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5-HT1A autoreceptors mediate negative feedback inhibition of serotonergic neurons and play a critical role in regulating 5-HT signaling involved in shaping the functional response of major forebrain targets, such as the amygdala, supporting complex behavioral processes. A common functional variation (C(-1019)G) in the human 5-HT1A gene (HTR1A) represents one potential source of such inter-individual variability. Both in vitro and in vivo the -1019G blocks transcriptional repression leading to increased autoreceptor expression. Thus, the -1019G may contribute to relatively decreased 5-HT signaling at postsynaptic forebrain target sites via increased negative feedback.
To use imaging genetics to evaluate the effects of HTR1A C(-1019)G on amygdala reactivity in 89 healthy adults and employ path analyses to explore the impact of HTR1A-mediated variability in amygdala reactivity on individual differences in trait anxiety. We hypothesized that the -1019G, which potentially results in decreased 5-HT signaling, would be associated with relatively decreased amygdala reactivity and related trait anxiety.
Consistent with prior findings, the -1019G was associated with significantly decreased threat-related amygdala reactivity. Importantly, this effect was independent of that associated with another common functional polymorphism impacting 5-HT signaling, namely the 5-HTTLPR. While there were no direct genotype effects on trait anxiety, the HTR1A C(-1019)G indirectly predicted 9.2% of interindividual variability in trait anxiety through its effects on amygdala reactivity.
Our findings further implicate relatively increased 5-HT signaling, associated with genetic variation mediating increased 5-HT1A autoreceptors, in driving amygdala reactivity and trait anxiety. Moreover, they provide empirical documentation of the basic premise that genetic variation impacts emergent behavioral processes related to psychiatric disease risk indirectly by biasing the response of underlying neural circuitries.
The amygdala, through its extensive interactions with other limbic and cortical regions, plays a central role in the generation of emotional behaviors1. Moreover, abnormal amygdala function has been implicated in psychiatric disorders, such as depression and anxiety 2, often characterized by abnormal emotional responses. Converging preclinical and clinical evidence indicates that amygdala functioning is sensitive to the effects of central serotonin (5-HT)3, whose principle forebrain innervation is provided by the midbrain dorsal raphe nuclei (DRN). Multiple mechanisms involving de novo biosynthesis, vesicular release, active reuptake, metabolic degradation as well as a myriad of both pre- and postsynaptic receptors contribute to the regulation of 5-HT neurotransmission and its subsequent modulation of brain function (see Holmes, 2008 for detailed review). In general, component processes that affect the magnitude of signaling (e.g., biosynthesis, reuptake, autoregulation) rather than localized effects on target neurons (e.g., postsynaptic receptors) represent key bottlenecks in 5-HT regulation of neural circuit function. Crucial among these is activation of somatodendritic 5-HT1A autoreceptors, which mediate negative feedback on DRN neurons resulting in decreased 5-HT release at postsynaptic targets in the forebrain4. Using a multimodal neuroimaging strategy, we previously reported that the density of 5-HT1A autoreceptors accounts for 30–44% of variability in amygdala reactivity in healthy adults5, confirming the important role of 5-HT1A autoreceptors in modulating the activity of serotonergic target regions.
As a likely consequence of its impact on 5-HT release, variability in 5-HT1A autoreceptor function has been linked to personality traits and psychiatric illnesses6–8. It has also been suggested that 5-HT1A autoreceptors constitute a critical pharmacotherapeutic target. One such example is the desensitization of these receptors after chronic administration of selective serotonin reuptake inhibitor (SSRI) antidepressant drugs9, which may participate in correcting pathologically decreased 5-HT neurotransmission10 and altered amygdala reactivity observed in patients with major depression11. Given the critical role of 5-HT1A autoreceptors in regulating 5-HT signaling and its resulting influence on the functioning of major brain targets, such as the amygdala, as well as complex behavioral processes, it is important to identify sources of emergent variability in 5-HT1A function.
