Periventricular nodular heterotopia (PNH) is the most common form of malformation of cortical development (MCD) in adulthood but its genetic basis remains unknown in most sporadic cases. We have recently developed a strategy to identify novel candidate genes for MCDs and to directly confirm their causative role in vivo.
Birth defects that involve the cerebral cortex — also known as malformations of cortical development (MCD) — are important causes of intellectual disability and account for 20-40% of drug-resistant epilepsy in childhood. High-resolution brain imaging has facilitated in vivo identification of a large group of MCD phenotypes. Despite the advances in brain imaging, genomic analysis and generation of animal models, a straightforward workflow to systematically prioritize candidate genes and to test functional effects of putative mutations is missing. To overcome this problem, an experimental strategy enabling the identification of novel causative genes for MCD was developed and validated. This strategy is based on identifying candidate genomic regions or genes via array-CGH or whole-exome sequencing and characterizing the effects of their inactivation or of overexpression of specific mutations in developing rodent brains via in utero electroporation. This approach led to the identification of the C6orf70 gene, encoding for a putative vesicular protein, to the pathogenesis of periventricular nodular heterotopia, a MCD caused by defective neuronal migration.
The cerebral cortex plays a key role in cognitive and intellectual processes and is involved in emotional control as well as learning and memory. It is therefore not surprising that many neurological and psychiatric diseases result from malformations of cortical development (MCD). The etiology of MCD is complex since both acquired and genetic factors are involved. The cumulative prevalence of genetically determined proportion of MCD is about 2% and they are sporadic in most cases. For instance, the incidence of congenital brain dysgenesis was estimated to be higher than 1% in the human population, and some forms of MCD are observed in more than 14% of all patients with epilepsy and in 40% of severe or intractable epilepsy1,2.
Periventricular Nodular Heterotopia (PNH) is one of the most common MCDs and is caused by abnormal migration of neurons from the ventricular zone (VZ) to the developing cerebral cortex. The failure of neurons to migrate results in clusters of heterotopic neurons along the walls of the lateral ventricles which can usually be visualized using Magnetic Resonance Imaging (MRI). The clinical, anatomic and imaging features of PNH are heterogeneous. Nodules may range from small and unilateral to bilateral and symmetric. Common clinical sequelae include epilepsy and intellectual disabilities3. Mutations in the Filamin A (or FLNA) gene, which maps in Xq28, were found in 100% of families with X-linked bilateral PNH and in 26% of sporadic patients3,4. A rare, recessive form of PNH caused by mutations in the ARFGEF2 gene, which maps in 20q13, has been reported in two consanguineous families5. Recently, biallelic mutations in genes encoding the receptor-ligand cadherin pair DCHS1 and FAT4 have been identified in nine patients affected by a multisystemic disorder that includes PNH6. PNH has also been associated with fragile X syndrome7, Williams syndrome8, 22q11 microdeletion syndrome9, duplications at 5p1510, deletions at 1p3611, 5q14.3-q1512, 6p2513 and 6q terminal deletion syndrome14,15,16,17,18,19, suggesting that additional causative genes are scattered throughout the genome. However, for approximately 74% of sporadic PNH patients the genetic basis remains to be elucidated17.
Classical gene mapping approaches such as array Comparative Genomic Hybridization (array-CGH) have proven to be a powerful tools for the detection of sub-microscopic chromosomal abnormalities, however, the genomic regions identified using this approach are often large and contain numerous genes.
The advent of massive parallel sequencing techniques (i.e. Whole-Exome Sequencing (WES) and Whole-Genome sequencing (WGS)) has substantially reduced both the cost and the time required to sequence an entire human exome or genome. Nevertheless, interpretation of WES and WGS data remains challenging in the majority of cases, since for each patient tens to hundreds (or even thousands, depending from the type of analysis) variants emerge from data filtering.
To speed up the process of identifying novel MCD causative genes, a novel systematic strategy combining array-CGH, WES and in utero electroporation (IUE) screening of candidate genes was designed. IUE allows to selectively inactivate (or overexpress) specific genes or mutations in rodent brains, enabling rapid evaluation of their involvement in corticogenesis18,19. RNAi mediated-knockdown or overexpression of one or more candidate genes is expected to cause, when the gene is associated with disease development, localized defects in neuronal migration and/or maturation. Upon the identification of a gene whose inactivation (or overexpression) reproduces the phenotype observed in patients in rodents, it becomes an outstanding candidate for the screening in sporadic patients with MCD. Using this approach, we recently revealed the crucial contribution of the C6orf70 gene (also known as ERMARD) in PNH pathogenesis in patients harbouring 6q27 chromosomal deletions16.
