Quantitative trait transcripts for nicotine resistance in Drosophila melanogaster

Effects of cocaine, nicotine, and marijuana exposure in Drosophila Melanogaster development: A systematic review and meta-analysis

Abuse-related drug usage is a public health issue. Drosophila melanogaster has been used as an animal model to study the biological effects of these psychoactive substances in preclinical studies. Our objective in this review is to evaluate the adverse effects produced by cocaine, nicotine, and marijuana during the development of D. melanogaster. We searched experimental studies in which D. melanogaster was exposed to these three psychoactive drugs in seven online databases up to January 2023. Two reviewers independently extracted the data. Fifty-one studies met eligibility criteria and were included in the data extraction: nicotine (n = 26), cocaine (n = 20), and marijuana (n = 5). Fifteen studies were eligible for meta-analysis. Low doses (∼0.6 mM) of nicotine increased locomotor activity in fruit flies, while high doses (≥3 mM) led to a decrease. Similarly, exposure to cocaine increased locomotor activity, resulting in decreased climbing response in D. melanogaster. Studies with exposure to marijuana did not present a profile for our meta-analysis. However, this drug has been less associated with locomotor changes, but alterations in body weight and fat content and changes in cardiac function. Our analyses have shown that fruit flies exposed to drugs of abuse during different developmental stages, such as larvae and adults, exhibit molecular, morphological, behavioral, and survival changes that are dependent on the dosage. These phenotypes resemble the adverse effects of psychoactive substances in clinical medicine.

RNA Sequencing of the Pituitary Gland and Association Analyses Reveal PRKG2 as a Candidate Gene for Growth and Carcass Traits in Chinese Ningdu Yellow Chickens

Growth and carcass traits are of great economic importance to the chicken industry. The candidate genes and mutations associated with growth and carcass traits can be utilized to improve chicken growth. Therefore, the identification of these genes and mutations is greatly importance. In this study, a total of 17 traits related to growth and carcass were measured in 399 Chinese Ningdu yellow chickens. RNA sequencing (RNA-seq) was performed to detect candidate genes using 12 pituitary gland samples (six per group), which exhibited extreme growth and carcass phenotypes: either a high live weight and carcass weight (H group) or a low live weight and carcass weight (L group). A differential expression analysis, utilizing RNA-seq, between the H and L groups identified 428 differentially expressed genes (DEGs), including 110 up-regulated genes and 318 down-regulated genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the identified genes showed a significant enrichment of 158 GO terms and two KEGG pathways, including response to stimulus and neuroactive ligand-receptor interaction, respectively. Furthermore, RNA-seq data, qRT–PCR, and quantitative trait transcript (QTT) analysis results suggest that the PRKG2 gene is an important candidate gene for growth and carcass traits of Chinese Ningdu yellow chickens. More specifically, association analyses of a single nucleotide polymorphism (SNP) in PRKG2 and growth and carcass traits showed that the SNP rs16400745 was significantly associated with 12 growth and carcass traits (P < 0.05), such as carcass weight (P = 9.68E-06), eviscerated weight (P = 3.04E-05), and semi-eviscerated weight (P = 2.14E-04). Collectively, these results provide novel insights into the genetic basis of growth in Chinese Ningdu yellow chickens and the SNP rs16400745 reported here could be incorporated into the selection programs involving this breed.

An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs

Understanding the genetic factors behind meat quality traits is of great significance to animal breeding and production. We previously conducted a genome-wide association study (GWAS) for meat quality traits in a White Duroc × Erhualian F2 pig population using Illumina porcine 60K SNP data. Here, we further investigate the functional candidate genes and their network modules associated with meat quality traits by integrating transcriptomics and GWAS information. Quantitative trait transcript (QTT) analysis, gene expression QTL (eQTL) mapping, and weighted gene co-expression network analysis (WGCNA) were performed using the digital gene expression (DGE) data from 493 F2 pig's muscle and liver samples. Among the quantified 20,108 liver and 23,728 muscle transcripts, 535 liver and 1,014 muscle QTTs corresponding to 416 and 721 genes, respectively, were found to be significantly (p < 5 × 10^-4) correlated with 22 meat quality traits measured on longissiums dorsi muscle (LM) or semimembranosus muscle (SM). Transcripts associated with muscle glycolytic potential (GP) and pH values were enriched for genes involved in metabolic process. There were 42 QTTs (for 32 genes) shared by liver and muscle tissues, of which 10 QTTs represent GP- and/or pH-related genes, such as JUNB, ATF3, and PPP1R3B. Furthermore, a genome-wide eQTL mapping revealed a total of 3,054 eQTLs for all annotated transcripts in muscle (p < 2.08 × 10^-5), including 1,283 cis-eQTLs and 1771 trans-eQTLs. In addition, WGCNA identified five modules relevant to glycogen metabolism pathway and highlighted the connections between variations in meat quality traits and genes involved in energy process. Integrative analysis of GWAS loci, eQTL, and QTT demonstrated GALNT15/GALNTL2 and HTATIP2 as strong candidate genes for drip loss and pH drop from postmortem 45 min to 24 h, respectively. Our findings provide valuable insights into the genetic basis of meat quality traits and greatly expand the number of candidate genes that may be valuable for future functional analysis and genetic improvement of meat quality.

Allele Specific Expression (ASE) analysis between Bos Taurus and Bos Indicus cows using RNA-Seq data at SNP level and gene level

In the current study, allele specific expression analysis was performed in two subspecies cows (Bos taurus and Bos indicus) at SNP and gene levels. RNA-Seq data of 21,078,477 and 20940063 paired end reads from pooling of whole blood samples (Leukocyte) from 40 US Holstein (Bos Taurus) and 45 Cholistani cows (Bos indicus) obtained from SRA database in NCBI. Quality control and trimming of row RNA-Seq data were processed by FASTQC and Trimmomatic softwares. The transcriptome was assembled by TopHat2 software in two cow's population by aligning and mapping the RNA-Seq reads on bovine reference genome. The SNPs were discovered by Samtools software and ASE analysis was performed by Chi-square test. Results showed that 50183 and 137954 SNPs were discovered on the assembled transcriptome of Holstein and Cholistani cow samples, respectively, and 15308 SNPs were common in both breeds. 10158 SNPs from 50183 (20%) in Holstein and 31523 SNPs from 137954 (23%) in Cholistani cows were identified as ASE-SNPs. Reference allele and alternative allele count in Holstein and Cholistani cows were 3041 and 7155, respectively. Among 131 discovered SNPs in 41 genes with different expression in Holstein and Cholistani cows, 31 ASE-SNPs (5 in Holstein; 26 in Cholistani cows) were discovered.

Causal Inference Methods to Integrate Omics and Complex Traits

Major biotechnological advances have facilitated a tremendous boost to the collection of (gen-/transcript-/prote-/methyl-/metabol-)omics data in very large sample sizes worldwide. Coordinated efforts have yielded a deluge of studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) disease biomarkers may simply be correlates or consequences of the explored diseases. To move beyond this realm, Mendelian randomization provides a principled framework to integrate information on omics- and disease-associated genetic variants to pinpoint molecular traits causally driving disease development. In this review, we show the latest advances in this field, flag up key challenges for the future, and propose potential solutions.

