Increased Circulating Regulatory T Cells in Medicated People with Schizophrenia

Abstract

Immunological abnormalities are increasingly reported in people with schizophrenia, but no clear functional biomarkers associated with genetic correlates of the disease have been found. Regulatory T cells (Tregs) are key immunoregulatory cells involved in the control of inflammatory processes and their functions are directly related to the human leucocyte antigen (HLA) gene, which has been implicated in schizophrenia genetic studies. However, there is a lack of studies reporting Treg status in people with schizophrenia. In the current study, the proportion of circulating Tregs was examined using flow cytometry in 26 medicated participants with schizophrenia and 17 healthy controls. Psychiatric symptoms and cognitive function were evaluated using the Scale for the Assessment of Negative Symptoms, the Brief Psychiatric Rating Scale, and the MATRICS Consensus Cognitive Battery. The proportion of Tregs was found to be significantly greater in the schizophrenia group compared to healthy controls. No differences were observed in total lymphocyte counts or CD3⁺ and CD4 ⁺ T cells, confirming a specific effect for Tregs. Elevated Tregs in schizophrenia correlated with fewer negative symptoms, a core domain of the illness. These results suggest that Tregs may contribute to improved negative symptoms in schizophrenia, possibly by counteracting on-going inflammatory processes.

1. Introduction

Schizophrenia (SZ) is a complex heterogeneous disease with recognized genetic risk factors. Genetic studies have identified more than 100 loci conferring risk for the disease and there is consensus today for a polygenic etiology of SZ (Purcell et al., 2009). Genome wide association studies (GWAS) using large cohorts have strongly implicated polymorphisms in the chromosome 6p21.3–22.1 region as one the most consistent genomic regions associated with the disease (Agartz et al., 2011; Bergen et al., 2012; Stefansson et al., 2009; Zhang et al., 2013). This region corresponds to a highly variable and recombinant portion of the human genome coding for human leucocyte antigen (HLA) proteins, which are also known as major histocompatibility complex (MHC) molecules (Stefansson et al., 2009). Due to their involvement in cellular immune functions, including antigen processing and tolerance, it is believed that the pathophysiology of SZ is related, at least in part, to disrupted immune processes that influence the responses of the immune system and its interaction with the central nervous system, with particular effects during prenatal neurodevelopment (Brown, 2011; Elmer and McAllister, 2012; McAllister, 2014). Indeed, there are an increasing number of studies pointing to an ongoing inflammatory process in SZ characterized by elevated inflammatory markers in peripheral blood (Doorduin et al., 2009; Fineberg and Ellman, 2013; Muller et al., 2012; Saetre et al., 2007).

Cytokines, chemokines and C-reactive protein are among the most commonly studied immune function biomarkers in SZ (Brown et al., 2004; Fatjo-Vilas et al., 2012; Fineberg and Ellman, 2013; Hope et al., 2013; Miller et al., 2013a; Reale et al., 2011). These molecules have gained relevance in clinical research as they may contribute to the underlying pathophysiological processes and genetic underpinnings associated with SZ. Nevertheless, cytokines and chemokines are involved in multiple cellular functions that may or may not be directly linked with MHC function. Thus, immune biomarkers directly related with MHC function may provide a more stable biomarker, as well as provide potential mechanistic insights on specific pathological cellular processes.

