scholarly journals Transmission distortion and genetic incompatibilities between alleles in a multigenerational mouse advanced intercross line

Genetics ◽  
2021 ◽  
Author(s):  
Danny Arends ◽  
Stefan Kärst ◽  
Sebastian Heise ◽  
Paula Korkuc ◽  
Deike Hesse ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Complex Traits ◽  
Inbred Strains ◽  
Parental Origin ◽  
Protein Protein Interactions ◽  
Sequencing Data ◽  
Synonymous Snps ◽  
Advanced Intercross Line ◽  
Apoptosis Pathways ◽  
Overrepresentation Analysis

Abstract While direct additive and dominance effects on complex traits have been mapped repeatedly, additional genetic factors contributing to the heterogeneity of complex traits have been scarcely investigated. To assess genetic background effects, we investigated transmission ratio distortions (TRDs) of alleles from parent to offspring using an advanced intercross line (AIL) of an initial cross between the mouse inbred strains C57BL/6NCrl (B6N) and BFMI860-12 (BFMI). 341 males of generation 28 and their respective 61 parents and 66 grandparents were genotyped using Mega Mouse Universal Genotyping Arrays (MegaMUGA). TRDs were investigated using allele transmission asymmetry tests, and pathway overrepresentation analysis was performed. Sequencing data was used to test for overrepresentation of non-synonymous SNPs in TRD regions. Genetic incompatibilities were tested using the Bateson-Dobzhansky-Muller two-locus model. 62 TRD regions were detected, many in close proximity to the telocentric centromere. TRD regions contained 44.5% more non-synonymous SNPs than randomly selected regions (182 vs. 125.9 ± 17.0, P < 1x10−4). Testing for genetic incompatibilities between TRD regions identified 29 genome-wide significant incompatibilities between TRD regions (P(BF) < 0.05). Pathway overrepresentation analysis of genes in TRD regions showed that DNA methylation, epigenetic regulation of RNA, and meiotic/meiosis regulation pathways were affected independent of the parental origin of the TRD. Paternal BFMI TRD regions showed overrepresentation in the small interfering RNA (siRNA) biogenesis and in the metabolism of lipids and lipoproteins. Maternal B6N TRD regions harbored genes involved in meiotic recombination, cell death, and apoptosis pathways. The analysis of genes in TRD regions suggests the potential distortion of protein-protein interactions influencing obesity and diabetic retinopathy as a result of disadvantageous combinations of allelic variants in Aass, Pgx6 and Nme8. Using an AIL significantly improves the resolution at which we can investigate TRD. Our analysis implicates distortion of protein-protein interactions as well as meiotic drive as the underlying mechanisms leading to the observed TRD in our AIL. Furthermore, genes with large amounts of non-synonymous SNPs located in TRD regions are more likely to be involved in pathways that are related to the phenotypic differences between the parental strains. Genes in these TRD regions provide new targets for investigating genetic adaptation, protein-protein interactions, and determinants of complex traits such as obesity.

scholarly journals Transmission distortion and genetic incompatibilities between alleles in a multigenerational mouse advanced intercross line

2021 ◽  
Author(s):  
Danny Arends ◽  
Stefan Kärst ◽  
Sebastian Heise ◽  
Paula Korkuc ◽  
Deike Hesse ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Complex Traits ◽  
Genetic Background ◽  
Inbred Strains ◽  
Parental Origin ◽  
Protein Protein Interactions ◽  
Sequencing Data ◽  
Synonymous Snps ◽  
Advanced Intercross Line ◽  
Overrepresentation Analysis

