Around one in 100 children are born with heart defects caused by congenital heart disease. Studying the genetic sequences of people with congenital heart disease has revealed many genes that may play a role in causing the condition, but few of these findings have been confirmed experimentally in animal model systems.

The fruit fly species Drosophila melanogaster is often used in genetic studies because it is a relatively simple organism. The insights gained from studying flies are often valuable for determining the direction of subsequent investigations in more complex animals – such as humans – that involve experiments that are more costly and less efficient.

Zhu, Fu et al. have now used fruit flies to investigate the effects of 134 genes that have been suggested to contribute to congenital heart disease. The investigation used a method that rapidly allowed the activity of specific genes to be altered in the flies. The effects that these alterations had on many aspects of heart development, structure and activity were then measured. Of all the genes tested, 70 caused heart defects in the flies. Several of these genes help to modify the structure of proteins called histones; these modifications play important roles in heart cell formation and growth.

Further tests showed that the effects of specific genetic errors that had been identified in people with congenital heart disease could be reliably reproduced in the flies. This may allow individual cases of congenital heart disease to be replicated and studied closely in the lab, helping to create treatments that are personalised to each patient.

Studying congenital heart disease in flies provides a fast and simple first step in understanding the roles that different genes play in the disease. Moving forward, precise gene editing techniques could be used to generate flies to examine the role of each of the genetic mutations that occur in individual patients. Ultimately, when gene editing techniques are ready to be used in humans, this could lead to cures for congenital heart disease at the DNA level, so that these mutations won’t be passed on to the next generation.

A major problem confronting large-scale genomic sequencing studies of disease patients is to identify those genetic variants that actually represent disease-causing mutations. This is particularly true for candidate genes that have not previously been evaluated in an experimental model system for involvement in disease-relevant biological processes. Functional gene validation systems in model organisms are therefore essential. While a definitive assignment of disease association should be established through the analysis of mutant gene phenotypes in a mammalian model, this approach is not practical for rapid high-throughput initial screening of large numbers of candidate genes. Drosophila, by contrast, possesses the attributes of a simple animal model system that make it ideally suited for this role (Bier, 2005; Chien et al., 2002).

Use of Drosophila as a model to elucidate molecular disease mechanisms is well established and amply documented (Cagan, 2011; Na et al., 2015; Zhang et al., 2013; Diop and Bodmer, 2015; Bier and Bodmer, 2004; Owusu-Ansah and Perrimon, 2014). It has been established that 75% of human disease associated genes are represented in the fly genome by functional homologs (Reiter et al., 2001). Although it is difficult to directly link Drosophila developmental defects to patient symptoms, the Drosophila system can serve as a testing platform for gene function in development, which can be used to test disease association for a large number of candidate genes identified from patient genomic sequencing.

Congenital heart disease (CHD) affects 0.8% of live births (Hoffman, 1990, 2002; Reller et al., 2008). Genetic factors are strongly implicated in CHD pathogenesis, but the great majority of genes (accounting for an estimated 75% of cases) remain unidentified (Gelb et al., 2013). Drosophila has served as a model to study genes related to CHD for over 20 years, based on the evolutionarily conserved genetic basis of heart development (Bier and Bodmer, 2004; Olson, 2006; Yi et al., 2006). The Drosophila heart (Figure 1A) is a rhythmically beating linear tube composed of parallel rows of fused contractile cardiac cells, flanked by adherent pericardial cells (Vogler and Bodmer, 2015). The heart functions to maintain circulation of hemolymph (insect ‘blood’) throughout the body cavity. In a typical beating cycle, hemolymph enters the posteriorly-located heart chamber in the abdomen and is pumped anteriorly through the aorta towards the head and brain. Although structurally of relatively low complexity, Drosophila and human heart development are both largely directed by the same highly conserved regulatory networks (Vogler and Bodmer, 2015). To demonstrate that the fly is the ideal in vivo animal model for cost-effective, rapidly informative, and efficient screening of candidate disease genes, we have developed a Drosophila validation system to screen candidate genes identified in a large-scale genomic sequencing study of congenital heart disease patients.

