Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits

Murine Alox8 versus the human ALOX15B ortholog: differences and similarities

Human arachidonate 15-lipoxygenase type B is a lipoxygenase that catalyzes the peroxidation of arachidonic acid at carbon-15. The corresponding murine ortholog however has 8-lipoxygenase activity. Both enzymes oxygenate polyunsaturated fatty acids in S-chirality with singular reaction specificity, although they generate a different product pattern. Furthermore, while both enzymes utilize both esterified fatty acids and fatty acid hydro(pero)xides as substrates, they differ with respect to the orientation of the fatty acid in their substrate-binding pocket. While ALOX15B accepts the fatty acid "tail-first," Alox8 oxygenates the free fatty acid with its "head-first." These differences in substrate orientation and thus in regio- and stereospecificity are thought to be determined by distinct amino acid residues. Towards their biological function, both enzymes share a commonality in regulating cholesterol homeostasis in macrophages, and Alox8 knockdown is associated with reduced atherosclerosis in mice. Additional roles have been linked to lung inflammation along with tumor suppressor activity. This review focuses on the current knowledge of the enzymatic activity of human ALOX15B and murine Alox8, along with their association with diseases.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease

Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer's-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer's disease.

Establishment and application of a silkworm CRISPR/Cas9 tool for conditionally manipulating gene disruption in the epidermis

Spatial or temporal specific gene knockout system is a valuable tool for studying the molecular mechanisms underlying developmental processes. The integument is essential for insect fitness and survival, but tools for dissecting function of genes in this tissue are lacking. In this study, we firstly identified an epidermis specifically expressed gene of the domesticated silkworm, BmCPG25, by comparative transcriptomic analysis. Furthermore, a transgenic silkworm expressing the RNA dependent CRISPR-Cas9 protein driven by the regulatory region of the BmCPG25 was established. Immunochemistry analysis showed the endonuclease was specifically expressed in the nuclear of epidermal cells. We also validated the efficiency of this system by disrupting the function of an epidermis specifically expressed cuticular protein gene (Cpr21) and a ubiquitously expressed cuticular gene (Cph18), respectively. In summary, we successfully constructed a conditional knockout toolkit to manipulate the gene editing in epidermal cells, which provides a valuable approach to study the molecular mechanism of integument development.

Mutational sources of trans-regulatory variation affecting gene expression in Saccharomyces cerevisiae

Heritable variation in a gene’s expression arises from mutations impacting cis- and trans-acting components of its regulatory network. Here, we investigate how trans-regulatory mutations are distributed within the genome and within a gene regulatory network by identifying and characterizing 69 mutations with trans-regulatory effects on expression of the same focal gene in Saccharomyces cerevisiae. Relative to 1766 mutations without effects on expression of this focal gene, we found that these trans-regulatory mutations were enriched in coding sequences of transcription factors previously predicted to regulate expression of the focal gene. However, over 90% of the trans-regulatory mutations identified mapped to other types of genes involved in diverse biological processes including chromatin state, metabolism, and signal transduction. These data show how genetic changes in diverse types of genes can impact a gene’s expression in trans, revealing properties of trans-regulatory mutations that provide the raw material for trans-regulatory variation segregating within natural populations.

Genetic architecture modulates diet-induced hepatic mRNA and miRNA expression profiles in Diversity Outbred mice

Genetic approaches in model organisms have consistently demonstrated that molecular traits such as gene expression are under genetic regulation, similar to clinical traits. The resulting expression quantitative trait loci (eQTL) have revolutionized our understanding of genetic regulation and identified numerous candidate genes for clinically relevant traits. More recently, these analyses have been extended to other molecular traits such as protein abundance, metabolite levels, and miRNA expression. Here, we performed global hepatic eQTL and microRNA expression quantitative trait loci (mirQTL) analysis in a population of Diversity Outbred mice fed two different diets. We identified several key features of eQTL and mirQTL, namely differences in the mode of genetic regulation (cis or trans) between mRNA and miRNA. Approximately 50% of mirQTL are regulated by a trans-acting factor, compared to ∼25% of eQTL. We note differences in the heritability of mRNA and miRNA expression and variance explained by each eQTL or mirQTL. In general, cis-acting variants affecting mRNA or miRNA expression explain more phenotypic variance than trans-acting variants. Finally, we investigated the effect of diet on the genetic architecture of eQTL and mirQTL, highlighting the critical effects of environment on both eQTL and mirQTL. Overall, these data underscore the complex genetic regulation of two well-characterized RNA classes (mRNA and miRNA) that have critical roles in the regulation of clinical traits and disease susceptibility

Sex-Biased Molecular Signature for Overall Survival of Liver Cancer Patients

Sex/gender disparity has been shown in the incidence and prognosis of many types of diseases, probably due to differences in genes, physiological conditions such as hormones, and lifestyle between the sexes. The mortality and survival rates of many cancers, especially liver cancer, differ between men and women. Due to the pronounced sex/gender disparity, considering sex/gender may be necessary for the diagnosis and treatment of liver cancer. By analyzing research articles through a PubMed literature search, the present review identified 12 genes which showed practical relevance to cancer and sex disparities. Among the 12 sex-specific genes, 7 genes (BAP1, CTNNB1, FOXA1, GSTO1, GSTP1, IL6, and SRPK1) showed sex-biased function in liver cancer. Here we summarized previous findings of cancer molecular signature including our own analysis, and showed that sex-biased molecular signature CTNNB1^High, IL6^High, RHOA^High and GLIPR1^Low may serve as a female-specific index for prediction and evaluation of OS in liver cancer patients. This review suggests a potential implication of sex-biased molecular signature in liver cancer, providing a useful information on diagnosis and prediction of disease progression based on gender.

Genetic Architecture Modulates Diet-Induced Hepatic mRNA and miRNA Expression Profiles in Diversity Outbred Mice

Genetic approaches in model organisms have consistently demonstrated that molecular traits such as gene expression are under genetic regulation, similar to clinical traits. The resulting expression quantitative trait loci (eQTL) have revolutionized our understanding of genetic regulation and identified numerous candidate genes for clinically relevant traits. More recently, these analyses have been extended to other molecular traits such as protein abundance, metabolite levels, and miRNA expression. Here, we performed global hepatic eQTL and microRNA expression quantitative trait loci (mirQTL) analysis in a population of Diversity Outbred mice fed two different diets. We identified several key features of eQTL and mirQTL, namely differences in the mode of genetic regulation (cis or trans) between mRNA and miRNA. Approximately 50% of mirQTL are regulated by a trans-acting factor, compared to ∼25% of eQTL. We note differences in the heritability of mRNA and miRNA expression and variance explained by each eQTL or mirQTL. In general, cis-acting variants affecting mRNA or miRNA expression explain more phenotypic variance than trans-acting variants. Lastly, we investigated the effect of diet on the genetic architecture of eQTL and mirQTL, highlighting the critical effects of environment on both eQTL and mirQTL. Overall, these data underscore the complex genetic regulation of two well-characterized RNA classes (mRNA and miRNA) that have critical roles in the regulation of clinical traits and disease susceptibility.

