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Background: Autosomal recessive primary microcephaly (MCPH) is a rare neurodevelopmental disease with severe microcephaly at birth due to a pronounced reduction in brain volume and intellectual disability. Biallelic mutations in the WD repeat-containing protein 62 gene WDR62 are the genetic cause of MCPH2. However, the exact underlying pathomechanism of MCPH2 remains to be clarified.
Methods/results: We characterized the clinical, radiological, and cellular features that add to the human MCPH2 phenotype. Exome sequencing followed by Sanger sequencing in a German family with two affected daughters with primary microcephaly revealed in the index patient the compound heterozygous mutations c. 1313G>A (p.R438H) / c.2864-2867delACAG (p.D955Afs*112) of WDR62, the second of which is novel. Radiological examination displayed small frontal lobes, corpus callosum hypoplasia, simplified hippocampal gyration, and cerebellar hypoplasia. We investigated the cellular phenotype in patient-derived lymphoblastoid cells and compared it with that of healthy female controls. WDR62 expression in the patient's immortalized lymphocytes was deranged, and mitotic spindle defects as well as abnormal centrosomal protein localization were apparent.
Conclusion: We propose that a disruption of centrosome integrity and/or spindle organization may play an important role in the development of microcephaly in MCPH2.
RNA sequencing (RNA-seq) has become a powerful tool to understand molecular mechanisms and/or developmental programs. It provides a fast, reliable and cost-effective method to access sets of expressed elements in a qualitative and quantitative manner. Especially for non-model organisms and in absence of a reference genome, RNA-seq data is used to reconstruct and quantify transcriptomes at the same time. Even SNPs, InDels, and alternative splicing events are predicted directly from the data without having a reference genome at hand. A key challenge, especially for non-computational personnal, is the management of the resulting datasets, consisting of different data types and formats. Here, we present TBro, a flexible de novo transcriptome browser, tackling this challenge. TBro aggregates sequences, their annotation, expression levels as well as differential testing results. It provides an easy-to-use interface to mine the aggregated data and generate publication-ready visualizations. Additionally, it supports users with an intuitive cart system, that helps collecting and analysing biological meaningful sets of transcripts. TBro’s modular architecture allows easy extension of its functionalities in the future. Especially, the integration of new data types such as proteomic quantifications or array-based gene expression data is straightforward. Thus, TBro is a fully featured yet flexible transcriptome browser that supports approaching complex biological questions and enhances collaboration of numerous researchers.