Figure 1 with 1 supplement see all

Download asset Open asset

In 2013 the Pediatric Cardiac Genomics Consortium (PCGC) published a large-scale sequence analysis of sporadically occurring CHD cases (Zaidi et al., 2013), providing an opportunity to conduct systematic model system based experimental studies into the genetic basis of CHD, informed from the outset by clinical data. Such studies will ultimately facilitate the development of diagnostic, therapeutic, and preventive approaches, and precision medicine interventions (Ashley, 2015) for CHD. The initial report (Zaidi et al., 2013) of 249 protein-altering de novo mutations from 362 severe cases of CHD identified 223 candidate disease genes involved in diverse biochemical pathways, with 26 genes selected as particular genes-of-interest based upon bioinformatics criteria (Zaidi et al., 2013). We used Drosophila developmental genetics to quantitatively evaluate the phenotypes associated with heart-specific RNAi mediated gene silencing of fly homologs of mutated candidate disease genes. 174 genes (78%) had clear fly homologs, consistent with the published estimate of 75% conservation of disease gene homologs (Reiter et al., 2001). Interestingly, the ‘top 26’ genes identified by the PCGC study (Zaidi et al., 2013) all have conserved Drosophila homologs. Some of these genes were previously identified as having roles in Drosophila heart development, in the context of a global in vivo RNAi screen for candidate fly heart genes. (Neely et al., 2010) The current study used a stronger heart-specific driver to validate candidate CHD genes.

We developed a highly efficient cardiac-targeted gene silencing approach in flies, and used this to examine effects on heart structure and function for fly homologs of 134 candidate disease genes published by the PCGC. We employed a quantitative phenotypic screening protocol that evaluated developmental lethality (pre-adult mortality), heart structure (Figure 1A; including morphology, cardiac myofibrillar density, cardiac collagen deposition, and cardioblast cell number) and adult longevity. We found that 52% of the tested fly homologs are required for cardiac development and function in flies. We also developed a gene replacement testing strategy involving simultaneous heart-specific silencing of an endogenous fly gene homolog and expression of either wild type or patient-derived mutant alleles of the candidate human disease gene.

To achieve a highly efficient heart-specific gene knock down, we generated a strong cardiac cell Gal4 driver featuring 4 tandem repeats of the Drosophila Hand gene cardiac enhancer (Han and Olson, 2005). We compared the levels of Gal4 RNA in heart tissues of transgenic flies carrying the new 4XHand-Gal4 driver construct versus flies carrying the original, established Hand-Gal4 driver (featuring a single Hand enhancer element). As anticipated, 4XHand promoted significantly higher heart cell expression of Gal4 mRNA (Figure 1B). We then compared the Hand-Gal4 and 4XHand-Gal4 drivers together with UAS-Gene-IR RNAi based silencing constructs (Ni et al., 2008, 2011) for gene knock down efficiency when used to silence expression (i.e. lower heart cell RNA levels) of Drosophila genes CG8184 (Figure 1C) and Lid (Figure 1D). Consistent with the higher Gal4 expression, 4XHand was significantly more effective at silencing target gene expression through RNAi. To further confirm the utility of 4XHand-Gal4 for screening purposes, we comparatively evaluated the Hand-Gal4 and 4XHand-Gal4 constructs driving heart-specific expression of UAS-RNAi transgenes targeting eight Drosophila genes for developmental lethal (death at pre-adult stage) effects. The Hand enhancer is active during development from embryonic stages, and we reasoned that Hand enhancer-driven silencing of genes known to be essential for heart development would cause developmental lethality. For this assay, male and female flies of the appropriate genotypes were crossed to produce progeny carrying the indicated UAS-gene silencing (i.e. RNAi) interfering RNA (IR) construct, the expression of which was driven by Hand-Gal4 or 4XHand-Gal4. Progeny embryos were collected and allowed to develop under standard conditions. The results were quantitatively expressed as a Mortality Index (MI), the percentage of flies that died before adult emergence from the pupa stage. We found that for all eight tested genes, significant developmental lethal effects of heart-specific gene silencing were only observed in flies carrying a 4XHand-Gal4 driver (Figure 1—figure supplement 1).