Common sequence variation in the human 5-HT1A gene (HTR1A) represents one potential source of such inter-individual variability. Recently, a relatively frequent single nucleotide polymorphism, C(-1019)G, in the promoter region of HTR1A was demonstrated to impact transcriptional regulation of the gene through altered binding of the transcription factors human nuclear deformed epidermal autoregulatory factor-1 (DEAF-1)-related (NUDR)/Deaf-1 and Hairy/Enhancer-of-split-5 (Hes5). Specifically, the -1019G allele abolishes repression of the promoter by DEAF-1 and partially impairs Hes5-mediated repression and, as a consequence, is associated with increased HTR1A protein and binding12. Consistent with this finding, in vivo human positron emission tomography (PET) has revealed increased 5-HT1A autoreceptor density in both healthy adults and depressed patients carrying the -1019G allele13. However, a similar effect was not observed in an earlier PET study14. Regardless, the in vitro effects of the HTR1A -1019G allele and the more general relationship documented between increased 5-HT1A autoreceptor density and decreased amygdala reactivity5 suggest that this common functional genetic variation may contribute significantly to the emergence of inter-individual variability in amygdala reactivity.
In the current study, we used imaging genetics, a strategy previously implemented to identify the neurobiological impact of common functional variation in other serotonin-related genes15–20, to evaluate the effects of HTR1A C(-1019)G variant on amygdala reactivity in response to an archival fMRI challenge paradigm in 89 healthy adults. We hypothesized that in comparison to the HTR1A -1019C allele, the -1019G allele, which abolishes DEAF-1 and impairs Hes5-mediated transcriptional repression leading to increased 5-HT1A autoreceptor density, would be associated with relatively lower amygdala reactivity putatively reflecting increased negative feedback and, consequently, decreased 5-HT signaling. In addition, because prior studies have linked individual differences in anxiety with amygdala function21–24, we employed path analyses to explore the relationship between HTR1A-mediated variability in amygdala reactivity and individual differences in trait anxiety.
Our a priori focus on 5-HT1A autoreceptors and not postsynaptic 5-HT1A heteroreceptors is driven by two major findings. The first is our earlier discovery that 5-HT1A autoreceptors account for a greater proportion of variability in amygdala reactivity than local postsynaptic heteroreceptors5. The second is recent in vitro data illustrating cell-specific effects of the -1019G allele on transcriptional repression. Specifically, the G allele leads to consistently increased expression of 5-HT1A autoreceptors, but does not consistently alter and sometimes even decreases expression of postsynaptic receptors25. This in vitro finding is supported by the in vivo study documenting increased density of autoreceptors but not postsynaptic cortical or limbic receptors in -1019G allele carriers13. Collectively, these results suggest 5-HT1A autoreceptors and not heteroreceptors account for the majority of variability in amygdala reactivity, and that the -1019G allele may specifically affect the regulated expression of 5-HT1A autoreceptors.
A total of 108 participants were recruited from the Adult Health and Behavior (AHAB) project, an archival database encompassing detailed measures of behavioral and biological traits among a community sample of 1,379 non-patient, middle-aged volunteers. Written informed consent according to the guidelines of the University of Pittsburgh Institutional Review Board was provided by all participants prior to their participation in our neuroimaging subcomponent of AHAB. All participants included in our analyses were in good general health and free of the following study exclusions: (1) medical diagnoses of cancer, stroke, diabetes requiring insulin treatment, chronic kidney or liver disease, or a lifetime history of psychotic symptoms; (2) use of psychotropic, glucocorticoid, or cardiovascular (e.g., antihypertensive, antiarrythmic) medication; (3) conditions affecting cerebral blood flow and metabolism (e.g., hypertension); and (4) any current DSM-IV Axis I disorder as assessed by the Structured Clinical Interview for DSM-IV (SCID), non-patient edition26.
Both AHAB and our smaller neuroimaging study have been developed for the explicit purpose of facilitating hypothesis-driven investigations of variables possibly mediating inter-individual variability in behavioral traits representing potential predictive markers of physical and mental health. In fact, combinations of neuroimaging, behavioral and molecular genetics data from a variable number of our 103 participants (range: 31 – 89) have been utilized in several prior studies examining biological pathways mediating inter-individual variability in behaviorally-relevant brain function27–32. In the current study, overlapping HTR1A C(-1019)G genotype and threat-related amygdala reactivity data were available in 89 adult Caucasian volunteers.
