Integrating global gene expression analysis and genetics

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.

Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics

RNA-seq has become a state-of-the-art technique for transcriptomic studies. Advances in both RNA-seq techniques and the corresponding analysis tools and pipelines have unprecedently shaped our understanding in almost every aspects of plant sciences. Notably, the integration of huge amount of RNA-seq with other omic data sets in the model plants and major crop species have facilitated plant regulomics, while the RNA-seq analysis has still been primarily used for differential expression analysis in many less-studied plant species. To unleash the analytical power of RNA-seq in plant species, especially less-studied species and biomass crops, we summarize recent achievements of RNA-seq analysis in the major plant species and representative tools in the four types of application: (1) transcriptome assembly, (2) construction of expression atlas, (3) network analysis, and (4) structural alteration. We emphasize the importance of expression atlas, coexpression networks and predictions of gene regulatory relationships in moving plant transcriptomes toward regulomics, an omic view of genome-wide transcription regulation. We highlight what can be achieved in plant research with RNA-seq by introducing a list of representative RNA-seq analysis tools and resources that are developed for certain minor species or suitable for the analysis without species limitation. In summary, we provide an updated digest on RNA-seq tools, resources and the diverse applications for plant research, and our perspective on the power and challenges of short-read RNA-seq analysis from a regulomic point view. A full utilization of these fruitful RNA-seq resources will promote plant omic research to a higher level, especially in those less studied species.

Methylation quantitative trait loci are largely consistent across disease states in Crohn’s disease

Recently, we identified 1,189 CpG sites whose DNA methylation level in blood associated with Crohn’s disease. Here, we examined associations between DNA methylation and genetic variants to identify methylation quantitative trait loci across disease states in (1) 402 blood samples from 164 newly diagnosed pediatric Crohn’s disease patients taken at 2 time points (diagnosis and follow-up), and 74 non-inflammatory bowel disease controls, (2) 780 blood samples from a non-Crohn’s disease adult population, and (3) 40 ileal biopsies (17 Crohn’s disease cases and 23 non-inflammatory bowel disease controls) from group (1). Genome-wide DNAm profiling and genotyping were performed using the Illumina MethylationEPIC and Illumina Multi-Ethnic arrays. SNP-CpG associations were identified via linear models adjusted for age, sex, disease status, disease subtype, estimated cell proportions, and genotype-based principal components. In total, we observed 535,448 SNP-CpG associations between 287,881 SNPs and 12,843 CpG sites (P < 8.21 × 10−14). Associations were highly consistent across different ages, races, disease states, and tissue types, suggesting that the majority of these methylation quantitative trait loci participate in common gene regulation. However, genes near CpGs associated with inflammatory bowel disease SNPs were enriched for 18 KEGG pathways relevant to inflammatory bowel disease-linked immune function and inflammatory responses. We observed suggestive evidence for a small number of tissue-specific associations and disease-specific associations in ileum, though larger studies will be needed to confirm these results. Our study concludes that the vast majority of blood-derived methylation quantitative trait loci are common across individuals, though a subset may be involved in processes related to Crohn’s disease. Independent cohort studies will be required to validate these findings.

Identification of New Genes and Loci Associated With Bone Mineral Density Based on Mendelian Randomization

Bone mineral density (BMD) is a complex and highly hereditary trait that can lead to osteoporotic fractures. It is estimated that BMD is mainly affected by genetic factors (about 85%). BMD has been reported to be associated with both common and rare variants, and numerous loci related to BMD have been identified by genome-wide association studies (GWAS). We systematically integrated expression quantitative trait loci (eQTL) data with GWAS summary statistical data. We mainly focused on the loci, which can affect gene expression, so Summary data-based Mendelian randomization (SMR) analysis was implemented to investigate new genes and loci associated with BMD. We identified 12,477 single-nucleotide polymorphisms (SNPs) regulating 564 genes, which are associated with BMD. The genetic mechanism we detected could make a contribution in the density of BMD in individuals and play an important role in understanding the pathophysiology of cataclasis.

