Effect of genetic variation in a Drosophila model of diabetes-associated misfolded human proinsulin

A Drosophila screen identifies a role for histone methylation in ER stress preconditioning

Stress preconditioning occurs when transient, sublethal stress events impact an organism's ability to counter future stresses. Although preconditioning effects are often noted in the literature, very little is known about the underlying mechanisms. To model preconditioning, we exposed a panel of genetically diverse Drosophila melanogaster to a sublethal heat shock and measured how well the flies survived subsequent exposure to endoplasmic reticulum (ER) stress. The impact of preconditioning varied with genetic background, ranging from dying half as fast to 4 and a half times faster with preconditioning compared to no preconditioning. Subsequent association and transcriptional analyses revealed that histone methylation, and transcriptional regulation are both candidate preconditioning modifier pathways. Strikingly, almost all subunits (7/8) in the Set1/COMPASS complex were identified as candidate modifiers of preconditioning. Functional analysis of Set1 knockdown flies demonstrated that loss of Set1 led to the transcriptional dysregulation of canonical ER stress genes during preconditioning. Based on these analyses, we propose a preconditioning model in which Set1 helps to establish an interim transcriptional "memory" of previous stress events, resulting in a preconditioned response to subsequent stress.

Using Drosophila to identify naturally occurring genetic modifiers of amyloid beta 42- and tau-induced toxicity

Alzheimer's disease is characterized by 2 pathological proteins, amyloid beta 42 and tau. The majority of Alzheimer's disease cases in the population are sporadic and late-onset Alzheimer's disease, which exhibits high levels of heritability. While several genetic risk factors for late-onset Alzheimer's disease have been identified and replicated in independent studies, including the ApoE ε4 allele, the great majority of the heritability of late-onset Alzheimer's disease remains unexplained, likely due to the aggregate effects of a very large number of genes with small effect size, as well as to biases in sample collection and statistical approaches. Here, we present an unbiased forward genetic screen in Drosophila looking for naturally occurring modifiers of amyloid beta 42- and tau-induced ommatidial degeneration. Our results identify 14 significant SNPs, which map to 12 potential genes in 8 unique genomic regions. Our hits that are significant after genome-wide correction identify genes involved in neuronal development, signal transduction, and organismal development. Looking more broadly at suggestive hits (P < 10-5), we see significant enrichment in genes associated with neurogenesis, development, and growth as well as significant enrichment in genes whose orthologs have been identified as significantly or suggestively associated with Alzheimer's disease in human GWAS studies. These latter genes include ones whose orthologs are in close proximity to regions in the human genome that are associated with Alzheimer's disease, but where a causal gene has not been identified. Together, our results illustrate the potential for complementary and convergent evidence provided through multitrait GWAS in Drosophila to supplement and inform human studies, helping to identify the remaining heritability and novel modifiers of complex diseases.

A Drosophila screen identifies a role for histone methylation in ER stress preconditioning

Stress preconditioning occurs when transient, sublethal stress events impact an organism's ability to counter future stresses. Although preconditioning effects are often noted in the literature, very little is known about the underlying mechanisms. To model preconditioning, we exposed a panel of genetically diverse Drosophila melanogaster to a sublethal heat shock and measured how well the flies survived subsequent exposure to endoplasmic reticulum (ER) stress. The impact of preconditioning varied with genetic background, ranging from dying half as fast to four and a half times faster with preconditioning compared to no preconditioning. Subsequent association and transcriptional analyses revealed that histone methylation, transcriptional regulation, and immune status are all candidate preconditioning modifier pathways. Strikingly, almost all subunits (7/8) in the Set1/COMPASS complex were identified as candidate modifiers of preconditioning. Functional analysis of Set1 knockdown flies demonstrated that loss of Set1 led to the transcriptional dysregulation of canonical ER stress genes during preconditioning. Based on these analyses, we propose a model of preconditioning in which Set1 helps to establish an interim transcriptional 'memory' of previous stress events, resulting in a preconditioned response to subsequent stress.

Regulation of autophagy, lipid metabolism, and neurodegenerative pathology by heparan sulfate proteoglycans

Heparan sulfate modified proteins or proteoglycans (HSPGs) are an abundant class of cell surface and extracellular matrix molecules. They serve important co-receptor functions in the regulation of signaling as well as membrane trafficking. Many of these activities directly affect processes associated with neurodegeneration including uptake and export of Tau protein, disposition of Amyloid Precursor Protein-derived peptides, and regulation of autophagy. In this review we focus on the impact of HSPGs on autophagy, membrane trafficking, mitochondrial quality control and biogenesis, and lipid metabolism. Disruption of these processes are a hallmark of Alzheimer's disease (AD) and there is evidence that altering heparan sulfate structure and function could counter AD-associated pathological processes. Compromising presenilin function in several systems has provided instructive models for understanding the molecular and cellular underpinnings of AD. Disrupting presenilin function produces a constellation of cellular deficits including accumulation of lipid, disruption of autophagosome to lysosome traffic and reduction in mitochondrial size and number. Inhibition of heparan sulfate biosynthesis has opposing effects on all these cellular phenotypes, increasing mitochondrial size, stimulating autophagy flux to lysosomes, and reducing the level of intracellular lipid. These findings suggest a potential mechanism for countering pathology found in AD and related disorders by altering heparan sulfate structure and influencing cellular processes disrupted broadly in neurodegenerative disease. Vertebrate and invertebrate model systems, where the cellular machinery of autophagy and lipid metabolism are conserved, continue to provide important translational guideposts for designing interventions that address the root cause of neurodegenerative pathology.