In this regard, CD3⁺ T cells are directly related to MHC function; these cells depend on permanent interactions involving T cell receptor-MHC complexes during development, differentiation and survival of the T cell repertoire (Charles A Janeway, 2001). Thus, CD3⁺ T cells may constitute a valuable biomarker of the disease in relation to functional variation of the MHC locus. Moreover, increasing evidence from basic rodent studies implicates different types of CD3⁺ T cells in particular brain functions and behavioral processes (Kipnis et al., 2008; Kipnis et al., 2012), which may provide specific insights about endophenotypes or symptoms of the disease. However, studies reporting on various aspects of CD3 ⁺ T cell biology in SZ are relatively uncommon (Craddock et al., 2007; Ding et al., 2014; Drexhage et al., 2011; Fernandez-Egea et al., 2016; Miller et al., 2013b). CD3⁺ T cells differentiate into two major types, expressing one of two different cell surface markers, CD8 or CD4, which allow specific interactions with either MHC class I or class II respectively. Within the CD4⁺ type, regulatory T cells (CD4⁺/CD25⁺/Foxp3⁺; Tregs) are an attractive CD3⁺ T cell subtype for a biomarker in SZ, since they function to provide important immunomodulatory control over other T cell subtypes and inflammatory processes (Arce-Sillas et al., 2016; Miyara et al., 2009; Rodriguez-Perea et al., 2016). The development and functions of these cells are directly related to the expression of the transcription factor Foxp3 and the CD25 cell surface marker within the CD3⁺/CD4⁺ T cell subtype (Kitagawa and Sakaguchi, 2017; Mohr et al., 2018). Tregs have broad immune suppressive activity on diverse immune cells, which requires MHC cellular interactions. Therefore, in the present study we compared the proportions of Tregs in healthy controls and stable medicated participants with SZ and examined the relationship between Tregs and negative symptoms and cognitive performance, two core domains of the illness.

2. Methods

2.1. Study participants and data and sample collection

All participants were recruited from the Maryland Psychiatric Research Center, University of Maryland in Catonsville, MD between February and November of 2017. Participants were between 18 and 64 years of age and of either sex and any race. Participants with schizophrenia were required to meet Diagnostic and Statistical Manual (DSM)-5 criteria (American Psychiatric Association, 2013) for schizophrenia or schizoaffective disorder, able to give informed consent and considered clinically stable as defined by use of the same antipsychotic treatment for 30 days prior to the study. Healthy controls were included if they were free of all major psychiatric illness as determined by the Structured Clinical Interview for Diagnosis-5, and able to give informed consent. Exclusion criteria for both participants with schizophrenia and healthy controls included a DSM-5 diagnosis of alcohol or substance misuse disorders currently or in the last 3 months, any current active systemic infection with fever (temperature > 38°C), any long-term untreated chronic infections, pregnancy, and other conditions whose pathology and treatments may alter their immune status. The study was approved by the University of Maryland Institutional Review Board (IRB), reliance was granted to UMB IRB by the Department of Health and Mental Hygiene IRB, and the study was approved by the Spring Grove Hospital Research Committee. Twenty-six participants with schizophrenia and 17 healthy controls met inclusion and exclusion criteria, passed the Evaluation to Sign Consent (ESC), signed informed consent and were enrolled in the study.

2.2. Laboratory assays

All study participants arrived between 7:00–8:00 am under fasting conditions. Participants had their blood drawn for flow cytometry, a complete blood count (CBC) and a Chemistry 14 panel. All processing of blood was completed immediately. Samples were maintained at 2–5°C and processed for flow cytometry within 2 h of collection using a stringent collection and transport protocol. Samples for the CBC and Chemistry 14 were sent to LabCorp (Burlington, NC, USA) for standard processing. In addition to the blood draws, demographic information, height, weight, sex/gender, past medical history, current medications, and blood pressure data were collected on the same day.

2.3. Neuropsychological and psychiatric symptom evaluation

The MATRICS Consensus Cognitive Battery (MCCB) was used to assess neuropsychological test performance. The MCCB is specifically designed to assess cognition in people with schizophrenia. The MCCB is comprised of 10 tests, which assess seven cognitive domains. The MCCB composite score is a standardized mean of the seven domain scores. T-scores are standardized to normative data, and have an estimated mean of 50 and SD of 10 in the general healthy population (Nuechterlein et al., 2008).