We combined the new 4XHand-Gal4 driver with available fly lines carrying UAS-Gene-IR RNAi based silencing constructs to evaluate for heart specific essential function (i.e. MI) the homologs of all genes from the PCGC study for which multiple independent UAS-RNAi lines are available (134 total genes, Supplementary file 1). We extracted the data for the PCGC top 26 genes, identified as most-promising candidate disease genes on the basis of multiple bioinformatics-based criteria (Figure 2A). We reasoned that if these criteria alone were truly informative, the majority of these genes would yield higher MI scores, overall, than observed among all 134 tested genes representing the PCGC total list. As shown in Figure 2 we observed MI values ranging across a spectrum spanning Normal (≤6%, equivalent to nonbiased control genes), Low (7–30%), Medium (31–60%), to High (61–100%). Importantly, we found that the distribution of MI values across all ranges was not significantly different for the top 26 genes compared to the entire cohort of 134 candidate genes tested (Figure 2A). Thus, selection of candidate CHD genes on the basis of bioinformatics criteria alone did not enrich for genes inducing more severe developmental mortality phenotypes (indicative of more genes with essential roles in either heart development, tissue maintenance, or function). This observation emphasizes the importance of screening and selecting most-promising candidate genes on the basis of functional assays. To determine if the set of 134 candidate genes in itself can be demonstrated to be enriched for heart-essential genes, we examined previously obtained data from a screen of approximately 800 Drosophila genes silenced using the 4XHand-Gal4; UAS-RNAi system. These genes were randomly chosen to screen for effects on pericardial nephrocyte function (cardioblast lineage cells in which the Hand enhancer is active), so they are unbiased with respect to essential heart roles. We found that only 6% of these genes showed any level of developmental lethality when silenced (i.e. 94% normal). Thus, the PCGC genomic association study, though not gene selection based upon bioinformatics criteria, did enrich significantly for heart-essential genes as assayed in Drosophila. That such a small number of unbiased genes conferred a phenotype of silencing-induced developmental lethality also suggests that overexpression of 4xHand-Gal4 itself did not sensitize flies to RNAi mediated knockdown effects.

Figure 2

Download asset Open asset

We also stratified and compared MI values we obtained for all of the 134 Drosophila genes as functions of expression level of murine homologs during mouse embryonic heart development, as reported in the PCGC study (and one basis for selection of top 26 genes) (Zaidi et al., 2013). As shown in Figure 2C, high heart expression (HHE) during development did not correlate with greater severity of developmental lethality associated with gene silencing in Drosophila heart. Contrary to expectation, low heart expression (LHE) of murine homologs was associated with relatively higher severity of Drosophila gene silencing. These observations again emphasize the importance of applying functional screening assays to the results of genomic sequencing studies. We noted nevertheless that the top 26 genes all have Drosophila homologs, including 22 that are highly conserved. When mortality was assayed as a function of degree of fly-human conservation among all 134 tested genes (Figure 2D) we did observe greater mortality among highly conserved genes compared to less conserved genes. Not surprisingly, phylogenetic conservation indeed correlated positively with MI, a result that further supports using Drosophila as a model for functional evaluation of candidate genes for disease association.

In order to confirm that RNAi based phenotypes were indeed associated with target gene mRNA reduction, we measured RNA levels in heart tissue from flies expressing silencing constructs targeting six different genes from the top 26 group. These genes were chosen for analysis because they represent a range of different cellular functions, and include three genes for which silencing did not induce developmental lethality. MI values for the six genes ranged from normal to 43%. As shown in Figure 3, heart RNA levels of target genes were all significantly reduced. Furthermore, we tested developmental lethal phenotypes for all top 26 genes using two or more independent RNAi silencing fly lines for each gene, and obtained consistent MI results in all cases (data not shown).

Figure 3

Download asset Open asset

Eight of the top 26 gene homologs encode products involved in histone H3K4 and H3K27 methylation (Figures 2A and 4). Epigenetic activating (H3K4me) and inactivating (H3K27me) marks are associated with selective activation of promoters and enhancers, and chromatin marks are associated with specification and differentiation of cardiac cells (Wamstad et al., 2012), murine heart development (He et al., 2012; Delgado-Olguín et al., 2012), and cardiac pathogenesis (Kaneda et al., 2009; Zhang and Liu, 2015). Two additional top 26 genes (SUV420H1 and HUWE1) also encode conserved histone modifying proteins. Thus 42% of the top candidate genes from the PCGC study are involved in histone modification pathways, and all but one are highly conserved. Because chromatin modification pathway associated genes represent the major functional class among the ‘top 26’, we chose to examine in more detail the roles of these genes in Drosophila heart morphogenesis and function.

