Impact of natural genetic variation on gene expression dynamics

Effects of biodegradable microplastics on the crustacean isopod Idotea balthica basteri Audouin, 1826

Plastic pollution is a notable environmental issue, being plastic widespread and characterized by long lifetime. Serious environmental problems are caused by the improper management of plastic end-of-life. In fact, plastic litter is currently detected in any environment. Biodegradable Polymers (BPs) are promising materials if correctly applied and managed at their end of life, to minimize environmental problems. However, poor data on the fate and toxicity of BPs on marine organisms still limit their applicability. In this work we tested the effects of five biodegradable polymers (polybutylene succinate, PBS; polybutylene succinate-co-butylene adipate, PBSA; polycaprolactone, PCL; poly (3-hydroxybutyrates, PHB; polylactic acid, PLA) widely used for several purposes. Adult individuals of the isopod Idotea balthica basteri were fed on these polymers for twenty-seven days by adding biodegradable microplastic polymers (BMPs) to formulated feeds at two concentrations, viz. 0.84 and 8.4 g/kg feed. The plastic fragments affected the mortality rates of the isopods, as well as the expression levels of eighteen genes (tested by Real Time qPCR) involved in stress response and detoxification processes. Our findings confirmed that I. balthica basteri is a convenient model organism to study the response to environmental pollution and emerging contaminants in the aquatic environment, and highlighted the need for the correct use of BMPs.

Expression interplay of genes coding for calcium-binding proteins and transcription factors during the osmotic phase provides insights on salt stress response mechanisms in bread wheat

Bread wheat is an important crop for the human diet, but the increasing soil salinization is reducing the yield. The Ca2+ signaling events at the early stages of the osmotic phase of salt stress are crucial for the acclimation response of the plants through the performance of calcium-sensing proteins, which activate or repress transcription factors (TFs) that affect the expression of downstream genes. Physiological, genetic mapping, and transcriptomics studies performed with the contrasting genotypes Syn86 (synthetic, salt-susceptible) and Zentos (elite cultivar, salt-tolerant) were integrated to gain a comprehensive understanding of the salt stress response. The MACE (Massive Analysis of cDNA 3'-Ends) based transcriptome analysis until 4 h after stress exposure revealed among the salt-responsive genes, the over-representation of genes coding for calcium-binding proteins. The functional and structural diversity within this category was studied and linked with the expression levels during the osmotic phase in the contrasting genotypes. The non-EF-hand category from calcium-binding proteins was found to be enriched for the susceptibility response. On the other side, the tolerant genotype was characterized by a faster and higher up-regulation of genes coding for proteins with EF-hand domain, such as RBOHD orthologs, and TF members. This study suggests that the interplay of calcium-binding proteins, WRKY, and AP2/ERF TF families in signaling pathways at the start of the osmotic phase can affect the expression of downstream genes. The identification of SNPs in promoter sequences and 3' -UTR regions provides insights into the molecular mechanisms controlling the differential expression of these genes through differential transcription factor binding affinity or altered mRNA stability.

Transcriptome-wide Mendelian randomization during CD4+ T cell activation reveals immune-related drug targets for cardiometabolic diseases

Immunity has shown potentials in informing drug development for cardiometabolic diseases, such as type 2 diabetes (T2D) and coronary artery disease (CAD). Here, we performed a transcriptome-wide Mendelian randomization (MR) study to estimate the putative causal effects of 11,021 gene expression profiles during CD4+ T cells activation on the development of T2D and CAD. Robust MR and colocalization evidence was observed for 162 genes altering T2D risk and 80 genes altering CAD risk, with 12% and 16% respectively demonstrating CD4+ T cell specificity. We observed temporal causal patterns during T cell activation in 69 gene-T2D pairs and 34 gene-CAD pairs. These genes were eight times more likely to show robust genetic evidence. We further identified 25 genes that were targets for drugs under clinical investigation, including LIPA and GCK. This study provides evidence to support immune-to-metabolic disease connections, and prioritises immune-mediated drug targets for cardiometabolic diseases.

Integrative Genetic Variation, DNA Methylation, and Gene Expression Analysis of Escitalopram and Aripiprazole Treatment Outcomes in Depression: A CAN-BIND-1 Study

INTRODUCTION

Little is known about the interplay between genetics and epigenetics on antidepressant treatment (1) response and remission, (2) side effects, and (3) serum levels. This study explored the relationship among single nucleotide polymorphisms (SNPs), DNA methylation (DNAm), and mRNA levels of four pharmacokinetic genes, CYP2C19, CYP2D6, CYP3A4, and ABCB1, and its effect on these outcomes.

METHODS

The Canadian Biomarker Integration Network for Depression-1 dataset consisted of 177 individuals with major depressive disorder treated for 8 weeks with escitalopram (ESC) followed by 8 weeks with ESC monotherapy or augmentation with aripiprazole. DNAm quantitative trait loci (mQTL), identified by SNP-CpG associations between 20 SNPs and 60 CpG sites in whole blood, were tested for associations with our outcomes, followed by causal inference tests (CITs) to identify methylation-mediated genetic effects.

