Using Transcription Modules to Identify Expression Clusters Perturbed in Williams-Beuren Syndrome

The genetic dissection of the phenotypes associated with Williams-Beuren Syndrome (WBS) is advancing thanks to the study of individuals carrying typical or atypical structural rearrangements, as well as in vitro and animal studies. However, little is known about the global dysregulations caused by the WBS deletion. We profiled the transcriptomes of skin fibroblasts from WBS patients and compared them to matched controls. We identified 868 differentially expressed genes that were significantly enriched in extracellular matrix genes, major histocompatibility complex (MHC) genes, as well as genes in which the products localize to the postsynaptic membrane. We then used public expression datasets from human fibroblasts to establish transcription modules, sets of genes coexpressed in this cell type. We identified those sets in which the average gene expression was altered in WBS samples. Dysregulated modules are often interconnected and share multiple common genes, suggesting that intricate regulatory networks connected by a few central genes are disturbed in WBS. This modular approach increases the power to identify pathways dysregulated in WBS patients, thus providing a testable set of additional candidates for genes and their interactions that modulate the WBS phenotypes

Can a dog be jealous?

Whether humans alone experience complex emotions like jealousy or envy remains hotly debated, partly because of the difficulty of measuring them without a verbal report. Cook, Berns and colleagues use functional brain imaging to identify in dogs neural responses very similar to those evoked by jealousy in humans. When dogs see their caregiver reward a facsimile dog, their amygdala is activated and the strength of this response predicts aggressive behavior — just as jealousy leads to aggression in humans. The authors conclude that dogs feel something very similar to human jealousy. This novel and creative study tackles one of the most vexing challenges in neuroscience — understanding the unstated thoughts and feelings of others — with practical applications that go beyond getting closer to man’s best friend. The issue of whether a dog can be jealous nevertheless remains far from settled, as we discuss below

Autism and Williams syndrome: Dissimilar socio-cognitive profiles with similar patterns of abnormal gene expression in the blood

Autism spectrum disorders and Williams syndrome exhibit quite opposite features in the social domain, but also share some common underlying behavioral and cognitive deficits. It is not clear, however, which genes account for the attested differences (and similarities) in the socio-cognitive domain. In this article, we adopted a comparative molecular approach and looked for genes that might be differentially (or similarly) regulated in the blood of subjects with these two conditions. We found a significant overlap between differentially expressed genes compared to neurotypical controls, with most of them exhibiting a similar trend in both conditions, but with genes being more dysregulated in Williams syndrome than in autism spectrum disorders. These genes are involved in aspects of brain development and function (particularly dendritogenesis) and are expressed in brain areas (particularly the cerebellum, the thalamus, and the striatum) of relevance for the autism spectrum disorder and the Williams syndrome etiopathogenesi

Development of Biclustering Techniques for Gene Expression Data Modeling and Mining

