The molecular mechanisms underlying schizophrenia remain largely unknown. Although schizophrenia is a mental disorder, there is increasing evidence to indicate that inflammatory processes driven by diverse environmental factors play a significant role in its development. With gene expression studies having been conducted across a variety of sample types, e.g., blood and postmortem brain, it is possible to investigate convergent signatures that may reveal interactions between the immune and nervous systems in schizophrenia pathophysiology. We conducted two meta-analyses of schizophrenia microarray gene expression data (N=474) and non-psychiatric control (N=485) data from postmortem brain and blood. Then, we assessed whether significantly dysregulated genes in schizophrenia could be shared between blood and brain. To validate our findings, we selected a top gene candidate and analyzed its expression by RT-qPCR in a cohort of schizophrenia subjects stabilized by atypical antipsychotic monotherapy (N=29) and matched controls (N=31). Meta-analyses highlighted inflammation as the major biological process associated with schizophrenia and that the chemokine receptor CX3CR1 was significantly down-regulated in schizophrenia. This differential expression was also confirmed in our validation cohort. Given both the recent data demonstrating selective CX3CR1 expression in subsets of neuroimmune cells, as well as behavioral and neuropathological observations of CX3CR1 deficiency in mouse models, our results of reduced CX3CR1 expression adds further support for a role played by monocyte/microglia in the neurodevelopment of schizophrenia.

Major depressive disorder (MDD) is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS) model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice) as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice) with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG), and the anterior cingulate cortex (ACC). Hierarchical clustering and rank-rank hypergeometric overlap (RRHO) procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE) patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C), whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1), as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1). These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD.

Familial dysautonomia (FD) is a rare neurodegenerative disease caused by a mutation in intron 20 of the IKBKAP gene (c.2204+6T>C), leading to tissue-specific skipping of exon 20 and a decrease in the synthesis of the encoded protein IKAP (also known as ELP1). Small non-coding RNAs known as microRNAs (miRNAs) are important post-transcriptional regulators of gene expression and play an essential role in the nervous system development and function. To better understand the neuronal specificity of IKAP loss, we examined expression of miRNAs in human olfactory ecto-mesenchymal stem cells (hOE-MSCs) from five control individuals and five FD patients. We profiled the expression of 373 miRNAs using microfluidics and reverse transcription coupled to quantitative PCR (RT-qPCR) on two biological replicate series of hOE-MSC cultures from healthy controls and FD patients. This led to the total identification of 26 dysregulated miRNAs in FD, validating the existence of a miRNA signature in FD. We then selected the nine most discriminant miRNAs for further analysis. The signaling pathways affected by these dysregulated miRNAs were largely within the nervous system. In addition, many targets of these dysregulated miRNAs had been previously demonstrated to be affected in FD models. Moreover, we found that four of our nine candidate miRNAs target the neuron-specific splicing factor NOVA1. We demonstrated that overexpression of miR-203a-3p leads to a decrease of NOVA1, counter-balanced by an increase of IKAP, supporting a potential interaction between NOVA1 and IKAP. Taken together, these results reinforce the choice of miRNAs as potential therapeutic targets and suggest that NOVA1 could be a regulator of FD pathophysiology.

FD is a rare neurodegenerative disorder caused by a mutation of the IKBKAP gene, which induces low expression levels of the Elongator subunit IKAP/hELP1 protein. A rational strategy for FD treatment could be to identify drugs increasing IKAP/hELP1 expression levels by blocking protein degradation pathways such as the 26S proteasome. Proteasome inhibitors are promising molecules emerging in cancer treatment and could thus constitute an enticing pharmaceutical strategy for FD treatment. Therefore, we tested three proteasome inhibitors on FD human olfactory ecto-mesenchymal stem cells (hOE-MSCs): two approved by the Food and Drug Administration (FDA) and European Medicines Agency (EMA), bortezomib and carfilzomib, as well as epoxomicin. Although all 3 inhibitors demonstrated activity in correcting IKBKAP mRNA aberrant splicing, carfilzomib was superior in enhancing IKAP/hELP1 quantity. Moreover, we observed a synergistic effect of suboptimal doses of carfilzomib on kinetin in improving IKBKAP isoforms ratio and IKAP/hELP1 expression levels allowing to counterbalance carfilzomib toxicity. Finally, we identified several dysregulated miRNAs after carfilzomib treatment that target proteasome-associated mRNAs and determined that IKAP/hELP1 deficiency in FD pathology is correlated to an overactivity of the 26S proteasome. Altogether, these results reinforce the rationale for using chemical compounds inhibiting the 26S proteasome as an innovative option for FD and a promising therapeutic pathway for many other neurodegenerative diseases.

Major depressive disorder (MDD) is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS) model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice) as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice) with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG), and the anterior cingulate cortex (ACC). Hierarchical clustering and rank-rank hypergeometric overlap (RRHO) procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE) patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C), whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1), as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1). These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD.

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