Figure 4 with 1 supplement see all

Download asset Open asset

We analyzed heart phenotypes resulting from cardiac-specific silencing of the eight genes involved in H3K4 and H3K27 methylation (Figure 4). Silencing kismet yielded an adult heart phenotype in which the heart tube was severely affected (Figure 4B) with very low cardiac myofibrillar density (Figure 4C), elevated Pericardin (collagen) deposition (Figure 4D), and reduced numbers of cardioblasts (Figure 4E). Adult fly longevity was significantly affected, with approximately 50% of flies dead by day 25 (Figure 4F). During development, kismet silencing caused 54% mortality (Figure 2A). In larvae, cardiac myofibrillar density was reduced with pronounced heart tube filament disruption (Figure 4G,H), and Pericardin over-expression with disorganized ECM deposition (Figure 4G,I). Trx-silenced adult flies lacked cardiac actin and heart tube (Figure 4B,C), exhibited severely reduced Pericardin (Figure 4D), almost no cardioblast cells (Figure 4E), and all were dead at day 25 after emergence, compared to 20% mortality among control flies (Figure 4F). Trx silencing caused 35% developmental lethality (Figure 2A). In larvae, knock down of trx induced abnormal heart tube structure (Figure 4G) with reduced cardiac myofibrillar density (Figure 4G,H) and reduced Pericardin (Figure 4G,I). Silencing of wds caused complete developmental lethality (Figure 2A) and a dramatic larva heart phenotype (Figure 4G) featuring abnormal cardiac actin organization and fiber density (Figure 4H), and extremely high levels of Pericardin (Figure 4G,I). Taken together, these results strongly suggest that H3K4 methylation is critical for normal heart development and function, a conclusion supported by the observation that knockdown of the H3K4 de-methylase Lid had no phenotypic consequences (Figure 4C–F,H,I; Figure 4—figure supplement 1). Similarly, silencing of SMOX (involved in H3K27 de-methylation) resulted in minimal developmental mortality (MI 7%, Figure 2A) but normal cardiac myofibrillar density and Pericardin levels in larvae (Figure 4H,I). The adult heart phenotype was essentially normal, as was adult mortality at day 25 (Figure 4).

Ubiquitination of H2BK120 (Figure 4A) is required for H3K4 methylation, and we observed that silencing of the UbcD6 gene resulted in 84% developmental mortality (Figure 2A) and reduced adult longevity (Figure 4F; Figure 4—figure supplement 1). The adult heart was severely affected (Figure 4—figure supplement 1) with greatly reduced cardiac myofibrillar density (Figure 4C), elevated Pericardin (Figure 4D), and reduced cardioblast cell numbers (Figure 4E). Silencing of Bre1 and Scny also caused developmental mortality and cardiac myofibrillar density was somewhat reduced in larvae and adults (Figure 4C,H). Pericardin levels, cardioblast cell numbers, and adult mortality at day 25 were essentially normal (Figure 4D,E,F,I). By contrast, H3K4 de-methylation did not appear critical as evidenced by the essentially normal heart phenotype, normal MI, and normal adult longevity observed upon Lid silencing. (Figure 4 and Figure 4—figure supplement 1). These results further demonstrate the utility of the Drosophila system to functionally test large-scale clinical genomics data and gain insights into fundamental pathways contributing to disease pathogenesis.

We used dye angiography to measure effects on heart function of cardiac-specific silencing of the eight genes involved in H3K4 and H3K27 methylation (Figure 5). Briefly, fluorescent dye was injected into the posterior abdomen of pharate (pre-eclosion) adult flies, where it entered the heart chamber and was pumped anteriorly. Dye accumulation in the head over a 30 s time interval provided a measurement of cardiac efficiency (Figure 5A,B) (Drechsler et al., 2013). We observed that in normal control flies fluorescent dye is readily detectable in the head within 15 s of injection, and significantly accumulated by 30 s. By contrast, silencing of candidate CHD gene homologs involved in the methylation of H3K4 and H3K27 profoundly impaired cardiac function (Figure 5B,C). Silencing of Smox and Lid, however, did not measurably impair heart function as measured in this assay. These observations indicate that H3K4 and H3K27 methylation, and not demethylation, is essential for heart function, consistent with our findings with regard to cardiac tissue morphology and effects on developmental lethality and adult longevity.

Figure 5

Download asset Open asset

Gene silencing using available and well-established UAS-RNAi fly lines allows rapid and efficient functional screening of large numbers of candidate genes. We also established an in vivo platform to analyze the cardiac phenotypes of specific patient-derived alleles of candidate CHD genes in Drosophila, employing a ‘gene replacement’ strategy. As a proof-of-concept, we tested the ability of a wild type human WDR5 allele to rescue heart phenotypes induced by silencing of the endogenous wds Drosophila homolog. Heart-specific silencing of wds caused 100% developmental lethality and abnormal heart morphology in late larvae characterized by reduced cardiac myofibers and severe over-deposition of Pericardin (Figure 6). We found that simultaneously overexpressing a wild type human WDR5 allele reduced developmental lethality almost 7-fold, significantly restored cardiac myofibrillar density, and reduced Pericardin levels essentially to normal (Figure 6). By contrast, when the endogenous wds expression was ‘replaced’ by a CHD patient-derived WDR5-K7Q mutant allele, developmental lethality remained quite elevated, cardiac myofibrillar density remained abnormally low (relative both to Control and wild type WDR5 rescue), and Pericardin levels were lowered but still significantly higher than both Control and wild type WDR5 rescue. These observations illustrate the structure-function homologies between human and fly ‘heart genes’ that support use of Drosophila for functional validation of candidate CHD genes. Moreover, they demonstrate that the ‘gene replacement’ strategy can be used to quantitatively assess phenotypes induced by patient-specific alleles of candidate disease genes.

