Software helps ID hard-to-interpret Alagille-causing gene mutations
Tools helped find 7 previously unconfirmed splicing-disturbing JAG1 variants
Software tools may help predict whether certain hard-to-interpret mutations in the JAG1 gene affect splicing, a process that can direct how a protein is produced, a study shows.
The tools, along with additional functional tests, let the researchers identify seven splicing-disturbing JAG1 mutations that hadn’t been previously confirmed.
JAG1 is the gene most commonly mutated in Alagille syndrome. Having a better understanding of the potential effects of the gene’s mutations on splicing and the protein that results from it can aid in identifying variants that truly cause Alagille.
The study, “Investigation of cryptic JAG1 splice variants as a cause of Alagille syndrome and performance evaluation of splice predictor tools,” was published in HGG Advances.
Alagille is an inherited disease that can result in abnormalities in the liver, heart, blood vessels, bones, and other organs. Most cases are caused by mutations in the JAG1 gene that affect the production or function of the JAG1 protein, which is crucial for embryos to develop normally. Most Alagille-causing JAG1 mutations result in a shorter protein being produced that doesn’t work properly.
Splicing and Alagille syndrome
Splicing is a natural process that removes dispensable sections of genetic information (introns) and joins those containing information needed to build a protein (exons) in messenger RNA (mRNA), an intermediate molecule derived from DNA that guides protein production. Abnormalities in splicing can change the length and sequence of the resulting protein.
Splicing mutations, or those that affect splicing, in the JAG1 gene have been reported to cause Alagille. While most of the mutations occur at well-defined, or canonical, splicing-related positions near the exon-intron connection, a few map to intronic regions far from these canonical positions, and are referred to as noncanonical intronic variants.
Also, missense mutations, which result in only one amino acid — a building block of protein — being swapped for another in the resulting protein, can also create new splicing positions, also known as cryptic splice sites, within introns, thereby affecting normal splicing.
“Missense variants, which do not result in early protein truncation, and noncanonical splice variants, which may or may not interfere with normal splicing, both lead to uncertain associations with disease and often require functional analysis of the protein product or RNA to confirm [if they cause disease],” the researchers wrote. “Therefore, confirmation of aberrant splicing from noncanonical intronic variants or through the generation of [new splicing sites] by variation within the coding region is immediately relevant to diagnostics.”
Evaluating effects of splicing in JAG1 mutations
Researchers in the U.S. sought to evaluate the splicing effects of potential cryptic splice variants in Alagille patients through mRNA analysis and two splice prediction software tools, called SpliceAI and Pangolin.
The tools assign a zero to 1 score to mutations, with a higher score meaning there’s a greater chance the mutation will affect splicing. In both tools, a score above 0.2 has been used as the minimum cutoff for a greater likelihood of aberrant splicing.
From an initial 479 Alagille patients who carried JAG1 mutations, 90 had either missense or noncanonical intronic variants. Ten patients (six males, four females) with ages ranging from 6 months to 32 years, had potential cryptic splice mutations, based on SpliceAI and Pangolin and available blood cell samples to perform mRNA analysis, and were included in the final analysis. The patients carried 10 unique mutations: five noncanonical intronic variants and five missense variants.
A mRNA analysis on all 10 mutations showed that three missense and all five noncanonical intronic mutations did affect splicing, as predicted by the splice prediction software tools.
“We show that three JAG1 missense variants and five intronic variants outside of the canonical splice site positions identified in individuals with [Alagille syndrome] cause abnormal splicing and are likely [disease-causing],” the researchers wrote.
A review of existing research found 23 noncanonical and cryptic splice variants in the JAG1 gene, most of which were predicted to affect splicing, but that hadn’t been experimentally confirmed. This was the case of seven of the eight JAG1 mutations that resulted in aberrant splicing in the current study.
By combining all the data, the researchers were able to identify a more accurate score threshold of SpliceAI and Pangolin for predicting whether a mutation affects splicing. They found that a score higher than 0.6 was very good at detecting such mutations.
The study showed the software tools were less reliable for mutations with scores below 0.2, however. In these cases, the software sometimes predicted the mutations didn’t affect splicing when they actually did.
“These results improve genomic diagnostics for [Alagille syndrome] by confirming splice effects for seven variants and suggest that the integration of splice prediction tools with RNA analysis is important to ensure accurate clinical variant classifications,” the researchers wrote. “We expect this will provide guidance for the incorporation of these tools during clinical variant classification of JAG1 and potentially other disease genes.”
About the Author