Mouse genome-wide association and systems genetics identifies Lhfp as a regulator of bone mass

Bone mineral density (BMD) is a strong predictor of osteoporotic fracture. It is also one of the most heritable disease-associated quantitative traits. As a result, there has been considerable effort focused on dissecting its genetic basis. Here, we performed a genome-wide association study (GWAS) in a panel of inbred strains to identify associations influencing BMD. This analysis identified a significant (P = 3.1 x 10⁻¹²) BMD locus on Chromosome 3@52.5 Mbp that replicated in two separate inbred strain panels and overlapped a BMD quantitative trait locus (QTL) previously identified in a F2 intercross. The association mapped to a 300 Kbp region containing four genes; Gm2447, Gm20750, Cog6, and Lhfp. Further analysis found that Lipoma HMGIC Fusion Partner (Lhfp) was highly expressed in bone and osteoblasts. Furthermore, its expression was regulated by a local expression QTL (eQTL), which overlapped the BMD association. A co-expression network analysis revealed that Lhfp was strongly connected to genes involved in osteoblast differentiation. To directly evaluate its role in bone, Lhfp deficient mice (Lhfp^-/-) were created using CRISPR/Cas9. Consistent with genetic and network predictions, bone marrow stromal cells (BMSCs) from Lhfp^-/- mice displayed increased osteogenic differentiation. Lhfp^-/- mice also had elevated BMD due to increased cortical bone mass. Lastly, we identified SNPs in human LHFP that were associated (P = 1.2 x 10⁻⁵) with heel BMD. In conclusion, we used GWAS and systems genetics to identify Lhfp as a regulator of osteoblast activity and bone mass.

Osteoporosis is a common, chronic disease characterized by low bone mineral density (BMD) that puts millions of Americans at high risk of fracture. Variation in BMD in the general population is, in large part, determined by genetic factors. To identify novel genes influencing BMD, we performed a genome-wide association study in a panel of inbred mouse strains. We identified a locus on Chromosome 3 strongly associated with BMD. Using a combination of systems genetics approaches, we connected the expression of the Lhfp gene with BMD-associated genetic variants and predicted it influenced BMD by altering the activity of bone-forming osteoblasts. Using mice deficient in Lhfp, we demonstrated that Lhfp negatively regulates bone formation and BMD. These data suggest that inhibiting Lhfp may represent a novel therapeutic strategy to increase BMD and decrease the risk of fracture.

Networking in Biology: The Hybrid Rat Diversity Panel

One of the most fruitful resources for systems genetic studies of nonhuman mammals is a panel of inbred strains that exhibits significant genetic diversity between strains but genetic stability (isogenicity) within strains. These characteristics allow for fine mapping of complex phenotypes (QTLs) and provide statistical power to identify loci which contribute nominally to the phenotype. This type of resource also allows the planning and performance of investigations using the same genetic backgrounds over several generations of the test animals. Often, rats are preferred over mice for physiologic and behavioral studies because of their larger size and more distinguishable anatomy (particularly for their central nervous system). The Hybrid Rat Diversity Panel (HRDP) is a panel of inbred rat strains, which combines two recombinant inbred panels (the HXB/BXH, 30 strains; the LEXF/FXLE, 34 strains and 35 more strains of inbred rats which were selected for genetic diversity, based on their fully sequenced genomes and/or thorough genotyping). The genetic diversity and statistical power of this panel for mapping studies rivals or surpasses currently available panels in mouse. The genetic stability of this panel makes it particularly suitable for collection of high-throughput omics data as relevant technology becomes available for engaging in truly integrative systems biology. The PhenoGen website ( http://phenogen.org ) is the repository for the initial transcriptome data, making the raw data, the processed data, and the analysis results, e.g., organ-specific protein coding and noncoding transcripts, isoform analysis, expression quantitative trait loci, and co-expression networks, available to the research public. The data sets and tools being developed will complement current efforts to analyze the human transcriptome and its genetic controls (the Genotype-Tissue Expression Project (GTEx)) and allow for dissection of genetic networks that predispose to particular phenotypes and gene-by-environment interactions that are difficult or even impossible to study in humans. The HRDP is an essential population for exploring truly integrative systems genetics.

Dissecting the Genetics of Osteoporosis using Systems Approaches

Osteoporosis is a condition characterized by low bone mineral density (BMD) and an increased risk of fracture. Traits contributing to osteoporotic fracture are highly heritable, indicating that a comprehensive understanding of bone requires a thorough understanding of the genetic basis of bone traits. Towards this goal, genome-wide association studies (GWASs) have identified over 500 loci associated with bone traits. However, few of the responsible genes have been identified, and little is known of how these genes work together to influence systems-level bone function. In this review, we describe how systems genetics approaches can be used to fill these knowledge gaps.

Integration of summary data from GWAS and eQTL studies identified novel causal BMD genes with functional predictions

PURPOSE

Osteoporosis is a common global health problem characterized by low bone mineral density (BMD) and increased risk of fracture. Genome-wide association studies (GWAS) have identified >100 genetic loci associated with BMD. However, the functional genes responsible for most associations remain largely unknown. We conducted an innovative summary statistic data-based Mendelian randomization (SMR) analysis to identify novel causal genes associated with BMD and explored their potential functional significance.