Advances in cerebral palsy biomarkers

Cerebral palsy (CP), defined as a group of nonprogressive disorders of movement and posture, is the most common cause of severe neurodisability in children. The prevalence of CP is the same across the globe, affecting approximately 17 million people worldwide. Cerebral Palsy is an umbrella term used to describe the disease due to its inherent heterogeneity. For instance, CP has multiple (1) causes; (2) clinical types; (3) patterns of neuropathology on brain imaging and (4) it's associated with several developmental pathologies such as intellectual disability, autism, epilepsy, and visual impairment. Understanding its physiopathology is crucial to developing protective strategies. Despite its importance, there is still insufficient progress in the areas of CP prediction, early diagnosis, treatment, and prevention. Herein we describe the current risk factors and biomarkers used for the diagnosis and prediction of CP. With the advancement in biomarker discovery, we predict that our understanding of the etiopathophysiology of CP will also increase, lending to more opportunities for developing novel treatments and prognosis.

Genetic Signatures of Drug Response Variability in Drosophila melanogaster

Knowledge of the genetic basis underlying variation in response to environmental exposures or treatments is important in many research areas. For example, knowing the set of causal genetic variants for drug responses could revolutionize personalized medicine. We used Drosophila melanogaster to investigate the genetic signature underlying behavioral variability in response to methylphenidate (MPH), a drug used in the treatment of attention-deficit/hyperactivity disorder. We exposed a wild-type D. melanogaster population to MPH and a control treatment, and observed an increase in locomotor activity in MPH-exposed individuals. Whole-genome transcriptomic analyses revealed that the behavioral response to MPH was associated with abundant gene expression alterations. To confirm these patterns in a different genetic background and to further advance knowledge on the genetic signature of drug response variability, we used a system of inbred lines, the Drosophila Genetic Reference Panel (DGRP). Based on the DGRP, we showed that the behavioral response to MPH was strongly genotype-dependent. Using an integrative genomic approach, we incorporated known gene interactions into the genomic analyses of the DGRP, and identified putative candidate genes for variability in drug response. We successfully validated 71% of the investigated candidate genes by gene expression knockdown. Furthermore, we showed that MPH has cross-generational behavioral and transcriptomic effects. Our findings establish a foundation for understanding the genetic mechanisms driving genotype-specific responses to medical treatment, and highlight the opportunities that integrative genomic approaches have in optimizing medical treatment of complex diseases.

Kernel size-related genes revealed by an integrated eQTL analysis during early maize kernel development

In maize, kernel traits strongly impact overall grain yields, and it is known that sophisticated spatiotemporal programs of gene expression coordinate kernel development, so advancing our knowledge of kernel development can help efforts to improve grain yields. Here, using phenotype, genotype and transcriptomics data of maize kernels at 5 and 15 days after pollination (DAP) for a large association mapping panel, we employed multiple quantitative genetics approaches-genome-wide association studies (GWAS) as well as expression quantitative trait loci (eQTL) and quantitative trait transcript (QTT) analyses-to gain insights about molecular genetic basis of kernel development in maize. This resulted in the identification of 137 putative kernel length-related genes at 5 DAP, of which 43 are located in previously reported QTL regions. Strikingly, we identified an eQTL that overlaps the locus encoding a maize homolog of the recently described m⁶ A methylation reader protein ECT2 from Arabidopsis; this putative ^epi eQTL is associated with 53 genes and may represent a master epi-transcriptomic regulator of kernel development. Notably, among the genes associated with this ^epi eQTL, 10 are for the main storage proteins in the maize endosperm (zeins) and two are known regulators of zein expression or endosperm development (Opaque2 and ZmICE1). Collectively, beyond cataloging and characterizing genomic attributes of a large number of eQTL associated with kernel development in maize, our study highlights how an eQTL approach can bolster the impact of both GWAS and QTT studies and can drive insights about the basic biology of plants.

The developmental and genetic trajectory of coloration in the guppy (Poecilia reticulata)

Examining the association between trait variation and development is crucial for understanding the evolution of phenotypic differences. Male guppy ornamental caudal fin coloration is one trait that shows a striking degree of variation within and between guppy populations. Males initially have no caudal fin coloration, then gradually develop it as they reach sexual maturity. For males, there is a trade-off between female preference for caudal fin coloration and increased visibility to predators. This trade-off may reach unique endpoints in males from different predation regimes. Caudal fin coloration includes black melanin, orange/yellow pteridines or carotenoids, and shimmering iridescence. This study examined the phenotypic trajectory and genetics associated with color development. We found that black coloration always developed first, followed by orange/yellow, then iridescence. The ordering and timing of color appearance was the same regardless of predation regime. The increased expression of melanin synthesis genes correlated well with the visual appearance of black coloration, but there was no correlation between carotenoids or pteridine synthesis gene expression and the appearance of orange/yellow. The lack of orange/yellow coloration in earlier male caudal fin developmental stages may be due to reduced expression of genes underlying the development of orange/yellow xanthophores.

Evolutionary compromises to metabolic toxins: Ammonia and urea tolerance in Drosophila suzukii and Drosophila melanogaster

The invasive pest Drosophila suzukii has evolved morphological and behavioural adaptations to lay eggs under the skin of fresh fruits. This results in severe damage to a wide range of small fruits. Drosophila suzukii females typically lay few eggs per fruit, preferring healthy fruits. Hence, larvae are exposed to a reduced amount of nitrogenous waste. Differently, the innocuous Drosophila melanogaster lays eggs on fermented fruits already infested by conspecifics, with larvae developing in a crowded environment with the accumulation of nitrogenous waste such as ammonia and urea. These compounds derive from nitrogen metabolism, protein degradation, and amino acids catabolism and are relatively toxic at high concentrations in an organism. The observed differences in oviposition site and larval ecological niche suggest that these species might differ in behavioural and physiological mechanisms used to cope with nitrogenous waste. We investigated how different concentrations of ammonia and urea affect oviposition and larval development in both species. Females and larvae of D. suzukii showed greater susceptibility to high concentrations of both compounds, with a dramatic decrease in the number of eggs laid and egg viability. Moreover, we tested the chemotactic response of third instar larvae to high concentrations of the compounds. Interestingly, ammonia resulted in a repulsive behaviour in respect of the control and urea groups. To better understand the pathways underlying these differences, we evaluated the effect on ornithine aminotransferase and glutathione-S-transferase, two enzymes involved in nitrogen metabolism and stress response that are expressed during larval development. Both ammonia and urea significantly reduced the expression of these enzymes in D. suzukii compared to D. melanogaster. This shows how the ecological shift of D. suzukii to fresh fruit is accompanied by less efficient detoxifying and excretory mechanisms, with important implications for evolutionary biology and applied research. Our data suggest that the ecological shift of D. suzukii to fresh fruit as oviposition substrate is accompanied by a reduced tolerance to metabolic toxins during larval development.