Background/Objectives: While direct additive and dominance effects on complex traits have been mapped repeatedly, additional genetic factors contributing to the heterogeneity of complex traits have been scarcely investigated. To assess genetic background effects, we investigated transmission ratio distortions (TRDs) of alleles from parent to offspring using an advanced intercross line (AIL) of an initial cross between the mouse inbred strains C57BL/6NCrl (B6N) and BFMI860-12 (BFMI). Subjects/Methods: 341 males of generation 28 and their respective 61 parents and 66 grandparents were genotyped using Mega Mouse Universal Genotyping Arrays (MegaMUGA). TRDs were investigated using allele transmission asymmetry tests, and pathway overrepresentation analysis was performed. Sequencing data was used to test for overrepresentation of non-synonymous SNPs in TRD regions. Genetic incompatibilities were tested using the Bateson-Dobzhansky-Muller two-locus model. Results: 62 TRD regions were detected, many in close proximity to the telocentric centromere. TRD regions contained 44.5% more non-synonymous SNPs than randomly selected regions (182 vs. 125.9 17.0, P < 1x10-4). Testing for genetic incompatibilities between TRD regions identified 29 genome-wide significant incompatibilities between TRD regions (P(BF) < 0.05). Pathway overrepresentation analysis of genes in TRD regions showed that DNA methylation, epigenetic regulation of RNA, and meiotic/meiosis regulation pathways were affected independent of the parental origin of the TRD. Paternal BFMI TRD regions showed overrepresentation in the small interfering RNA (siRNA) biogenesis and in the metabolism of lipids and lipoproteins. Maternal B6N TRD regions harbored genes involved in meiotic recombination, cell death, and apoptosis pathways. The analysis of genes in TRD regions suggests the potential distortion of protein-protein interactions accounting for obesity and diabetic retinopathy as a result of disadvantageous combinations of allelic variants in Aass, Pgx6 and Nme8. Conclusions: Since genes in TRD regions showed a significant increase in the number of non-synonymous SNPs, these loci likely co-evolved to ensure protein-protein interaction compatibility, survival and optimal adaptation to the genetic background environment. Genes in these regions provide new targets for investigating genetic adaptation, protein-protein interactions, and determinants of complex traits such as obesity.

scholarly journals An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 460
Author(s):  
Valentina Cipriani ◽  
Nikolas Pontikos ◽  
Gavin Arno ◽  
Panagiotis I. Sergouniotis ◽  
Eva Lenassi ◽  
...  
Keyword(s):  
Molecular Diagnosis ◽  
Protein Interactions ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Sequencing Data ◽  
Variant Prioritization ◽  
Real Patient ◽  
Human Phenotype ◽  
Support Tool ◽  
Variant Frequency

Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients’ sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein–protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients’ HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization.

S100 proteins and their influence on pro-survival pathways in cancer

2004 ◽  
Vol 82 (4) ◽  
pp. 508-515 ◽  
Author(s):  
Ethan D Emberley ◽  
Leigh C Murphy ◽  
Peter H Watson
Keyword(s):  
Cancer Progression ◽  
Protein Interactions ◽  
S100 Proteins ◽  
Amino Acid Residues ◽  
Protein Protein Interactions ◽  
Effector Molecules ◽  
Common Structure ◽  
Survival Pathways ◽  
Response Differentiation ◽  
Apoptosis Pathways

The S100 gene family is composed of at least 20 members that share a common structure defined in part by the Ca2+ binding EF-hand motif. These genes which are expressed in a discriminate fashion in specific cells and tissues, have been described to have either an intracellular or extracellular function, or both. S100 proteins are implicated in the immune response, differentiation, cytoskeleton dynamics, enzyme activity, Ca2+ homeostasis and growth. A potential role for S100 proteins in neoplasia stems from these activities and from the observation that several S100 proteins have altered levels of expression in different stages and types of cancer. While the precise role and importance of S100 proteins in the development and promotion of cancer is poorly understood, it appears that the binding of Ca2+ is essential for exposing amino acid residues that are important in forming protein-protein interactions with effector molecules. The identity of some of these effector molecules has also now begun to emerge, and with this the elucidation of the signaling pathways that are modulated by these proteins. Some of these interactions are consistent with the diverse functions noted above. Others suggest that, many S100s may also promote cancer progression through specific roles in cell survival and apoptosis pathways. This review summarizes these findings and their implications.

Modulating the Bcl-2 Family of Apoptosis Suppressors for Potential Therapeutic Benefit in Cancer

Hematology ◽  
2005 ◽  
Vol 2005 (1) ◽  
pp. 226-230 ◽  
Author(s):  
Gordon C. Shore ◽  
Jean Viallet
Keyword(s):  
Protein Interactions ◽  
Therapeutic Benefit ◽  
Protein Protein Interactions ◽  
Cancer Treatments ◽  
The Family ◽  
Challenges And Opportunities ◽  
Apoptosis Pathways ◽  
Current Therapies ◽  
Cellular Oncogenes ◽  
Potential Therapeutic Benefit

Abstract Members of the BCL-2 family of proteins regulate and execute many cell intrinsic apoptosis pathways, including those arising from dysregulated expression of cellular oncogenes. Since pro-survival members of the family are often strongly elevated in diverse cancers, with the potential to confer resistance to both endogenous cell death stimuli and many cancer treatments, there has been intense interest to develop strategies to therapeutically modulate their activity. Although encouraging genetic and pharmacological preclinical proof of concept has been obtained, the challenge for clinical development will be to devise strategies that address the fact that multiple pro-survival members are typically up-regulated in a given cancer and the family operates primarily through protein-protein interactions. Moreover, since several current therapies themselves are known to stimulate the levels of one or more family members, there will be additional challenges (and opportunities) in exploiting this target in the clinic. In this review, we describe the rationale for targeting the BCL-2 family of apoptosis suppressors in cancer and the progress that has been made in modulating the family by small molecule antagonists.

