SEARCH

SEARCH BY CITATION

Keywords:

  • brain anatomy;
  • major depression;
  • genetics;
  • mouse;
  • animal model

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. 3-D MRM
  6. RESULTS
  7. DISCUSSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

We have recently reported the creation and initial characterization of an etiology-based recombinant mouse model of a severe and inherited form of Major Depressive Disorder (MDD). This was achieved by replacing the corresponding mouse DNA sequence with a 6-base DNA sequence from the human CREB1 promoter that is associated with the development of MDD in men and women from families identified by probands with recurrent, early-onset MDD (RE-MDD). Individuals in these families are also at increased risk for childhood developmental disorders and late life neurodegenerative disorders. The current study used three-dimensional magnetic resonance microscopy (3D-MRM) to determine the effect of the resulting humanized mutation of the mouse Creb1 gene on the anatomy of the mouse brain. Homozygous mutant mice manifested prominent increases in the volume and surface area of the lateral ventricles, as well as reduced volume of the anterior corpus callosum, compared to age/sex-matched wild-type mice. No significant genotype effects were observed on the volume or surface area of total brain, or several brain regions sometimes observed to be abnormal in human depression, including hippocampus, amygdala, or striatum. These findings suggest that at least some forms of MDD result from abnormal brain development produced by inherited genetic variants. © 2013 Wiley Periodicals, Inc.


INTRODUCTION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. 3-D MRM
  6. RESULTS
  7. DISCUSSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Major depressive disorder (MDD) has an estimated lifetime prevalence of about 16.5% (F/M ratio ∼2) and is a leading cause of suffering and disability worldwide [Kessler et al., 2005; Lopez et al., 2006]. It is also an important contributor to mortality from all causes, including suicide, and has been estimated to be among the costliest medical disorders from an economic perspective [Zubenko et al., 2001; Greenberg et al., 2003]. Unfortunately, our understanding of the underlying cause of this disorder remains rudimentary, and existing treatments are often only partially effective or ineffective and are commonly associated with poorly tolerated side effects. Twin and adoption studies indicate that genetic factors account for 40–70% of the risk of developing MDD [for review see Zubenko et al., 2001]. The inherited determinants appear to be many, and may differ from case to case, but some of the relevant factors are becoming known, including a rare sequence of the human CREB1 promoter we have observed in some individuals with early onset recurrent depression [Zubenko and Hughes, 2012].

A better understanding of the cellular and molecular brain mechanisms that lead to the expression of MDD and the mechanism of action of existing antidepressants seems prerequisite to preventing or reducing the global burden of this major public health problem. Studies of MDD are constrained by numerous factors including the complexity of the brain and our limited understanding of normal brain functioning, the inaccessibility of the brain in living subjects, and the limitations inherent in postmortem studies. The development of a valid animal model for any form of MDD that reflects the brain mechanisms that lead to MDD could significantly accelerate the pace toward achieving these goals.

The laboratory mouse has many features that make it an attractive model organism for the study of human diseases, including their striking similarity to humans in anatomy, physiology, and genetics, and shared features of the brain in limbic structures thought to mediate mood and reward. We have recently reported the creation and initial characterization of the first etiology-based recombinant mouse model of MDD [Zubenko and Hughes, 2011, 2012]. This was achieved by replacing the corresponding mouse DNA sequence with a 6-base DNA sequence from the human CREB1 promoter that is associated with the development of MDD in both men and women from families identified by probands with recurrent, early-onset MDD (RE-MDD). This congenic mutant C57BL/6NTac mouse line is based on a rare, highly penetrant, pathogenic mutation in the human CREB1 promoter [Zubenko and Hughes, 2010], rather than psychological hypotheses or stress paradigms, and it mimics the brain mechanism that leads to MDD in some humans, rather than symptoms or antidepressant responsiveness. In our initial characterization, the mutant mice exhibited several features that were reminiscent of the human disorder, including alterations of brain anatomy, gene expression, behavior, as well as increased infant mortality [Zubenko and Hughes, 2011].

