Identification of genetic drivers of plasma lipoprotein size in the Diversity Outbred mouse population

Despite great progress in understanding lipoprotein physiology, there is still much to be learned about the genetic drivers of lipoprotein abundance, composition, and function. We used ion mobility spectrometry to survey 16 plasma lipoprotein subfractions in 500 Diversity Outbred mice maintained on a Western-style diet. We identified 21 quantitative trait loci (QTL) affecting lipoprotein abundance. To refine the QTL and link them to disease risk in humans, we asked if the human homologs of genes located at each QTL were associated with lipid traits in human genome-wide association studies. Integration of mouse QTL with human genome-wide association studies yielded candidate gene drivers for 18 of the 21 QTL. This approach enabled us to nominate the gene encoding the neutral ceramidase, Asah2, as a novel candidate driver at a QTL on chromosome 19 for large HDL particles (HDL-2b). To experimentally validate Asah2, we surveyed lipoproteins in Asah2-/- mice. Compared to wild-type mice, female Asah2-/- mice showed an increase in several lipoproteins, including HDL. Our results provide insights into the genetic regulation of circulating lipoproteins, as well as mechanisms by which lipoprotein subfractions may affect cardiovascular disease risk in humans.

Expression of tau and phosphorylated tau in the brain of normal and hemiparkinsonian rhesus macaques

Tau is a neuronal protein involved in microtubule stabilization and intracellular vesicle transport in axons. In neurodegenerative disorders termed "tauopathies," like Alzheimer's and Parkinson's disease, tau becomes hyperphosphorylated and forms intracellular inclusions. Rhesus macaques are widely used for studying ageing processes and modeling neurodegenerative disorders, yet little is known about endogenous tau expression in their brains. In this study, immunohistochemical methods were used to map and characterize total tau, 3R- and 4R-tau isoforms, and phosphorylated tau (pThr231-tau and pSer202/Thr205-tau/AT8) expression bilaterally in 16 brain regions of normal and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced hemiparkinsonian adult rhesus macaques. Tau-immunoreactivity (-ir), including both 3R and 4R isoforms, was observed throughout the brain, with varying regional intensities. The anterior cingulate cortex, entorhinal cortex, and hippocampus displayed the most robust tau-ir, while the subthalamic nucleus and white matter regions had minimal expression. Tau was present in neurons of gray matter regions; it was preferentially observed in fibers of the globus pallidus and substantia nigra and in cell bodies of the thalamus and subthalamic nucleus. In white matter regions, tau was abundantly present in oligodendrocytes. Additionally, neuronal pThr231-tau-ir was abundant in all brain regions, but not AT8-ir. Differences in regional and intracellular protein expression were not detected between control subjects and both brain hemispheres of MPTP-treated animals. Specifically, tau-ir in the substantia nigra of all subjects colocalized with GABAergic neurons. Overall, this report provides an in-depth characterization of tau expression in the rhesus macaque brain to facilitate future investigations for understanding and modeling tau pathology in this species.

Lipidomic QTL in Diversity Outbred mice identifies a novel function for α/β hydrolase domain 2 (Abhd2) as an enzyme that metabolizes phosphatidylcholine and cardiolipin

We and others have previously shown that genetic association can be used to make causal connections between gene loci and small molecules measured by mass spectrometry in the bloodstream and in tissues. We identified a locus on mouse chromosome 7 where several phospholipids in liver showed strong genetic association to distinct gene loci. In this study, we integrated gene expression data with genetic association data to identify a single gene at the chromosome 7 locus as the driver of the phospholipid phenotypes. The gene encodes α/β-hydrolase domain 2 (Abhd2), one of 23 members of the ABHD gene family. We validated this observation by measuring lipids in a mouse with a whole-body deletion of Abhd2. The Abhd2KO mice had a significant increase in liver levels of phosphatidylcholine and phosphatidylethanolamine. Unexpectedly, we also found a decrease in two key mitochondrial lipids, cardiolipin and phosphatidylglycerol, in male Abhd2KO mice. These data suggest that Abhd2 plays a role in the synthesis, turnover, or remodeling of liver phospholipids.

Lipidomic QTL in Diversity Outbred mice identifies a novel function for α/β hydrolase domain 2 ( Abhd2 ) as an enzyme that metabolizes phosphatidylcholine and cardiolipin

We and others have previously shown that genetic association can be used to make causal connections between gene loci and small molecules measured by mass spectrometry in the bloodstream and in tissues. We identified a locus on mouse chromosome 7 where several phospholipids in liver showed strong genetic association to distinct gene loci. In this study, we integrated gene expression data with genetic association data to identify a single gene at the chromosome 7 locus as the driver of the phospholipid phenotypes. The gene encodes α/β-hydrolase domain 2 ( Abhd2 ), one of 23 members of the ABHD gene family. We validated this observation by measuring lipids in a mouse with a whole-body deletion of Abhd2 . The Abhd2 KO mice had a significant increase in liver levels of phosphatidylcholine and phosphatidylethanolamine. Unexpectedly, we also found a decrease in two key mitochondrial lipids, cardiolipin and phosphatidylglycerol, in male Abhd2 KO mice. These data suggest that Abhd2 plays a role in the synthesis, turnover, or remodeling of liver phospholipids.

Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes

BACKGROUND

The spread of the COVID-19 (SARS-CoV-2) and the surging number of cases across the United States have resulted in full hospitals and exhausted health care workers. Limited availability and questionable reliability of the data make outbreak prediction and resource planning difficult. Any estimates or forecasts are subject to high uncertainty and low accuracy to measure such components. The aim of this study is to apply, automate, and assess a Bayesian time series model for the real-time estimation and forecasting of COVID-19 cases and number of hospitalizations in Wisconsin healthcare emergency readiness coalition (HERC) regions.

METHODS

This study makes use of the publicly available Wisconsin COVID-19 historical data by county. Cases and effective time-varying reproduction number [Formula: see text] by the HERC region over time are estimated using Bayesian latent variable models. Hospitalizations are estimated by the HERC region over time using a Bayesian regression model. Cases, effective Rt, and hospitalizations are forecasted over a 1-day, 3-day, and 7-day time horizon using the last 28 days of data, and the 20%, 50%, and 90% Bayesian credible intervals of the forecasts are calculated. The frequentist coverage probability is compared to the Bayesian credible level to evaluate performance.

RESULTS

For cases and effective [Formula: see text], all three time horizons outperform the three credible levels of the forecast. For hospitalizations, all three time horizons outperform the 20% and 50% credible intervals of the forecast. On the contrary, the 1-day and 3-day periods underperform the 90% credible intervals. Questions about uncertainty quantification should be re-calculated using the frequentist coverage probability of the Bayesian credible interval based on observed data for all three metrics.