The Brief Psychiatric Rating Scale (BPRS) total score was used to measure global psychopathology. The four BPRS positive symptom items - conceptual disorganization, suspiciousness, hallucinatory behavior, and unusual thought content – were used to measure positive psychotic symptoms (Overall JE, 1962). The Scale for the Assessment of Negative Symptoms (SANS) minus the global items, inappropriate affect, poverty of content of speech, and attention items, was used to evaluate negative symptoms (Andreasen, 1982; Buchanan et al., 2007). The Calgary Depression Rating Scale (CDS), which was specifically designed to assess depressive symptoms in people with schizophrenia (Addington et al., 1993) was used to measure depressive symptoms. Finally, the Clinical Global Impression (CGI) severity item was used to assess global illness severity (Guy W, 1976). The MCCB and symptom assessments were conducted immediately after the blood samples were obtained.

2.4. Flow cytometry and T cell analyses

Peripheral blood mononuclear cells (PBMCs) were prepared using Ficoll density gradient separation following manufacture’s guidelines (Ficoll-Paque™ PLUS Media, GE Healthcare™). Briefly, after centrifugation the mononuclear cell layer was washed twice and re-suspended in 1 ml MACS buffer (1X PBS, 2% FBS, 2 mM EDTA). After determining the cell concentration, approximately 1–1.5 × 10⁶ cells were washed twice with PBS and re-suspended in 1 ml PBS buffer and 1 μl viability marker (Fixable Viability Dye eFluor™ 506, eBioscience, Cat#: 65–0866-14). Cells were then incubated at 4°C in the dark for 30 min and washed twice with PBS. Cell surface epitopes for CD3, CD4, CD25, and CD45RA were labeled before fixation. For this, cells were re-suspended in 100 µl PBS buffer containing a combination of fluorescence conjugated anti-human antibodies including: PE-CD3 (BioLegend Catalog #: 317308); FITC-CD4 (BioLegend Catalog #: 357405); PE/Cy7-CD45RA (BioLegend Catalog #: 304126); PerCP-Cy5.5; and CD25-APC (Miltenyi Biotec Inc. Catalog #: 130–098-213). Cells were incubated at 4°C in the dark for 30 to 60 min. After surface staining, the cells were fixed and permeabilized overnight at 4°C, washed again and then labeled for Foxp3 with e-fluor 450-Foxp3 (FOXP3 Monoclonal Antibody (236A/E7), eBioscience™, Catalog #: 48–4777-42) at 4°C in the dark for 1 h. The samples were then washed and resuspended in fresh buffer for analysis. Flow cytometric analysis was performed using a BDTM LSR II flow cytometer (BD Biosciences, San Jose, California). Lymphocytes were gated on forward scatter (FSC) versus side scatter (SSC), corrected for live/dead and single cells (Fig. 1A-C), and 500,000 events collected for initial gating and then standardized to 50,000 lymphocytes for analysis using FlowJo version 10 software (Tree Star, Ashland, Oregon) with biexponential scaling. Tregs were quantified within the CD3⁺ and CD4⁺ gate as CD25⁺/Foxp3⁺ (Fig 1A, B). Tregs negative for the CD45RA antigen were defined as activated Tregs (Fig. 1C) and used as marker of functional Tregs (Miyara et al., 2009). Blood from two collection tubes per participant were processed individually. This generated duplicate data per single participant. Variability between samples for individual participants was less than 5%.

Figure 1. >Analysis of Tregs by flow cytometry.

Representative flow cytometry dot plots showing the population of total CD4+ cells (A), CD4⁺/CD25⁺/Foxp3⁺ regulatory T cells (B) and CD45RA^neg activated regulatory T cells (C). Student’s t tests were conducted between healthy controls (HC) and schizophrenia (SZ) patients for D) CD4⁺ T cells, E) regulatory T cells (p = 0.019), and F) activated regulatory T cells. N = 17 HC and 26 SZ patients.

