out as previously described 20. Relative expression values were calculated relative to the housekeeping genes GAPDH and ACTB using 2-Δct. Primers were designed using NCBI primer-BLAST. Primer sequences are as follows: IFNG forward, 5’-TCTTTTGGATGCTCTGGTCA-3’; IFNG reverse, 5’-TTCAGCTCTGCATCGTTTTG-3’; CXCL10 forward, 5’- ATTTGCTGCCTTATCTTTCTG-3’, CXCL10 reverse, 5’-TCTCACCCTTCTTTTTCATTGTAG-3’; IRF1 forward, 5’- ATGAGACCCTGGCTAGAG-3’, IRF1 reverse 5’-AAGCATCCGGTACACTCG-3’; STAT1 forward, 5’-GCCAAAGGAAGCACCAGAGCCAAT-3’; STAT1 reverse, 5’-AGGAGACATGGGGAGCAGGTTGT-3’; TLR4 forward, 5’-TGGAAGTTGAACGAATGGAATGTG-3’; TLR4 reverse, 5’-ACCAGAACTGCTACAACAGATACT −3’; GAPDH forward, 5’-CGAGATCCCTCCAAAATCAA-3’; GAPDH reverse, 5’-TTCACACCCATGACGAACAT-3’; ACTB forward, 5’-TGAAGGTAGTTTCGTGGATGC-3’; and ACTB reverse, 5’-TCCCTGGAGAAGAGCTACGA-3’.

2.3. Statistical Analysis

Based on the overall pattern of gene expression for the selected genes a composite JAK-STAT1 signature score was computed for each participant. Composite scores are often used in the biomedical sciences to represent a biological process, such as activation of intracellular signaling pathways21. The JAK-STAT1 composite score was calculated for each gene expression data point followed by averaging the z-scores of the five genes (IFNG, STAT1, IRF1, TLR4 and CXCL10) for each participant 22. SPSS (version 22.0 for Windows) was used for all statistical analyses. Group differences for demographic, clinical data were analyzed by using Chi Square tests, independent samples t-tests and one-way analysis of variance (ANOVA). For ANOVA that yielded significant results, Tukey post hoc tests or planned comparisons were used to identify significant pair-wise group differences. Analysis of covariance (ANCOVA) was used to confirm group differences after controlling for potential confounds (race for CXCL10 and BMI for the JAK-STAT1 composite). Error bars on all graphs represent standard error of the mean. All bivariate correlations were run using Spearman’s rho. Lastly, a 3-stage hierarchical multiple linear regression model was performed to predict the JAK-STAT1 composite signature scores, examining specifically the relative contribution of individual descriptive variables, clinical symptomology, duration of illness and illness acuity in participants with psychosis (schizophrenia and bipolar disorder with psychosis).

3. RESULTS

3.1. Demographics

Table 1 summarizes participant characteristics. There was no significant difference between participants with a psychiatric diagnosis and non-psychiatric controls for age or sex. Ethnicity and BMI were different between the groups. However, there was no correlation of BMI with mRNA expression of any of the assayed genes. Additionally, mRNA expression did not differ significantly by ethnicity for IFNG, STAT1, IRF1, or TLR4, though there was a significant difference for CXCL10 (F(3,122)=3.27, p=.02), with increased expression in Caucasian participants compared to Black participants (p=.05) and to Hispanic participants (p=.04). There was no difference in mRNA expression for any of the genes when comparing male and female participants. There was a significant association of expression of IFNG, STAT1, IRF1, TLR4, but not CXCL10, with age. This association with age is addressed in section 3.8. Comparison of participants who reported a physical illness and the remaining sample did not reveal any significant differences in the JAK-STAT1 composite score (see section 3.2; data not shown). For the five participants who reported immune system problems the JAK-STAT1 composite score was highly variable as was illness duration, indicating no overall influence on the results presented here.

