Uncovering genes and regulatory pathways related to urinary albumin excretion

Belief propagation in genotype-phenotype networks

Graphical models have proven to be a valuable tool for connecting genotypes and phenotypes. Structural learning of phenotype-genotype networks has received considerable attention in the post-genome era. In recent years, a dozen different methods have emerged for network inference, which leverage natural variation that arises in certain genetic populations. The structure of the network itself can be used to form hypotheses based on the inferred direct and indirect network relationships, but represents a premature endpoint to the graphical analyses. In this work, we extend this endpoint. We examine the unexplored problem of perturbing a given network structure, and quantifying the system-wide effects on the network in a node-wise manner. The perturbation is achieved through the setting of values of phenotype node(s), which may reflect an inhibition or activation, and propagating this information through the entire network. We leverage belief propagation methods in Conditional Gaussian Bayesian Networks (CG-BNs), in order to absorb and propagate phenotypic evidence through the network. We show that the modeling assumptions adopted for genotype-phenotype networks represent an important sub-class of CG-BNs, which possess properties that ensure exact inference in the propagation scheme. The system-wide effects of the perturbation are quantified in a node-wise manner through the comparison of perturbed and unperturbed marginal distributions using a symmetric Kullback-Leibler divergence. Applications to kidney and skin cancer expression quantitative trait loci (eQTL) data from different mus musculus populations are presented. System-wide effects in the network were predicted and visualized across a spectrum of evidence. Sub-pathways and regions of the network responded in concert, suggesting co-regulation and coordination throughout the network in response to phenotypic changes. We demonstrate how these predicted system-wide effects can be examined in connection with estimated class probabilities for covariates of interest, e.g. cancer status. Despite the uncertainty in the network structure, we demonstrate the system-wide predictions are stable across an ensemble of highly likely networks. A software package, geneNetBP, which implements our approach, was developed in the R programming language.

Survival analysis and microarray profiling identify Cd40 as a candidate for the Salmonella susceptibility locus, Ity5

The outcome of infection with Salmonella Typhimurium in mouse models of human typhoid fever is dependent upon a coordinated complex immune response. A panel of recombinant congenic strains (RCS) derived from reciprocal backcross of A/J and C57BL/6J mice was screened for their susceptibility to Salmonella infection and two susceptibility loci, Ity4 (Immunity to Typhimurium locus 4) and Ity5, were identified. We validated Ity5 in a genetic environment free of the impact of Ity4 using a cross between A/J and 129S6. Using a time-series analysis of genome-wide transcription during infection, comparing A/J with AcB60 mice having a C57BL/6J-derived Ity5 interval, we have identified the differential expression of the positional candidate gene Cd40, Cd40-associated signaling pathways, and the differential expression of numerous genes expressed in neutrophils. CD40 is known to coordinate T cell-dependent B-cell responses and myeloid cell activation. In fact, CD40 signaling is altered in A/J mice as seen by impaired IgM upregulation during infection, decreased Ig class switching, neutropenia, reduced granulocyte recruitment in response to infection and inflammation, and decreased ERK1/2 activity. These results suggest that altered CD40 signaling and granulocyte recruitment in response to infection are responsible for the Ity5-associated Salmonella susceptibility of A/J mice.

A roadmap for the genetic analysis of renal aging

Several studies show evidence for the genetic basis of renal disease, which renders some individuals more prone than others to accelerated renal aging. Studying the genetics of renal aging can help us to identify genes involved in this process and to unravel the underlying pathways. First, this opinion article will give an overview of the phenotypes that can be observed in age-related kidney disease. Accurate phenotyping is essential in performing genetic analysis. For kidney aging, this could include both functional and structural changes. Subsequently, this article reviews the studies that report on candidate genes associated with renal aging in humans and mice. Several loci or candidate genes have been found associated with kidney disease, but identification of the specific genetic variants involved has proven to be difficult. CUBN, UMOD, and SHROOM3 were identified by human GWAS as being associated with albuminuria, kidney function, and chronic kidney disease (CKD). These are promising examples of genes that could be involved in renal aging, and were further mechanistically evaluated in animal models. Eventually, we will provide approaches for performing genetic analysis. We should leverage the power of mouse models, as testing in humans is limited. Mouse and other animal models can be used to explain the underlying biological mechanisms of genes and loci identified by human GWAS. Furthermore, mouse models can be used to identify genetic variants associated with age-associated histological changes, of which Far2, Wisp2, and Esrrg are examples. A new outbred mouse population with high genetic diversity will facilitate the identification of genes associated with renal aging by enabling high-resolution genetic mapping while also allowing the control of environmental factors, and by enabling access to renal tissues at specific time points for histology, proteomics, and gene expression.

