Intellectual disability (ID) is reported to affect 1.5–3% of the global population and represents a class of neurodevelopmental disorders characterized by cognitive impairments that result in lifelong educational, social, and financial consequences for patients and their caregivers (van Bokhoven, 2011; Leonard and Wen, 2002). ID disorders are diagnosed during early childhood and are defined by an IQ score of less than 70 with deficits in adaptive behaviors (Ropers, 2010). However, despite these profound burdens, little is known regarding the pathogenesis of ID disorders, particularly how disruptions in genetic and neuronal regulatory programs contribute to cognitive and behavioral dysfunction.

Advances in comparative genomic hybridization and whole-exome sequencing have revealed strong associations between ID and mutations in genes encoding chromatin-modifying enzymes (Elbert and Bérubé, 2014; Harmeyer et al., 2017; Kong et al., 2018; Parkel et al., 2013). These proteins post-translationally modify chromatin by inserting or removing chemical moieties from histone tails to regulate DNA accessibility or alter the recruitment proteins needed to modulate transcriptional initiation or elongation (van Bokhoven, 2011; Liefke et al., 2010; Liu et al., 2014; van Oevelen et al., 2008; Secombe et al., 2007; Vallianatos and Iwase, 2015). One class of ID-associated chromatin modifiers is the lysine demethylase 5 (KDM5) family of transcriptional regulators, with mammals encoding four KDM5 paralogs: KDM5A, KDM5B, KDM5C, and KDM5D. Loss-of-function mutations in KDM5A, KDM5B, and KDM5C are associated with ID, with genetic variants in KDM5C associated with a disorder known as mental retardation, X-linked, syndromic, Claes–Jensen type (MRXSCJ, OMIM# 300534).key. 

The generation of KDM5 knockout animal models has greatly assisted in our ability to investigate the neuromorphological and behavioral consequences of KDM5 loss of function. Previous in vitro studies examining rat cerebellar granular neurons and pyramidal neurons of prepared mouse basolateral amygdala slices demonstrate that Kdm5c knockout results in dendritic spine abnormalities (Iwase et al., 2016). Similarly, loss-of-function mutations in rbr-2, the sole Caenorhabditis elegans Kdm5c ortholog, result in axonal growth and guidance defects (Mariani et al., 2016). Additionally, Kdm5c knockout mice display behavioral deficits that are analogous to those exhibited by patients with pathogenic KDM5C variants, such as increased aggression, learning and memory impairments, and decreased seizure thresholds (Iwase et al., 2016; Scandaglia et al., 2017). Together, these studies suggest that the neuromorphological and functional impairments resulting from loss of orthologous KDM5 proteins are likely to be attributed to altered gene expression within neurons.

KDM5 proteins demethylate trimethyl groups on lysine 4 of histone H3 (H3K4me3) via the enzymatic activity of their Jumonji C (JmjC) domains (Liefke et al., 2010; Liu et al., 2014; Secombe et al., 2007; van Oevelen et al., 2008). High levels of H3K4me3 near transcriptional start sites (TSS) are associated with actively transcribed genes, suggesting that KDM5 proteins can dynamically regulate transcription (Greer and Shi, 2012). Prevailing models linking alterations in KDM5 family protein function to ID suggest that loss of JmjC-mediated demethylase activity is a key driver of neuronal dysfunction (Belalcazar et al., 2021; Mariani et al., 2016; Scandaglia et al., 2017; Vallianatos and Iwase, 2015; Vallianatos et al., 2020; Zamurrad et al., 2018). For example, Vallianatos and colleagues have demonstrated that the neuronal and behavioral phenotypes observed in Kdm5c knockout mice can be rescued by reducing levels of the H3K4 methyltransferase KMT2A (Vallianatos et al., 2020). Similarly, Drosophila KDM5 acts in a demethylase-dependent manner to regulate long- and short-term olfactory memory (Zamurrad et al., 2018).