Genomic resources for dissecting the role of non-protein coding variation in gene-environment interactions

The majority of single nucleotide variants (SNVs) identified in Genome Wide Association Studies (GWAS) fall within non-protein coding DNA and have the potential to alter gene expression. Non-protein coding DNA can control gene expression by acting as transcription factor (TF) binding sites or by regulating the organization of DNA into chromatin. SNVs in non-coding DNA sequences can disrupt TF binding and chromatin structure and this can result in pathology. Further, environmental health studies have shown that exposure to xenobiotics can disrupt the ability of TFs to regulate entire gene networks and result in pathology. However, there is a large amount of interindividual variability in exposure-linked health outcomes. One explanation for this heterogeneity is that genetic variation and exposure combine to disrupt gene regulation, and this eventually manifests in disease. Many resources exist that annotate common variants from GWAS and combine them with conservation, functional genomics, and TF binding data. These annotation tools provide clues regarding the biological implications of an SNV, as well as lead to the generation of hypotheses regarding potentially disrupted target genes, epigenetic markers, pathways, and cell types. Collectively this information can be used to predict how SNVs can alter an individual's response to exposure and disease risk. A basic understanding of the regulatory information contained within non-protein coding DNA is needed to predict the biological consequences of SNVs, and to determine how these SNVs impact exposure-related disease. We hope that this review will aid in the characterization of disease-associated genetic variation in the non-protein coding genome.

The role of the LTB4-BLT1 axis in health and disease

Leukotriene B4 (LTB4) is a major type of lipid mediator that is rapidly generated from arachidonic acid through sequential action of 5-lipoxygenase (5-LO), 5-lipoxygenase-activating protein (FLAP) and LTA4 hydrolase (LTA4H) in response to various stimuli. LTB4 is well known to be a chemoattractant for leukocytes, particularly neutrophils, via interaction with its high-affinity receptor BLT1. Extensive attention has been paid to the role of the LTB4-BLT1 axis in acute and chronic inflammatory diseases, such as infectious diseases, allergy, autoimmune diseases, and metabolic disease via mediating recruitment and/or activation of different types of inflammatory cells depending on different stages or the nature of inflammatory response. Recent studies also demonstrated that LTB4 acts on non-immune cells via BLT1 to initiate and/or amplify pathological inflammation in various tissues. In addition, emerging evidence reveals a complex role of the LTB4-BLT1 axis in cancer, either tumor-inhibitory or tumor-promoting, depending on the different target cells. In this review, we summarize both established understanding and the most recent progress in our knowledge about the LTB4-BLT1 axis in host defense, inflammatory diseases and cancer.

Identification and Functional Prediction of Long Intergenic Non-coding RNAs Related to Subcutaneous Adipose Development in Pigs

An increasing number of studies have shown that long intergenic non-coding RNAs (lincRNAs) are a very important class of non-coding RNAs that plays a vital role in many biological processes. Adipose tissue is an important place for storing energy, but few studies on lincRNAs were related to pig subcutaneous fat development. Here, we used published RNA-seq data from subcutaneous adipose tissue of Italian Large White pigs and identified 252 putative lincRNAs, wherein 34 were unannotated. These lincRNAs had relatively shorter length, lower number of exons, and lower expression level compared with protein-coding transcripts. Gene ontology and pathway analysis indicated that the adjacent genes of lincRNAs were involved in lipid metabolism. In addition, differentially expressed lincRNAs (DELs) between low and high backfat thickness pigs were identified. Through the detection of quantitative trait locus (QTL), DELs were mainly located in QTLs related to adipose development. Based on the expression correlation of DEL genes and their differentially expressed potential target genes, we constructed a co-expression network and a potential pathway of DEL's effect on lipid metabolism. Our study identified and analyzed lincRNAs in subcutaneous adipose tissue, and results suggested that lincRNAs may be involved in the regulation of subcutaneous fat development. Our findings provided new insights into the biological function of porcine lincRNAs.

Post-genomic behavioral genetics: From revolution to routine

What was once expensive and revolutionary-full-genome sequence-is now affordable and routine. Costs will continue to drop, opening up new frontiers in behavioral genetics. This shift in costs from the genome to the phenome is most notable in large clinical studies of behavior and associated diseases in cohorts that exceed hundreds of thousands of subjects. Examples include the Women's Health Initiative (www.whi.org), the Million Veterans Program (www.

RESEARCH

va.gov/MVP), the 100 000 Genomes Project (genomicsengland.co.uk) and commercial efforts such as those by deCode (www.decode.com) and 23andme (www.23andme.com). The same transition is happening in experimental neuro- and behavioral genetics, and sample sizes of many hundreds of cases are becoming routine (www.genenetwork.org, www.mousephenotyping.org). There are two major consequences of this new affordability of massive omics datasets: (1) it is now far more practical to explore genetic modulation of behavioral differences and the key role of gene-by-environment interactions. Researchers are already doing the hard part-the quantitative analysis of behavior. Adding the omics component can provide powerful links to molecules, cells, circuits and even better treatment. (2) There is an acute need to highlight and train behavioral scientists in how best to exploit new omics approaches. This review addresses this second issue and highlights several new trends and opportunities that will be of interest to experts in animal and human behaviors.

Genetical genomics of growth in a chicken model

BACKGROUND

The genetics underlying body mass and growth are key to understanding a wide range of topics in biology, both evolutionary and developmental. Body mass and growth traits are affected by many genetic variants of small effect. This complicates genetic mapping of growth and body mass. Experimental intercrosses between individuals from divergent populations allows us to map naturally occurring genetic variants for selected traits, such as body mass by linkage mapping. By simultaneously measuring traits and intermediary molecular phenotypes, such as gene expression, one can use integrative genomics to search for potential causative genes.

RESULTS

In this study, we use linkage mapping approach to map growth traits (N = 471) and liver gene expression (N = 130) in an advanced intercross of wild Red Junglefowl and domestic White Leghorn layer chickens. We find 16 loci for growth traits, and 1463 loci for liver gene expression, as measured by microarrays. Of these, the genes TRAK1, OSBPL8, YEATS4, CEP55, and PIP4K2B are identified as strong candidates for growth loci in the chicken. We also show a high degree of sex-specific gene-regulation, with almost every gene expression locus exhibiting sex-interactions. Finally, several trans-regulatory hotspots were found, one of which coincides with a major growth locus.

CONCLUSIONS

These findings not only serve to identify several strong candidates affecting growth, but also show how sex-specificity and local gene-regulation affect growth regulation in the chicken.

Genetics of metabolic syndrome: potential clues from wild-derived inbred mouse strains

The metabolic syndrome (MetS) is a complex constellation of metabolic abnormalities including obesity, abnormal glucose metabolism, dyslipidemia, and elevated blood pressure that together substantially increase risk for cardiovascular disease and Type 2 diabetes. Both genetic and environmental factors contribute to the development of MetS, but this process is still far from understood. Human studies have revealed only part of the underlying basis. Studies in mice offer many strengths that can complement human studies to help elucidate the etiology and pathophysiology of MetS. Here we review the ways mice can contribute to MetS research. In particular, we focus on the information that can be obtained from studies of the inbred strains, with specific focus on the phenotypes of the wild-derived inbred strains. These are newly derived inbred strains that were created from wild-caught mice. They contain substantial genetic variation that is not present in the classical inbred strains, have phenotypes of relevance for MetS, and various mouse strain resources have been created to facilitate the mining of this new genetic variation. Thus studies using wild-derived inbred strains hold great promise for increasing our understanding of MetS.