Regulation at Drosophila's Malic Enzyme highlights the complexity of transvection and its sensitivity to genetic background

Transvection, a type of trans-regulation of gene expression in which regulatory elements on one chromosome influence elements on a paired homologous chromosome, is itself a complex biological phenotype subject to modification by genetic background effects. However, relatively few studies have explored how transvection is affected by distal genetic variation, perhaps because it is strongly influenced by local regulatory elements and chromosomal architecture. With the emergence of the "hub" model of transvection and a series of studies showing variation in transvection effects, it is becoming clear that genetic background plays an important role in how transvection influences gene transcription. We explored the effects of genetic background on transvection by performing two independent genome wide association studies (GWASs) using the Drosophila genetic reference panel (DGRP) and a suite of Malic enzyme (Men) excision alleles. We found substantial variation in the amount of transvection in the 149 DGRP lines used, with broad-sense heritability of 0.89 and 0.84, depending on the excision allele used. The specific genetic variation identified was dependent on the excision allele used, highlighting the complex genetic interactions influencing transvection. We focussed primarily on genes identified as significant using a relaxed P-value cutoff in both GWASs. The most strongly associated genetic variant mapped to an intergenic single nucleotide polymorphism (SNP), located upstream of Tiggrin (Tig), a gene that codes for an extracellular matrix protein. Variants in other genes, such transcription factors (CG7368 and Sima), RNA binding proteins (CG10418, Rbp6, and Rig), enzymes (AdamTS-A, CG9743, and Pgant8), proteins influencing cell cycle progression (Dally and Eip63E) and signaling proteins (Atg-1, Axo, Egfr, and Path) also associated with transvection in Men. Although not intuitively obvious how many of these genes may influence transvection, some have been previously identified as promoting or antagonizing somatic homolog pairing. These results identify several candidate genes to further explore in the understanding of transvection in Men and in other genes regulated by transvection. Overall, these findings highlight the complexity of the interactions involved in gene regulation, even in phenotypes, such as transvection, that were traditionally considered to be primarily influenced by local genetic variation.

Intragenic compensation through the lens of deep mutational scanning

A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01005-w.

A natural genetic variation screen identifies insulin signaling, neuronal communication, and innate immunity as modifiers of hyperglycemia in the absence of Sirt1

Variation in the onset, progression, and severity of symptoms associated with metabolic disorders such as diabetes impairs the diagnosis and treatment of at-risk patients. Diabetes symptoms, and patient variation in these symptoms, are attributed to a combination of genetic and environmental factors, but identifying the genes and pathways that modify diabetes in humans has proven difficult. A greater understanding of genetic modifiers and the ways in which they interact with metabolic pathways could improve the ability to predict a patient's risk for severe symptoms, as well as enhance the development of individualized therapeutic approaches. In this study, we use the Drosophila Genetic Reference Panel to identify genetic variation influencing hyperglycemia associated with loss of Sirt1 function. Through analysis of individual candidate functions, physical interaction networks, and gene set enrichment analysis, we identify not only modifiers involved in canonical glucose metabolism and insulin signaling, but also genes important for neuronal signaling and the innate immune response. Furthermore, reducing the expression of several of these candidates suppressed hyperglycemia, making them potential candidate therapeutic targets. These analyses showcase the diverse processes contributing to glucose homeostasis and open up several avenues of future investigation.

The Genetics of Diabetes: What We Can Learn from Drosophila

Diabetes mellitus is a heterogeneous disease characterized by hyperglycemia due to impaired insulin secretion and/or action. All diabetes types have a strong genetic component. The most frequent forms, type 1 diabetes (T1D), type 2 diabetes (T2D) and gestational diabetes mellitus (GDM), are multifactorial syndromes associated with several genes’ effects together with environmental factors. Conversely, rare forms, neonatal diabetes mellitus (NDM) and maturity onset diabetes of the young (MODY), are caused by mutations in single genes. Large scale genome screenings led to the identification of hundreds of putative causative genes for multigenic diabetes, but all the loci identified so far explain only a small proportion of heritability. Nevertheless, several recent studies allowed not only the identification of some genes as causative, but also as putative targets of new drugs. Although monogenic forms of diabetes are the most suited to perform a precision approach and allow an accurate diagnosis, at least 80% of all monogenic cases remain still undiagnosed. The knowledge acquired so far addresses the future work towards a study more focused on the identification of diabetes causal variants; this aim will be reached only by combining expertise from different areas. In this perspective, model organism research is crucial. This review traces an overview of the genetics of diabetes and mainly focuses on Drosophila as a model system, describing how flies can contribute to diabetes knowledge advancement.

Natural genetic variation in Drosophila melanogaster reveals genes associated with Coxiella burnetii infection

The gram-negative bacterium Coxiella burnetii is the causative agent of Query (Q) fever in humans and coxiellosis in livestock. Host genetics are associated with C. burnetii pathogenesis both in humans and animals; however, it remains unknown if specific genes are associated with severity of infection. We employed the Drosophila Genetics Reference Panel to perform a genome-wide association study to identify host genetic variants that affect host survival to C. burnetii infection. The genome-wide association study identified 64 unique variants (P < 10⁻⁵) associated with 25 candidate genes. We examined the role each candidate gene contributes to host survival during C. burnetii infection using flies carrying a null mutation or RNAi knockdown of each candidate. We validated 15 of the 25 candidate genes using at least one method. This is the first report establishing involvement of many of these genes or their homologs with C. burnetii susceptibility in any system. Among the validated genes, FER and tara play roles in the JAK/STAT, JNK, and decapentaplegic/TGF-β signaling pathways which are components of known innate immune responses to C. burnetii infection. CG42673 and DIP-ε play roles in bacterial infection and synaptic signaling but have no previous association with C. burnetii pathogenesis. Furthermore, since the mammalian ortholog of CG13404 (PLGRKT) is an important regulator of macrophage function, CG13404 could play a role in host susceptibility to C. burnetii through hemocyte regulation. These insights provide a foundation for further investigation regarding the genetics of C. burnetii susceptibility across a wide variety of hosts.