RESULTS

Eleven cis-SNP-CpG pairs (q<0.05) constituting four unique SNPs were identified. Although no significant associations were observed between mQTLs and response/remission, CYP2C19 rs4244285 was associated with treatment-related weight gain (q=0.027) and serum concentrations of ESCadj (q<0.001). Between weeks 2-4, 6.7% and 14.9% of those with *1/*1 (normal metabolizers) and *1/*2 (intermediate metabolizers) genotypes, respectively, reported ≥2 lbs of weight gain. In contrast, the *2/*2 genotype (poor metabolizers) did not report weight gain during this period and demonstrated the highest ESCadj concentrations. CITs did not indicate that these effects were epigenetically mediated.

DISCUSSION

These results elucidate functional mechanisms underlying the established associations between CYP2C19 rs4244285 and ESC pharmacokinetics. This mQTL SNP as a marker for antidepressant-related weight gain needs to be further explored.

Systems genetics analysis reveals the common genetic basis for pain sensitivity and cognitive function

BACKGROUND

There is growing evidence of a strong correlation between pain sensitivity and cognitive function under both physiological and pathological conditions. However, the detailed mechanisms remain largely unknown. In the current study, we sought to explore candidate genes and common molecular mechanisms underlying pain sensitivity and cognitive function with a transcriptome-wide association study using recombinant inbred mice from the BXD family.

METHODS

The pain sensitivity determined by Hargreaves' paw withdrawal test and cognition-related phenotypes were systematically analyzed in 60 strains of BXD mice and correlated with hippocampus transcriptomes, followed by quantitative trait locus (QTL) mapping and systems genetics analysis.

RESULTS

The pain sensitivity showed significant variability across the BXD strains and co-varies with cognitive traits. Pain sensitivity correlated hippocampual genes showed a significant involvement in cognition-related pathways, including glutamatergic synapse, and PI3K-Akt signaling pathway. Moreover, QTL mapping identified a genomic region on chromosome 4, potentially regulating the variation of pain sensitivity. Integrative analysis of expression QTL mapping, correlation analysis, and Bayesian network modeling identified Ring finger protein 20 (Rnf20) as the best candidate. Further pathway analysis indicated that Rnf20 may regulate the expression of pain sensitivity and cognitive function through the PI3K-Akt signaling pathway, particularly through interactions with genes Ppp2r2b, Ppp2r5c, Col9a3, Met, Rps6, Tnc, and Kras.

CONCLUSIONS

Our study demonstrated that pain sensitivity is associated with genetic background and Rnf20-mediated PI3K-Akt signaling may involve in the regulation of pain sensitivity and cognitive functions.

Neurodegenerative disorders: From clinicopathology convergence to systems biology divergence

Neurodegenerative diseases are multifactorial. This means that several genetic, epigenetic, and environmental factors contribute to their emergence. Therefore, for the future management of these highly prevalent diseases, it is necessary to change perspective. If a holistic viewpoint is assumed, the phenotype (the clinicopathological convergence) emerges from the perturbation of a complex system of functional interactions among proteins (systems biology divergence). The systems biology top-down approach starts with the unbiased collection of sets of data generated through one or more -omics techniques and has the aim to identify the networks and the components that participate in the generation of a phenotype (disease), often without any available a priori knowledge. The principle behind the top-down method is that the molecular components that respond similarly to experimental perturbations are somehow functionally related. This allows the study of complex and relatively poorly characterized diseases without requiring extensive knowledge of the processes under investigation. In this chapter, the use of a global approach will be applied to the comprehension of neurodegeneration, with a particular focus on the two most prevalent ones, Alzheimer's and Parkinson's diseases. The final purpose is to distinguish disease subtypes (even with similar clinical manifestations) to launch a future of precision medicine for patients with these disorders.

Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks

BACKGROUND

Genome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation.

RESULTS

This paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration's Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness.

CONCLUSIONS

To our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies.

Cardiac physiology and metabolic gene expression during late organogenesis among F. heteroclitus embryo families from crosses between pollution-sensitive and -resistant parents

BACKGROUND

The teleost fish Fundulus heteroclitus inhabit estuaries heavily polluted with persistent and bioaccumulative chemicals. While embryos of parents from polluted sites are remarkably resistant to toxic sediment and develop normally, embryos of parents from relatively clean estuaries, when treated with polluted sediment extracts, are developmentally delayed, displaying deformities characteristic of pollution-induced embryotoxicity. To gain insight into parental effects on sensitive and resistant phenotypes during late organogenesis, we established sensitive, resistant, and crossed embryo families using five female and five male parents from relatively clean and predominantly PAH-polluted estuaries each, measured heart rates, and quantified individual embryo expression of 179 metabolic genes.

RESULTS

Pollution-induced embryotoxicity manifested as morphological deformities, significant developmental delays, and altered cardiac physiology was evident among sensitive embryos resulting from crosses between females and males from relatively clean estuaries. Significantly different heart rates among several geographically unrelated populations of sensitive, resistant, and crossed embryo families during late organogenesis and pre-hatching suggest site-specific adaptive cardiac physiology phenotypes relative to pollution exposure. Metabolic gene expression patterns (32 genes, 17.9%, at p < 0.05; 11 genes, 6.1%, at p < 0.01) among the embryo families indicate maternal pollutant deposition in the eggs and parental effects on gene expression and metabolic alterations.