CONCLUSIONS

We present an approach to automate the real-time estimation and forecasting of cases and hospitalizations and corresponding uncertainty using publicly available data. The models were able to infer short-term trends consistent with reported values at the HERC region level. Additionally, the models were able to accurately forecast and estimate the uncertainty of the measurements. This study can help identify the most affected regions and major outbreaks in the near future. The workflow can be adapted to other geographic regions, states, and even countries where decision-making processes are supported in real-time by the proposed modeling system.

pH-Responsive Polymer Nanoparticles for Efficient Delivery of Cas9 Ribonucleoprotein With or Without Donor DNA

Clustered regularly interspaced short palindromic repeat (CRISPR)-associated protein 9 (Cas9) may offer new therapeutics for genetic diseases through gene disruption via nonhomologous end joining (NHEJ) or gene correction via homology-directed repair (HDR). However, clinical translation of CRISPR technology is limited by the lack of safe and efficient delivery systems. Here, facilely fabricated pH-responsive polymer nanoparticles capable of safely and efficiently delivering Cas9 ribonucleoprotein alone (termed NHEJ-NP, diameter = 29.4 nm), or together with donor DNA (termed HDR-NP, diameter = 33.3 nm) are reported. Moreover, intravenously, intratracheally, and intramuscularly injected NHEJ-NP induces efficient gene editing in mouse liver, lung, and skeletal muscle, respectively. Intramuscularly injected HDR-NP also leads to muscle strength recovery in a Duchenne muscular dystrophy mouse model. NHEJ-NP and HDR-NP possess many desirable properties including high payload loading content, small and uniform sizes, high editing efficiency, good biocompatibility, low immunogenicity, and ease of production, storage, and transport, making them great interest for various genome editing applications with clinical potentials.

Interventions to Disrupt Coronavirus Disease Transmission at a University, Wisconsin, USA, August-October 2020

University settings have demonstrated potential for coronavirus disease (COVID-19) outbreaks; they combine congregate living, substantial social activity, and a young population predisposed to mild illness. Using genomic and epidemiologic data, we describe a COVID-19 outbreak at the University of Wisconsin-Madison, Madison, Wisconsin, USA. During August-October 2020, a total of 3,485 students, including 856/6,162 students living in dormitories, tested positive. Case counts began rising during move-in week, August 25-31, 2020, then rose rapidly during September 1-11, 2020. The university initiated multiple prevention efforts, including quarantining 2 dormitories; a subsequent decline in cases was observed. Genomic surveillance of cases from Dane County, in which the university is located, did not find evidence of transmission from a large cluster of cases in the 2 quarantined dorms during the outbreak. Coordinated implementation of prevention measures can reduce COVID-19 spread in university settings and may limit spillover to the surrounding community.

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

Importance

A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed.

Objective

To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection.

Design, Setting, and Participants

This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases.

Exposure

State-level stay-at-home orders during the COVID-19 pandemic.

Main Outcomes and Measures

The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state.

Results

Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was -0.586 (95% CI, -0.742 to -0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results.

Conclusions and Relevance

These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.

Genetic determinants of gut microbiota composition and bile acid profiles in mice

The microbial communities that inhabit the distal gut of humans and other mammals exhibit large inter-individual variation. While host genetics is a known factor that influences gut microbiota composition, the mechanisms underlying this variation remain largely unknown. Bile acids (BAs) are hormones that are produced by the host and chemically modified by gut bacteria. BAs serve as environmental cues and nutrients to microbes, but they can also have antibacterial effects. We hypothesized that host genetic variation in BA metabolism and homeostasis influence gut microbiota composition. To address this, we used the Diversity Outbred (DO) stock, a population of genetically distinct mice derived from eight founder strains. We characterized the fecal microbiota composition and plasma and cecal BA profiles from 400 DO mice maintained on a high-fat high-sucrose diet for ~22 weeks. Using quantitative trait locus (QTL) analysis, we identified several genomic regions associated with variations in both bacterial and BA profiles. Notably, we found overlapping QTL for Turicibacter sp. and plasma cholic acid, which mapped to a locus containing the gene for the ileal bile acid transporter, Slc10a2. Mediation analysis and subsequent follow-up validation experiments suggest that differences in Slc10a2 gene expression associated with the different strains influences levels of both traits and revealed novel interactions between Turicibacter and BAs. This work illustrates how systems genetics can be utilized to generate testable hypotheses and provide insight into host-microbe interactions.

Gene loci associated with insulin secretion in islets from non-diabetic mice

Genetic susceptibility to type 2 diabetes is primarily due to β-cell dysfunction. However, a genetic study to directly interrogate β-cell function ex vivo has never been previously performed. We isolated 233,447 islets from 483 Diversity Outbred (DO) mice maintained on a Western-style diet, and measured insulin secretion in response to a variety of secretagogues. Insulin secretion from DO islets ranged >1,000-fold even though none of the mice were diabetic. The insulin secretory response to each secretagogue had a unique genetic architecture; some of the loci were specific for one condition, whereas others overlapped. Human loci that are syntenic to many of the insulin secretion QTL from mouse are associated with diabetes-related SNPs in human genome-wide association studies. We report on three genes, Ptpn18, Hunk and Zfp148, where the phenotype predictions from the genetic screen were fulfilled in our studies of transgenic mouse models. These three genes encode a non-receptor type protein tyrosine phosphatase, a serine/threonine protein kinase, and a Krϋppel-type zinc-finger transcription factor, respectively. Our results demonstrate that genetic variation in insulin secretion that can lead to type 2 diabetes is discoverable in non-diabetic individuals.

Host Genotype and Gut Microbiome Modulate Insulin Secretion and Diet-Induced Metabolic Phenotypes

Genetic variation drives phenotypic diversity and influences the predisposition to metabolic disease. Here, we characterize the metabolic phenotypes of eight genetically distinct inbred mouse strains in response to a high-fat/high-sucrose diet. We found significant variation in diabetes-related phenotypes and gut microbiota composition among the different mouse strains in response to the dietary challenge and identified taxa associated with these traits. Follow-up microbiota transplant experiments showed that altering the composition of the gut microbiota modifies strain-specific susceptibility to diet-induced metabolic disease. Animals harboring microbial communities with enhanced capacity for processing dietary sugars and for generating hydrophobic bile acids showed increased susceptibility to metabolic disease. Notably, differences in glucose-stimulated insulin secretion between different mouse strains were partially recapitulated via gut microbiota transfer. Our results suggest that the gut microbiome contributes to the genetic and phenotypic diversity observed among mouse strains and provide a link between the gut microbiome and insulin secretion.

The Transcription Factor Nfatc2 Regulates β-Cell Proliferation and Genes Associated with Type 2 Diabetes in Mouse and Human Islets

Human genome-wide association studies (GWAS) have shown that genetic variation at >130 gene loci is associated with type 2 diabetes (T2D). We asked if the expression of the candidate T2D-associated genes within these loci is regulated by a common locus in pancreatic islets. Using an obese F2 mouse intercross segregating for T2D, we show that the expression of ~40% of the T2D-associated genes is linked to a broad region on mouse chromosome (Chr) 2. As all but 9 of these genes are not physically located on Chr 2, linkage to Chr 2 suggests a genomic factor(s) located on Chr 2 regulates their expression in trans. The transcription factor Nfatc2 is physically located on Chr 2 and its expression demonstrates cis linkage; i.e., its expression maps to itself. When conditioned on the expression of Nfatc2, linkage for the T2D-associated genes was greatly diminished, supporting Nfatc2 as a driver of their expression. Plasma insulin also showed linkage to the same broad region on Chr 2. Overexpression of a constitutively active (ca) form of Nfatc2 induced β-cell proliferation in mouse and human islets, and transcriptionally regulated more than half of the T2D-associated genes. Overexpression of either ca-Nfatc2 or ca-Nfatc1 in mouse islets enhanced insulin secretion, whereas only ca-Nfatc2 was able to promote β-cell proliferation, suggesting distinct molecular pathways mediating insulin secretion vs. β-cell proliferation are regulated by NFAT. Our results suggest that many of the T2D-associated genes are downstream transcriptional targets of NFAT, and may act coordinately in a pathway through which NFAT regulates β-cell proliferation in both mouse and human islets.