The next-generation sequencing technologies can generate large-scale biological data with higher resolution, better accuracy, and lower technical variation than the arraybased counterparts. RNA sequencing (RNA-Seq) can generate genome-scale gene expression data in biological samples at a given moment, facilitating a better understanding of cell functions at genetic and cellular levels. The abundance of gene expression datasets provides an opportunity to identify genes with similar expression patterns across multiple conditions, i.e., co-expression gene modules (CEMs). Genomescale identification of CEMs can be modeled and solved by biclustering, a twodimensional data mining technique that allows clustering of rows and columns in a gene expression matrix, simultaneously. Compared with traditional clustering that targets global patterns, biclustering can predict local patterns. This unique feature makes biclustering very useful when applied to big gene expression data since genes that participate in a cellular process are only active in specific conditions, thus are usually coexpressed under a subset of all conditions. The combination of biclustering and large-scale gene expression data holds promising potential for condition-specific functional pathway/network analysis. However, existing biclustering tools do not have satisfied performance on high-resolution RNA-Seq data, majorly due to the lack of (i) a consideration of high sparsity of RNA-Seq data, especially for scRNA-Seq data, and (ii) an understanding of the underlying transcriptional regulation signals of the observed gene expression values. QUBIC2, a novel biclustering algorithm, is designed for large-scale bulk RNA-Seq and single-cell RNA-seq (scRNA-Seq) data analysis. Critical novelties of the algorithm include (i) used a truncated model to handle the unreliable quantification of genes with low or moderate expression; (ii) adopted the Gaussian mixture distribution and an information-divergency objective function to capture shared transcriptional regulation signals among a set of genes; (iii) utilized a Dual strategy to expand the core biclusters, aiming to save dropouts from the background; and (iv) developed a statistical framework to evaluate the significances of all the identified biclusters. Method validation on comprehensive data sets suggests that QUBIC2 had superior performance in functional modules detection and cell type classification. The applications of temporal and spatial data demonstrated that QUBIC2 could derive meaningful biological information from scRNA-Seq data. Also presented in this dissertation is QUBICR. This R package is characterized by an 82% average improved efficiency compared to the source C code of QUBIC. It provides a set of comprehensive functions to facilitate biclustering-based biological studies, including the discretization of expression data, query-based biclustering, bicluster expanding, biclusters comparison, heatmap visualization of any identified biclusters, and co-expression networks elucidation. In the end, a systematical summary is provided regarding the primary applications of biclustering for biological data and more advanced applications for biomedical data. It will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency

A CELL REPROGRAMMING-BASED APPROACH TO STUDY 7Q11.23 GENE DOSAGE IMBALANCES IN WILLIAMS BEUREN SYNDROME AND AUTISM SPECTRUM DISORDER

Symmetrical gene dosage imbalances at 7q11.23 chromosomal region cause two unique neurodevelopmental diseases, Williams Beuren Syndrome (WBS) and the 7q11.23 microduplication associated to autistic spectrum disorder (7dup-ASD). Although both these diseases share common features such as intellectual disability and craniofacial dysmorphism, they can be distinguished by distinct social and language abilities: WBS patients characterized by hypersociality and comparatively well-preserved language skills while 7dup-ASD is associated with impairment in social interaction and communicative skills. The involvement of same genetic interval in these disease, points out to small subset of dosage-sensitive genes affecting cognition, social behavior and communication skills. Among the genes in the deleted region, some were shown to contribute to the abnormalities in these patients through transgenic mice models and individual case reports. However, the precise cellular and molecular phenotypes associated with these syndromes in disease-relevant cell-types are unknown due to the scarce availability of primary diseased tissues. Transcription factor induced somatic cell reprogramming has bypassed such fundamental limitation and has enabled us to model human diseases, elucidate their pathogenesis and discover new therapeutics by screening small chemicals/drugs on these models. During my PhD studies, I focused on the functional dissection of these complementary diseases at the level of transcriptional deregulation in patient-derived iPSC and its differentiated derivatives such as neural crest stem cells, mesenchymal stem cells, and neural progenitors. To this end, we have assembled a unique cohort of typical WBS, atypical WBS (patient with a partial deletion) and 7dup-ASD patients (along with unaffected relatives), and then I used mRNA reprogramming to establish and characterize at least 3 independent iPSC lines from a total of 12 individuals. High throughput mRNA sequencing on iPSC revealed critical transcriptional derangements in disease-relevant pathways already at the pluripotent state. These alterations found to be selectively amplified upon differentiation into disease-relevant lineages, thereby establishing the value of large iPSC cohorts in the elucidation of disease-relevant developmental pathways. Finally, we created an open-access web-based platform to make accessible our multi-layered datasets and integrate contributions by the entire community working on the molecular dissection of the 7q11.23 syndromes