Finally, there was no significant difference in JAK-STAT1 signature gene expression between participants with psychosis who were taking antipsychotics at the time of the blood draw (n=74) compared to those who were not using antipsychotics (n=13) for any of the genes measured. Using the JAK-STAT1 composite score we further examined for differences in relation to treatment with non-antipsychotic psychotropics (antidepressants, sedatives and mood stabilizers), as well as other medications that could impact immune function (medications for diabetes, hypertension and cholesterol). The only significant difference in the JAK-STAT1 composite score was in participants treated for diabetes (M=1.04, SD=3.16, n=19) compared with the remaining sample (M=−.38, SD=2.46, n=111) (t128=−2.22, p=.03).

3.2. Co-expression of JAK-STAT1 signature genes

mRNA expression of all JAK-STAT1 signature genes were correlated with one another across the entire participant sample (Table 2). Co-expression of these genes within the participant sample indicates shared signaling mechanisms and supports their use as a combined measure of cellular phenotype, in this case activation of the IFN-γ regulated JAK-STAT1 pathway. Thus, a composite JAK-STAT1 signature score was computed for each participant and used as an overall measure of transcriptional activity of the JAK-STAT1 pathway in subsequent analyses.

Table 2.

Correlation of JAK-STAT1 signature genes mRNA expression within all participants. Displayed as Spearman’s rho, p-value, number of participants.

IFNG CXCL10 IRF1 STAT1
CXCL10 .26
.003
124
IRF1 .25
.005
128		 .28
.002
125
STAT1 .43
<.001
127		 .31
.001
125		 .51
<.001
128
TLR4 .31
<.001
127		 .24
.008
123		 .29
.001
127		 .55
<.001
126
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3.3. mRNA expression of specific JAK-STAT1 signature genes is decreased in participants with psychosis

Next, JAK-STAT1 signature genes were compared between participants with psychosis and controls. Contrary to our expectation that the JAK-STAT1 transcriptional signature would be elevated in psychosis, mRNA expression of IFNG (t128=2.71, p=.008), CXCL10 (t124=3.69, p<.001) and IRF1 (t128=3.06, p=.003) and the JAK-STAT1 composite score (t129=2.47, p=.02) were significantly decreased in participants with psychosis (Figure 2). STAT1 (t127=1.45, p=.15) and TLR4 (t128=0.50, p=.62) expression did not demonstrate a significant difference between participants with psychosis and controls. The results for CXCL10 remained significant after controlling for ethnicity (p<.001), and for the JAK-STAT1 composite score after controlling for BMI (p=.02).

Figure 2.

Figure 2.

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Relative mRNA expression of each gene displayed by diagnostic category. Statistically significant t-tests denoted by **p<.01, ***p<.001.

3.4. The JAK-STAT1 signature is decreased in participants with psychosis early in illness and normalizes over the duration of the illness

When the JAK-STAT1 transcriptional signature was analyzed in relation to illness duration in participants with psychosis, mRNA expression of IFNG (r2=.48, p<.001, n=78) IRF1 (r2=.32, p=.004, n=78), STAT1 (r2=.34, p=.003, n=78), TLR4 (r2=.25, p=.03, n=80) and the JAK-STAT1 signature score (r2=.28, p=.001, n=80), demonstrated a positive correlation with duration of illness (onset of psychosis to time of blood draw), but this relationship was not statistically significant for CXCL10 (r2=.21, p=.07, n=76). To assess the association of illness duration and JAK-STAT1 signature gene expression in relation to control participants, illness duration was converted to a categorical variable of short, medium and long illness duration groups. Planned comparisons demonstrated that there was a significant difference in mRNA expression between controls and participants with an illness duration of 0–17 years for IFNG, CXCL10, IRF1, STAT1 and for the JAK-STAT1 composite score, but this was not significantly different for TLR4 (Figure 3). mRNA expression of all genes and the JAK-STAT1 composite score demonstrated a significant difference when comparing participants with a short versus long illness duration. Furthermore, there were no significant differences when comparing control participant mRNA expression with the longest illness duration group of 35–51 years for any genes or the JAK-STAT1 signature score. The results for CXCL10 remained significant after controlling for ethnicity with decreased CXCL10 in the short illness duration group compared with controls (p<.001) and with the long illness duration group (p=.02). The JAK-STAT1 composite score also remained decreased in participants with a short illness duration compared with controls (p<.001) and with the long illness duration group p<.001) after controlling for BMI. Overall, these data suggest that the JAK-STAT1 signature is suppressed early in illness course and normalizes over the duration of the illness.