Genome-wide association studies (GWAS) and linkage studies provide a powerful way to establish a causal connection between a gene locus and a physiological or pathophysiological phenotype. We wondered if candidate genes associated with type 2 diabetes in human populations, in addition to being causal for the disease, could also be intermediate traits in a pathway leading to disease. In addition, we wished to know if there were any regulatory loci that could coordinately drive the expression of these genes in pancreatic islets and thus complete a pathway; i.e. Driver → GWAS candidate expression → type 2 diabetes. Using data from a mouse intercross between a diabetes-susceptible and a diabetes-resistant mouse strain, we found that the expression of ~40% of >130 candidate GWAS genes genetically mapped to a hot spot on mouse chromosome 2. Using a variety of statistical methods, we identified the transcription factor Nfatc2 as the candidate driver. Follow-up experiments showed that overexpression of Nfatc2 does indeed affect the expression of the GWAS genes and regulates β-cell proliferation and insulin secretion. The work shows that in addition to being causal, GWAS candidate genes can be intermediate traits in a pathway leading to disease. Model organisms can be used to explore these novel causal pathways.

Fine Mapping of a QTL Associated with Kernel Row Number on Chromosome 1 of Maize

The genetic factors underlying changes in ear morphology, and particularly the inheritance of kernel row number (KRN), have been broadly investigated in diverse mapping populations in maize (Zea mays L.). In this study, we mapped a region on the long arm of chromosome 1 containing a QTL for KRN. This work was performed using a set of recombinant chromosome nearly isogenic lines (RCNILs) derived from a BC₂S₃ population produced using the inbred maize line W22 and teosinte (Zea mays ssp. parviglumis) as the parents. A set of 48 RCNILs was evaluated in the field during the summer of 2013 in order to perform the mapping. A QTL for KRN was found that explained approximately 51% of the phenotypic variance and had a 1.5-LOD confidence interval of 203 kb. Seven genes are described in this interval. One of these candidate genes may have been the target of domestication processes in maize and contributed to the shift from two kernel row ears in teosinte to a highly polystichous ear in maize.

Genetic control of obesity, glucose homeostasis, dyslipidemia and fatty liver in a mouse model of diet-induced metabolic syndrome

BACKGROUND/OBJECTIVES

Both genetic and dietary factors contribute to the metabolic syndrome (MetS) in humans and animal models. Characterizing their individual roles as well as relationships among these factors is critical for understanding MetS pathogenesis and developing effective therapies. By studying phenotypic responsiveness to high-risk versus control diet in two inbred mouse strains and their derivatives, we estimated the relative contributions of diet and genetic background to MetS, characterized strain-specific combinations of MetS conditions, and tested genetic and phenotypic complexity on a single substituted chromosome.

METHODS

Ten measures of metabolic health were assessed in susceptible C57BL/6 J and resistant A/J male mice fed either a control or a high-fat, high-sucrose (HFHS) diet, permitting estimates of the relative influences of strain, diet and strain-diet interactions for each trait. The same traits were measured in a panel of C57BL/6 J (B6)-Chr(A/J) chromosome substitution strains (CSSs) fed the HFHS diet, followed by characterization of interstrain relationships, covariation among metabolic traits and quantitative trait loci (QTLs) on Chromosome 10.

RESULTS

We identified significant genetic contributions to nine of ten metabolic traits and significant dietary influence on eight. Significant strain-diet interaction effects were detected for four traits. Although a range of HFHS-induced phenotypes were observed among the CSSs, significant associations were detected among all traits but one. Strains were grouped into three clusters based on overall phenotype and specific CSSs were identified with distinct and reproducible trait combinations. Finally, several Chr10 regions were shown to control the severity of MetS conditions.

CONCLUSIONS

Generally strong genetic and dietary effects validate these CSSs as a multifactorial model of MetS. Although traits tended to segregate together, considerable phenotypic heterogeneity suggests that underlying genetic factors influence their co-occurrence and severity. Identification of multiple QTLs within and among strains highlights both the complexity of genetically regulated, diet-induced MetS and the ability of CSSs to prioritize candidate loci for mechanistic studies.

Spink2 modulates apoptotic susceptibility and is a candidate gene in the Rgcs1 QTL that affects retinal ganglion cell death after optic nerve damage

The Rgcs1 quantitative trait locus, on mouse chromosome 5, influences susceptibility of retinal ganglion cells to acute damage of the optic nerve. Normally resistant mice (DBA/2J) congenic for the susceptible allele from BALB/cByJ mice exhibit susceptibility to ganglion cells, not only in acute optic nerve crush, but also to chronic inherited glaucoma that is characteristic of the DBA/2J strain as they age. SNP mapping of this QTL has narrowed the region of interest to 1 Mb. In this region, a single gene (Spink2) is the most likely candidate for this effect. Spink2 is expressed in retinal ganglion cells and is increased after optic nerve damage. This gene is also polymorphic between resistant and susceptible strains, containing a single conserved amino acid change (threonine to serine) and a 220 bp deletion in intron 1 that may quantitatively alter endogenous expression levels between strains. Overexpression of the different variants of Spink2 in D407 tissue culture cells also increases their susceptibility to the apoptosis-inducing agent staurosporine in a manner consistent with the differential susceptibility between the DBA/2J and BALB/cByJ strains.

Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets

Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin^ob/ob mutation (Lep^ob). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein–protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

Alzheimer's disease and type 2 diabetes are two common aging-related diseases. Numerous studies have shown that the two diseases are associated. However, the mechanisms of such connection are not clear. Both diseases are complex diseases that are induced by multiple genetic factors and the environment. To understand the molecular network regulated by complex genetic factors causing type 2 diabetes, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant and diabetic mouse strains. We measured genotypes, clinical traits, and expression profiling in five tissues for each mouse. We then performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait, and developed a novel method for inferring key regulators for regulating plasma insulin. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

A quantitative map of the liver mitochondrial phosphoproteome reveals posttranslational control of ketogenesis

Mitochondria are dynamic organelles that play a central role in a diverse array of metabolic processes. Elucidating mitochondrial adaptations to changing metabolic demands and the pathogenic alterations that underlie metabolic disorders represent principal challenges in cell biology. Here, we performed multiplexed quantitative mass spectrometry-based proteomics to chart the remodeling of the mouse liver mitochondrial proteome and phosphoproteome during both acute and chronic physiological transformations in more than 50 mice. Our analyses reveal that reversible phosphorylation is widespread in mitochondria, and is a key mechanism for regulating ketogenesis during the onset of obesity and type 2 diabetes. Specifically, we have demonstrated that phosphorylation of a conserved serine on Hmgcs2 (S456) significantly enhances its catalytic activity in response to increased ketogenic demand. Collectively, our work describes the plasticity of this organelle at high resolution and provides a framework for investigating the roles of proteome restructuring and reversible phosphorylation in mitochondrial adaptation.