PROPAGATION OF DYSREGULATION ACROSS GENE EXPRESSION LAYERS IN 7Q11.23 CNV ASSOCIATED DEVELOPMENTAL DISORDERS

Williams-Beuren Syndrome (WBS) and 7q11.23 microduplication syndrome (7dup), two multi-systemic developmental disorders, arise from symmetrical copy number variations of the same region on chromosome 7q. In WBS patients this region is deleted, whereas it is duplicated in 7dup individuals. These syndromes display a striking combination of shared and opposite clinical manifestations at the level of neuro-cognitive, craniofacial and cardiovascular features, thus pointing to a remarkable dosage-sensitive effect of a small group of genes on the development and maintenance of complex traits such as sociality, language and facial morphology. We derived a large cohort of induced pluripotent stem cells (iPSCs) from samples with WBS, 7dup and from healthy controls, and we demonstrated that, already at the pluripotent state, the transcriptome is dysregulated in pathways that map onto disease-related features. Moreover, these pathways were selectively dysregulated in differentiated lineages, thus demonstrating an anticipatory power of the pluripotent state. Building on these results, we expanded the view on the dysregulation in pluripotency by measuring three layers of gene expression: transcriptome, translatome and proteome. We mapped the propagation of differences across layers by integrating ribosome profiling and SWATH-MS proteomics, and we probed the extent to which a translation initiation factor included in the CNV, EIF4H, was responsible for the regulation of translation. We found that each layer of gene expression has its own differentially expressed genes, whose degrees of propagation can change between layers. Moreover, differentially expressed genes can cluster by different ways of propagation when they are compared to the levels of EIF4H

RNAseq analysis of heart tissue from mice treated with atenolol and isoproterenol reveals a reciprocal transcriptional response.

The transcriptional response to many widely used drugs and its modulation by genetic variability is poorly understood. Here we present an analysis of RNAseq profiles from heart tissue of 18 inbred mouse strains treated with the β-blocker atenolol (ATE) and the β-agonist isoproterenol (ISO). Differential expression analyses revealed a large set of genes responding to ISO (n = 1770 at FDR = 0.0001) and a comparatively small one responding to ATE (n = 23 at FDR = 0.0001). At a less stringent definition of differential expression, the transcriptional responses to these two antagonistic drugs are reciprocal for many genes, with an overall anti-correlation of r = -0.3. This trend is also observed at the level of most individual strains even though the power to detect differential expression is significantly reduced. The inversely expressed gene sets are enriched with genes annotated for heart-related functions. Modular analysis revealed gene sets that exhibit coherent transcription profiles across some strains and/or treatments. Correlations between these modules and a broad spectrum of cardiovascular traits are stronger than expected by chance. This provides evidence for the overall importance of transcriptional regulation for these organismal responses and explicits links between co-expressed genes and the traits they are associated with. Gene set enrichment analysis of differentially expressed groups of genes pointed to pathways related to heart development and functionality. Our study provides new insights into the transcriptional response of the heart to perturbations of the β-adrenergic system, implicating several new genes that had not been associated to this system previously

RNAseq analysis of heart tissue from mice treated with atenolol and isoproterenol reveals a reciprocal transcriptional response

Abstract Background The transcriptional response to many widely used drugs and its modulation by genetic variability is poorly understood. Here we present an analysis of RNAseq profiles from heart tissue of 18 inbred mouse strains treated with the β-blocker atenolol (ATE) and the β-agonist isoproterenol (ISO). Results Differential expression analyses revealed a large set of genes responding to ISO (n = 1770 at FDR = 0.0001) and a comparatively small one responding to ATE (n = 23 at FDR = 0.0001). At a less stringent definition of differential expression, the transcriptional responses to these two antagonistic drugs are reciprocal for many genes, with an overall anti-correlation of r = −0.3. This trend is also observed at the level of most individual strains even though the power to detect differential expression is significantly reduced. The inversely expressed gene sets are enriched with genes annotated for heart-related functions. Modular analysis revealed gene sets that exhibit coherent transcription profiles across some strains and/or treatments. Correlations between these modules and a broad spectrum of cardiovascular traits are stronger than expected by chance. This provides evidence for the overall importance of transcriptional regulation for these organismal responses and explicits links between co-expressed genes and the traits they are associated with. Gene set enrichment analysis of differentially expressed groups of genes pointed to pathways related to heart development and functionality. Conclusions Our study provides new insights into the transcriptional response of the heart to perturbations of the β-adrenergic system, implicating several new genes that had not been associated to this system previously