Long-read sequencing reveals the complex splicing profile of the psychiatric risk gene CACNA1C in human brain

Abstract

RNA splicing is a key mechanism linking genetic variation with psychiatric disorders. Splicing profiles are particularly diverse in brain and difficult to accurately identify and quantify. We developed a new approach to address this challenge, combining long-range PCR and nanopore sequencing with a novel bioinformatics pipeline. We identify the full-length coding transcripts of CACNA1C in human brain. CACNA1C is a psychiatric risk gene that encodes the voltage-gated calcium channel Ca V 1.2. We show that CACNA1C's transcript profile is substantially more complex than appreciated, identifying 38 novel exons and 241 novel transcripts. Importantly, many of the novel variants are abundant, and predicted to encode channels with altered function. The splicing profile varies between brain regions, especially in cerebellum. We demonstrate that human transcript diversity (and thereby protein isoform diversity) remains undercharacterised, and provide a feasible and cost-effective methodology to address this. A detailed understanding of isoform diversity will be essential for the translation of psychiatric genomic findings into pathophysiological insights and novel psychopharmacological targets.

Targeting microglia L‐type voltage‐dependent calcium channels for the treatment of central nervous system disorders

“…In addition to differential subunit distribution, the α1 L-VDCC subunit genes are also alternatively spliced between different cells and tissues. In the brain, there are hundreds of splice variants of Cav1.2 (Clark et al, 2019) and different Cav1.3 splice variants regulate dendritic spine morphology on neurons (Stanika et al, 2016).…”

“…The overall uncertainty about whether microglia even express L-VDCCs may be due to activation-dependent differential expression similar to other microglia channels (Nguyen et al, 2017), or an uncharacterized splice variant of L-VDCC subunits that is microglia-specific, similar to that which is observed in T lymphocytes (Kotturi & Jefferies, 2005;Matza et al, 2016). In the brain, there are hundreds of splice variants of Cav1.2 (Clark et al, 2019) and it is not clear which, if any of these splice variants may be specific to microglia. Microglia may express their own voltage-insensitive unique splice variant of L-VDCCs similar to T lymphocytes (Kotturi & Jefferies, 2005) which would explain the elusiveness of typical L-VDCC currents on microglia.…”

“…Cav1.3 is a better target for PD and may also represent a better target for novel anti-inflammatory agents targeted to microglia. Recent advances in long-read sequencing have revealed the extensive number and diversity of variants present in the brain (Clark et al, 2019), some of which are most certainly specific to cells of interest, including subtypes of neurons, microglia, and astrocytes. With these splice variant sequences, we can characterize the electrophysiological and pharmacodynamic properties of each of these variants and identify their cell type-specific distribution.…”

Dynamic nanopore long-read sequencing analysis of HIV-1 splicing events during the early steps of infection

“…It has been shown to sequence short and ultra-long reads of hundreds of kilobases without bias for high or low GC contents [37]. ONT sequencing has been successfully applied to study alternative splicing of several human transcripts [38][39][40] and to explore viral transcriptomes [41][42][43][44][45][46][47][48][49]. We took advantage of this technology to produce HIV-1 reads long enough to cover all the possible splice junctions present on viral isoforms.…”

Accessing a New Dimension in TP53 Biology: Multiplex Long Amplicon Digital PCR to Specifically Detect and Quantitate Individual TP53 Transcripts

“…In addition, our method overcomes the limitations of standard RT-qPCR or short-read RNA sequencing methods [8] and will complement long-read RNA sequencing technologies. It can be used to determine whether there is more transcript complexity than can be inferred from short read RNA sequencing data, or as a more sensitive method to confirm and/or precisely quantitate splice forms identified by sequencing approaches [37]. It can also provide a laboratory method to corroborate inferential splice forms or splicing intermediates identified by bioinformatic and deep learning techniques [38,39].…”