Lotrich and Pollock Wang, et al 2003). Vasopressin (Keck et al 2003), neuropeptide Y (Caberlotto et al 1998; Pandey et al 2003), and dynorphin (Chen et al 2002; Newton et al 2002) may also play roles. Ion channels and synaptic transmission As noted above, 5-HT transmission and subsequent intracellular transduction cascades can also effect ion channels, thereby affecting neuronal excitability and neurotransmitter release (Nunoki et al 1989; Blier and de Montigny 1994; Levitan 1994; Wischmeyer and Karschin 1996; Fairchild et al 2003). One example is the G-protein- gated inwardly rectifying potassium channel (GIRK), which regulates membrane excitability. At least three subunits for GIRK are native to hippocampal neurons, Kir3.1, Kir3.2A, and Kir3.2C, and these may mediate some 5-HT1A responses (Fairchild et al 2003). SSRIs can also decrease synaptic function in select areas by influencing calcium channels. For example, inhibition of P/Q-type calcium channels in cerebrocortical neurons by fluoxetine can be mediated by a PKC pathway, resulting in decreased neurotransmission (Wang et al 2003). Consistent with these observations, blockade of calcium channels using verapamil or diltiazem can attenuate the effectiveness of SSRIs in animal models of depression (Srivastava and Nath 2000). Other molecules affecting synaptic transmission have been similarly implicated in the downstream effects of antidepressants. For example Pyk2 (proline-rich tyrosine kinase 2) is a tyrosine kinase that can modify the activity of NMDA receptors and voltage-gated potassium channels in the lateral septum, enhancing synaptic transmission and long-term potentiation (Sheehan et al 2003). Other potential moderators of antidepressant response include apolipoprotein E (ApoE) and various immunologic cytokines that may affect depression (Song et al 1998; Alvarez et al 2002; Anisman et al 2002; Anisman and Merali 2002; Murphy GM et al 2003a; Yu, Chen, Hong, et al 2003). Unanticipated candidates based on microarray and genomic screening A recent microarray study examining whole frontal cortex (Landgrebe et al 2002) has identified 16 genes that were influenced by SSRI treatment in mice – all were down- regulated. Only one related to signal transduction, six were involved in metabolism, two in cell-cycle regulation, and 26 one was a homeobox transcription factor. An alternative approach has been to examine the effect of chronic stress in tree shrews, using a subtractive hippocampal cDNA library. Chronic stress decreased the expression of neural growth factor, membrane glycoprotein 6a, CDC-like kinase 1, and G-protein subunit αq. Treatment with clomipramine mitigated these transcriptome changes (Alfonso et al 2004). If confirmed, the findings from this nascent technology may eventually provide unsuspected, novel candidate genes for association analyses. Summary A number of neurotransmitter and neuromodulatory systems are associated both with mood disorders such as MDD and response to antidepressant treatment. Each of these systems offers a biologically plausible target for selection of candidate genes affecting SSRI response. Empirically delineating the genetic polymorphisms in these various systems that, in fact, influence SSRI response will be an important goal for the next generation of studies examining treatment outcome. Given that many of the proteins, such as adenylyl cyclase, Gi subunits, and protein phosphatases have multiple isoforms, selecting specific isoforms for genetic analysis may require first determining which isoforms are involved in neuronal SSRI-influenced pathways. Further, once a specific gene is chosen for analysis, the next step requires selecting which polymorphism(s) within the gene to study. Because genes often have multiple polymorphisms, it is unfeasible to assess them all. Conversely, studies that test only a single polymorphism may erroneously conclude that a gene is not involved in SSRI response, but miss testing an important functional polymorphism in that gene. Often, it is ideal to choose polymorphisms with known functional affects. It may be useful to choose polymorphisms that have had previously demonstrated associations with other phenotypes of interest such as MDD. Ultimately, selecting a subset of several polymorphisms within a gene for haplotype analysis may provide improved power over choosing only a single polymorphism for study (Hoehe et al 2003). This is of particular relevance for interpreting those studies that report a lack of association for single polymorphisms. If the complete gene including its regulatory regions is not examined, functionally important genetic differences may be missed and lack of importance for a gene erroneously inferred. This is a critical shortcoming in many recent papers reporting “negative” findings. Neuropsychiatric Disease and Treatment 2005:1(1)