KDM5 family proteins can also regulate transcription independently of their demethylase activity by associating with other chromatin-modifying proteins (Gajan et al., 2016; Lee et al., 2007; Lee et al., 2009; Nishibuchi et al., 2014). For example, the HDAC complex member SIN3A has been shown in vitro to interact with Drosophila KDM5 and regulate overlapping subsets of genes. In mice, SIN3A is required for neuronal development (Gajan et al., 2016; Witteveen et al., 2016) with mutations in SIN3A associated with Witteveen–Kolk syndrome (OMIM# 613406), a neurodevelopmental disorder characterized by developmental delay and ID (Witteveen et al., 2016). Additionally, a subset of ID-associated KDM5C missense mutations have been shown in vitro not to affect H3K4me3 demethylase activity, yet alter transcriptional outputs (Brookes et al., 2015; Vallianatos et al., 2018). Collectively, these data provide strong evidence that disruption of KDM5 protein function may impact multiple transcriptional pathways critical to neuronal development and function.

Here, we utilize Drosophila, which encodes a single, highly conserved KDM5 ortholog known as kdm5 (previously known as little imaginal discs [lid]), to investigate the genetic and neuromorphological consequences of KDM5 loss during neurodevelopment. Our analyses focus on a group of neurons known as Kenyon cells, which form a bilateral, neuropil-rich structure known as the mushroom body (MB). The MB is essential for orchestrating a diverse repertoire of cognitive processes and is thus routinely used to study neuroanatomical changes associated with mutations in ID-related genes (Androschuk et al., 2015; Aso et al., 2014; Dubnau et al., 2001; Heisenberg et al., 1985). In fact, loss-of-function mutations in orthologous genes associated with ID, such as the Fragile X syndrome gene fmr1, the Down syndrome gene dscam, and the ZC3H14 autosomal-recessive ID gene dnab2, result in severe morphological defects of the MB (Hattori et al., 2007; Kelly et al., 2016; Michel et al., 2004; Zhan et al., 2004).

The development of the MB is dependent on four MB neuroblasts (MBNBs) per hemisphere dividing asymmetrically throughout development. Each MBNB gives rise to another MBNB and a ganglion mother cell (GMC), which in turn divides symmetrically to form two Kenyon cells. Three subclasses of Kenyon cells give rise to the MB and are born in a highly regulated and sequential manner with tight temporal control. The first-born Kenyon cells are referred to as γ Kenyon cells and develop between the embryonic and mid-third-instar larval stage, giving rise to the γ lobes. The α′/β′ lobes are the next to develop, followed by the α/β lobes during the late larval and pupal stages. Notably, specification of these Kenyon cell subsets is transcriptionally regulated through the timed expression of a number of transcription factors (Bates et al., 2010; Marchetti and Tavosanis, 2017; Syed et al., 2017).

We show here that kdm5 gene knockout and shRNA-mediated depletion of kdm5 within GMCs and immature neurons both result in profound MB structural defects. Furthermore, using an in vivo transcriptomics-based approach, we identify subsets of genes within GMCs and immature Kenyon cells that are downregulated upon kdm5 depletion. One such gene, prospero (pros), encodes a homeodomain-containing transcription factor that is required for axon pathfinding and growth in other neuronal cell types; however, its importance in regulating MB development remains unexplored. We find here that MB-specific knockdown of pros leads to aberrant MB formation and demonstrate that KDM5 directly binds to and regulates pros expression within GMCs and immature neurons. As approximately half of KDM5 regulated genes are bound by Pros, we propose a model by which KDM5 regulates the expression of Pros and its targets to promote proper MB formation. Our studies thus provide the first in vivo analysis of KDM5 within a specific cell population, revealing a key kdm5-pros genetic pathway critical for neurodevelopment.

Here, we utilize in vivo transcriptional and binding approaches coupled with neuromorphological analyses to demonstrate that KDM5 is essential for the development of the adult MB. Although previous work in murine models has shown that knockout of the kdm5 ortholog Kdm5c leads to dendritic spine abnormalities in cortical brain regions, these studies utilized ubiquitous knockout systems that were not amenable to spatiotemporal genetic manipulation (Iwase et al., 2016; Vallianatos et al., 2020). Drosophila, however, provides a genetically tractable system to modulate gene expression in a spatiotemporally restrictive manner. Using the MB as a model for neuronal development, we demonstrate that KDM5 expression within GMCs and immature neurons is necessary and sufficient for proper neuropil development. Depletion of KDM5 within mature subpopulations of Kenyon cells did not affect gross MB structure, suggesting that there exists a critical neurodevelopmental window during which KDM5 is required to regulate genetic programs essential for axonal growth and guidance. Notably, the MB growth and guidance defects we observed upon depletion of KDM5 phenocopied those seen in other Drosophila ID models, such as the fmr1 and dnab2 RNA binding protein mutants (Kelly et al., 2016; Michel et al., 2004). It is thus possible that ID genes may function synergistically within common neurodevelopmental pathways to affect neuronal architecture and function.

