| Literature DB >> 25002852 |
Vaibhav A Diwadkar1, Angela Bustamante2, Harinder Rai1, Monica Uddin3.
Abstract
The recent sociodevelopmental cognitive model of schizophrenia/psychosis is a highly influential and compelling compendium of research findings. Here, we present logical extensions to this model incorporating ideas drawn from epigenetic mediation of psychiatric disease, and the plausible effects of epigenetics on the emergence of brain network function and dysfunction in adolescence. We discuss how gene-environment interactions, effected by epigenetic mechanisms, might in particular mediate the stress response (itself heavily implicated in the emergence of schizophrenia). Next, we discuss the plausible relevance of this framework for adolescent genetic risk populations, a risk group characterized by vexing and difficult-to-explain heterogeneity. We then discuss how exploring relationships between epigenetics and brain network dysfunction (a strongly validated finding in risk populations) can enhance understanding of the relationship between stress, epigenetics, and functional neurobiology, and the relevance of this relationship for the eventual emergence of schizophrenia/psychosis. We suggest that these considerations can expand the impact of models such as the sociodevelopmental cognitive model, increasing their explanatory reach. Ultimately, integration of these lines of research may enhance efforts of early identification, intervention, and treatment in adolescents at-risk for schizophrenia.Entities:
Keywords: adolescence; brain networks; epigenetics; risk; schizophrenia
Year: 2014 PMID: 25002852 PMCID: PMC4066368 DOI: 10.3389/fpsyt.2014.00071
Source DB: PubMed Journal: Front Psychiatry ISSN: 1664-0640 Impact factor: 4.157
Figure 1Overview of working model. HPA axis reactivity is determined both by intrinsic genetic factors and stressful environmental (including pre-natal) experiences. Stressful exposures induce a glucocorticoid (i.e., cortisol) cascade that then induces DNAm changes in HPA axis genes in the blood. These changes are expected to be more pronounced in at-risk adolescents, particularly those who may already exhibit sub-clinical psychopathology, such as negative symptoms. Risk-associated, blood-derived DNAm differences in HPA axis and related stress sensitivity genes are hypothesized to index metrics of brain function including activation patterns and effective connectivity in stress-sensitive brain regions. The activation patterns are reproduced from Diwadkar (13) and reflect engagement of an extended face-processing network in controls and high-risk subjects during a continuous emotion-processing task. These activations are most likely generated by complex dynamic interactions between brain networks that are represented in the figure below. The figure presents a putative combination of intrinsic connections between brain regions activated during such a task, and the contextual modulation of specific intrinsic connections by dynamic task elements. The role of effective connectivity analyses is to recover and estimate parameter values for intrinsic and modulatory connections that a) may be different in the diseased or risk state and b) may plausibly be under epigenetic mediation. The figure is adapted and reprinted from: Mehta and Binder (124), with permission from Elsevier; adapted by permission from Macmillan Publishers Ltd.: Frontiers in Neuropsychiatric Imaging and Stimulation (108). Reproduced with permission, Copyright © (2012) American Medical Association. All rights reserved.
Summary of genome-wide studies reporting differential DNA methylation.
| Gene name | Pathway | Studies in blood | Studies in brain | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Reference | Blood | Blood cell | Method | Data available? | Reference | Brain | Brain tissue | Method | Data available? | ||
| Dopamine catabolism | ( | Increased (cg13175282, cg06860277) DNA methylation in SCZ patients | Whole blood | 450 K | GEO | ( | Increased DNA methylation (cg12457376) in SCZ patients; DM between SCZ sub-groups, increased (cg00107488) and decreased (cg12728623, cg07579946, cg04856117, cg06787004) DNA methylation | Frontal cortex | 450 K | Three supplemental tables including all DM CpGs | |
| DNA methylation | NA | No SCZ-related DM reported in genome-wide blood-based studies to date | NA | NA | NA | ( | Decreased DNA methylation (cg06128182, cg01347596) in SCZ patients; DM between SCZ sub-groups, increased (cg21892967) and decreased (cg12053136 and cg26705765) DNA methylation | Frontal cortex | 450 K | Three supplemental tables including all DM CpGs | |
| GC-receptor chaperone complex | ( | Decreased (cg15260466) DNA methylation in SCZ patients | Whole blood | 450 K | GEO | ( | Increased DNA methylation (cg00779206) in SCZ patients; increased (cg00779206) DNA methylation between SCZ sub-groups | Frontal cortex | 450 K | Three supplemental tables including all DM CpGs | |
| GC-receptor chaperone complex; HPA axis gene | ( | Decreased (cg25114611) DNA methylation in SCZ patients | Whole blood | 450 K | GEO | ( | DM between SCZ sub-groups. Increased (cg19226017, cg17030679, cg07061368) and decreased (cg14284211 and cg01294490) DNA methylation. | Frontal cortex | 450 K | Three supplemental tables including all DM CpGs | |
| GC-receptor chaperone complex | ( | Increased (cg10833014 HSP90AA1) and decreased (cg07086455 | Whole blood | 450 K | GEO | ( | Increased | Frontal cortex | 450 K | Three supplemental tables including all DM CpGs | |
| ( | Peripheral blood cells | 27K | Included supplement of the 603 DM CpGs | ||||||||
| GC-receptor; HPA axis gene | ( | Decreased (cg06968181 and cg17617527) DNA methylation in SCZ patients | Whole blood | 450 K | GEO | ( | Decreased (cg06613263 and cg07733851) DNA methylation between SCZ sub-groups | Frontal cortex | 450 K | Three supplemental tables including all DM CpGs | |
| GC-receptor chaperone complex | NA | No SCZ-related DM reported in genome-wide blood-based studies to date | NA | NA | NA | ( | Decreased DNA methylation (cg20253639) in SCZ patients | Frontal cortex | 450 K | Three supplemental tables including all DM CpGs | |
| Dopaminergic system | ( | Schizophrenia-associated DNA methylation (increased beta 0.05 avg) differences in discordant monozygotic twins | Whole blood | 27 K | Only list top 100 DM CpGs | ( | Decreased DNA methylation (cg01204634, cg05030481, cg24756227) in SCZ patients; decreased (cg24756227, cg16392193, cg16180821) DNA methylation between SCZ sub-groups | Frontal cortex | 450 K | Three supplemental tables including all DM CpGs | |
| ( | Hypomethylation (cg26205131) in first episode schizophrenia patients | Peripheral blood cells | 27 K | Included supplement of the 603 DM CpGs | |||||||
| ( | Increased (cg1161677) and decreased (cg22659953) DNA methylation in SCZ patients | Whole blood | 450 K | GEO | |||||||
| Serotonergic system | NA | No SCZ-related DM reported in genome-wide blood-based studies to date | NA | NA | NA | ( | Decreased DNA methylation (cg03363743) in SCZ patients; decreased (cg26126367 and cg03363743) DNA methylation between SCZ sub-groups | Frontal cortex | 450 K | Three supplemental tables including all DM CpGs | |
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Gene-expression omnibus (GEO) is a public repository of functional genomic data accessible via NCBI.
The Horvath data were analyzed using GEO2R.