Identification of genetic modifiers of lifespan on a high sugar diet in the Drosophila Genetic Reference Panel

Genome-wide association studies (GWAS) have become beneficial in identifying genetic variants underlying susceptibility to various complex diseases and conditions, including obesity. Utilizing the Drosophila Genetic Reference Panel (DGRP), we performed a GWAS of lifespan of 193 genetically distinct lines on a high sugar diet (HSD). The DGRP analysis pipeline determined the most significant lifespan associated polymorphisms were within loci of genes involved in: neural processes, behavior, development, and apoptosis, among other functions. Next, based on the relevance to obesity pathology, and the availability of transgenic RNAi lines targeting the genes we identified, whole-body in vivo knockdown of several candidate genes was performed. We utilized the GAL4-UAS binary expression system to independently validate the impacts of these loci on Drosophila lifespan during HSD. These loci were largely confirmed to affect lifespan in that HSD setting, as well as a normal diet setting. However, we also detected unexpected dietary effects of the HSD, including inconsistent diet effects on lifespan relative to a normal diet and a strong downregulation of feeding quantity.

A Drosophila screen identifies NKCC1 as a modifier of NGLY1 deficiency

N-Glycanase 1 (NGLY1) is a cytoplasmic deglycosylating enzyme. Loss-of-function mutations in the NGLY1 gene cause NGLY1 deficiency, which is characterized by developmental delay, seizures, and a lack of sweat and tears. To model the phenotypic variability observed among patients, we crossed a Drosophila model of NGLY1 deficiency onto a panel of genetically diverse strains. The resulting progeny showed a phenotypic spectrum from 0 to 100% lethality. Association analysis on the lethality phenotype, as well as an evolutionary rate covariation analysis, generated lists of modifying genes, providing insight into NGLY1 function and disease. The top association hit was Ncc69 (human NKCC1/2), a conserved ion transporter. Analyses in NGLY1-/- mouse cells demonstrated that NKCC1 has an altered average molecular weight and reduced function. The misregulation of this ion transporter may explain the observed defects in secretory epithelium function in NGLY1 deficiency patients.

Machine Learning Representation of Loss of Eye Regularity in a Drosophila Neurodegenerative Model

The fruit fly compound eye is a premier experimental system for modeling human neurodegenerative diseases. The disruption of the retinal geometry has been historically assessed using time-consuming and poorly reliable techniques such as histology or pseudopupil manual counting. Recent semiautomated quantification approaches rely either on manual region-of-interest delimitation or engineered features to estimate the extent of degeneration. This work presents a fully automated classification pipeline of bright-field images based on orientated gradient descriptors and machine learning techniques. An initial region-of-interest extraction is performed, applying morphological kernels and Euclidean distance-to-centroid thresholding. Image classification algorithms are trained on these regions (support vector machine, decision trees, random forest, and convolutional neural network), and their performance is evaluated on independent, unseen datasets. The combinations of oriented gradient + gaussian kernel Support Vector Machine [0.97 accuracy and 0.98 area under the curve (AUC)] and fine-tuned pre-trained convolutional neural network (0.98 accuracy and 0.99 AUC) yielded the best results overall. The proposed method provides a robust quantification framework that can be generalized to address the loss of regularity in biological patterns similar to the Drosophila eye surface and speeds up the processing of large sample batches.

An Insulin-Sensitive Circular RNA that Regulates Lifespan in Drosophila

Circular RNAs (circRNAs) are abundant and accumulate with age in neurons of diverse species. However, only few circRNAs have been functionally characterized, and their role during aging has not been addressed. Here, we use transcriptome profiling during aging and find that accumulation of circRNAs is slowed down in long-lived insulin mutant flies. Next, we characterize the in vivo function of a circRNA generated by the sulfateless gene (circSfl), which is consistently upregulated, particularly in the brain and muscle, of diverse long-lived insulin mutants. Strikingly, lifespan extension of insulin mutants is dependent on circSfl, and overexpression of circSfl alone is sufficient to extend the lifespan. Moreover, circSfl is translated into a protein that shares the N terminus and potentially some functions with the full-length Sfl protein encoded by the host gene. Our study demonstrates that insulin signaling affects global circRNA accumulation and reveals an important role of circSfl during aging in vivo.

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Accumulation of circRNAs with age is slowed down in long-lived insulin mutant flies

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A circRNA encoded by the sulfateless gene is induced in long-lived insulin mutants

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Overexpression of circSfl extends the lifespan of fruit flies

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CircSfl is translated, and the resulting peptide is sufficient to extend lifespan

Unexpected cell type-dependent effects of autophagy on polyglutamine aggregation revealed by natural genetic variation in C. elegans

BACKGROUND

Monogenic protein aggregation diseases, in addition to cell selectivity, exhibit clinical variation in the age of onset and progression, driven in part by inter-individual genetic variation. While natural genetic variants may pinpoint plastic networks amenable to intervention, the mechanisms by which they impact individual susceptibility to proteotoxicity are still largely unknown.