Genetic Background is a Key Determinant of Glomerular Extracellular Matrix Composition and Organization

Glomerular disease often features altered histologic patterns of extracellular matrix (ECM). Despite this, the potential complexities of the glomerular ECM in both health and disease are poorly understood. To explore whether genetic background and sex determine glomerular ECM composition, we investigated two mouse strains, FVB and B6, using RNA microarrays of isolated glomeruli combined with proteomic glomerular ECM analyses. These studies, undertaken in healthy young adult animals, revealed unique strain- and sex-dependent glomerular ECM signatures, which correlated with variations in levels of albuminuria and known predisposition to progressive nephropathy. Among the variation, we observed changes in netrin 4, fibroblast growth factor 2, tenascin C, collagen 1, meprin 1-α, and meprin 1-β. Differences in protein abundance were validated by quantitative immunohistochemistry and Western blot analysis, and the collective differences were not explained by mutations in known ECM or glomerular disease genes. Within the distinct signatures, we discovered a core set of structural ECM proteins that form multiple protein-protein interactions and are conserved from mouse to man. Furthermore, we found striking ultrastructural changes in glomerular basement membranes in FVB mice. Pathway analysis of merged transcriptomic and proteomic datasets identified potential ECM regulatory pathways involving inhibition of matrix metalloproteases, liver X receptor/retinoid X receptor, nuclear factor erythroid 2-related factor 2, notch, and cyclin-dependent kinase 5. These pathways may therefore alter ECM and confer susceptibility to disease.

Genetic regulation of Zfp30, CXCL1, and neutrophilic inflammation in murine lung

Allergic asthma is a complex disease characterized in part by granulocytic inflammation of the airways. In addition to eosinophils, neutrophils (PMN) are also present, particularly in cases of severe asthma. We sought to identify the genetic determinants of neutrophilic inflammation in a mouse model of house dust mite (HDM)-induced asthma. We applied an HDM model of allergic asthma to the eight founder strains of the Collaborative Cross (CC) and 151 incipient lines of the CC (preCC). Lung lavage fluid was analyzed for PMN count and the concentration of CXCL1, a hallmark PMN chemokine. PMN and CXCL1 were strongly correlated in preCC mice. We used quantitative trait locus (QTL) mapping to identify three variants affecting PMN, one of which colocalized with a QTL for CXCL1 on chromosome (Chr) 7. We used lung eQTL data to implicate a variant in the gene Zfp30 in the CXCL1/PMN response. This genetic variant regulates both CXCL1 and PMN by altering Zfp30 expression, and we model the relationships between the QTL and these three endophenotypes. We show that Zfp30 is expressed in airway epithelia in the normal mouse lung and that altering Zfp30 expression in vitro affects CXCL1 responses to an immune stimulus. Our results provide strong evidence that Zfp30 is a novel regulator of neutrophilic airway inflammation.

Dissection of complex gene expression using the combined analysis of pleiotropy and epistasis

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.

A major X-linked locus affects kidney function in mice

Chronic kidney disease is a common disease with increasing prevalence in the western population. One common reason for chronic kidney failure is diabetic nephropathy. Diabetic nephropathy and hyperglycemia are characteristics of the mouse inbred strain KK/HlJ, which is predominantly used as a model for metabolic syndrome due to its inherited glucose intolerance and insulin resistance. We used KK/HlJ, an albuminuria-sensitive strain, and C57BL/6J, an albuminuria-resistant strain, to perform a quantitative trait locus (QTL) cross to identify the genetic basis for chronic kidney failure. Albumin-creatinine ratio (ACR) was measured in 130 F2 male offspring. One significant QTL was identified on chromosome (Chr) X and four suggestive QTL were found on Chrs 6, 7, 12, and 13. Narrowing of the QTL region was focused on the X-linked QTL and performed by incorporating genotype and expression analyses for genes located in the region. From the 485 genes identified in the X-linked QTL region, a few candidate genes were identified using a combination of bioinformatic evidence based on genomic comparison of the parental strains and known function in urine homeostasis. Finally, this study demonstrates the significance of the X chromosome in the genetic determination of albuminuria.

