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Albuminuria is an important marker of nephropathy that increases the risk of progressive renal and chronic cardiovascular diseases. The genetic basis of kidney disease is well-established in humans and rodent models, but the causal genes remain to be identified. We applied several genetic strategies to map and refine genetic loci affecting albuminuria in mice and translated the findings to human kidney disease. First, we measured albuminuria in mice from 33 inbred strains, used the data for haplotype association mapping (HAM), and detected 10 genomic regions associated with albuminuria. Second, we performed eight F2 intercrosses between genetically diverse strains to identify six loci underlying albuminuria, each of which was concordant to kidney disease loci in humans. Third, we used the Oak Ridge National Laboratory incipient Collaborative Cross subpopulation to detect an additional novel quantitative trait loci (QTL) underlying albuminuria. We also performed a ninth intercross, between genetically similar strains, that substantially narrowed an albuminuria QTL on Chromosome 17 to a region containing four known genes. Finally, we measured renal gene expression in inbred mice to detect pathways highly correlated with albuminuria. Expression analysis also identified Glcci1, a gene known to affect podocyte structure and function in zebrafish, as a strong candidate gene for the albuminuria QTL on Chromosome 6. Overall, these findings greatly enhance our understanding of the genetic basis of albuminuria in mice and may guide future studies into the genetic basis of kidney disease in humans. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's Disease (AD) is a complex multifaceted disease that is mediated by multiple unidentified genetic and environmental factors. One such factor that has been implicated, but is yet to be fully understood, is the role of transposable elements (TE) in AD. Here, we explore the role and scope of TEs using genetically diverse population of mice carrying familial AD mutations (AD‐BXDs) and their non‐transgenic littermates (Ntg‐BXDs). Method: We quantified the expression levels of all known transposable elements (TEs) in the hippocampus using TEtranscripts (v.0.36) in 28 different mouse strains carrying familial AD mutations (AD‐BXDs) and in their non‐transgenic littermates (Ntg‐BXDs) at 6 and 14 months of age. Differentially abundant TEs as a function of genotype (Ntg vs 5XFAD) and age (6m vs 14m) were assessed using DESeq2. We assessed heritability of TE expression for TEs that were identified as significantly differential expressed from the above comparisons, to obtain an estimate of heritable variation in a genetically diverse population. Lastly, we correlated TE expression to short‐term and long‐term memory using contextual fear conditioning. Result: Differential expression analysis revealed higher number of differentially expressed TEs in the 14‐month age group between AD‐BxD and their Ntg littermate controls as compared to the 6‐month age group. Majority of these differentially expressed TE are upregulated and belong to the category of LTR transposons. Importantly, we observe high average heritable variation (h26m‐Ntg=0.65, h26m‐5xFAD=0.58, h214m‐Ntg =0.44, h214m‐5xFAD=0.79) across our genetically diverse AD‐BXD panel. Lastly, we find significant negative correlations among contextual fear conditioning phenotypes and TE expression in the 5XFAD genotype group in both our 6m and 14m age groups. Conclusion: Our findings reveal a role of transposable elements, particularly LTR transposons across our genetically diverse population of mice, specifically in our 14m 5xFAD cohort. This finding is in line with previously published studies of the prevalence of LTR transposons in tauopathy models but is the first to assess the abundance and report on the extent of heritable variation of TE along with their correlation to cognitive behavior in a genetically diverse but translationally relevant mouse population of human AD. [ABSTRACT FROM AUTHOR]

Abstract: The identification of genes that modify pathological ocular phenotypes in mouse models may improve our understanding of disease mechanisms and lead to new treatment strategies. Here, we identify modifier loci affecting photoreceptor cell loss in homozygous Mfrp rd6 mice, which exhibit a slowly progressive photoreceptor degeneration. A cohort of 63 F2 homozygous Mfrp rd6 mice from a (B6.C3Ga-Mfrp rd6 /J × CAST/EiJ) F1 intercross exhibited a variable number of cell bodies in the retinal outer nuclear layer at 20 weeks of age. Mice were genotyped with a panel of single nucleotide polymorphism markers, and genotypes were correlated with phenotype by quantitative trait locus (QTL) analysis to map modifier loci. A genome-wide scan revealed a statistically significant, protective candidate locus on CAST/EiJ Chromosome 1 and suggestive modifier loci on Chromosomes 6 and 11. Multiple regression analysis of a three-QTL model indicated that the modifier loci on Chromosomes 1 and 6 together account for 26% of the observed phenotypic variation, while the modifier locus on Chromosome 11 explains only an additional 4%. Our findings indicate that the severity of the Mfrp rd6 retinal degenerative phenotype in mice depends on the strain genetic background and that a significant modifier locus on CAST/EiJ Chromosome 1 protects against Mfrp rd6 -associated photoreceptor loss. [Copyright &y& Elsevier]

Histamine plays pivotal role in normal physiology and dysregulated production of histamine or signaling through histamine receptors (HRH) can promote pathology. Previously, we showed that Bordetella pertussis or pertussis toxin can induce histamine sensitization in laboratory inbred mice and is genetically controlled by Hrh1/HRH1. HRH1 allotypes differ at three amino acid residues with P 263 -V 313 -L 331 and L 263 -M 313 -S 331 , imparting sensitization and resistance respectively. Unexpectedly, we found several wild-derived inbred strains that carry the resistant HRH1 allotype (L 263 -M 313 -S 331 ) but exhibit histamine sensitization. This suggests the existence of a locus modifying pertussis-dependent histamine sensitization. Congenic mapping identified the location of this modifier locus on mouse chromosome 6 within a functional linkage disequilibrium domain encoding multiple loci controlling sensitization to histamine. We utilized interval-specific single-nucleotide polymorphism (SNP) based association testing across laboratory and wild-derived inbred mouse strains and functional prioritization analyses to identify candidate genes for this modifier locus. Atg7, Plxnd1, Tmcc1, Mkrn2, Il17re, Pparg, Lhfpl4, Vgll4, Rho and Syn2 are candidate genes within this modifier locus, which we named Bphse, enhancer of Bordetella pertussis induced histamine sensitization. Taken together, these results identify, using the evolutionarily significant diversity of wild-derived inbred mice, additional genetic mechanisms controlling histamine sensitization., (© 2023. The Author(s).)