We additionally performed RNA pol II TaDa analyses within GMCs and immature neurons to survey gene expression changes resulting from loss of kdm5. Although previous transcriptomic studies have utilized extracted RNA from dissected tissues or from neuronal or fibroblast cultures as input for RNA-seq (Brookes et al., 2015; Iwase et al., 2016; Liu and Secombe, 2015; Scandaglia et al., 2017; Vallianatos et al., 2020; Wei et al., 2016; Zamurrad et al., 2018), this is the first study describing changes to gene expression resulting from cell-specific loss of kdm5. This technique allowed us to survey gene expression changes within GMCs and immature neurons of kdm5 null and cell-type-specific knockdown animals, revealing a number of DEGs associated with neurodevelopment. It is important to note, however, that since we induced expression of UAS-dam and UAS-dam-Pol II beginning during the late larval stages, a subset of our data may reflect changes to gene expression within GMCs and immature neurons that are not part of the MB lineage. Nevertheless, TaDa induction during the larval stage was brief (<24 hr at 29°C), with the vast majority of transcriptomic changes occurring within MB-GMCs and immature Kenyon cells, which are some of the few cell types able to proliferate up to 96 hr after pupal formation (Truman and Bate, 1988).

Our data suggest that one means by which KDM5 functions in neurons is by binding to and regulating the expression of the homeodomain-containing transcription factor Pros. Pros is a cell fate determinant that is expressed in most neuronal precursors and immature neurons and is involved in cell cycle exit, neuronal differentiation, and axonal development (Choksi et al., 2006; Doe et al., 1991; Froldi et al., 2015; Tea et al., 2010; Vaessin et al., 1991). Loss of pros results in axonal routing defects within embryonic motor and sensory neurons (Vaessin et al., 1991) and can lead to the miswiring of olfactory projection neuron dendrites (Miyoshi et al., 2015; Tea et al., 2010). However, despite its demonstrated importance in other neuronal contexts, a role for Drosophila Pros or its orthologs in regulating axonal growth and guidance programs has remained largely uncharacterized.

Pros is evolutionarily well-conserved, with the mammalian ortholog PROX1 having been shown to promote neuronal differentiation and migration. Specifically, studies utilizing chick and mouse neural progenitor cells demonstrate that PROX1 is essential for cell cycle exit and differentiation of neuronal precursors via the transcriptional repression of Notch1 (Kaltezioti et al., 2010; Lavado et al., 2010). Additionally, studies leveraging conditional knockout mouse strains have shown that PROX1 regulates GABAergic cortical interneuron migration during embryonic and postnatal development (Miyoshi et al., 2015). However, despite its demonstrated importance in other neuronal contexts, a role for PROX1 or its orthologs in regulating axonal growth and guidance programs has remained largely uncharacterized. Here, we demonstrate that depletion of Drosophila Pros within MBNBs, MB-GMCs, and immature Kenyon cells leads to severe MB growth and guidance defects. Importantly, Pros is expressed primarily in neural precursors and immature neurons at a time when KDM5 function is critical for MB development (Doe et al., 1991; Vaessin et al., 1991).

Our transcriptomic data further reveal that a number of Pros interactors are dysregulated upon kdm5 depletion. For example, the direct Pros target nerfin-1, which encodes a zinc finger transcription factor required for early axon pathfinding by most CNS neurons in the Drosophila embryo (Kuzin et al., 2005; Liu et al., 2020), was significantly downregulated. Additionally, genes encoding Toy and Tap, which have been shown to regulate MB morphology (Furukubo-Tokunaga et al., 2009; Yuan et al., 2016), were similarly affected. Transcriptional dysregulation of pros and its targets may thus provide important molecular insight into the neuronal phenotypes associated with kdm5 loss of function.