Ethics Statement: Wistar rats were mated, maintained and used in the INMED animal facilities, in agreement with European Union and French legislation.
1. DNA Extraction and Quantification for Array-CGH and WES
2. Array-CGH Protocol
3. WES Protocol
4. Plasmid DNA Preparation for In Utero Electroporation
5. In Utero Electroporation
6. Brain Sample Preparation
7. Confocal Imaging and Quantitative Analysis
The experimental strategy designed to identify novel MCD causative genes is recapitulated in Figure 1.
By performing array-CGH in a cohort of 155 patients with developmental brain abnormalities variably combining PNH (Figure 2A), corpus callosum dysgenesis, colpocephaly, cerebellar hypoplasia and polymicrogyria associated with epilepsy, ataxia and cognitive impairment, we identified a 1.2 Mb minimal critical deletion in 6q27 shared by 12 patients (Figure 2B)21. The genomic region contains four known genes (THBS2, PHF10, TCTE3 and DLL1) and two predicted genes (WDR27 and C6orf70) (Figure 2B).
In parallel to array-CGH, WES analysis in 14 patients with isolated bilateral PNH and no Copy Number Variations analysis was carried out, identifying one patient with a de novo mutation (c.752T>A: pIle250Asn) in the predicted C6orf70 gene (Genbank accession number NM_018341.1) (Figure 3).
To confirm that the mutation identified in C6orf70 had a causative role in PNH and to investigate whether haploinsufficiency of the other genes mapping in 6q27 may influence the phenotype, their role in vivo on neuronal migration was explored using the in utero RNAi-mediated knockdown approach23,24 to silence their expression in rat cortical neural progenitors. Only genes expressed in the developing rat cortex, PHF10, C6orf70 and DLL1, were tested (Figure 4A). Different short hairpin RNAs (shRNAs) were generated targeting the coding sequences of these three genes. ShRNAs were then introduced into neuroprogenitors in the rat neocortex by in utero electroporation at embryonic day 15 (E15), when neurons committed to upper cortical layers are generated. Green fluorescent protein was used as transfection reporter. Relative distribution of GFP-positive cells was examined 5 days after electroporation. Knockdown of Dll1 and Phf10 resulted in a slight delay of radial neuronal migration (data not shown), whereas knockdown of C6orf70 impaired neuronal migration and gave rise to the development of heterotopic nodules highly reminiscent of those observed in Flna knockdown model (Figure 4B)24. Conversely, transfections with the ineffective C6orf70 mismatch shRNA had no obvious impact on neuronal migration (Figure 4B).
Figure 1: Workflow for the identification of novel MCD causative genes identification. Schematic representation of the different approaches that can be used to identify novel disease causing genes. Boxes coloured in yellow represent the approaches described in the present paper. Please click here to view a larger version of this figure.
Figure 2: 6q27 deletions cause abnormal brain development with PNH. (A) Patient 2. T2-weighed coronal (left panel) and T1-weighed axial (right panel) sections showing PNH lining the wall of the left temporal lobe (white and black arrowhead) below an unfolded insular cortex. (B) Patient 11. T1-weighted coronal section, showing bilateral heterotopic nodules along the walls of the temporal horns (white arrowheads). (C) Patient 12. T2-weighed coronal section. On the left, PNH is still visible (black arrowhead) and the ventricles are dilated. (D) Schematic representation of deletions of the 6q27 region identified in PNH patients using array-CGH (upper part). The horizontal, dashed lines represent deletions identified in patients. The size of the minimal critical deleted region is also indicated and contains four known genes: THBS2, PHF10, TCTE3 and DLL1 and two predicted genes: WDR27 and C6orf70 (upper part). Please click here to view a larger version of this figure.
Figure 3: Sequencing results. ( ) T2- weighted axial section showing bilateral PNH in the frontal horns (white arrowheads) in the patient carrying the c.752T>A mutation in C6orf70. (B) Sanger sequencing showing that the mutation identified by WES occurred de novo. The position of the mutation is indicated by black arrowheads. Please click here to view a larger version of this figure.