METHODS

After quality control of the largest GWAS meta-analysis data of BMD and the largest expression quantitative trait loci (eQTL) meta-analysis data from peripheral blood samples, 5967 genes were tested using the SMR method. Another eQTL data was used to verify the results. Next we performed a fine-mapping association analysis to investigate the functional SNP in the identified loci. Weighted gene co-expression network analysis (WGCNA) was used to explore functional relationships for the identified novel genes with known putative osteoporosis genes. Further, we assessed functions of the identified genes through in vitro cellular study or previous functional studies.

RESULTS

We identified two potentially causal genes (ASB16-AS1 and SYN2) associated with BMD. SYN2 was a novel osteoporosis candidate gene and ASB16-AS1 locus was known to be associated with BMD but was not the nearest gene to the top GWAS SNP. Fine-mapping association analysis showed that rs184478 and rs795000 was predicted to be possible causal SNPs in ASB16-AS1 and SYN2, respectively. ASB16-AS1 co-expressed with several known putative osteoporosis risk genes. In vitro cellular study showed that over-expressed ASB16-AS1 increased the expression of osteoblastogenesis related genes (BMP2 and ALPL), indicating its functional significance.

CONCLUSION

Our findings support that ASB16-AS1 and SYN2 may represent two novel functional genes underlying BMD variation. The findings provide a basis for further functional mechanistic studies.

Using GWAS to identify novel therapeutic targets for osteoporosis

Osteoporosis is a common, increasingly prevalent, global health burden characterized by low bone mineral density (BMD) and increased risk of fracture. Despite its significant impact on human health, there is currently a lack of highly effective treatments free of side effects for osteoporosis. Therefore, a major goal in the field is to identify new drug targets. Genetic discovery has been shown to be effective in the unbiased identification of novel drug targets and genome-wide association studies (GWASs) have begun to provide insight into genetic basis of osteoporosis. Over the last decade, GWASs have led to the identification of ∼100 loci associated with BMD and other bone traits related to risk of fracture. However, there have been limited efforts to identify the causal genes underlying the GWAS loci or the mechanisms by which GWAS loci alter bone physiology. In this review, we summarize the current state of the field and discuss strategies for causal gene discovery and the evidence that the novel genes underlying GWAS loci are likely to be a new source of drug targets.

Integrative analysis of GWASs, human protein interaction, and gene expression identified gene modules associated with BMDs

CONTEXT

To date, few systems genetics studies in the bone field have been performed. We designed our study from a systems-level perspective by integrating genome-wide association studies (GWASs), human protein-protein interaction (PPI) network, and gene expression to identify gene modules contributing to osteoporosis risk.

METHODS

First we searched for modules significantly enriched with bone mineral density (BMD)-associated genes in human PPI network by using 2 large meta-analysis GWAS datasets through a dense module search algorithm. One included 7 individual GWAS samples (Meta7). The other was from the Genetic Factors for Osteoporosis Consortium (GEFOS2). One was assigned as a discovery dataset and the other as an evaluation dataset, and vice versa.

RESULTS

In total, 42 modules and 129 modules were identified significantly in both Meta7 and GEFOS2 datasets for femoral neck and spine BMD, respectively. There were 3340 modules identified for hip BMD only in Meta7. As candidate modules, they were assessed for the biological relevance to BMD by gene set enrichment analysis in 2 expression profiles generated from circulating monocytes in subjects with low versus high BMD values. Interestingly, there were 2 modules significantly enriched in monocytes from the low BMD group in both gene expression datasets (nominal P value <.05). Two modules had 16 nonredundant genes. Functional enrichment analysis revealed that both modules were enriched for genes involved in Wnt receptor signaling and osteoblast differentiation.

CONCLUSION

We highlighted 2 modules and novel genes playing important roles in the regulation of bone mass, providing important clues for therapeutic approaches for osteoporosis.

Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density

Patient bone mineral density (BMD) predicts the likelihood of osteoporotic fracture. While substantial progress has been made toward elucidating the genetic determinants of BMD, our understanding of the factors involved remains incomplete. Here, using a systems genetics approach in the mouse, we predicted that bicaudal C homolog 1 (Bicc1), which encodes an RNA-binding protein, is responsible for a BMD quantitative trait locus (QTL) located on murine chromosome 10. Consistent with this prediction, mice heterozygous for a null allele of Bicc1 had low BMD. We used a coexpression network-based approach to determine how Bicc1 influences BMD. Based on this analysis, we inferred that Bicc1 was involved in osteoblast differentiation and that polycystic kidney disease 2 (Pkd2) was a downstream target of Bicc1. Knock down of Bicc1 and Pkd2 impaired osteoblastogenesis, and Bicc1 deficiency-dependent osteoblast defects were rescued by Pkd2 overexpression. Last, in 2 human BMD genome-wide association (GWAS) meta-analyses, we identified SNPs in BICC1 and PKD2 that were associated with BMD. These results, in both mice and humans, identify Bicc1 as a genetic determinant of osteoblastogenesis and BMD and suggest that it does so by regulating Pkd2 transcript levels.