2.5. Statistics

The demographic information is reported descriptively. The mean standard deviation of clinical data and neuropsychological outcomes for each group was calculated using Student’s t tests and Chi Square tests. The flow cytometry samples were de-identified so that those analyzing the samples were blind to group identity. Paired comparisons were conducted for proportion of T cell subtypes using Student’s t-tests. Spearman’s correlation analyses were used to determine the relationship between Tregs and psychiatric symptoms and neurocognitive performance. Linear regression models included covariates such as gender, smoking status and BMI to examine the impact of these variables on outcomes. All analyses were conducted using SAS 9.4 (SAS Institute, NC, USA). Data are reported as mean ± SD; while p ≤ 0.05 is considered significant.

3. Results

3.1. Demographics and sample characteristics

Demographic, clinical characteristics, BMI and blood counts are listed in Table 1. There were no significant group differences on any demographic variable, BMI, white blood cell count, total lymphocytes or in any other blood cell type. Medication use in the schizophrenia group is listed in the table. None of the healthy controls were treated with a psychotropic medication.

Table 1. Demographic and clinical characteristics.

Demographic and clinical characteristics of the sample including blood cell counts and medications. No differences were observed in any parameter except medications. FGA & SGA: First and second-generation antipsychotic respectively.

	Schizophrenia (N = 26)	Healthy Controls (N = 17)	Statistics
Age (years; mean ± SD)	45.5 ± 11.0	39.7 ± 12.4	t = −1.62 p = 0.112
Sex (male, N, %)	N = 19 (47%)	N = 9 (53%)	χ2 = 1.8 p = 0.176
Race (N, %)
White AA Asian Mixed	N = 9 (35%) N = 15 (58%) N = 0 N = 2 (8%)	N = 8 (47%) N = 6 (35%) N = 1 N = 2 (12%)	χ2 = 3.1 p = 3.7
Cigarette smoker current	10 (38%)	3 (18%)	χ2 = 2.1 p = 0.15
Body mass index (kg/m2)	32.4 ± 10.0	30.1 ± 6.1	t = −0.92 p = 0.36
White blood cell count (x103/μl)	6.3 ± 2.8	5.6 ± 1.9	t = 0.78 p = 0.44
Neutrophils (x103/μl)	3.9 ± 2.3	3.2 ± 1.3	t = −125 p = 0.22
Lymphocytes (x103/μl)	1.8 ± 0.7	1.8 ± 0.6	t = −0.10 p = 0.91
Monocytes (x103/μl)	0.5 ± 0.3	0.4 ± 0.2	t = −1.14 p = 0.26
Eosinophils (x103/μl)	0.2 ± 0.1	0.2 ± 0.1	t = 0.45 p = 0.66
Basophils (x103/μl)	0.0 ± 0.01	0.0 ± 0.01	t = 0.27 p = 0.78
Medications
Clozapine  SGA  FGA	N = 9 (35%) N = 7 (27%) N = 10 (38%)	NA
Antidepressants	N = 9 (35%)	NA
Anxiolytics	N = 8 (31%)	NA

3.2. Tregs are increased in participants with schizophrenia

The proportion of Tregs was higher in participants with SZ compared to the healthy controls (7.1 ± 0.7 versus 4.7 ± 0.6 respectively, t = 2.44, p = 0.02; Figure 1-E). No significant differences between groups were observed in the proportion of CD3⁺ cells from the parent lymphocyte gate, or in total CD4⁺ cells from the parent CD3⁺ population (Fig. 1-D) indicating a specific effect for Tregs. Likewise, no significant differences were observed in the proportion of activated Tregs (CD45RA^neg.) between participants with SZ and healthy controls (Fig. 1-F) suggesting an overall increase in functional Tregs. These results were confirmed by applying an alternate gating strategy by obtaining a univariate histogram for Foxp3 within the CD4⁺/CD25⁺ gate (not shown). This yielded similar results of increased Foxp3 expression in Tregs in the SZ group compared to controls. These results show an increased proportion of circulating Tregs in participants with SZ, which is specific for this T cell subtype. As the total lymphocyte counts were almost identical between the two groups, and no group differences were found for CD3⁺ or CD4⁺ cells, these results translate to increased total numbers of Tregs in participants with SZ with respect to controls. While no relationships were noted between smoking, BMI, age and Tregs, logistic regression analyses were performed with Tregs as the dependent variable to assess the contribution of group, age, BMI, and smoking status on Tregs. The only variable for which there was a significant effect was diagnosis (F = 5.47; p = 0.025), suggesting that higher Tregs in SZ is not due to age (F = 0.46; p = 0.50), smoking (F = 0.18; p = 0.67) or BMI (F = 0.02; p = 0.89), three variables that are influenced by inflammation.