In the current study, we used three-dimensional magnetic resonance microscopy (3D-MRM) to confirm and extend our initial findings suggesting that the pathogenic Creb1 allele was associated with significant ventricular enlargement in homozygous mice, without an increase in total brain volume. The volumes and surface areas of whole brains and both lateral and third ventricles were compared between age/sex matched groups consisting of six wild type (WT) and six homozygous (Hom) mutant mice. Analogous comparisons were also performed for the corpus callosum, hippocampus, amygdala, and striatum, regions that have been implicated in the pathophysiology of MDD [for reviews see Cummings, 1995; Drevets, 2000; Drevets et al., 2008].

MATERIALS AND METHODS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. 3-D MRM
  6. RESULTS
  7. DISCUSSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Mouse breeding and genotyping

The construction and initial characterization of our congenic C57BL/6NTac mouse line carrying the 5′-TCCCCG-3′ sequence at positions −170 to −165 of the recombinant Creb1 allele has been previously published [Zubenko and Hughes, 2011]. Adult homozygous mutant mice and age- and sex-matched WT mice used in this study were bred at The Jackson Laboratory (JAX, Bar Harbor, ME), an AAALAC-accredited (Association for Assessment and Accreditation of Laboratory Animal Care International) facility that employs specific-pathogen-free vivaria. Mice were provided fresh food and water ad libitum. Genotyping of these animals for the recombinant Creb1 allele was performed at the University of Pittsburgh, using frozen tail biopsies as previously described [Zubenko and Hughes, 2011]. Protocols involving mice were approved by Institutional Animal Care and Use Committees of JAX and the University of Pittsburgh.

3-D MRM

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. 3-D MRM
  6. RESULTS
  7. DISCUSSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Three-dimensional magnetic resonance microscopy (3-D MRM) was performed at the Pittsburgh NMR Center for Biomedical Research at Carnegie Mellon University, using minor modifications of methods described by Koshibu et al. [2004, 2005]. Brains were imaged using a Bruker AVANCE DBX 11.7 Tesla, 89-mm vertical bore, microimaging system, equipped with a Micro 2.5 gradient set and a 15 mm birdcage resonator (Bruker Biospin, Billerica, MA). Following perfusion and fixation, intact brain specimens were immersed in phosphate-buffered saline, sealed in a plastic tube, and positioned in the magnet. The sample temperature was maintained at 15°C. Following pilot scans, two co-registered 3D data sets were acquired for each brain. One data set was acquired with a RARE (Rapid Acquisition with Relaxation Enhancement) T2-weighted sequence, with an effective TE of 48 ms (TR/TE 900/12 ms, RARE factor = 8, NA = 6). The other data set was acquired with a diffusion-weighted spin-echo (TR/TE = 900/25 ms, NA = 4) with the diffusion gradient (b-value = 1,900 s/mm2) applied along the rostral–caudal axis. Both of these data sets were acquired with a 256 × 128 × 128 matrix that was zero-filled to 256 × 256 × 256, yielding a final isotropic resolution of 62 µm. The total imaging time for each brain was approximately 20 hr. While volumetric studies of human brain typically employ a T1-weighted sequence, established methods for similar studies in mice substitute a T2-weighted sequence [Koshibu et al., 2004, 2005]. This is because mice have significantly less white matter than humans, and because the high field strength used for high-resolution MRM (11.7 T) results in T1 values that are prohibitively long without the aid of a T1 contrast agent.

This imaging technique was chosen over traditional histological methods to minimize distortion artifacts produced by the extreme treatment of the brain tissue during fixation, sectioning, and mounting and for greater accuracy in determining structure volumes. Thus, MRM in intact tissues produces morphometric measurements with less variability compared with histological approaches, permitting a smaller statistical sampling size and a more rapid analysis as described by Koshibu et al. [2004, 2005].