Relating quantitative variation within a behavior to variation in transcription

Many studies have shown that variation in transcription is associated with changes in behavioral state, or with variation within a state, but little has been done to address if the same genes are involved in both. Here, we investigate the transcriptional basis of variation in parental provisioning using two species of burying beetle, Nicrophorus orbicollis and Nicrophorus vespilloides. We used RNA-seq to compare transcription in parents that provided high amounts of provisioning behavior versus low amounts in males and females of each species. We found no overarching transcriptional patterns distinguishing high from low caring parents, and no informative transcripts that displayed particularly large expression differences in either sex. However, we did find subtler gene expression differences between high and low provisioning parents that are consistent across both sexes and species. Furthermore, we show that transcripts previously implicated in transitioning into parental care in N. vespilloides had high variance in the levels of transcription and were unusually likely to display differential expression between high and low provisioning parents. Thus, quantitative behavioral variation appears to reflect many transcriptional differences of small effect. Furthermore, the same transcripts required for the transition between behavioral states are also related to variation within a behavioral state.

Identifying genes for neurobehavioural traits in rodents: progress and pitfalls

Identifying genes and pathways that contribute to differences in neurobehavioural traits is a key goal in psychiatric research. Despite considerable success in identifying quantitative trait loci (QTLs) associated with behaviour in laboratory rodents, pinpointing the causal variants and genes is more challenging. For a long time, the main obstacle was the size of QTLs, which could encompass tens if not hundreds of genes. However, recent studies have exploited mouse and rat resources that allow mapping of phenotypes to narrow intervals, encompassing only a few genes. Here, we review these studies, showcase the rodent resources they have used and highlight the insights into neurobehavioural traits provided to date. We discuss what we see as the biggest challenge in the field - translating QTLs into biological knowledge by experimentally validating and functionally characterizing candidate genes - and propose that the CRISPR/Cas genome-editing system holds the key to overcoming this obstacle. Finally, we challenge traditional views on inbred versus outbred resources in the light of recent resource and technology developments.

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

Panx3 links body mass index and tumorigenesis in a genetically heterogeneous mouse model of carcinogen-induced cancer

Background

Body mass index (BMI) has been implicated as a primary factor influencing cancer development. However, understanding the relationship between these two complex traits has been confounded by both environmental and genetic heterogeneity.

Methods

In order to gain insight into the genetic factors linking BMI and cancer, we performed chemical carcinogenesis on a genetically heterogeneous cohort of interspecific backcross mice ((Mus Spretus × FVB/N) F1 × FVB/N). Using this cohort, we performed quantitative trait loci (QTL) analysis to identify regions linked to BMI. We then performed an integrated analysis incorporating gene expression, sequence comparison between strains, and gene expression network analysis to identify candidate genes influencing both tumor development and BMI.

Results

Analysis of QTL linked to tumorigenesis and BMI identified several loci associated with both phenotypes. Exploring these loci in greater detail revealed a novel relationship between the Pannexin 3 gene (Panx3) and both BMI and tumorigenesis. Panx3 is positively associated with BMI and is strongly tied to a lipid metabolism gene expression network. Pre-treatment Panx3 gene expression levels in normal skin are associated with tumor susceptibility and inhibition of Panx function strongly influences inflammation.

Conclusions

These studies have identified several genetic loci that influence both BMI and carcinogenesis and implicate Panx3 as a candidate gene that links these phenotypes through its effects on inflammation and lipid metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0334-8) contains supplementary material, which is available to authorized users.

Weak Epistasis Generally Stabilizes Phenotypes in a Mouse Intercross

The extent and strength of epistasis is commonly unresolved in genetic studies, and observed epistasis is often difficult to interpret in terms of biological consequences or overall genetic architecture. We investigated the prevalence and consequences of epistasis by analyzing four body composition phenotypes—body weight, body fat percentage, femoral density, and femoral circumference—in a large F2 intercross of B6-lit/lit and C3.B6-lit/lit mice. We used Combined Analysis of Pleiotropy and Epistasis (CAPE) to examine interactions for the four phenotypes simultaneously, which revealed an extensive directed network of genetic loci interacting with each other, circulating IGF1, and sex to influence these phenotypes. The majority of epistatic interactions had small effects relative to additive effects of individual loci, and tended to stabilize phenotypes towards the mean of the population rather than extremes. Interactive effects of two alleles inherited from one parental strain commonly resulted in phenotypes closer to the population mean than the additive effects from the two loci, and often much closer to the mean than either single-locus model. Alternatively, combinations of alleles inherited from different parent strains contribute to more extreme phenotypes not observed in either parental strain. This class of phenotype-stabilizing interactions has effects that are close to additive and are thus difficult to detect except in very large intercrosses. Nevertheless, we found these interactions to be useful in generating hypotheses for functional relationships between genetic loci. Our findings suggest that while epistasis is often weak and unlikely to account for a large proportion of heritable variance, even small-effect genetic interactions can facilitate hypotheses of underlying biology in well-powered studies.

The role of statistical epistasis in the genetic architecture of complex traits has been of great interest to the genetics community since Fisher introduced the concept in 1918. However, assessing epistasis in human and model organism populations has been impeded by limited statistical power. To mitigate this limitation, we analyzed bone and body composition traits in an unusually large mouse intercross population of over 2000 mice, paired with a recently-developed computational approach that leverages information to detect interactions across multiple phenotypes. We discovered a large network of highly significant genetic interactions between variants that influence complex body composition traits. Although epistasis was abundant, the interaction network was dominated by epistasis that stabilizes phenotypes by reducing phenotypic deviation from the parent strains. Nevertheless, the observed network provides an overview of genetic architecture and specific hypotheses of how QTL combine to affect phenotypes. These findings suggest that epistatic effects are generally of lesser magnitude than main QTL effects, and therefore are unlikely to account for major components of variance, but also reinforce genetic interaction analysis as a potent tool for dissecting the biology of complex traits.

Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data

Understanding causal relationships among large numbers of variables is a fundamental goal of biomedical sciences and can be facilitated by Directed Acyclic Graphs (DAGs) where directed edges between nodes represent the influence of components of the system on each other. In an observational setting, some of the directions are often unidentifiable because of Markov equivalency. Additional exogenous information, such as expert knowledge or genotype data can help establish directionality among the endogenous variables. In this study, we use the method of principle component analysis to extract information across the genome in order to generate a robust statistical causal network among phenotypes, the variables of primary interest. The method is applied to 590,020 SNP genotypes measured on 1596 individuals to generate the statistical causal network of 13 cardiovascular disease risk factor phenotypes. First, principal component analysis was used to capture information across the genome. The principal components were then used to identify a robust causal network structure, GDAG, among the phenotypes. Analyzing a robust causal network over risk factors reveals the flow of information in direct and alternative paths, as well as determining predictors and good targets for intervention. For example, the analysis identified BMI as influencing multiple other risk factor phenotypes and a good target for intervention to lower disease risk.