Figure 3.

Figure 3.

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Relative mRNA expression of each gene displayed by illness duration category. L to R: Control - control participants; Illness duration categories: Short - 0–17 year illness duration; Medium: 18–34 year illness duration; Long: 35–51 year illness duration. Statistically significant planned comparisons denoted by *p<.05, **p<.01, ***p<.001

3.5. The JAK-STAT1 signature is decreased in participants with psychosis who are hospitalized with an acute exacerbation of psychosis

Next,, comparisons in JAK-STAT1 signature gene expression were made between participants with psychosis who were hospitalized for an acute exacerbation of psychosis at the time of the blood draw, and participants who took part in the study as outpatients. Participants seen on the inpatient unit are usually experiencing a more severe psychotic manifestation, whereas those being monitored on an outpatient basis are generally more functionally stable. When we compared the JAK-STAT1 transcriptional signature in these hospitalized versus outpatient groups we found that mRNA expression of IFNG, CXCL10, IRF1 and STAT1, and the JAK-STAT1 signature score were decreased in the hospitalized group compared to controls (Figure 4). IFNG, STAT1 and TLR4 expression, and the JAK-STAT1 signature score, were decreased in hospitalized participants compared with outpatients, whereas CXCL10 and IRF1 mRNA expression was decreased in both hospitalized participants and outpatients compared to controls. CXCL10 remained significantly decreased in both hospitalized participants (p=.004) and outpatients (p=.001) compared with controls after controlling for ethnicity, and the JAK-STAT1 composite score remained decreased in hospitalized participants compared with both controls (p<.001) and outpatients (p=.002) after controlling for BMI. Thus, hospitalization for an acute exacerbation of psychosis is associated with a suppressed JAK-STAT1 transcriptional signature though results for participants with outpatient status were more varied.

Figure 4.

Figure 4.

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Relative mRNA expression of each gene displayed by illness status group. Statistically significant post-hoc comparisons denoted by *p<.05, **p<.01, ***p<.001.

3.6. There is a negative association of JAK-STAT1 signature gene expression and psychopathology

Grouping by illness status (hospitalized and outpatient groups) is one strategy with which to highlight alterations to biological signals related to illness severity and functioning. Additionally, the presence and severity of specific symptom domains can be quantified using the PANSS. While PANSS scores did not significantly vary with illness duration, scores were greater in the hospitalized participant group compared to outpatient group (t87=6.39, p<.001). PANSS scores were used to examine for a relationship of JAK-STAT1 mediated gene expression with measures of psychopathology on a continuum from control participants to those with a diagnosis of psychosis. IFNG (n=130) demonstrated a negative correlation with PANSS Positive (r2=−.342, p<.001), Negative (r2=−.321, p=<.001) and General (r2=−.347, p<.001) subscales. CXCL10 (n=126) was also negatively correlated with PANSS Positive (r2=−.258, p=.004), Negative (r2=−.248, p=.005) and General (r2=−.256, p=.004) subscales. However, expression levels of IRF1, STAT1 and TLR4 were not correlated with these measures of psychopathology. The JAK-STAT1 pathway composite score (n=130) demonstrated a negative correlation with PANSS Positive (r2=−.262, p=.003), Negative (r2=−.220, p=.012) and General (r2=−.250, p=.004) subscales.

3.7. Interaction of illness duration and status in relation to JAK-STAT1 signature gene expression

In order to determine whether participants who are hospitalized with an acute exacerbation of psychosis demonstrated a suppressed JAK-STAT1 signature regardless of illness duration, the association of the JAK-STAT1 signature composite score with illness duration was assessed in each illness status group. Within the hospitalized participant group alone, there was no correlation of the JAK-STAT1 signature score with illness duration (Figure 5). When outpatients alone were selected, there was a strong correlation with illness duration, demonstrating that it is this group of participants driving the positive association of JAK-STAT1 signature expression with illness duration. Thus, the results demonstrate that both hospitalization and shorter illness duration are associated with suppressed JAK-STAT1 signature gene expression. Whereas, in the presence of chronic and persistent but not acute psychopathology, the expression of JAK-STAT1 signature genes normalizes as illness duration lengthens.