Digital Segmentation

The whole brain, brain parenchyma, lateral and third ventricles, corpus callosum, hippocampus, amygdala, and striatum were digitally segmented in 3D in a semiautomated fashion with the software package Amira version 4.1 (VGS, Burlington, MA). This segmentation relied on the intrinsic contrast provided by co-registered T2- and diffusion-weighted data sets to delineate the boundaries of regions of interest (ROIs), as identified by the Allen Reference Atlas v2 [Lein et al., 2007; Dong, 2008; Website: ©2012 Allen Institute for Brain Science. Allen Mouse Brain Atlas [Internet]. Available from: http://mouse.brain-map.org/]. Each ROI was segmented by a single investigator (HBH) using the data set that provided the most unambiguous boundary. The resulting segmented brain regions from a WT mouse are illustrated in Figure 1. A color-enhanced version of this figure, along with color videos of segmented brains from WT and mutant mice are provided as supplementary on-line material.

image

Figure 1. Segmentation of MRM brain images. Mouse brain images, acquired by magnetic resonance microscopy, were segmented into six different regions, as seen in these coronal (A), sagittal (B) and dorsoventral (C) views of the brain of a male mouse. The six regions were the hippocampus (horizontal lines), amygdala (vertical lines), striatum (left diagonal lines), third ventricle (right diagonal lines), lateral ventricles (stipple), and corpus callosum (crosshatch).

Download figure to PowerPoint

Segmentation began with the identification of the brain parenchyma, whose boundaries were defined by tissue-aqueous (tissue-buffer or tissue-CSF) interfaces most readily identified in diffusion-weighted images where aqueous fluids appear black. These boundaries were delineated using an algorithm that selected contiguous voxels falling below a user-defined intensity threshold. The resulting image was inspected slice by slice in all three dimensions and any voxels that were misspecified were manually corrected. For consistency, the parenchyma included the two short horns that project in an anterior and superior direction, and excluded the brainstem posterior to the aqueduct. This procedure was also used to define the boundaries of the lateral and third ventricles.

Segmentation of the remaining ROIs (corpus callosum, hippocampus, amygdala, and striatum) relied exclusively on the T2-weighted scans, where the boundaries were more readily discerned. The boundaries of the corpus callosum, hippocampus, and amygdala were determined in sequential coronal sections as defined in the atlas. The corpus callosum was divided into anterior and posterior regions using the level where the corpus callosum bifurcates into two bilateral sheets as the landmark. The striatum was comprised by the caudoputamen, fundus of the striatum, and nucleus accumbens. The segmentation of the collection of these three subcortical structures required the use of coronal, sagittal, and horizontal views.

The surfaces of each segmented ROI were rendered using the SurfaceGen module and the surface area and volume of each ROI were calculated using the SurfaceArea module. The total surface area and total volume of each brain were calculated by summing of the corresponding values for the brain parenchyma, lateral ventricles, and third ventricle.

Statistical Analysis

Continuous variables were expressed as means ± SD. A two-way analysis of variance (ANOVA) was used to confirm the adequacy of age- and sex-matching of mutant and WT mice. Two-way ANOVAs were also used to evaluate the effects of sex and genotype on the volumes and surface areas of ROIs. Significant effects detected by two-way ANOVAs were further evaluated by pairwise comparison of means using the LSD post hoc test. Statistical analysis was performed using PASW Statistics (formerly SPSS, IBM, Armonk, NY) version 18. Power estimates, based on our data and those presented in Koshibu et al. [2004, 2005], indicate that it is 80% likely that volume changes of 5% of whole brain, 40% in the ventricles, 5–8% in the corpus callosum, 5–10% in hippocampus, 10–15% in amygdala, and 10% in striatum would be detected by this sample size at a confidence level of 0.05 (PASS, NCSS, Kaysville, UT). Corresponding power estimates indicate that it is 80% likely that surface area changes of 5–8% of whole brain, 25% in the ventricles, 5% in the corpus callosum, 8–10% in hippocampus, 8–10% in amygdala, and 5–8% in striatum would be detected by this sample size at a confidence level of 0.05.

RESULTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. 3-D MRM
  6. RESULTS
  7. DISCUSSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Three-dimensional magnetic resonance microscopy (3D-MRM) was used to compare the brain anatomy of 6 (3M, 3F) homozygous mutant mice with a mean age of 20.9 ± SD 0.6 weeks, and 6 (3M, 3F) age- and sex-matched WT mice with a mean age of 21.0 ± SD 0.2 weeks [for genotype, F(1,8) = 0.07, P = 0.80; for sex, F(1,8) = 0.852, P = 0.383; genotype × sex interaction, F(1,8) = 2.504, P = 0.15]. Adult mice of this age are old enough to have fully developed brains and young enough that senescent changes would not yet have occurred. As shown in Table I, the lateral ventricles of the mutant mice exhibited more than a fivefold average increase (3.78 mm3) in volume compared to the WT mice (P = 0.008). In contrast, the corpus callosum of the mutant mice exhibited a 6.1% average decrease (0.56 mm3) in volume compared to the WT mice (P = 0.019). The latter finding was attributable to a loss of volume from the anterior portion of the corpus callosum. The genotype effect on the volumes of the lateral ventricles and corpus callosum are illustrated in Figures 2 and 3, respectively, as well as by the movies provided as supplementary on-line material.

Table I. Genotype and Sex Effects on Volumes of Brain Regions
 Brain regionGenotypeVolume, mm3 (mean ± SD)Difference (%)F(1,8)P-Value
  • *

    P < 0.05.

Genotype effectTotal brainWT307.63 ± 12.59−0.70.0810.784
  Hom305.34 ± 17.97   
 ParenchymaWT306.51 ± 12.36−2.10.6040.459
  Hom300.09 ± 18.46   
 Lateral ventriclesWT0.74 ± 0.58+510.812.0750.008*
  Hom4.52 ± 2.33   
 Third ventricleWT0.39 ± 0.28+87.22.8150.132
  Hom0.73 ± 0.38   
 Corpus callosumWT9.19 ± 0.40−6.18.6430.019*
  Hom8.63 ± 0.23   
 AnteriorWT6.67 ± 0.45−9.610.4880.012*
  Hom6.03 ± 0.38   
 PosteriorWT2.50 ± 0.27+3.20.4960.501
  Hom2.58 ± 0.21   
 HippocampusWT19.14 ± 0.60−3.01.0650.332
  Hom18.57 ± 1.28   
 AmygdalaWT9.23 ± 1.25−5.50.5640.474
  Hom8.72 ± 0.90   
 StriatumWT20.29 ± 1.64+2.80.3280.582
  Hom20.85 ± 1.50   
Sex effectTotal brainF314.91 ± 15.37−3.14.3740.070
  M298.07 ± 8.83   
 ParenchymaF311.63 ± 15.88−4.44.0810.078
  M294.97 ± 9.96   
 Lateral ventriclesF2.78 ± 2.60−10.10.0740.792
  M2.48 ± 2.74   
 Third ventricleF0.50 ± 0.28+26.00.3850.552
  M0.63 ± 0.45   
 Corpus callosumF9.02 ± 0.39−2.41.4370.265
  M8.80 ± 0.46   
 AnteriorF6.59 ± 0.51−7.35.9400.041*
  M6.11 ± 0.44   
 PosteriorF2.41 ± 0.22+10.84.3070.072
  M2.67 ± 0.18   
 HippocampusF19.25 ± 1.03−4.22.0710.188
  M18.45 ± 0.87   
 AmygdalaF9.21 ± 0.71−5.10.4800.508
  M8.74 ± 1.37   
 StriatumF20.77 ± 1.68−2.00.1790.683
  M20.36 ± 1.49   
image

Figure 2. Three-dimensional brain images with the lateral and third ventricles highlighted (white) within translucent renderings of the brain surface. The volume of the ventricles is increased in the mutant mice of both sexes, compared to age/sex-matched WT mice. The olfactory bulbs are in the lower left corner of each image. F, female; M, male. Scale bar (2 mm) shown.