Arachidonate 5-lipoxygenase (ALOX5) gene polymorphism is associated with Alzheimer's disease and body mass index

Dementias of old age, in particular Alzheimer's disease (AD), pose a growing threat to the longevity and quality of life of individuals as well as whole societies world-wide. The risk factors are both genetic and environmental (life-style) and there is an overlap with similar factors predisposing to cardiovascular diseases (CVD). Using a case-control genetic approach, we have identified a SNP (rs10507391) in ALOX5 gene, previously associated with an increased risk of stroke, as a novel genetic risk factor for AD. ALOX5 gene encodes a 5'-lipoxygenase (5'-LO) activating protein (FLAP), a crucial component of the arachidonic acid/leukotriene inflammatory cascade. A-allele of rs4769874 polymorphism increases the risk of AD 1.41-fold (p<0.0001), while AA genotype does so 1.79-fold (p<0.0001). In addition, GG genotype of rs4769874 polymorphism is associated with a modest increase in body mass index (BMI). We discuss potential biochemical mechanisms linking the SNP to AD and suggest possible preventive pharmacotherapies some of which are based on commonly available natural products. Finally, we set the newly identified AD risk factors into a broader context of similar CVD risk factors to generate a more comprehensive picture of interacting genetics and life-style habits potentially leading to the deteriorating mental health in the old age.

Metabolites of arachidonic acid and linoleic acid in early stages of non-alcoholic fatty liver disease--A pilot study

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is a spectrum of liver conditions related to fat infiltration. The role of liver triacylglycerol accumulation in NAFLD is not fully understood.

METHODS

Twenty-four patients, 12 in the first and 12 in the second stage of NAFLD, were prospectively enrolled in this study. Biochemical parameters and eicosanoids (HETE and HODE) were compared between the first and the second stage of hepatic steatosis and the effect of a 6-month dietary intervention on these parameters was evaluated. Eicosanoid profiles were extracted from 0.5 ml of plasma using solid-phase extraction RP-18 SPE columns. The HPLC separations were performed on a 1260 liquid chromatograph.

RESULTS

Patients with stage I NAFLD had a significantly higher level of HDL cholesterol and a lower level of 5-HETE. Patients with grade II steatosis had higher concentrations of 9-HODE. Following the six-month dietary intervention, hepatic steatosis resolved completely in all patients. This resulted in a significant decrease in the concentrations of all eicosanoids (LX4, 16-HETE, 13-HODE, 9-HODE, 15-HETE, 12-HETE, 5-oxoETE, 5-HETE) and key biochemical parameters (BMI, insulin, HOMA-IR, liver enzymes).

CONCLUSION

A significant reduction in the analyzed eicosanoids and a parallel reduction in fatty liver confirmed the usefulness of HETE and HODE in the assessment of NAFLD.

Genetic regulation of bone metabolism in the chicken: similarities and differences to Mammalian systems

Birds have a unique bone physiology, due to the demands placed on them through egg production. In particular their medullary bone serves as a source of calcium for eggshell production during lay and undergoes continuous and rapid remodelling. We take advantage of the fact that bone traits have diverged massively during chicken domestication to map the genetic basis of bone metabolism in the chicken. We performed a quantitative trait locus (QTL) and expression QTL (eQTL) mapping study in an advanced intercross based on Red Junglefowl (the wild progenitor of the modern domestic chicken) and White Leghorn chickens. We measured femoral bone traits in 456 chickens by peripheral computerised tomography and femoral gene expression in a subset of 125 females from the cross with microarrays. This resulted in 25 loci for female bone traits, 26 loci for male bone traits and 6318 local eQTL loci. We then overlapped bone and gene expression loci, before checking for an association between gene expression and trait values to identify candidate quantitative trait genes for bone traits. A handful of our candidates have been previously associated with bone traits in mice, but our results also implicate unexpected and largely unknown genes in bone metabolism. In summary, by utilising the unique bone metabolism of an avian species, we have identified a number of candidate genes affecting bone allocation and metabolism. These findings can have ramifications not only for the understanding of bone metabolism genetics in general, but could also be used as a potential model for osteoporosis as well as revealing new aspects of vertebrate bone regulation or features that distinguish avian and mammalian bone.

In this work we seek to further the understanding of bone genetics by mapping bone traits and gene expression in the chicken. Bone in female birds is special due to egg production. In this study, we combine the genetic mapping of bone traits with bone gene expression to find candidate quantitative trait genes that explain the differences between wild and domestic chickens in terms of bone production. The concept of combining genetic mapping and gene expression mapping is not new, and has already been successful in isolating bone-related genes in mammals, however this is the first time it has been applied to an avian system with such unique bone modelling processes. We aim to reveal new molecular mechanisms of bone regulation, and many of the candidates we find are new, highlighting the potential this technique has to identify the potential differences between avian and mammalian bone biology.

Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders

A disruptive approach to therapeutic discovery and development is required in order to significantly improve the success rate of drug discovery for central nervous system (CNS) disorders. In this review, we first assess the key factors contributing to the frequent clinical failures for novel drugs. Second, we discuss cancer translational research paradigms that addressed key issues in drug discovery and development and have resulted in delivering drugs with significantly improved outcomes for patients. Finally, we discuss two emerging technologies that could improve the success rate of CNS therapies: human induced pluripotent stem cell (hiPSC)-based studies and multiscale biology models. Coincident with advances in cellular technologies that enable the generation of hiPSCs directly from patient blood or skin cells, together with methods to differentiate these hiPSC lines into specific neural cell types relevant to neurological disease, it is also now possible to combine data from large-scale forward genetics and post-mortem global epigenetic and expression studies in order to generate novel predictive models. The application of systems biology approaches to account for the multiscale nature of different data types, from genetic to molecular and cellular to clinical, can lead to new insights into human diseases that are emergent properties of biological networks, not the result of changes to single genes. Such studies have demonstrated the heterogeneity in etiological pathways and the need for studies on model systems that are patient-derived and thereby recapitulate neurological disease pathways with higher fidelity. In the context of two common and presumably representative neurological diseases, the neurodegenerative disease Alzheimer's Disease, and the psychiatric disorder schizophrenia, we propose the need for, and exemplify the impact of, a multiscale biology approach that can integrate panomic, clinical, imaging, and literature data in order to construct predictive disease network models that can (i) elucidate subtypes of syndromic diseases, (ii) provide insights into disease networks and targets and (iii) facilitate a novel drug screening strategy using patient-derived hiPSCs to discover novel therapeutics for CNS disorders.