CONCLUSION

Heart rate differences among sensitive, resistant, and crossed embryos is a reliable phenotype for further explorations of adaptive mechanisms. While metabolic gene expression patterns among embryo families are suggestive of parental effects on several differentially expressed genes, a definitive adaptive signature and metabolic cost of resistant phenotypes is unclear and shows unexpected sensitive-resistant crossed embryo expression profiles. Our study highlights physiological and metabolic gene expression differences during a critical embryonic stage among pollution sensitive, resistant, and crossed embryo families, which may contribute to underlying resistance mechanisms observed in natural F. heteroclitus populations living in heavily contaminated estuaries.

Cis-regulatory variants affect gene expression dynamics in yeast

Evolution of cis-regulatory sequences depends on how they affect gene expression and motivates both the identification and prediction of cis-regulatory variants responsible for expression differences within and between species. While much progress has been made in relating cis-regulatory variants to expression levels, the timing of gene activation and repression may also be important to the evolution of cis-regulatory sequences. We investigated allele-specific expression (ASE) dynamics within and between Saccharomyces species during the diauxic shift and found appreciable cis-acting variation in gene expression dynamics. Within-species ASE is associated with intergenic variants, and ASE dynamics are more strongly associated with insertions and deletions than ASE levels. To refine these associations, we used a high-throughput reporter assay to test promoter regions and individual variants. Within the subset of regions that recapitulated endogenous expression, we identified and characterized cis-regulatory variants that affect expression dynamics. Between species, chimeric promoter regions generate novel patterns and indicate constraints on the evolution of gene expression dynamics. We conclude that changes in cis-regulatory sequences can tune gene expression dynamics and that the interplay between expression dynamics and other aspects of expression is relevant to the evolution of cis-regulatory sequences.

Chemerin levels and its genetic variants are associated with Gestational Diabetes Mellitus: a hospital-based study in a Chinese cohort

BACKGROUND

Gestational diabetes mellitus (GDM) is a glucose intolerance condition encounters for the first time in a fraction of pregnant women. The role of different host inflammatory molecules in GDM etiology has been deciphered. Chemerin is a chemoattractant protein primarily associated with the pathogenesis of type 2 diabetes, obesity, and metabolic syndrome. However, the association of chemerin and its genetic variants with the predisposition of GDM is not clear, and our present study is aimed to address the issue.

MATERIALS AND METHODS

A total of 703 Chinese women comprising of GDM (n=303), glucose tolerant pregnant women (n=211), and non-pregnant glucose tolerant controls (n=189) were recruited in the present investigation. GDM was diagnosed according to the World Health Organization recommendation for diagnosis of gestational diabetes during pregnancy. Plasma levels of chemerin were quantified by an Enzyme-linked Immunosorbent Assay (ELISA). Common variants in the chemerin gene (rs4721, rs17173617, rs7806429, and rs17173608) were genotyped by using TaqMan assay.

RESULTS

Plasma chemerin level was found higher in subjects with GDM as compared to glucose tolerant pregnant and non-pregnant women. Further, a positive correlation between plasma chemerin and HOMA-IR index suggesting an essential role of chemerin in mediating insulin resistance. Variants of rs4721 and rs17173608 polymorphisms were associated with lower levels of plasma chemerin and low HOMA-IR index. Furthermore, mutants of rs4721 and rs17173608 polymorphisms were associated with protection against the development of GDM in the Chinese cohort.

CONCLUSIONS

Plasma chemerin is elevated in GDM patients. Genetic variation in chemerin gene associated with lower plasma levels of chemerin, HOMA-IR index and protects against the development of GDM in Chinese.

Stratifying Cumulus Cell Samples Based on Molecular Profiling to Help Resolve Biomarker Discrepancies and to Predict Oocyte Developmental Competence

To increase the efficiency of assisted reproductive techniques (ART), molecular studies have been performed to identify the best predictive biomarkers for selecting the most suitable germ cells for fertilization and the best embryo for intra-uterine transfer. However, across different studies, no universal markers have been found. In this study, we addressed this issue by generating gene expression and CpG methylation profiles of outer cumulus cells obtained during intra-cytoplasmic sperm injection (ICSI). We also studied the association of the generated genomic data with the clinical parameters (spindle presence, zona pellucida birefringence, pronuclear pattern, estrogen level, endometrium size and lead follicle size) and the pregnancy result. Our data highlighted the presence of several parameters that affect analysis, such as inter-individual differences, inter-treatment differences, and, above all, specific treatment protocol differences. When comparing the pregnancy outcome following the long protocol (GnRH agonist) of ovarian stimulation, we identified the single gene markers (NME6 and ASAP1, FDR < 5%) which were also correlated with endometrium size, upstream regulators (e.g., EIF2AK3, FSH, ATF4, MKNK1, and TP53) and several bio-functions related to cell death (apoptosis) and cellular growth and proliferation. In conclusion, our study highlighted the need to stratify samples that are very heterogeneous and to use pathway analysis as a more reliable and universal method for identifying markers that can predict oocyte development potential.

Transcriptome profiling at osmotic and ionic phases of salt stress response in bread wheat uncovers trait-specific candidate genes

BACKGROUND

Bread wheat is one of the most important crops for the human diet, but the increasing soil salinization is causing yield reductions worldwide. Improving salt stress tolerance in wheat requires the elucidation of the mechanistic basis of plant response to this abiotic stress factor. Although several studies have been performed to analyze wheat adaptation to salt stress, there are still some gaps to fully understand the molecular mechanisms from initial signal perception to the onset of responsive tolerance pathways. The main objective of this study is to exploit the dynamic salt stress transcriptome in underlying QTL regions to uncover candidate genes controlling salt stress tolerance in bread wheat. The massive analysis of 3'-ends sequencing protocol was used to analyze leave samples at osmotic and ionic phases. Afterward, stress-responsive genes overlapping QTL for salt stress-related traits in two mapping populations were identified.