Genetic mapping of paternal sorting of mitochondria in cucumber

Mitochondria are organelles that have their own DNA; serve as the powerhouses of eukaryotic cells; play important roles in stress responses, programmed cell death, and ageing; and in the vast majority of eukaryotes, are maternally transmitted. Strict maternal transmission of mitochondria makes it difficult to select for better-performing mitochondria, or against deleterious mutations in the mitochondrial DNA. Cucumber is a useful plant for organellar genetics because its mitochondria are paternally transmitted and it possesses one of the largest mitochondrial genomes among all eukaryotes. Recombination among repetitive motifs in the cucumber mitochondrial DNA produces rearrangements associated with strongly mosaic (MSC) phenotypes. We previously reported nuclear control of sorting among paternally transmitted mitochondrial DNAs. The goal of this project was to map paternal sorting of mitochondria as a step towards its eventual cloning. We crossed single plants from plant introduction (PI) 401734 and Cucumis sativus var. hardwickii and produced an F(2) family. A total of 425 F(2) plants were genotyped for molecular markers and testcrossed as the female with MSC16. Testcross families were scored for frequencies of wild-type versus MSC progenies. Discrete segregations for percent wild-type progenies were not observed and paternal sorting of mitochondria was therefore analyzed as a quantitative trait. A major quantitative trait locus (QTL; LOD >23) was mapped between two simple sequence repeats encompassing a 459-kb region on chromosome 3. Nuclear genes previously shown to affect the prevalence of mitochondrial DNAs (MSH1, OSB1, and RECA homologs) were not located near this major QTL on chromosome 3. Sequencing of this region from PI 401734, together with improved annotation of the cucumber genome, should result in the eventual cloning of paternal sorting of mitochondria and provide insights about nuclear control of organellar-DNA sorting.

Tsc2, a positional candidate gene underlying a quantitative trait locus for hepatic steatosis

Nonalchoholic fatty liver disease (NAFLD) is the most common cause of liver dysfunction and is associated with metabolic diseases, including obesity, insulin resistance, and type 2 diabetes. We mapped a quantitative trait locus (QTL) for NAFLD to chromosome 17 in a cross between C57BL/6 (B6) and BTBR mouse strains made genetically obese with the Lep(ob/ob) mutation. We identified Tsc2 as a gene underlying the chromosome 17 NAFLD QTL. Tsc2 functions as an inhibitor of mammalian target of rapamycin, which is involved in many physiological processes, including cell growth, proliferation, and metabolism. We found that Tsc2(+/-) mice have increased lipogenic gene expression in the liver in an insulin-dependent manner. The coding single nucleotide polymorphism between the B6 and BTBR strains leads to a change in the ability to inhibit the expression of lipogenic genes and de novo lipogenesis in AML12 cells and to promote the proliferation of Ins1 cells. This difference is due to a different affinity of binding to Tsc1, which affects the stability of Tsc2.

Positional cloning of a type 2 diabetes quantitative trait locus; tomosyn-2, a negative regulator of insulin secretion

We previously mapped a type 2 diabetes (T2D) locus on chromosome 16 (Chr 16) in an F2 intercross from the BTBR T (+) tf (BTBR) Lep^ob/ob and C57BL/6 (B6) Lep^ob/ob mouse strains. Introgression of BTBR Chr 16 into B6 mice resulted in a consomic mouse with reduced fasting plasma insulin and elevated glucose levels. We derived a panel of sub-congenic mice and narrowed the diabetes susceptibility locus to a 1.6 Mb region. Introgression of this 1.6 Mb fragment of the BTBR Chr 16 into lean B6 mice (B6.16^BT36–38) replicated the phenotypes of the consomic mice. Pancreatic islets from the B6.16^BT36–38 mice were defective in the second phase of the insulin secretion, suggesting that the 1.6 Mb region encodes a regulator of insulin secretion. Within this region, syntaxin-binding protein 5-like (Stxbp5l) or tomosyn-2 was the only gene with an expression difference and a non-synonymous coding single nucleotide polymorphism (SNP) between the B6 and BTBR alleles. Overexpression of the b-tomosyn-2 isoform in the pancreatic β-cell line, INS1 (832/13), resulted in an inhibition of insulin secretion in response to 3 mM 8-bromo cAMP at 7 mM glucose. In vitro binding experiments showed that tomosyn-2 binds recombinant syntaxin-1A and syntaxin-4, key proteins that are involved in insulin secretion via formation of the SNARE complex. The B6 form of tomosyn-2 is more susceptible to proteasomal degradation than the BTBR form, establishing a functional role for the coding SNP in tomosyn-2. We conclude that tomosyn-2 is the major gene responsible for the T2D Chr 16 quantitative trait locus (QTL) we mapped in our mouse cross. Our findings suggest that tomosyn-2 is a key negative regulator of insulin secretion.

Humans carry many genetic variants that confer small effects on metabolic traits relevant to type 2 diabetes. These effects are amplified by environmental stressors like obesity. We used morbid obesity as a sensitizer to identify genes that contribute to the diabetes susceptibility of the BTBR mouse strain. Using mapping and breeding strategies, we were able to narrow a genetic region to one containing just 13 genes. One of these genes, tomosyn-2, emerged as a prime candidate. Our functional studies showed that tomosyn-2 is an inhibitor of insulin secretion, and it binds to the proteins involved in the fusion of insulin containing granules with the plasma membrane. We found a coding mutation and demonstrated that this mutation affects the stability of the protein product. Our work with Tomosyn-2 provides new insights into the regulation of insulin secretion and emphasizes that negative regulation is critical for avoiding insulin-induced hypoglycemia.

FoxM1 is up-regulated by obesity and stimulates beta-cell proliferation

beta-Cell mass expansion is one mechanism by which obese animals compensate for insulin resistance and prevent diabetes. FoxM1 is a transcription factor that can regulate the expression of multiple cell cycle genes and is necessary for the maintenance of adult beta-cell mass, beta-cell proliferation, and glucose homeostasis. We hypothesized that FoxM1 is up-regulated by nondiabetic obesity and initiates a transcriptional program leading to beta-cell proliferation. We performed gene expression analysis on islets from the nondiabetic C57BL/6 Leptin(ob/ob) mouse, the diabetic BTBR Leptin(ob/ob) mouse, and an F2 Leptin(ob/ob) population derived from these strains. We identified obesity-driven coordinated up-regulation of islet Foxm1 and its target genes in the nondiabetic strain, correlating with beta-cell mass expansion and proliferation. This up-regulation was absent in the diabetic strain. In the F2 Leptin(ob/ob) population, increased expression of Foxm1 and its target genes segregated with higher insulin and lower glucose levels. We next studied the effects of FOXM1b overexpression on isolated mouse and human islets. We found that FoxM1 stimulated mouse and human beta-cell proliferation by activating many cell cycle phases. We asked whether FOXM1 expression is also responsive to obesity in human islets by collecting RNA from human islet donors (body mass index range: 24-51). We found that the expression of FOXM1 and its target genes is positively correlated with body mass index. Our data suggest that beta-cell proliferation occurs in adult obese humans in an attempt to expand beta-cell mass to compensate for insulin resistance, and that the FoxM1 transcriptional program plays a key role in this process.