Brown adipose tissue (BAT) regulates metabolic physiology. However, nearly all mechanistic studies of BAT protein function occur in a single inbred mouse strain, which has limited the understanding of generalizable mechanisms of BAT regulation over physiology. Here, we perform deep quantitative proteomics of BAT across a cohort of 163 genetically defined diversity outbred mice, a model that parallels the genetic and phenotypic variation found in humans. We leverage this diversity to define the functional architecture of the outbred BAT proteome, comprising 10,479 proteins. We assign co-operative functions to 2,578 proteins, enabling systematic discovery of regulators of BAT. We also identify 638 proteins that correlate with protection from, or sensitivity to, at least one parameter of metabolic disease. We use these findings to uncover SFXN5, LETMD1, and ATP1A2 as modulators of BAT thermogenesis or adiposity, and provide OPABAT as a resource for understanding the conserved mechanisms of BAT regulation over metabolic physiology., Competing Interests: Declaration of interests F.E.M. and N.V.B. and are currently employees of Calico Life Sciences, LLC. T.B. is currently an employee of Roche Diagnostics. E.T.C. is a founder, equity holder, and consultant for Matchpoint Therapeutics and Aevum Therapeutics. B.M.S. is a founder, equity holder, and consultant for Aevum Therapeutics., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Epistasis, or gene-gene interaction, contributes substantially to trait variation in organisms ranging from yeast to humans, and modeling epistasis directly is critical to understanding the genotype-phenotype map. However, inference of genetic interactions is challenging compared to inference of individual allele effects due to low statistical power. Furthermore, genetic interactions can appear inconsistent across different quantitative traits, presenting a challenge for the interpretation of detected interactions. Here we present a method called the Combined Analysis of Pleiotropy and Epistasis (CAPE) that combines information across multiple quantitative traits to infer directed epistatic interactions. By combining information across multiple traits, CAPE not only increases power to detect genetic interactions but also interprets these interactions across traits to identify a single interaction that is consistent across all observed data. This method generates informative, interpretable interaction networks that explain how variants interact with each other to influence groups of related traits. This method could potentially be used to link genetic variants to gene expression, physiological endophenotypes, and higher-level disease traits.

Global transcript expression experiments are commonly used to investigate the biological processes that underlie complex traits. These studies can exhibit complex patterns of pleiotropy when trans-acting genetic factors influence overlapping sets of multiple transcripts. Dissecting these patterns into biological modules with distinct genetic etiology can provide models of how genetic variants affect specific processes that contribute to a trait. Here we identify transcript modules associated with pleiotropic genetic factors and apply genetic interaction analysis to disentangle the regulatory architecture in a mouse intercross study of kidney function. The method, called the combined analysis of pleiotropy and epistasis (CAPE), has been previously used to model genetic networks for multiple physiological traits. It simultaneously models multiple phenotypes to identify direct genetic influences as well as influences mediated through genetic interactions. We first identify candidate trans expression quantitative trait loci (eQTL) and the transcripts potentially affected. We then clustered the transcripts into modules of co-expressed genes, from which we compute summary module phenotypes. Finally, we applied CAPE to map the network of interacting module QTL (modQTL) affecting the gene modules. The resulting network mapped how multiple modQTL both directly and indirectly affect modules associated with metabolic functions and biosynthetic processes. This work demonstrates how the integration of pleiotropic signals in gene expression data can be used to infer a complex hypothesis of how multiple loci interact to co-regulate transcription programs, thereby providing additional constraints to prioritize validation experiments.

Albuminuria is an important marker of nephropathy that increases the risk of progressive renal and chronic cardiovascular diseases. The genetic basis of kidney disease is well-established in humans and rodent models, but the causal genes remain to be identified. We applied several genetic strategies to map and refine genetic loci affecting albuminuria in mice and translated the findings to human kidney disease. First, we measured albuminuria in mice from 33 inbred strains, used the data for haplotype association mapping (HAM), and detected 10 genomic regions associated with albuminuria. Second, we performed eight F(2) intercrosses between genetically diverse strains to identify six loci underlying albuminuria, each of which was concordant to kidney disease loci in humans. Third, we used the Oak Ridge National Laboratory incipient Collaborative Cross subpopulation to detect an additional novel quantitative trait loci (QTL) underlying albuminuria. We also performed a ninth intercross, between genetically similar strains, that substantially narrowed an albuminuria QTL on Chromosome 17 to a region containing four known genes. Finally, we measured renal gene expression in inbred mice to detect pathways highly correlated with albuminuria. Expression analysis also identified Glcci1, a gene known to affect podocyte structure and function in zebrafish, as a strong candidate gene for the albuminuria QTL on Chromosome 6. Overall, these findings greatly enhance our understanding of the genetic basis of albuminuria in mice and may guide future studies into the genetic basis of kidney disease in humans.