Download figure to PowerPoint

image

Figure 3. Registered T2-weighted coronal views through the corpus callosum (CC) at approximate Bregma position 0.845 mm. The thickness of the CC is reduced in the mutant mice of both sexes, compared to age/sex-matched WT mice. The enlargement of the lateral ventricles (LV) is also clearly visible. AC, anterior commissure; S, striatum; F, female; M, male. Scale bar (2 mm) shown.

Download figure to PowerPoint

Male mice tended to have modestly smaller total brain and parenchymal volumes (P = 0.070 and 0.078, respectively), a smaller volume of the anterior corpus callosum (P = 0.041), and a trend toward a larger posterior corpus callosum (P = 0.072) compared to age-matched female mice (Table I). A larger sample size and wider age range would be necessary to more adequately characterize the sexual dimorphism of mouse brain anatomy across the life span. No genotype × sex effects on the volumes of any of the ROIs were observed.

Consistent with the observed increase in the volume of the lateral ventricles, the mutant mice exhibited more than a 2.5-fold increase (25.41 mm2) in surface area compared to WT mice (P = 0.002, Table II). No other significant genotype effects, no sex effects, and no genotype × sex interactions were observed on the surface areas of the remaining ROIs.

Table II. Genotype and Sex Effects on Surface Areas of Brain Regions
 Brain regionGenotypeVolume, mm2 (mean ± SD)Difference (%)F(1,8)P-Value
  • *

    P < 0.05.

Genotype effectTotal brainWT323.54 ± 13.73+0.80.1070.752
  Hom326.03 ± 12.30   
 Lateral ventriclesWT10.12 ± 6.44+251.120.6000.002*
  Hom35.53 ± 10.67   
 Third ventricleWT9.89 ± 3.49+20.60.8030.396
  Hom11.93 ± 3.73   
 Corpus callosumWT125.23 ± 3.60+0.20.0050.947
  Hom125.45 ± 6.27   
 AnteriorWT83.13 ± 5.08−1.70.2390.638
  Hom81.68 ± 5.56   
 PosteriorWT45.80 ± 5.20+3.40.5960.462
  Hom47.24 ± 1.21   
 HippocampusWT65.63 ± 1.36−2.10.9640.355
  Hom64.27 ± 3.51   
 AmygdalaWT42.95 ± 3.85−2.60.3060.596
  Hom41.83 ± 2.38   
 StriatumWT73.73 ± 5.60+1.10.0590.814
  Hom74.52 ± 5.94   
Sex effectTotal brainF329.70 ± 9.54−3.01.6630.233
  M319.87 ± 13.94   
 Lateral ventriclesF24.23 ± 15.96−11.60.2530.628
  M21.41 ± 16.83   
 Third ventricleF10.48 ± 3.22+8.30.1450.713
  M11.35 ± 4.22   
 Corpus callosumF125.76 ± 5.81−0.70.0660.804
  M124.92 ± 4.25   
 AnteriorF84.57 ± 5.96−5.12.140.182
  M80.23 ± 3.35   
 PosteriorF44.86 ± 4.14+7.43.1300.115
  M48.17 ± 2.46   
 HippocampusF66.10 ± 2.76−4.42.7440.136
  M63.81 ± 2.13   
 AmygdalaF42.92 ± 2.04−2.60.2790.612
  M41.85 ± 4.05   
 StriatumF76.54 ± 6.20−6.32.2120.175
  M71.70 ± 3.81   

DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. 3-D MRM
  6. RESULTS
  7. DISCUSSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

