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Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning

ABSTRACT: Most genetic variants in the human genome reside in non-coding regions, where they can perturb regulatory elements, influence gene expression, and contribute to various phenotypes and diseases. However, identifying such functionally relevant genetic variation remains challenging. Here we integrate personal genomics, allele-specific gene regulation, and deep learning predictions to map the impact of non-coding variation in its native allelic and regulatory context. We identify and validate hundreds of cell-type-specific transcription factor binding events disrupted by genetic variants, providing mechanistic insights underlying allele-specific gene regulation. Using this framework, we discover a rare variant that modulates PIK3R5 gene expression by disrupting an OCT2 binding site within a distal enhancer. Our study establishes a generalisable strategy for interpreting non-coding regulatory variation, enabling systematic dissection of variant effects across diverse biological systems and offering an alternative framework to investigate the disease mechanisms.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): B Cell, Blood

DISEASE(S): Lymphoma

SUBMITTER: Miguel Hernandez Quiles

LAB HEAD: Michiel Vermeulen

PROVIDER: PXD069116 | Pride | 2026-04-22

REPOSITORIES: Pride

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			Action	DRS
	20241128_Exploris_Evosep_EV1137_V109_DNA_PD_dimethyl_MHQ_7.raw	Raw
	20241128_Exploris_Evosep_EV1137_V109_DNA_PD_dimethyl_MHQ_8.raw	Raw
	mqpar.xml	Xml
	proteinGroups.txt	Txt

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Project description:The interaction between transcription factors and genomic DNA forms the basis for spatio-temporal control of gene expression. Therefore, these interactions and their impact on disease and cellular fate are extensively studied on a global level, mainly using techniques based on next-generation sequencing. These techniques, however, do not allow an unbiased study of proteins or entire protein complexes that bind to a certain DNA sequence. In recent years, DNA pull-downs followed by quantitative mass spectrometry were introduced to close this gap. Established protocols, however, are based on metabolic labeling techniques or require enormous amounts of cellular material, thus restricting the method to cell lines grown in culture. Furthermore, they require substantial amount of expertise, thus keeping this technique restricted to a limited number of laboratories. Here, we introduce a high-throughput compatible, LC-MS/MS based DNA pull-down that combines on-bead digestion with direct dimethyl labeling or label-free protein quantification. We demonstrate, that our method can efficiently identify transcription factors binding to their known consensus DNA motifs when using nuclear extracts from model cell lines. Subsequently, we apply the method to study DNA-protein interactions in primary foreskin fibroblasts and peripheral blood mononuclear cells (PBMCs) freshly isolated from human donors. We show that the same DNA sequence binds different sets of proteins in an established model cell line as opposed to PBMCs. This stresses the importance of selecting relevant cell extracts for any interaction in question. In-depth nuclear proteomes with absolute quantification of close to 7,000 proteins in K562 cells and PBMCs clearly link these differential interactions to differences in protein abundance. In conclusion, our approach, applicable to primary material and capable of profiling DNA-protein interactions in high-throughput, will likely prove itself as a useful screening platform and will provide invaluable functional data, for example through integration with large scale GWAS.

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Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning

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