Cholecystokinin is up-regulated in obese mouse islets and expands beta-cell mass by increasing beta-cell survival

An absolute or functional deficit in beta-cell mass is a key factor in the pathogenesis of diabetes. We model obesity-driven beta-cell mass expansion by studying the diabetes-resistant C57BL/6-Leptin(ob/ob) mouse. We previously reported that cholecystokinin (Cck) was the most up-regulated gene in obese pancreatic islets. We now show that islet cholecystokinin (CCK) is up-regulated 500-fold by obesity and expressed in both alpha- and beta-cells. We bred a null Cck allele into the C57BL/6-Leptin(ob/ob) background and investigated beta-cell mass and metabolic parameters of Cck-deficient obese mice. Loss of CCK resulted in decreased islet size and reduced beta-cell mass through increased beta-cell death. CCK deficiency and decreased beta-cell mass exacerbated fasting hyperglycemia and reduced hyperinsulinemia. We further investigated whether CCK can directly affect beta-cell death in cell culture and isolated islets. CCK was able to directly reduce cytokine- and endoplasmic reticulum stress-induced cell death. In summary, CCK is up-regulated by islet cells during obesity and functions as a paracrine or autocrine factor to increase beta-cell survival and expand beta-cell mass to compensate for obesity-induced insulin resistance.

Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes

Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

Effect of anesthesia, positioning, time, and feeding on the proventriculus: keel ratio of clinically healthy parrots

Healthy, adult Hispaniolan Amazon parrots (Amazona ventralis) were imaged on three occasions to determine the effects of anesthesia, patient rotation, feeding, and short/long-term temporal factors on the proventriculus:keel ratio. Increasing rotation up to 15 degrees from right lateral resulted in increased inability to measure the proventriculus in up to 44% of birds, meaning that the proventriculus:keel ratio could not be calculated from those radiographs. There was a significant difference between the proventriculus:keel ratio for individual parrots when quantified 3 weeks apart. Despite this difference, all ratios remained within normal limits. No significant effect was identified due to anesthesia, feeding, fasting, or repeated imaging through an 8-h period. Interobserver agreement for measurability and correlation for the proventriculus:keel ratio values was high. It is recommended that the proventriculus:keel ratio be calculated from anesthetized parrots to attain images in true lateral recumbency. Ratio fluctuations within the normal range between radiographs obtained on different dates may be observed in normal parrots.

CAUSAL GRAPHICAL MODELS IN SYSTEMS GENETICS: A UNIFIED FRAMEWORK FOR JOINT INFERENCE OF CAUSAL NETWORK AND GENETIC ARCHITECTURE FOR CORRELATED PHENOTYPES

Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenotype network structure. In this paper we develop a QTL-driven phenotype network method (QTLnet) to jointly infer a causal phenotype network and associated genetic architecture for sets of correlated phenotypes. Randomization of alleles during meiosis and the unidirectional influence of genotype on phenotype allow the inference of QTLs causal to phenotypes. Causal relationships among phenotypes can be inferred using these QTL nodes, enabling us to distinguish among phenotype networks that would otherwise be distribution equivalent. We jointly model phenotypes and QTLs using homogeneous conditional Gaussian regression models, and we derive a graphical criterion for distribution equivalence. We validate the QTLnet approach in a simulation study. Finally, we illustrate with simulated data and a real example how QTLnet can be used to infer both direct and indirect effects of QTLs and phenotypes that co-map to a genomic region.

Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice

Type 2 diabetes results from severe insulin resistance coupled with a failure of b cells to compensate by secreting sufficient insulin. Multiple genetic loci are involved in the development of diabetes, although the effect of each gene on diabetes susceptibility is thought to be small. MicroRNAs (miRNAs) are noncoding 19-22-nucleotide RNA molecules that potentially regulate the expression of thousands of genes. To understand the relationship between miRNA regulation and obesity-induced diabetes, we quantitatively profiled approximately 220 miRNAs in pancreatic islets, adipose tissue, and liver from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mice. More than half of the miRNAs profiled were expressed in all three tissues, with many miRNAs in each tissue showing significant changes in response to genetic obesity. Furthermore, several miRNAs in each tissue were differentially responsive to obesity in B6 versus BTBR mice, suggesting that they may be involved in the pathogenesis of diabetes. In liver there were approximately 40 miRNAs that were downregulated in response to obesity in B6 but not BTBR mice, indicating that genetic differences between the mouse strains play a critical role in miRNA regulation. In order to elucidate the genetic architecture of hepatic miRNA expression, we measured the expression of miRNAs in genetically obese F2 mice. Approximately 10% of the miRNAs measured showed significant linkage (miR-eQTLs), identifying loci that control miRNA abundance. Understanding the influence that obesity and genetics exert on the regulation of miRNA expression will reveal the role miRNAs play in the context of obesity-induced type 2 diabetes.

Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice

Type 2 diabetes results from severe insulin resistance coupled with a failure of b cells to compensate by secreting sufficient insulin. Multiple genetic loci are involved in the development of diabetes, although the effect of each gene on diabetes susceptibility is thought to be small. MicroRNAs (miRNAs) are noncoding 19-22-nucleotide RNA molecules that potentially regulate the expression of thousands of genes. To understand the relationship between miRNA regulation and obesity-induced diabetes, we quantitatively profiled approximately 220 miRNAs in pancreatic islets, adipose tissue, and liver from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mice. More than half of the miRNAs profiled were expressed in all three tissues, with many miRNAs in each tissue showing significant changes in response to genetic obesity. Furthermore, several miRNAs in each tissue were differentially responsive to obesity in B6 versus BTBR mice, suggesting that they may be involved in the pathogenesis of diabetes. In liver there were approximately 40 miRNAs that were downregulated in response to obesity in B6 but not BTBR mice, indicating that genetic differences between the mouse strains play a critical role in miRNA regulation. In order to elucidate the genetic architecture of hepatic miRNA expression, we measured the expression of miRNAs in genetically obese F2 mice. Approximately 10% of the miRNAs measured showed significant linkage (miR-eQTLs), identifying loci that control miRNA abundance. Understanding the influence that obesity and genetics exert on the regulation of miRNA expression will reveal the role miRNAs play in the context of obesity-induced type 2 diabetes.

Transcriptomic profiling of bovine IVF embryos revealed candidate genes and pathways involved in early embryonic development

Background

Early embryonic loss is a large contributor to infertility in cattle. Although genetic factors are known to affect early embryonic development, the discovery of such factors has been a serious challenge. The objective of this study was to identify genes differentially expressed between blastocysts and degenerative embryos at early stages of development.

Results

Using microarrays, genome-wide RNA expression was profiled and compared for in vitro fertilization (IVF) - derived blastocysts and embryos undergoing degenerative development up to the same time point. Surprisingly similar transcriptomic profiles were found in degenerative embryos and blastocysts. Nonetheless, we identified 67 transcripts that significantly differed between these two groups of embryos at a 15% false discovery rate, including 33 transcripts showing at least a two-fold difference. Several signaling and metabolic pathways were found to be associated with the developmental status of embryos, among which were previously known important steroid biosynthesis and cell communication pathways in early embryonic development.

Conclusions

This study presents the first direct and comprehensive comparison of transcriptomes between IVF blastocysts and degenerative embryos, providing important information for potential genes and pathways associated with early embryonic development.

Major QTL for carrot color are positionally associated with carotenoid biosynthetic genes and interact epistatically in a domesticated x wild carrot cross

We performed QTL analyses for pigment content on a carotenoid biosynthesis function map based on progeny of a wild white carrot (QAL) which accumulates no pigments x domesticated orange carrot (B493), one of the richest sources of carotenoid pigments-mainly provitamin A alpha- and beta- carotenes. Two major interacting loci, Y and Y(2) on linkage groups 2 and 5, respectively, control much variation for carotenoid accumulation in carrot roots. They are associated with carotenoid biosynthetic genes zeaxanthin epoxidase and carotene hydroxylase and carotenoid dioxygenase gene family members as positional candidate genes. Dominant Y allele inhibits carotenoid accumulation. When Y is homozygous recessive, carotenoids that accumulate are either only xanthophylls in Y(2)__ plants, or both carotenes and xanthophylls, in y(2) y(2) plants. These two genes played a major role in carrot domestication and account for the significant role that modern carrot plays in vitamin A nutrition.