Advantages of RNA-seq compared to RNA microarrays for transcriptome profiling of anterior cruciate ligament tears

Microarrays and RNA-seq are at the forefront of high throughput transcriptome analyses. Since these methodologies are based on different principles, there are concerns about the concordance of data between the two techniques. The concordance of RNA-seq and microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed in clinically derived ligament tissues. To demonstrate the concordance between RNA-seq and microarrays and to assess potential benefits of RNA-seq over microarrays, we assessed differences in transcript expression in anterior cruciate ligament (ACL) tissues based on time-from-injury. ACL remnants were collected from patients with an ACL tear at the time of ACL reconstruction. RNA prepared from torn ACL remnants was subjected to Agilent microarrays (N = 24) and RNA-seq (N = 8). The correlation of biological replicates in RNA-seq and microarrays data was similar (0.98 vs. 0.97), demonstrating that each platform has high internal reproducibility. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarrays values were moderate. The cross-platform concordance for differentially expressed transcripts or enriched pathways was linearly correlated (r = 0.64). RNA-Seq was superior in detecting low abundance transcripts and differentiating biologically critical isoforms. Additional independent validation of transcript expression was undertaken using microfluidic PCR for selected genes. PCR data showed 100% concordance (in expression pattern) with RNA-seq and microarrays data. These findings demonstrate that RNA-seq has advantages over microarrays for transcriptome profiling of ligament tissues when available and affordable. Furthermore, these findings are likely transferable to other musculoskeletal tissues where tissue collection is challenging and cells are in low abundance. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:484-497, 2018.

Naturally Segregating Variation at Ugt86Dd Contributes to Nicotine Resistance in Drosophila melanogaster

Identifying the sequence polymorphisms underlying complex trait variation is a key goal of genetics research, since knowing the precise causative molecular events allows insight into the pathways governing trait variation. Genetic analysis of complex traits in model systems regularly starts by constructing QTL maps, but generally fails to identify causative sequence polymorphisms. Previously we mapped a series of QTL contributing to resistance to nicotine in a Drosophila melanogaster multiparental mapping resource and here use a battery of functional tests to resolve QTL to the molecular level. One large-effect QTL resided over a cluster of UDP-glucuronosyltransferases, and quantitative complementation tests using deficiencies eliminating subsets of these detoxification genes revealed allelic variation impacting resistance. RNAseq showed that Ugt86Dd had significantly higher expression in genotypes that are more resistant to nicotine, and anterior midgut-specific RNA interference (RNAi) of this gene reduced resistance. We discovered a segregating 22-bp frameshift deletion in Ugt86Dd, and accounting for the InDel during mapping largely eliminates the QTL, implying the event explains the bulk of the effect of the mapped locus. CRISPR/Cas9 editing of a relatively resistant genotype to generate lesions in Ugt86Dd that recapitulate the naturally occurring putative loss-of-function allele, leads to a large reduction in resistance. Despite this major effect of the deletion, the allele appears to be very rare in wild-caught populations and likely explains only a small fraction of the natural variation for the trait. Nonetheless, this putatively causative coding InDel can be a launchpad for future mechanistic exploration of xenobiotic detoxification.

Relating quantitative variation within a behavior to variation in transcription

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

Identification of Ceruloplasmin as a Gene that Affects Susceptibility to Glomerulonephritis Through Macrophage Function

Crescentic glomerulonephritis (Crgn) is a complex disorder where macrophage activity and infiltration are significant effector causes. In previous linkage studies using the uniquely susceptible Wistar Kyoto (WKY) rat strain, we have identified multiple crescentic glomerulonephritis QTL (Crgn) and positionally cloned genes underlying Crgn1 and Crgn2, which accounted for 40% of total variance in glomerular inflammation. Here, we have generated a backcross (BC) population (n = 166) where Crgn1 and Crgn2 were genetically fixed and found significant linkage to glomerular crescents on chromosome 2 (Crgn8, LOD = 3.8). Fine mapping analysis by integration with genome-wide expression QTLs (eQTLs) from the same BC population identified ceruloplasmin (Cp) as a positional eQTL in macrophages but not in serum. Liquid chromatography-tandem mass spectrometry confirmed Cp as a protein QTL in rat macrophages. WKY macrophages overexpress Cp and its downregulation by RNA interference decreases markers of glomerular proinflammatory macrophage activation. Similarly, short incubation with Cp results in a strain-dependent macrophage polarization in the rat. These results suggest that genetically determined Cp levels can alter susceptibility to Crgn through macrophage function and propose a new role for Cp in early macrophage activation.

From integrative genomics to systems genetics in the rat to link genotypes to phenotypes

Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease.

Identification of microRNAs associated with allergic airway disease using a genetically diverse mouse population

Background

Allergic airway diseases (AADs) such as asthma are characterized in part by granulocytic airway inflammation. The gene regulatory networks that govern granulocyte recruitment are poorly understood, but evidence is accruing that microRNAs (miRNAs) play an important role. To identify miRNAs that may underlie AADs, we used two complementary approaches that leveraged the genotypic and phenotypic diversity of the Collaborative Cross (CC) mouse population. In the first approach, we sought to identify miRNA expression quantitative trait loci (eQTL) that overlap QTL for AAD-related phenotypes. Specifically, CC founder strains and incipient lines of the CC were sensitized and challenged with house dust mite allergen followed by measurement of granulocyte recruitment to the lung. Total lung RNA was isolated and miRNA was measured using arrays for CC founders and qRT-PCR for incipient CC lines.

Results

Among CC founders, 92 miRNAs were differentially expressed. We measured the expression of 40 of the most highly expressed of these 92 miRNAs in the incipient lines of the CC and identified 18 eQTL corresponding to 14 different miRNAs. Surprisingly, half of these eQTL were distal to the corresponding miRNAs, and even on different chromosomes. One of the largest-effect local miRNA eQTL was for miR-342-3p, for which we identified putative causal variants by bioinformatic analysis of the effects of single nucleotide polymorphisms on RNA structure. None of the miRNA eQTL co-localized with QTL for eosinophil or neutrophil recruitment. In the second approach, we constructed putative miRNA/mRNA regulatory networks and identified three miRNAs (miR-497, miR-351 and miR-31) as candidate master regulators of genes associated with neutrophil recruitment. Analysis of a dataset from human keratinocytes transfected with a miR-31 inhibitor revealed two target genes in common with miR-31 targets correlated with neutrophils, namely Oxsr1 and Nsf.

Conclusions

miRNA expression in the allergically inflamed murine lung is regulated by genetic loci that are smaller in effect size compared to mRNA eQTL and often act in trans. Thus our results indicate that the genetic architecture of miRNA expression is different from mRNA expression. We identified three miRNAs, miR-497, miR-351 and miR-31, that are candidate master regulators of genes associated with neutrophil recruitment. Because miR-31 is expressed in airway epithelia and is predicted to target genes with known links to neutrophilic inflammation, we suggest that miR-31 is a potentially novel regulator of airway inflammation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1732-9) contains supplementary material, which is available to authorized users.

Genetic approaches in comparative and evolutionary physiology

Whole animal physiological performance is highly polygenic and highly plastic, and the same is generally true for the many subordinate traits that underlie performance capacities. Quantitative genetics, therefore, provides an appropriate framework for the analysis of physiological phenotypes and can be used to infer the microevolutionary processes that have shaped patterns of trait variation within and among species. In cases where specific genes are known to contribute to variation in physiological traits, analyses of intraspecific polymorphism and interspecific divergence can reveal molecular mechanisms of functional evolution and can provide insights into the possible adaptive significance of observed sequence changes. In this review, we explain how the tools and theory of quantitative genetics, population genetics, and molecular evolution can inform our understanding of mechanism and process in physiological evolution. For example, lab-based studies of polygenic inheritance can be integrated with field-based studies of trait variation and survivorship to measure selection in the wild, thereby providing direct insights into the adaptive significance of physiological variation. Analyses of quantitative genetic variation in selection experiments can be used to probe interrelationships among traits and the genetic basis of physiological trade-offs and constraints. We review approaches for characterizing the genetic architecture of physiological traits, including linkage mapping and association mapping, and systems approaches for dissecting intermediary steps in the chain of causation between genotype and phenotype. We also discuss the promise and limitations of population genomic approaches for inferring adaptation at specific loci. We end by highlighting the role of organismal physiology in the functional synthesis of evolutionary biology.

