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Lateralized nervous system function is phylogenetically old but fundamentally important for human brain function. Although altered in developmental and psychiatric disorders, we know little about its genetics. To understand the genetic origins of hemispheric specialization, we investigated laterality in a genetic disorder, Williams Syndrome (WS), caused by ~ 27 deleted genes on 7q11.2. Using a multidisciplinary approach combining individuals’ molecular genetic, electrophysiological, and behavioral data, we identify reversed lateralization, from right to left hemisphere for perceiving direction of motion in WS and show hemispheric strengths are inversely correlated. Moreover, we correlate decreased transcript levels of the deleted gene BUD23 , with strength of the reversed lateralization and with decreased performance in mental rotation, another right hemisphere lateralized function. The results implicate dosed BUD23, an 18S ribosomal RNA methyltransferase, in human brain laterality, support an evolutionary origin and provide altered lateralization as a novel mechanism for impaired cognition in genetic and behavioral disorders.

The molecular and neural mechanisms regulating human social-emotional behaviors are fundamentally important but largely unknown; unraveling these requires a genetic systems neuroscience analysis of human models. Williams Syndrome (WS), a condition caused by deletion of ~28 genes, is associated with a gregarious personality, strong drive to approach strangers, difficult peer interactions, and attraction to music. WS provides a unique opportunity to identify endogenous human gene-behavior mechanisms. Social neuropeptides including oxytocin (OT) and arginine vasopressin (AVP) regulate reproductive and social behaviors in mammals, and we reasoned that these might mediate the features of WS. Here we established blood levels of OT and AVP in WS and controls at baseline, and at multiple timepoints following a positive emotional intervention (music), and a negative physical stressor (cold). We also related these levels to standardized indices of social behavior. Results revealed significantly higher median levels of OT in WS versus controls at baseline, with a less marked increase in AVP. Further, in WS, OT and AVP increased in response to music and to cold, with greater variability and an amplified peak release compared to controls. In WS, baseline OT but not AVP, was correlated positively with approach, but negatively with adaptive social behaviors. These results indicate that WS deleted genes perturb hypothalamic-pituitary release not only of OT but also of AVP, implicating more complex neuropeptide circuitry for WS features and providing evidence for their roles in endogenous regulation of human social behavior. The data suggest a possible biological basis for amygdalar involvement, for increased anxiety, and for the paradox of increased approach but poor social relationships in WS. They also offer insight for translating genetic and neuroendocrine knowledge into treatments for disorders of social behavior.

Background Down syndrome (DS) is a leading model for studying therapeutic implications of co‐occurring conditions, including Alzheimer's disease (AD), intellectual disabilities (ID), and disturbances in cardiovascular (CV), pulmonary (P), and immune (Im) systems. These conditions cause significant suffering and remain unmet medical needs. Recent advances in iPSCs (induced pluripotent stem cells) derived from individuals with DS have helped accelerate research translation to clinical care. Method This study presents a unique panel of iPSCs designed to explore the contributions of distinct chromosomes, genomic genes, and gender to AD, ID, and CV, P, and Im systems. The Utah iPSC collection includes 17 cell lines from individuals with DS, most of which have paired fibroblast and lymphoblastoid cell lines. These individuals underwent deep phenotyping, including multimodal neural imaging, cognition assessments, and data from seven independent Pan‐Omics databases. The panel includes samples from identical twins discordant for DS, partial trisomies (three without APP duplication), and individuals with genetic variants such as apolipoprotein e2/e2. We used proteomics, phosphoproteomics, RNA sequencing, microRNA analysis, metabolomics, and lipidomics to examine gene influences, including APP, IFNAR, ITSN1, DYRK1A, TTC3, DSCAM, MMU17, and their homologs. Result The iPSC panel provides valuable insights into AD, DS, and related systems by distinguishing gene influences. Integration of 14 multidimensional datasets—spanning behavioral, cognitive, and neural imaging data—offers a detailed view of brain function, behavior, and organ development. This comprehensive approach allows for the dissection of AD and DS pathogenesis and the modeling of multiple human organs and cell types Conclusion This work creates a comprehensive resource to understand DS at the organismal, cellular, and systems levels. The Utah iPSC collection is an invaluable tool for accelerating research translation into therapeutic strategies to improve the quality of life for individuals with DS and related conditions.