Figure 6

Download asset Open asset

Our results demonstrate the value of the Drosophila model system to functionally screen and validate candidate disease genes identified through large scale sequencing efforts. The high-throughput testing platform described here can extend gene association data to quantitative, in vivo morphological and functional criteria. On the basis of this screening, the most promising genes can justifiably be advanced to more costly, time consuming, and technically challenging validation platforms required to definitively identify and confirm disease-causing gene variants. Such advanced testing, we presume, will encompass gene function analysis in a mammalian model system.

The advantages of functional screening using RNAi-based silencing lines are to some extent offset by certain limitations. From the outset, of human disease genes, approximately 25% are not represented by Drosophila homologs. False negatives may occur if RNAi expression does not sufficiently reduce mRNA levels. However, we tested a number of RNAi lines by qRT-PCR and found no examples of ineffective gene silencing. False negatives may also occur when missense mutations produce gain-of-function (GOF) alleles, and we cannot accurately predict the degree to which this could confound our analysis. This problem, we note, is mitigated to a degree by the fact that genes altered by GOF mutations would in many instances normally encode proteins with cardiac cell functions, and thus yield silencing induced phenotypes in our functional screening system. False positives can arise from so-called ‘off target’ effects of a given RNAi targeting construct. We controlled against misleading results from both RNAi silencing inefficiency and off target effects by employing the latest-generation available RNAi lines, and testing multiple such lines for each of the top 26 candidate genes. This precaution against false negative and false positive results further limited the total number of candidate genes we could screen to 134 (60% of the CHD candidate genes published by the PCGC). Nevertheless, our in vivo screening system rapidly and efficiently confirmed roles in heart development, tissue maintenance, or cardiac function for 72 candidate genes.

Phenotypic analysis based on gene silencing, while effective for high throughput screening, represents a first step in validation and characterization of candidate mutations. We also demonstrated an initial ‘gene replacement’ strategy that takes further advantage of the available resources and sophisticated genetics of the Drosophila model system to quantitatively characterize the phenotype induced by expression of a specific patient-derived mutant allele. This approach may prove highly valuable as a testing platform for precision medicine based therapeutic drugs. Ultimately, the knowledge that a specific mutation contributes to disease pathogenesis opens the door to the application of precise gene-editing interventions, strategies for which are currently being developed based on CRISPR/Cas-9 mediated genome editing (Long et al., 2016; Carroll et al., 2016; Long et al., 2014). The high throughput candidate gene validation studies reported here represent the essential first step in the functional confirmation, phenotypic characterization, and ultimate precision treatment of disease-associated mutations.

1. 1. Ashley EA

   (2015) The precision medicine initiative: a new national effort

   JAMA 313:2119–2120.

   https://doi.org/10.1001/jama.2015.3595

   • PubMed
   • Google Scholar
2. 1. Bier E
   2. Bodmer R

   (2004) Drosophila, an emerging model for cardiac disease

   Gene 342:1–11.

   https://doi.org/10.1016/j.gene.2004.07.018

   • PubMed
   • Google Scholar
3. 1. Bier E

   (2005) Drosophila, the golden bug, emerges as a tool for human genetics

   Nature Reviews Genetics 6:9–23.

   https://doi.org/10.1038/nrg1503

   • PubMed
   • Google Scholar
4. 1. Brand AH
   2. Perrimon N

   (1993)

   Targeted gene expression as a means of altering cell fates and generating dominant phenotypes

   Development 118:401–415.

   • PubMed
   • Google Scholar
5. 1. Cagan RL

   (2011) The Drosophila nephrocyte

   Current Opinion in Nephrology and Hypertension 20:409–415.

   https://doi.org/10.1097/MNH.0b013e328347ae02

   • PubMed
   • Google Scholar
6. 1. Carroll KJ
   2. Makarewich CA
   3. McAnally J
   4. Anderson DM
   5. Zentilin L
   6. Liu N
   7. Giacca M
   8. Bassel-Duby R
   9. Olson EN

   (2016) A mouse model for adult cardiac-specific gene deletion with CRISPR/Cas9

   PNAS 113:338–343.

   https://doi.org/10.1073/pnas.1523918113

   • PubMed
   • Google Scholar
7. 1. Chien S
   2. Reiter LT
   3. Bier E
   4. Gribskov M

   (2002) Homophila: human disease gene cognates in Drosophila

   Nucleic Acids Research 30:149–151.

