Reduced number of peripheral natural killer cells in schizophrenia but not in bipolar disorder – a computational deconvolution study Paweł Karpiński1, Dorota Frydecka2, Maria M. Sąsiadek1, Błażej Misiak1,2 1 - Department of Genetics, Wroclaw Medical University, Wroclaw, Poland 2 - Department of Psychiatry, Wroclaw Medical University, Wroclaw, Poland Introduction Several lines of evidence indicate the involvement of immune-inflammatory processes in the pathophysiology of schizophrenia (SZ) and bipolar disorder (BD) [1, 2]. Indeed, there are studies showing elevated levels of pro-inflammatory cytokines, overproduction of acute phase proteins, increased levels of various autoantibodies and microglial activation in patients with SZ and BD [1, 2]. In addition, it has been reported that patients with schizophrenia and BD might present with changes in proportions of various lymphocyte populations [3, 4]. However, studies addressing this issue measured lymphocyte proportions using flow cytommetry and fluorescence-activated cell sorting techniques (FACS), which have several limitations. Therefore, in this study we aimed to investigate leukocyte cell proportions estimated by means of microarray expression deconvolution using publicly available datasets from SZ and BD patients. In turn, ANCOVA revealed significant and independent effects of group (SZ patients vs. controls) and sex differences on the levels of NK cells. Indeed, females had significantly lower levels of NK cells (for details see [5]). There were no significant differences in the levels of NK cells between acutely relapsed SZ patients and those in remission (analysis based on one dataset GSE27383, see [5] for details). There were no significant differences in the levels of NK cells between BD patients and controls. Fig. 1 Proportions of leukocyte counts in patients with sepsis [5]. Material and methods Data preprocessing and deconvolution Results of this study were presented in the article published in Brain, Behavior and Immunity [5]. All expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Our datasets included 199 SZ patients, 49 BD patients and 218 controls. We also used other 73 samples to validate the preprocessing and deconvolution steps. A detailed description of preprocessing and normalization procedures was provided in our previous article [5]. After data normalization, we utilized the CIBERSORT algorithm (1000 iterations) and the LM22 gene signature to predict relative proportions of 21 human hematopoietic cell phenotypes (mast cells were excluded from signature) [6]. Subsequently, we aggregated 21 cell subsets into 10 major cell types (B cells, plasma B cells, T CD4+, T CD8+, NK cells, gamma delta T cells, monocytes together with macrophages, dendritic cells, eosinophils and neutrophils) [6]. Cell proportions were predicted in each dataset separately. To validate the CIBERSORT algorithm, we performed an initial analysis of leukocyte counts in data from patients with sepsis, who are characterized by well-defined shifts in leukocyte counts. Recent studies have demonstrated that septic patients display an extensive depletion of peripheral gamma delta T cells, T CD4+ cells, T CD8+ cells, NK cells, and B lymphocytes, whereas numbers of circulating neutrophils are highly increased [7]. Statistics Assessment of data normality and homogeneity of variance was performed using the Anderson-Darling test and the Levene’s test. Between-group differences were analysed using the t-test or non-parametric statistics (depending on normality and homogeneity of variance). Effects of age and sex differences were tested using the Analysis of Covariance (ANCOVA). In case of non-normal distribution, data was transforemd using the arcsine transformation before performing ANCOVA. Results were adjusted for multiple testing using the Bonferroni correction. The STATISTICA 10 software was used to perform ANCOVA, while the rest of statistical analyses were performed using the ‘‘nortest” and ‘‘compareGroups” packages in R statistical software. Fig. 2 The levels of NK cells in patients with SZ and BD [5]. p = 0.049 p < 0.001 p = 0.002 p = 0.001 p = 0.548 p = 0.497 Conclusions Reduced number of peripheral NK cells might occur in patients with SZ providing further support for the immune-inflammatory hypothesis of psychotic disorders. Observational studies investigating the impact of antipsychotic treatment on NK cells counts are required to explore medication effects. Computational deconvolution might reliably measure counts of peripheral lymphocytes. Future studies should compare results from computational deconvolution and flow cytometry techniques in detecting subthreshol inflammatory state in patients with SZ and BD. Results As demonstrated in Fig. 1, counts of lymphocyte subtypes and neutrophils calculated by the CIBERSORT algorithm accurately reflected the main changes in circulating blood cells described in patients with sepsis, when compared to healthy controls. There were no significant differences between patients with SZ or BD and healthy individuals with respect to the majority of cell subtypes (data not shown). However, the levels of NK cells were significantly lower in medicated SZ patients and, to a lesser extent in drug-naive SZ patients, compared to controls (Fig. 2). This association remained significant after Bonferroni correction in datasets from medicated SZ patients (p < 0.008). References 1. Khandaker et al. Lancet Psychiatry 2015; 2:258-270; 2. Leboyer et al. J Affect Disord 2012; 141:1-10; 3. Miller et al. Biol Psychiatry 2013;73:993-998; 4. Barbosa et al. Int J Neuropsychopharmacol 2015,18; 5. Karpiński et al. Brain Behav Immun 2016;54:194-200; 6. Newman et al. Nat Methods 2015;12:453-457; 6. De Pablo et al. Biomed Res Int 2014;671087