High molecular weight DNA was isolated from EDTA anticoagulated whole blood samples obtained from all participants using a salting out procedure. Each sample was genotyped using PCR amplification and fluorescence polarization primers. Primers were designed to produce a 272 bp fragment containing the HTR1A C(-1019)G SNP(rs6295; primers available upon request). PCR was carried out for 35 cycles at annealing temperature 55 °C in a reaction mixture containing 1.5 mM Mg++. Resulting products were cleaned by 1.5 h incubation with Exo-SAP (USB). Genotyping of the C to G transversion was performed on the LJL AnalystHT (Molecular Devices). In addition to HTR1A C(-1019)G, the 5-HTTLPR, MAOA 30-bp VNTR and TPH2 G(-844)T were all genotyped using published protocols16,20,33. All of these genotypes were scored by two independent readers by comparison to sequence verified standards and all call rates were >95%. No additional polymorphisms in HTR1A were examined in our study.
We used the program STRUCTURE34 to evaluate presence of genetic substructure in the sample. Fifteen ancestry informative markers (rs1022106, rs1335995, rs1439564, rs1502812, rs1860300, rs548146, rs705388, rs715994, rs720517, rs722743, rs730899, rs734204, rs9059966, rs1328994, rs1485405), which are unlikely to be related to phenotypes of interest, were genotyped for this analysis. We ran STRUCTURE assuming a model with admixture, correlated allele frequencies, individual α parameters and independent FST for all subpopulations. We tested models with 1, 2, 3 and 4 subpopulations using a burn-in of 40,000 followed by 80,000 repetitions and compared the likelihoods of models fitting the data.
The Spielberger State-Trait Anxiety Inventory (STAI) is a self-report scale indexing the frequency with which individuals perceive encountered situations to be threatening and to respond to such situations with subjective feelings of apprehension and tension35. STAI has been used extensively as a clinical and research instrument, including as an endophenotype in genetic association studies of candidate genes for neuropsychiatric disorders. The STAI consists of two scales, one assessing the general tendency to be anxious as a personality trait (STAI-Trait) and one measuring the degree of anxiety at a particular moment as a situation-dependent state (STAI-State). In this study, only the STAI-Trait version of the scale was administered as trait scores better reflect dispositional anxiety35.
The experimental fMRI paradigm consisted of four blocks of a face processing task interleaved with 5 blocks of a sensorimotor control task30,31,33,36. Subject performance (accuracy and reaction time) was monitored during all scans. During the face processing task, subjects viewed a trio of faces (expressing either anger or fear) and selected one of two faces (bottom) identical to a target face (top). Angry and fearful facial expressions can represent honest indicators of ecologically-valid threat, especially that related to conspecific challengers37. Within this context, we interpret the amygdala activation elicited by our task as being threat-related. Each face processing block consisted of six images, balance for sex and target affect (angry or fearful) all derived from a standard set of pictures of facial affect38. During the sensorimotor control blocks, subjects viewed a trio of simple geometric shapes (circles, vertical and horizontal ellipses) and selected one of two shapes (bottom) identical to a target shape (top). Each sensorimotor control block consisted of six different shape trios. All blocks were preceded by a brief instruction (“Match Faces” or “Match Shapes”) lasting 2 seconds. In the face processing blocks, each of the six face trios was presented for 4 seconds with a variable inter-stimulus interval of 2–6 sec (mean = 4 sec) for a total block length of 48 seconds. In the sensorimotor control blocks, each of the six shape trios was presented for 4 seconds with a fixed inter-stimulus of 2 seconds for a total block length of 36 seconds. Total task time was 390 seconds. As we were not interested in neural networks associated with face-specific processing per se, but rather in eliciting a maximal amygdala response across all subjects that we could then interrogate for genotype effects, we chose not to use neutral faces as control stimuli because neutral faces can be subjectively experienced as affectively laden or ambiguous and thus engage the amygdala21,39.
Each participant was scanned using a Siemens 3T MAGNETOM Allegra developed specifically for advanced brain imaging applications and characterized by increased T2* sensitivity and fast gradients (slew rate = 400 T/m/s) which minimize echo-spacing thereby reducing EPI geometric distortions and improving image quality. BOLD functional images were acquired with a gradient echo EPI sequence (TR/TE = 2000/25 msec, FOV = 20 cm, matrix = 64 × 64) which covered 34 inter-leaved axial slices (3mm slice thickness) aligned with the AC-PC plane and encompassing the entire cerebrum and the majority of the cerebellum. All scanning parameters were selected to optimize the quality of the BOLD signal while maintaining a sufficient number of slices to acquire whole-brain data. Before the collection of fMRI data for each participant, we acquired a reference EPI scan that we visually inspected for artifacts (e.g., ghosting), as well as for good signal across the entire volume of acquisition, including the amygdala and ventral striatum. Additionally, an autoshimming procedure was conducted before the acquisition of BOLD data in each subject to minimize field inhomogeneities. The fMRI data from all 89 subjects included in this study were cleared of such problems.