Although we demonstrate that KDM5 binds to the pros gene and is needed for its activation, we do not know the precise mechanism by which this occurs. We predict, however, that it is independent of its canonical histone demethylase activity as animals lacking this enzymatic function have MBs that are phenotypically indistinguishable from those of wild type (Zamurrad et al., 2018). Because flies lacking KDM5 histone demethylase activity have cognitive deficits (Zamurrad et al., 2018), it is likely that KDM5 regulates neuronal development and function via multiple distinct mechanisms. Consistent with this model, several ID-associated mutations in KDM5C have been shown to alter H3K4me3-directed enzymatic activity in vitro, whereas others do not (Brookes et al., 2015; Vallianatos et al., 2018). Based on these data and the results reported here, it is likely that ID-causing mutations in human KDM5A, KDM5B, or KDM5C might fall into three classes: the first may only affect KDM5 demethylase activity, the second may only affect non-enzymatic activities necessary for transcriptional regulation, and the third may affect both. The extent to which canonical and non-canonical activities are disrupted may, indeed, correlate with the severity of the cognitive deficit or presence of syndromic features. Future work will further elucidate how individual patient mutations may disrupt these transcriptional regulatory mechanisms to influence neuronal and behavioral outputs, thus providing a promising strategy for predicting and treating patients with pathogenic variants in KDM5 family genes.

TaDa data have been deposited in GEO under the accession codes GSE156010 and GSE166116.

1. 1. Hatch HAM
   2. Belalcazar HM
   3. Marshall OJ
   4. Secombe J

   (2021) NCBI Gene Expression Omnibus

   ID GSE156010. Targeted DamID analyses of neural progenitor cells and immature neurons of kdm5[140] and kdm5 shRNA Drosophila larvae and pupae.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE156010
2. 1. Hatch HAM
   2. Belalcazar HM
   3. Marshall OJ
   4. Secombe J

   (2021) NCBI Gene Expression Omnibus

   ID GSE166116. Genome-wide binding profiles of KDM5 in Drosophila GMCs and immature neurons.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE166116

1. 1. Liu X
   2. Shen J
   3. Xie L
   4. Wei Z
   5. Wong C
   6. Li Y
   7. Zheng X
   8. Li P
   9. Song Y

   (2020) NCBI Gene Expression Omnibus

   ID GSE136413. Genome-wide binding profiles of HP1a and Prospero in Drosophila central brain neural precursors and neurons.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136413
2. 1. Drelon C
   2. Belalcazar B
   3. Secombe J

   (2018) NCBI Gene Expression Omnibus

   ID GSE109201. RNA-seq analysis of kdm5 null mutant wing discs.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109201

1. 1. Androschuk A
   2. Al-Jabri B
   3. Bolduc FV

   (2015) From learning to memory: what flies can tell us about intellectual disability treatment

   Frontiers in Psychiatry 6:1–16.

   https://doi.org/10.3389/fpsyt.2015.00085

   • PubMed
   • Google Scholar
2. 1. Aso Y
   2. Hattori D
   3. Yu Y
   4. Johnston RM
   5. Iyer NA
   6. Ngo TT
   7. Dionne H
   8. Abbott LF
   9. Axel R
   10. Tanimoto H
   11. Rubin GM

   (2014) The neuronal architecture of the mushroom body provides a logic for associative learning

   eLife 3:e04577.

   https://doi.org/10.7554/eLife.04577

   • PubMed
   • Google Scholar
3. 1. Aughey GN
   2. Estacio Gomez A
   3. Thomson J
   4. Yin H
   5. Southall TD

   (2018) CATaDa reveals global remodelling of chromatin accessibility during stem cell differentiation in vivo

   eLife 7:e32341.

   https://doi.org/10.7554/eLife.32341

   • PubMed
   • Google Scholar
4. 1. Aughey GN
   2. Cheetham SW
   3. Southall TD

   (2019) DamID as a versatile tool for understanding gene regulation

   Development 146:dev173666.