Figure 5.

Figure 5.

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Correlation of the JAK-STAT1 composite score and illness duration for hospitalized and outpatient groups. **p<.01

3.8. Illness duration is the primary predictor of JAK-STAT1 signature gene expression in psychosis

The findings outlined so far demonstrate that JAK-STAT1 signature gene expression varies with clinical measures including illness duration, status and psychopathology. To examine which contributing factors best explain variation in the JAK-STAT1 composite score, a hierarchical regression was used in the sample of participants with psychosis (n=79). Prior to conducing a hierarchical multiple regression, the relevant assumptions of this statistical analysis were tested 23. The collinearity statistics (i.e., Tolerance and VIF) were all within accepted limits, thus the assumption of multicollinearity were satisfied 24. An examination of the Mahalanobis distance scores indicated no multivariate outliers. Residual and scatter plots indicated the assumptions of normality, linearity and homoscedasticity were all satisfied 25. A three stage hierarchical multiple regression was conducted with the JAK-STAT1 composite score as the dependent variable. Descriptive variables (age, sex, ethnicity, BMI and current antipsychotic status) were entered at stage one of the regression. PANSS positive, negative and general subscales were entered at stage two, and illness duration and illness status were entered at stage three. The regression statistics are reported in Table 3.

Table 3:

Hierarchical multiple regression stage 3: Prediction of JAK-STAT1 signature gene expression (n=79)

Stage Variable B SE β t p VIF
1. Age −.007 .043 −.034 −.161 .873 4.238
Sex .953 .611 .176 1.559 .124 1.200
Ethnicity −.081 .210 −.041 −.368 .700 1.078
BMI −.029 .036 −.090 −.798 .427 1.206
Current antipsychotic status .717 .915 .094 .784 .436 1.347
2. PANSS positive subscale .003 .086 .007 .034 .973 3.884
PANSS negative subscale −.011 .060 −.029 −.175 .861 2.578
PANSS general subscale .018 .064 .065 .279 .781 5.101
3. Illness status .457 .908 .081 .503 .617 2.434
Illness duration .091 .036 .494 2.539 .013 3.564
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Note. Stage 1 R2= .213, p= .004; Stage 2 R2= .216, p= .03; Stage 3 R2= .288, p= .007.

The hierarchical multiple regression revealed that at stage one, descriptive variables contributed significantly to the regression model, (F(5,72) = 3.90, p=.004) accounting for 21% of the variance in JAK-STAT1 signature gene expression. Introducing the clinical symptomology variables at Stage 2 also significantly contributed to the regression model (F(8,69) = 2.38, p=.02) accounting for less than 1% of the variance in JAK-STAT1 signature gene expression. Finally, in stage 3, the addition of illness duration and illness status explained an additional 7% of the variation and this change in R2 was significant (F(10,67) = 2.71, p=.007). When considering the full model in stage 3, we observe that only illness duration (p=.01) significantly contributed to the prediction of alterations in JAK-STAT1 signature gene expression in participants with psychosis.

Thus, none of the demographic or medication variables, including age, sex, ethnicity, BMI or antipsychotic medication status significantly predicted JAK-STAT1 signature gene expression when all independent variables were included. While we found no effect of antipsychotic status in this model, an influence of antipsychotic medications on these immune parameters should not be discounted due to heterogeneity in the sample related to medication type, dose and length of treatment. In section 3.5 JAK-STAT1 signature expression varied with illness status, but when included in the full model did not significantly predict the JAK-STAT1 composite score in participants with psychosis. As highlighted, illness duration uniquely predicted JAK-STAT1 signature gene expression. Though we found a difference in JAK-STAT1 signature expression in participants treated for diabetes, when this variable was included in the regression model it was not a significant predictor and did not alter these main findings (data not shown).