A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis

The identification of quantitative trait loci (QTL) and their interactions is a crucial step toward the discovery of genes responsible for variation in experimental crosses. The problem is best viewed as one of model selection, and the most important aspect of the problem is the comparison of models of different sizes. We present a penalized likelihood approach, with penalties on QTL and pairwise interactions chosen to control false positive rates. This extends the work of Broman and Speed to allow for pairwise interactions among QTL. A conservative version of our penalized LOD score provides strict control over the rate of extraneous QTL and interactions; a more liberal criterion is more lenient on interactions but seeks to maintain control over the rate of inclusion of false loci. The key advance is that one needs only to specify a target false positive rate rather than a prior on the number of QTL and interactions. We illustrate the use of our model selection criteria as exploratory tools; simulation studies demonstrate reasonable power to detect QTL. Our liberal criterion is comparable in power to two Bayesian approaches.

Determination of a sedative protocol for use in California sea lions (Zalophus californianus) with neurologic abnormalities undergoing electroencephalographic examination

Sedation in sea lions exhibiting abnormal neurologic signs may require modification of established sedatior protocols because of the likely interaction between effects of the sedative and physiologic changes in diseased animals The effects of two sedative combinations, 0.07 mg/kg medetomidine and 0.07 mg/kg medetomidine plus 0.2 mg/kg butorphanol, were compared between California sea lions (Zalophus californianus) with signs of neurologic dysfunctior (n=33) and without neurologic signs (n=8). Sedation depth was scored on a scale of 0 (no effect) to 4 (profound sedation) assessed by response to auditory, tactile, and visual stimuli at the time of perceived maximal sedative effect In the medetomidine-alone group, sea lions with neurologic signs attained a median sedation score of 4 compared to a median sedation score of 1 in the clinically normal sea lions. Sea lions with and without neurologic signs giver medetomidine-butorphanol attained a median sedation score of 4. No statistically significant difference in time to induction and respiratory rate was found between the two sedation protocols in all sea lions. In the sea lions with neurologic signs, the recovery time from medetomidine-butorphanol sedation was prolonged (P < 0.01) and minimum recorded heart rates, although remaining within normal physiologic limits, were lower (P = 0.02) when compared to the sea lions administered medetomidine alone. Muscle jerks were observed in many animals given medetomidine-butorphanol and were detrimental to the diagnostic quality of the electroencephalogram (EEG) recording. Medetomidine alone at a dose rate of 0.07 mg/kg thus provides adequate and safe sedation in sea lions with neurologic signs undergoing EEG evaluation.

Bayesian quantitative trait loci mapping for multiple traits

Most quantitative trait loci (QTL) mapping experiments typically collect phenotypic data on multiple correlated complex traits. However, there is a lack of a comprehensive genomewide mapping strategy for correlated traits in the literature. We develop Bayesian multiple-QTL mapping methods for correlated continuous traits using two multivariate models: one that assumes the same genetic model for all traits, the traditional multivariate model, and the other known as the seemingly unrelated regression (SUR) model that allows different genetic models for different traits. We develop computationally efficient Markov chain Monte Carlo (MCMC) algorithms for performing joint analysis. We conduct extensive simulation studies to assess the performance of the proposed methods and to compare with the conventional single-trait model. Our methods have been implemented in the freely available package R/qtlbim (http://www.qtlbim.org), which greatly facilitates the general usage of the Bayesian methodology for unraveling the genetic architecture of complex traits.

Rgcs1, a dominant QTL that affects retinal ganglion cell death after optic nerve crush in mice

Background

Intrinsic apoptosis of neuronal somas is one aspect of neurodegenerative diseases that can be influenced by genetic background. Genes that affect this process may act as susceptibility alleles that contribute to the complex genetic nature of these diseases. Retinal ganglion cell death is a defining feature of the chronic and genetically complex neurodegenerative disease glaucoma. Previous studies using an optic nerve crush procedure in inbred mice, showed that ganglion cell resistance to crush was affected by the Mendelian-dominant inheritance of 1–2 predicted loci. To assess this further, we bred and phenotyped a large population of F2 mice derived from a resistant inbred strain (DBA/2J) and a susceptible strain (BALB/cByJ).

Results

Genome wide mapping of the F2 mice using microsatellite markers, detected a single highly significant quantitative trait locus in a 25 cM (58 Mb) interval on chromosome 5 (Chr5.loc34-59 cM). No interacting loci were detected at the resolution of this screen. We have designated this locus as Retinal ganglion cell susceptible 1, Rgcs1. In silico analysis of this region revealed the presence of 578 genes or expressed sequence tags, 4 of which are highly expressed in the ganglion cell layer of the mammalian retina, and 2 of which are suspected susceptibility alleles in chronic neurodegenerative diseases. In addition, 25 genes contain 36 known single nucleotide polymorphisms that create nonsynonymous amino acid changes between the two parental strains. Collectively, this analysis has identified 7 potential candidate genes that may affect ganglion cell death.

Conclusion

The process of ganglion cell death is likely one of the many facets of glaucoma susceptibility. A novel dominant locus has been identified that affects sensitivity of ganglion cells to optic nerve crush. The allele responsible for this sensitivity may also be a susceptibility allele for glaucoma.

Inferring causal phenotype networks from segregating populations

A major goal in the study of complex traits is to decipher the causal interrelationships among correlated phenotypes. Current methods mostly yield undirected networks that connect phenotypes without causal orientation. Some of these connections may be spurious due to partial correlation that is not causal. We show how to build causal direction into an undirected network of phenotypes by including causal QTL for each phenotype. We evaluate causal direction for each edge connecting two phenotypes, using a LOD score. This new approach can be applied to many different population structures, including inbred and outbred crosses as well as natural populations, and can accommodate feedback loops. We assess its performance in simulation studies and show that our method recovers network edges and infers causal direction correctly at a high rate. Finally, we illustrate our method with an example involving gene expression and metabolite traits from experimental crosses.

A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility

Insulin resistance is necessary but not sufficient for the development of type 2 diabetes. Diabetes results when pancreatic beta-cells fail to compensate for insulin resistance by increasing insulin production through an expansion of beta-cell mass or increased insulin secretion. Communication between insulin target tissues and beta-cells may initiate this compensatory response. Correlated changes in gene expression between tissues can provide evidence for such intercellular communication. We profiled gene expression in six tissues of mice from an obesity-induced diabetes-resistant and a diabetes-susceptible strain before and after the onset of diabetes. We studied the correlation structure of mRNA abundance and identified 105 co-expression gene modules. We provide an interactive gene network model showing the correlation structure between the expression modules within and among the six tissues. This resource also provides a searchable database of gene expression profiles for all genes in six tissues in lean and obese diabetes-resistant and diabetes-susceptible mice, at 4 and 10 wk of age. A cell cycle regulatory module in islets predicts diabetes susceptibility. The module predicts islet replication; we found a strong correlation between (2)H(2)O incorporation into islet DNA in vivo and the expression pattern of the cell cycle module. This pattern is highly correlated with that of several individual genes in insulin target tissues, including Igf2, which has been shown to promote beta-cell proliferation, suggesting that these genes may provide a link between insulin resistance and beta-cell proliferation.

Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin^ob/ob and the diabetes-susceptible BTBR leptin^ob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identifying individual genes and their potential roles in molecular pathways leading to disease remains a challenge. In this study, we include transcriptional and metabolic profiling in genomic analyses to address this limitation. We investigated an F2 intercross between the diabetes-resistant C57BL/6 leptin^ob/ob and the diabetes-susceptible BTBR leptin^ob/ob mouse strains that segregates for genotype and diabetes-related physiological traits; blood glucose, plasma insulin and body weight. Our study shows that liver metabolites (comprised of amino acids, organic acids, and acyl-carnitines) map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal, testable networks for control of specific metabolic processes in liver. We apply an in vitro study to confirm the validity of this integrative method, and thus provide a novel approach to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.

Phenotypic and transcriptomic changes associated with potato autopolyploidization

Polyploidy is remarkably common in the plant kingdom and polyploidization is a major driving force for plant genome evolution. Polyploids may contain genomes from different parental species (allopolyploidy) or include multiple sets of the same genome (autopolyploidy). Genetic and epigenetic changes associated with allopolyploidization have been a major research subject in recent years. However, we know little about the genetic impact imposed by autopolyploidization. We developed a synthetic autopolyploid series in potato (Solanum phureja) that includes one monoploid (1x) clone, two diploid (2x) clones, and one tetraploid (4x) clone. Cell size and organ thickness were positively correlated with the ploidy level. However, the 2x plants were generally the most vigorous and the 1x plants exhibited less vigor compared to the 2x and 4x individuals. We analyzed the transcriptomic variation associated with this autopolyploid series using a potato cDNA microarray containing approximately 9000 genes. Statistically significant expression changes were observed among the ploidies for approximately 10% of the genes in both leaflet and root tip tissues. However, most changes were associated with the monoploid and were within the twofold level. Thus, alteration of ploidy caused subtle expression changes of a substantial percentage of genes in the potato genome. We demonstrated that there are few genes, if any, whose expression is linearly correlated with the ploidy and can be dramatically changed because of ploidy alteration.

An efficient Bayesian model selection approach for interacting quantitative trait loci models with many effects

We extend our Bayesian model selection framework for mapping epistatic QTL in experimental crosses to include environmental effects and gene-environment interactions. We propose a new, fast Markov chain Monte Carlo algorithm to explore the posterior distribution of unknowns. In addition, we take advantage of any prior knowledge about genetic architecture to increase posterior probability on more probable models. These enhancements have significant computational advantages in models with many effects. We illustrate the proposed method by detecting new epistatic and gene-sex interactions for obesity-related traits in two real data sets of mice. Our method has been implemented in the freely available package R/qtlbim (http://www.qtlbim.org) to facilitate the general usage of the Bayesian methodology for genomewide interacting QTL analysis.

SORCS1: a novel human type 2 diabetes susceptibility gene suggested by the mouse

OBJECTIVE

A small number of susceptibility genes for human type 2 diabetes have been identified by candidate gene analysis or positional cloning. Genes found to influence diabetes or related traits in mice are likely to be susceptibility genes in humans. SorCS1 is the gene identified as responsible for the mouse chromosome 19 T2dm2 quantitative trait locus for fasting insulin levels, acting via impaired insulin secretion and increased islet disruption in obese females. Genes that impair compensatory insulin secretion in response to obesity-induced insulin resistance may be particularly relevant to human diabetes. Thus, we sought to determine whether variation in the human SORCS1 gene was associated with diabetes-related traits.

RESEARCH DESIGN AND METHODS

We assessed the contribution of variation in SORCS1 to human insulin-related traits in two distinct Mexican-American cohorts. One cohort (the Mexican-American Coronary Artery Disease [MACAD] cohort) consisted of nondiabetic individuals, allowing assessment of genetic association with subclinical intermediate insulin-related traits; the second cohort (the San Antonio Family Diabetes Study [SAFADS]) contained individuals with diabetes, allowing association analyses with overt disease.

RESULTS

We first found association of SORCS1 single nucleotide polymorphisms and haplotypes with fasting insulin levels and insulin secretion in the MACAD cohort. Similar to our results in the mice, the genetic association was strongest in overweight women. We then observed association with diabetes risk and age at diagnosis in women of the SAFADS cohort.

CONCLUSIONS

Identification of SORCS1 as a novel gene affecting insulin secretion and diabetes risk is likely to provide important insight into the biology of obesity-induced type 2 diabetes.

Bayesian mapping of genomewide interacting quantitative trait loci for ordinal traits

Development of statistical methods and software for mapping interacting QTL has been the focus of much recent research. We previously developed a Bayesian model selection framework, based on the composite model space approach, for mapping multiple epistatic QTL affecting continuous traits. In this study we extend the composite model space approach to complex ordinal traits in experimental crosses. We jointly model main and epistatic effects of QTL and environmental factors on the basis of the ordinal probit model (also called threshold model) that assumes a latent continuous trait underlies the generation of the ordinal phenotypes through a set of unknown thresholds. A data augmentation approach is developed to jointly generate the latent data and the thresholds. The proposed ordinal probit model, combined with the composite model space framework for continuous traits, offers a convenient way for genomewide interacting QTL analysis of ordinal traits. We illustrate the proposed method by detecting new QTL and epistatic effects for an ordinal trait, dead fetuses, in a F(2) intercross of mice. Utility and flexibility of the method are also demonstrated using a simulated data set. Our method has been implemented in the freely available package R/qtlbim, which greatly facilitates the general usage of the Bayesian methodology for genomewide interacting QTL analysis for continuous, binary, and ordinal traits in experimental crosses.

Abdominal obesity in BTBR male mice is associated with peripheral but not hepatic insulin resistance

Insulin resistance is a common feature of obesity. BTBR mice have more fat mass than most other inbred mouse strains. On a chow diet, BTBR mice have elevated insulin levels relative to the C57BL/6J (B6) strain. Male F1 progeny of a B6 x BTBR cross are insulin resistant. Previously, we reported insulin resistance in isolated muscle and in isolated adipocytes in this strain. Whereas the muscle insulin resistance was observed only in male F1 mice, adipocyte insulin resistance was also present in male BTBR mice. We examined in vivo mechanisms of insulin resistance with the hyperinsulinemic euglycemic clamp technique. At 10 wk of age, BTBR and F1 mice had a >30% reduction in whole body glucose disposal primarily due to insulin resistance in heart, soleus muscle, and adipose tissue. The increased adipose tissue mass and decreased muscle mass in BTBR and F1 mice were negatively and positively correlated with whole body glucose disposal, respectively. Genes involved in focal adhesion, actin cytoskeleton, and inflammation were more highly expressed in BTBR and F1 than in B6 adipose tissue. The BTBR and F1 mice have higher levels of testosterone, which may be related to the pathological changes in adipose tissue that lead to systemic insulin resistance. Despite profound peripheral insulin resistance, BTBR and F1 mice retained hepatic insulin sensitivity. These studies reveal a genetic difference in body composition that correlates with large differences in peripheral insulin sensitivity.