The arachidonic acid metabolome serves as a conserved regulator of cholesterol metabolism

Cholesterol metabolism is closely interrelated with cardiovascular disease in humans. Dietary supplementation with omega-6 polyunsaturated fatty acids including arachidonic acid (AA) was shown to favorably affect plasma LDL-C and HDL-C. However, the underlying mechanisms are poorly understood. By combining data from a GWAS screening in >100,000 individuals of European ancestry, mediator lipidomics, and functional validation studies in mice, we identify the AA metabolome as an important regulator of cholesterol homeostasis. Pharmacological modulation of AA metabolism by aspirin induced hepatic generation of leukotrienes (LTs) and lipoxins (LXs), thereby increasing hepatic expression of the bile salt export pump Abcb11. Induction of Abcb11 translated in enhanced reverse cholesterol transport, one key function of HDL. Further characterization of the bioactive AA-derivatives identified LX mimetics to lower plasma LDL-C. Our results define the AA metabolomeasconserved regulator of cholesterol metabolism, and identify AA derivatives as promising therapeutics to treat cardiovascular disease in humans.

Genetic influences on brain gene expression in rats selected for tameness and aggression

Interindividual differences in many behaviors are partly due to genetic differences, but the identification of the genes and variants that influence behavior remains challenging. Here, we studied an F2 intercross of two outbred lines of rats selected for tame and aggressive behavior toward humans for >64 generations. By using a mapping approach that is able to identify genetic loci segregating within the lines, we identified four times more loci influencing tameness and aggression than by an approach that assumes fixation of causative alleles, suggesting that many causative loci were not driven to fixation by the selection. We used RNA sequencing in 150 F2 animals to identify hundreds of loci that influence brain gene expression. Several of these loci colocalize with tameness loci and may reflect the same genetic variants. Through analyses of correlations between allele effects on behavior and gene expression, differential expression between the tame and aggressive rat selection lines, and correlations between gene expression and tameness in F2 animals, we identify the genes Gltscr2, Lgi4, Zfp40, and Slc17a7 as candidate contributors to the strikingly different behavior of the tame and aggressive animals.

Expression quantitative trait loci infer the regulation of isoflavone accumulation in soybean (Glycine max L. Merr.) seed

BACKGROUND

Mapping expression quantitative trait loci (eQTL) of targeted genes represents a powerful and widely adopted approach to identify putative regulatory variants. Linking regulation differences to specific genes might assist in the identification of networks and interactions. The objective of this study is to identify eQTL underlying expression of four gene families encoding isoflavone synthetic enzymes involved in the phenylpropanoid pathway, which are phenylalanine ammonia-lyase (PAL; EC 4.3.1.5), chalcone synthase (CHS; EC 2.3.1.74), 2-hydroxyisoflavanone synthase (IFS; EC1.14.13.136) and flavanone 3-hydroxylase (F3H; EC 1.14.11.9). A population of 130 recombinant inbred lines (F5:11), derived from a cross between soybean cultivar 'Zhongdou 27' (high isoflavone) and 'Jiunong 20' (low isoflavone), and a total of 194 simple sequence repeat (SSR) markers were used in this study. Overlapped loci of eQTLs and phenotypic QTLs (pQTLs) were analyzed to identify the potential candidate genes underlying the accumulation of isoflavone in soybean seed.

RESULTS

Thirty three eQTLs (thirteen cis-eQTLs and twenty trans-eQTLs) underlying the transcript abundance of the four gene families were identified on fifteen chromosomes. The eQTLs between Satt278-Sat_134, Sat_134-Sct_010 and Satt149-Sat_234 underlie the expression of both IFS and CHS genes. Five eQTL intervals were overlapped with pQTLs. A total of eleven candidate genes within the overlapped eQTL and pQTL were identified.

CONCLUSIONS

These results will be useful for the development of marker-assisted selection to breed soybean cultivars with high or low isoflavone contents and for map-based cloning of new isoflavone related genes.

Heritability and genetic basis of protein level variation in an outbred population

The genetic basis of heritable traits has been studied for decades. Although recent mapping efforts have elucidated genetic determinants of transcript levels, mapping of protein abundance has lagged. Here, we analyze levels of 4084 GFP-tagged yeast proteins in the progeny of a cross between a laboratory and a wild strain using flow cytometry and high-content microscopy. The genotype of trans variants contributed little to protein level variation between individual cells but explained >50% of the variance in the population’s average protein abundance for half of the GFP fusions tested. To map trans-acting factors responsible, we performed flow sorting and bulk segregant analysis of 25 proteins, finding a median of five protein quantitative trait loci (pQTLs) per GFP fusion. Further, we find that cis-acting variants predominate; the genotype of a gene and its surrounding region had a large effect on protein level six times more frequently than the rest of the genome combined. We present evidence for both shared and independent genetic control of transcript and protein abundance: More than half of the expression QTLs (eQTLs) contribute to changes in protein levels of regulated genes, but several pQTLs do not affect their cognate transcript levels. Allele replacements of genes known to underlie trans eQTL hotspots confirmed the correlation of effects on mRNA and protein levels. This study represents the first genome-scale measurement of genetic contribution to protein levels in single cells and populations, identifies more than a hundred trans pQTLs, and validates the propagation of effects associated with transcript variation to protein abundance.

A three step network based approach (TSNBA) to finding disease molecular signature and key regulators: a case study of IL-1 and TNF-alpha stimulated inflammation

A disease molecular signature is a set of biomolecular features that are prognostic of clinical phenotypes and indicative of underlying pathology. It is of great importance to develop computational approaches for finding more relevant molecular signatures. Based upon the hypothesis that various components in a molecular signature are more likely to share similar patterns, we introduced a novel three step network based approach (TSNBA) to identify the molecular signature and key pathological regulators. Protein-protein interaction (PPI) network and ranking algorithm were integrated in the first step to find pathology related proteins with high accuracy. It was followed by the second step to further screen with co-expression patterns for better pathology enrichment. Context likelihood of relatedness (CLR) algorithm was used in the third step to infer gene regulatory networks and identify key transcription regulators. We applied this approach to study IL-1 (interleukin-1) and TNF-alpha (tumor necrosis factor-alpha) stimulated inflammation. TSNBA identified inflammatory signature with high accuracy and outperformed 5 competing methods namely fold change, degree, interconnectivity, neighborhood score and network propagation based approaches. The best molecular signature, with 80% (40/50) confirmed inflammatory genes, was used to predict inflammation related genes. As a result, 8 out of 10 predicted inflammation genes that were not included in the benchmark Entrez Gene database were validated by literature evidence. Furthermore, 23 of the 32 predicted inflammation regulators were validated by literature evidence. The rest 9 were also validated with TF (transcription factor) binding site analysis. In conclusion, we developed an efficient strategy for disease molecular signature finding and key pathological regulator identification.

Modular network construction using eQTL data: an analysis of computational costs and benefits

Background: In this paper, we consider analytic methods for the integrated analysis of genomic DNA variation and mRNA expression (also named as eQTL data), to discover genetic networks that are associated with a complex trait of interest. Our focus is the systematic evaluation of the trade-off between network size and network search efficiency in the construction of these networks.

Results: We developed a modular approach to network construction, building from smaller networks to larger ones, thereby reducing the search space while including more variables in the analysis. The goal is achieving a lower computational cost while maintaining high confidence in the resulting networks. As demonstrated in our simulation results, networks built in this way have low node/edge false discovery rate (FDR) and high edge sensitivity comparing to greedy search. We further demonstrate our method in a data set of cellular responses to two chemotherapeutic agents: docetaxel and 5-fluorouracil (5-FU), and identify biologically plausible networks that might describe resistances to these drugs.