RESULTS

Among the over-represented salt-responsive gene categories, the early up-regulation of calcium-binding and cell wall synthesis genes found in the tolerant genotype are presumably strategies to cope with the salt-related osmotic stress. On the other hand, the down-regulation of photosynthesis-related and calcium-binding genes, and the increased oxidative stress response in the susceptible genotype are linked with the greater photosynthesis inhibition at the osmotic phase. The specific up-regulation of some ABC transporters and Na+/Ca2+ exchangers in the tolerant genotype at the ionic stage indicates their involvement in mechanisms of sodium exclusion and homeostasis. Moreover, genes related to protein synthesis and breakdown were identified at both stress phases. Based on the linkage disequilibrium blocks, salt-responsive genes within QTL intervals were identified as potential components operating in pathways leading to salt stress tolerance. Furthermore, this study conferred evidence of novel regions with transcription in bread wheat.

CONCLUSION

The dynamic transcriptome analysis allowed the comparison of osmotic and ionic phases of the salt stress response and gave insights into key molecular mechanisms involved in the salt stress adaptation of contrasting bread wheat genotypes. The leveraging of the highly contiguous chromosome-level reference genome sequence assembly facilitated the QTL dissection by targeting novel candidate genes for salt tolerance.

Developmental plasticity shapes social traits and selection in a facultatively eusocial bee

Developmental processes are an important source of phenotypic variation, but the extent to which this variation contributes to evolutionary change is unknown. We used integrative genomic analyses to explore the relationship between developmental and social plasticity in a bee species that can adopt either a social or solitary lifestyle. We find genes regulating this social flexibility also regulate development, and positive selection on these genes is influenced by their function during development. This suggests that developmental plasticity may influence the evolution of sociality. Our additional finding of genetic variants linked to differences in social behavior sheds light on how phenotypic variation derived from development may become encoded into the genome, and thus contribute to evolutionary change.

Developmental plasticity generates phenotypic variation, but how it contributes to evolutionary change is unclear. Phenotypes of individuals in caste-based (eusocial) societies are particularly sensitive to developmental processes, and the evolutionary origins of eusociality may be rooted in developmental plasticity of ancestral forms. We used an integrative genomics approach to evaluate the relationships among developmental plasticity, molecular evolution, and social behavior in a bee species (Megalopta genalis) that expresses flexible sociality, and thus provides a window into the factors that may have been important at the evolutionary origins of eusociality. We find that differences in social behavior are derived from genes that also regulate sex differentiation and metamorphosis. Positive selection on social traits is influenced by the function of these genes in development. We further identify evidence that social polyphenisms may become encoded in the genome via genetic changes in regulatory regions, specifically in transcription factor binding sites. Taken together, our results provide evidence that developmental plasticity provides the substrate for evolutionary novelty and shapes the selective landscape for molecular evolution in a major evolutionary innovation: Eusociality.

Genetic Architecture of Gene Expression in European and African Americans: An eQTL Mapping Study in GENOA

Most existing expression quantitative trait locus (eQTL) mapping studies have been focused on individuals of European ancestry and are underrepresented in other populations including populations with African ancestry. Lack of large-scale well-powered eQTL mapping studies in populations with African ancestry can both impede the dissemination of eQTL mapping results that would otherwise benefit individuals with African ancestry and hinder the comparable analysis for understanding how gene regulation is shaped through evolution. We fill this critical knowledge gap by performing a large-scale in-depth eQTL mapping study on 1,032 African Americans (AA) and 801 European Americans (EA) in the GENOA cohort. We identified a total of 354,931 eSNPs in AA and 371,309 eSNPs in EA, with 112,316 eSNPs overlapped between the two. We found that eQTL harboring genes (eGenes) are enriched in metabolic pathways and tend to have higher SNP heritability compared to non-eGenes. We found that eGenes that are common in the two populations tend to be less conserved than eGenes that are unique to one population, which are less conserved than non-eGenes. Through conditional analysis, we found that eGenes in AA tend to harbor more independent eQTLs than eGenes in EA, suggesting potentially diverse genetic architecture underlying expression variation in the two populations. Finally, the large sample sizes in GENOA allow us to construct accurate expression prediction models in both AA and EA, facilitating powerful transcriptome-wide association studies. Overall, our results represent an important step toward revealing the genetic architecture underlying expression variation in African Americans.

Distinguishing CPT gene family members and vetting the sequence structure of a putative rubber synthesizing variant in Hevea brasiliensis