Fine-mapping nicotine resistance loci in Drosophila using a multiparent advanced generation inter-cross population

Animals in nature are frequently challenged by toxic compounds, from those that occur naturally in plants as a defense against herbivory, to pesticides used to protect crops. On exposure to such xenobiotic substances, animals mount a transcriptional response, generating detoxification enzymes and transporters that metabolize and remove the toxin. Genetic variation in this response can lead to variation in the susceptibility of different genotypes to the toxic effects of a given xenobiotic. Here we use Drosophila melanogaster to dissect the genetic basis of larval resistance to nicotine, a common plant defense chemical and widely used addictive drug in humans. We identified quantitative trait loci (QTL) for the trait using the DSPR (Drosophila Synthetic Population Resource), a panel of multiparental advanced intercross lines. Mapped QTL collectively explain 68.4% of the broad-sense heritability for nicotine resistance. The two largest-effect loci-contributing 50.3 and 8.5% to the genetic variation-map to short regions encompassing members of classic detoxification gene families. The largest QTL resides over a cluster of ten UDP-glucuronosyltransferase (UGT) genes, while the next largest QTL harbors a pair of cytochrome P450 genes. Using RNAseq we measured gene expression in a pair of DSPR founders predicted to harbor different alleles at both QTL and showed that Ugt86Dd, Cyp28d1, and Cyp28d2 had significantly higher expression in the founder carrying the allele conferring greater resistance. These genes are very strong candidates to harbor causative, regulatory polymorphisms that explain a large fraction of the genetic variation in larval nicotine resistance in the DSPR.

Expression quantitative trait locus analysis for translational medicine

Expression quantitative trait locus analysis has emerged as an important component of efforts to understand how genetic polymorphisms influence disease risk and is poised to make contributions to translational medicine. Here we review how expression quantitative trait locus analysis is aiding the identification of which gene(s) within regions of association are causal for a disease or phenotypic trait; the narrowing down of the cell types or regulators involved in the etiology of disease; the characterization of drivers and modifiers of cancer; and our understanding of how different environments and cellular contexts can modify gene expression. We also introduce the concept of transcriptional risk scores as a means of refining estimates of individual liability to disease based on targeted profiling of the transcripts that are regulated by polymorphisms jointly associated with disease and gene expression.

Identification of quantitative trait transcripts for growth traits in the large scales of liver and muscle samples

Growth-related traits are economically important traits to the pig industry. Identification of causative gene and mutation responsible for growth-related QTL will facilitate the improvement of pig growth through marker-assisted selection. In this study, we applied whole genome gene expression and quantitative trait transcript (QTT) analyses in 497 liver and 586 longissimus dorsi muscle samples to identify candidate genes and dissect the genetic basis of pig growth in a white Duroc × Erhualian F2 resource population. A total of 20,108 transcripts in liver and 23,728 transcripts in muscle with expression values were used for association analysis between gene expression level and phenotypic value. At the significance threshold of P < 0.0005, we identified a total of 169 and 168 QTTs for nine growth-related traits in liver and muscle, respectively. We also found that some QTTs were correlated to more than one trait. The QTTs identified here showed high tissue specificity. We did not identify any QTTs that were associated with one trait in both liver and muscle. Through an integrative genomic approach, we identified SDR16C5 as the important candidate gene in pig growth trait. These findings contribute to further identification of the causative genes for porcine growth traits and facilitate improvement of pig breeding.

Adaptation to nicotine in the facultative tobacco-feeding hemipteran Bemisia tabaci

BACKGROUND

Plant defensive metabolites such as nicotine can provide barriers to host-range expansion by generalist herbivores. Nicotine is one of the most abundant and toxic plant secondary metabolites in nature and is defined by high toxicity to plant-feeding insects. There is significant variation in nicotine tolerance among Bemisia tabaci (tobacco whitefly) isolates. Some nicotine-tolerant B. tabaci strains can consume 40-fold higher nicotine levels than susceptible strains, and also show cross-resistance to neonicotinoid insecticides. In this study, biological and molecular assays were used to investigate the responses of B. tabaci strains that differ in their ability to tolerate dietary nicotine.

RESULTS

Egg laying and honeydew secretion bioassays as well as gene expression microarrays were used to measure B. tabaci biological parameters and gene transcripts misregulated in response to nicotine in resistant and susceptible strains. The resistant B. tabaci strain laid significantly fewer eggs and excreted more honeydew on a tobacco strain with high levels of nicotine, suggesting a fitness cost effect. The molecular response was drastic in the susceptible strain, while the resistant strain exhibited moderate response. Higher expression of the previously identified CYP6CM1 P450 monooxygenase gene related to the resistance to neonicotinoids, as well as other P450s and metabolic genes, was identified in the resistant and susceptible strains after exposure to nicotine.

CONCLUSIONS

Nicotine is a very toxic plant natural compound, and its mode of action resembles that of synthetic neonicotinoids. The biological and molecular responses observed in this study suggest that nicotine may play an important role in providing barriers for host-plant expansion by generalists, and may act as a natural factor that contributes to the development of insect populations resistant to synthetic pesticides.

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.

Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells

To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-Seq and microarray technologies in transcriptome profiling. A comparison of data sets derived from RNA-Seq and Affymetrix platforms using the same set of samples showed a high correlation between gene expression profiles generated by the two platforms. However, it also demonstrated that RNA-Seq was superior in detecting low abundance transcripts, differentiating biologically critical isoforms, and allowing the identification of genetic variants. RNA-Seq also demonstrated a broader dynamic range than microarray, which allowed for the detection of more differentially expressed genes with higher fold-change. Analysis of the two datasets also showed the benefit derived from avoidance of technical issues inherent to microarray probe performance such as cross-hybridization, non-specific hybridization and limited detection range of individual probes. Because RNA-Seq does not rely on a pre-designed complement sequence detection probe, it is devoid of issues associated with probe redundancy and annotation, which simplified interpretation of the data. Despite the superior benefits of RNA-Seq, microarrays are still the more common choice of researchers when conducting transcriptional profiling experiments. This is likely because RNA-Seq sequencing technology is new to most researchers, more expensive than microarray, data storage is more challenging and analysis is more complex. We expect that once these barriers are overcome, the RNA-Seq platform will become the predominant tool for transcriptome analysis.