3.3. Neuropsychological test performance and symptoms correlations with Tregs

Data distribution for main diagnostic symptoms is shown in Figure 2. As expected, compared to healthy controls, participants with SZ had higher BPRS, SANS and CGI scores and lower MCCB composite score (Fig 2A-D). While there were trends for higher depressive symptoms in SZ and lower attention scores, these differences were not significant. The participants with SZ were stable with a low level of symptom presentation.

Figure 2. Psychiatric symptoms.

Psychiatric symptoms and neurocognitive function in the schizophrenia group (SZ) and healthy controls (HC). A) BPRS: Brief Psychiatric Rating Scale total score. B) SANS: Scale for the Assessment of Negative Symptoms. C) MCCB: MATRICS Consensus Cognitive Battery. D) CGI: Clinical Global Impression. Scores are shown as Mean ± SD. **** Significant p values (≤ 0.0001)

Spearman’s correlations were used to examine the relationship of Tregs and symptom domains. Healthy controls displayed no relationship between Tregs and symptoms in any domain or cognitive performance, with the exception of higher Tregs being correlated to higher working memory performance (r = 0.49; p = 0.05; data not shown). In the SZ group, Tregs were associated with lower SANS total scores (r = −0.38; p = 0.05; Fig 3A, B); the relationship between Tregs and SANS total score was primarily driven by the correlations observed with the alogia (r = −0.46; p = 0.018) and affective blunting (r = −0.36; p = 0.07) scales (Table 2). These correlations for Tregs were not observed for CD3⁺ or CD4⁺ T cells indicating that these populations are not influencing these outcomes. Age, smoking status and BMI were not related to Tregs and did not contribute significantly to any group effects. There were no significant relationships observed for BPRS total or positive symptom scores.

Figure 3. Correlations of Tregs with total SANS scores.

Correlation plots for percentage of Tregs from the CD4⁺ group of cells and total SANS scores in healthy controls and participants with schizophrenia

Table 2. Correlations of T cells with psychiatric symptoms and cognition in schizophrenia patients.

For the schizophrenia group, the SANS total score as well as domains of alogia and affective blunting suggest less impaired negative symptoms with higher Tregs based on significant correlations (see text). Higher CD4 cells were correlated to higher visual memory performance. Significant p values are noted in bold.

	CD3+	CD4+	Tregs
BPRS Total	r = −0.25 p = 0.22	r = 0.05 p = 0.80	r = −0.01 p = 0.96
Positive symptoms	r = −0.24 p = 0.25	r = 0.16 p = 0.44	r = 0.06 p = 0.76
SANS total score	r = 0.08 p = 0.71	r = −0.28 p = 0.17	r = −0.38 p = 0.05
SANS Anhedonia	r = 0.23 p = 0.26	r = −0.53 p = 0.005	r = −0.18 p = 0.38
SANS Alogia	r = 0.04 p = 0.86	r = −0.35 p = 0.08	r = −0.46 p = 0.02
SANS Avolition	r = −0.03 p = 0.90	r = −0.13 p = 0.52	r = −0.20 p = 0.32
SANS Affective blunting	r = −0.08 p = 0.70	r = 0.08 p = 0.69	r = −0.36 p = 0.07
Calgary Depression Rating Scale	r = −0.16 p = 0.44	r = −0.05 p = 0.79	r = −0.14 p = 0.48
Clinical Global Impression	r = −0.06 p = 0.76	r = −0.20 p = 0.32	r = 0.02 p = 0.91
MCCB composite	r = −0.08 p = 0.71	r = 0.15 p = 0.46	r = 0.31 p = 0.12
Speed of processing	r = 0.12 p = 0.56	r = −0.07 p = 0.73	r = 0.29 p = 0.15
Attention/vigilance	r = −0.03 p = 0.88	r = 0.04 p = 0.86	r = 0.26 p = 0.20
Working memory	r = 0.21 p = 0.31	r = −0.28 p = 0.17	r = 0.26 p = 0.20
Verbal learning and memory	r = −0.05 p = 0.79	r = 0.24 p = 0.24	r = −0.03 p = 0.89
Visual learning and memory	r = −0.34 p = 0.08	r = 0.46 p = 0.02	r = 0.03 p = 0.87
Reasoning and problem solving	r = −0.00 p = 0.96	r = −0.03 p = 0.88	r = 0.33 p = 0.09