   https://doi.org/10.1093/nar/30.1.149

   • PubMed
   • Google Scholar
8. 1. Delgado-Olguín P
   2. Huang Y
   3. Li X
   4. Christodoulou D
   5. Seidman CE
   6. Seidman JG
   7. Tarakhovsky A
   8. Bruneau BG

   (2012) Epigenetic repression of cardiac progenitor gene expression by Ezh2 is required for postnatal cardiac homeostasis

   Nature Genetics 44:343–347.

   https://doi.org/10.1038/ng.1068

   • PubMed
   • Google Scholar
9. 1. Diop SB
   2. Bodmer R

   (2015) Gaining insights into diabetic cardiomyopathy from Drosophila

   Trends in Endocrinology & Metabolism 26:618–627.

   https://doi.org/10.1016/j.tem.2015.09.009

   • PubMed
   • Google Scholar
10. 1. Drechsler M
    2. Schmidt AC
    3. Meyer H
    4. Paululat A

    (2013) The conserved ADAMTS-like protein lonely heart mediates matrix formation and cardiac tissue integrity

    PLoS Genetics 9:e1003616.

    https://doi.org/10.1371/journal.pgen.1003616

    • PubMed
    • Google Scholar
11. 1. Gelb B
    2. Brueckner M
    3. Chung W
    4. Goldmuntz E
    5. Kaltman J
    6. Kaski JP
    7. Kim R
    8. Kline J
    9. Mercer-Rosa L
    10. Porter G
    11. Roberts A
    12. Rosenberg E
    13. Seiden H
    14. Seidman C
    15. Sleeper L
    16. Tennstedt S
    17. Kaltman J
    18. Schramm C
    19. Burns K
    20. Pearson G
    21. Rosenberg E
    22. Pediatric Cardiac Genomics Consortium

    (2013) The congenital heart disease genetic network study: rationale, design, and early results

    Circulation Research 112:698–706.

    https://doi.org/10.1161/CIRCRESAHA.111.300297

    • PubMed
    • Google Scholar
12. 1. Han Z
    2. Olson EN

    (2005) Hand is a direct target of Tinman and GATA factors during Drosophila cardiogenesis and hematopoiesis

    Development 132:3525–3536.

    https://doi.org/10.1242/dev.01899

    • PubMed
    • Google Scholar
13. 1. Han Z
    2. Yi P
    3. Li X
    4. Olson EN

    (2006) Hand, an evolutionarily conserved bHLH transcription factor required for Drosophila cardiogenesis and hematopoiesis

    Development 133:1175–1182.

    https://doi.org/10.1242/dev.02285

    • PubMed
    • Google Scholar
14. 1. He A
    2. Ma Q
    3. Cao J
    4. von Gise A
    5. Zhou P
    6. Xie H
    7. Zhang B
    8. Hsing M
    9. Christodoulou DC
    10. Cahan P
    11. Daley GQ
    12. Kong SW
    13. Orkin SH
    14. Seidman CE
    15. Seidman JG
    16. Pu WT

    (2012) Polycomb repressive complex 2 regulates normal development of the mouse heart

    Circulation Research 110:406–415.

    https://doi.org/10.1161/CIRCRESAHA.111.252205

    • PubMed
    • Google Scholar
15. 1. Hoffman JI
    2. Kaplan S

    (2002) The incidence of congenital heart disease

    Journal of the American College of Cardiology 39:1890–1900.

    https://doi.org/10.1016/S0735-1097(02)01886-7

    • PubMed
    • Google Scholar
16. 1. Hoffman JI

    (1990) Congenital heart disease: incidence and inheritance

    Pediatric Clinics of North America 37:25–43.

    https://doi.org/10.1016/S0031-3955(16)36830-4

    • PubMed
    • Google Scholar
17. 1. Hu Y
    2. Flockhart I
    3. Vinayagam A
    4. Bergwitz C
    5. Berger B
    6. Perrimon N
    7. Mohr SE

    (2011) An integrative approach to ortholog prediction for disease-focused and other functional studies

    BMC Bioinformatics 12:357.

    https://doi.org/10.1186/1471-2105-12-357

    • PubMed
    • Google Scholar
18. 1. Kaneda R
    2. Takada S
    3. Yamashita Y
    4. Choi YL
    5. Nonaka-Sarukawa M
    6. Soda M
    7. Misawa Y
    8. Isomura T
    9. Shimada K
    10. Mano H

    (2009) Genome-wide histone methylation profile for heart failure

    Genes to Cells 14:69–77.

    https://doi.org/10.1111/j.1365-2443.2008.01252.x

    • PubMed
    • Google Scholar
19. 1. Livak KJ
    2. Schmittgen TD

    (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method

    Methods 25:402–408.