The Mammalian Phenotype Ontology as a unifying standard for experimental and high-throughput phenotyping data

The Mammalian Phenotype Ontology (MP) is a structured vocabulary for describing mammalian phenotypes and serves as a critical tool for efficient annotation and comprehensive retrieval of phenotype data. Importantly, the ontology contains broad and specific terms, facilitating annotation of data from initial observations or screens and detailed data from subsequent experimental research. Using the ontology structure, data are retrieved inclusively, i.e., data annotated to chosen terms and to terms subordinate in the hierarchy. Thus, searching for "abnormal craniofacial morphology" also returns annotations to "megacephaly" and "microcephaly," more specific terms in the hierarchy path. The development and refinement of the MP is ongoing, with new terms and modifications to its organization undergoing continuous assessment as users and expert reviewers propose expansions and revisions. A wealth of phenotype data on mouse mutations and variants annotated to the MP already exists in the Mouse Genome Informatics database. These data, along with data curated to the MP by many mouse mutagenesis programs and mouse repositories, provide a platform for comparative analyses and correlative discoveries. The MP provides a standard underpinning to mouse phenotype descriptions for existing and future experimental and large-scale phenotyping projects. In this review we describe the MP as it presently exists, its application to phenotype annotations, the relationship of the MP to other ontologies, and the integration of the MP within large-scale phenotyping projects. Finally we discuss future application of the MP in providing standard descriptors of the phenotype pipeline test results from the International Mouse Phenotype Consortium projects.

Identification of the nephropathy-susceptibility locus HIVAN4

HIVAN1, HIVAN2, and HIVAN3 are nephropathy-susceptibility loci previously identified in the HIV-1 transgenic mouse, a model of collapsing glomerulopathy. The HIVAN1 and HIVAN2 loci modulate expression of Nphs2, which encodes podocin and several other podocyte-expressed genes. To identify additional loci predisposing to nephropathy, we performed a genome-wide scan in 165 backcross mice generated between the nephropathy-sensitive HIV-1-transgenic FVB/NJ (TgFVB) strain and the resistant Balb/cJ (BALB) strain. We identified a major susceptibility locus (HIVAN4) on chromosome 6 G3-F3, with BALB alleles conferring a twofold reduction in severity (peak LOD score = 4.0). Similar to HIVAN1 and HIVAN2, HIVAN4 modulated expression of Nphs2, indicating a common pathway underlying these loci. We independently confirmed the HIVAN4 locus in a sister TgFVB colony that experienced a dramatic loss of nephropathy subsequent to a breeding bottleneck. In this low-penetrance line, 3% of the genome was admixed with BALB alleles, suggesting a remote contamination event. The admixture localized to discrete segments on chromosome 2 and at the HIVAN4 locus. HIVAN4 candidate genes include killer lectin-like receptor genes as well as A2m and Ptpro, whose gene products are enriched in the glomerulus and interact with HIV-1 proteins. In summary, these data identify HIVAN4 as a major quantitative trait locus for nephropathy and a transregulator of Nphs2. Furthermore, similar selective breeding strategies may help identify further susceptibility loci.

Genetic loci that affect aristolochic acid-induced nephrotoxicity in the mouse

Aristolochic acids (AA) are plant-derived nephrotoxins and carcinogens found in traditional medicines and herbal remedies. AA causes aristolochic acid nephropathy (AAN) and is a suspected environmental agent in Balkan endemic nephropathy (BEN) and its associated upper urothelial cancer. Approximately 5-10% of individuals exposed to AA develop renal insufficiency and/or cancer; thus a genetic predisposition to AA sensitivity has been proposed. The mouse is an established animal model of AAN, and inbred murine strains vary in AA sensitivity, confirming the genetic predisposition. We mapped quantitative trait loci (QTL) correlated with proximal tubule dysfunction after exposure to AA in an F2 population of mice, derived from breeding an AA-resistant strain (C57BL/6J) and an AA-sensitive strain (DBA/2J). A single main QTL was identified on chromosome 4 (Aanq1); three other interacting QTLs, (Aanq2-4) also were detected. The Aanq1 region was also detected in untreated mice, raising the possibility that preexisting differences in proximal tubule function may affect the severity of AA-elicited toxicity. This study lays the groundwork for identifying the genetic pathways contributing to AA sensitivity in the mouse and will further our understanding of human susceptibility to AA found widely in traditional medicines.

A Bayesian framework for inference of the genotype-phenotype map for segregating populations

Complex genetic interactions lie at the foundation of many diseases. Understanding the nature of these interactions is critical to developing rational intervention strategies. In mammalian systems hypothesis testing in vivo is expensive, time consuming, and often restricted to a few physiological endpoints. Thus, computational methods that generate causal hypotheses can help to prioritize targets for experimental intervention. We propose a Bayesian statistical method to infer networks of causal relationships among genotypes and phenotypes using expression quantitative trait loci (eQTL) data from genetically randomized populations. Causal relationships between network variables are described with hierarchical regression models. Prior distributions on the network structure enforce graph sparsity and have the potential to encode prior biological knowledge about the network. An efficient Monte Carlo method is used to search across the model space and sample highly probable networks. The result is an ensemble of networks that provide a measure of confidence in the estimated network topology. These networks can be used to make predictions of system-wide response to perturbations. We applied our method to kidney gene expression data from an MRL/MpJ × SM/J intercross population and predicted a previously uncharacterized feedback loop in the local renin-angiotensin system.