We have previously reported the creation and initial characterization of an etiology-based recombinant mouse model of MDD [Zubenko and Hughes, 2011]. This was achieved by replacing the corresponding mouse DNA sequence with a 6-base DNA sequence from the human CREB1 promoter that is associated with the development of MDD in men and women from families identified by probands with RE-MDD. The principal finding of the current study is that homozygous mutant mice of either sex manifested enlargement in the volume and surface area of the lateral ventricles and a reduction in the volume of the (anterior) corpus callosum, revealing an abnormality of brain development. These brain changes were not apparent from the examination of the intact brain, or measurements of total brain weight, volume, or surface area. No genotype × sex interactions were observed, consistent with the observation that the pathogenic CREB1 mutation was associated with MDD in both male and female members of RE-MDD families, as well as the results of published transfection experiments employing human CREB1 promoter-reporter gene constructs [Zubenko and Hughes, 2010]. Continuing the theme of abnormal development, decreases in the litter size and survival of mouse pups produced by breeding pairs that carried the mutant Creb1 allele may be related to the increase in infant mortality observed in RE-MDD families [Zubenko et al., 2001].

Published transfection experiments indicate that the pathogenic human CREB1 promoter variant carried by the recombinant mouse line significantly reduces CREB1 promoter activity [Zubenko and Hughes, 2010]. Consistent with this finding, the level of CREB protein in the cerebral cortex of homozygous mutant mice is reduced by 44% [Zubenko and Hughes, 2011]. The first case of a sporadic multiple malformation syndrome due to a dominant negative CREB1 mutation in a human newborn has recently been reported [Kitazawa et al., 2012]. At autopsy, this patient exhibited extreme enlargement in the cerebral ventricles and agenesis of the corpus callosum, features that coincide anatomically with the milder brain abnormalities of our homozygous mutant mice. The consistency of these observations in humans and the recombinant mouse provide support for the likely validity of the mouse model for studying human brain disorders.

Our current results confirm and extend our initial report of ventricular enlargement in this recombinant mouse model of MDD. Homozygous mutant adult mice of both sexes exhibited a large relative increase (511%) in the volume of the lateral ventricles compared to age/sex-matched WT mice, and a trend toward an increase in the average volume of the third ventricle (87.2%) that did not reach statistical significance. While a significant relative decrease was observed in the average volume of the corpus callosum of the mutant mice, the absolute reduction in volume of this region (0.56 mm3) did not account entirely for the increased ventricular volume. However, it should be recognized that while the relative increase in the volume of the lateral ventricles is large, the absolute change (3.78 mm3) represents only 1.2% of the total brain volume. It is possible that small reductions in the volumes of multiple brain structures, below the detection limits of our study, also contribute to the increased ventricular volume. Finally, it should be noted that the average ventricular volumes were associated with larger standard deviations than most other ROIs, perhaps related to the complexity of these structures, the small group size, or measurement error.

The brain anatomic changes exhibited by the recombinant mouse model warrant consideration in the context of those reported for mood and related psychiatric disorders. The evidence for ventricular enlargement or alterations in corpus callosum volume in patients with MDD is mixed [for review see Savitz and Drevets, 2009], although imaging studies of subjects with RE-MDD are lacking. It is tempting to speculate that the selective reduction in volume of the anterior corpus callosum of the mouse brain may compromise inter-hemispheric communication between the left and right frontal cortices, regions that make important contributions to brain circuits that underlie the clinical biology of MDD in humans [for reviews see Cummings, 1995; Drevets, 2000; Drevets et al., 2008].

In contrast to the mixed findings for mood disorders, the reduction in the size of the corpus callosum is one of the most consistent brain anatomic findings among patients with autism [Piven et al., 1997; Habalan et al., 2009]. Furthermore, genetic mapping studies of agenesis of the corpus callosum in human newborns have implicated serine/threonine kinase AKT3 [Boland et al., 2007], a participant in the CREB signaling pathway [Du and Montminy, 1998], in this congenital developmental disorder. It is tempting to hypothesize a potential developmental overlap between RE-MDD and other developmental brain disorders including autism. The elevated rates of Disorders Usually First Apparent in Infancy, Childhood, or Adolescence in our RE-MDD families is consistent with this hypothesis [Zubenko et al., 2001].