   https://doi.org/10.1242/dev.173666

   • PubMed
   • Google Scholar
5. 1. Bates KE
   2. Sung CS
   3. Robinow S

   (2010) The unfulfilled gene is required for the development of mushroom body neuropil in Drosophila

   Neural Development 5:4.

   https://doi.org/10.1186/1749-8104-5-4

   • PubMed
   • Google Scholar
6. 1. Belalcazar HM
   2. Hendricks EL
   3. Zamurrad S
   4. Liebl FLW
   5. Secombe J

   (2021) The histone demethylase KDM5 is required for synaptic structure and function at the Drosophila neuromuscular junction

   Cell Reports 34:108753.

   https://doi.org/10.1016/j.celrep.2021.108753

   • PubMed
   • Google Scholar
7. 1. Bellosta P
   2. Gallant P

   (2010) Myc function in Drosophila

   Genes & Cancer 1:542–546.

   https://doi.org/10.1177/1947601910377490

   • PubMed
   • Google Scholar
8. 1. Bhaumik SR
   2. Smith E
   3. Shilatifard A

   (2007) Covalent modifications of histones during development and disease pathogenesis

   Nature Structural & Molecular Biology 14:1008–1016.

   https://doi.org/10.1038/nsmb1337

   • PubMed
   • Google Scholar
9. 1. Brookes E
   2. Laurent B
   3. Õunap K
   4. Carroll R
   5. Moeschler JB
   6. Field M
   7. Schwartz CE
   8. Gecz J
   9. Shi Y

   (2015) Mutations in the intellectual disability gene KDM5C reduce protein stability and demethylase activity

   Human Molecular Genetics 24:2861–2872.

   https://doi.org/10.1093/hmg/ddv046

   • PubMed
   • Google Scholar
10. 1. Chen K
    2. Luan X
    3. Liu Q
    4. Wang J
    5. Chang X
    6. Snijders AM
    7. Mao JH
    8. Secombe J
    9. Dan Z
    10. Chen JH
    11. Wang Z
    12. Dong X
    13. Qiu C
    14. Chang X
    15. Zhang D
    16. Celniker SE
    17. Liu X

    (2019) Drosophila histone demethylase KDM5 regulates social behavior through immune control and gut Microbiota maintenance

    Cell Host & Microbe 25:537–552.

    https://doi.org/10.1016/j.chom.2019.02.003

    • PubMed
    • Google Scholar
11. 1. Choksi SP
    2. Southall TD
    3. Bossing T
    4. Edoff K
    5. de Wit E
    6. Fischer BE
    7. van Steensel B
    8. Micklem G
    9. Brand AH

    (2006) Prospero acts as a binary switch between self-renewal and differentiation in Drosophila neural stem cells

    Developmental Cell 11:775–789.

    https://doi.org/10.1016/j.devcel.2006.09.015

    • PubMed
    • Google Scholar
12. 1. Doe CQ
    2. Chu-LaGraff Q
    3. Wright DM
    4. Scott MP

    (1991) The Prospero gene specifies cell fates in the Drosophila central nervous system

    Cell 65:451–464.

    https://doi.org/10.1016/0092-8674(91)90463-9

    • PubMed
    • Google Scholar
13. 1. Doupé DP
    2. Marshall OJ
    3. Dayton H
    4. Brand AH
    5. Perrimon N

    (2018) Drosophila intestinal stem and progenitor cells are major sources and regulators of homeostatic niche signals

    PNAS 115:12218–12223.

    https://doi.org/10.1073/pnas.1719169115

    • PubMed
    • Google Scholar
14. 1. Drelon C
    2. Belalcazar HM
    3. Secombe J

    (2018) The histone demethylase KDM5 is essential for larval growth in Drosophila

    Genetics 209:773–787.

    https://doi.org/10.1534/genetics.118.301004

    • PubMed
    • Google Scholar
15. 1. Drelon C
    2. Rogers MF
    3. Belalcazar HM
    4. Secombe J

    (2019) The histone demethylase KDM5 controls developmental timing in Drosophila by promoting prothoracic gland endocycles

    Development 146:dev182568.

