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Identification of non-coding genetic variants in samples from hypoxemic respiratory disease patients that affect the transcriptional response to hypoxia.

ABSTRACT: A wide range of diseases course with an unbalance between the consumption of oxygen by tissues and its supply. This situation triggers a transcriptional response, mediated by the hypoxia inducible factors (HIFs), that aims to restore oxygen homeostasis. Little is known about the inter-individual variation in this response and its role in the progression of disease. Herein, we sought to identify common genetic variants mapping to hypoxia response elements (HREs) and characterize their effect on transcription. To this end, we constructed a list of genome-wide HIF-binding regions from publicly available experimental datasets and studied the genetic variability in these regions by targeted re-sequencing of genomic samples from 96 chronic obstructive pulmonary disease and 144 obstructive sleep apnea patients. This study identified 14 frequent variants disrupting potential HREs. The analysis of the genomic regions containing these variants by means of reporter assays revealed that variants rs1009329, rs6593210 and rs150921338 impaired the transcriptional response to hypoxia. Finally, using genome editing we confirmed the functional role of rs6593210 in the transcriptional regulation of EGFR. In summary, we found that inter-individual variability in non-coding regions affect the response to hypoxia and could potentially impact on the progression of pulmonary diseases.

SUBMITTER: Roche O

PROVIDER: S-EPMC5100585 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Identification of non-coding genetic variants in samples from hypoxemic respiratory disease patients that affect the transcriptional response to hypoxia.

Roche Olga O Deguiz María Laura ML Tiana María M Galiana-Ribote Clara C Martinez-Alcazar Daniel D Rey-Serra Carlos C Ranz-Ribeiro Beatriz B Casitas Raquel R Galera Raúl R Fernández-Navarro Isabel I Sánchez-Cuéllar Silvia S Bernard Virginie V Ancochea Julio J Wasserman Wyeth W WW García-Rio Francisco F Jimenez Benilde B Del Peso Luis L

Nucleic acids research 20160912 19

A wide range of diseases course with an unbalance between the consumption of oxygen by tissues and its supply. This situation triggers a transcriptional response, mediated by the hypoxia inducible factors (HIFs), that aims to restore oxygen homeostasis. Little is known about the inter-individual variation in this response and its role in the progression of disease. Herein, we sought to identify common genetic variants mapping to hypoxia response elements (HREs) and characterize their effect on t ...[more]

PMID: 27625398

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Project description:BACKGROUND:Identification of functional non-coding variants and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in reference samples allow functional annotation of the genome, but do not provide ready answers regarding the effects of non-coding variants on phenotypes. A promising computational approach is to build models that predict TF-DNA binding from sequence, and use such models to score a variant's impact on TF binding strength. Here, we asked if this mechanistic approach to variant interpretation can be combined with information on genotype-phenotype associations to discover transcription factors regulating phenotypic variation among individuals. RESULTS:We developed a statistical approach that integrates phenotype, genotype, gene expression, TF ChIP-seq, and Hi-C chromatin interaction data to answer this question. Using drug sensitivity of lymphoblastoid cell lines as the phenotype of interest, we tested if non-coding variants statistically linked to the phenotype are enriched for strong predicted impact on DNA binding strength of a TF and thus identified TFs regulating individual differences in the phenotype. Our approach relies on a new method for predicting variant impact on TF-DNA binding that uses a combination of biophysical modeling and machine learning. We report statistical and literature-based support for many of the TFs discovered here as regulators of drug response variation. We show that the use of mechanistically driven variant impact predictors can identify TF-drug associations that would otherwise be missed. We examined in depth one reported association-that of the transcription factor ELF1 with the drug doxorubicin-and identified several genes that may mediate this regulatory relationship. CONCLUSION:Our work represents initial steps in utilizing predictions of variant impact on TF binding sites for discovery of regulatory mechanisms underlying phenotypic variation. Future advances on this topic will be greatly beneficial to the reconstruction of phenotype-associated gene regulatory networks.