cis-Prenyltransferases (cis-PTs) constitute a large family of enzymes conserved during evolution and present in all domains of life. cis-PTs catalyze the cis-1,4-polymerization of isoprene units to generate isoprenoids with carbon skeletons varying from C10 (neryl pyrophosphate) to C > 10,000 (natural rubber). Though the previously reported CPTs in Hevea are designated based on sequence variations, their classification was done mostly by phylogenetic analysis using a mixture of partial as well as full length sequences often excluding the UTRs. In this context an attempt was made to reclassify the CPTs strictly based on their sequence similarity and distinguish the members putatively associated with rubber biosynthesis from the others. Extensive computational analysis was carried out on CPT sequences obtained from public resources and whole genome assemblies of Hevea. Based on the results from BLAST analysis, multiple sequence alignments of protein, nucleotide and untranslated regions, open reading frame analysis, gene prediction analysis and sequence length variations, we conclude that there exists mainly three CPTs namely RubCPT1, RubCPT2 and RubCPT3 putatively associated with rubber biosynthesis in Hevea brasiliensis. The rest were categorised as variants of dehydrodolichyl diphosphate synthase (DHDDS) involved in the synthesis of dolichols having short chain isoprenoids. Analysis of the sequence structure of the most highly expressed RubCPT1 in latex revealed the allele richness and diversity of this important variant prevailing in the popular rubber clones. Haplotypes consisting of SNPs with high degree of heterozygosity were also identified. Segregation and linkage disequilibrium analysis confirmed that recombination is the major contributor towards the generation of allelic diversity rather than point mutations. Alternatively, gene expression analysis indicated the possibility of association between specific haplotypes and RubCPT1 expression in Hevea clones which may have downstream impact up to the level of rubber production. The conclusions from this study may pave way for the identification and better understanding of CPTs directly involved with natural rubber biosynthesis in Hevea and the SNP data generated may aid in the development of molecular markers putatively associated with yield in rubber.

From Genotype to Phenotype: A Primer on the Functional Follow-up of Genome-Wide Association Studies in Cardiovascular Disease

Genome-wide association studies (GWASs) have implicated many human genomic loci in the development of complex traits. The loci identified by these studies are potentially involved in novel pathways that contribute to disease pathophysiology. However, eventual therapeutic targeting of these pathways relies on bridging the gap between genetic association and function, a task that first requires validation of causal genetic variants, casual genes, and directionality of effect. Executing this task requires basic knowledge of interpreting GWAS results and prioritizing candidates for further study, in addition to understanding the experimental methods available for evaluating candidate variants. Here we review the basic genetic principles of genome-wide association studies, the computational and experimental tools used for identifying causal variants and genes, and salient illustrative examples of how cardiovascular loci have undergone functional investigation.

iFORM/eQTL: an ultrahigh-dimensional platform for inferring the global genetic architecture of gene transcripts

Knowledge about how changes in gene expression are encoded by expression quantitative trait loci (eQTLs) is a key to construct the genotype-phenotype map for complex traits or diseases. Traditional eQTL mapping is to associate one transcript with a single marker at a time, thereby limiting our inference about a complete picture of the genetic architecture of gene expression. Here, we implemented an ultrahigh-dimensional variable selection model to build a computing platform that can systematically scan main effects and interaction effects among all possible loci and identify a set of significant eQTLs modulating differentiation and function of gene expression. This platform, named iFORM/eQTL, was assembled by forward-selection-based procedures to tackle complex covariance structures of gene-gene interactions. iFORM/eQTL can particularly discern the role of cis-QTLs, trans-QTLs and their epistatic interactions in gene expression. Results from the reanalysis of a published genetic and genomic data set through iFORM/eQTL gain new discoveries on the genetic origin of gene expression differentiation in Caenorhabditis elegans, which could not be detected by a traditional one-locus/one-transcript analysis approach.

Population transcriptomic characterization of the genetic and expression variation of a candidate progenitor of Miscanthus energy crops

The use of transcriptome data in the study of the population genetics of a species can capture faint signals of both genetic variation and expression variation and can provide a broad picture of a species' genomic response to environmental conditions. In this study, we characterized the genetic and expression diversity of Miscanthus lutarioriparius by comparing more than 16,225 transcripts obtained from 78 individuals, belonging to 10 populations distributed across the species' entire geographic range. We only observed a low level of nucleotide diversity (π = 0.000434) among the transcriptome data of these populations, which is consistent with highly conserved sequences of functional elements and protein-coding genes captured with this method. Tests of population divergence using the transcriptome data were consistent with previous microsatellite data but proved to be more sensitive, particularly if gene expression variation was considered as well. For example, the analysis of expression data showed that genes involved in photosynthetic processes and responses to temperature or reactive oxygen species stimuli were significantly enriched in certain populations. This differential gene expression was primarily observed among populations and not within populations. Interestingly, nucleotide diversity was significantly negatively correlated with expression diversity within populations, while this correlation was positive among populations. This suggests that genetic and expression variation play separate roles in adaptation and population persistence. Combining analyses of genetic and gene expression variation represents a promising approach for studying the population genetics of wild species and may uncover both adaptive and nonadaptive processes.

A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation

Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension). We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

Complex diseases such as hypertension often involve maladaptive autonomic nervous system control over the cardiovascular, renal, hepatic, immune, and endocrine systems. We studied the pathogenesis of physiological homeostasis by examining the temporal dynamics of gene expression levels from multiple organs in an animal model of autonomic dysfunction characterized by cardiovascular disease, metabolic dysregulation, and immune system aberrations. We employed a data-driven modeling approach to jointly predict continuous gene expression dynamics and gene regulatory interactions across organs in the disease and control phenotypes. We combined our analyses of multi-organ gene regulatory network dynamics and connectivity with bioinformatic analyses of genetic mutations that could regulate gene expression. Our multi-organ modeling approach to investigate the mechanisms of complex disease pathogenesis revealed novel candidates for therapeutic interventions against the development and progression of complex diseases involving autonomic nervous system dysfunction.