    https://doi.org/10.1242/dev.182568

    • PubMed
    • Google Scholar
16. 1. Dubnau J
    2. Grady L
    3. Kitamoto T
    4. Tully T

    (2001) Disruption of neurotransmission in Drosophila mushroom body blocks retrieval but not acquisition of memory

    Nature 411:476–480.

    https://doi.org/10.1038/35078077

    • PubMed
    • Google Scholar
17. 1. Elbert A
    2. Bérubé NG

    (2014) Chromatin structure and intellectual disability syndromes

    Neuroscience and Biobehavioral Reviews 46:161–174.

    https://doi.org/10.5772/55730

    • Google Scholar
18. 1. Froldi F
    2. Szuperak M
    3. Weng CF
    4. Shi W
    5. Papenfuss AT
    6. Cheng LY

    (2015) The transcription factor Nerfin-1 prevents reversion of neurons into neural stem cells

    Genes & Development 29:129–143.

    https://doi.org/10.1101/gad.250282.114

    • PubMed
    • Google Scholar
19. 1. Furukubo-Tokunaga K
    2. Adachi Y
    3. Kurusu M
    4. Walldorf U

    (2009) Brain patterning defects caused by mutations of the twin of eyeless gene in Drosophila melanogaster

    Fly 3:263–269.

    https://doi.org/10.4161/fly.10385

    • PubMed
    • Google Scholar
20. 1. Gajan A
    2. Barnes VL
    3. Liu M
    4. Saha N
    5. Pile LA

    (2016) The histone demethylase dKDM5/LID interacts with the SIN3 histone deacetylase complex and shares functional similarities with SIN3

    Epigenetics & Chromatin 9:1–23.

    https://doi.org/10.1186/s13072-016-0053-9

    • PubMed
    • Google Scholar
21. 1. Gombos R
    2. Migh E
    3. Antal O
    4. Mukherjee A
    5. Jenny A
    6. Mihaly J

    (2015) The formin DAAM functions as molecular effector of the planar cell polarity pathway during axonal development in Drosophila

    Journal of Neuroscience 35:10154–10167.

    https://doi.org/10.1523/JNEUROSCI.3708-14.2015

    • Google Scholar
22. 1. Gratz SJ
    2. Wildonger J
    3. Harrison MM
    4. O'Connor-Giles KM

    (2013) CRISPR/Cas9-mediated genome engineering and the promise of designer flies on demand

    Fly 7:249–255.

    https://doi.org/10.4161/fly.26566

    • PubMed
    • Google Scholar
23. 1. Greer EL
    2. Shi Y

    (2012) Histone methylation: a dynamic mark in health, disease and inheritance

    Nature Reviews Genetics 13:343–357.

    https://doi.org/10.1038/nrg3173

    • Google Scholar
24. 1. Grewal SS
    2. Li L
    3. Orian A
    4. Eisenman RN
    5. Edgar BA

    (2005) Myc-dependent regulation of ribosomal RNA synthesis during Drosophila development

    Nature Cell Biology 7:295–302.

    https://doi.org/10.1038/ncb1223

    • PubMed
    • Google Scholar
25. 1. Harmeyer KM
    2. Facompre ND
    3. Herlyn M
    4. Basu D

    (2017) JARID1 histone demethylases: emerging targets in Cancer

    Trends in Cancer 3:713–725.

    https://doi.org/10.1016/j.trecan.2017.08.004

    • PubMed
    • Google Scholar
26. 1. Hattori D
    2. Demir E
    3. Kim HW
    4. Viragh E
    5. Zipursky SL
    6. Dickson BJ

    (2007) Dscam diversity is essential for neuronal wiring and self-recognition

    Nature 449:223–227.

    https://doi.org/10.1038/nature06099

    • PubMed
    • Google Scholar
27. 1. Hayashi S
    2. Ito K
    3. Sado Y
    4. Taniguchi M
    5. Akimoto A
    6. Takeuchi H
    7. Aigaki T
    8. Matsuzaki F
    9. Nakagoshi H
    10. Tanimura T
    11. Ueda R
    12. Uemura T
    13. Yoshihara M
    14. Goto S

    (2002) GETDB, a database compiling expression patterns and molecular locations of a collection of Gal4 enhancer traps