There were no significant associations between Tregs and MCCB total score or any domain score (see Table 2). In contrast, there was a significant correlation between higher CD4⁺ T cells and better visual learning and memory performance (r = 0.46; p = 0.02). Notably, the correlations for CD4⁺ cells found in both HC and SZ groups in the cognitive domain were not found for Tregs, suggesting that other CD4⁺ T cell subtypes, but not Tregs, are associated with cognitive performance.

3.4. Psychiatric Medications and Tregs

All participants with SZ received antipsychotic medications (see Table 1 for type of antipsychotic). A subgroup of participants with SZ was also medicated with antidepressants and anxiolytics. Thus, we examined the contribution of antipsychotic, antidepressant or anxiolytic medications on Tregs within the SZ group. Moreover, among antipsychotic use, clozapine, second-generation antipsychotics (SGA) and first generation antipsychotics (FGA) were also examined with respect to Tregs. There was no significant difference in contribution among the antipsychotic classes on the prevalence of Tregs, (F = 1.25; p = 0.31). Also, there was no effect of antidepressant (F = 2.50; p = 0.13) or with anxiolytic (F = 1.34; p = 0.26) use on the prevalence of Tregs. These results suggest that psychotropic medications had no detectable effect on Treg prevalence in this study.

4. Discussion

To our knowledge, this is the first study reporting on Tregs in SZ using the canonical marker Foxp3 as a marker of Tregs (Kitagawa and Sakaguchi, 2017; Miyara et al., 2009; Mohr et al., 2018; Rodriguez-Perea et al., 2016). Despite a modest sample size, the present study revealed an important effect size showing an increased proportion of Tregs in medicated participants with SZ compared to healthy controls. The difference in Tregs was not related to an acute exacerbation of symptoms as the participants with SZ were required to be clinically stable to enter the study. Potential confounders, including BMI, smoking status and age, were not correlated with the numbers of Tregs. The group difference was specific for Tregs, since there were no significant differences in the proportion of CD4⁺ T cells. Comparable proportions of activated Tregs (CD45RA^neg) between groups and increased expression of Foxp3 in SZ suggest that the increase relates to the generation and maintenance of functional Tregs, possibly mediating immunosuppressive or anti-inflammatory functions (Miyara et al., 2009; Mohr et al., 2018). Interestingly, higher Tregs were significantly associated with lower negative symptoms, especially alogia and affective blunting, which suggests that an increase in Tregs in SZ is associated with better emotional expression.