Significance of functional disease-causal/susceptible variants identified by whole-genome analyses for the understanding of human diseases

Human genome variation may cause differences in traits and disease risks. Disease-causal/susceptible genes and variants for both common and rare diseases can be detected by comprehensive whole-genome analyses, such as whole-genome sequencing (WGS), using next-generation sequencing (NGS) technology and genome-wide association studies (GWAS). Here, in addition to the application of an NGS as a whole-genome analysis method, we summarize approaches for the identification of functional disease-causal/susceptible variants from abundant genetic variants in the human genome and methods for evaluating their functional effects in human diseases, using an NGS and in silico and in vitro functional analyses. We also discuss the clinical applications of the functional disease causal/susceptible variants to personalized medicine.

Population transcriptomics uncovers the regulation of gene expression variation in adaptation to changing environment

Expression variation plays an important role in plant adaptation, but little is known about the factors impacting the expression variation when population adapts to changing environment. We used RNA-seq data from 80 individuals in 14 Miscanthus lutarioriparius populations, which were transplanted into a harsh environment from native habitat, to investigate the expression level, expression diversity and genetic diversity for genes expressed in both environments. The expression level of genes with lower expression level or without SNP tended to be more changeable in new environment, which suggested highly expressed genes experienced stronger purifying selection than those at lower level. Low proportion of genes with population effect confirmed the weak population structure and frequent gene flow in these populations. Meanwhile, the number of genes with environment effect was the most frequent compared with that with population effect. Our results showed that environment and genetic diversity were the main factors determining gene expression variation in population. This study could facilitate understanding the mechanisms of global gene expression variation when plant population adapts to changing environment.

A block mixture model to map eQTLs for gene clustering and networking

To study how genes function in a cellular and physiological process, a general procedure is to classify gene expression profiles into categories based on their similarity and reconstruct a regulatory network for functional elements. However, this procedure has not been implemented with the genetic mechanisms that underlie the organization of gene clusters and networks, despite much effort made to map expression quantitative trait loci (eQTLs) that affect the expression of individual genes. Here we address this issue by developing a computational approach that integrates gene clustering and network reconstruction with genetic mapping into a unifying framework. The approach can not only identify specific eQTLs that control how genes are clustered and organized toward biological functions, but also enable the investigation of the biological mechanisms that individual eQTLs perturb in a signaling pathway. We applied the new approach to characterize the effects of eQTLs on the structure and organization of gene clusters in Caenorhabditis elegans. This study provides the first characterization, to our knowledge, of the effects of genetic variants on the regulatory network of gene expression. The approach developed can also facilitate the genetic dissection of other dynamic processes, including development, physiology and disease progression in any organisms.

From GWAS to function: lessons from blood cells

Haematopoiesis, or the process of formation of mature blood cells from committed progenitors, represents an accessible and well-studied paradigm of cell differentiation and lineage specification. Genetic association studies provide a powerful approach to discover new genes, biological pathways and mechanisms underlying haematopoietic development. Here, we highlight recent findings of genomewide association studies (GWAS) linking 145 genomic loci to traits affecting the formation of red and white cells and platelets in European and other ancestries. We present strategies to address the main challenges in GWAS discoveries, particularly to find functional and regulatory effects of genetic variants, and to identify genes through which these genetic variants affect haematological phenotypes. We argue that studies of haematological trait variation provide an ideal paradigm for understanding the function of GWAS-associated variants owing to the accessible nature of cells, simple cellular phenotype and focused efforts to characterize the genetic and epigenetic factors influencing the regulatory landscape in highly pure mature cell populations.

Reconstructing and analysing cellular states, space and time from gene expression profiles of many cells and single cells

Gene expression profiling is a fast, cheap and standardised analysis that provides a high dimensional measurement of the state of a biological sample, including of single cells. Computational methods to reconstruct the composition of samples and spatial and temporal information from expression profiles are described, as well as how they can be used to describe the effects of genetic variation.

Natural genetic variation impacts expression levels of coding, non-coding, and antisense transcripts in fission yeast

Our current understanding of how natural genetic variation affects gene expression beyond well-annotated coding genes is still limited. The use of deep sequencing technologies for the study of expression quantitative trait loci (eQTLs) has the potential to close this gap. Here, we generated the first recombinant strain library for fission yeast and conducted an RNA-seq-based QTL study of the coding, non-coding, and antisense transcriptomes. We show that the frequency of distal effects (trans-eQTLs) greatly exceeds the number of local effects (cis-eQTLs) and that non-coding RNAs are as likely to be affected by eQTLs as protein-coding RNAs. We identified a genetic variation of swc5 that modifies the levels of 871 RNAs, with effects on both sense and antisense transcription, and show that this effect most likely goes through a compromised deposition of the histone variant H2A.Z. The strains, methods, and datasets generated here provide a rich resource for future studies.