Figure 4: Expression analysis of the genes localized in the 6q27 minimal critical region and C6orf70-knockdown alters radial migration of cortical neurons. (A) Quantitative RT-PCR showing the expression of genes contained in the 6q27 critical region in the rat cerebral cortex. Cyclophilin A was used for normalization. (B) Representative neocortical coronal sections of E20 rat brains 5 days after electroporation with either Green Fluorescent Protein construct alone (control), or combined with shRNA targeting the C6orf70 coding sequence (C6orf70-shCDS) or with the relative ineffective mismatch construct (C6orf70-shCDSmm). Flna-knockdown (FLNA-shCDS) was used as control of impaired neuronal migration. In utero electroporation with either the C6orf70-shCDS or FLNA-shCDS induced the arrest of GFP-positive cells within the ventricular zone (VZ) (white arrows), whereas the cells expressing GFP alone or in combination with the mismatch construct did not alter neuronal migration. Scale bars = 200 µm. Please click here to view a larger version of this figure.
MCDs are important causes of intellectual disability and account for 20-40% of drug-resistant childhood epilepsy1,2. Interest in MCDs has increased dramatically over the past decade as a result of two major factors. The first is the improvement in brain imaging (particularly MRI), which allows physicians and scientists to visualize many brain malformations that were not previously recognized. The other is the evolution of genetic tools that have allowed the identification of many novel MCD causative genes. This has vastly improved our knowledge of the mechanisms that underlie brain development and function, and allowed for more accurate genetic counselling.
In the past, researchers focused their efforts primarily on causative gene identification, leaving the design of functional assays to clarify their role in brain development to later stages. This has become more difficult due to the progression from the study of rare, recurrent genetic disorders to more common sporadic disorders for which traditional gene finding methods are not amenable. Current approaches to identify causative genes often allow the identification of relatively large regions of the genome containing numerous genes (array-CGH) or several variants in a number of candidate genes which are hard to validate (WES or WGS).
Array-CGH and WES (or WGS) approaches have broadened the mutation spectrum for many genetic disorders. Nevertheless, some critical limitations still remain. For instance, array-CGH needs high-quality DNA and fails to identify balanced translocations or small deletions/duplications including those involving one or few exons of a single gene. To overcome this problem, array-CGH may be performed at a higher resolution than conventional probe spacing (e.g. using 1M array-CGH kit) or substituted with SNP-array analysis. WES often does not cover large intragenic regions and fails to identify deep intronic mutations. In addition, sometimes the coverage may be too low to identify causative mutations (especially in case of mutations with low percentage of mosaicism). Another critical step for WES is that data analysis and filtering still require a considerable effort. To increase the coverage of WES, the number of patients in a single experiment may be reduced or different capture kits may be used at the same time.
IUE is the most appropriate approach to analyse the impact of genes knockdown on neuronal migration. However, investigations on other steps of cortical developmental, such as neurogenesis and neuronal maturation, are hindered by some technical limitations. Indeed, IUE performed before E13 is often unsuccessful whereas investigations at later stages are restricted by the high rate of postnatal lethality associated to this procedure. In addition, gene-knockdown efficiency may differ among electroporated embryos leading to considerable phenotypic heterogeneity.
Overall, the present protocol has four major critical steps. Although it is not part of the protocol described in the present paper, we have to point out that the first fundamental step to be taken into account is the selection of patients to be enrolled in such studies. Indeed, the process of identifying novel MCD genes requires clinical and imaging investigations in a cohort of patients for whom the phenotype should be as homogeneous as possible. Collecting patients with highly homogeneous phenotype increases the chance of identifying causative mutations in a given gene. However, the minimum number of patients to be enrolled in such studies to achieve success is hard to predict, since it greatly depends on the mutation rate of the different genes. For example, for genes such as FLNA, which is mutated in 100% of familial cases of X-linked bilateral PNH and in 26% of sporadic patients, the number of patients needed to identify multiple hits in the gene could be relatively low. Conversely, for genes with low mutation rate, the number of patients to be screened is higher. For example, in the case of C6orf70, we were able to identify a single causative mutation only upon screening 64 patients (14 through WES and 50 through conventional Sanger sequencing)16, estimating a mutation rate for this gene of about 1.5%. The second critical step is the exclusion of mutations in all known MCD genes in order to identify novel causative genes. Thanks to the advent of novel next generation sequencing technologies, mutation screening of known and candidate genes may now be performed in a single experiment. However, appropriate variants filtering should be used to avoid the presence of an excessive number of false positives to be experimentally confirmed and to filter out potential causative mutations. Indeed, the number of candidate genes/variants is particularly high in WES experiments. If the involvement of a given gene is suspected, mutation screening should also be complemented by MLPA analysis, to exclude microdeletions or microduplications. The third critical point is the fact that chromosomal rearrangements are strongly influenced by the location of the breakpoint. In this context, it is worth to exclude, to the greatest possible extent, the disruption or the displacement of cis-regulatory elements distal to genes not included into the deletion/duplication. Finally, in vivo RNAi experiments assume that causative genes play a direct role in neuronal migration during embryonic stages. However, the etiology of MCD, including PNH, is heterogeneous and the in utero approach could fail to detect the effects of genes involved in developmental steps including neuronal proliferation or cell survival. In addition, the low complexity of the rodent brain could mask the impact of the knockdown or the overexpression of a given candidate gene, which may be more evident in the human brain.