RESULTS

We have previously shown that natural variation modifies polyglutamine (polyQ) aggregation phenotypes in C. elegans muscle cells. Here, we find that a genomic locus from C. elegans wild isolate DR1350 causes two genetically separable aggregation phenotypes, without changing the basal activity of muscle proteostasis pathways known to affect polyQ aggregation. We find that the increased aggregation phenotype was due to regulatory variants in the gene encoding a conserved autophagy protein ATG-5. The atg-5 gene itself conferred dosage-dependent enhancement of aggregation, with the DR1350-derived allele behaving as hypermorph. Surprisingly, increased aggregation in animals carrying the modifier locus was accompanied by enhanced autophagy activation in response to activating treatment. Because autophagy is expected to clear, not increase, protein aggregates, we activated autophagy in three different polyQ models and found a striking tissue-dependent effect: activation of autophagy decreased polyQ aggregation in neurons and intestine, but increased it in the muscle cells.

CONCLUSIONS

Our data show that cryptic natural variants in genes encoding proteostasis components, although not causing detectable phenotypes in wild-type individuals, can have profound effects on aggregation-prone proteins. Clinical applications of autophagy activators for aggregation diseases may need to consider the unexpected divergent effects of autophagy in different cell types.

Natural Genetic Variation Screen in Drosophila Identifies Wnt Signaling, Mitochondrial Metabolism, and Redox Homeostasis Genes as Modifiers of Apoptosis

Apoptosis is the primary cause of degeneration in a number of neuronal, muscular, and metabolic disorders. These diseases are subject to a great deal of phenotypic heterogeneity in patient populations, primarily due to differences in genetic variation between individuals. This creates a barrier to effective diagnosis and treatment. Understanding how genetic variation influences apoptosis could lead to the development of new therapeutics and better personalized treatment approaches. In this study, we examine the impact of the natural genetic variation in the Drosophila Genetic Reference Panel (DGRP) on two models of apoptosis-induced retinal degeneration: overexpression of p53 or reaper (rpr). We identify a number of known apoptotic, neural, and developmental genes as candidate modifiers of degeneration. We also use Gene Set Enrichment Analysis (GSEA) to identify pathways that harbor genetic variation that impact these apoptosis models, including Wnt signaling, mitochondrial metabolism, and redox homeostasis. Finally, we demonstrate that many of these candidates have a functional effect on apoptosis and degeneration. These studies provide a number of avenues for modifying genes and pathways of apoptosis-related disease.

Genetic Modifiers of Neurodegeneration in a Drosophila Model of Parkinson's Disease

Disease phenotypes can be highly variable among individuals with the same pathogenic mutation. There is increasing evidence that background genetic variation is a strong driver of disease variability in addition to the influence of environment. To understand the genotype-phenotype relationship that determines the expressivity of a pathogenic mutation, a large number of backgrounds must be studied. This can be efficiently achieved using model organism collections such as the Drosophila Genetic Reference Panel (DGRP). Here, we used the DGRP to assess the variability of locomotor dysfunction in a LRRK2 G2019S Drosophila melanogaster model of Parkinson's disease (PD). We find substantial variability in the LRRK2 G2019S locomotor phenotype in different DGRP backgrounds. A genome-wide association study for candidate genetic modifiers reveals 177 genes that drive wide phenotypic variation, including 19 top association genes. Genes involved in the outgrowth and regulation of neuronal projections are enriched in these candidate modifiers. RNAi functional testing of the top association and neuronal projection-related genes reveals that pros, pbl, ct, and CG33506 significantly modify age-related dopamine neuron loss and associated locomotor dysfunction in the Drosophila LRRK2 G2019S model. These results demonstrate how natural genetic variation can be used as a powerful tool to identify genes that modify disease-related phenotypes. We report novel candidate modifier genes for LRRK2 G2019S that may be used to interrogate the link between LRRK2, neurite regulation and neuronal degeneration in PD.

Genetic Network Complexity Shapes Background-Dependent Phenotypic Expression

The phenotypic consequences of a given mutation can vary across individuals. This so-called background effect is widely observed, from mutant fitness of loss-of-function variants in model organisms to variable disease penetrance and expressivity in humans; however, the underlying genetic basis often remains unclear. Taking insights gained from recent large-scale surveys of genetic interaction and suppression analyses in yeast, we propose that the genetic network context for a given mutation may shape its propensity of exhibiting background-dependent phenotypes. We argue that further efforts in systematically mapping the genetic interaction networks beyond yeast will provide not only key insights into the functional properties of genes, but also a better understanding of the background effects and the (un)predictability of traits in a broader context.

