Integrative network analysis reveals biological pathways associated with Williams syndrome

Abstract

Background Williams syndrome (WS) is a neurodevelopmental disorder that has been attributed to heterozygous deletions in chromosome 7q11.23 and exhibits a variety of physical, cognitive, and behavioral features. However, the genetic basis of this phenotypic variability is unclear. In this study, we identified genetic clues underlying these complex phenotypes. Methods Neurobehavioral function was assessed in WS patients and healthy controls. Total RNA was extracted from peripheral blood and subjected to microarray analysis, RNA‐sequencing, and qRT‐PCR. Weighted gene co‐expression network analysis was performed to identify specific alterations related to intermediate disease phenotypes. To functionally interpret each WS‐related module, gene ontology and disease‐related gene enrichment were examined. We also investigated the micro (mi)RNA expression profiles and miRNA co‐expression networks to better explain the regulation of the transcriptome in WS. Results Our analysis identified four significant co‐expression modules related to intermediate WS phenotypes. Notably, the three upregulated WS‐related modules were composed exclusively of genes located outside the 7q11.23 region. They were significantly enriched in genes related to B‐cell activation, RNA processing, and RNA transport. BCL11A, which is known for its association with speech disorders and intellectual disabilities, was identified as one of the hub genes in the top WS‐related module. Finally, these key upregulated mRNA co‐expression modules appear to be inversely correlated with a specific downregulated WS‐related miRNA co‐expression module. Conclusions Dysregulation of the mRNA/miRNA network involving genes outside of the 7q11.23 region is likely related to the complex phenotypes observed in WS patients.

Birdsong as a window into language origins and evolutionary neuroscience

“…In this sense, BAZ1B may be connected to neural crest changes that occur during neotenization, and influence a subset of specialized traits including verbal fluency and sociality that are enhanced in WS patients. Notably, BCL11A was among the most highly interconnected genes in a transcriptional network generated from peripheral blood from WS patients [123]. In the juvenile Area X, expression levels of BCL11A and BAZ1B positively correlate with levels of morning song modulation measured by the VI paradigm [9].…”

Integrated DNA methylation analysis reveals a potential role for ANKRD30B in Williams syndrome

“…All blood samples were collected on the same day as clinical assessments. All patients were confirmed to have typical 7q11.23 deletions based on genomic quantitative PCR as previously described [ 16 ]. This study was approved by institutional ethics committees at each participating institution.…”

“…Using the ELMER R package (version 2.6.1) in supervised mode, the methylation and expression data were integrated to investigate GRNs and identify related master regulator transcription factors [ 13 ]. Briefly, for this analysis the processed methylation data β -values, as described above, were used, combined with published expression data [ 16 ]. More details are provided in the Supplemental Data .…”

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

“…All samples had RNA integrity number (RIN) values over 8. An Agilent SurePrint G3 Human GE v2 8x60K Microarray (G4851B) (Agilent Technologies) was subsequently used for analyzing transcriptome changes, as described in more detail in Kimura et al (2019). Gene expression data can be retrieved from Gene Expression Omnibus (GSE 89594).…”

“…In cases with a known etiology, like in Williams syndrome (WS), robust gene-to-phenotype correlations are also difficult to establish, particularly, for cognitive and behavioral deficits (see Korenberg et al, 2000;Tassabehji, 2003;Karmiloff-Smith et al, 2012;Ghaffari et al, 2018 among others for discussion), seemingly because these problems result in most cases from the dysregulation of several other genes outside the affected genomic regions (e.g. Lalli et al, 2016or Kimura et al, 2019 for WS). Consequently, for these clinical conditions, it is of particular interest to examine the expression pattern of genes across the whole genome.…”