Sex- and age-interacting eQTLs in human complex diseases

Many complex human diseases exhibit sex or age differences in gene expression. However, the presence and the extent of genotype-specific variations in gene regulation are largely unknown. Here, we report results of a comprehensive analysis of expression regulation of genetic variation related to 11,672 complex disease-associated SNPs as a function of sex and age in whole-blood-derived RNA from 5254 individuals. At false discovery rate <0.05, we identified 14 sex- and 10 age-interacting expression quantitative trait loci (eQTLs). We show that these eQTLs are also associated with many sex- or age-associated traits. These findings provide important context regarding the regulation of phenotypes by genotype-environment interaction.

Transcriptome study of differential expression in schizophrenia

Schizophrenia genome-wide association studies (GWAS) have identified common SNPs, rare copy number variants (CNVs) and a large polygenic contribution to illness risk, but biological mechanisms remain unclear. Bioinformatic analyses of significantly associated genetic variants point to a large role for regulatory variants. To identify gene expression abnormalities in schizophrenia, we generated whole-genome gene expression profiles using microarrays on lymphoblastoid cell lines (LCLs) from 413 cases and 446 controls. Regression analysis identified 95 transcripts differentially expressed by affection status at a genome-wide false discovery rate (FDR) of 0.05, while simultaneously controlling for confounding effects. These transcripts represented 89 genes with functions such as neurotransmission, gene regulation, cell cycle progression, differentiation, apoptosis, microRNA (miRNA) processing and immunity. This functional diversity is consistent with schizophrenia's likely significant pathophysiological heterogeneity. The overall enrichment of immune-related genes among those differentially expressed by affection status is consistent with hypothesized immune contributions to schizophrenia risk. The observed differential expression of extended major histocompatibility complex (xMHC) region histones (HIST1H2BD, HIST1H2BC, HIST1H2BH, HIST1H2BG and HIST1H4K) converges with the genetic evidence from GWAS, which find the xMHC to be the most significant susceptibility locus. Among the differentially expressed immune-related genes, B3GNT2 is implicated in autoimmune disorders previously tied to schizophrenia risk (rheumatoid arthritis and Graves' disease), and DICER1 is pivotal in miRNA processing potentially linking to miRNA alterations in schizophrenia (e.g. MIR137, the second strongest GWAS finding). Our analysis provides novel candidate genes for further study to assess their potential contribution to schizophrenia.

Comparative study of RNA-seq- and microarray-derived coexpression networks in Arabidopsis thaliana

MOTIVATION

Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. They have been used for hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. So far, the main platform for expression data has been DNA microarrays; however, the recent development of RNA-seq allows for higher accuracy and coverage of transcript populations. It is therefore important to assess the potential for biological investigation of coexpression networks derived from this novel technique in a condition-independent dataset.

RESULTS

We collected 65 publicly available Illumina RNA-seq high quality Arabidopsis thaliana samples and generated Pearson correlation coexpression networks. These networks were then compared with those derived from analogous microarray data. We show how Variance-Stabilizing Transformed (VST) RNA-seq data samples are the most similar to microarray ones, with respect to inter-sample variation, correlation coefficient distribution and network topological architecture. Microarray networks show a slightly higher score in biology-derived quality assessments such as overlap with the known protein-protein interaction network and edge ontological agreement. Different coexpression network centralities are investigated; in particular, we show how betweenness centrality is generally a positive marker for essential genes in A.thaliana, regardless of the platform originating the data. In the end, we focus on a specific gene network case, showing that although microarray data seem more suited for gene network reverse engineering, RNA-seq offers the great advantage of extending coexpression analyses to the entire transcriptome.

