Identifying chromosomal subpopulations based on their recombination histories advances the study of the genetic basis of phenotypic traits

The E3 ligase RBCK1 reduces the sensitivity of ccRCC to sunitinib through the ANKRD35-MITD1-ANXA1 axis

Despite the promise of targeted tyrosine kinase inhibitors (TKIs), such as sunitinib, in the extension of survival time in patients with clear cell renal cell carcinoma (ccRCC) progression or metastasis, the patients eventually succumb to inevitable drug resistance. Protein degradation executed by the ubiquitin-dependent proteasome system played an important role in determining the sensitivity of ccRCC to sunitinib. Here, we applied the bioinformatic analysis to identify that E3 ligase RBCK1 was elevated in the sunitinib-resistant renal cancer cell lines or patient specimens. The subsequent in vitro or in vivo studies demonstrated that RBCK1 contributed to decreasing the sensitivity of ccRCC to sunitinib. Then, we showed that inhibition of RBCK1 inactivated the AKT and MAPK signaling pathways, which might be one of the main reasons why RBCK1 induces sunitinib resistance in ccRCC cells. Mechanistically, our results indicated that RBCK1 promotes the degradation of ANKRD35 and that ANKRD35 destabilizes MITD1 by binding with SUMO2 in ccRCC cells. In addition, we showed that the RBCK1-ANKRD35-MITD1-ANXA1 axis regulates the phosphorylation of AKT and ERK and contributes to the dysregulation of sunitinib in ccRCC cells. Therefore, we identified a novel mechanism for regulating the sensitivity of sunitinib in ccRCC. Therefore, we elucidated a novel mechanism by which RBCK1 regulates sunitinib sensitivity in ccRCC.

The early-life exposome modulates the effect of polymorphic inversions on DNA methylation

Polymorphic genomic inversions are chromosomal variants with intrinsic variability that play important roles in evolution, environmental adaptation, and complex traits. We investigated the DNA methylation patterns of three common human inversions, at 8p23.1, 16p11.2, and 17q21.31 in 1,009 blood samples from children from the Human Early Life Exposome (HELIX) project and in 39 prenatal heart tissue samples. We found inversion-state specific methylation patterns within and nearby flanking each inversion region in both datasets. Additionally, numerous inversion-exposure interactions on methylation levels were identified from early-life exposome data comprising 64 exposures. For instance, children homozygous at inv-8p23.1 and higher meat intake were more susceptible to TDH hypermethylation (P = 3.8 × 10⁻²²); being the inversion, exposure, and gene known risk factors for adult obesity. Inv-8p23.1 associated hypermethylation of GATA4 was also detected across numerous exposures. Our data suggests that the pleiotropic influence of inversions during development and lifetime could be substantially mediated by allele-specific methylation patterns which can be modulated by the exposome.

Analysis of the relationship between presence of common DNA sequence inversions and DNA methylation patterns suggests a role for environmental exposures (such as food intake) in mediating inversion state-specific methylation patterns.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

The Rat Genome Database (RGD) facilitates genomic and phenotypic data integration across multiple species for biomedical research

Model organism research is essential for discovering the mechanisms of human diseases by defining biologically meaningful gene to disease relationships. The Rat Genome Database (RGD, ( https://rgd.mcw.edu )) is a cross-species knowledgebase and the premier online resource for rat genetic and physiologic data. This rich resource is enhanced by the inclusion and integration of comparative data for human and mouse, as well as other human disease models including chinchilla, dog, bonobo, pig, 13-lined ground squirrel, green monkey, and naked mole-rat. Functional information has been added to records via the assignment of annotations based on sequence similarity to human, rat, and mouse genes. RGD has also imported well-supported cross-species data from external resources. To enable use of these data, RGD has developed a robust infrastructure of standardized ontologies, data formats, and disease- and species-centric portals, complemented with a suite of innovative tools for discovery and analysis. Using examples of single-gene and polygenic human diseases, we illustrate how data from multiple species can help to identify or confirm a gene as involved in a disease and to identify model organisms that can be studied to understand the pathophysiology of a gene or pathway. The ultimate aim of this report is to demonstrate the utility of RGD not only as the core resource for the rat research community but also as a source of bioinformatic tools to support a wider audience, empowering the search for appropriate models for human afflictions.