Sex differences and hormonal effects on gut microbiota composition in mice.

We previously reported quantitation of gut microbiota in a panel of 89 different inbred strains of mice, and we now examine the question of sex differences in microbiota composition. When the total population of 689 mice was examined together, several taxa exhibited significant differences in abundance between sexes but a larger number of differences were observed at the single strain level, suggesting that sex differences can be obscured by host genetics and environmental factors. We also examined a subset of mice on chow and high fat diets and observed sex-by-diet interactions. We further investigated the sex differences using gonadectomized and hormone treated mice from 3 different inbred strains. Principal coordinate analysis with unweighted UniFrac distances revealed very clear effects of gonadectomy and hormone replacement on microbiota composition in all 3 strains. Moreover, bile acid analyses showed gender-specific differences as well as effects of gonodectomy, providing one possible mechanism mediating sex differences in microbiota composition.

Genetic and environmental control of host-gut microbiota interactions.

Genetics provides a potentially powerful approach to dissect host-gut microbiota interactions. Toward this end, we profiled gut microbiota using 16s rRNA gene sequencing in a panel of 110 diverse inbred strains of mice. This panel has previously been studied for a wide range of metabolic traits and can be used for high-resolution association mapping. Using a SNP-based approach with a linear mixed model, we estimated the heritability of microbiota composition. We conclude that, in a controlled environment, the genetic background accounts for a substantial fraction of abundance of most common microbiota. The mice were previously studied for response to a high-fat, high-sucrose diet, and we hypothesized that the dietary response was determined in part by gut microbiota composition. We tested this using a cross-fostering strategy in which a strain showing a modest response, SWR, was seeded with microbiota from a strain showing a strong response, A×B19. Consistent with a role of microbiota in dietary response, the cross-fostered SWR pups exhibited a significantly increased response in weight gain. To examine specific microbiota contributing to the response, we identified various genera whose abundance correlated with dietary response. Among these, we chose Akkermansia muciniphila, a common anaerobe previously associated with metabolic effects. When administered to strain A×B19 by gavage, the dietary response was significantly blunted for obesity, plasma lipids, and insulin resistance. In an effort to further understand host-microbiota interactions, we mapped loci controlling microbiota composition and prioritized candidate genes. Our publicly available data provide a resource for future studies.

The pH of chemistry assays plays an important role in monoclonal immunoglobulin interferences.

OBJECTIVES: Immunoglobulin paraproteins can interfere with multiple chemistry assays. We want to investigate the mechanisms of immunoglobulin interference. DESIGN AND METHODS: Serum samples containing paraproteins from the index patient and eight additional patients were used to investigate the interference with the creatinine and total protein assays on the Beckman Coulter AU5400/2700 analyzer, and to determine the effects of pH and ionic strength on the precipitation of different immunoglobulins in these patient samples. RESULTS: The paraprotein interference with the creatinine and total protein assays was caused by the precipitation of IgM paraprotein in the index patient's samples under alkaline assay conditions. At extremely high pH (12-13) and extremely low pH (1-2) and low ionic strength, paraprotein formed large aggregates in samples from the index patient but not from other patients. CONCLUSIONS: The pH and ionic strength are the key factors that contribute to protein aggregation and precipitation which interfere with the creatinine and total protein measurements on AU5400/2700. The different amino acid sequence of each monoclonal paraprotein will determine the pH and ionic strength at which the paraprotein will precipitate.

Allele-specific expression and eQTL analysis in mouse adipose tissue.

BACKGROUND: The simplest definition of cis-eQTLs versus trans, refers to genetic variants that affect expression in an allele specific manner, with implications on underlying mechanism. Yet, due to technical limitations of expression microarrays, the vast majority of eQTL studies performed in the last decade used a genomic distance based definition as a surrogate for cis, therefore exploring local rather than cis-eQTLs. RESULTS: In this study we use RNAseq to explore allele specific expression (ASE) in adipose tissue of male and female F1 mice, produced from reciprocal crosses of C57BL/6J and DBA/2J strains. Comparison of the identified cis-eQTLs, to local-eQTLs, that were obtained from adipose tissue expression in two previous population based studies in our laboratory, yields poor overlap between the two mapping approaches, while both local-eQTL studies show highly concordant results. Specifically, local-eQTL studies show ~60% overlap between themselves, while only 15-20% of local-eQTLs are identified as cis by ASE, and less than 50% of ASE genes are recovered in local-eQTL studies. Utilizing recently published ENCODE data, we also find that ASE genes show significant bias for SNPs prevalence in DNase I hypersensitive sites that is ASE direction specific. CONCLUSIONS: We suggest a new approach to analysis of allele specific expression that is more sensitive and accurate than the commonly used fisher or chi-square statistics. Our analysis indicates that technical differences between the cis and local-eQTL approaches, such as differences in genomic background or sex specificity, account for relatively small fraction of the discrepancy. Therefore, we suggest that the differences between two eQTL mapping approaches may facilitate sorting of SNP-eQTL interactions into true cis and trans, and that a considerable portion of local-eQTL may actually represent trans interactions.