MDD is also commonly associated with degenerative disorders that become clinically evident in later life and the rate of Alzheimer-type dementia (AD) is elevated by more than twofold among the members of RE-MDD families [Zubenko et al., 2001]. Since apoptotic events contribute to neuronal loss in AD as well as during organogenesis [Zubenko, 2004], this pathway provides a potential mechanism linking the pathophysiology of RE-MDD with developmental disorders that emerge in early life and degenerative disorders whose clinical features emerge in late life.

Mounting evidence suggests that each of the major mental disorders is heterogeneous in cause. In addition, apparently different disorders may share alterations in fundamental functional and regulatory pathways. Both genetic background and non-genetic factors shape the final behavioral phenotype that becomes manifest in each affected individual [Cross Disorders Group of the Psychiatric Genomics Consortium, 2013; Rucker et al., 2013]. The findings of this study provide clues to the connection between a highly penetrant genetic determinant and high level (anatomic) features of one severe, strongly familial form of MDD. However, our experimental approach and the results of this study may also be relevant to efforts to understand other major mental disorders and comorbid medical disorders that aggregate in the same families. Future studies that examine the effects of the pathogenic Creb1 promoter in the mouse and human brain should provide further insight into the biology of RE-MDD and related disorders.

ACKNOWLEDGMENTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. 3-D MRM
  6. RESULTS
  7. DISCUSSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

This work was supported by a research contract from McLean Hospital (G.S.Z. and B.M.C.); the Pittsburgh NMR Center for Biomedical Research (P41EB001977) at Carnegie Mellon University; and the Shane Richard Brown Fund, University of Pittsburgh. Consultation and support from Dr. Chien Ho, and the technical assistance of Ms. Lesley Foley, were greatly appreciated.

