Trends in Neurosciences
OpinionCircuit-based framework for understanding neurotransmitter and risk gene interactions in schizophrenia
Introduction
Schizophrenia affects nearly 1% of the population [1]. Clinically, the disorder is characterized by positive symptoms (psychosis, hallucinations and paranoia), negative symptoms (flat affect, poor attention, lack of motivation and deficits in social function) and cognitive deficits. Population, family and twin studies indicate that schizophrenia is highly heritable, but no single gene has a strong effect. Rather, the disorder is due to the synergistic interaction of multiple genes and environmental factors [2]. Recent association and linkage studies have identified over a dozen risk genes for schizophrenia [3]. Another line of research has focused on neurotransmitter systems and, again, the evidence, rather than identifying a single factor, points to abnormalities in multiple systems: glutamate, GABA, dopamine and acetylcholine have all been implicated. There is therefore a strong need for an integrative approach to explain how multiple genes and neurotransmitters can interact in a synergistic way to produce the disorder. In this review, we will describe neural circuitry that provides a framework for understanding many of these interactions. Our description builds on several previous integrative approaches 4, 5 but extends that work in several ways, notably by suggesting a systems-level explanation for the changes in GABAergic interneurons that occur in schizophrenia.
Section snippets
GABA hypofunction
Studies of postmortem brain tissue have provided strong evidence that the GABAergic system is impaired in schizophrenia (this is termed hypofunction). These studies showed reductions in the concentration of cortical GABA [6] and the activity of glutamate decarboxylase (GAD) [7], the enzyme that synthesizes GABA. These observations were confirmed and extended in subsequent studies showing alteration in several presynaptic components of the GABAergic system 8, 9, 10, 11, 12, 13, 14. The GABA
NMDA hypofunction
The NMDA hypofunction theory of schizophrenia (reduced NMDA channel function) is based on two findings: (i) dissociative anesthetics (PCP, MK801 and ketamine) are antagonists of NMDA receptors, and (ii) when abused, these drugs induce a condition that resembles schizophrenia [28]. In laboratory experiments, a subanesthetic dose of ketamine given to normal volunteers induces the symptoms of schizophrenia more effectively than any other known drug 29, 30, 31, 32. NMDA antagonists reproduce both
NMDA/GABA interaction: disinhibition
In pyramidal cells, excitatory postsynaptic potential (EPSPs) are generated primarily by AMPA channels; the main role of NMDA channels in these cells is in the synaptic plasticity that underlies learning. It was therefore unclear why administration of NMDA antagonist to humans should have large effects on mental processes not related to learning. A key finding [53] was the discovery that NMDA channels contribute strongly to the EPSP in interneurons and that acute inhibition of these channels
NMDARs on interneurons as a sensor for homeostatic regulation of pyramidal cell firing
The existence of an NMDAR-mediated component of the EPSP in interneurons helps to connect the NMDA and GABA hypotheses, but does not explain the decreased expression of GAD and parvalbumin. Here we propose a novel explanation of this decrease. Our starting point is the idea that a major function of the fast-spiking interneurons is the homeostatic regulation of overall pyramidal cell firing. These interneurons sum the responses from hundreds of pyramidal cells and then inhibit these cells, thus
The hyperdopaminergic state and the role of the hippocampus
Dopamine was the first neurotransmitter system to be strongly implicated in schizophrenia. Antagonists of the D2 receptor reduce the positive symptoms of the disorder 85, 86, and standard drug treatments of schizophrenia remain based on this antagonism. By implication, it would seem that the disease might be due to a hyperdopaminergic state (excess dopamine). Consistent with this, increasing dopamine release with amphetamine produces positive symptoms in normal subjects [87]. Direct evidence
Disinhibition might produce some cognitive symptoms by reducing gamma oscillations
In the above section, we explored how malfunction of the feedback loop between pyramidal cells and fast-spiking interneurons could affect the dopamine system. In addition, this loop is directly involved in the generation of gamma oscillations and there are now strong reasons for believing that abnormalities in these oscillations could contribute to some of the symptoms of schizophrenia. Gamma frequency (30–100 Hz) oscillations are present in the local field potential and EEG, reflecting the
The cholinergic system: reversing disinhibition and cognitive deficits
The disinhibition model described above is also useful in understanding the role of the cholinergic system in schizophrenia [118]. One hint of the relevance of the cholinergic system to schizophrenia is that the prevalence of smoking among individuals with schizophrenia exceeds 70%, 2- to 4-fold higher than in the general population [119]. This heavy use of nicotine is believed to be an attempt at self-medication [120]. In controlled experiments on schizophrenia patients, nicotine has been
Toward a circuit-based explanation of synergistic gene action
The goal of systems biology is to understand how genes work together in biochemical and cellular networks to produce function. Such an integrated understanding is of special importance in schizophrenia research because the disorder results from the synergistic interaction of many risk genes, none of which has a large effect. To determine whether genes act synergistically, it is necessary to have a circuit-based model. This is illustrated by analysis of NMDAR function; these receptors are
Acknowledgements
The authors would like to thank Gordon Fain for advice on the manuscript and Margarita Behrens and Claudia Racca for useful discussions. J.E.L., J.T.C., D.C.J. and R.W.G. are grateful for the support of NIH Conte Center grant P50 MH060450. F.M.B. has been supported by NIH grants MH42261, MH31862 and MH60450. S.H. has been supported by MH067999 and MH070560. R.W.G. gratefully acknowledges support from the Department of Veterans Affairs.
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