Regulation of galactomannan biosynthesis in coffee seeds

The seed of Coffea arabica accumulates large amounts of cell wall storage polysaccharides (CWSPs) of the mannan family in the cell walls of the endosperm. The variability induced by the growing environment and extensive pairwise correlation analysis with stringent significance thresholds was used to investigate transcript-transcript and transcript-metabolite relationships among 26 sugar-related genes, and the amount of CWSPs and seven soluble low molecular weight carbohydrates in the developing coffee endosperm. A dense module of nine quantitatively co-expressed genes was detected at the mid-developmental stage when CWSPs accumulate. This module included the five genes of the core galactomannan synthetic machinery, namely genes coding for the enzymes needed to assemble the mannan backbone (mannan synthase, ManS), and genes that introduce the galactosyl side chains (galactosyltransferase, GMGT), modulate the post-depositional degree of galactose substitution (α-galactosidase), and produce the nucleotide sugar building blocks GDP-mannose and UDP-galactose (mannose-1P guanyltransferase and UDP-glucose 4'-epimerase, respectively). The amount of CWSPs stored in the endosperm at the onset of their accumulation was primarily and quantitatively modulated at the transcriptional level (i.e. positively correlated with the expression level of these key galactomannan biosynthetic genes). This analysis also suggests a role for sorbitol and raffinose family oligosaccharides as transient auxiliary sources of building blocks for galactomannan synthesis. Finally, a microarray-based analysis of the developing seed transcriptome revealed that all genes of the core galactomannan synthesis machinery grouped in a single cluster of 209 co-expressed genes. Analysis of the gene composition of this cluster revealed remarkable functional coherence and identified transcription factors that putatively control galactomannan biosynthesis in coffee.

Systems genetics approaches to understand complex traits

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.

Genetic complexity in a Drosophila model of diabetes-associated misfolded human proinsulin

Drosophila melanogaster has been widely used as a model of human Mendelian disease, but its value in modeling complex disease has received little attention. Fly models of complex disease would enable high-resolution mapping of disease-modifying loci and the identification of novel targets for therapeutic intervention. Here, we describe a fly model of permanent neonatal diabetes mellitus and explore the complexity of this model. The approach involves the transgenic expression of a misfolded mutant of human preproinsulin, hINS^C96Y, which is a cause of permanent neonatal diabetes. When expressed in fly imaginal discs, hINS^C96Y causes a reduction of adult structures, including the eye, wing, and notum. Eye imaginal discs exhibit defects in both the structure and the arrangement of ommatidia. In the wing, expression of hINS^C96Y leads to ectopic expression of veins and mechano-sensory organs, indicating disruption of wild-type signaling processes regulating cell fates. These readily measurable “disease” phenotypes are sensitive to temperature, gene dose, and sex. Mutant (but not wild-type) proinsulin expression in the eye imaginal disc induces IRE1-mediated XBP1 alternative splicing, a signal for endoplasmic reticulum stress response activation, and produces global change in gene expression. Mutant hINS transgene tester strains, when crossed to stocks from the Drosophila Genetic Reference Panel, produce F₁ adults with a continuous range of disease phenotypes and large broad-sense heritability. Surprisingly, the severity of mutant hINS-induced disease in the eye is not correlated with that in the notum in these crosses, nor with eye reduction phenotypes caused by the expression of two dominant eye mutants acting in two different eye development pathways, Drop (Dr) or Lobe (L), when crossed into the same genetic backgrounds. The tissue specificity of genetic variability for mutant hINS-induced disease has, therefore, its own distinct signature. The genetic dominance of disease-specific phenotypic variability in our model of misfolded human proinsulin makes this approach amenable to genome-wide association study in a simple F₁ screen of natural variation.

Mapping genetic determinants of coronary microvascular remodeling in the spontaneously hypertensive rat

The mechanisms underlying coronary microvascular remodeling and dysfunction, which are critical determinants of abnormal myocardial blood flow regulation in human hypertension, are poorly understood. The spontaneously hypertensive rat (SHR) exhibits many features of human hypertensive cardiomyopathy. We demonstrate that remodeling of intramural coronary arterioles is apparent in the SHR already at 4 weeks of age, i.e. before the onset of systemic hypertension. To uncover possible genetic determinants of coronary microvascular remodeling, we carried out detailed histological and histomorphometric analysis of the heart and coronary vasculature in 30 weeks old SHR, age-matched Brown Norway (BN-Lx) parentals and BXH/HXB recombinant inbred (RI) strains. Using previously mapped expression quantitative trait loci (eQTLs), we carried out a genome-wide association analysis between genetic determinants of cardiac gene expression and histomorphometric traits. This identified 36 robustly mapped eQTLs in the heart which were associated with medial area of intramural coronary arterioles [false discovery rate (FDR) ~5%]. Transcripts, which were both under cis-acting genetic regulation and significantly correlated with medial area (FDR <5%), but not with blood pressure indices, were prioritized and four candidate genes were identified (Rtel1, Pla2g5, Dnaja4 and Rcn2) according to their expression levels and biological functions. Our results demonstrate that genetic factors play a role in the development of coronary microvascular remodeling and suggest blood pressure independent candidate genes for further functional experiments.

Down-regulation of Decapping Protein 2 mediates chronic nicotine exposure-induced locomotor hyperactivity in Drosophila

Long-term tobacco use causes nicotine dependence via the regulation of a wide range of genes and is accompanied by various health problems. Studies in mammalian systems have revealed some key factors involved in the effects of nicotine, including nicotinic acetylcholine receptors (nAChRs), dopamine and other neurotransmitters. Nevertheless, the signaling pathways that link nicotine-induced molecular and behavioral modifications remain elusive. Utilizing a chronic nicotine administration paradigm, we found that adult male fruit flies exhibited locomotor hyperactivity after three consecutive days of nicotine exposure, while nicotine-naive flies did not. Strikingly, this chronic nicotine-induced locomotor hyperactivity (cNILH) was abolished in Decapping Protein 2 or 1 (Dcp2 or Dcp1) -deficient flies, while only Dcp2-deficient flies exhibited higher basal levels of locomotor activity than controls. These results indicate that Dcp2 plays a critical role in the response to chronic nicotine exposure. Moreover, the messenger RNA (mRNA) level of Dcp2 in the fly head was suppressed by chronic nicotine treatment, and up-regulation of Dcp2 expression in the nervous system blocked cNILH. These results indicate that down-regulation of Dcp2 mediates chronic nicotine-exposure-induced locomotor hyperactivity in Drosophila. The decapping proteins play a major role in mRNA degradation; however, their function in the nervous system has rarely been investigated. Our findings reveal a significant role for the mRNA decapping pathway in developing locomotor hyperactivity in response to chronic nicotine exposure and identify Dcp2 as a potential candidate for future research on nicotine dependence.

Systems-level approaches reveal conservation of trans-regulated genes in the rat and genetic determinants of blood pressure in humans

AIMS

Human genome-wide association studies (GWAS) of hypertension identified only few susceptibility loci with large effect that were replicated across populations. The vast majority of genes detected by GWAS has small effect and the regulatory mechanisms through which these genetic variants cause disease remain mostly unclear. Here, we used comparative genomics between human and an established rat model of hypertension to explore the transcriptional mechanisms mediating the effect of genes identified in 15 hypertension GWAS.