A Genomic Reference Panel for Drosophila serrata

Here we describe a collection of re-sequenced inbred lines of Drosophila serrata, sampled from a natural population situated deep within the species endemic distribution in Brisbane, Australia. D. serrata is a member of the speciose montium group whose members inhabit much of south east Asia and has been well studied for aspects of climatic adaptation, sexual selection, sexual dimorphism, and mate recognition. We sequenced 110 lines that were inbred via 17-20 generations of full-sib mating at an average coverage of 23.5x with paired-end Illumina reads. 15,228,692 biallelic SNPs passed quality control after being called using the Joint Genotyper for Inbred Lines (JGIL). Inbreeding was highly effective and the average levels of residual heterozygosity (0.86%) were well below theoretical expectations. As expected, linkage disequilibrium decayed rapidly, with r² dropping below 0.1 within 100 base pairs. With the exception of four closely related pairs of lines which may have been due to technical errors, there was no statistical support for population substructure. Consistent with other endemic populations of other Drosophila species, preliminary population genetic analyses revealed high nucleotide diversity and, on average, negative Tajima’s D values. A preliminary GWAS was performed on a cuticular hydrocarbon trait, 2-Me-C₂₈ revealing 4 SNPs passing Bonferroni significance residing in or near genes. One gene Cht9 may be involved in the transport of CHCs from the site of production (oenocytes) to the cuticle. Our panel will facilitate broader population genomic and quantitative genetic studies of this species and serve as an important complement to existing D. melanogaster panels that can be used to test for the conservation of genetic architectures across the Drosophila genus.

Charting the genotype-phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel

Understanding the genetic architecture (causal molecular variants, their effects, and frequencies) of quantitative traits is important for precision agriculture and medicine and predicting adaptive evolution, but is challenging in most species. The Drosophila melanogaster Genetic Reference Panel (DGRP) is a collection of 205 inbred strains with whole genome sequences derived from a single wild population in Raleigh, NC, USA. The large amount of quantitative genetic variation, lack of population structure, and rapid local decay of linkage disequilibrium in the DGRP and outbred populations derived from DGRP lines present a favorable scenario for performing genome-wide association (GWA) mapping analyses to identify candidate causal genes, polymorphisms, and pathways affecting quantitative traits. The many GWA studies utilizing the DGRP have revealed substantial natural genetic variation for all reported traits, little evidence for variants with large effects but enrichment for variants with low P-values, and a tendency for lower frequency variants to have larger effects than more common variants. The variants detected in the GWA analyses rarely overlap those discovered using mutagenesis, and often are the first functional annotations of computationally predicted genes. Variants implicated in GWA analyses typically have sex-specific and genetic background-specific (epistatic) effects, as well as pleiotropic effects on other quantitative traits. Studies in the DGRP reveal substantial genetic control of environmental variation. Taking account of genetic architecture can greatly improve genomic prediction in the DGRP. These features of the genetic architecture of quantitative traits are likely to apply to other species, including humans. WIREs Dev Biol 2018, 7:e289. doi: 10.1002/wdev.289 This article is categorized under: Invertebrate Organogenesis > Flies.

Charting the genotype-phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel

Understanding the genetic architecture (causal molecular variants, their effects, and frequencies) of quantitative traits is important for precision agriculture and medicine and predicting adaptive evolution, but is challenging in most species. The Drosophila melanogaster Genetic Reference Panel (DGRP) is a collection of 205 inbred strains with whole genome sequences derived from a single wild population in Raleigh, NC, USA. The large amount of quantitative genetic variation, lack of population structure, and rapid local decay of linkage disequilibrium in the DGRP and outbred populations derived from DGRP lines present a favorable scenario for performing genome-wide association (GWA) mapping analyses to identify candidate causal genes, polymorphisms, and pathways affecting quantitative traits. The many GWA studies utilizing the DGRP have revealed substantial natural genetic variation for all reported traits, little evidence for variants with large effects but enrichment for variants with low P-values, and a tendency for lower frequency variants to have larger effects than more common variants. The variants detected in the GWA analyses rarely overlap those discovered using mutagenesis, and often are the first functional annotations of computationally predicted genes. Variants implicated in GWA analyses typically have sex-specific and genetic background-specific (epistatic) effects, as well as pleiotropic effects on other quantitative traits. Studies in the DGRP reveal substantial genetic control of environmental variation. Taking account of genetic architecture can greatly improve genomic prediction in the DGRP. These features of the genetic architecture of quantitative traits are likely to apply to other species, including humans. WIREs Dev Biol 2018, 7:e289. doi: 10.1002/wdev.289 This article is categorized under: Invertebrate Organogenesis > Flies.

Genetic backgrounds and hidden trait complexity in natural populations

Dissecting the genetic basis of natural phenotypic variation is a major goal in biology. We know that most traits are strongly heritable. However, their genetic architecture is a long-standing question, which is unfortunately confounded by the lack of complete knowledge of the genetic components as well as their phenotypic effect in a specific genetic background. Many genetic variants are known to affect phenotypes but the same functional variant can have a different effect on the phenotype in different individuals of the same species. Understanding the impact of genetic background on the expressivity of a given phenotype is essential because this effect complicates our ability to predict phenotype from genotype. Here, we briefly review recent progress on the exploration of the effect of genetic background and we discuss how a deeper characterization of the inheritance, expressivity and genetic interactions hidden behind the phenotypic landscape of natural variation could provide a better understanding of the relationship between genotype and phenotype.