Although we found no relationship between Tregs and psychotropic medications, as all participants with SZ were medicated and stable, increases in Tregs may be the result of medication effects on Tregs and/or these cells may be mediators of treatment response. The only other available study reporting on Tregs in SZ indicates that either of these possibilities are feasible, in particular with respect to negative symptoms (Fernandez-Egea et al., 2016). Indeed, Fernandez-Egea and colleagues found reduced Tregs in a sample of 18 treatment resistant clozapine-treated participants with SZ compared to matched controls. Within the SZ sample, reduced Tregs were associated with more severe negative symptoms, which is consistent with our findings. In addition, Fernandez-Egea and colleagues found a significant relationship between increased Tregs and better neuropsychological test performance. Although we did not find significant relationships between Tregs and neuropsychological test performance, some of the observed correlations were in the direction of increased Tregs being associated with better performance (i.e: p = 0.09 for reasoning and problem solving and p = 0.15 for speed of processing, Table 2). While neither study examined prospective changes in Treg populations across disease state, it is plausible that Tregs may relate to treatment efficacy. Related to this, a study examining antipsychotic-naïve, first episode people with SZ found that an increased proportion of Th17 cells correlated with positive and negative syndrome scale (PANSS) scores, and were decreased by chronic treatment with risperidone (Ding et al., 2014). The pro-inflammatory role of Th17 cells is well established (Song et al., 2014) and it is possible that one of the therapeutic effects of Tregs may be mediated by suppressing Th17 cells (Knochelmann et al., 2018). Since both Th17 and Tregs are subtypes of CD3⁺/CD4⁺ cells, these findings may represent functional outcomes related to the MHC region, which, as previously discussed, has been implicated in SZ by GWAS studies. Thus, the potential value of Tregs as a functional biomarker associated with genetic disease risk deserves further investigation. Also of note, the serine/threonine kinase 1(AKT1) gene, previously implicated in schizophrenia by several studies (Nicodemus et al., 2010; Emamian ES., 2012; Wockner et al., 2014) has direct regulatory roles on Tregs through interaction with the forkhead box O (Foxo) family of transcription factors, which in turn regulates Foxp3 expression and function (Ouyang et al., 2010; 2012). Therefore, down- or upstream mechanisms of Treg homeostasis may provide insights on biological processes involving diverse genes associated with the disease.

As previously discussed, specific correlations were found in the SZ sample with Tregs and domains of affect. Moreover, correlations between CD4⁺ cells and domains of cognitive functioning were observed for both control and SZ groups. While these findings in humans require further confirmation in larger cohorts, the association of CD4⁺ T cells with improved cognition and Tregs with emotional expression is consistent with basic rodent studies showing a role for these cells in emotional regulation and cognitive processes (Brynskikh et al., 2008; Clark et al., 2016; Clark et al., 2018; Derecki et al., 2010; Herkenham and Kigar, 2017; Kim et al., 2012; Kipnis et al., 2004; Kipnis et al., 2012). While the exact mechanisms on how CD4⁺ T cells in general, and Tregs in particular mediate these effects are not entirely clear, there is growing consensus that direct interactions with the central nervous system (CNS) are required (reviewed by (Filiano et al., 2017). Together, these studies provide support for the proposition that Tregs may not only be useful biomarkers directly associated with MHC variations, but they may also map specific neurobehavioral traits of immune-CNS interactions mediated through the MHC locus.

4.1. Limitations

Major limitations are the small sample size and the analysis performed at a single point in time. As discussed throughout the manuscript, our study cannot determine if medication status contributes to higher Tregs in SZ. While many variables may confound these results, age, smoking, or BMI were not found related to the prevalence of any T cell subtype. Moreover, participants were not enrolled if they currently tested positive for illicit drugs, limiting their potential effect on Tregs in this study.

4.2. Conclusions

This is the first study analyzing Tregs in schizophrenia using the canonical marker Foxp3, revealing increased proportions in stable participants with SZ, which inversely correlate with negative symptoms, suggesting that increases in this cell population may counter balance putative processes promoting and/or maintaining negative symptoms, in particular alogia and possibly affective blunting. Further studies including additional T cells subsets, such as Th17 cells, measured at multiple time-points and under different treatment protocols are warranted.

Highlights.

• ◦ Regulatory T cells are increased in the blood of stable medicated schizophrenia patients

• ◦ This increase corresponds to functional cells according to canonical

• ◦ markers

• ◦ A negative correlation between regulatory T cells and negative symptoms was detected

• ◦ A positive correlation for both schizophrenia and healthy controls was

• ◦ found for cognition and CD4 T cells

• ◦ These results supports the use of regulatory T cells as valid biomarkers in schizophrenia