METHODS AND RESULTS

Time series analysis of radiotelemetric blood pressure (BP) was performed to assess 11 parameters of BP variation in recombinant inbred strains derived from the spontaneously hypertensive rat. BP data were integrated with ∼27 000 expression quantative trait loci (eQTLs) mapped across seven tissues, detecting >8000 significant associations between eQTL genes and BP variation in the rat. We then compiled a large catalogue of human genes from GWAS of hypertension and identified a subset of 2292 rat-human orthologous genes. Expression levels for 795 (34%) of these genes correlated with BP variation across rat tissues: 51 genes were cis-regulated, whereas 459 were trans-regulated and enriched for 'calcium signalling pathway' (P = 9.6 × 10(-6)) and 'ion channel' genes (P = 3.5 × 10(-7)), which are important determinants of hypertension. We identified 158 clusters of trans-eQTLs, annotated the underlying 'master regulator' genes and found significant over-representation in the human hypertension gene set (enrichment P = 5 × 10(-4)).

CONCLUSION

We showed extensive conservation of trans-regulated genes and their master regulators between rat and human hypertension. These findings reveal that small-effect genes associated with hypertension by human GWAS are likely to exert their action through coordinate regulation of pathogenic pathways.

Integrative genomics in cardiovascular medicine

Integrative genomics studies have greatly advanced our understanding of cardiovascular pathophysiology over the last decade. Here, we highlight the strengths and challenges of this cutting-edge approach and provide examples where novel insights have arisen through the integration of multi-level genomic information and cardiac physiology. Going forward, the integration of comprehensive next-generation sequencing data sets with quantitative phenotypes at the molecular, cellular, and whole-heart level using advanced modelling approaches provides an unprecedented opportunity for cardiovascular science.

Characterization and comparison of the leukocyte transcriptomes of three cattle breeds

In this study, mRNA-Seq was used to characterize and compare the leukocyte transcriptomes from two taurine breeds (Holstein and Jersey), and one indicine breed (Cholistani). At the genomic level, we identified breed-specific base changes in protein coding regions. Among 7,793,425 coding bases, only 165 differed between Holstein and Jersey, and 3,383 (0.04%) differed between Holstein and Cholistani, 817 (25%) of which resulted in amino acid changes in 627 genes. At the transcriptional level, we assembled transcripts and estimated their abundances including those from more than 3,000 unannotated intergeneic regions. Differential gene expression analysis showed a high similarity between Holstein and Jersey, and a much greater difference between the taurine breeds and the indicine breed. We identified gene ontology pathways that were systematically altered, including the electron transport chain and immune response pathways that may contribute to different levels of heat tolerance and disease resistance in taurine and indicine breeds. At the post-transcriptional level, sequencing mRNA allowed us to identify a number of genes undergoing differential alternative splicing among different breeds. This study provided a high-resolution survey of the variation between bovine transcriptomes at different levels and may provide important biological insights into the phenotypic differentiation among cattle breeds.

Integrated genomic approaches to identification of candidate genes underlying metabolic and cardiovascular phenotypes in the spontaneously hypertensive rat

The spontaneously hypertensive rat (SHR) is a widely used rodent model of hypertension and metabolic syndrome. Previously we identified thousands of cis-regulated expression quantitative trait loci (eQTLs) across multiple tissues using a panel of rat recombinant inbred (RI) strains derived from Brown Norway and SHR progenitors. These cis-eQTLs represent potential susceptibility loci underlying physiological and pathophysiological traits manifested in SHR. We have prioritized 60 cis-eQTLs and confirmed differential expression between the parental strains by quantitative PCR in 43 (72%) of the eQTL transcripts. Quantitative trait transcript (QTT) analysis in the RI strains showed highly significant correlation between cis-eQTL transcript abundance and clinically relevant traits such as systolic blood pressure and blood glucose, with the physical location of a subset of the cis-eQTLs colocalizing with “physiological” QTLs (pQTLs) for these same traits. These colocalizing correlated cis-eQTLs (c3-eQTLs) are highly attractive as primary susceptibility loci for the colocalizing pQTLs. Furthermore, sequence analysis of the c3-eQTL genes identified single nucleotide polymorphisms (SNPs) that are predicted to affect transcription factor binding affinity, splicing and protein function. These SNPs, which potentially alter transcript abundance and stability, represent strong candidate factors underlying not just eQTL expression phenotypes, but also the correlated metabolic and physiological traits. In conclusion, by integration of genomic sequence, eQTL and QTT datasets we have identified several genes that are strong positional candidates for pathophysiological traits observed in the SHR strain. These findings provide a basis for the functional testing and ultimate elucidation of the molecular basis of these metabolic and cardiovascular phenotypes.

Swedish mutation within amyloid precursor protein modulates global gene expression towards the pathogenesis of Alzheimer's disease

The Swedish mutation (K595N/M596L) of amyloid precursor protein (APP-swe) has been known to increase abnormal cleavage of cellular APP by Beta-secretase (BACE), which causes tau protein hyperphosphorylation and early-onset Alzheimer's disease (AD). Here, we analyzed the effect of APP-swe in global gene expression using deep transcriptome sequencing technique. We found 283 genes were down-regulated and 348 genes were up-regulated in APP-swe expressing H4-swe cells compared to H4 wild-type cells from a total of approximately 74 million reads of 38 base pairs from each transcriptome. Two independent mechanisms such as kinase and phosphatase signaling cascades leading hyperphosphorylation of tau protein were regulated by the expression of APP-swe. Expressions of catalytic subunit as well as several regulatory subunits of protein phosphatases 2A were decreased. In contrast, expressions of tau-phosphorylating glycogen synthase kinase 3ß (GSK-3ß), cyclin dependent kinase 5 (CDK5), and cAMP-dependent protein kinase A (PKA) catalytic subunit were increased. Moreover, the expression of AD-related Aquaporin 1 and presenilin 2 expression was regulated by APP-swe. Taken together, we propose that the expression of APP-swe modulates global gene expression directed to AD pathogenesis.

The effects of weak genetic perturbations on the transcriptome of the wing imaginal disc and its association with wing shape in Drosophila melanogaster

A major objective of genomics is to elucidate the mapping between genotypic and phenotypic space as a step toward understanding how small changes in gene function can lead to elaborate phenotypic changes. One approach that has been utilized is to examine overall patterns of covariation between phenotypic variables of interest, such as morphology, physiology, and behavior, and underlying aspects of gene activity, in particular transcript abundance on a genome-wide scale. Numerous studies have demonstrated that such patterns of covariation occur, although these are often between samples with large numbers of unknown genetic differences (different strains or even species) or perturbations of large effect (sexual dimorphism or strong loss-of-function mutations) that may represent physiological changes outside of the normal experiences of the organism. We used weak mutational perturbations in genes affecting wing development in Drosophila melanogaster that influence wing shape relative to a co-isogenic wild type. We profiled transcription of 1150 genes expressed during wing development in 27 heterozygous mutants, as well as their co-isogenic wild type and one additional wild-type strain. Despite finding clear evidence of expression differences between mutants and wild type, transcriptional profiles did not covary strongly with shape, suggesting that information from transcriptional profiling may not generally be predictive of final phenotype. We discuss these results in the light of possible attractor states of gene expression and how this would affect interpretation of covariation between transcriptional profiles and other phenotypes.