We believe that the integration of genomics and in vivo functional studies will help to develop new diagnostic tools for the identification of new MCD causative genes. This strategy could also provide new animal models to test therapeutic targets and understand the pathophysiology of MCD, which have so far been limited by the lack of experimental models and limited access to brain tissue from affected patients. Deciphering the molecular pathways that are associated with MCD disorders will also provide valuable new information about physiological brain development in general.
The authors have nothing to disclose.
We thank Dr. G. McGillivray, Pr. J. Clayton-Smith, Pr. W.B. Dobyns, Pr. P. Striano, Pr. I.E. Scheffer, Pr. S.P. Roberston and Pr. S.F. Berkovic for providing MCD patients. We thank Dr. F. Michel and D. Mei for technical advices and help. This work was supported by funding from the Seven Framework Programme of the EU, DESIRE project, contract number: Health-F2-602531-2013, (to V.C., R.G., A.R., A.F. and C.C.), INSERM (to A.R. and C.C.), Fondation Jérôme Lejeune (R13083AA to A.F, E.P.P and C.C) and Région Provence Alpes Côte d'Azur (APO2014 – DEMOTIC to C.C. and A.A.D). D.A.K is an EMBO Young Investigator and is supported by FWF grants (I914 and P24367).
Picospritzer III | Parker Hannifin Corp | P/N 051-0500-900 | Intracellular Microinjection Dispense Systems |
Fast Green FCF | Sigma-Aldrich | F7252 | Fast green allow visual monitoring of the injection |
BTX ECM 830 electroporator | BTX Harvard Apparatus | 45-0002 | The ECM 830 is a Square Wave Pulse generator designed for in vitro and in vivo applications |
Microtome HM 650 V | Microm | 10076838 | Microtome HM 650 V vibratome 240V 50/60Hz with vibrating blade |
FluoView 300 | Olympus | The Olympus FluoViewTM 300 is a point-scanning, point-detection, confocal laser scanning microscope designed for biology research application | |
eCELLence software | Glance Vision Technologies | eCELLence is software designed for the quantitive analysis of cell migration | |
Agilent Microarray Scanner Bundle | Array slides | ||
for 1 x 244K, 2 x 105K, 4 x 44K or 8 x 15K | Agilent | Agilent p/n G4900DA, G2565CA or G2565BA | |
for 1 x 1M, 2 x 400K, 4 x 180K or 8 x 60K | Agilent | Agilent p/n G4900DA or G2565CA | |
Hybridization Chamber, stainless | Agilent | Agilent p/n G2534A | Chamber for array CGH hybridization |
Hybridization Chamber gasket slides, 5-pack | Gasket for array CGH hybridization | ||
for 1-pack microarrays or | Agilent | Agilent p/n G2534-60003 | |
for 2-pack microarrays or | Agilent | Agilent p/n G2534-60002 | |
for 4-pack microarrays or | Agilent | Agilent p/n G2534-60011 | |
for 8-pack microarrays | Agilent | Agilent p/n G2534-60014 | |
Hybridization oven | Agilent | Agilent p/n G2545A | |
Hybridization oven rotator for Agilent Microarray Hybridization Chambers | Agilent | Agilent p/n G2530-60029 | |
Thermal cycler with heated lid | Agilent | Agilent p/n G8800A or equivalent | Termal cycler for incubations |
1.5 mL RNase-free Microfuge Tube | Ambion | p/n AM12400 or equivalent | Microcentrifuge |
Magnetic stir bar | Corning | p/n 401435 or equivalent | Instrument for stirring |
Qubit Fluorometer | Life Technologies | p/n Q32857 | Instrument for DNA quantification |
Qubit dsDNA BR Assay Kit, for use with the Qubit fluorometer | Invitrogen | p/n Q32850 | Kit for Qubit fluorometer |
UV-VIS spectrophotometer | Thermo Scientific | NanoDrop 8000 or 2000, or equivalent | Instrument for DNA quantification |
P10, P20, P200 and P1000 pipettes | Pipetman or equivalent | DNA dispensation | |
Vacuum Concentrator | Thermo Scientific | p/n DNA120-115 or equivalent |