The systems genetics resource: a web application to mine global data for complex disease traits.

The Systems Genetics Resource (SGR) (http://systems.genetics.ucla.edu) is a new open-access web application and database that contains genotypes and clinical and intermediate phenotypes from both human and mouse studies. The mouse data include studies using crosses between specific inbred strains and studies using the Hybrid Mouse Diversity Panel. SGR is designed to assist researchers studying genes and pathways contributing to complex disease traits, including obesity, diabetes, atherosclerosis, heart failure, osteoporosis, and lipoprotein metabolism. Over the next few years, we hope to add data relevant to deafness, addiction, hepatic steatosis, toxin responses, and vascular injury. The intermediate phenotypes include expression array data for a variety of tissues and cultured cells, metabolite levels, and protein levels. Pre-computed tables of genetic loci controlling intermediate and clinical phenotypes, as well as phenotype correlations, are accessed via a user-friendly web interface. The web site includes detailed protocols for all of the studies. Data from published studies are freely available; unpublished studies have restricted access during their embargo period.

Limited RNA editing in exons of mouse liver and adipose.

Several studies have investigated RNA-DNA differences (RDD), presumably due to RNA editing, with conflicting results. We report a rigorous analysis of RDD in exonic regions in mice, taking into account critical biases in RNA-Seq analysis. Using deep-sequenced F1 reciprocal inbred mice, we mapped 40 million RNA-Seq reads per liver sample and 180 million reads per adipose sample. We found 7300 apparent hepatic RDDs using a multiple-site mapping procedure, compared with 293 RDD found using a unique-site mapping procedure. After filtering for repeat sequence, splice junction proximity, undirectional strand, and extremity read bias, 63 RDD remained. In adipose tissue unique-site mapping identified 1667 RDD, and after applying the same four filters, 188 RDDs remained. In both tissues, the filtering procedure increased the proportion of canonical (A-to-I and C-to-U) editing events. The genomic DNA of 12 RDD sites among the potential 63 hepatic RDD was tested by Sanger sequencing, three of which proved to be due to unreferenced SNPs. We validated seven liver RDD with Sequenom technology, including two noncanonical, Gm5424 C-to-I(G) and Pisd I(G)-to-A RDD. Differences in diet, sex, or genetic background had very modest effects on RDD occurrence. Only a small number of apparent RDD sites overlapped between liver and adipose, indicating a high degree of tissue specificity. Our findings underscore the importance of properly filtering for bias in RNA-Seq investigations, including the necessity of confirming the DNA sequence to eliminate unreferenced SNPs. Based on our results, we conclude that RNA editing is likely limited to hundreds of events in exonic RNA in liver and adipose.

Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice.

Obesity is a highly heritable disease driven by complex interactions between genetic and environmental factors. Human genome-wide association studies (GWAS) have identified a number of loci contributing to obesity; however, a major limitation of these studies is the inability to assess environmental interactions common to obesity. Using a systems genetics approach, we measured obesity traits, global gene expression, and gut microbiota composition in response to a high-fat/high-sucrose (HF/HS) diet of more than 100 inbred strains of mice. Here we show that HF/HS feeding promotes robust, strain-specific changes in obesity that are not accounted for by food intake and provide evidence for a genetically determined set point for obesity. GWAS analysis identified 11 genome-wide significant loci associated with obesity traits, several of which overlap with loci identified in human studies. We also show strong relationships between genotype and gut microbiota plasticity during HF/HS feeding and identify gut microbial phylotypes associated with obesity.

Applying perceptual and adaptive learning techniques for teaching introductory histopathology.

BACKGROUND: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. METHODS: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses) that appear during a learning session based on each learner's accuracy and response time (RT). We developed a perceptual and adaptive learning module (PALM) that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a "Score" that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. RESULTS: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1(st)-year students, but not significantly so for 2(nd)-year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1(st) and 2(nd) year students suggesting good retention of pattern recognition. Student evaluations were very favorable. CONCLUSION: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students.

Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits.

We have developed an association-based approach using classical inbred strains of mice in which we correct for population structure, which is very extensive in mice, using an efficient mixed-model algorithm. Our approach includes inbred parental strains as well as recombinant inbred strains in order to capture loci with effect sizes typical of complex traits in mice (in the range of 5% of total trait variance). Over the last few years, we have typed the hybrid mouse diversity panel (HMDP) strains for a variety of clinical traits as well as intermediate phenotypes and have shown that the HMDP has sufficient power to map genes for highly complex traits with resolution that is in most cases less than a megabase. In this essay, we review our experience with the HMDP, describe various ongoing projects, and discuss how the HMDP may fit into the larger picture of common diseases and different approaches.