How well do you know your mutation? Complex effects of genetic background on expressivity, complementation, and ordering of allelic effects

For a given gene, different mutations influence organismal phenotypes to varying degrees. However, the expressivity of these variants not only depends on the DNA lesion associated with the mutation, but also on factors including the genetic background and rearing environment. The degree to which these factors influence related alleles, genes, or pathways similarly, and whether similar developmental mechanisms underlie variation in the expressivity of a single allele across conditions and among alleles is poorly understood. Besides their fundamental biological significance, these questions have important implications for the interpretation of functional genetic analyses, for example, if these factors alter the ordering of allelic series or patterns of complementation. We examined the impact of genetic background and rearing environment for a series of mutations spanning the range of phenotypic effects for both the scalloped and vestigial genes, which influence wing development in Drosophila melanogaster. Genetic background and rearing environment influenced the phenotypic outcome of mutations, including intra-genic interactions, particularly for mutations of moderate expressivity. We examined whether cellular correlates (such as cell proliferation during development) of these phenotypic effects matched the observed phenotypic outcome. While cell proliferation decreased with mutations of increasingly severe effects, surprisingly it did not co-vary strongly with the degree of background dependence. We discuss these findings and propose a phenomenological model to aid in understanding the biology of genes, and how this influences our interpretation of allelic effects in genetic analysis.

Different mutations in a gene, or in genes with related functions, can have effects of varying severity. Studying sets of mutations and analyzing how they interact are essential components of a geneticist's toolkit. However, the effects caused by a mutation depend not only on the mutation itself, but on additional genetic variation throughout an organism's genome and on the environment that organism has experienced. Therefore, identifying how the genomic and environmental context alter the expression of mutations is critical for making reliable inferences about how genes function. Yet studies on this context dependence have largely been limited to single mutations in single genes. We examined how the genomic and environmental context influence the expression of multiple mutations in two related genes affecting the fruit fly wing. Our results show that the genetic and environmental context generally affect the expression of related mutations in similar ways. However, the interactions between two different mutations in a single gene sometimes depended strongly on context. In addition, cell proliferation in the developing wing and adult wing size were not affected by the genetic and environmental context in similar ways in mutant flies, suggesting that variation in cell growth cannot fully explain how mutations affect wings. Overall, our findings show that context can have a big impact on the interpretation of genetic experiments, including how we draw conclusions about gene function and cause-and-effect relationships.

Dissecting genetic architecture of startle response in Drosophila melanogaster using multi-omics information

Startle behavior is important for survival, and abnormal startle responses are related to several neurological diseases. Drosophila melanogaster provides a powerful system to investigate the genetic underpinnings of variation in startle behavior. Since mechanically induced, startle responses and environmental conditions can be readily quantified and precisely controlled. The 156 wild-derived fully sequenced lines of the Drosophila Genetic Reference Panel (DGRP) were used to identify SNPs and transcripts associated with variation in startle behavior. The results validated highly significant effects of 33 quantitative trait SNPs (QTSs) and 81 quantitative trait transcripts (QTTs) directly associated with phenotypic variation of startle response. We also detected QTT variation controlled by 20 QTSs (tQTSs) and 73 transcripts (tQTTs). Association mapping based on genomic and transcriptomic data enabled us to construct a complex genetic network that underlies variation in startle behavior. Based on principles of evolutionary conservation, human orthologous genes could be superimposed on this network. This study provided both genetic and biological insights into the variation of startle response behavior of Drosophila melanogaster, and highlighted the importance of genetic network to understand the genetic architecture of complex traits.

Variation in Position Effect Variegation Within a Natural Population

Changes in chromatin state may drive changes in gene expression, and it is of growing interest to understand the population genetic forces that drive differences in chromatin state. Here, we use the phenomenon of position effect variegation (PEV), a well-studied proxy for chromatin state, to survey variation in PEV among a naturally derived population. Further, we explore the genetic architecture of natural variation in factors that modify PEV. While previous mutation screens have identified over 150 suppressors and enhancers of PEV, it remains unknown to what extent allelic variation in these modifiers mediate interindividual variation in PEV. Is natural variation in PEV mediated by segregating genetic variation in known Su(var) and E(var) genes, or is the trait polygenic, with many variants mapping elsewhere in the genome? We designed a dominant mapping study that directly answers this question and suggests that the bulk of the variance in PEV does not map to genes with prior annotated impact to PEV. Instead, we find enrichment of top P-value ranked associations that suggest impact to active promoter and transcription start site proximal regions. This work highlights extensive variation in PEV within a population, and provides a quantitative view of the role naturally segregating autosomal variants play in modifying PEV-a phenomenon that continues to shape our understanding of chromatin state and epigenetics.

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.

Charting the genotype-phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel

Understanding the genetic architecture (causal molecular variants, their effects, and frequencies) of quantitative traits is important for precision agriculture and medicine and predicting adaptive evolution, but is challenging in most species. The Drosophila melanogaster Genetic Reference Panel (DGRP) is a collection of 205 inbred strains with whole genome sequences derived from a single wild population in Raleigh, NC, USA. The large amount of quantitative genetic variation, lack of population structure, and rapid local decay of linkage disequilibrium in the DGRP and outbred populations derived from DGRP lines present a favorable scenario for performing genome-wide association (GWA) mapping analyses to identify candidate causal genes, polymorphisms, and pathways affecting quantitative traits. The many GWA studies utilizing the DGRP have revealed substantial natural genetic variation for all reported traits, little evidence for variants with large effects but enrichment for variants with low P-values, and a tendency for lower frequency variants to have larger effects than more common variants. The variants detected in the GWA analyses rarely overlap those discovered using mutagenesis, and often are the first functional annotations of computationally predicted genes. Variants implicated in GWA analyses typically have sex-specific and genetic background-specific (epistatic) effects, as well as pleiotropic effects on other quantitative traits. Studies in the DGRP reveal substantial genetic control of environmental variation. Taking account of genetic architecture can greatly improve genomic prediction in the DGRP. These features of the genetic architecture of quantitative traits are likely to apply to other species, including humans. WIREs Dev Biol 2018, 7:e289. doi: 10.1002/wdev.289 This article is categorized under: Invertebrate Organogenesis > Flies.