Whole-brain image analysis was completed using the general linear model of SPM2 (http://www.fil.ion.ucl.ac.uk/spm). Images for each subject were realigned to the first volume in the time series to correct for head motion, spatially normalized into a standard stereotactic space (Montreal Neurological Institute template) using a 12-parameter affine model and smoothed to minimize noise and residual difference in gyral anatomy with a Gaussian filter, set at 6 mm full-width at half-maximum. Voxel-wise signal intensities were ratio normalized to the whole-brain global mean. These preprocessed data sets were analyzed using second-level random effects models that account for both scan-to-scan and participant-to-participant variability to determine task-specific regional responses.
Following preprocessing, linear contrasts employing canonical hemodynamic response functions were used to estimate condition-specific (i.e., faces > shapes) blood oxygen level-dependent activation for each individual and scan. These individual contrast images (i.e., weighted sum of the beta images) were then used in second-level random effects models to determine 1) mean condition-specific amygdala reactivity using one-sample t-tests, 2) main effects of HTR1A genotype on amygdala reactivity and 3) the relationship between amygdala reactivity and STAI-Trait using multiple regression (with 5-HTTLPR genotype as a covariate).
Our amygdala region of interest (ROI) was constructed using the Talairach Daemon option of the WFU PickAtlas Tool (v1.04). Exploratory analyses of genotype effects were conducted in prefrontal regions, namely orbitofrontal cortex (BA11), ventrolateral prefrontal cortex (BA47) and dorsolateral prefrontal cortex (BA9/44/45), exhibiting a main effect of task. These ROIs were also defined using the PickAtlas. All analyses were thresholded at a voxel level of p < 0.05, FDR corrected for multiple comparisons within an inclusive mask of activations of interest, and an extent threshold of at least 10 contiguous voxels. Because of our a priori, directionally-specific hypotheses and our use of a rigorous random-effects model, these statistical thresholds effectively control for “false positives” arising from multiple comparisons. Moreover, these statistical thresholds have recently been demonstrated to effectively limit “false positive” associations in imaging genetics studies below 5% (0.2–4.1%) and are, in fact, conservative40.. All neuroimaging data are reported using the coordinate system of Talairach & Tournoux.
A path model was used to examine the relationship between HTR1A genotype, amygdala reactivity, and trait anxiety. The cluster selected for the path analysis exhibited overlapping effects of genotype and trait anxiety, and was identified by applying a mask created from the activation cluster correlated with trait anxiety to a subsequent regression analysis between amygdala reactivity and HTR1A genotype. This two-step approach revealed a single activation cluster in the right amygdala exhibiting effects of both trait anxiety and genotype. Extracted activation values from the maximally activated voxel in the amygdala cluster showing overlapping effects of HTR1A genotype and STAI-Trait were fitted using Mplus 4.041, which can test indirect effects through two complementary methods 42,43. First, Mplus 4.0 uses a product of coefficients test (also called the Sobel method42) to quantify the magnitude of the indirect effects with more power than some other widely used methods44. Second, Mplus 4.0 constructs unbiased confidence intervals using bootstrapping methods which can represent a more powerful test in smaller samples such as ours, because bootstrapping methods do not assume normality of the distribution of the indirect effects43. While not the focus of our indirect effects analysis, path models generated in Mplus 4.0 can be tested for fit of the hypothesized model to the observed data. In our analyses, fit of a path model was considered acceptable if it had a non-significant chi-square fit statistic (χ2), a Root Mean Square Error of Approximation (RMSEA) smaller than .08, and a Standardized Root Mean Square Residual (SRMR) close to 045. Consistent with our general proposal that genetic effects on behavior are mediated through their effects on brain function29,46, we predicted that the link between HTR1A genotype and trait anxiety would be mediated through amygdala reactivity (i.e., HTR1A genotype predicts amygdala reactivity which, in turn, predicts trait anxiety). However, to explore possible direct links between HTR1A genotype and trait anxiety, we also modeled a direct path between these two variables. As this direct path was non-significant and the overall model fit decreased, this direct path was dropped from the final model.