scholarly journals Whole Genome Sequence of Two Wild-Derived Mus musculus domesticus Inbred Strains, LEWES/EiJ and ZALENDE/EiJ, with Different Diploid Numbers

2016 ◽  
Vol 6 (12) ◽  
pp. 4211-4216 ◽  
Author(s):  
Andrew P Morgan ◽  
John P Didion ◽  
Anthony G Doran ◽  
James M Holt ◽  
Leonard McMillan ◽  
...  
Keyword(s):  
Mus Musculus ◽  
Complex Traits ◽  
Geographic Origin ◽  
Inbred Strains ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Mus Musculus Domesticus ◽  
Sequencing Data ◽  
Ongoing Research ◽  
Burrows Wheeler Transform

Abstract Wild-derived mouse inbred strains are becoming increasingly popular for complex traits analysis, evolutionary studies, and systems genetics. Here, we report the whole-genome sequencing of two wild-derived mouse inbred strains, LEWES/EiJ and ZALENDE/EiJ, of Mus musculus domesticus origin. These two inbred strains were selected based on their geographic origin, karyotype, and use in ongoing research. We generated 14× and 18× coverage sequence, respectively, and discovered over 1.1 million novel variants, most of which are private to one of these strains. This report expands the number of wild-derived inbred genomes in the Mus genus from six to eight. The sequence variation can be accessed via an online query tool; variant calls (VCF format) and alignments (BAM format) are available for download from a dedicated ftp site. Finally, the sequencing data have also been stored in a lossless, compressed, and indexed format using the multi-string Burrows-Wheeler transform. All data can be used without restriction.

scholarly journals Why FRET over genomics?

1999 ◽  
Vol 1 (2) ◽  
pp. 93-99 ◽  
Author(s):  
DINO A. DE ANGELIS
Keyword(s):  
Protein Interactions ◽  
Complex Traits ◽  
High Capacity ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Regulatory Sequences ◽  
Gene Products ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interactions ◽  
Individual Gene

De Angelis, Dino A. Why FRET over genomics? Physiol. Genomics 1: 93–99, 1999.—Genetic information is being uncovered quickly and in vast amounts through the largely automated sequencing of genomes from all kinds of organisms. As this information becomes available, enormous challenges are emerging on three levels: first, functions will have to be assigned to individual gene products; second, factors that influence the expression level of these gene products will have to be identified; and third, allelic variants that act alone or in combination to give rise to complex traits will have to be characterized. Because of the sheer size of genomes, methods that can streamline or automate these processes are highly desirable. Fluorescence is an attractive readout for such high-throughput tasks because of the availability of equipment designed to detect light-emitting compounds with great speed and high capacity. The following is an overview of the achievements and potential of fluorescence resonance energy transfer (FRET) as applied in three areas of genomics: the identification of single-nucleotide polymorphisms, the detection of protein-protein interactions, and the genomewide analysis of regulatory sequences.

scholarly journals Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

2020 ◽  
Author(s):  
Carlos Cruchaga ◽  
Chengran Yang ◽  
Fabiana Geraldo Farias ◽  
Laura Ibanez ◽  
Brooke Sadler ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Complex Traits ◽  
Drug Targets ◽  
Genetic Architecture ◽  
Protein Protein Interactions ◽  
Regulate Gene Expression ◽  
Tissue Specific ◽  
Protein Levels ◽  
Novel Biomarkers ◽  
Health And Disease

Abstract Understanding the tissue-specific genetic architecture of protein levels is instrumental to understand the biology of health and disease. We generated a genomic atlas of protein levels in multiple neurologically relevant tissues (380 brain, 835 cerebrospinal fluid (CSF) and 529 plasma), by profiling thousands of proteins (713 CSF, 931 plasma and 1079 brain) in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. cis-pQTL were more likely to be shared across tissues but trans-pQTL tend to be tissue-specific. Between 44% to 68.2% of the pQTL do not colocalize with expression, splicing, methylation or histone QTLs, indicating that protein levels have a different genetic architecture to those that regulate gene expression. By combining our pQTL with Mendelian Randomization approaches we identified potential novel biomarkers and drug targets for neurodegenerative diseases including Alzheimer disease and frontotemporal dementia. Here we present the first multi-tissue study yielding hundred of novel pQTLs. This data will be instrumental to identify the functional gene from GWAS signals, identify novel biological protein-protein interactions, identify novel potential biomarkers and drug targets for complex traits.

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