HLA peptide-binding pocket diversity modulates immunological complications after cord blood transplant in acute leukaemia.

Pocket motifs and their amino acid positions of HLA molecules are known to govern antigen presentation to effector cells. Our objective was to analyse their influence on the risk of graft-versus-host disease (GVHD) and relapse after umbilical cord blood transplant (UCBT). The transplant characteristics of 849 patients with acute leukaemia were obtained from the Eurocord/EBMT database. Higher acute (a) GVHD was associated with homozygosity of UCB HLA-C amino acid positions 77 and 80 (NN/KK) (p = 0.008). Severe aGVHD was associated with HLA-A pocket B YSAVMENVHY motif (p = 0.002) and NN and RR genotypes of the HLA-C amino acid positions 77 and 156 (p = 0.006 and p = 0.002). Such risk was also increased in case of recipient and UCB mismatches in P4 (p < 0.0001) and P9 (p = 0.003) pockets of HLA-DQB1 alleles. For chronic GVHD, the pocket B YYAVMEISNY motif of the HLA-B*15:01 allele and the absence of mismatch between recipient and UCB in the P6 pocket of HLA-DRB1 were associated with a lower risk (p = 0.0007 and p = 0.0004). In relapse, both UCB pocket B YFAVMENVHY belonging to HLA-A*32:01 and recipient pocket B YDSVGENYQY motif of the HLA-C*07:01 allele were associated with higher risk (p = 0.0026 and p = 0.015). We provide clues on HLA-mediated cellular interactions and their role in the development of GVHD and relapse.

Immune-based Machine learning prediction of diagnosis and illness state in schizophrenia and bipolar disorder.

BACKGROUND: Schizophrenia and bipolar disorder frequently face significant delay in diagnosis, leading to being missed or misdiagnosed in early stages. Both disorders have also been associated with trait and state immune abnormalities. Recent machine learning-based studies have shown encouraging results using diagnostic biomarkers in predictive models, but few have focused on immune-based markers. Our main objective was to develop supervised machine learning models to predict diagnosis and illness state in schizophrenia and bipolar disorder using only a panel of peripheral kynurenine metabolites and cytokines. METHODS: The cross-sectional I-GIVE cohort included hospitalized acute bipolar patients (n = 205), stable bipolar outpatients (n = 116), hospitalized acute schizophrenia patients (n = 111), stable schizophrenia outpatients (n = 75) and healthy controls (n = 185). Serum kynurenine metabolites, namely tryptophan (TRP), kynurenine (KYN), kynurenic acid (KA), quinaldic acid (QUINA), xanthurenic acid (XA), quinolinic acid (QUINO) and picolinic acid (PICO) were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS), while V-plex Human Cytokine Assays were used to measure cytokines (interleukin-6 (IL-6), IL-8, IL-17, IL-12/IL23-P40, tumor necrosis factor-alpha (TNF-ɑ), interferon-gamma (IFN-γ)). Supervised machine learning models were performed using JMP Pro 17.0.0. We compared a primary analysis using nested cross-validation to a split set as sensitivity analysis. Post-hoc, we re-ran the models using only the significant features to obtain the key markers. RESULTS: The models yielded a good Area Under the Curve (AUC) (0.804, Positive Prediction Value (PPV) = 86.95; Negative Prediction Value (NPV) = 54.61) for distinguishing all patients from controls. This implies that a positive test is highly accurate in identifying the patients, but a negative test is inconclusive. Both schizophrenia patients and bipolar patients could each be separated from controls with a good accuracy (SCZ AUC 0.824; BD AUC 0.802). Overall, increased levels of IL-6, TNF-ɑ and PICO and decreased levels of IFN-γ and QUINO were predictive for an individual being classified as a patient. Classification of acute versus stable patients reached a fair AUC of 0.713. The differentiation between schizophrenia and bipolar disorder yielded a poor AUC of 0.627. CONCLUSIONS: This study highlights the potential of using immune-based measures to build predictive classification models in schizophrenia and bipolar disorder, with IL-6, TNF-ɑ, IFN-γ, QUINO and PICO as key candidates. While machine learning models successfully distinguished schizophrenia and bipolar disorder from controls, the challenges in differentiating schizophrenic from bipolar patients likely reflect shared immunological pathways by the both disorders and confounding by a larger state-specific effect. Larger multi-centric studies and multi-domain models are needed to enhance reliability and translation into clinic.

B Cell-activating factor (BAFF): A promising trans-nosographic biomarker of inflammation and autoimmunity in bipolar disorder and schizophrenia.

