Cytokine pathway disruption in a mouse model of schizophrenia induced by Munc18-1a overexpression in the brain

Equivalent change enrichment analysis: assessing equivalent and inverse change in biological pathways between diverse experiments

Background

In silico functional genomics have become a driving force in the way we interpret and use gene expression data, enabling researchers to understand which biological pathways are likely to be affected by the treatments or conditions being studied. There are many approaches to functional genomics, but a number of popular methods determine if a set of modified genes has a higher than expected overlap with genes known to function as part of a pathway (functional enrichment testing). Recently, researchers have started to apply such analyses in a new way: to ask if the data they are collecting show similar disruptions to biological functions compared to reference data. Examples include studying whether similar pathways are perturbed in smokers vs. users of e-cigarettes, or whether a new mouse model of schizophrenia is justified, based on its similarity in cytokine expression to a previously published model. However, there is a dearth of robust statistical methods for testing hypotheses related to these questions and most researchers resort to ad hoc approaches. The goal of this work is to develop a statistical approach to identifying gene pathways that are equivalently (or inversely) changed across two experimental conditions.

Results

We developed Equivalent Change Enrichment Analysis (ECEA). This is a new type of gene enrichment analysis based on a statistic that we call the equivalent change index (ECI). An ECI of 1 represents a gene that was over or under-expressed (compared to control) to the same degree across two experiments. Using this statistic, we present an approach to identifying pathways that are changed in similar or opposing ways across experiments. We compare our approach to current methods on simulated data and show that ECEA is able to recover pathways exhibiting such changes even when they exhibit complex patterns of regulation, which other approaches are unable to do. On biological data, our approach recovered pathways that appear directly connected to the condition being studied.

Conclusions

ECEA provides a new way to perform gene enrichment analysis that allows researchers to compare their data to existing datasets and determine if a treatment will cause similar or opposing genomic perturbations.

Astroglial correlates of neuropsychiatric disease: From astrocytopathy to astrogliosis

Complex roles for astrocytes in health and disease continue to emerge, highlighting this class of cells as integral to function and dysfunction of the nervous system. In particular, escalating evidence strongly implicates a range of changes in astrocyte structure and function associated with neuropsychiatric diseases including major depressive disorder, schizophrenia, and addiction. These changes can range from astrocytopathy, degeneration, and loss of function, to astrogliosis and hypertrophy, and can be either adaptive or maladaptive. Evidence from the literature indicates a myriad of changes observed in astrocytes from both human postmortem studies as well as preclinical animal models, including changes in expression of glial fibrillary protein, as well as changes in astrocyte morphology and astrocyte-mediated regulation of synaptic function. In this review, we seek to provide a comprehensive assessment of these findings and consequently evidence for common themes regarding adaptations in astrocytes associated with neuropsychiatric disease. While results are mixed across conditions and models, general findings indicate decreased astrocyte cellular features and gene expression in depression, chronic stress and anxiety, but increased inflammation in schizophrenia. Changes also vary widely in response to different drugs of abuse, with evidence reflective of features of astrocytopathy to astrogliosis, varying across drug classes, route of administration and length of withdrawal.

Profiling inflammatory signatures of schizophrenia: A cross-sectional and meta-analysis study

We aimed to profile a broad panel of inflammatory markers in patients with schizophrenia and healthy controls. Additionally, we performed a meta-analysis of chemokine alterations that have not been subjected to quantitative synthesis so far. We recruited 78 patients with schizophrenia and 78 healthy controls, and measured inflammatory markers using the Luminex technology. After adjustment for multiple testing, we found elevated levels of interleukin (IL)-1 receptor antagonist (IL-1RA), IL-6, IL-7, IL-8, IL-9, IL-10, IL-13, interferon-γ, eotaxin-1, granulocyte-macrophage colony-stimulating factor (GM-CSF), monocyte chemoattractant protein-1 (MCP-1), platelet-derived growth factor with two B subunits (PDGF-BB), macrophage inflammatory protein (MIP)-1α, MIP-1β, vascular endothelial growth factor A (VEGF-A) and RANTES in multiple-episode schizophrenia (MES) patients. These differences, except for the difference in eotaxin-1 levels, appeared to be significant after co-varying for the dosage of antipsychotics. There were no significant differences in the levels of immune markers between first-episode schizophrenia (FES) patients and controls. Our meta-analysis revealed elevated levels of MCP-1 in first-episode psychosis (FEP) patients and MES individuals. Other chemokine alterations (elevated levels of IL-8, eotaxin-1 and MIP-1β) were present only in MES patients. Our results indicate that dysregulation of immune response in schizophrenia develops with illness progression or appears as a long-term medication effect. Chemokine alterations are another example of aberrant immune response in schizophrenia patients. Elevated levels of MCP-1 might represent trait markers since these alterations were found in FEP and MES patients. Other chemokine alterations might be the markers of disease progression or might represent medication effects.