Conclusion: In this study, we suggest that guided comprehensive searches for parsimonious networks should be considered as an alternative to greedy network searches.

Genomic networks of hybrid sterility

Hybrid dysfunction, a common feature of reproductive barriers between species, is often caused by negative epistasis between loci ("Dobzhansky-Muller incompatibilities"). The nature and complexity of hybrid incompatibilities remain poorly understood because identifying interacting loci that affect complex phenotypes is difficult. With subspecies in the early stages of speciation, an array of genetic tools, and detailed knowledge of reproductive biology, house mice (Mus musculus) provide a model system for dissecting hybrid incompatibilities. Male hybrids between M. musculus subspecies often show reduced fertility. Previous studies identified loci and several X chromosome-autosome interactions that contribute to sterility. To characterize the genetic basis of hybrid sterility in detail, we used a systems genetics approach, integrating mapping of gene expression traits with sterility phenotypes and QTL. We measured genome-wide testis expression in 305 male F2s from a cross between wild-derived inbred strains of M. musculus musculus and M. m. domesticus. We identified several thousand cis- and trans-acting QTL contributing to expression variation (eQTL). Many trans eQTL cluster into eleven 'hotspots,' seven of which co-localize with QTL for sterility phenotypes identified in the cross. The number and clustering of trans eQTL-but not cis eQTL-were substantially lower when mapping was restricted to a 'fertile' subset of mice, providing evidence that trans eQTL hotspots are related to sterility. Functional annotation of transcripts with eQTL provides insights into the biological processes disrupted by sterility loci and guides prioritization of candidate genes. Using a conditional mapping approach, we identified eQTL dependent on interactions between loci, revealing a complex system of epistasis. Our results illuminate established patterns, including the role of the X chromosome in hybrid sterility. The integrated mapping approach we employed is applicable in a broad range of organisms and we advocate for widespread adoption of a network-centered approach in speciation genetics.

Biomarkers for combat-related PTSD: focus on molecular networks from high-dimensional data

Posttraumatic stress disorder (PTSD) and other deployment-related outcomes originate from a complex interplay between constellations of changes in DNA, environmental traumatic exposures, and other biological risk factors. These factors affect not only individual genes or bio-molecules but also the entire biological networks that in turn increase or decrease the risk of illness or affect illness severity. This review focuses on recent developments in the field of systems biology which use multidimensional data to discover biological networks affected by combat exposure and post-deployment disease states. By integrating large-scale, high-dimensional molecular, physiological, clinical, and behavioral data, the molecular networks that directly respond to perturbations that can lead to PTSD can be identified and causally associated with PTSD, providing a path to identify key drivers. Reprogrammed neural progenitor cells from fibroblasts from PTSD patients could be established as an in vitro assay for high throughput screening of approved drugs to determine which drugs reverse the abnormal expression of the pathogenic biomarkers or neuronal properties.

Molecular and neurologic responses to chronic alcohol use

This chapter provides an overview of current knowledge on the molecular and clinical aspects of chronic alcohol effects on the central nervous system. This drug is almost ubiquitous, widely enjoyed socially, but produces a diverse spectrum of neurologic disease when abused. Acutely, alcohol interacts predominantly with γ-aminobutyric acid-A (GABA-A) and N-methyl-d-aspartate (NMDA) receptors, but triggers diverse signaling events within well-defined neural pathways. These events result in adaptive changes in gene expression that ultimately produce two major states: addiction and toxicity. Epigenetic modifications of chromatin could lead to long-lived or even transgenerational changes in gene expression, thus producing aspects of the heritability of alcohol use disorders (AUD) and long-term behaviors such as recidivism. The diverse clinical syndromes produced by chronic alcohol actions in the central nervous system reflect the molecular pathology and predominantly involve aspects of tolerance/withdrawal, selective vulnerability (manifest as central pontine myelinolysis, Marchiafava-Bignami disease), and additional environmental factors (e.g., thiamine deficiency in Wernicke-Korsakoff's syndrome). Additionally, deleterious aspects of chronic alcohol on signaling, synaptic transmission, and cell toxicity lead to primary alcoholic dementia. Genetically determined aspects of myelin structure and alcohol actions on myelin gene expression may be a prominent molecular mechanism resulting in a predisposition to, or causation of, AUD and multiple other neurologic complications of chronic alcohol. The dramatic progress made in understanding molecular actions of alcohol holds great promise for our eventual treatment or prevention of AUD and neurologic complications resulting from chronic alcohol abuse.

The genetic basis for individual differences in mRNA splicing and APOBEC1 editing activity in murine macrophages

Alternative splicing and mRNA editing are known to contribute to transcriptome diversity. Although alternative splicing is pervasive and contributes to a variety of pathologies, including cancer, the genetic context for individual differences in isoform usage is still evolving. Similarly, although mRNA editing is ubiquitous and associated with important biological processes such as intracellular viral replication and cancer development, individual variations in mRNA editing and the genetic transmissibility of mRNA editing are equivocal. Here, we have used linkage analysis to show that both mRNA editing and alternative splicing are regulated by the macrophage genetic background and environmental cues. We show that distinct loci, potentially harboring variable splice factors, regulate the splicing of multiple transcripts. Additionally, we show that individual genetic variability at the Apobec1 locus results in differential rates of C-to-U(T) editing in murine macrophages; with mouse strains expressing mostly a truncated alternative transcript isoform of Apobec1 exhibiting lower rates of editing. As a proof of concept, we have used linkage analysis to identify 36 high-confidence novel edited sites. These results provide a novel and complementary method that can be used to identify C-to-U editing sites in individuals segregating at specific loci and show that, beyond DNA sequence and structural changes, differential isoform usage and mRNA editing can contribute to intra-species genomic and phenotypic diversity.

Body composition and gene expression QTL mapping in mice reveals imprinting and interaction effects

BACKGROUND

Shifts in body composition, such as accumulation of body fat, can be a symptom of many chronic human diseases; hence, efforts have been made to investigate the genetic mechanisms that underlie body composition. For example, a few quantitative trait loci (QTL) have been discovered using genome-wide association studies, which will eventually lead to the discovery of causal mutations that are associated with tissue traits. Although some body composition QTL have been identified in mice, limited research has been focused on the imprinting and interaction effects that are involved in these traits. Previously, we found that Myostatin genotype, reciprocal cross, and sex interacted with numerous chromosomal regions to affect growth traits.

RESULTS

Here, we report on the identification of muscle, adipose, and morphometric phenotypic QTL (pQTL), translation and transcription QTL (tQTL) and expression QTL (eQTL) by applying a QTL model with additive, dominance, imprinting, and interaction effects. Using an F2 population of 1000 mice derived from the Myostatin-null C57BL/6 and M16i mouse lines, six imprinted pQTL were discovered on chromosomes 6, 9, 10, 11, and 18. We also identified two IGF1 and two Atp2a2 eQTL, which could be important trans-regulatory elements. pQTL, tQTL and eQTL that interacted with Myostatin, reciprocal cross, and sex were detected as well. Combining with the additive and dominance effect, these variants accounted for a large amount of phenotypic variation in this study.