Immune dysregulation is an important aspect of schizophrenia (SZ) and bipolar disorders (BD) pathophysiology, including not only inflammatory but also autoimmune process reflective of abnormal humoral immune responses. Given that B cell-activating factor (BAFF) is an integral aspect of B lymphocyte regulation, the current study investigated BAFF in SZ and BD. 255 SZ patients, 407 BD patients and 185 healthy controls (HC) were investigated across three aspects of soluble BAFF (sBAFF) by (i) comparing sBAFF circulatory levels across SZ, BD and HC, (ii) determining potential correlations between the circulating levels of sBAFF and the genotype distribution of a functionally relevant polymorphism, namely the TNFSF13B 3'UTR insertion-deletion polymorphism (GCTGT>A), (iii) analyzing relationships between both sBAFF levels and 3'UTR insertion-deletion genotypes and disease risk, patients clinical characteristics and circulating levels of potent inflammatory molecules. In addition, in subsets of patients, we also searched for possible correlations between sBAFF levels and stigma of past infectious events as well as positivity for circulating systemic autoantibodies or those directed against central nervous system (CNS) structures. Studying blood derived serum and DNA, weobserved that circulating sBAFF levels were significantly higher in SZ and BD patients, versus HC (p = 5.3*10-10and p = 4.4*10-09). Patients experiencing acute episodes, versus stable patients, in between acute episodes, exhibited higher sBAFF levels (p = 0.017).In SZ patients, positive correlations were observed between elevated sBAFF levels and: (i) elevated positive psychotic symptoms (PANSS pos), (ii) history of childhood trauma (physical abuse), and (iii) low scores on global functioning (GAF) (p = 0.024, p = 0.024, and p = 0.041).We also found that the distribution of the BAFF Ins/Del genotypes was significantly correlated with circulating sBAFF levels in SZ and BD patients (p = 0.0004). Elevated sBAFF levels were also correlated with increased levels of pro-inflammatory markers in both SZ and BD cohorts (p < 0.001). Regarding infectious stigma, only patients seropositive, versus seronegative, for herpes simplex virus (HSV)1 immunoglobulin (Ig)G antibodies exhibited a significant association with high sBAFF levels (p = 0.013). In contrast, positivity for systemic or CNS autoantibodies was significantly associated with reduced sBAFF levels, compared to patients without autoantibodies (p = 0.0017). Overall, our findings indicate that BAFF may be a promising trans-nosographic biomarker of inflammation that is likely to offer predictive, diagnostic, and prognostic tools for the management of SZ and BD. The results therefore have practicable clinical utility given the availability of immunotherapeutic treatment options including targeted monoclonal antibodies against BAFF.

A replication study of sHLA-E influence on schizophrenia and bipolar disorder.

OBJECTIVES: Schizophrenia (SZ) and bipolar disorders (BP) are chronic and severe neuropsychiatric diseases. These disorders are tightly related to immune deregulations. In the current study, we intended to replicate the previously reported involvement of the soluble HLA-E isoforms (sHLA-E) in the risk of developing the two conditions along with disease severity in a Tunisian population group. PATIENTS AND METHODS: One hundred and twenty-four patients with schizophrenia and 121 with bipolar disorder meeting the DSM-IV criteria along 111 healthy controls were included in this present case-control study. The soluble HLA-E isoforms circulating levels were measured using the ELISA method. The statistical analyses were performed using Kruskal-Wallis and Wilcoxon rank sum tests by R software and GraphPad prism 9. RESULTS: We found that the sHLA-E circulating levels were significantly higher in BP patients as compared to healthy controls (P<0.0001) and that such increases were mainly observed in patients during an acute phase of their disease (P<0.0001). In SZ patients, while we failed to observe an association with the levels of sHLA-E in the entire SZ sample, we found that high sHLA-E levels characterized stabilized patients in comparison with those during an acute episode (P=0.022). Finally, we did not observe any association between sHLA-E circulating levels and symptoms assessed by the classical clinical scales either in BP or SZ patients. CONCLUSION: Overall, the present findings replicate in a Tunisian population group the previously demonstrated implication of sHLA-E circulating levels in the risk of developing BP or SZ in a French patient cohort. Such replication allows to consider HLA-E as a potent and true inflammatory marker in the context of the two disorders.

Low C4A copy numbers and higher HERV gene insertion contributes to increased risk of SLE, with absence of association with disease phenotype and disease activity.

Low copy numbers (CNs) of C4 genes are associated with systemic autoimmune disorders and affects autoantibody diversity and disease subgroups. The primary objective of this study was to characterize diversity of complement (C4) and C4-Human Endogenous Retrovirus (HERV) gene copy numbers in SLE. We also sought to assess the association of C4 and C4-HERV CNs with serum complement levels, autoantibodies, disease phenotypes and activity. Finally, we checked the association of C4 and HERV CNs with specific HLA alleles. Genomic DNA from 70 SLE and 90 healthy controls of south Indian Tamil origin were included. Demographic, clinical and serological data was collected in a predetermined proforma. CNs of C4A and C4B genes and the frequency of insertion of 6.4kb HERV within C4 gene (C4AL, C4BL) was determined using droplet digital polymerase chain reaction (ddPCR). A four digit high resolution HLA genotyping was done using next generation sequencing. In our cohort, the total C4 gene copies ranged from 2 to 6. Compared to controls, presence of two or less copies of C4A gene was associated with SLE risk (p = 0.005; OR = 2.79; 95% CI = 1.29-6.22). Higher frequency of HERV insertion in C4A than in C4B increases such risk (p = 0.000; OR = 12.67; 95% CI = 2.80-115.3). AL-AL-AL-BS genotype was significantly higher in controls than SLE (9%vs1%, p = 0.04; OR = 0.15, 95% CI = 0.00-0.16). Distribution of HLA alleles was not different in SLE compared to controls as well as in SLE subjects with ≤ 2 copies and > 2 copies of C4A, but HLA allele distribution was diverse in subjects with C4B ≤ 2 copies and > 2 copies. Finally, there was no correlation between the C4 and the C4-HERV diversity and complement levels, autoantibodies, disease phenotypes and activity. In conclusion, our data show that, low C4A copy number and higher insertion of HERV-K in C4A increases the risk for SLE. C4 and C4-HERV CNs did not correlate with serum complements, autoantibodies, disease phenotypes and activity in SLE. Further validation in a larger homogenous SLE cohort is needed.