Integrative genetic analysis of allergic inflammation in the murine lung

Airway allergen exposure induces inflammation among individuals with atopy that is characterized by altered airway gene expression, elevated levels of T helper type 2 cytokines, mucus hypersecretion, and airflow obstruction. To identify the genetic determinants of the airway allergen response, we employed a systems genetics approach. We applied a house dust mite mouse model of allergic airway disease to 151 incipient lines of the Collaborative Cross, a new mouse genetic reference population, and measured serum IgE, airway eosinophilia, and gene expression in the lung. Allergen-induced serum IgE and airway eosinophilia were not correlated. We detected quantitative trait loci (QTL) for airway eosinophilia on chromosome (Chr) 11 (71.802-87.098 megabases [Mb]) and allergen-induced IgE on Chr 4 (13.950-31.660 Mb). More than 4,500 genes expressed in the lung had gene expression QTL (eQTL), the majority of which were located near the gene itself. However, we also detected approximately 1,700 trans-eQTL, and many of these trans-eQTL clustered into two regions on Chr 2. We show that one of these loci (at 147.6 Mb) is associated with the expression of more than 100 genes, and, using bioinformatics resources, fine-map this locus to a 53 kb-long interval. We also use the gene expression and eQTL data to identify a candidate gene, Tlcd2, for the eosinophil QTL. Our results demonstrate that hallmark allergic airway disease phenotypes are associated with distinct genetic loci on Chrs 4 and 11, and that gene expression in the allergically inflamed lung is controlled by both cis and trans regulatory factors.

Identification of genetic variants associated with alternative splicing using sQTLseekeR

Identification of genetic variants affecting splicing in RNA sequencing population studies is still in its infancy. Splicing phenotype is more complex than gene expression and ought to be treated as a multivariate phenotype to be recapitulated completely. Here we represent the splicing pattern of a gene as the distribution of the relative abundances of a gene’s alternative transcript isoforms. We develop a statistical framework that uses a distance-based approach to compute the variability of splicing ratios across observations, and a non-parametric analogue to multivariate analysis of variance. We implement this approach in the R package sQTLseekeR and use it to analyze RNA-Seq data from the Geuvadis project in 465 individuals. We identify hundreds of single nucleotide polymorphisms (SNPs) as splicing QTLs (sQTLs), including some falling in genome-wide association study SNPs. By developing the appropriate metrics, we show that sQTLseekeR compares favorably with existing methods that rely on univariate approaches, predicting variants that behave as expected from mutations affecting splicing.

RNA sequencing has enabled the global analysis of both gene expression levels and splicing events. Here, the authors develop a multivariate approach that is able to identify SNPs that influence splicing, and investigate the overlap of these with functional domains across the genome, including previously identified GWAS signals.

Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression

MOTIVATION

A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression or metabolomics studies. A common approach is to perform a univariate test between each genotype-phenotype pair, and then to apply a stringent significance cutoff to account for the large number of tests performed. However, this approach has limited ability to uncover dependencies involving multiple variables. Another trend in the current genetics is the investigation of the impact of rare variants on the phenotype, where the standard methods often fail owing to lack of power when the minor allele is present in only a limited number of individuals.

RESULTS

We propose a new statistical approach based on Bayesian reduced rank regression to assess the impact of multiple SNPs on a high-dimensional phenotype. Because of the method's ability to combine information over multiple SNPs and phenotypes, it is particularly suitable for detecting associations involving rare variants. We demonstrate the potential of our method and compare it with alternatives using the Northern Finland Birth Cohort with 4702 individuals, for whom genome-wide SNP data along with lipoprotein profiles comprising 74 traits are available. We discovered two genes (XRCC4 and MTHFD2L) without previously reported associations, which replicated in a combined analysis of two additional cohorts: 2390 individuals from the Cardiovascular Risk in Young Finns study and 3659 individuals from the FINRISK study.

AVAILABILITY AND IMPLEMENTATION

R-code freely available for download at http://users.ics.aalto.fi/pemartti/gene_metabolome/.

Novel insights into the regulatory architecture of CD4+ T cells in rheumatoid arthritis

Rheumatoid arthritis (RA) is the most frequent autoimmune chronic inflammatory disease of the joints and it is characterized by the inflammation of the synovial membrane and the subsequent destruction of the joints. In RA, CD4+ T cells are the main drivers of disease initiation and the perpetuation of the damaging inflammatory process. To date, however, the genetic regulatory mechanisms of CD4+ T cells associated with RA etiology are poorly understood. The genome-wide analysis of expression quantitative trait loci (eQTL) in disease-relevant cell types is a recent genomic integration approach that is providing significant insights into the genetic regulatory mechanisms of many human pathologies. The objective of the present study was to analyze, for the first time, the genome-wide genetic regulatory mechanisms associated with the gene expression of CD4+ T cells in RA. Whole genome gene expression profiling of CD4+ T cells and the genome-wide genotyping (598,258 SNPs) of 29 RA patients with an active disease were performed. In order to avoid the excessive burden of multiple testing associated with genome-wide trans-eQTL analysis, we developed and implemented a novel systems genetics approach. Finally, we compared the genomic regulation pattern of CD4+ T cells in RA with the genomic regulation observed in reference lymphoblastoid cell lines (LCLs). We identified a genome-wide significant cis-eQTL associated with the expression of FAM66C gene (P = 6.51e−9). Using our new systems genetics approach we identified six statistically significant trans-eQTLs associated with the expression of KIAA0101 (P<7.4e−8) and BIRC5 (P = 5.35e−8) genes. Finally, comparing the genomic regulation profiles between RA CD4+ T cells and control LCLs we found 20 genes showing differential regulatory patterns between both cell types. The present genome-wide eQTL analysis has identified new genetic regulatory elements that are key to the activity of CD4+ T cells in RA.