4. DISCUSSION

The potential involvement of the immune system in the pathophysiology of psychotic disorders is receiving renewed interest in recent years. It has become apparent that both peripheral and central immune cells, which are predominantly considered for their role in host defense against pathogens, are also intricately involved in multiple aspects of CNS development and homeostasis. Much of the data to date indicates that psychosis may be associated with mild but chronic systemic inflammation. However, there is a paucity of data illustrating alterations to immune cellular phenotype and the manner in which these immune measures relate to clinical characteristics 2. Here, we chose to investigate the JAK-STAT1 transcriptional signature as this signaling pathway plays a vital role in multiple aspects of immunity, including activation of myeloid cells to a proinflammatory state 26,27.

We demonstrate that expression of a panel of genes representative of IFN-γ mediated JAK-STAT1 activity indeed reveal alterations to this transcriptional signature in psychosis. Interestingly however, while we had hypothesized increased activity of the JAK-STAT1 pathway based on its known role in inflammation, we instead found that this signature was decreased in participants with psychosis who were early in illness duration and in those who were hospitalized for an acute exacerbation of psychosis compared to controls. Our data further demonstrated that this initial suppression of JAK-STAT1 transcriptional activity reverses over the course of the illness, with gene expression normalizing as illness duration lengthens. When we examined the relationship of JAK-STAT1 signature gene expression with illness duration in each illness status group we found that in participants who are hospitalized, regardless of the duration of the illness, the JAK-STAT1 signature gene is suppressed, whereas in the presence of chronic and persistent but not acute psychopathology, expression of JAK-STAT1 signature genes normalizes as illness duration lengthens. Additionally, some but not all of the JAK-STAT1 signature genes were negatively correlated with measures of symptom presence and severity as measured by the PANSS. A hierarchical regression was used to determine the relative contribution of each of the described variables to JAK-STAT1 mediated transcriptional activity. Controlling first for demographic characteristics, we demonstrate that overall illness duration is the strongest predictor of JAK-STAT1 signature gene expression in participants with psychosis.

This variability in immune cell gene expression over the course of the illness is perhaps not surprising given the high degree of phenotypic plasticity these cells exhibit in response to microenvironmental perturbations. Indeed, some serum cytokine levels also seem to demonstrate alterations based on illness stage and have thus been designated state or trait markers 1. However, the suppressed JAK-STAT1 signature we found early in illness and with hospitalization is unexpected given that these variables are usually most strongly associated with a proinflammatory state. These data suggest that the nature of immune alterations in psychosis are not merely ‘proinflammatory’ but instead involve a more complex dysregulation of immune and inflammatory cellular activity that will be better understood in the context of illness stage and severity. To our knowledge this is the first study to directly investigate the JAK-STAT1 transcriptional signature in psychosis, and thus it provides important insight into the functional state of circulating immune cells.

The decreased JAK-STAT1 signature expression observed here may be indicative of an immune state in which these circulating cells, particularly monocytes, are skewed more towards an anti-inflammatory or tissue remodeling phenotype 28. These phenotypic changes are induced under physiological conditions that include the presence of the T-helper 2 cytokines IL-4 and IL-13, high levels of proinflammatory ligands, and acute stress, any of which may be present in psychosis earlier in illness and during periods of illness exacerbation 29–34.

Whether these peripheral immune cell changes are pathophysiologically relevant for psychosis or are an epiphenomenon of the illness remains to be established. The interaction between the peripheral immune system and CNS is an area of intense investigation, and routes of communication in both directions have been elucidated 35. This allows for speculation that the immune stimulus is emanating from the center and will be registered in circulating cells that are equipped to survey these signals. Furthermore, immune stimuli that originate in the periphery affect the blood brain barrier, which demonstrates alterations in psychosis 36,37. Peripherally derived immune cells are found at these brains boundaries, including the meninges and choroid plexus 38 and play complex roles in CNS function. Interestingly, it was recently demonstrated that IFN-γ secreted by t-cells in the meninges mediates normal social behavior in mice via the effects of IFN-γ on GABAergic interneuron firing in the prefrontal cortex 5. Other studies highlight the importance of IFNs including IFN-γ for appropriate immune surveillance at parenchymal borders 39. Thus, our findings of decreased expression of the IFN-γ-JAK-STAT1 transcriptional signature in circulating immune cells in psychosis in the earlier stages of illness and during periods of illness exacerbation are of theoretical interest, though the potential cause of these changes requires further investigation.