ABCC6 localizes to the mitochondria-associated membrane.

RATIONALE: Mutations of the orphan transporter ABCC6 (ATP-binding cassette, subfamily C, member 6) cause the connective tissue disorder pseudoxanthoma elasticum. ABCC6 was thought to be located on the plasma membrane of liver and kidney cells. OBJECTIVE: Mouse systems genetics and bioinformatics suggested that ABCC6 deficiency affects mitochondrial gene expression. We therefore tested whether ABCC6 associates with mitochondria. METHODS AND RESULTS: We found ABCC6 in crude mitochondrial fractions and subsequently pinpointed its localization to the purified mitochondria-associated membrane fraction. Cell-surface biotinylation in hepatocytes confirmed that ABCC6 is intracellular. Abcc6-knockout mice demonstrated mitochondrial abnormalities and decreased respiration reserve capacity. CONCLUSIONS: Our finding that ABCC6 localizes to the mitochondria-associated membrane has implications for its mechanism of action in normal and diseased states.

Abcc6 deficiency causes increased infarct size and apoptosis in a mouse cardiac ischemia-reperfusion model.

OBJECTIVE: ABCC6 genetic deficiency underlies pseudoxanthoma elasticum (PXE) in humans, characterized by ectopic calcification, and early cardiac disease. The spectrum of PXE has been noted in Abcc6-deficient mice, including dystrophic cardiac calcification. We tested the role of Abcc6 in response to cardiac ischemia-reperfusion (I/R) injury. METHODS AND RESULTS: To determine the role of Abcc6 in cardioprotection, we induced ischemic injury in mice in vivo by occluding the left anterior descending artery (30 minutes) followed by reperfusion (48 hours). Infarct size was increased in Abcc6-deficient mice compared with wild-type controls. Additionally, an Abcc6 transgene significantly reduced infarct size on the background of a naturally occurring Abcc6 deficiency. There were no differences in cardiac calcification following I/R, but increased cardiac apoptosis was noted in Abcc6-deficient mice. Previous studies have implicated the bone morphogenetic protein (BMP) signaling pathway in directing calcification, and here we showed that the BMP responsive transcription factors pSmad1/5/8 were increased in hearts of Abcc6 mice. Consistent with this finding, BMP4 and BMP9 were increased and activin receptor-like kinase-2 and endoglin were downregulated in cardiac extracts from Abcc6-deficient mice versus controls. CONCLUSIONS: These data identify Abcc6 as a novel modulator of cardiac myocyte survival after I/R. This cardioprotective mechanism may involve inhibition of the BMP signaling pathway, which modulates apoptosis.

Mouse genome-wide association and systems genetics identify Asxl2 as a regulator of bone mineral density and osteoclastogenesis.

Significant advances have been made in the discovery of genes affecting bone mineral density (BMD); however, our understanding of its genetic basis remains incomplete. In the current study, genome-wide association (GWA) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel (HMDP) to identify and functionally characterize novel BMD genes. In the HMDP, a GWA of total body, spinal, and femoral BMD revealed four significant associations (-log10P>5.39) affecting at least one BMD trait on chromosomes (Chrs.) 7, 11, 12, and 17. The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes. This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous (NS) SNPs. This analysis revealed that the most significant BMD SNP on Chr. 12 was a NS SNP in the additional sex combs like-2 (Asxl2) gene that was predicted to be functional. The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD. To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains. Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells. In bone, osteoclasts are bone-resorbing cells of myeloid origin, suggesting that Asxl2 may play a role in osteoclast differentiation. In agreement, the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts. This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits.

Genes and pathways contributing to obesity: a systems biology view.

Systems biology is an expanding discipline that utilizes high-throughput data from multiple sources to develop models of biologic processes. This chapter reviews basic systems biology concepts and how they inform the traditional search for genes and pathways involved in disease pathogenesis. Systems approaches yield networks representing interactions and relationships among elements, with subnetworks or modules being functional groupings of these. For genetics of common disease-related traits such as obesity, integrative genetics is representative of a top-down systems biology approach, bringing together high-throughput genotyping, global tissue mRNA expression data, and phenotypic data. Coexpression network analysis yields network models that allow identification of groups of coexpressed genes that can be related to particular traits. Combining this with genetics has shown that genetic variation at disease associated loci act though influencing such modules. Constraint-based reconstruction of metabolic networks represents a bottom-up systems biology approach that can be utilized to model the effects of genetic variation. The progressively increasing ability to generate high-throughput data of various types will promote continued application of systems approaches to better understand the processes by which genetic variation influences disease-related traits.

A high-resolution association mapping panel for the dissection of complex traits in mice.