A Bystander Mechanism Explains the Specific Phenotype of a Broadly Expressed Misfolded Protein

Misfolded proteins in transgenic models of conformational diseases interfere with proteostasis machinery and compromise the function of many structurally and functionally unrelated metastable proteins. This collateral damage to cellular proteins has been termed 'bystander' mechanism. How a single misfolded protein overwhelms the proteostasis, and how broadly-expressed mutant proteins cause cell type-selective phenotypes in disease are open questions. We tested the gain-of-function mechanism of a R37C folding mutation in an endogenous IGF-like C.elegans protein DAF-28. DAF-28(R37C) is broadly expressed, but only causes dysfunction in one specific neuron, ASI, leading to a distinct developmental phenotype. We find that this phenotype is caused by selective disruption of normal biogenesis of an unrelated endogenous protein, DAF-7/TGF-β. The combined deficiency of DAF-28 and DAF-7 biogenesis, but not of DAF-28 alone, explains the gain-of-function phenotype—deficient pro-growth signaling by the ASI neuron. Using functional, fluorescently-tagged protein, we find that, in animals with mutant DAF-28/IGF, the wild-type DAF-7/TGF-β is mislocalized to and accumulates in the proximal axon of the ASI neuron. Activation of two different branches of the unfolded protein response can modulate both the developmental phenotype and DAF-7 mislocalization in DAF-28(R37C) animals, but appear to act through divergent mechanisms. Our finding that bystander targeting of TGF-β explains the phenotype caused by a folding mutation in an IGF-like protein suggests that, in conformational diseases, bystander misfolding may specify the distinct phenotypes caused by different folding mutations.

Correct protein folding and localization ensures cellular health. Dedicated proteostasis machinery assists in protein folding and protects against misfolding. Yet, folding mutations cause many conformational diseases, including neurodegenerative diseases and certain types of diabetes and cancer. Misfolded disease-related proteins interfere with proteostasis machinery, causing global misfolding in the cell. How this global mechanism leads to the specific phenotypes in different conformational diseases is unknown. Moreover, mutant misfolded proteins that only damage specific cell-types in disease often lose this cell-selectivity when overexpressed in genetic models. Here we use an endogenous folding mutation in a C. elegans secreted IGF-like protein, DAF-28, that causes dysfunction in one neuron and a specific developmental phenotype, despite expression in many cells. We find that misfolding of mutant DAF-28 causes mislocalization and defective function of another, wild-type growth factor that is expressed in the affected neuron, the TGF-β protein DAF-7. Decrease in DAF-7 function explains the observed developmental phenotype. This targeting of the bystander protein DAF-7 by the misfolded mutant DAF-28 is specific and is not caused by the global stress. Our data suggest that rather than global effects, it is the selective targeting of specific susceptible bystander proteins that defines the specific phenotypes in conformational diseases.

Drosophila as a Model for Diabetes and Diseases of Insulin Resistance

Despite the importance of insulin signaling pathways in human disease, initial concerns that insect physiology and sugar metabolism differ enough from humans that flies would not model human disease hampered research in this area. However, during the past 10-15 years, evidence has accumulated that flies can indeed model various aspects of diabetes and related human disorders. This cluster of diseases impact insulin and insulin signaling pathways, fields which have been discussed in many excellent review articles in recent years. In this chapter, we restrict our focus to specific examples of diabetes-related disease models in Drosophila, discussing the advantages and limitations of these models in light of physiological similarities and differences between insects and mammals. We discuss features of metabolism and sugar regulation that are shared between flies and mammals, and specific Drosophila models for Type 1 and Type 2 diabetes, Metabolic syndrome, and related abnormalities including insulin resistance and heart disease. We conclude that fly models for diabetes and related disorders enhance our ability to identify genes and discern functional interactions that can be exploited for disease intervention.

Genotypic Context and Epistasis in Individuals and Populations

Genes encode components of coevolved and interconnected networks. The effect of genotype on phenotype therefore depends on genotypic context through gene interactions known as epistasis. Epistasis is important in predicting phenotype from genotype for an individual. It is also examined in population studies to identify genetic risk factors in complex traits and to predict evolution under selection. Paradoxically, the effects of genotypic context in individuals and populations are distinct and sometimes contradictory. We argue that predicting genotype from phenotype for individuals based on population studies is difficult and, especially in human genetics, likely to result in underestimating the effects of genotypic context.

1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana

Arabidopsis thaliana serves as a model organism for the study of fundamental physiological, cellular, and molecular processes. It has also greatly advanced our understanding of intraspecific genome variation. We present a detailed map of variation in 1,135 high-quality re-sequenced natural inbred lines representing the native Eurasian and North African range and recently colonized North America. We identify relict populations that continue to inhabit ancestral habitats, primarily in the Iberian Peninsula. They have mixed with a lineage that has spread to northern latitudes from an unknown glacial refugium and is now found in a much broader spectrum of habitats. Insights into the history of the species and the fine-scale distribution of genetic diversity provide the basis for full exploitation of A. thaliana natural variation through integration of genomes and epigenomes with molecular and non-molecular phenotypes.