Down syndrome (DS) is a leading model for studying therapeutic implications of co-occurring conditions, including Alzheimer's disease (AD), intellectual disabilities (ID), and disturbances in cardiovascular (CV), pulmonary (P), and immune (Im) systems. These conditions cause significant suffering and remain unmet medical needs. Recent advances in iPSCs (induced pluripotent stem cells) derived from individuals with DS have helped accelerate research translation to clinical care. This study presents a unique panel of iPSCs designed to explore the contributions of distinct chromosomes, genomic genes, and gender to AD, ID, and CV, P, and Im systems. The Utah iPSC collection includes 17 cell lines from individuals with DS, most of which have paired fibroblast and lymphoblastoid cell lines. These individuals underwent deep phenotyping, including multimodal neural imaging, cognition assessments, and data from seven independent Pan-Omics databases. The panel includes samples from identical twins discordant for DS, partial trisomies (three without APP duplication), and individuals with genetic variants such as apolipoprotein e2/e2. We used proteomics, phosphoproteomics, RNA sequencing, microRNA analysis, metabolomics, and lipidomics to examine gene influences, including APP, IFNAR, ITSN1, DYRK1A, TTC3, DSCAM, MMU17, and their homologs. The iPSC panel provides valuable insights into AD, DS, and related systems by distinguishing gene influences. Integration of 14 multidimensional datasets-spanning behavioral, cognitive, and neural imaging data-offers a detailed view of brain function, behavior, and organ development. This comprehensive approach allows for the dissection of AD and DS pathogenesis and the modeling of multiple human organs and cell types CONCLUSION: This work creates a comprehensive resource to understand DS at the organismal, cellular, and systems levels. The Utah iPSC collection is an invaluable tool for accelerating research translation into therapeutic strategies to improve the quality of life for individuals with DS and related conditions.

A significant current challenge in human genetics is the identification of interacting genetic loci mediating complex polygenic disorders. One of the best characterized polygenic diseases is Down syndrome (DS), which results from an extra copy of part or all of chromosome 21. A short interval near the distal tip of chromosome 21 contributes to congenital heart defects (CHD), and a variety of indirect genetic evidence suggests that multiple candidate genes in this region may contribute to this phenotype. We devised a tiered genetic approach to identify interacting CHD candidate genes. We first used the well vetted Drosophila heart as an assay to identify interacting CHD candidate genes by expressing them alone and in all possible pairwise combinations and testing for effects on rhythmicity or heart failure following stress. This comprehensive analysis identified DSCAM and COL6A2 as the most strongly interacting pair of genes. We then over-expressed these two genes alone or in combination in the mouse heart. While over-expression of either gene alone did not affect viability and had little or no effect on heart physiology or morphology, co-expression of the two genes resulted in ≈50% mortality and severe physiological and morphological defects, including atrial septal defects and cardiac hypertrophy. Cooperative interactions between DSCAM and COL6A2 were also observed in the H9C2 cardiac cell line and transcriptional analysis of this interaction points to genes involved in adhesion and cardiac hypertrophy. Our success in defining a cooperative interaction between DSCAM and COL6A2 suggests that the multi-tiered genetic approach we have taken involving human mapping data, comprehensive combinatorial screening in Drosophila, and validation in vivo in mice and in mammalian cells lines should be applicable to identifying specific loci mediating a broad variety of other polygenic disorders.

We propose a generic method for the statistical analysis of collections of anatomical shape complexes, namely sets of surfaces that were previously segmented and labeled in a group of subjects. The method estimates an anatomical model, the template complex, that is representative of the population under study. Its shape reflects anatomical invariants within the dataset. In addition, the method automatically places control points near the most variable parts of the template complex. Vectors attached to these points are parameters of deformations of the ambient 3D space. These deformations warp the template to each subject's complex in a way that preserves the organization of the anatomical structures. Multivariate statistical analysis is applied to these deformation parameters to test for group differences. Results of the statistical analysis are then expressed in terms of deformation patterns of the template complex, and can be visualized and interpreted. The user needs only to specify the topology of the template complex and the number of control points. The method then automatically estimates the shape of the template complex, the optimal position of control points and deformation parameters. The proposed approach is completely generic with respect to any type of application and well adapted to efficient use in clinical studies, in that it does not require point correspondence across surfaces and is robust to mesh imperfections such as holes, spikes, inconsistent orientation or irregular meshing. The approach is illustrated with a neuroimaging study of Down syndrome (DS). The results demonstrate that the complex of deep brain structures shows a statistically significant shape difference between control and DS subjects. The deformation-based modelingis able to classify subjects with very high specificity and sensitivity, thus showing important generalization capability even given a low sample size. We show that the results remain significant even if the number of control points, and hence the dimension of variables in the statistical model, are drastically reduced. The analysis may even suggest that parsimonious models have an increased statistical performance. The method has been implemented in the software Deformetrica, which is publicly available at www.deformetrica.org. [Display omitted] •Construction of anatomical models from surface data segmented in a series of images•Models reflect the anatomical organization of complexes of brain structures.•Space deformations measure shape differences between different subjects' complexes.•Multivariate statistics are computed on few adapted deformation parameters.•Shape statistics are displayed as interpretable deformation patterns.