    https://doi.org/10.1006/meth.2001.1262

    • PubMed
    • Google Scholar
20. 1. Long C
    2. Amoasii L
    3. Mireault AA
    4. McAnally JR
    5. Li H
    6. Sanchez-Ortiz E
    7. Bhattacharyya S
    8. Shelton JM
    9. Bassel-Duby R
    10. Olson EN

    (2016) Postnatal genome editing partially restores dystrophin expression in a mouse model of muscular dystrophy

    Science 351:400–403.

    https://doi.org/10.1126/science.aad5725

    • PubMed
    • Google Scholar
21. 1. Long C
    2. McAnally JR
    3. Shelton JM
    4. Mireault AA
    5. Bassel-Duby R
    6. Olson EN

    (2014) Prevention of muscular dystrophy in mice by CRISPR/Cas9-mediated editing of germline DNA

    Science 345:1184–1188.

    https://doi.org/10.1126/science.1254445

    • PubMed
    • Google Scholar
22. 1. Na J
    2. Sweetwyne MT
    3. Park AS
    4. Susztak K
    5. Cagan RL

    (2015) Diet-Induced podocyte dysfunction in Drosophila and mammals

    Cell Reports 12:636–647.

    https://doi.org/10.1016/j.celrep.2015.06.056

    • PubMed
    • Google Scholar
23. 1. Neely GG
    2. Kuba K
    3. Cammarato A
    4. Isobe K
    5. Amann S
    6. Zhang L
    7. Murata M
    8. Elmén L
    9. Gupta V
    10. Arora S
    11. Sarangi R
    12. Dan D
    13. Fujisawa S
    14. Usami T
    15. Xia CP
    16. Keene AC
    17. Alayari NN
    18. Yamakawa H
    19. Elling U
    20. Berger C
    21. Novatchkova M
    22. Koglgruber R
    23. Fukuda K
    24. Nishina H
    25. Isobe M
    26. Pospisilik JA
    27. Imai Y
    28. Pfeufer A
    29. Hicks AA
    30. Pramstaller PP
    31. Subramaniam S
    32. Kimura A
    33. Ocorr K
    34. Bodmer R
    35. Penninger JM

    (2010) A global in vivo Drosophila RNAi screen identifies NOT3 as a conserved regulator of heart function

    Cell 141:142–153.

    https://doi.org/10.1016/j.cell.2010.02.023

    • PubMed
    • Google Scholar
24. 1. Ni JQ
    2. Markstein M
    3. Binari R
    4. Pfeiffer B
    5. Liu LP
    6. Villalta C
    7. Booker M
    8. Perkins L
    9. Perrimon N

    (2008) Vector and parameters for targeted transgenic RNA interference in Drosophila melanogaster

    Nature Methods 5:49–51.

    https://doi.org/10.1038/nmeth1146

    • PubMed
    • Google Scholar
25. 1. Ni JQ
    2. Zhou R
    3. Czech B
    4. Liu LP
    5. Holderbaum L
    6. Yang-Zhou D
    7. Shim HS
    8. Tao R
    9. Handler D
    10. Karpowicz P
    11. Binari R
    12. Booker M
    13. Brennecke J
    14. Perkins LA
    15. Hannon GJ
    16. Perrimon N

    (2011) A genome-scale shRNA resource for transgenic RNAi in Drosophila

    Nature Methods 8:405–407.

    https://doi.org/10.1038/nmeth.1592

    • PubMed
    • Google Scholar
26. 1. Olson EN

    (2006) Gene regulatory networks in the evolution and development of the heart

    Science 313:1922–1927.

    https://doi.org/10.1126/science.1132292

    • PubMed
    • Google Scholar
27. 1. Owusu-Ansah E
    2. Perrimon N

    (2014) Modeling metabolic homeostasis and nutrient sensing in Drosophila: implications for aging and metabolic diseases

    Disease Models & Mechanisms 7:343–350.

    https://doi.org/10.1242/dmm.012989

    • PubMed
    • Google Scholar
28. 1. Reiter LT
    2. Potocki L
    3. Chien S
    4. Gribskov M
    5. Bier E

    (2001) A systematic analysis of human disease-associated gene sequences in Drosophila melanogaster

    Genome Research 11:1114–1125.

    https://doi.org/10.1101/gr.169101

    • PubMed
    • Google Scholar
29. 1. Reller MD
    2. Strickland MJ
    3. Riehle-Colarusso T
    4. Mahle WT
    5. Correa A

    (2008) Prevalence of congenital heart defects in metropolitan Atlanta, 1998-2005

    The Journal of Pediatrics 153:807–813.