The distribution of our observed genotype frequencies (Table 1) from the total cohort of 89 Caucasian subjects (C/C= 25, C/G= 36, GG= 28) was consistent with prior reports and did not deviate from Hardy Weinberg equilibrium (χ2 = 0.38, P = 0.83). STRUCTURE analyses revealed no evidence of genetic substructure in our sample (log probability of data for k = 1, 2, 3 and 4 subpopulations in STRUCTURE were 1292.6, −1304.6, −1287.6 and −1296.3, respectively) and thus, no further adjustments were made to control for type I or type II errors attributable to genetic stratification. Moreover, HTR1A genotype groups did not differ (all χ2s < 4.47, all P’s > 0.11) in the distribution of multiple additional functional serotonin related polymorphisms (i.e., 5-HTTLPR, MAOA 30-bp VNTR & TPH2 G(-844)T) previously linked with variability in amygdala reactivity16,20,33. This was true for either the two (i.e., C/C versus G carriers) or three (i.e., C/C, C/G & G/G) genotype group classification schemes. Finally, genotype groups (using either the 2- or 3-genotype classification schemes) did not differ with respect to age, sex distribution, history of mood or anxiety disorders, task performance or STAI-Trait, which was normally distributed in our sample (Table 1). Although the distribution of the 5-HTTLPR across HTR1A genotype groups was random, we nevertheless entered 5-HTTLPR genotype as a covariate in our neuroimaging data analysis because of its well-documented effect on amygdala reactivity47 which may be mediated through altered 5-HT1A autoreceptor density14.
The main effects of task contrast, faces > shapes, was associated with significant bilateral amygdala reactivity across all subjects. Regression analyses, corrected for effects of 5-HTTLPR, revealed a significant effect of HTR1A genotype on bilateral amygdala reactivity (Figure 1). This pattern was confirmed using ANCOVA on the extracted maximal voxel amygdala activation values (right hemisphere: F(2,86) = 3.66, P = 0.030; left hemisphere: F(2,86) = 3.21, P = 0.045). Post hoc analyses revealed significant differences between individuals with the C/C genotype and those with either C/G (right hemisphere: t59 = 2.11, P = 0.039; left hemisphere: t57 = 2.06, P = 0.045) or G/G (right hemisphere: t47 = 2.59, P = 0.013; left hemisphere: t51 = 2.45, P = 0.018). There was no significant difference between C/G and G/G genotypes (right hemisphere: t62 = 0.95, P = 0.35; left hemisphere: t62 = 0.73, P = 0.47). As these results indicated C(-1019)G effects on amygdala reactivity were independent of -1019G allele load, all subsequent analyses were conducted using a simplified two genotype classification scheme (i.e., C/C homozygotes versus G carriers). Statistical parametric analyses using this two genotype classification confirmed the results of the ANCOVA by identifying significant differences between C/C homozygotes and G carriers (right hemisphere: 36 voxels; z = 2.86, P < 0.05; left hemisphere: 27 voxels; z = 2.58, P < 0.05). Finally, exploratory analyses of HTR1A genotype effects on task-related activation in prefrontal regions of interest did not reveal any statistically significant effects.
Mean single-subject activation values from the maximal voxel in the right amygdala cluster exhibiting a correlation with both HTR1A genotype and STAI-Trait (Figure 2A) were extracted for use in our path models. Analyses in Mplus 4.0 using these extracted values revealed no significant direct path between HTR1A genotype and STAI-Trait in the model (B = −2.13, SE = 1.95, P > 0.25) and thus, this path was dropped. In contrast, analyses in Mplus 4.0 revealed significant direct paths from HTR1A genotype to amygdala reactivity (B = 0.91, SE = 0.31, P < 0.01) and from amygdala reactivity to STAI-Trait (B = 1.76, SE = 0.59, P < 0.01) (Figure 2B). Moreover, the indirect path from HTR1A genotype to SATI-T through amygdala reactivity was significant (αβ = −1.60, SE = 0.73, P < 0.05). This model accounted for 9.2% of the variability in STAI-Trait scores, indicating that relatively decreased amygdala reactivity contributes to decreased trait anxiety in -1019G carriers and that the effect of HTR1A genotype on trait anxiety is through its effect on amygdala reactivity. The bootstrapped confidence interval for this estimate did not contain 0 further indicating a significant indirect effect. The proposed model also had an acceptable fit (χ2 = 1.35, ns, RMSEA = 0.06, SRMR = 0.05). In addition, the results were consistent across different models and extraction methods. The indirect effect was significant in the model containing the direct path from HTR1A genotype to STAI-Trait (αβ = −1.41, SE = 0.70, P < 0.05), as well as when using a model containing the mean value extracted from the entire activation cluster rather than maximal voxel (αβ = −1.33, SE = .66, P < 0.05).