Here we focused our investigation on one gene signature, IFN-γ mediated JAK-STAT1 signaling, highlighting the temporal and functional complexity of immune alterations in psychosis. This signature was measured in total PBMCs, a mixed cell population which includes lymphocytes, monocytes and dendritic cells, demonstrating overall altered activity in circulating mononuclear immune cells. These cells function as an interacting system, but each has specialized roles, with particular effector molecules serving specific functions that depend on cell type and context 40–42. Future studies should thus focus on isolated immune cell types as well as measuring alternative physiological signatures alongside the JAK-STAT1 signature to determine corresponding phenotypic alterations. Furthermore, the present analysis would have benefited from concurrent measurement of serum IFN-γ levels, and thus further research should also include serum cytokine measurements alongside gene expression signatures to paint a comprehensive picture of the dynamic changes to immune activity in relation to stage, severity and treatment of psychosis.

Highlights.

Circulating immune cell transcriptional signatures are altered in psychosis

The JAK-STAT1 signature is suppressed in early illness and with high acuity

The JAK-STAT1 signature increases over the course of the illness

Acknowledgements:

the authors would like to thank all individuals who participated in this study

Funding: this work was supported in part by NIH grants R01MH094358 (R.P.S.) and UL1TR002003 (J.K.M.)

Footnotes

Declarations of interest: none

Informed consent: informed consent was obtained from all participants prior to the initiation of any study procedures