Project description:Studies on autism spectrum disorder (ASD) have amassed substantial evidence for the role of genetics in the disease's phenotypic manifestation. A large number of coding and non-coding variants with low penetrance likely act in a combinatorial manner to explain the variable forms of ASD. However, many of these combined interactions, both additive and epistatic, remain undefined. Coalitional game theory (CGT) is an approach that seeks to identify players (individual genetic variants or genes) who tend to improve the performance-association to a disease phenotype of interest-of any coalition (subset of co-occurring genetic variants) they join. This method has been previously applied to boost biologically informative signal from gene expression data and exome sequencing data but remains to be explored in the context of cooperativity among non-coding genomic regions. We describe our extension of previous work, highlighting non-coding chromosomal regions relevant to ASD using CGT on alteration data of 4595 fully sequenced genomes from 756 multiplex families. Genomes were encoded into binary matrices for three types of non-coding regions previously implicated in ASD and separated into ASD (case) and unaffected (control) samples. A player metric, the Shapley value, enabled determination of individual variant contributions in both sets of cohorts. A total of 30 non-coding positions were found to have significantly elevated player scores and likely represent significant contributors to the genetic coordination underlying ASD. Cross-study analyses revealed that a subset of mutated non-coding regions (all of which are in human accelerated regions (HARs)) and related genes are involved in biological pathways or behavioral outcomes known to be affected in autism, suggesting the importance of single nucleotide polymorphisms (SNPs) within HARs in ASD. These findings support the use of CGT in identifying hidden yet influential non-coding players from large-scale genomic data, to better understand the precise underpinnings of complex neurodevelopmental disorders such as autism.

Project description:BackgroundAcute hypoxemic respiratory failure (HRF) is associated with high morbidity and mortality, but its heterogeneity challenges the identification of effective therapies. Defining subphenotypes with distinct prognoses or biologic features can improve therapeutic trials, but prior work has focused on ARDS, which excludes many acute HRF patients. We aimed to characterize persistent and resolving subphenotypes in the broader HRF population.MethodsIn this secondary analysis of 2 independent prospective ICU cohorts, we included adults with acute HRF, defined by invasive mechanical ventilation and PaO2-to-FIO2 ratio ≤ 300 on cohort enrollment (n = 768 in the discovery cohort and n = 1715 in the validation cohort). We classified patients as persistent HRF if still requiring mechanical ventilation with PaO2-to-FIO2 ratio ≤ 300 on day 3 following ICU admission, or resolving HRF if otherwise. We estimated relative risk of 28-day hospital mortality associated with persistent HRF, compared to resolving HRF, using generalized linear models. We also estimated fold difference in circulating biomarkers of inflammation and endothelial activation on cohort enrollment among persistent HRF compared to resolving HRF. Finally, we stratified our analyses by ARDS to understand whether this was driving differences between persistent and resolving HRF.ResultsOver 50% developed persistent HRF in both the discovery (n = 386) and validation (n = 1032) cohorts. Persistent HRF was associated with higher risk of death relative to resolving HRF in both the discovery (1.68-fold, 95% CI 1.11, 2.54) and validation cohorts (1.93-fold, 95% CI 1.50, 2.47), after adjustment for age, sex, chronic respiratory illness, and acute illness severity on enrollment (APACHE-III in discovery, APACHE-II in validation). Patients with persistent HRF displayed higher biomarkers of inflammation (interleukin-6, interleukin-8) and endothelial dysfunction (angiopoietin-2) than resolving HRF after adjustment. Only half of persistent HRF patients had ARDS, yet exhibited higher mortality and biomarkers than resolving HRF regardless of whether they qualified for ARDS.ConclusionPatients with persistent HRF are common and have higher mortality and elevated circulating markers of lung injury compared to resolving HRF, and yet only a subset are captured by ARDS definitions. Persistent HRF may represent a clinically important, inclusive target for future therapeutic trials in HRF.

Dataset Information

Identification of non-coding genetic variants in samples from hypoxemic respiratory disease patients that affect the transcriptional response to hypoxia.

Publications

Identification of non-coding genetic variants in samples from hypoxemic respiratory disease patients that affect the transcriptional response to hypoxia.

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