    Genesis 34:58–61.

    https://doi.org/10.1002/gene.10137

    • PubMed
    • Google Scholar
28. 1. Heisenberg M
    2. Borst A
    3. Wagner S
    4. Byers D

    (1985) Drosophila mushroom body mutants are deficient in olfactory learning

    Journal of Neurogenetics 2:1–30.

    https://doi.org/10.3109/01677068509100140

    • PubMed
    • Google Scholar
29. 1. Henry GL
    2. Davis FP
    3. Picard S
    4. Eddy SR

    (2012) Cell type-specific genomics of Drosophila neurons

    Nucleic Acids Research 40:9691–9704.

    https://doi.org/10.1093/nar/gks671

    • PubMed
    • Google Scholar
30. 1. Hulsen T
    2. de Vlieg J
    3. Alkema W

    (2008) BioVenn - a web application for the comparison and visualization of biological lists using area-proportional venn diagrams

    BMC Genomics 9:488.

    https://doi.org/10.1186/1471-2164-9-488

    • PubMed
    • Google Scholar
31. 1. Ito K
    2. Hotta Y

    (1992) Proliferation pattern of postembryonic neuroblasts in the brain of Drosophila melanogaster

    Developmental Biology 149:134–148.

    https://doi.org/10.1016/0012-1606(92)90270-Q

    • Google Scholar
32. 1. Iwase S
    2. Brookes E
    3. Agarwal S
    4. Badeaux AI
    5. Ito H
    6. Vallianatos CN
    7. Tomassy GS
    8. Kasza T
    9. Lin G
    10. Thompson A
    11. Gu L
    12. Kwan KY
    13. Chen C
    14. Sartor MA
    15. Egan B
    16. Xu J
    17. Shi Y

    (2016) A mouse model of X-linked intellectual disability associated with impaired removal of histone methylation

    Cell Reports 14:1000–1009.

    https://doi.org/10.1016/j.celrep.2015.12.091

    • PubMed
    • Google Scholar
33. 1. Jenett A
    2. Rubin GM
    3. Ngo TT
    4. Shepherd D
    5. Murphy C
    6. Dionne H
    7. Pfeiffer BD
    8. Cavallaro A
    9. Hall D
    10. Jeter J
    11. Iyer N
    12. Fetter D
    13. Hausenfluck JH
    14. Peng H
    15. Trautman ET
    16. Svirskas RR
    17. Myers EW
    18. Iwinski ZR
    19. Aso Y
    20. DePasquale GM
    21. Enos A
    22. Hulamm P
    23. Lam SC
    24. Li HH
    25. Laverty TR
    26. Long F
    27. Qu L
    28. Murphy SD
    29. Rokicki K
    30. Safford T
    31. Shaw K
    32. Simpson JH
    33. Sowell A
    34. Tae S
    35. Yu Y
    36. Zugates CT

    (2012) A GAL4-driver line resource for Drosophila neurobiology

    Cell Reports 2:991–1001.

    https://doi.org/10.1016/j.celrep.2012.09.011

    • PubMed
    • Google Scholar
34. 1. Kaltezioti V
    2. Kouroupi G
    3. Oikonomaki M
    4. Mantouvalou E
    5. Stergiopoulos A
    6. Charonis A
    7. Rohrer H
    8. Matsas R
    9. Politis PK

    (2010) Prox1 regulates the notch1-mediated inhibition of neurogenesis

    PLOS Biology 8:e1000565.

    https://doi.org/10.1371/journal.pbio.1000565

    • PubMed
    • Google Scholar
35. 1. Kelly SM
    2. Bienkowski R
    3. Banerjee A
    4. Melicharek DJ
    5. Brewer ZA
    6. Marenda DR
    7. Corbett AH
    8. Moberg KH

    (2016) The Drosophila ortholog of the Zc3h14 RNA binding protein acts within neurons to pattern axon projection in the developing brain

    Developmental Neurobiology 76:93–106.

    https://doi.org/10.1002/dneu.22301

    • PubMed
    • Google Scholar
36. 1. Kong SY
    2. Kim W
    3. Lee HR
    4. Kim HJ