CONCLUSIONS

Our study indicates that both imprinting and interaction effects are important components of the genetic model of body composition traits. Furthermore, the integration of eQTL and traditional QTL mapping may help to explain more phenotypic variation than either alone, thereby uncovering more molecular details of how tissue traits are regulated.

Rewiring of human lung cell lineage and mitotic networks in lung adenocarcinomas

Analysis of gene expression patterns in normal tissues and their perturbations in tumors can help to identify the functional roles of oncogenes or tumor suppressors and identify potential new therapeutic targets. Here, gene expression correlation networks were derived from 92 normal human lung samples and patient-matched adenocarcinomas. The networks from normal lung show that NKX2-1 is linked to the alveolar type 2 lineage, and identify PEBP4 as a novel marker expressed in alveolar type 2 cells. Differential correlation analysis shows that the NKX2-1 network in tumors includes pathways associated with glutamate metabolism, and identifies Vaccinia-related kinase (VRK1) as a potential drug target in a tumor-specific mitotic network. We show that VRK1 inhibition cooperates with inhibition of PARP signaling to inhibit growth of lung tumor cells. Targeting of genes that are recruited into tumor mitotic networks may provide a wider therapeutic window than that seen by inhibition of known mitotic genes.

Molecular biology of atherosclerosis

At least 468 individual genes have been manipulated by molecular methods to study their effects on the initiation, promotion, and progression of atherosclerosis. Most clinicians and many investigators, even in related disciplines, find many of these genes and the related pathways entirely foreign. Medical schools generally do not attempt to incorporate the relevant molecular biology into their curriculum. A number of key signaling pathways are highly relevant to atherogenesis and are presented to provide a context for the gene manipulations summarized herein. The pathways include the following: the insulin receptor (and other receptor tyrosine kinases); Ras and MAPK activation; TNF-α and related family members leading to activation of NF-κB; effects of reactive oxygen species (ROS) on signaling; endothelial adaptations to flow including G protein-coupled receptor (GPCR) and integrin-related signaling; activation of endothelial and other cells by modified lipoproteins; purinergic signaling; control of leukocyte adhesion to endothelium, migration, and further activation; foam cell formation; and macrophage and vascular smooth muscle cell signaling related to proliferation, efferocytosis, and apoptosis. This review is intended primarily as an introduction to these key signaling pathways. They have become the focus of modern atherosclerosis research and will undoubtedly provide a rich resource for future innovation toward intervention and prevention of the number one cause of death in the modern world.

Partial restoration of macrophage alteration from diet-induced obesity in response to Porphyromonas gingivalis infection

Obesity is a chronic inflammatory disease that weakens macrophage innate immune response to infections. Since M1 polarization is crucial during acute infectious diseases, we hypothesized that diet-induced obesity inhibits M1 polarization of macrophages in the response to bacterial infections. Bone marrow macrophages (BMMΦ) from lean and obese mice were exposed to live Porphyromonas gingivalis (P. gingivalis) for three incubation times (1 h, 4 h and 24 h). Flow cytometry analysis revealed that the M1 polarization was inhibited after P. gingivalis exposure in BMMΦ from obese mice when compared with BMMΦ from lean counterparts. Using a computational approach in conjunction with microarray data, we identified switching genes that may differentially control the behavior of response pathways in macrophages from lean and obese mice. The two most prominent switching genes were thrombospondin 1 and arginase 1. Protein expression levels of both genes were higher in obese BMMΦ than in lean BMMΦ after exposure to P. gingivalis. Inhibition of either thrombospondin 1 or arginase 1 by specific inhibitors recovered the M1 polarization of BMMΦ from obese mice after P. gingivalis exposure. These data indicate that thrombospondin 1 and arginase 1 are important bacterial response genes, whose regulation is altered in macrophages from obese mice.

5-lipoxygenase-activating protein as a modulator of olanzapine-induced lipid accumulation in adipocyte

Experiments were performed in 3T3-L1 preadipocytes differentiated in vitro into adipocytes. Cells were treated with olanzapine and a 5-lipoxygenase (5-LOX) activating protein (FLAP) inhibitor MK-886. Lipid content was measured using an Oil Red O assay; 5-LOX and FLAP mRNA content was measured using quantitative real-time PCR; the corresponding protein contents were measured using quantitative Western blot assay. Olanzapine did not affect the cell content of 5-LOX mRNA and protein; it decreased FLAP mRNA and protein content at day five but not 24 hours after olanzapine addition. In the absence of MK-886, low concentrations of olanzapine increased lipid content only slightly, whereas a 56% increase was induced by 50 μM olanzapine. A 5-day cotreatment with 10 μM MK-886 potentiated the lipid increasing action of low concentrations of olanzapine. In contrast, in the presence of 50 μM olanzapine nanomolar and low micromolar concentrations of MK-886 reduced lipid content. These data suggest that FLAP system in adipocytes is affected by olanzapine and that it may modify how these cells respond to the second-generation antipsychotic drugs (SGADs). Clinical studies could evaluate whether the FLAP/5-LOX system could play a role in setting a variable individual susceptibility to the metabolic side effects of SGADs.

Integrative analysis using module-guided random forests reveals correlated genetic factors related to mouse weight

Complex traits such as obesity are manifestations of intricate interactions of multiple genetic factors. However, such relationships are difficult to identify. Thanks to the recent advance in high-throughput technology, a large amount of data has been collected for various complex traits, including obesity. These data often measure different biological aspects of the traits of interest, including genotypic variations at the DNA level and gene expression alterations at the RNA level. Integration of such heterogeneous data provides promising opportunities to understand the genetic components and possibly genetic architecture of complex traits. In this paper, we propose a machine learning based method, module-guided Random Forests (mgRF), to integrate genotypic and gene expression data to investigate genetic factors and molecular mechanism underlying complex traits. mgRF is an augmented Random Forests method enhanced by a network analysis for identifying multiple correlated variables of different types. We applied mgRF to genetic markers and gene expression data from a cohort of F2 female mouse intercross. mgRF outperformed several existing methods in our extensive comparison. Our new approach has an improved performance when combining both genotypic and gene expression data compared to using either one of the two types of data alone. The resulting predictive variables identified by mgRF provide information of perturbed pathways that are related to body weight. More importantly, the results uncovered intricate interactions among genetic markers and genes that have been overlooked if only one type of data was examined. Our results shed light on genetic mechanisms of obesity and our approach provides a promising complementary framework to the “genetics of gene expression” analysis for integrating genotypic and gene expression information for analyzing complex traits.

