The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases
Abstract Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts. Here, we present GENDULF (GENetic moDULators identiFication), o...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2020-12-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.20209701 |
| _version_ | 1797281938992529408 |
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| author | Noam Auslander Daniel M Ramos Ivette Zelaya Hiren Karathia Thomas O. Crawford Alejandro A Schäffer Charlotte J Sumner Eytan Ruppin |
| author_facet | Noam Auslander Daniel M Ramos Ivette Zelaya Hiren Karathia Thomas O. Crawford Alejandro A Schäffer Charlotte J Sumner Eytan Ruppin |
| author_sort | Noam Auslander |
| collection | DOAJ |
| description | Abstract Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts. Here, we present GENDULF (GENetic moDULators identiFication), one of the first methods to facilitate prediction of disease modifiers using healthy and diseased tissue gene expression data. GENDULF is designed for monogenic diseases in which the mechanism is loss of function leading to reduced expression of the mutated gene. When applied to cystic fibrosis, GENDULF successfully identifies multiple, previously established disease modifiers, including EHF, SLC6A14, and CLCA1. It is then utilized in spinal muscular atrophy (SMA) and predicts U2AF1 as a modifier whose low expression correlates with higher SMN2 pre‐mRNA exon 7 retention. Indeed, knockdown of U2AF1 in SMA patient‐derived cells leads to increased full‐length SMN2 transcript and SMN protein expression. Taking advantage of the increasing availability of transcriptomic data, GENDULF is a novel addition to existing strategies for prediction of genetic disease modifiers, providing insights into disease pathogenesis and uncovering novel therapeutic targets. |
| first_indexed | 2024-03-07T17:04:42Z |
| format | Article |
| id | doaj.art-3de22a2ac37c4d438a901282d752a3e5 |
| institution | Directory Open Access Journal |
| issn | 1744-4292 |
| language | English |
| last_indexed | 2024-03-07T17:04:42Z |
| publishDate | 2020-12-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj.art-3de22a2ac37c4d438a901282d752a3e52024-03-03T02:46:54ZengSpringer NatureMolecular Systems Biology1744-42922020-12-011612n/an/a10.15252/msb.20209701The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseasesNoam Auslander0Daniel M Ramos1Ivette Zelaya2Hiren Karathia3Thomas O. Crawford4Alejandro A Schäffer5Charlotte J Sumner6Eytan Ruppin7Cancer Data Science Laboratory (CDSL) National Cancer InstituteNational Institutes of Health Bethesda MD USADepartment of Neuroscience Johns Hopkins University School of Medicine Baltimore MD USAInterdepartmental Program in Bioinformatics University of California Los Angeles Los Angeles CA USALaboratory of Receptor Biology and Gene Expression National Cancer InstituteNational Institutes of Health MD USADepartment of Pediatrics Johns Hopkins University School of Medicine Baltimore MD USACancer Data Science Laboratory (CDSL) National Cancer InstituteNational Institutes of Health Bethesda MD USADepartment of Neuroscience Johns Hopkins University School of Medicine Baltimore MD USACancer Data Science Laboratory (CDSL) National Cancer InstituteNational Institutes of Health Bethesda MD USAAbstract Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts. Here, we present GENDULF (GENetic moDULators identiFication), one of the first methods to facilitate prediction of disease modifiers using healthy and diseased tissue gene expression data. GENDULF is designed for monogenic diseases in which the mechanism is loss of function leading to reduced expression of the mutated gene. When applied to cystic fibrosis, GENDULF successfully identifies multiple, previously established disease modifiers, including EHF, SLC6A14, and CLCA1. It is then utilized in spinal muscular atrophy (SMA) and predicts U2AF1 as a modifier whose low expression correlates with higher SMN2 pre‐mRNA exon 7 retention. Indeed, knockdown of U2AF1 in SMA patient‐derived cells leads to increased full‐length SMN2 transcript and SMN protein expression. Taking advantage of the increasing availability of transcriptomic data, GENDULF is a novel addition to existing strategies for prediction of genetic disease modifiers, providing insights into disease pathogenesis and uncovering novel therapeutic targets.https://doi.org/10.15252/msb.20209701cystic fibrosisdigenic inheritancegene expressionmodifier genespinal muscular atrophy |
| spellingShingle | Noam Auslander Daniel M Ramos Ivette Zelaya Hiren Karathia Thomas O. Crawford Alejandro A Schäffer Charlotte J Sumner Eytan Ruppin The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases Molecular Systems Biology cystic fibrosis digenic inheritance gene expression modifier gene spinal muscular atrophy |
| title | The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases |
| title_full | The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases |
| title_fullStr | The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases |
| title_full_unstemmed | The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases |
| title_short | The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases |
| title_sort | gendulf algorithm mining transcriptomics to uncover modifier genes for monogenic diseases |
| topic | cystic fibrosis digenic inheritance gene expression modifier gene spinal muscular atrophy |
| url | https://doi.org/10.15252/msb.20209701 |
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