Systems genetic analysis of osteoblast-lineage cells

The osteoblast-lineage consists of cells at various stages of maturation that are essential for skeletal development, growth, and maintenance. Over the past decade, many of the signaling cascades that regulate this lineage have been elucidated; however, little is known of the networks that coordinate, modulate, and transmit these signals. Here, we identify a gene network specific to the osteoblast-lineage through the reconstruction of a bone co-expression network using microarray profiles collected on 96 Hybrid Mouse Diversity Panel (HMDP) inbred strains. Of the 21 modules that comprised the bone network, module 9 (M9) contained genes that were highly correlated with prototypical osteoblast maker genes and were more highly expressed in osteoblasts relative to other bone cells. In addition, the M9 contained many of the key genes that define the osteoblast-lineage, which together suggested that it was specific to this lineage. To use the M9 to identify novel osteoblast genes and highlight its biological relevance, we knocked-down the expression of its two most connected “hub” genes, Maged1 and Pard6g. Their perturbation altered both osteoblast proliferation and differentiation. Furthermore, we demonstrated the mice deficient in Maged1 had decreased bone mineral density (BMD). It was also discovered that a local expression quantitative trait locus (eQTL) regulating the Wnt signaling antagonist Sfrp1 was a key driver of the M9. We also show that the M9 is associated with BMD in the HMDP and is enriched for genes implicated in the regulation of human BMD through genome-wide association studies. In conclusion, we have identified a physiologically relevant gene network and used it to discover novel genes and regulatory mechanisms involved in the function of osteoblast-lineage cells. Our results highlight the power of harnessing natural genetic variation to generate co-expression networks that can be used to gain insight into the function of specific cell-types.

The osteoblast-lineage consists of a range of cells from osteogenic precursors that mature into bone-forming osteoblasts to osteocytes that are entombed in bone. Each cell in the lineage serves a number of distinct and critical roles in the growth and maintenance of the skeleton, as well as many extra-skeletal functions. Over the last decade, many of the major regulatory pathways governing the differentiation and activity of these cells have been discovered. In contrast, little is known regarding the composition or function of gene networks within the lineage. The goal of this study was to increase our understanding of how genes are organized into networks in osteoblasts. Towards this goal, we used microarray gene expression profiles from bone to identify a group of genes that formed a network specific to the osteoblast-lineage. We used the knowledge of this network to identify novel genes that are important for regulating various aspects of osteoblast function. These data improve our understanding of the gene networks operative in cells of the osteoblast-lineage.

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.

Genetics and regulatory impact of alternative polyadenylation in human B-lymphoblastoid cells

Gene expression varies widely between individuals of a population, and regulatory change can underlie phenotypes of evolutionary and biomedical relevance. A key question in the field is how DNA sequence variants impact gene expression, with most mechanistic studies to date focused on the effects of genetic change on regulatory regions upstream of protein-coding sequence. By contrast, the role of RNA 3'-end processing in regulatory variation remains largely unknown, owing in part to the challenge of identifying functional elements in 3' untranslated regions. In this work, we conducted a genomic survey of transcript ends in lymphoblastoid cells from genetically distinct human individuals. Our analysis mapped the cis-regulatory architecture of 3' gene ends, finding that transcript end positions did not fall randomly in untranslated regions, but rather preferentially flanked the locations of 3' regulatory elements, including miRNA sites. The usage of these transcript length forms and motifs varied across human individuals, and polymorphisms in polyadenylation signals and other 3' motifs were significant predictors of expression levels of the genes in which they lay. Independent single-gene experiments confirmed the effects of polyadenylation variants on steady-state expression of their respective genes, and validated the regulatory function of 3' cis-regulatory sequence elements that mediated expression of these distinct RNA length forms. Focusing on the immune regulator IRF5, we established the effect of natural variation in RNA 3'-end processing on regulatory response to antigen stimulation. Our results underscore the importance of two mechanisms at play in the genetics of 3'-end variation: the usage of distinct 3'-end processing signals and the effects of 3' sequence elements that determine transcript fate. Our findings suggest that the strategy of integrating observed 3'-end positions with inferred 3' regulatory motifs will prove to be a critical tool in continued efforts to interpret human genome variation.

Regulation of gene expression in atherosclerosis: insights from microarray studies in monocytes/macrophages

Atherosclerosis is a pathological phenomenon in which the walls of large arteries thicken and lose elasticity as a result of the growth of atheromatous lesions. It is a complex, multifactorial disease that involves several cell types and various pathobiological processes. Its genetic basis has not yet been deciphered, but it is related to complex multigene patterns influenced by environmental interactions. In this review, we focus specifically on the application of microarrays to atherosclerosis research using monocytes and monocyte-derived macrophages, as these are key cells in all phases of atherosclerosis, from the formation of foam cells to the destabilization and rupture of the atherosclerotic plaque. These studies have provided relevant information on genes involved in atherosclerosis development, contributing to our understanding of the molecular mechanisms that underlie this complex disease.