Quantitative Assessment of Eye Phenotypes for Functional Genetic Studies Using Drosophila melanogaster

About two-thirds of the vital genes in the Drosophila genome are involved in eye development, making the fly eye an excellent genetic system to study cellular function and development, neurodevelopment/degeneration, and complex diseases such as cancer and diabetes. We developed a novel computational method, implemented as Flynotyper software (http://flynotyper.sourceforge.net), to quantitatively assess the morphological defects in the Drosophila eye resulting from genetic alterations affecting basic cellular and developmental processes. Flynotyper utilizes a series of image processing operations to automatically detect the fly eye and the individual ommatidium, and calculates a phenotypic score as a measure of the disorderliness of ommatidial arrangement in the fly eye. As a proof of principle, we tested our method by analyzing the defects due to eye-specific knockdown of Drosophila orthologs of 12 neurodevelopmental genes to accurately document differential sensitivities of these genes to dosage alteration. We also evaluated eye images from six independent studies assessing the effect of overexpression of repeats, candidates from peptide library screens, and modifiers of neurotoxicity and developmental processes on eye morphology, and show strong concordance with the original assessment. We further demonstrate the utility of this method by analyzing 16 modifiers of sine oculis obtained from two genome-wide deficiency screens of Drosophila and accurately quantifying the effect of its enhancers and suppressors during eye development. Our method will complement existing assays for eye phenotypes, and increase the accuracy of studies that use fly eyes for functional evaluation of genes and genetic interactions.

Multiple testing correction in linear mixed models

BACKGROUND

Multiple hypothesis testing is a major issue in genome-wide association studies (GWAS), which often analyze millions of markers. The permutation test is considered to be the gold standard in multiple testing correction as it accurately takes into account the correlation structure of the genome. Recently, the linear mixed model (LMM) has become the standard practice in GWAS, addressing issues of population structure and insufficient power. However, none of the current multiple testing approaches are applicable to LMM.

RESULTS

We were able to estimate per-marker thresholds as accurately as the gold standard approach in real and simulated datasets, while reducing the time required from months to hours. We applied our approach to mouse, yeast, and human datasets to demonstrate the accuracy and efficiency of our approach.

CONCLUSIONS

We provide an efficient and accurate multiple testing correction approach for linear mixed models. We further provide an intuition about the relationships between per-marker threshold, genetic relatedness, and heritability, based on our observations in real data.

Candidate genetic modifiers of retinitis pigmentosa identified by exploiting natural variation in Drosophila

Individuals carrying the same pathogenic mutation can present with a broad range of disease outcomes. While some of this variation arises from environmental factors, it is increasingly recognized that the background genetic variation of each individual can have a profound effect on the expressivity of a pathogenic mutation. In order to understand this background effect on disease-causing mutations, studies need to be performed across a wide range of backgrounds. Recent advancements in model organism biology allow us to test mutations across genetically diverse backgrounds and identify the genes that influence the expressivity of a mutation. In this study, we used the Drosophila Genetic Reference Panel, a collection of ∼200 wild-derived strains, to test the variability of the retinal phenotype of the Rh1(G69D) Drosophila model of retinitis pigmentosa (RP). We found that the Rh1(G69D) retinal phenotype is quite a variable quantitative phenotype. To identify the genes driving this extensive phenotypic variation, we performed a genome-wide association study. We identified 106 candidate genes, including 14 high-priority candidates. Functional testing by RNAi indicates that 10/13 top candidates tested influence the expressivity of Rh1(G69D). The human orthologs of the candidate genes have not previously been implicated as RP modifiers and their functions are diverse, including roles in endoplasmic reticulum stress, apoptosis and retinal degeneration and development. This study demonstrates the utility of studying a pathogenic mutation across a wide range of genetic backgrounds. These candidate modifiers provide new avenues of inquiry that may reveal new RP disease mechanisms and therapies.

Quantitative characterization of adhesion and stiffness of corneal lens of Drosophila melanogaster using atomic force microscopy

Atomic force Microscopy (AFM) has become a versatile tool in biology due to its advantage of high-resolution imaging of biological samples close to their native condition. Apart from imaging, AFM can also measure the local mechanical properties of the surfaces. In this study, we explore the possibility of using AFM to quantify the rough eye phenotype of Drosophila melanogaster through mechanical properties. We have measured adhesion force, stiffness and elastic modulus of the corneal lens using AFM. Various parameters affecting these measurements like cantilever stiffness and tip geometry are systematically studied and the measurement procedures are standardized. Results show that the mean adhesion force of the ommatidial surface varies from 36 nN to 16 nN based on the location. The mean stiffness is 483 ± 5 N/m, and the elastic modulus is 3.4 ± 0.05 GPa (95% confidence level) at the center of ommatidia. These properties are found to be different in corneal lens of eye expressing human mutant tau gene (mutant). The adhesion force, stiffness and elastic modulus are decreased in the mutant. We conclude that the measurement of surface and mechanical properties of D. melanogaster using AFM can be used for quantitative evaluation of 'rough eye' surface.

Drosophila gains traction as a repurposed tool to investigate metabolism

The use of fruit flies has recently emerged as a powerful experimental paradigm to study the core aspects of energy metabolism. The fundamental need for lipid and carbohydrate processing and storage across species dictates that the central regulators that control metabolism are highly conserved through evolution. Accordingly, the Drosophila system is being used to identify human disease genes and has the potential to model successfully human disorders that center on excessive caloric intake and metabolic dysfunction, including diet-induced lipotoxicity and type 2 diabetes. We review here recent progress on this front and contend that increasing such efforts will yield unexpectedly high rates of experimental return, thereby leading to novel approaches in the treatment of obesity and its comorbidities.

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.