Consistent with our hypothesis, the HTR1A -1019G allele was associated with significantly decreased threat-related amygdala reactivity. This effect was independent of -1019G allele load, with both C/G and G/G individuals exhibiting significantly reduced amygdala reactivity in comparison with C/C homozygotes, as well as occult genetic stratification and other functional 5-HT polymorphisms, most notably the 5-HTTLPR, impacting amygdala reactivity16,20,33. Path models revealed no significant direct genotype effect on trait anxiety. The marginal nature of this relationship (P > 0.25) is consistent with previous studies in relatively small samples which are likely insufficiently powered to detect direct effects between genotype and distal behavioral phenotypes. In contrast, HTR1A C(-1019)G and amygdala reactivity indirectly predicted a significant proportion (9.2%) of individual differences in trait anxiety through their respective indirect and direct paths.
Our observation of decreased amygdala reactivity in carriers of the -1019G is specifically consistent with the in vitro12 and in vivo13 effects of the -1019G allele (i.e., increased 5-HT1A autoreceptor expression associated with the -1019G), as well as our previous study demonstrating an inverse relationship between 5-HT1A autoreceptor density and amygdala reactivity5. This pattern is more generally consistent with that reported for other common functional polymorphisms, namely the 5-HTTLPR short allele16,47 and MAOA low-activity alleles20, also associated with relatively increased 5-HT signaling. Collectively, these findings further implicate relatively increased 5-HT signaling, regardless of the putative molecular mechanism, in driving amygdala reactivity and related behavioral processes such as anxiety48. Not only does this parallel the effects of increased 5-HT in animal models49–53, but also a recent study demonstrating that acute blockade of 5-HT reuptake with IV citalopram results in dose-dependent potentiation of human amygdala reactivity54.
Although this convergent data strongly implicates 5-HT in driving amygdala reactivity, the detailed molecular mechanisms through which such effects are mediated is not fully understood. This effect likely reflects the complex co-expression of inhibitory and excitatory postsynaptic 5-HT receptor subtypes on both glutamatergic projection neurons and GABAergic interneurons of the amygdala55. For example, 5-HT-induced inhibition of glutamatergic activity in the lateral amygdala, which processes afferent sensory information, may be mediated through activation of excitatory serotonergic receptors on interneurons56. However, agonism of excitatory 5-HT2A/2C and 5-HT3 postsynaptic receptors can increase the activity of both projection neurons and interneurons, and agonism of 5-HT1A postsynaptic receptors can decrease activity of interneurons57. Furthermore, while excitatory postsynaptic 5-HT2A/2C receptors have been localized to both projection and interneurons58 and are thus capable of both increasing and decreasing amygdala activity, a recent study suggests that 5-HT2A/2C receptors mediate the potentiation of amygdala-related conditioned fear responses following acute 5-HT reuptake inhibition49. The synaptic localization of 5-HT receptors may also bias the net effect of 5-HT on amygdala reactivity. In other forebrain target regions, inhibitory 5-HT1A receptors are localized within the synapse while excitatory 5-HT2A/2C receptors are extrasynaptic4. Thus, a greater level of 5-HT release (i.e., volume transmission) may be necessary to evoke stimulation of these targets while a lesser level evokes inhibition. It is possible that the decreased 5-HT release associated with the -1019G biases toward greater inhibition of amygdala target neurons (via preferential stimulation of synaptic 5-HT1A) reflected as decreased reactivity in BOLD fMRI. However, this putative mechanism is dependent on the appropriate expression of 5-HT receptor subtypes which remains largely unknown. Finally, although in vivo assays of 5-HT1A autoreceptor density indicate a functional effect of the -1019G13, our observed differences in amygdala reactivity may reflect early neurodevelopmental phenomena associated with altered 5-HT signaling59. In fact, only transgenic inactivation of the murine 5-HT1A gene during early development and not adulthood is associated with altered anxiety-like behaviors60.