Decoding the informational properties of the RNA polymerase II Carboxy Terminal Domain

BACKGROUND

The largest sub-unit of RNA polymerase II, Rpb1p, has long been known to be subject to post-translational modifications that influence various aspects of pre-mRNA processing. However, the portion of the Rpb1p molecule subject to these modifications - the carboxy-terminal domain or CTD - remains the subject of much fascination. Intriguingly, the CTD possesses a unique repetitive structure consisting of multiple repeats of the heptapeptide sequence, Y(1)S(2)P(3)T(4)S(5)P(6)S(7). While these repeats are critical for viability, they are not required for basal transcriptional activity in vitro. This suggests that - even though the CTD is not catalytically essential - it must perform other critical functions in eukaryotes.

PRESENTATION OF THE HYPOTHESIS

By formally applying the long-standing mathematical principles of information theory, I explore the hypothesis that complex post-translational modifications of the CTD represent a means for the dynamic "programming" of Rpb1p and thus for the discrete modulation of the expression of specific gene subsets in eukaryotes.

TESTING THE HYPOTHESIS

Empirical means for testing the informational capacity and regulatory potential of the CTD - based on simple genetic analysis in yeast model systems - are put forward and discussed.

IMPLICATIONS OF THE HYPOTHESIS

These ideas imply that the controlled manipulation of CTD effectors could be used to "program" the CTD and thus to manipulate biological processes in eukaryotes in a definable manner.

Gene expression in the Parkinson's disease brain

The study of gene expression has undergone a transformation in the past decade as the benefits of the sequencing of the human genome have made themselves felt. Increasingly, genome wide approaches are being applied to the analysis of gene expression in human disease as a route to understanding the underlying pathogenic mechanisms. In this review, we will summarise current state of gene expression studies of the brain in Parkinson's disease, and examine how these techniques can be used to gain an insight into aetiology of this devastating disorder.

Bridging global gene expression candidates in first trimester placentas with susceptibility loci from linkage studies of preeclampsia

Preeclampsia is as a leading cause of maternal and perinatal morbidity and mortality. Prevention, early identification, and individualized treatments may become feasible if reliable early biomarkers can be developed. Towards a systems biology framework, this review synthesizes prior linkage studies and genome scans of preeclampsia with candidates identified in a global gene expression microarray analysis of chorionic villus sampling (CVS) specimens from women who subsequently developed preeclampsia. Nearly 40% of these CVS candidate genes occurred in previously identified susceptibility loci for preeclampsia. Integration of genetic epidemiologic and functional gene expression data could help to elucidate preeclampsia as a complex disease resulting from multiple maternal and fetal/placental factors that each contributes a greater or lesser effect. These loci and related candidate genes are set to substantially improve insights into the first trimester pathogenesis of this pregnancy disorder.

Genetic dissection of behavioral flexibility: reversal learning in mice

BACKGROUND

Behavioral inflexibility is a feature of schizophrenia, attention-deficit/hyperactivity disorder, and behavior addictions that likely results from heritable deficits in the inhibitory control over behavior. Here, we investigate the genetic basis of individual differences in flexibility, measured using an operant reversal learning task.

METHODS

We quantified discrimination acquisition and subsequent reversal learning in a cohort of 51 BXD strains of mice (2-5 mice/strain, n = 176) for which we have matched data on sequence, gene expression in key central nervous system regions, and neuroreceptor levels.

RESULTS

Strain variation in trials to criterion on acquisition and reversal was high, with moderate heritability (∼.3). Acquisition and reversal learning phenotypes did not covary at the strain level, suggesting that these traits are effectively under independent genetic control. Reversal performance did covary with dopamine D2 receptor levels in the ventral midbrain, consistent with a similar observed relationship between impulsivity and D2 receptors in humans. Reversal, but not acquisition, is linked to a locus on mouse chromosome 10 with a peak likelihood ratio statistic at 86.2 megabase (p < .05 genome-wide). Variance in messenger RNA levels of select transcripts expressed in neocortex, hippocampus, and striatum correlated with the reversal learning phenotype, including Syn3, Nt5dc3, and Hcfc2.

CONCLUSIONS

This work demonstrates the clear trait independence between, and genetic control of, discrimination acquisition and reversal and illustrates how globally coherent data sets for a single panel of highly related strains can be interrogated and integrated to uncover genetic sources and molecular and neuropharmacological candidates of complex behavioral traits relevant to human psychopathology.

