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An Integrative Neuroscience Program Linking Mouse Genes to Cognition and Disease
Connecting the Molecular Mechanisms of Learning Between Mouse and Human
The recognition that sensory information is encoded in patterns of action potentials and transmitted into the brain (Adrian, 1928) led to the prediction that there must be some "metabolic" mechanism in neurons that is capable of detecting specific patterns of activity and converting them into some "structural" changes (Hebb, 1949). This hypothesis was made experimentally tractable when electrophysiologists found that synapses from the hippocampus, a region of the brain involved with learning, could be stimulated with different patterns of action potentials, and these patterns would induce increases or decreases in the efficiency of communication between two neurons (Bliss & Lomo, 1973). This system allowed pharmacological studies (Collingridge, Kehl, & McLennan, 1983) and mouse genetic studies to be used to identify molecules previously unknown in this process of synaptic plasticity (Grant et al., 1992). By using these methods, a large body of data accumulated during the 1990s, which essentially implicated in excess of 100 proteins in this biology without providing any unifying scheme or molecular hypothesis (Sanes & Lichtman, 1999).
Within this dataset, it was well established that a receptor-ion channel, known as the N-methyl-D-aspartate receptor (NR), was an essential component. The NR is a receptor for the excitatory neurotransmitter glutamate and on activation allows Ca2+ influx via its central pore. As is inappropriately illustrated in many textbooks and reviews, it would appear that this receptor simply sits in the membrane at the postsynaptic side of the synapse, where it injects Ca2+ into the dendrite, which then diffuses to activate a variety of enzymes that seem to float freely in the cytoplasm. These enzymes then drive various poorly understood signaling pathways that control neuronal properties. The first evidence that the NR and signaling proteins do not function in this way came when transgenic mice carrying the mutation in the Post Synaptic Density 95 protein (PSD-95), which normally binds the NR, were found to produce striking changes in the properties of synaptic plasticity and learning (Migaud et al.,1998). This work predicted that there are multiprotein signaling complexes comprised of NR and PSD-95 with other proteins, which control learning.
This genetic evidence for a multiprotein complex was used to justify a proteomic analysis: biochemical isolation of the protein complexes from brain and identification of proteins using mass spectrometry and immunoblotting (Husi & Grant, 2001a; Husi, Ward, Choudhary, Blackstock & Grant, 2000). These methods, which have general applicability to other receptor complexes (Husi & Grant, 2001b), showed that the NR PSD-95 complexes were approximately 2,000-3,000 kDa, which is several-fold more than would be expected if it was simply the NR subunits alone. A picture emerged of 75 or more proteins that could be broadly categorised into five classes: neurotransmitter receptors, cell adhesion molecules, adaptors, signaling enzymes and cytoskeletal proteins (for more details see Husi et al., 2000). A major surprise in this study was that at least 27 proteins from the complexes were known to be required for synaptic plasticity and 18 for learning in rodents and were from each of the five classes of complex components. Thus the organisation of these proteins into these multiprotein complexes suggests that they work together in a large "machine", not unlike many other multiprotein molecular machines. This importance of this concept is that it removes the focus of interest away from the individual molecules onto the function of the overall machine. My colleagues and I (Migaud et al., 1998) have proposed that these complexes are a "device" for detecting patterns of synaptic activity and for converting this information into intracellular signals that store the information in the cell. In this way, electrical information can be translated into cellular memory.
These properties were at the basis of Hebb's postulate, and these complexes have been described as Hebbosomes, multiprotein complexes that convert patterns of neuronal activity into cellular changes underlying learning. It is beyond the scope of this chapter to broadly discuss Hebbosomes, except to indicate that there are families of such complexes, which different molecular composition, which confer specific properties to different synapses.
The characterisation of hebbosomes has significant implications for human genetics. Three genes previously known in humans to be involved with cognitive defects were also found to encode proteins found in the complexes. These include two signal transduction enzymes: neurofibromin (also known as NF-1 and mutant in the neurofibromatosis syndrome) and RSK-2 (mutant in the Coffin Lowry syndrome) and the adhesion protein L1 (mutant in CRASH syndrome). These observations open the exciting possibility that other human cognitive disorders that have a genetic component may involve genes encoding the proteins in these complexes. In this way, the named genes from the mouse studies can be used as candidate genes in a human association study.
In the simplest setting, knowing that a mouse gene is important for behaviour is a reasonable starting point for a human study. There are a number of potential weaknesses in this setting. For example, the human gene may not be as important to the human as it is to the mouse. A stronger starting point is not to rely on a single gene but to use the information about that gene to build up a set of genes. As described in the example above, one could consider that PSD-95 was a starting point, because the mouse knock out had severe learning deficits (Migaud et al., 1998). By isolating the PSD-95 containing complexes and using proteomic tools, it became clear that at least 75 proteins could now be considered candidates for association studies. Thus a nongenetic strategy, such as proteomics, can be used in conjunction with the genetics to identify molecules involved with learning.
A Multilayer Organisation
The G2C can be organised into four layers (see Figure1). These layers are briefly summarized here and discussed in more detail later.
The entry point for the strategy (Layer 1) is molecular information derived from basic science studies. Strong emphasis is placed on the value of genetically modifiable organisms with nervous systems (invertebrates: fruit fly, Drosophila; worm, Caenorhabditis elegans; vertebrates: mouse, Mus musculus; zebra fish, Danio rerio). Through the use of genetic screens and mutations, these organisms have generated lists of proteins that are involved with various phenotypes. Compiling the set of genes that are involved in a common phenotype (e.g. learning) or involved in a multiprotein complex, or some other ways of classifying sets, produces useful information for a human genotyping study. A prototype for this set is that derived from the molecular studies of the multiprotein complexes (Hebbosomes) underlying acquisition of learning (Husi et al., 2000).
Layer 2 of the G2C takes forward the candidate genes from Layer 1 into human genotyping. Using genome sequencing technology, human single-nucleotide polymorphisms (SNPs) can be determined for all genes in the set and DNAs from relevant humans genotyped. Given the rapid pace of the SNP identification and characterization, information covering the first phase of this should be available in the public domain in the very near future.
Layer 3 of the G2C is aimed at validating the biological significance of variant alleles found in humans. Here functional assays are required, and mouse ES cell technology again is used to provide several complementary in vivo and in vitro approaches. One could assemble a wide range of molecular and neuroscience methods in a highly interactive research program. These neurobiological studies can e linked to human neurobiological studies, thus providing a broad framework of connections at many levels of analysis.
There will be an important role for informatics at all stages of the G2C, and Layer 4 is the platform for this technology. This will include access to existing databases as well as generating new databases. These databases and links should generate a novel and valuable resource for the scientific and medical community.
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