    https://doi.org/10.1016/j.jpeds.2008.05.059

    • PubMed
    • Google Scholar
30. 1. Vogler G
    2. Bodmer R

    (2015) Cellular mechanisms of Drosophila heart morphogenesis

    Journal of Cardiovascular Development and Disease 2:2–16.

    https://doi.org/10.3390/jcdd2010002

    • PubMed
    • Google Scholar
31. 1. Wamstad JA
    2. Alexander JM
    3. Truty RM
    4. Shrikumar A
    5. Li F
    6. Eilertson KE
    7. Ding H
    8. Wylie JN
    9. Pico AR
    10. Capra JA
    11. Erwin G
    12. Kattman SJ
    13. Keller GM
    14. Srivastava D
    15. Levine SS
    16. Pollard KS
    17. Holloway AK
    18. Boyer LA
    19. Bruneau BG

    (2012) Dynamic and coordinated epigenetic regulation of developmental transitions in the cardiac lineage

    Cell 151:206–220.

    https://doi.org/10.1016/j.cell.2012.07.035

    • PubMed
    • Google Scholar
32. 1. Yi P
    2. Han Z
    3. Li X
    4. Olson EN

    (2006) The mevalonate pathway controls heart formation in Drosophila by isoprenylation of Ggamma1

    Science 313:1301–1303.

    https://doi.org/10.1126/science.1127704

    • PubMed
    • Google Scholar
33. 1. Zaidi S
    2. Choi M
    3. Wakimoto H
    4. Ma L
    5. Jiang J
    6. Overton JD
    7. Romano-Adesman A
    8. Bjornson RD
    9. Breitbart RE
    10. Brown KK
    11. Carriero NJ
    12. Cheung YH
    13. Deanfield J
    14. DePalma S
    15. Fakhro KA
    16. Glessner J
    17. Hakonarson H
    18. Italia MJ
    19. Kaltman JR
    20. Kaski J
    21. Kim R
    22. Kline JK
    23. Lee T
    24. Leipzig J
    25. Lopez A
    26. Mane SM
    27. Mitchell LE
    28. Newburger JW
    29. Parfenov M
    30. Pe'er I
    31. Porter G
    32. Roberts AE
    33. Sachidanandam R
    34. Sanders SJ
    35. Seiden HS
    36. State MW
    37. Subramanian S
    38. Tikhonova IR
    39. Wang W
    40. Warburton D
    41. White PS
    42. Williams IA
    43. Zhao H
    44. Seidman JG
    45. Brueckner M
    46. Chung WK
    47. Gelb BD
    48. Goldmuntz E
    49. Seidman CE
    50. Lifton RP

    (2013) De novo mutations in histone-modifying genes in congenital heart disease

    Nature 498:220–223.

    https://doi.org/10.1038/nature12141

    • PubMed
    • Google Scholar
34. 1. Zhang F
    2. Zhao Y
    3. Chao Y
    4. Muir K
    5. Han Z

    (2013) Cubilin and amnionless mediate protein reabsorption in Drosophila nephrocytes

    Journal of the American Society of Nephrology 24:209–216.

    https://doi.org/10.1681/ASN.2012080795

    • PubMed
    • Google Scholar
35. 1. Zhang QJ
    2. Liu ZP

    (2015) Histone methylations in heart development, congenital and adult heart diseases

    Epigenomics 7:321–330.

    https://doi.org/10.2217/epi.14.60

    • PubMed
    • Google Scholar

Author details

1. Jun-yi Zhu

   Center for Cancer and Immunology Research, Children's National Medical Center, Washington, United States

   Contribution

   J-yZ, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft

   Contributed equally with

   Yulong Fu

   Competing interests

   The authors declare that no competing interests exist.

2. Yulong Fu

   Center for Cancer and Immunology Research, Children's National Medical Center, Washington, United States

   Contribution

   YF, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft

   Contributed equally with

   Jun-yi Zhu

   Competing interests

   The authors declare that no competing interests exist.

3. Margaret Nettleton

   Center for Cancer and Immunology Research, Children's National Medical Center, Washington, United States

   Contribution

   MN, Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   The authors declare that no competing interests exist.

4. Adam Richman

   Center for Cancer and Immunology Research, Children's National Medical Center, Washington, United States

   Contribution

   AR, Data curation, Validation, Investigation, Methodology, Writing—original draft

   Competing interests

   The authors declare that no competing interests exist.

5. Zhe Han

   1. Center for Cancer and Immunology Research, Children's National Medical Center, Washington, United States
   2. Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, United States

   Contribution

   ZH, Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   zhan@childrensnational.org

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-5177-7798

• 3,606

  views

• 784

  downloads

• 43

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.