Investigating a macromolecular complex: the toolkit of methods

Structural biologists studying macromolecular complexes spend considerable effort doing strictly "non-structural" work: investigating the physiological relevance and biochemical properties of a complex, preparing homogeneous samples for structural analysis, and experimentally validating structure-based hypotheses regarding function or mechanism. Familiarity with the diverse perspectives and techniques available for studying complexes helps in the critical assessment of non-structural data, expedites the pre-structural characterization of a complex and facilitates the investigation of function. Here we survey the approaches and techniques used to study macromolecular complexes from various viewpoints, including genetics, cell and molecular biology, biochemistry/biophysics, structural biology, and systems biology/bioinformatics. The aim of this overview is to heighten awareness of the diversity of perspectives and experimental tools available for investigating complexes and of their usefulness for the structural biologist.

Transcriptomics and proteomics in human African trypanosomiasis: current status and perspectives

Human African trypanosomiasis, or sleeping sickness, is a neglected vector-borne parasitic disease caused by protozoa of the species Trypanosoma brucei sensu lato. Within this complex species, T. b. gambiense is responsible for the chronic form of sleeping sickness in Western and Central Africa, whereas T. b. rhodesiense causes the acute form of the disease in East Africa. Presently, 1.5 million disability-adjusted life years (DALYs) per year are lost due to sleeping sickness. In addition, on the basis of the mortality, the disease is ranked ninth out of 25 human infectious and parasitic diseases in Africa. Diagnosis is complex and needs the intervention of a specialized skilled staff; treatment is difficult and expensive and has potentially life-threatening side effects. The use of transcriptomic and proteomic technologies, currently in rapid development and increasing in sensitivity and discriminating power, is already generating a large panel of promising results. The objective of these technologies is to significantly increase our knowledge of the molecular mechanisms governing the parasite establishment in its vector, the development cycle of the parasite during the parasite's intra-vector life, its interactions with the fly and the other microbial inhabitants of the gut, and finally human host-trypanosome interactions. Such fundamental investigations are expected to provide opportunities to identify key molecular events that would constitute accurate targets for further development of tools dedicated to field work for early, sensitive, and stage-discriminant diagnosis, epidemiology, new chemotherapy, and potentially vaccine development, all of which will contribute to fighting the disease. The present review highlights the contributions of the transcriptomic and proteomic analyses developed thus far in order to identify potential targets (genes or proteins) and biological pathways that may constitute a critical step in the identification of new targets for the development of new tools for diagnostic and therapeutic purposes.

Identification of a gene module associated with BMD through the integration of network analysis and genome-wide association data

Bone mineral density (BMD) is influenced by a complex network of gene interactions; therefore, elucidating the relationships between genes and how those genes, in turn, influence BMD is critical for developing a comprehensive understanding of osteoporosis. To investigate the role of transcriptional networks in the regulation of BMD, we performed a weighted gene coexpression network analysis (WGCNA) using microarray expression data on monocytes from young individuals with low or high BMD. WGCNA groups genes into modules based on patterns of gene coexpression. and our analysis identified 11 gene modules. We observed that the overall expression of one module (referred to as module 9) was significantly higher in the low-BMD group (p = .03). Module 9 was highly enriched for genes belonging to the immune system-related gene ontology (GO) category "response to virus" (p = 7.6 × 10(-11)). Using publically available genome-wide association study data, we independently validated the importance of module 9 by demonstrating that highly connected module 9 hubs were more likely, relative to less highly connected genes, to be genetically associated with BMD. This study highlights the advantages of systems-level analyses to uncover coexpression modules associated with bone mass and suggests that particular monocyte expression patterns may mediate differences in BMD.

Proinflammatory fecal mRNA and childhood bacterial enteric infections

INTRODUCTION: Assessment of specific mRNAs in human samples is useful in characterizing disease. However, mRNA in human stool has been understudied. RESULTS: Compared to controls, infected stools showed increased transcripts of IL-1β, IL-8 and calprotectin. mRNA and protein concentrations correlated for IL-8, but not for calprotectin. DISCUSSION: Stool mRNA quantification offers a potentially useful, noninvasive way to assess inflammation in the gastrointestinal tract, and may be more sensitive than EIA. METHODS: We purified fecal RNA from 46 children infected with Campylobacter jejuni, Escherichia coli O157:H7, Salmonella spp. or Shigella sonnei and 26 controls and compared the proportions of IL-1β, IL-8, osteoprotegerin and calprotectin mRNA between groups using qRT-PCR. We determined the concentrations of calprotectin, IL-8 and osteoprotegerin by enzyme immunoassays in cognate specimens.

Systems biology asks new questions about sex differences

Females and males differ in physiology and in the incidence and progression of diseases. The sex-biased proximate factors causing sex differences in phenotype include direct effects of gonadal hormones and of genes represented unequally in the genome because of their X- or Y-linkage. Novel systems approaches have begun to assess the magnitude and character of sex differences in organization of gene networks on a genome-wide scale. These studies identify functionally related modules of genes that are coexpressed differently in males and females, and sites in the genome that regulate gene networks in a sex-specific manner. Measurement of the aggregate behavior of genes uncovers novel sex differences that can be related more effectively to susceptibility to disease.