Methods for quantifying gene expression in ecoimmunology: from qPCR to RNA-Seq

Historically, the use of cutting-edge molecular techniques to study immunological gene expression and related cellular pathways has been largely limited to model organisms. Few studies have been performed that quantify the molecular immunological responses of non-model species, especially in response to environmental factors, life-history events, or exposure to parasites. This dearth of information has largely occurred due to the lack of available non-model species-specific gene sequences and immunological reagents and also due to prohibitively expensive technology. However, with the rapid development of various sequencing and transcriptomic technologies, profiling the gene expression of non-model organisms has become possible. Technologies and concepts explored here include an overview of current technologies for quantifying gene expression, including: qPCR, multiplex branched DNA assays, microarrays, and profiling gene expression (RNA sequencing [RNA-Seq]) based on next-generation sequencing. Examples of the advancement of these technologies in non-model systems are discussed. Additionally, applications, limitations, and feasibility of the use of these methodologies in non-model systems to address questions in ecological immunology and disease-ecology are specifically addressed.

Systems genetics: From GWAS to disease pathways

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.

Coronary heart disease-associated variation in TCF21 disrupts a miR-224 binding site and miRNA-mediated regulation

Genome-wide association studies (GWAS) have identified chromosomal loci that affect risk of coronary heart disease (CHD) independent of classical risk factors. One such association signal has been identified at 6q23.2 in both Caucasians and East Asians. The lead CHD-associated polymorphism in this region, rs12190287, resides in the 3′ untranslated region (3′-UTR) of TCF21, a basic-helix-loop-helix transcription factor, and is predicted to alter the seed binding sequence for miR-224. Allelic imbalance studies in circulating leukocytes and human coronary artery smooth muscle cells (HCASMC) showed significant imbalance of the TCF21 transcript that correlated with genotype at rs12190287, consistent with this variant contributing to allele-specific expression differences. 3′ UTR reporter gene transfection studies in HCASMC showed that the disease-associated C allele has reduced expression compared to the protective G allele. Kinetic analyses in vitro revealed faster RNA-RNA complex formation and greater binding of miR-224 with the TCF21 C allelic transcript. In addition, in vitro probing with Pb²⁺ and RNase T1 revealed structural differences between the TCF21 variants in proximity of the rs12190287 variant, which are predicted to provide greater access to the C allele for miR-224 binding. miR-224 and TCF21 expression levels were anti-correlated in HCASMC, and miR-224 modulates the transcriptional response of TCF21 to transforming growth factor-β (TGF-β) and platelet derived growth factor (PDGF) signaling in an allele-specific manner. Lastly, miR-224 and TCF21 were localized in human coronary artery lesions and anti-correlated during atherosclerosis. Together, these data suggest that miR-224 interaction with the TCF21 transcript contributes to allelic imbalance of this gene, thus partly explaining the genetic risk for coronary heart disease associated at 6q23.2. These studies implicating rs12190287 in the miRNA-dependent regulation of TCF21, in conjunction with previous studies showing that this variant modulates transcriptional regulation through activator protein 1 (AP-1), suggests a unique bimodal level of complexity previously unreported for disease-associated variants.

Both genetic and environmental factors cumulatively contribute to coronary heart disease risk in human populations. Large-scale meta-analyses of genome-wide association studies have now leveraged common genetic variation to identify multiple sites of disease susceptibility; however, the causal mechanisms for these associations largely remain elusive. One of these disease-associated variants, rs12190287, resides in the 3′untranslated region of the vascular developmental transcription factor, TCF21. Intriguingly, this variant is shown to disrupt the seed binding sequence for microRNA-224, and through altered RNA secondary structure and binding kinetics, leads to dysregulated TCF21 gene expression in response to disease-relevant stimuli. Importantly TCF21 and miR-224 expression levels were perturbed in human atherosclerotic lesions. Along with our previous reports on the transcriptional regulatory mechanisms altered by this variant, these studies shed new light on the complex heritable mechanisms of coronary heart disease risk that are amenable to therapeutic intervention.

The effects of genetic variation on gene expression dynamics during development

The development of a multicellular organism and physiological responses require massive coordinated changes in gene expression across several cell and tissue types. Polymorphic regions of the genome that influence gene expression levels have been identified by expression quantitative trait locus (eQTL) mapping in many species, including loci that have cell-dependent, tissue-dependent and age-dependent effects. However, there has been no comprehensive characterization of how polymorphisms influence the complex dynamic patterns of gene expression that occur during development and in physiology. Here we describe an efficient experimental design to infer gene expression dynamics from single expression profiles in different genotypes, and apply it to characterize the effect of local (cis) and distant (trans) genetic variation on gene expression at high temporal resolution throughout a 12-hour period of the development of Caenorhabditis elegans. Taking dynamic variation into account identifies >50% more cis-eQTLs, including more than 900 that alter the dynamics of expression during this period. Local sequence polymorphisms extensively affect the timing, rate, magnitude and shape of expression changes. Indeed, many local sequence variants both increase and decrease gene expression, depending on the time-point profiled. Expression dynamics during this 12-hour period are also influenced extensively in trans by distal loci. In particular, several trans loci influence genes with quite diverse dynamic expression patterns, but they do so primarily during a common time interval. Trans loci can therefore act as modifiers of expression during a particular period of development. This study provides the first characterization, to our knowledge, of the effect of local and distant genetic variation on the dynamics of gene expression throughout an extensive time period. Moreover, the approach developed here should facilitate the genetic dissection of other dynamic processes, including potentially development, physiology and disease progression in humans.