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. 3-D MRM
  6. RESULTS
  7. DISCUSSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information
  • Boland E, Clayton-Smith J, Woo VG, McKee S, Manson FDC, Medne L, Zackai E, Swanson EA, Fitzpatrick D, Millen KJ, Sherr EH, Dobyns WB, Black GCM. 2007. Mapping of deletion and translocation breakpoints in 1q44 implicates the serine/threonine kinase ATK3 in postnatal microcephaly and agenesis of the corpus callosum. Am J Hum Genet 81:292303.
  • Cross Disorders Group of the Psychiatric Genomics Consortium. 2013. Identification of risk loci with shared effects on five major psychiatric disorders: A genome-wide analysis. Lancet 381:13711379.
  • Cummings JL. 1995. Anatomic and behavioral aspects of frontal-subcortical circuits. Ann NY Acad Sci 769:113.
  • Dong HW. 2008. Allen reference atlas: A digital color brain atlas of the C57BL/6J male mouse. Hoboken: John Wiley and Sons. 376p.
  • Drevets W. 2000. Neuroimaging studies of mood disorders. Biol Psychiatry 48:813829.
  • Drevets WC, Price JL, Durey ML. 2008. Brain structural and functional abnormalities in mood disorders: Implications for neurocircuitry models of depression. Brain Struct Funct 213:93118.
  • Du K, Montminy M. 1998. CREB is a regulatory target for the protein kinase Akt/PKB. J Biol Chem 273(49):3237732379.
  • Greenberg PE, Kessler RC, Birnbaum HG, Leong SA, Lowe SW, Berglund PA, Corey-Lisle PK. 2003. The economic burden of depression in the United States: How did it change between 1990 and 2000? J Clin Psychiatry 64:14651475.
  • Habalan AY, Pabalan M, Gupta N, Bansal R, Melhem NM, Fedorov S, Keshavan MS, Minshew NJ. 2009. Corpus callosum volume in children with autism. Psychiatry Res 174(1):5761.
  • Kessler RC, Chiu WT, Demler O, Merikangus KR, Walters EE. 2005. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 62(6):617627.
  • Kitazawa S, Kondo T, Mori K, Yokoyama N, Matsui M, Hitazawa R. 2012. A p.D116G mutation in CREB1 leads to novel multiple malformation syndrome resembling CrebA knockout mouse. Hum Mutat 33:651654.
  • Koshibu K, Levitt P, Ahrens ET. 2004. Sex-specific, postpuberty changes in mouse brain structures revealed by three-dimensional magnetic microscopy. NeuroImage 22:16361645.
  • Koshibu K, Ahrens ET, Levitt P. 2005. Postpubertal sex differentiation of forebrain structures and functions depend on transforming growth factor-alpha. J Neurosci 25(15):38703880.
  • Lein ES, Hawrylycz MJ, Ao N, Ayres M, Bensinger A, Bernard A, et al. 2007. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445:168176.
  • Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CL. 2006. Measuring the global burden of disease and risk factors, 1990–2001. In: Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CL, editors. Global burden of disease and risk factors. New York: The World Bank and Oxford University Press. pp 113.
  • Piven J, Bailey J, Ranson B, Arndt S. 1997. An MRI study of the corpus callosum in autism. Am J Psychiatry 154:10511056.
  • Rucker JJH, Breen G, Pinto D, Pedroso I, Lewis CM, Cohen-Woods S, Uher R, Schlosser A, Rivera M, Aitchison KJ, Craddock N, Owen MJ, Jones I, Korszun A, Muglia P, Barnes MR, Preisig M, Mors O, Gill M, Maier W, Rice J, Rietschel M, Holsboer F, Farmer AE, Craig IW, Scherer SW, McGuffin P. 2013. Genome-wide association analysis of copy number variation in recurrent depressive disorder. Mol Psychiatry 18:183189.
  • Savitz J, Drevets WC. 2009. Bipolar and major depressive disorder: Neuroimaging the developmental-degenerative divide. Neurosci Biobehav Rev 33:699771.
  • Zubenko GS. 2004. Major depressive disorder in Alzheimer's disease. In: Roose S, Sackheim H, editors. Late life depression. New York: Oxford University Press. pp 361369.
  • Zubenko GS, Hughes HB III. 2010. Effects of the A(-115)G variant on CREB1 promoter activity in two brain cell lines: Interactions with gonadal steroids. Am J Med Genet Part B 153B:13651372.
  • Zubenko GS, Hughes HB III. 2011. Replacement of homologous mouse DNA sequence with pathogenic 6-base human CREB1 promoter sequence creates murine model of major depressive disorder. Am J Med Genet Part B 156B:517531.
  • Zubenko GS, Hughes HB III. 2012. No evidence of non-homologous insertions in mouse model of MDD created by replacement of homologous mouse DNA sequence with pathogenic 6-base human CREB1 promoter sequence. Am J Med Genet Part B 159B(1):14.
  • Zubenko GS, Zubenko WN, Spiker DG, Giles DE, Kaplan BB. 2001. The malignancy of recurrent, early-onset major depression: A family study. Am J Med Genet Part B 105B(8):690699.

Supporting Information

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. 3-D MRM
  6. RESULTS
  7. DISCUSSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Additional supporting information may be found in the online version of this article at the publisher's web-site.

FilenameFormatSizeDescription
ajmb32198-sm-0001-SuppLegends-S1.doc23KLegends for Supplementary Material.
ajmb32198-sm-0001-SuppFig.tif742KFIG. S1. Segmentation of MRM brain images. Mouse brain images, acquired by magnetic resonance microscopy, were segmented into six different regions, as seen in these coronal (A), sagittal (B) and dorsoventral (C) views of the brain of a male mouse. The six regions were the hippocampus (blue, horizontal lines), amygdala (yellow, vertical lines), striatum (green, left diagonal lines), third ventricle (red, right diagonal lines), lateral ventricles (lavender, stipple), and corpus callosum (brown, crosshatch).
ajmb32198-sm-0001-SuppMovie-S1.mpg18946KB1 Hom F
ajmb32198-sm-0001-SuppMovie-S2.mpg23548KB3 Hom M
ajmb32198-sm-0001-SuppMovie-S3.mpg25741KB4 WT M
ajmb32198-sm-0001-SuppMovie-S4.mpg24493KB5 WT F

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.