Genome-wide expression profiling of in vivo-derived bloodstream parasite stages and dynamic analysis of mRNA alterations during synchronous differentiation in Trypanosoma brucei

Background

Trypanosomes undergo extensive developmental changes during their complex life cycle. Crucial among these is the transition between slender and stumpy bloodstream forms and, thereafter, the differentiation from stumpy to tsetse-midgut procyclic forms. These developmental events are highly regulated, temporally reproducible and accompanied by expression changes mediated almost exclusively at the post-transcriptional level.

Results

In this study we have examined, by whole-genome microarray analysis, the mRNA abundance of genes in slender and stumpy forms of T.brucei AnTat1.1 cells, and also during their synchronous differentiation to procyclic forms. In total, five biological replicates representing the differentiation of matched parasite populations derived from five individual mouse infections were assayed, with RNAs being derived at key biological time points during the time course of their synchronous differentiation to procyclic forms. Importantly, the biological context of these mRNA profiles was established by assaying the coincident cellular events in each population (surface antigen exchange, morphological restructuring, cell cycle re-entry), thereby linking the observed gene expression changes to the well-established framework of trypanosome differentiation.

Conclusion

Using stringent statistical analysis and validation of the derived profiles against experimentally-predicted gene expression and phenotypic changes, we have established the profile of regulated gene expression during these important life-cycle transitions. The highly synchronous nature of differentiation between stumpy and procyclic forms also means that these studies of mRNA profiles are directly relevant to the changes in mRNA abundance within individual cells during this well-characterised developmental transition.

Linking genes to diseases: it's all in the data

Genome-wide association analyses on large patient cohorts are generating large sets of candidate disease genes. This is coupled with the availability of ever-increasing genomic databases and a rapidly expanding repository of biomedical literature. Computational approaches to disease-gene association attempt to harness these data sources to identify the most likely disease gene candidates for further empirical analysis by translational researchers, resulting in efficient identification of genes of diagnostic, prognostic and therapeutic value. Existing computational methods analyze gene structure and sequence, functional annotation of candidate genes, characteristics of known disease genes, gene regulatory networks, protein-protein interactions, data from animal models and disease phenotype. To date, a few studies have successfully applied computational analysis of clinical phenotype data for specific diseases and shown genetic associations. In the near future, computational strategies will be facilitated by improved integration of clinical and computational research, and by increased availability of clinical phenotype data in a format accessible to computational approaches.

A method for isolating and analyzing human mRNA from newborn stool

Efforts to characterize the human transcriptome have largely been limited to blood, urine, and tissue analyses (i.e., normally sterile materials). We report here an extraction protocol using commercially available reagents to obtain high-yield, reverse-transcribable RNA from human stool. Quantitative reverse transcriptase polymerase chain reactions demonstrated minimal intra-specimen but considerable intra-subject variability over time of transcripts for interleukin-6 (IL-6), IL-8, epidermal growth factor (EGF), calprotectin, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH). This technique now expands opportunities to use the human fecal transcriptome to characterize gastrointestinal pathophysiology.

An integrative genetics approach to identify candidate genes regulating BMD: combining linkage, gene expression, and association

Numerous quantitative trait loci (QTLs) affecting bone traits have been identified in the mouse; however, few of the underlying genes have been discovered. To improve the process of transitioning from QTL to gene, we describe an integrative genetics approach, which combines linkage analysis, expression QTL (eQTL) mapping, causality modeling, and genetic association in outbred mice. In C57BL/6J x C3H/HeJ (BXH) F(2) mice, nine QTLs regulating femoral BMD were identified. To select candidate genes from within each QTL region, microarray gene expression profiles from individual F(2) mice were used to identify 148 genes whose expression was correlated with BMD and regulated by local eQTLs. Many of the genes that were the most highly correlated with BMD have been previously shown to modulate bone mass or skeletal development. Candidates were further prioritized by determining whether their expression was predicted to underlie variation in BMD. Using network edge orienting (NEO), a causality modeling algorithm, 18 of the 148 candidates were predicted to be causally related to differences in BMD. To fine-map QTLs, markers in outbred MF1 mice were tested for association with BMD. Three chromosome 11 SNPs were identified that were associated with BMD within the Bmd11 QTL. Finally, our approach provides strong support for Wnt9a, Rasd1, or both underlying Bmd11. Integration of multiple genetic and genomic data sets can substantially improve the efficiency of QTL fine-mapping and candidate gene identification.