Systems genetics relies on common genetic variants to elucidate biologic networks contributing to complex disease-related phenotypes. Mice are ideal model organisms for such approaches, but linkage analysis has been only modestly successful due to low mapping resolution. Association analysis in mice has the potential of much better resolution, but it is confounded by population structure and inadequate power to map traits that explain less than 10% of the variance, typical of mouse quantitative trait loci (QTL). We report a novel strategy for association mapping that combines classic inbred strains for mapping resolution and recombinant inbred strains for mapping power. Using a mixed model algorithm to correct for population structure, we validate the approach by mapping over 2500 cis-expression QTL with a resolution an order of magnitude narrower than traditional QTL analysis. We also report the fine mapping of metabolic traits such as plasma lipids. This resource, termed the Hybrid Mouse Diversity Panel, makes possible the integration of multiple data sets and should prove useful for systems-based approaches to complex traits and studies of gene-by-environment interactions.

Upstream transcription factor 1 influences plasma lipid and metabolic traits in mice.

Upstream transcription factor 1 (USF1) has been associated with familial combined hyperlipidemia, the metabolic syndrome, and related conditions, but the mechanisms involved are unknown. In this study, we report validation of Usf1 as a causal gene of cholesterol homeostasis, insulin sensitivity and body composition in mouse models using several complementary approaches and identify associated pathways and gene expression network modules. Over-expression of human USF1 in both transgenic mice and mice with transient liver-specific over-expression influenced metabolic trait phenotypes, including obesity, total cholesterol level, LDL/VLDL cholesterol and glucose/insulin ratio. Additional analyses of trait and hepatic gene expression data from an F2 population derived from C57BL/6J and C3H/HeJ strains in which there is a naturally occurring variation in Usf1 expression supported a causal role for Usf1 for relevant metabolic traits. Gene network and pathway analyses of the liver gene expression signatures in the F2 population and the hepatic over-expression model suggested the involvement of Usf1 in immune responses and metabolism, including an Igfbp2-centered module. In all three mouse model settings, notable sex specificity was observed, consistent with human studies showing differences in association with USF1 gene polymorphisms between sexes.

Disruption of the aortic elastic lamina and medial calcification share genetic determinants in mice.

BACKGROUND: Disruption of the elastic lamina, as an early indicator of aneurysm formation, and vascular calcification frequently occur together in atherosclerotic lesions of humans. METHODS AND RESULTS: We now report evidence of shared genetic basis for disruption of the elastic lamina (medial disruption) and medial calcification in an F(2) mouse intercross between C57BL/6J and C3H/HeJ on a hyperlipidemic apolipoprotein E (ApoE(-/-)) null BACKGROUND: gene, known to mediate myocardial calcification. Using transgenic complementation, we show that Abcc6 also contributes to aortic medial calcification. CONCLUSIONS: Our data indicate that calcification, though possibly contributory, does not always lead to medial disruption and that in addition to aneurysm formation, medial disruption may be the precursor to calcification.

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

A systems-based framework for understanding complex metabolic and cardiovascular disorders.

Common forms of metabolic and cardiovascular diseases involve the interplay of numerous genes as well as important environmental factors. Traditional biochemical and genetic approaches generally attempt to dissect these diseases one gene at a time, for example, by analysis of Mendelian forms or genetically engineered experimental organisms. But, it is also important to understand how the genes interact with each other and the environment, and how these interactions change in disease states. Technological advances, such as the development of expression arrays that allow quantification of all transcript levels in a cell or tissue, have made it feasible to globally monitor molecular phenotypes that underlie disease states. By applying statistical methods, relationships between DNA variation, gene expression patterns, and diseases can be modeled.

Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks.

We previously used high-density expression arrays to interrogate a genetic cross between strains C3H/HeJ and C57BL/6J and observed thousands of differences in gene expression between sexes. We now report analyses of the molecular basis of these sex differences and of the effects of sex on gene expression networks. We analyzed liver gene expression of hormone-treated gonadectomized mice as well as XX male and XY female mice. Differences in gene expression resulted in large part from acute effects of gonadal hormones acting in adulthood, and the effects of sex chromosomes, apart from hormones, were modest. We also determined whether there are sex differences in the organization of gene expression networks in adipose, liver, skeletal muscle, and brain tissue. Although coexpression networks of highly correlated genes were largely conserved between sexes, some exhibited striking sex dependence. We observed strong body fat and lipid correlations with sex-specific modules in adipose and liver as well as a sexually dimorphic network enriched for genes affected by gonadal hormones. Finally, our analyses identified chromosomal loci regulating sexually dimorphic networks. This study indicates that gonadal hormones play a strong role in sex differences in gene expression. In addition, it results in the identification of sex-specific gene coexpression networks related to genetic and metabolic traits.