Instrument to concentrate DNA |
SureTag Complete DNA Labeling Kit | Agilent | p/n 5190-4240 | DNA Labeling Kit (for Human Samples) |
Purification Columns (50 units) | Agilent | p/n 5190-3391 | DNA Labeling Kit (for Human Samples) |
AutoScreen A, 96-well plates | GE Healthcare | p/n 25-9005-98 | DNA Labeling Kit (for Human Samples) |
GenElute PCR Clean-Up Kit | Sigma-Aldrich | p/n NA1020 | DNA Labeling Kit (for Human Samples) |
Human Genomic DNA | p/n G1521 | DNA Labeling Kit (for Human Samples) | |
For CGH microarrays: | Promega | (female) or p/n G1471 (male) | Array CGH control DNA |
For CGH+SNP microarrays: | Coriell | p/n NA18507, NA18517, NA12891, NA12878, or NA18579 | Array CGH control DNA |
Oligo aCGH/ChIP-on-chip Wash Buffer Kit or | Agilent | p/n 5188-5226 | Array CGH hybridization and wash |
Oligo aCGH/ChIP-on-chip Wash Buffer 1 and | Agilent | p/n 5188-5221 | Array CGH hybridization and wash |
Oligo aCGH/ChIP-on-chip Wash Buffer 2 | Agilent | p/n 5188-5222 | Array CGH hybridization and wash |
Stabilization and Drying Solution | Agilent | p/n 5185-5979 | Array CGH hybridization and wash |
Oligo aCGH/ChIP-on-chip Hybridization Kit | Agilent | p/n 5188-5220 (25) or p/n 5188-5380 (100) | Array CGH hybridization and wash |
Human Cot-1 DNA | Agilent | p/n 5190-3393 | Array CGH hybridization and wash |
Agilent C scanner | Agilent | Scanner for array CGH slides | |
SureSelect XT2 Reagent Kit | Kit for target enrichment | ||
HiSeq platform (HSQ), 16 Samples | Agilent | p/n G9621A | |
HiSeq platform (HSQ), 96 Samples | Agilent | p/n G9621B | |
HiSeq platform (HSQ), 480 Samples | Agilent | p/n G9621C | |
MiSeq platform (MSQ), 16 Samples | Agilent | p/n G9622A | |
MiSeq platform (MSQ), 96 Samples | Agilent | p/n G9622B | |
MiSeq platform (MSQ), 480 Samples | Agilent | p/n G9622C | |
DNA 1000 Kit | Agilent | p/n 5067-1504 | Kit for the separation, sizing and quantification of dsDNA fragments from 25 to 1000 bp. |
High Sensitivity DNA Kit | Agilent | p/n 5067-4626 | Kit for analysis of fragmented DNA or DNA libraries. |
AMPure XP Kit | Kit for automated PCR purification. | ||
5 mL | Agencourt | p/n A63880 | |
60 mL | Agencourt | p/n A63881 | |
450 mL | Agencourt | p/n A63882 | |
Dynabeads MyOne Streptavidin T1 | Isolation and handling of biotinylated nucleic acids | ||
2 mL | Life Technologies | Cat #65601 | |
10 mL | Life Technologies | Cat #65602 | |
Quant-iT dsDNA BR Assay Kit, for the Qubit fluorometer | DNA quantification | ||
100 assays, 2-1000 ng | Life Technologies | Cat #Q32850 | |
500 assays, 2-1000 ng | Life Technologies | Cat #Q32853 | |
Qubit assay tubes | Life Technologies | p/n Q32856 | DNA quantification |
SureSelec tXT2 Capture Libraries | Agilent | depending on the experiment | Kit for libraries capture |
SureCycler 8800 Thermal Cycler | Agilent | p/n G8800A | DNA amplification |
96 well plate module for SureCycler 8800 Thermal Cycler | Agilent | p/n G8810A | DNA amplification |
SureCycler 8800-compatible 96-well plates | Agilent | p/n 410088 | DNA amplification |
Optical strip caps | Agilent | p/n 401425 | DNA amplification |
Tube cap strips, domed | Agilent | p/n 410096 | DNA amplification |
Compression mats | Agilent | p/n 410187 | DNA amplification |
2100 Bioanalyzer Laptop Bundle | Agilent | p/n G2943CA | DNA amplification |
2100 Bioanalyzer Electrophoresis Set | Agilent | p/n G2947CA | DNA amplification |
Covaris Sample Preparation System, E-series or S-series | Covaris | DNA shearing | |
Covaris sample holders | p/n 520078 | DNA shearing | |
Nutator plate mixer | BD Diagnostics | p/n 421105 or equivalent | Plate Mixer |
GaIIx | Illumina | next generation sequencing machine |