Transcriptional networks for alcohol sensitivity in Drosophila melanogaster

Understanding the genetic architecture of polygenic traits requires investigating how complex networks of interacting molecules mediate the effect of genetic variation on organismal phenotypes. We used a combination of P-element mutagenesis and analysis of natural variation in gene expression to predict transcriptional networks that underlie alcohol sensitivity in Drosophila melanogaster. We identified 139 unique P-element mutations (124 in genes) that affect sensitivity or resistance to alcohol exposure. Further analyses of nine of the lines showed that the P-elements affected expression levels of the tagged genes, and P-element excision resulted in phenotypic reversion. The majority of the mutations were in computationally predicted genes or genes with unexpected effects on alcohol phenotypes. Therefore we sought to understand the biological relationships among 21 of these genes by leveraging genetic correlations among genetically variable transcripts in wild-derived inbred lines to predict coregulated transcriptional networks. A total of 32 "hub" genes were common to two or more networks associated with the focal genes. We used RNAi-mediated inhibition of expression of focal genes and of hub genes connected to them in the network to confirm their effects on alcohol-related phenotypes. We then expanded the computational networks using the hub genes as foci and again validated network predictions. Iteration of this approach allows a stepwise expansion of the network with simultaneous functional validation. Although coregulated transcriptional networks do not provide information about causal relationships among their constituent transcripts, they provide a framework for subsequent functional studies on the genetic basis of alcohol sensitivity.

Use of the growing environment as a source of variation to identify the quantitative trait transcripts and modules of co-expressed genes that determine chlorogenic acid accumulation

Developing Coffea arabica seeds accumulate large amounts of chlorogenic acids (CGAs) as a storage form of phenylpropanoid derivatives, making coffee a valuable model to investigate the metabolism of these widespread plant phenolics. However, developmental and environmental regulations of CGA metabolism are poorly understood. In the present work, the expression of selected phenylpropanoid genes, together with CGA isomer profiles, was monitored throughout seed development across a wide set of contrasted natural environments. Although CGA metabolism was controlled by major developmental factors, the mean temperature during seed development had a direct impact on the time-window of CGA biosynthesis, as well as on final CGA isomer composition through subtle transcriptional regulations. We provide evidence that the variability induced by the environment is a useful tool to test whether CGA accumulation is quantitatively modulated at the transcriptional level, hence enabling detection of rate-limiting transcriptional steps [quantitative trait transcripts (QTTs)] for CGA biosynthesis. Variations induced by the environment also enabled a better description of the phenylpropanoid gene transcriptional network throughout seed development, as well as the detection of three temporally distinct modules of quantitatively co-expressed genes. Finally, analysis of metabolite-to-metabolite relationships revealed new biochemical characteristics of the isomerization steps that remain uncharacterized at the gene level.

Identifying genetic loci and spleen gene coexpression networks underlying immunophenotypes in BXD recombinant inbred mice

The immune system plays a pivotal role in the susceptibility to and progression of a variety of diseases. Due to a strong genetic basis, heritable differences in immune function may contribute to differential disease susceptibility between individuals. Genetic reference populations, such as the BXD (C57BL/6J × DBA/2J) panel of recombinant inbred (RI) mouse strains, provide unique models through which to integrate baseline phenotypes in healthy individuals with heritable risk for disease because of the ability to combine data collected from these populations across both multiple studies and time. We performed basic immunophenotyping (e.g., percentage of circulating B and T lymphocytes and CD4(+) and CD8(+) T cell subpopulations) in peripheral blood of healthy mice from 41 BXD RI strains to define the immunophenotypic variation in this strain panel and to characterize the genetic architecture that underlies these traits. Significant QTL models that explained the majority (50-77%) of phenotypic variance were derived for each trait and for the T:B cell and CD4(+):CD8(+) ratios. Combining QTL mapping with spleen gene expression data uncovered two quantitative trait transcripts, Ptprk and Acp1, as candidates for heritable differences in the relative abundance of helper and cytotoxic T cells. These data will be valuable in extracting genetic correlates of the immune system in the BXD panel. In addition, they will be a useful resource for prospective, phenotype-driven model selection to test hypotheses about differential disease or environmental susceptibility between individuals with baseline differences in the composition of the immune system.

Genotype and gene expression associations with immune function in Drosophila

It is now well established that natural populations of Drosophila melanogaster harbor substantial genetic variation associated with physiological measures of immune function. In no case, however, have intermediate measures of immune function, such as transcriptional activity of immune-related genes, been tested as mediators of phenotypic variation in immunity. In this study, we measured bacterial load sustained after infection of D. melanogaster with Serratia marcescens, Providencia rettgeri, Enterococcus faecalis, and Lactococcus lactis in a panel of 94 third-chromosome substitution lines. We also measured transcriptional levels of 329 immune-related genes eight hours after infection with E. faecalis and S. marcescens in lines from the phenotypic tails of the test panel. We genotyped the substitution lines at 137 polymorphic markers distributed across 25 genes in order to test for statistical associations among genotype, bacterial load, and transcriptional dynamics. We find that genetic polymorphisms in the pathogen recognition genes (and particularly in PGRP-LC, GNBP1, and GNBP2) are most significantly associated with variation in bacterial load. We also find that overall transcriptional induction of effector proteins is a significant predictor of bacterial load after infection with E. faecalis, and that a marker upstream of the recognition gene PGRP-SD is statistically associated with variation in both bacterial load and transcriptional induction of effector proteins. These results show that polymorphism in genes near the top of the immune system signaling cascade can have a disproportionate effect on organismal phenotype due to the amplification of minor effects through the cascade.

Genetic variation for resistance to infection is widespread among insects and other organisms. However, the extent to which this variation in resistance is mediated by changes in infection-induced gene expression is not known. In this study, we assayed expression of immune system genes and bacterial load after infection in a genotyped panel of lines of the model insect Drosophila melanogaster. We find that statistical associations between genetic variants and bacterial load tend to cluster in genes encoding proteins involved in microbial recognition. Variation in suppression of bacterial growth is also determined in part by genetic variation in the expression of downstream components of the immune system that function to directly kill bacteria, despite finding no genetic variation in any single of these effector gene significantly associated with phenotype. Instead, it appears that activity differences in upstream components of the pathway have a cascading effect that results in larger variation in the expression of coordinately regulated downstream effector genes. These results imply that the interactions among genes need to be taken into account when assessing the phenotypic consequences of genetic variation, as signaling cascades such as those in the immune response have the potential to amplify the phenotypic effects of minor genetic variation in individual genes.

Use of rat genomics for investigating the metabolic syndrome

The spontaneously hypertensive rat (SHR) is the most widely used animal model of essential hypertension and accompanying metabolic disturbances. In this model, the use of whole genome sequencing and gene expression profiling techniques, linkage and correlation analyses in recombinant inbred strains, and in vitro and in vivo functional studies in congenic and transgenic lines has recently enabled molecular identification of quantitative trait loci (QTLs) relevant to the metabolic syndrome: (1) a deletion variant in Cd36 (fatty acid translocase) responsible for QTLs on chromosome 4 associated with dyslipidemia, insulin resistance and hypertension, (2) mutated Srebf1 (sterol regulatory element binding factor 1) as a QTL on chromosome 10 influencing dietary-induced changes in hepatic cholesterol levels, and (3) Ogn (osteoglycin) as a QTL on chromosome 17 associated with left ventricular hypertrophy. In addition, selective replacement of the mitochondrial genome of the SHR with the mitochondrial genome of the Brown Norway rat influenced several major metabolic risk factors for type 2 diabetes and provided evidence that spontaneous variation in the mitochondrial genome per se can promote systemic metabolic disturbances relevant to the pathogenesis of metabolic syndrome. Owing to recent progress in the development of rat genomic resources, the pace of QTL identification and discovery of new disease mechanisms can be expected to accelerate in the near future.