In this study of eight rare atypical deletion cases with Williams-Beuren syndrome (WS; also known as 7q11.23 deletion syndrome) consisting of three different patterns of deletions, compared to typical WS and typically developing (TD) individuals, we show preliminary evidence of dissociable genetic contributions to brain structure and human cognition. Univariate and multivariate pattern classification results of morphometric brain patterns complemented by behavior implicate a possible role for the chromosomal region that includes: 1) GTF2I/GTF2IRD1 in visuo-spatial/motor integration, intraparietal as well as overall gray matter structures, 2) the region spanning ABHD11 through RFC2 including LIMK1, in social cognition, in particular approachability, as well as orbitofrontal, amygdala and fusiform anatomy, and 3) the regions including STX1A, and/or CYLN2 in overall white matter structure. This knowledge contributes to our understanding of the role of genetics on human brain structure, cognition and pathophysiology of altered cognition in WS. The current study builds on ongoing research designed to characterize the impact of multiple genes, gene-gene interactions and changes in gene expression on the human brain.

Although genetics is the most significant known determinant of human intelligence, specific gene contributions remain largely unknown. To accelerate understanding in this area, we have taken a new approach by studying the relationship between quantitative gene expression and intelligence in a cohort of 65 patients with Williams Syndrome (WS), a neurodevelopmental disorder caused by a 1.5 Mb deletion on chromosome 7q11.23. We find that variation in the transcript levels of the brain gene STX1A correlates significantly with intelligence in WS patients measured by principal component analysis (PCA) of standardized WAIS-R subtests, r = 0.40 (Pearson correlation, Bonferroni corrected p-value = 0.007), accounting for 15.6% of the cognitive variation. These results suggest that syntaxin 1A, a neuronal regulator of presynaptic vesicle release, may play a role in WS and be a component of the cellular pathway determining human intelligence.

Williams syndrome (WS) is a neurodevelopmental disorder associated with deletion of ∼ 20 contiguous genes in chromosome band 7q11.23. Individuals with WS exhibit mild to moderate mental retardation, but are relatively more proficient in specific language and musical abilities. We used tensor-based morphometry (TBM) to visualize the complex pattern of gray/white matter reductions in WS, based on fluid registration of structural brain images. 3D T1-weighted brain MRIs of 41 WS subjects (age [mean ± SD]: 29.2 ± 9.2 years; 23F/18M) and 39 age-matched healthy controls (age: 27.5 ± 7.4 years; 23F/16M) were fluidly registered to a minimum deformation target. Fine-scale volumetric differences were mapped between diagnostic groups. Local regions were identified where regional structure volumes were associated with diagnosis, and with intelligence quotient (IQ) scores. Brain asymmetry was also mapped and compared between diagnostic groups. WS subjects exhibited widely distributed brain volume reductions (∼ 10–15% reduction; P < 0.0002, permutation test). After adjusting for total brain volume, the frontal lobes, anterior cingulate, superior temporal gyrus, amygdala, fusiform gyrus and cerebellum were found to be relatively preserved in WS, but parietal and occipital lobes, thalamus and basal ganglia, and midbrain were disproportionally decreased in volume ( P < 0.0002). These regional volumes also correlated positively with performance IQ in adult WS subjects (age ≥ 30 years, P = 0.038). TBM facilitates 3D visualization of brain volume reductions in WS. Reduced parietal/occipital volumes may be associated with visuospatial deficits in WS. By contrast, frontal lobes, amygdala, and cingulate gyrus are relatively preserved or even enlarged, consistent with unusual affect regulation and language production in WS.

Orientation distribution functions (ODFs) are widely used to resolve fiber crossing problems in high angular resolution diffusion imaging (HARDI). The characteristics of the ODFs are often assessed using a visual criterion, although the use of objective criteria is also reported, which are directly borrowed from classic signal and image processing theory because they are intuitive and simple to compute. However, they are not always pertinent for the characterization of ODFs. We propose a more general paradigm for assessing the characteristics of ODFs. The idea consists in regarding an ODF as a three-dimensional (3D) point cloud, projecting the 3D point cloud onto an angle-distance map, constructing an angle-distance matrix, and calculating metrics such as length ratio, separability, and uncertainty. The results from both simulated and real data show that the proposed metrics allow for the assessment of the characteristics of ODFs in a quantitative and relatively complete manner.