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

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.

Patterns of structural variation define prostate cancer across disease states.

The complex genomic landscape of prostate cancer evolves across disease states under therapeutic pressure directed toward inhibiting androgen receptor (AR) signaling. While significantly altered genes in prostate cancer have been extensively defined, there have been fewer systematic analyses of how structural variation shapes the genomic landscape of this disease across disease states. We uniformly characterized structural alterations across 531 localized and 143 metastatic prostate cancers profiled by whole genome sequencing, 125 metastatic samples of which were also profiled via whole transcriptome sequencing. We observed distinct significantly recurrent breakpoints in localized and metastatic castration-resistant prostate cancers (mCRPC), with pervasive alterations in noncoding regions flanking the AR, MYC, FOXA1, and LSAMP genes enriched in mCRPC and TMPRSS2-ERG rearrangements enriched in localized prostate cancer. We defined 9 subclasses of mCRPC based on signatures of structural variation, each associated with distinct genetic features and clinical outcomes. Our results comprehensively define patterns of structural variation in prostate cancer and identify clinically actionable subgroups based on whole genome profiling.

Transient expression of a GABA receptor subunit during early development is critical for inhibitory synapse maturation and function.

Developing neural circuits, including GABAergic circuits, switch receptor types. But the role of early GABA receptor expression for establishment of functional inhibitory circuits remains unclear. Tracking the development of GABAergic synapses across axon terminals of retinal bipolar cells (BCs), we uncovered a crucial role of early GABAA receptor expression for the formation and function of presynaptic inhibitory synapses. Specifically, early α3-subunit-containing GABAA (GABAAα3) receptors are a key developmental organizer. Before eye opening, GABAAα3 gives way to GABAAα1 at individual BC presynaptic inhibitory synapses. The developmental downregulation of GABAAα3 is independent of GABAAα1 expression. Importantly, lack of early GABAAα3 impairs clustering of GABAAα1 and formation of functional GABAA synapses across mature BC terminals. This impacts the sensitivity of visual responses transmitted through the circuit. Lack of early GABAAα3 also perturbs aggregation of LRRTM4, the organizing protein at GABAergic synapses of rod BC terminals, and their arrangement of output ribbon synapses.