R/qtlbim: QTL with Bayesian Interval Mapping in experimental crosses

R/qtlbim is an extensible, interactive environment for the Bayesian Interval Mapping of QTL, built on top of R/qtl (Broman et al., 2003), providing Bayesian analysis of multiple interacting quantitative trait loci (QTL) models for continuous, binary and ordinal traits in experimental crosses. It includes several efficient Markov chain Monte Carlo (MCMC) algorithms for evaluating the posterior of genetic architectures, i.e. the number and locations of QTL, their main and epistatic effects and gene-environment interactions. R/qtlbim provides extensive informative graphical and numerical summaries, and model selection and convergence diagnostics of the MCMC output, illustrated through the vignette, example and demo capabilities of R (R Development Core Team 2006).

AVAILABILITY

The package is freely available from cran.r-project.org.

Impact of nonsense-mediated mRNA decay on the global expression profile of budding yeast

Nonsense-mediated mRNA decay (NMD) is a eukaryotic mechanism of RNA surveillance that selectively eliminates aberrant transcripts coding for potentially deleterious proteins. NMD also functions in the normal repertoire of gene expression. In Saccharomyces cerevisiae, hundreds of endogenous RNA Polymerase II transcripts achieve steady-state levels that depend on NMD. For some, the decay rate is directly influenced by NMD (direct targets). For others, abundance is NMD-sensitive but without any effect on the decay rate (indirect targets). To distinguish between direct and indirect targets, total RNA from wild-type (Nmd⁺) and mutant (Nmd⁻) strains was probed with high-density arrays across a 1-h time window following transcription inhibition. Statistical models were developed to describe the kinetics of RNA decay. 45% ± 5% of RNAs targeted by NMD were predicted to be direct targets with altered decay rates in Nmd⁻ strains. Parallel experiments using conventional methods were conducted to empirically test predictions from the global experiment. The results show that the global assay reliably distinguished direct versus indirect targets. Different types of targets were investigated, including transcripts containing adjacent, disabled open reading frames, upstream open reading frames, and those prone to out-of-frame initiation of translation. Known targeting mechanisms fail to account for all of the direct targets of NMD, suggesting that additional targeting mechanisms remain to be elucidated. 30% of the protein-coding targets of NMD fell into two broadly defined functional themes: those affecting chromosome structure and behavior and those affecting cell surface dynamics. Overall, the results provide a preview for how expression profiles in multi-cellular eukaryotes might be impacted by NMD. Furthermore, the methods for analyzing decay rates on a global scale offer a blueprint for new ways to study mRNA decay pathways in any organism where cultured cell lines are available.

Genes determine the structure of proteins through transcription and translation in which an RNA copy of the gene is made (mRNA) and then translated to make the protein. Cellular protein levels reflect the relative rates of mRNA synthesis and degradation, which are subject to multiple layers of controls. Mechanisms also exist to ensure the quality of each mRNA. One quality control mechanism called nonsense-mediated mRNA decay (NMD) triggers the rapid degradation of mRNAs containing coding errors that would otherwise lead to the production of non-functional or potentially deleterious proteins. NMD occurs in yeasts, plants, flies, worms, mice, and humans. In humans, NMD affects the etiology of genetic disorders by affecting the expression of genes that carry disease-causing mutations. Besides quality assurance, NMD plays another role in gene expression by controlling the abundance of hundreds of normal mRNAs that are devoid of coding errors. In this paper, the authors used DNA arrays to monitor the relative decay rates of all mRNAs in budding yeast and found a subset where decay rates were dependent on NMD. Many of the corresponding proteins perform related functional roles affecting both the structure and behavior of chromosomes and the structure and integrity of the cell surface.

Positional cloning of Sorcs1, a type 2 diabetes quantitative trait locus

We previously mapped the type 2 diabetes mellitus-2 locus (T2dm2), which affects fasting insulin levels, to distal chromosome 19 in a leptin-deficient obese F2 intercross derived from C57BL/6 (B6) and BTBR T+ tf/J (BTBR) mice. Introgression of a 7-Mb segment of the B6 chromosome 19 into the BTBR background (strain 1339A) replicated the reduced insulin linked to T2dm2. The 1339A mice have markedly impaired insulin secretion in vivo and disrupted islet morphology. We used subcongenic strains derived from 1339A to localize the T2dm2 quantitative trait locus (QTL) to a 242-kb segment comprising the promoter, first exon and most of the first intron of the Sorcs1 gene. This was the only gene in the 1339A strain for which we detected amino acid substitutions and expression level differences between mice carrying B6 and BTBR alleles of this insert, thereby identifying variation within the Sorcs1 gene as underlying the phenotype associated with the T2dm2 locus. SorCS1 binds platelet-derived growth factor, a growth factor crucial for pericyte recruitment to the microvasculature, and may thus have a role in expanding or maintaining the islet vasculature. Our identification of the Sorcs1 gene provides insight into the pathway underlying the pathophysiology of obesity-induced type 2 diabetes mellitus.

Combined expression trait correlations and expression quantitative trait locus mapping

Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. The consideration of gene expression correlation over a time or tissue dimension has proved valuable in predicting gene function. Here, we consider correlations over a genetic dimension. In addition to identifying coregulated genes, the genetic dimension also supplies us with information about the genomic locations of putative regulatory loci. We calculated correlations among approximately 45,000 expression traits derived from 60 individuals in an F₂ sample segregating for obesity and diabetes. By combining the correlation results with linkage mapping information, we were able to identify regulatory networks, make functional predictions for uncharacterized genes, and characterize novel members of known pathways. We found evidence of coordinate regulation of 174 G protein–coupled receptor protein signaling pathway expression traits. Of the 174 traits, 50 had their major LOD peak within 10 cM of a locus on Chromosome 2, and 81 others had a secondary peak in this region. We also characterized a Riken cDNA clone that showed strong correlation with stearoyl-CoA desaturase 1 expression. Experimental validation confirmed that this clone is involved in the regulation of lipid metabolism. We conclude that trait correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we studied only mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping alone.

In order to annotate gene function and identify potential members of regulatory networks, the authors explore correlation of expression profiles across a genetic dimension, namely genotypes segregating in a panel of 60 F₂ mice derived from a cross used to explore diabetes in obese mice. They first identified 6,016 seed transcripts for which they observe that the gene expression is linked to a particular region of the genome. Then they searched for transcripts whose expression is highly correlated with the seed transcripts and tested for enrichment of common biological functions among the lists of correlated transcripts. They found and explored the properties of 1,341 sets of transcripts that share a particular “gene ontology” term. Thirty-eight seeds in the G protein–coupled receptor protein signaling pathway were correlated with 174 transcripts, all of which are also annotated as G protein–coupled receptor protein signaling pathway and 131 of which share a regulatory locus on Chromosome 2. The authors note many of these findings would have been missed by simple expression quantitative trait loci analysis without the correlation step. The approach was used to identify a common set of genes involved in lipid metabolism.

Bayesian model selection for genome-wide epistatic quantitative trait loci analysis

The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.

Nonparametric estimation of the effects of quantitative trait loci

Interval mapping of quantitative trait loci from breeding experiments plays an important role in understanding the mechanisms of disease, both in humans and other organisms. Standard approaches to estimation involve parametric assumptions for the component distributions and may be sensitive to model misspecification. Some nonparametric tests have been studied. However, nonparametric estimation of the phenotypic distributions has not been considered in the genetics literature, even though such methods might provide essential nonparametric summaries for comparing different loci. We develop a sufficient condition for identifiability of the phenotypic distributions. Simple nonparametric estimators for the distributions are proposed for uncensored and right censored data. They have a closed form and their small and large sample properties are readily established. Their practical utility as numerical summaries which complement nonparametric tests is demonstrated on two recent genetics examples.