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References

  • 1.Goldsmith DR, Rapaport MH, Miller BJ. A meta-analysis of blood cytokine network alterations in psychiatric patients: Comparisons between schizophrenia, bipolar disorder and depression. In: Molecular Psychiatry. Vol 21. Nature Publishing Group; 2016:16961709. doi: 10.1038/mp.2016.3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Miller BJ, Goldsmith DR. Towards an Immunophenotype of Schizophrenia: Progress, Potential Mechanisms, and Future Directions. Neuropsychopharmacology. 2016;42(1):1–19. doi: 10.1038/npp.2016.211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Khandaker GM, Dantzer R. Is there a role for immune-to-brain communication in schizophrenia? Psychopharmacology (Berl). June 2015. doi: 10.1007/s00213-015-3975-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Bergink V, Gibney SM, Drexhage H a. Autoimmunity, inflammation, and psychosis: A search for peripheral markers. Biol Psychiatry. 2014;75(4):324–331. doi: 10.1016/j.biopsych.2013.09.037. [DOI] [PubMed] [Google Scholar]
  • 5.Filiano AJ, Xu Y, Tustison NJ, et al. Unexpected role of interferon-γ in regulating neuronal connectivity and social behaviour. Nature. 2016;535(7612):425–429. doi: 10.1038/nature18626. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Garré JM, Silva HM, Lafaille JJ, Yang G. CX3CR1+ monocytes modulate learning and learning-dependent dendritic spine remodeling via TNF-α. Nat Med. 2017;23(6):714–722. doi: 10.1038/nm.4340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Melbourne JK, Feiner B, Rosen C, Sharma RP. Targeting the Immune System With Pharmacotherapy in Schizophrenia. Curr Treat Options Psychiatry. 2017;4:139–151. doi: 10.1007/s40501-017-0114-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Upthegrove R, Manzanares-Teson N, Barnes NM. Cytokine function in medication-naive first episode psychosis: A systematic review and meta-analysis. Schizophr Res. 2014;155(1–3):101–108. doi: 10.1016/j.schres.2014.03.005. [DOI] [PubMed] [Google Scholar]
  • 9.Capuzzi E, Bartoli F, Crocamo C, Clerici M, Carra G. Acute variations of cytokine levels after antipsychotic treatment in drug-naive subjects with a first-episode psychosis: A meta-analysis. Neurosci Biobehav Rev. 2017;77:122–128. doi: 10.1016/j.neubiorev.2017.03.003. [DOI] [PubMed] [Google Scholar]
  • 10.Drexhage RC, Van Der Heul-Nieuwenhuijsen L, Padmos RC, et al. Inflammatory gene expression in monocytes of patients with schizophrenia: Overlap and difference with bipolar disorder. A study in naturalistically treated patients. Int J Neuropsychopharmacol. 2010;13(10):1369–1381. doi: 10.1017/S1461145710000799. [DOI] [PubMed] [Google Scholar]
  • 11.Chase KA, Rosen C, Gin H, et al. Metabolic and inflammatory genes in schizophrenia. Psychiatry Res. 2015;225(1–2):208–211. doi: 10.1016/j.psychres.2014.11.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Song X-Q, Lv L-X, Li W-Q, Hao Y-H, Zhao J-P. The interaction of nuclear factor-kappa B and cytokines is associated with schizophrenia. Biol Psychiatry. 2009;65(6):481–488. doi: 10.1016/j.biopsych.2008.10.018. [DOI] [PubMed] [Google Scholar]
  • 13.Schoenborn JR, Wilson CB. Regulation of Interferon-γ During Innate and Adaptive Immune Responses. Adv Immunol. 2007;96(07):41–101. doi: 10.1016/S0065-2776(07)96002-2. [DOI] [PubMed] [Google Scholar]
  • 14.Qiao Y, Giannopoulou EG, Chan CH, et al. Synergistic activation of inflammatory cytokine genes by interferon-γ-induced chromatin remodeling and toll-like receptor signaling. Immunity. 2013;39(3):454–469. doi: 10.1016/j.immuni.2013.08.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Sharma RP, Rosen C, Melbourne JK, Feiner B, Chase KA. Activated Phosphorylated STAT1 Levels as a Biologically Relevant Immune Signal in Schizophrenia. Neuroimmunomodulation. 2016;23(4):224–229. doi: 10.1159/000450581. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Satoh JI, Tabunoki H. A comprehensive profile of ChIP-Seq-based STAT1 target genes suggests the complexity of STAT1-mediated gene regulatory mechanisms. Gene Regul Syst Bio. 2013;2013(7):41–56. doi: 10.4137/GRSB.S11433. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Bosisio D, Polentarutti N, Sironi M, et al. Stimulation of toll-like receptor 4 expression in human mononuclear phagocytes by interferon- ␥ : a molecular basis for priming and synergism with bacterial lipopolysaccharide. Blood. 2002;99(9):3427–3431. [DOI] [PubMed] [Google Scholar]
  • 18.First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Patient Edition (SCID-I/P, 11/2002 Revision).; 2002. doi:M. [Google Scholar]
  • 19.Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13(2):261–76. [DOI] [PubMed] [Google Scholar]