Obesity has become a perilous global epidemic that can lead to complex diseases, such as diabetes and cardiovascular diseases. Much effort has been devoted to the studies of the genetic mechanisms that pillow the manifestation of obesity. Although a large quantity of experimental data has been accumulated lately using high-throughput techniques, our understanding of genetic mechanisms of obesity is still limited. The proposed method is motivated to address three critical issues that have impeded the existing methods. The first is the curse of dimensionality in selecting a subset of genetic elements related to the traits of interest from a large number of candidates. The second is genetic multiplicity underlying non-Mendelian traits, in which multiple genes are in interplay. The third issue is the integration of data from multiple sources in light of genetic multiplicity and curse of dimensionality. Here, we propose a new method, which augments the Random Forests method with a network-based analysis, to integrate genotypic and gene expression information and identify correlated multiple genetic elements underlying mouse weight. Our results shed light on complex genetic interactions underlying obesity, which can form viable hypotheses worthy of further investigation.

Polygenic molecular architecture underlying non-sexual cell aggregation in budding yeast

Cell aggregation in unicellular organisms, induced by either cell non-sexual adhesion to yield flocs and biofilm, or pheromone-driving sexual conjugation is of great significance in cellular stress response, medicine, and brewing industries. Most current literatures have focused on one form of cell aggregation termed flocculation and its major molecular determinants, the flocculation (FLO) family genes. Here, we implemented a map-based approach for dissecting the molecular basis of non-sexual cell aggregation in Saccharomyces cerevisiae. Genome-wide mapping has identified four major quantitative trait loci (QTL) underlying nature variation in the cell aggregation phenotype. High-resolution mapping following up with knockout and allele replacement experiments resolved the QTL into the underlying genes (AMN1, RGA1, FLO1, and FLO8) or even into the causative nucleotide. Genetic variation in the QTL genes can explain up to 46% of phenotypic variation of this trait. Of these genes, AMN1 plays the leading role, differing from the FLO family members, in regulating expression of cell clumping phenotype through inducing cell segregation defect. These findings provide novel insights into the molecular mechanism of how cell aggregation is regulated in budding yeast, and the data will be directly implicated to understand the molecular basis and evolutionary implications of cell aggregation in other fungus species.

Ribosomal protein L17, RpL17, is an inhibitor of vascular smooth muscle growth and carotid intima formation

BACKGROUND

Carotid intima-media thickening is associated with increased cardiovascular risk in humans. We discovered that intima formation and cell proliferation in response to carotid injury is greater in SJL/J (SJL) in comparison with C3HeB/FeJ (C3H/F) mice. The purpose of this study was to identify candidate genes contributing to intima formation.

METHODS AND RESULTS

We performed microarray and bioinformatic analyses of carotid arteries from C3H/F and SJL mice. Kyoto Encyclopedia of Genes and Genomes analysis showed that the ribosome pathway was significantly up-regulated in C3H/F in comparison with SJL mice. Expression of a ribosomal protein, RpL17, was >40-fold higher in C3H/F carotids in comparison with SJL. Aortic vascular smooth muscle cells from C3H/F grew slower in comparison to SJL. To determine the role of RpL17 in vascular smooth muscle cell growth regulation, we analyzed the relationship between RpL17 expression and cell cycle progression. Cultured vascular smooth muscle cells from mice, rats, and humans showed that RpL17 expression inversely correlated with growth as shown by decreased cells in S phase and increased cells in G(0)/G(1). To prove that RpL17 acted as a growth inhibitor in vivo, we used pluronic gel delivery of RpL17 small interfering RNA to C3H/F carotid arteries. This resulted in an 8-fold increase in the number of proliferating cells. Furthermore, following partial carotid ligation in SJL mice, RpL17 expression in the intima and media decreased, but the number of proliferating cells increased.

CONCLUSIONS

RpL17 acts as a vascular smooth muscle cell growth inhibitor (akin to a tumor suppressor) and represents a potential therapeutic target to limit carotid intima-media thickening.

Systems genetics: a novel approach to dissect the genetic basis of osteoporosis

From the early 1990s to the middle of the last decade, the search for genes influencing osteoporosis proved difficult with few successes. However, over the last 5 years this has begun to change with the introduction of genome-wide association (GWA) studies. In this short period of time, GWA studies have significantly accelerated the pace of gene discovery, leading to the identification of nearly 100 independent associations for osteoporosis-related traits. However, GWA does not specifically pinpoint causal genes or provide functional context for associations. Thus, there is a need for approaches that provide systems-level insight on how associated variants influence cellular function, downstream gene networks, and ultimately disease. In this review we discuss the emerging field of "systems genetics" and how it is being used in combination with and independent of GWA to improve our understanding of the molecular mechanisms involved in bone fragility.

Adipocytes secrete leukotrienes: contribution to obesity-associated inflammation and insulin resistance in mice

Leukotrienes (LTs) are potent proinflammatory mediators, and many important aspects of innate and adaptive immune responses are regulated by LTs. Key members of the LT synthesis pathway are overexpressed in adipose tissue (AT) during obesity, resulting in increased LT levels in this tissue. We observed that several mouse adipocyte cell lines and primary adipocytes from mice and humans both can secrete large amounts of LTs. Furthermore, this production increases with a high-fat diet (HFD) and positively correlates with adipocyte size. LTs produced by adipocytes play an important role in attracting macrophages and T cells in in vitro chemotaxis assays. Mice that are deficient for the enzyme 5-lipoxygenase (5-LO), and therefore lack LTs, exhibit a decrease in HFD-induced AT macrophage and T-cell infiltration and are partially protected from HFD-induced insulin resistance. Similarly, treatment of HFD-fed wild-type mice with the 5-LO inhibitor Zileuton also results in a reduction of AT macrophages and T cells, accompanied by a decrease in insulin resistance. Together, these findings suggest that LTs represent a novel target in the prevention or treatment of obesity-associated inflammation and insulin resistance.

Molecular signatures from omics data: from chaos to consensus

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.

Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers

A common inflammatome signature, as well as disease-specific expression patterns, was identified from 11 different rodent inflammatory disease models. Causal regulatory networks and the drivers of the inflammatome signature were uncovered and validated.

Representative inflammatome gene signatures, as well as disease model-specific gene signatures, were identified from 12 gene expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models.

The inflammatome signature is highly enriched for immune response-related genes, disease causal genes, and drug targets.

Regulatory relationships among the inflammatome signature genes were examined in over 70 causal networks derived from a number of large-scale genetic studies of multiple diseases, and the potential key drivers were uncovered and validated prospectively.

Over 70% of the inflammatome signature genes and over 50% of the key driver genes have not been reported in previous studies of common signatures in inflammatory conditions.

Common inflammatome gene signatures as well as disease-specific signatures were identified by analyzing 12 expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models. The inflammatome signature significantly overlaps with known drug targets and co-expressed gene modules linked to metabolic disorders and cancer. A large proportion of genes in this signature are tightly connected in tissue-specific Bayesian networks (BNs) built from multiple independent mouse and human cohorts. Both the inflammatome signature and the corresponding consensus BNs are highly enriched for immune response-related genes supported as causal for adiposity, adipokine, diabetes, aortic lesion, bone, muscle, and cholesterol traits, suggesting the causal nature of the inflammatome for a variety of diseases. Integration of this inflammatome signature with the BNs uncovered 151 key drivers that appeared to be more biologically important than the non-drivers in terms of their impact on disease phenotypes. The identification of this inflammatome signature, its network architecture, and key drivers not only highlights the shared etiology but also pinpoints potential targets for intervention of various common diseases.