Identification of genetic factors influencing metabolic dysregulation and retinal support for MacTel, a retinal disorder

Bonelli, Roberto; Jackson, Victoria E.; Prasad, Aravind; Munro, Jacob E.; Farashi, Samaneh; Heeren, Tjebo; Pontikos, Nikolas; Scheppke, Lea; Friedlander, Martin; Egan, Catherine; Allikmets, Rando; Ansell, Brendan R E; Bahlo, Melanie

doi:10.1038/s42003-021-01788-w

Cited by 26 publications

(27 citation statements)

References 77 publications

(133 reference statements)

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“…A handful of SNPs associated with eye diseases have been experimentally studied using retinal organoids derived from induced pluripotent stem cells. One such SNP is rs17421627, an index SNP from GWAS of MacTel representing a T-to-G substitution on chromosome 5 5,28 . We determined rs17421627 to be one of only five SNPs for MacTel with a predicted target gene and found the SNP to be most accessible in Müller glia and astrocytes (Fig.…”

Section: Resultsmentioning

confidence: 99%

Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

Wang

Nair

et al. 2022

Preprint

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Genome-wide association studies (GWAS) of eye disorders have identified hundreds of genetic variants associated with ocular disease. However, the vast majority of these variants are noncoding, making it challenging to interpret their function. Here, we present a joint single-cell atlas of gene expression and chromatin accessibility of the adult human retina with >50,000 cells, which we used to analyze noncoding single-nucleotide polymorphisms (SNPs) implicated by GWAS of age-related macular degeneration, glaucoma, diabetic retinopathy, myopia, and type 2 macular telangiectasia. We integrate this atlas with a HiChIP enhancer connectome, expression quantitative trait loci (eQTL) data, and base-resolution deep learning models to predict noncoding SNPs with causal roles in eye disease, assess SNP impact on transcription factor binding, and define their known and novel target genes. Our efforts nominate pathogenic SNP-target gene interactions for multiple vision disorders and provide a potentially powerful resource for interpreting noncoding variation in the eye.

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Section: Resultsmentioning

confidence: 99%

Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

Wang

Nair

et al. 2022

Preprint

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“…To place results from our analysis in healthy primate retina into the context of pathology, we mapped our data against serum-based investigations of two retinal disorders that exclusively affect the macula and have known associations with systemic metabolic changes, Macular Telangiectasia type 2 (MacTel) [5][6][7][8][9][10] and Age-related Macular Degeneration (AMD) [1][2][3][4] . To this end, we leveraged differential serum metabolite abundance results from our previously published MacTel study 5 , to compare serum metabolites in patients with MacTel compared to controls.…”

Section: The Regional Retinal Context Of Disease-related Metabolitesmentioning

confidence: 99%

“…For example, metabolomic studies performed on serum from Age-related Macular Degeneration (AMD) have identified associations between dysregulations of lipids as well as amino acids with AMD disease status or severity [1][2][3][4] . Similarly, metabolomic profiling of Macular telangiectasia type 2 (MacTel) patients identified serum levels of serine, and sphingolipids as an important MacTel risk factor [5][6][7][8][9][10] . However, it is not clear whether the systemic manifestations (i.e.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of metabolites in primate retina and its relevance to studies of human metabolic retinal disorders

Bonelli

Ansell

Woods

et al. 2022

Preprint

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The primate retina has evolved regional specialisations for specific visual functions. The macula is specialised towards high acuity vision and is an area that contains an increased density of cone photoreceptors and signal processing neurons. Different regions in the retina display unique susceptibility to pathology, with many retinal diseases primarily affecting the macula. To better understand the properties of different retinal areas we conducted an untargeted metabolomics analysis on full thickness punches from three different regions (macula, temporal peri-macula and periphery) of primate retina. Half of all metabolites identified showed differential abundance in at least one comparison between the three regions. The unique metabolic phenotype of different retinal regions is likely due to the differential distribution of different cell types in these regions reflecting the specific metabolic requirements of each cell type. Furthermore, mapping metabolomics results from macula-specific eye diseases onto the region-specific distributions of healthy primate retina revealed differential abundance defining systemic metabolic dysregulations that were region specific, highlighting how our results may help to better understand the pathobiology of retinal diseases with region specificity.

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“… 4 – 7 Genome-wide association studies (GWASs) frequently implicate non-coding regions to disease phenotypes. 6 , 8 – 11 Additionally, expression quantitative trait locus (eQTL) analyses have associated non-coding variants with changes in retinal gene expression. 12 , 13 Moreover, individual case studies have identified causal regulatory variants in retinal disorders, including blue cone monochromacy, non-syndromic congenital retinal non-attachment, and aniridia with foveal hypoplasia.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements

VandenBosch

Luu

Timms

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose Prior studies have demonstrated the significance of specific cis -regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learning approach, trained on epigenomic data from the adult human retina, to systematically quantify the predicted impact of cis -regulatory variants. Methods We used human retinal DNA accessibility data (ATAC-seq) to determine a set of 18.9k high-confidence, putative cis -regulatory elements. Eighty percent of these elements were used to train a machine learning model utilizing a gapped k -mer support vector machine–based approach. In silico saturation mutagenesis and variant scoring was applied to predict the functional impact of all potential single nucleotide variants within cis -regulatory elements. Impact scores were tested in a 20% hold-out dataset and compared to allele population frequency, phylogenetic conservation, transcription factor (TF) binding motifs, and existing massively parallel reporter assay data. Results We generated a model that distinguishes between human retinal regulatory elements and negative test sequences with 95% accuracy. Among a hold-out test set of 3.7k human retinal CREs, all possible single nucleotide variants were scored. Variants with negative impact scores correlated with higher phylogenetic conservation of the reference allele, disruption of predicted TF binding motifs, and massively parallel reporter expression. Conclusions We demonstrated the utility of human retinal epigenomic data to train a machine learning model for the purpose of predicting the impact of non-coding regulatory sequence variants. Our model accurately scored sequences and predicted putative transcription factor binding motifs. This approach has the potential to expedite the characterization of pathogenic non-coding sequence variants in the context of unexplained retinal disease. Translational Relevance This workflow and resulting dataset serve as a promising genomic tool to facilitate the clinical prioritization of functionally disruptive non-coding mutations in the retina.

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Identification of genetic factors influencing metabolic dysregulation and retinal support for MacTel, a retinal disorder

Cited by 26 publications

References 77 publications

Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

Spatial distribution of metabolites in primate retina and its relevance to studies of human metabolic retinal disorders

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements

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