Child Neurology: RNA Sequencing for the Diagnosis of Lissencephaly

Results

Based on the negative DNA testing, we pursued RNA sequencing through a research ethics board–approved research protocol. Because source material is a critical consideration for RNA-seq, and no brain tissue was available in this case, we wanted to understand which easily accessed tissues best mirrored the brain transcriptome. We focused on blood and skin fibroblasts, using transcriptome data from our in-house database and from GTex. While neither had profiles that fully matched the brain, fibroblasts much more closely resembled cortex in terms of number of genes expressed and expression levels (Figure, C). In particular, we reviewed tissue expression of LIS1 and other known brain malformation genes, and found they have substantially higher expression in fibroblasts, with levels nearly equivalent to cortical brain tissue.

We thus performed a skin biopsy, derived fibroblast cultures, and performed RNA-seq. The resulting total transcriptome analysis revealed reduced expression and allele imbalance of LIS1, with approximately 20 reads coming from 1 allele vs >500 from the other. Within the allele with reduced expression, we observed multiple splicing disruptions, including exon 4 skipping, although the low overall read count presented difficulty for the definitive interpretation of specific splicing alterations. Overall, the RNA-seq data were consistent with reduced expression of 1 LIS1 allele, supporting a haploinsufficiency mechanism, the most common mutational consequence associated with LIS1 mutation.

We repeated DNA sequence analysis with Sanger-based sequencing of LIS1 gene on a clinical basis. This demonstrated a pathogenic variant in exon 4 of LIS1 gene with a sequence change at c.164 G>A. This variant is predicted to create a premature stop codon at amino acid residue 55, p.Trp55*, with subsequent nonsense-mediated decay. Our transcriptome data support the reduction of expression from this allele, and suggest the variant promotes splicing changes in addition to nonsense-mediated decay. Given that heterozygous loss of expression/function variants in LIS1 are the known genetic pattern for LIS1-related lissencephaly, we concluded that this heterozygous stop variant is the cause of disease in our patient.

Discussion

Our study demonstrates the suitability of RNA-seq for identifying novel mutations and for providing functional evidence of mutation consequence. Using RNA-seq, we were able to accurately identify a variant in LIS1, and show that the resulting nonsense mutation promoted allele-specific loss of expression and abnormal RNA processing. Importantly, while our RNA-seq analysis ultimately pointed to a pathogenic variant in LIS1, because we captured the total transcriptome, we were potentially able to identify an abnormality in any gene expressed in the sample. For fibroblast transcriptome analysis, this includes essentially all genes associated with cortical malformations, as well as most genes associated with neurogenetic conditions. Overall, therefore, this technology is applicable for the investigation of any case not fully clarified or solved by multigene panel or WES.

In terms of RNA-seq as a diagnostic modality, our study reinforces the importance of using suitable source material for transcriptome analysis, and provides first proof of concept for using skin fibroblasts for the study of brain malformation genetics. This is particularly useful for clinical practice, given that brain tissue is often not available, and skin biopsies are an efficient and noninvasive bedside procedure that can assist in revealing an underlying genetic diagnosis. Of note, we show that the expression of genes associated with brain malformations is superior in fibroblasts as compared to blood, both in terms of number of genes expressed and overall levels of expression. This is important because only genes expressed at suitable levels (typically >1 transcript per million for expression and at least 5 reads for identifying splicing aberrations) can be meaningfully analyzed. Another potential source material for diseases of the CNS are lymphoblastoid cell lines, which have recently been shown to express a broad range of genes associated with neurodevelopmental disorders, and which have been successfully used to identify or clarify pathogenic variants that cause Cornelia de Lange syndrome.⁸

In addition, this case points to an important limitation of some genetic testing platforms, as the LIS1 variant in this case was missed in the initial analysis of patient DNA. The reasons why the variant was not detected are not clear, as overall mean read depth of the gene in the panel was >500×. One possibility is that the specific coverage of exon 4 was low, a likely hypothesis given that exon 4 is known to be challenging to capture at sufficient depth by next-generation sequencing. Overall, this illustrates the critical need to understand strengths and limitations of different genetic diagnostic technologies, and to not fully exclude causes until a definitive diagnosis has been established.

A final consideration is when to utilize RNA-seq in the diagnostic pathway. Multigene panel or WES remain first-line testing methodologies for mutation identification across the spectrum of neurogenetic disorders. However, these technologies do not provide information related to the noncoding genome, and in addition often identify variants that are of uncertain significance. RNA-seq is therefore an ideal next step modality in panel or WES-negative cases or cases with variants that are of uncertain significance, as it provides data on variants (coding or noncoding) that affect RNA levels and processing. Moving forward, with the emergence of whole genome sequencing (WGS) as a powerful and comprehensive technology for detection of pathogenic variants,⁹ RNA-seq is also well positioned to provide functional annotation and interpretation when paired with WGS.