Despite the consistency and convergence of our current findings with those from in vitro and in vivo assays of -1019G effects on 5-HT1A autoreceptors, our current results differ from two studies examining the effects of the HTR1A C(-1019)G on amygdala reactivity in patients with major depression19 and panic disorder61. In both patient populations, the -1019G allele, which is associated with relatively decreased amygdala reactivity in our sample of healthy adults, was associated with relatively increased amygdala reactivity. In the latter patient sample, however, this effect was limited to the left amygdala responses to happy expressions and there was no difference in amygdala activation to fearful expressions. In addition, the -1019G was associated with relatively decreased prefrontal activation to fearful expressions in these same patients. In contrast, we did not find a significant effect of HTR1A genotype on task-related prefrontal activation (see below for additional discussion). The presence or absence of psychopathology across these samples represents an obvious potential factor driving these differing patterns. The findings in the much smaller samples of patients may reflect an interaction of HTR1A genotype with ongoing pathological processes, as well as other genetic and/or environmental factors that act in concert to produce psychopathology62. The divergent effects reported in patients may also reflect additional variability in 5-HT signaling following chronic exposure to psychotropic medications, especially selective serotonin reuptake inhibitors (SSRIs). In these studies, all patients with major depression19 and half the patients with panic disorder61 were treated with SSRIs. Prospective studies in at-risk populations as well as in medication-naive patients pre- and post-treatment, are necessary to better characterize the relationship between HTR1A C(-1019)G genotype, amygdala reactivity, the emergence of psychopathology and therapeutic response.
Superficially, our current findings may appear contrary to reports linking the -1019G with increased risk for mood and anxiety disorders63, as well as increased neuroticism and harm avoidance64, all of which may be characterized by increased amygdala reactivity2,22,65,66. As emphasized in a recent review, available association studies between the -1019G and psychiatric liability are far from equivocal, and studies to date have been generally underpowered67. Furthermore, in contrast to the effects of the -1019G on autoreceptor expression several studies have documented decreased 5-HT1A autoreceptors in a range of mood and anxiety disorders68–73. Regardless, our existing data reflect only one factor involved in shaping both normal and pathological emotional responses to the environment, namely limbic drive in the form of amygdala reactivity. We did not observe significant genotype effects on task-related prefrontal activation. However, the ability to examine neurobehavioral effects of 5-HT signaling using BOLD fMRI is critically dependent on the challenge paradigms employed. Our paradigm is focused on threat-related amygdala and extended corticolimbic reactivity associated with “bottom-up” limbic drive. It is possible that more complex, top-down (e.g., emotion regulation) tasks may reveal effects of the -1019G allele extending to alterations in prefrontal regulatory circuitries whose dysfunction greatly contributes to and may characterize disorders of mood and emotion74,75. Given the importance of serotonin in the development and function of corticolimbic circuitries59, it is reasonable to speculate that decreased 5-HT signaling associated with the -1019G allele also reduces prefrontal activation in response to amygdala drive (possibly via decreased stimulation of excitatory postsynaptic 5-HT2A receptors located on glutamatergic pyramidal neurons). This, in turn, may lead to insufficient regulation of the amygdala and the emergence of pathological mood and emotion.
We believe that there is clearly a place for an alternative, neurobiologically-informed view in this literature. In this regard, our current findings, which are remarkably consistent with the basic biology of 5-HT as well as the C(-1019)G, provide an important mechanistic platform from which existing findings can be better appreciated and future, directionally-specific hypothesis driven association studies planned. Indeed, such staging has proved essential for advancing our understanding of many other genetic variants (e.g., COMT val158met, BDNF val66met, 5-HTTLPR). Our simple, reliable and robust paradigm has produced findings that constitute a necessary initial step towards understanding the influence of the HTR1A C(-1019)G on more complex circuitries and processes. More importantly, our current findings represent an important step in imaging genetics research by providing empirical documentation for the basic premise that genetic variation indirectly impacts emergent behavioral processes by biasing the response of underlying neural circuitries29,46.
We thank Sarah M. Brown for assistance with fMRI data collection and analyses. This work was supported by NIH grants HL040962 to SBM & MH072837 to ARH, as well as a NARSAD Young Investigator Award to ARH. LWH is supported by the predoctoral Training Program in Behavioral Brain Research (GM081760). PMF is supported by the predoctoral Multimodal Neuroimaging Training Program (DA023420). IH is supported by a Pittsburgh Mind Body Center postdoctoral fellowship. LWH, PMF and KEM are also supported by the University of Pittsburgh Center for the Neural Basis of Cognition.
Conflict of Interest Disclosure
The authors have no conflicts of interest or competing financial interests to disclose.