Genetical toxicogenomics in Drosophila identifies master-modulatory loci that are regulated by developmental exposure to lead

The genetics of gene expression in recombinant inbred lines (RILs) can be mapped as expression quantitative trait loci (eQTLs). So-called "genetical genomics" studies have identified locally acting eQTLs (cis-eQTLs) for genes that show differences in steady-state RNA levels. These studies have also identified distantly acting master-modulatory trans-eQTLs that regulate tens or hundreds of transcripts (hotspots or transbands). We expand on these studies by performing genetical genomics experiments in two environments in order to identify trans-eQTL that might be regulated by developmental exposure to the neurotoxin lead. Flies from each of 75 RIL were raised from eggs to adults on either control food (made with 250 microM sodium acetate), or lead-treated food (made with 250 microM lead acetate, PbAc). RNA expression analyses of whole adult male flies (5-10 days old) were performed with Affymetrix DrosII whole genome arrays (18,952 probesets). Among the 1389 genes with cis-eQTL, there were 405 genes unique to control flies and 544 genes unique to lead-treated ones (440 genes had the same cis-eQTLs in both samples). There are 2396 genes with trans-eQTL which mapped to 12 major transbands with greater than 95 genes. Permutation analyses of the strain labels but not the expression data suggests that the total number of eQTL and the number of transbands are more important criteria for validation than the size of the transband. Two transbands, one located on the 2nd chromosome and one on the 3rd chromosome, co-regulate 33 lead-induced genes, many of which are involved in neurodevelopmental processes. For these 33 genes, rather than allelic variation at one locus exerting differential effects in two environments, we found that variation at two different loci are required for optimal effects on lead-induced expression.

The genetics of quantitative traits: challenges and prospects

A major challenge in current biology is to understand the genetic basis of variation for quantitative traits. We review the principles of quantitative trait locus mapping and summarize insights about the genetic architecture of quantitative traits that have been obtained over the past decades. We are currently in the midst of a genomic revolution, which enables us to incorporate genetic variation in transcript abundance and other intermediate molecular phenotypes into a quantitative trait locus mapping framework. This systems genetics approach enables us to understand the biology inside the 'black box' that lies between genotype and phenotype in terms of causal networks of interacting genes.

A transcriptional network associated with natural variation in Drosophila aggressive behavior

A genome-wide screen of inbred Drosophila lines together with transcriptional network modeling reveals insights into the genetic bases of heritable aggression.

Background

Aggressive behavior is an important component of fitness in most animals. Aggressive behavior is genetically complex, with natural variation attributable to multiple segregating loci with allelic effects that are sensitive to the physical and social environment. However, we know little about the genes and genetic networks affecting natural variation in aggressive behavior. Populations of Drosophila melanogaster harbor quantitative genetic variation in aggressive behavior, providing an excellent model system for dissecting the genetic basis of naturally occurring variation in aggression.

Results

Correlating variation in transcript abundance with variation in complex trait phenotypes is a rapid method for identifying candidate genes. We quantified aggressive behavior in 40 wild-derived inbred lines of D. melanogaster and performed a genome-wide association screen for quantitative trait transcripts and single feature polymorphisms affecting aggression. We identified 266 novel candidate genes associated with aggressive behavior, many of which have pleiotropic effects on metabolism, development, and/or other behavioral traits. We performed behavioral tests of mutations in 12 of these candidate genes, and show that nine indeed affected aggressive behavior. We used the genetic correlations among the quantitative trait transcripts to derive a transcriptional genetic network associated with natural variation in aggressive behavior. The network consists of nine modules of correlated transcripts that are enriched for genes affecting common functions, tissue-specific expression patterns, and/or DNA sequence motifs.

Conclusions

Correlations among genetically variable transcripts that are associated with genetic variation in organismal behavior establish a foundation for understanding natural variation for complex behaviors in terms of networks of interacting genes.

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Because molecular machines control mRNA levels of multiple genes, we expect genetic variation in components of these machines would influence multiple genes in a similar fashion. We show that this assumption is correct by using correlation of mRNA levels measured from multiple tissues in mouse strain panels to detect shared genetic influences. These correlating groups of genes (CGGs) have collective properties that on average account for 52–79% of the variability of their constituent genes and can contain genes that encode functionally related proteins. We show that the genetic influences are essentially tissue-specific and, consequently, the same genetic variations in one animal may upregulate a CGG in one tissue but downregulate the CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. Thus, this class of genetic variation can result in complex inter- and intraindividual differences. This will create substantial challenges in humans, where multiple tissues are not readily available.

Co-regulated transcriptional networks contribute to natural genetic variation in Drosophila sleep

Sleep disorders are common in humans, and sleep loss increases the risk of obesity and diabetes1. Studies in Drosophila2, 3 have revealed molecular pathways4–7 and neural tissues8–10 regulating sleep; however, genes that maintain genetic variation for sleep in natural populations are unknown. Here, we characterized sleep in 40 wild-derived Drosophila lines and observed abundant genetic variation in sleep architecture. We associated sleep with genome-wide variation in gene expression11 to identify candidate genes. We independently confirmed that molecular polymorphisms in Catecholamines up are associated with variation in sleep; and that P-element mutations in four candidate genes affect sleep and gene expression. Transcripts associated with sleep grouped into biologically plausible genetically correlated transcriptional modules. We confirmed co-regulated gene expression using P-element mutants. Genes associated with sleep duration are evolutionarily conserved. Quantitative genetic analysis of natural phenotypic variation is an efficient method for revealing candidate genes and pathways.

Systems genetics of complex traits in Drosophila melanogaster

Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically inter-correlated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression, and transcription factor binding sites. The high transcriptional connectivity allows us to infer genetic networks and the function of predicted genes based on annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provides insight into the molecular basis of pleiotropy between complex traits.

Genomic consequences of background effects on scalloped mutant expressivity in the wing of Drosophila melanogaster

Genetic background effects contribute to the phenotypic consequences of mutations and are pervasive across all domains of life that have been examined, yet little is known about how they modify genetic systems. In part this is due to the lack of tractable model systems that have been explicitly developed to study the genetic and evolutionary consequences of background effects. In this study we demonstrate that phenotypic expressivity of the scalloped(E3) (sd(E3)) mutation of Drosophila melanogaster is background dependent and is the result of at least one major modifier segregating between two standard lab wild-type strains. We provide evidence that at least one of the modifiers is linked to the vestigial region and demonstrate that the background effects modify the spatial distribution of known sd target genes in a genotype-dependent manner. In addition, microarrays were used to examine the consequences of genetic background effects on the global transcriptome. Expression differences between wild-type strains were found to be as large as or larger than the effects of mutations with substantial phenotypic effects, and expression differences between wild type and mutant varied significantly between genetic backgrounds. Significantly, we demonstrate that the epistatic interaction between sd(E3) and an optomotor blind mutation is background dependent. The results are discussed within the context of developing a complex but more realistic view of the consequences of genetic background effects with respect to mutational analysis and studies of epistasis and cryptic genetic variation segregating in natural populations.