Identification of 27 allele-specific regulatory variants in Parkinson's disease using a massively parallel reporter assay.

Genome wide association studies (GWAS) have identified a number of genomic loci that are associated with Parkinson's disease (PD) risk. However, the majority of these variants lie in non-coding regions, and thus the mechanisms by which they influence disease development, and/or potential subtypes, remain largely elusive. To address this, we used a massively parallel reporter assay (MPRA) to screen the regulatory function of 5254 variants that have a known or putative connection to PD. We identified 138 loci with enhancer activity, of which 27 exhibited allele-specific regulatory activity in HEK293 cells. The identified regulatory variant(s) typically did not match the original tag variant within the PD associated locus, supporting the need for deeper exploration of these loci. The existence of allele specific transcriptional impacts within HEK293 cells, confirms that at least a subset of the PD associated regions mark functional gene regulatory elements. Future functional studies that confirm the putative targets of the empirically verified regulatory variants will be crucial for gaining a greater understanding of how gene regulatory network(s) modulate PD risk.

Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis.

Genome-wide association studies (GWAS) have associated 76 loci with the risk of developing melanoma. However, understanding the molecular basis of such associations has remained a challenge because most of these loci are in non-coding regions of the genome. Here, we integrated data on epigenomic markers, three-dimensional (3D) genome organization, and expression quantitative trait loci (eQTL) from melanoma-relevant tissues and cell types to gain novel insights into the mechanisms underlying melanoma risk. This integrative approach revealed a total of 151 target genes, both near and far away from the risk loci in linear sequence, with known and novel roles in the etiology of melanoma. Using protein-protein interaction networks, we identified proteins that interact-directly or indirectly-with the products of the target genes. The interacting proteins were enriched for known melanoma driver genes. Further integration of these target genes into tissue-specific gene regulatory networks revealed patterns of gene regulation that connect melanoma to its comorbidities. Our study provides novel insights into the biological implications of genetic variants associated with melanoma risk.

Nanobodies identify an activated state of the TRIB2 pseudokinase.

Tribbles proteins (TRIB1-3) are pseudokinases that recruit substrates to the COP1 ubiquitin ligase. TRIB2 was the first Tribbles ortholog to be implicated as a myeloid leukemia oncogene, because it recruits the C/EBPα transcription factor for ubiquitination by COP1. Here we report identification of nanobodies that bind the TRIB2 pseudokinase domain with low nanomolar affinity. A crystal structure of the TRIB2-Nb4.103 complex identified the nanobody to bind the N-terminal lobe of TRIB2, enabling specific recognition of TRIB2 in an activated conformation that is similar to the C/EBPα-bound state of TRIB1. Characterization in solution revealed that Nb4.103 can stabilize a TRIB2 pseudokinase domain dimer in a face-to-face manner. Conversely, a distinct nanobody (Nb4.101) binds through a similar epitope but does not readily promote dimerization. In combination, this study identifies features of TRIB2 that could be exploited for the development of inhibitors and nanobody tools for future investigation of TRIB2 function.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis.

BACKGROUND: There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression. MAIN BODY: We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data. CONCLUSION: We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.