  • 20.Chase KA, Cone JJ, Rosen C, Sharma RP. The value of interleukin 6 as a peripheral diagnostic marker in schizophrenia. BMC Psychiatry. 2016;16(1):152. doi: 10.1186/s12888-016-0866-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Berglund AE, Welsh EA, Eschrich SA. Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures. Int J Genomics. 2017;2017. doi: 10.1155/2017/2354564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Song M, Lin F, Ward SE, Fine JP, Hill C. Composite Variables: When and How. Nurs Res. 2013;62(1):45–49. doi: 10.1097/NNR.0b013e3182741948.Composite. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Tabachnick BG, Fidell LS. Using Multivariate Statistics. Boston, MC: Pearson Education Inc; 2007. [Google Scholar]
  • 24.Coakes SJ. SPSS: Analysis without Anguish: Version 12.0 for Windows. John Wiley & Son Australia, Ltd; 2005. [Google Scholar]
  • 25.Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate Data Analysis with Readings. 5th Editio. Englewood Cliffs, NJ: Prentice Hall; 1998. [Google Scholar]
  • 26.Hu X, Ivashkiv LB. Cross-regulation of Signaling Pathways by Interferon-γ: Implications for Immune Responses and Autoimmune Diseases. Immunity. 2009;31(4):539–550. doi: 10.1016/j.immuni.2009.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Rauch I, Müller M, Decker T. The regulation of inflammation by interferons and their STATs. Jak-Stat. 2013;2(1):e23820. doi: 10.4161/jkst.23820. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Hoeksema M a, de Winther MP. Epigenetic regulation of monocyte and macrophage function. Antioxid Redox Signal. 2016;6(31):ars.2016.6695. doi: 10.1089/ars.2016.6695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Pena OM, Pistolic J, Raj D, Fjell CD, Hancock REW. Endotoxin Tolerance Represents a Distinctive State of Alternative Polarization (M2) in Human Mononuclear Cells. J Immunol. 2011;186(12):7243–7254. doi: 10.4049/jimmunol.1001952. [DOI] [PubMed] [Google Scholar]
  • 30.Yona S, Gordon S. Inflammation: Glucocorticoids turn the monocyte switch. Immunol Cell Biol. 2007;85(2):81–82. doi: 10.1038/sj.icb.7100034. [DOI] [PubMed] [Google Scholar]
  • 31.Huber R, Bikker R, Welz B, Christmann M, Brand K. TNF Tolerance in Monocytes and Macrophages: Characteristics and Molecular Mechanisms. 2017;2017. doi: 10.1155/2017/9570129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Wohleb ES, McKim DB, Sheridan JF, Godbout JP. Monocyte trafficking to the brain with stress and inflammation: A novel axis of immune-to-brain communication that influences mood and behavior. Front Neurosci. 2015;9(JAN):1–17. doi: 10.3389/fnins.2014.00447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Fillman SG, Sinclair D, Fung SJ, Webster MJ, Weickert CS. Markers of in fl ammation and stress distinguish subsets of individuals with schizophrenia and bipolar disorder. Transl Psychiatry. 2014;4(2):e365–10. doi: 10.1038/tp.2014.8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Severance EG, Gressitt KL, Stallings CR, et al. Discordant patterns of bacterialtranslocation markers and implications for innate immune imbalances in schizophrenia. Schizophr Res. 2013;148(1–3):130–137. doi: 10.1016/j.schres.2013.05.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Dantzer R. Neuroimmune Interactions: From the Brain to the Immune System and Vice Versa. Physiol Rev. 2018:477–504. doi: 10.1152/physrev.00039.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Pollak TA, Drndarski S, Stone JM, David AS, McGuire P, Abbott NJ. The blood-brain barrier in psychosis. The Lancet Psychiatry. 2017;0366(17):1–14. doi: 10.1016/S2215-0366(17)30293-6. [DOI] [PubMed] [Google Scholar]
  • 37.Varatharaj A, Galea I. Brain, Behavior, and Immunity The blood-brain barrier in systemic inflammation. Brain Behav Immun. 2017;60:1–12. doi: 10.1016/j.bbi.2016.03.010. [DOI] [PubMed] [Google Scholar]
  • 38.Korin B, Ben-Shaanan TL, Schiller M, et al. High-dimensional, single-cell characterization of the brain’s immune compartment. Nat Neurosci. 2017;20(9). doi: 10.1038/nn.4610. [DOI] [PubMed] [Google Scholar]
  • 39.Kunis G, Baruch K, Rosenzweig N, et al. IFN-g-dependent activation of the brain’s choroid plexus for CNS immune surveillance and repair. Brain. 2013. doi: 10.1093/brain/awt259. [DOI] [PubMed] [Google Scholar]
  • 40.Baud V, Karin M. Signal transduction by tumor necrosis factor and its relatives. Trends Cell Biol. 2001;11(9):372–377. doi: 10.1016/S0962-8924(01)02064-5. [DOI] [PubMed] [Google Scholar]
  • 41.Reefman E, Kay JG, Wood SM, et al. Cytokine secretion is distinct from secretion of cytotoxic granules in NK cells. J Immunol. 2010;184(9):4852–4862. doi: 10.4049/jimmunol.0803954. [DOI] [PubMed] [Google Scholar]
  • 42.Trinschek B, Lüssi F, Haas J, et al. Kinetics of IL-6 Production Defines T Effector Cell Responsiveness to Regulatory T Cells in Multiple Sclerosis. PLoS One. 2013;8(10). doi: 10.1371/journal.pone.0077634. [DOI] [PMC free article] [PubMed] [Google Scholar]

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