Machine learning-driven identification of critical gene programs and key transcription factors in migraine
Abstract Background Migraine is a complex neurological disorder characterized by recurrent episodes of severe headaches. Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associated brain regions, remain unclear. This s...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | The Journal of Headache and Pain |
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| Online Access: | https://doi.org/10.1186/s10194-025-01950-3 |
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| author | Lei Zhang Yujie Li Yunhao Xu Wei Wang Guangyu Guo |
| author_facet | Lei Zhang Yujie Li Yunhao Xu Wei Wang Guangyu Guo |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | Abstract Background Migraine is a complex neurological disorder characterized by recurrent episodes of severe headaches. Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associated brain regions, remain unclear. This study applies machine learning techniques to explore region-specific gene expression profiles and identify critical gene programs and transcription factors linked to migraine pathogenesis. Methods We utilized single-nucleus RNA sequencing (snRNA-seq) data from 43 brain regions, along with genome-wide association study (GWAS) data, to investigate susceptibility to migraine. The cell-type-specific expression (CELLEX) algorithm was employed to calculate specific expression profiles for each region, while non-negative matrix factorization (NMF) was applied to decompose gene programs within the single-cell data from these regions. Following the annotation of brain region expression profiles and gene programs to the genome, we employed stratified linkage disequilibrium score regression (S-LDSC) to assess the associations between brain regions, gene programs, and migraine-related SNPs. Key transcription factors regulating critical gene programs were identified using a random forest model based on regulatory networks derived from the GTEx consortium. Results Our analysis revealed significant enrichment of migraine-associated single nucleotide polymorphisms (SNPs) in the posterior nuclear complex-medial geniculate nuclei (PoN_MG) of the thalamus, highlighting this region’s crucial role in migraine pathogenesis. Gene program 1, identified through NMF, was enriched in the calcium signaling pathway, a known contributor to migraine pathophysiology. Random forest analysis predicted ARID3A as the top transcription factor regulating gene program 1, suggesting its potential role in modulating calcium-related genes involved in migraine. Conclusion This study provides new insights into the molecular mechanisms underlying migraine, emphasizing the importance of the PoN_MG thalamic region, calcium signaling pathways, and key transcription factors like ARID3A. These findings offer potential avenues for developing targeted therapeutic strategies for migraine treatment. |
| format | Article |
| id | doaj-art-25f65770ecc84b37ac5c260e9ec2644e |
| institution | Kabale University |
| issn | 1129-2377 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | The Journal of Headache and Pain |
| spelling | doaj-art-25f65770ecc84b37ac5c260e9ec2644e2025-01-26T12:45:00ZengBMCThe Journal of Headache and Pain1129-23772025-01-0126111410.1186/s10194-025-01950-3Machine learning-driven identification of critical gene programs and key transcription factors in migraineLei Zhang0Yujie Li1Yunhao Xu2Wei Wang3Guangyu Guo4Clinical Systems Biology Laboratories, The First Affiliated Hospital of Zhengzhou UniversityAcademy of Medical Sciences of Zhengzhou UniversityClinical Systems Biology Laboratories, The First Affiliated Hospital of Zhengzhou UniversityHeadache Center, Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang UniversityClinical Systems Biology Laboratories, The First Affiliated Hospital of Zhengzhou UniversityAbstract Background Migraine is a complex neurological disorder characterized by recurrent episodes of severe headaches. Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associated brain regions, remain unclear. This study applies machine learning techniques to explore region-specific gene expression profiles and identify critical gene programs and transcription factors linked to migraine pathogenesis. Methods We utilized single-nucleus RNA sequencing (snRNA-seq) data from 43 brain regions, along with genome-wide association study (GWAS) data, to investigate susceptibility to migraine. The cell-type-specific expression (CELLEX) algorithm was employed to calculate specific expression profiles for each region, while non-negative matrix factorization (NMF) was applied to decompose gene programs within the single-cell data from these regions. Following the annotation of brain region expression profiles and gene programs to the genome, we employed stratified linkage disequilibrium score regression (S-LDSC) to assess the associations between brain regions, gene programs, and migraine-related SNPs. Key transcription factors regulating critical gene programs were identified using a random forest model based on regulatory networks derived from the GTEx consortium. Results Our analysis revealed significant enrichment of migraine-associated single nucleotide polymorphisms (SNPs) in the posterior nuclear complex-medial geniculate nuclei (PoN_MG) of the thalamus, highlighting this region’s crucial role in migraine pathogenesis. Gene program 1, identified through NMF, was enriched in the calcium signaling pathway, a known contributor to migraine pathophysiology. Random forest analysis predicted ARID3A as the top transcription factor regulating gene program 1, suggesting its potential role in modulating calcium-related genes involved in migraine. Conclusion This study provides new insights into the molecular mechanisms underlying migraine, emphasizing the importance of the PoN_MG thalamic region, calcium signaling pathways, and key transcription factors like ARID3A. These findings offer potential avenues for developing targeted therapeutic strategies for migraine treatment.https://doi.org/10.1186/s10194-025-01950-3MigraineGene programRandom forest |
| spellingShingle | Lei Zhang Yujie Li Yunhao Xu Wei Wang Guangyu Guo Machine learning-driven identification of critical gene programs and key transcription factors in migraine The Journal of Headache and Pain Migraine Gene program Random forest |
| title | Machine learning-driven identification of critical gene programs and key transcription factors in migraine |
| title_full | Machine learning-driven identification of critical gene programs and key transcription factors in migraine |
| title_fullStr | Machine learning-driven identification of critical gene programs and key transcription factors in migraine |
| title_full_unstemmed | Machine learning-driven identification of critical gene programs and key transcription factors in migraine |
| title_short | Machine learning-driven identification of critical gene programs and key transcription factors in migraine |
| title_sort | machine learning driven identification of critical gene programs and key transcription factors in migraine |
| topic | Migraine Gene program Random forest |
| url | https://doi.org/10.1186/s10194-025-01950-3 |
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