    (2018) The histone demethylase KDM5A is required for the repression of astrocytogenesis and regulated by the translational machinery in neural progenitor cells

    FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology 32:1108–1119.

    https://doi.org/10.1096/fj.201700780R

    • PubMed
    • Google Scholar
37. 1. Kuzin A
    2. Brody T
    3. Moore AW
    4. Odenwald WF

    (2005) Nerfin-1 is required for early axon guidance decisions in the developing Drosophila CNS

    Developmental Biology 277:347–365.

    https://doi.org/10.1016/j.ydbio.2004.09.027

    • PubMed
    • Google Scholar
38. 1. Langmead B
    2. Salzberg SL

    (2012) Fast gapped-read alignment with bowtie 2

    Nature Methods 9:357–359.

    https://doi.org/10.1038/nmeth.1923

    • PubMed
    • Google Scholar
39. 1. Lavado A
    2. Lagutin OV
    3. Chow LM
    4. Baker SJ
    5. Oliver G

    (2010) Prox1 is required for granule cell maturation and intermediate progenitor maintenance during brain neurogenesis

    PLOS Biology 8:e1000460.

    https://doi.org/10.1371/journal.pbio.1000460

    • PubMed
    • Google Scholar
40. 1. Lee MG
    2. Norman J
    3. Shilatifard A
    4. Shiekhattar R

    (2007) Physical and functional association of a trimethyl H3K4 demethylase and Ring6a/MBLR, a polycomb-like protein

    Cell 128:877–887.

    https://doi.org/10.1016/j.cell.2007.02.004

    • PubMed
    • Google Scholar
41. 1. Lee N
    2. Erdjument-Bromage H
    3. Tempst P
    4. Jones RS
    5. Zhang Y

    (2009) The H3K4 demethylase lid associates with and inhibits histone deacetylase Rpd3

    Molecular and Cellular Biology 29:1401–1410.

    https://doi.org/10.1128/MCB.01643-08

    • PubMed
    • Google Scholar
42. 1. Leonard H
    2. Wen X

    (2002) The epidemiology of mental retardation: challenges and opportunities in the new millennium

    Mental Retardation and Developmental Disabilities Research Reviews 8:117–134.

    https://doi.org/10.1002/mrdd.10031

    • PubMed
    • Google Scholar
43. 1. Li G
    2. Ruan X
    3. Auerbach RK
    4. Sandhu KS
    5. Zheng M
    6. Wang P
    7. Poh HM
    8. Goh Y
    9. Lim J
    10. Zhang J
    11. Sim HS
    12. Peh SQ
    13. Mulawadi FH
    14. Ong CT
    15. Orlov YL
    16. Hong S
    17. Zhang Z
    18. Landt S
    19. Raha D
    20. Euskirchen G
    21. Wei CL
    22. Ge W
    23. Wang H
    24. Davis C
    25. Fisher-Aylor KI
    26. Mortazavi A
    27. Gerstein M
    28. Gingeras T
    29. Wold B
    30. Sun Y
    31. Fullwood MJ
    32. Cheung E
    33. Liu E
    34. Sung WK
    35. Snyder M
    36. Ruan Y

    (2012) Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation

    Cell 148:84–98.

    https://doi.org/10.1016/j.cell.2011.12.014

    • PubMed
    • Google Scholar
44. 1. Li HH
    2. Kroll JR
    3. Lennox SM
    4. Ogundeyi O
    5. Jeter J
    6. Depasquale G
    7. Truman JW

    (2014) A GAL4 driver resource for developmental and behavioral studies on the larval CNS of Drosophila

    Cell Reports 8:897–908.

    https://doi.org/10.1016/j.celrep.2014.06.065

    • PubMed
    • Google Scholar
45. 1. Liefke R
    2. Oswald F
    3. Alvarado C
    4. Ferres-Marco D
    5. Mittler G
    6. Rodriguez P
    7. Dominguez M
    8. Borggrefe T

    (2010) Histone demethylase KDM5A is an integral part of the core Notch-RBP-J repressor complex

    Genes & Development 24:590–601.

    https://doi.org/10.1101/gad.563210

    • PubMed
    • Google Scholar
46. 1. Liu X
    2. Greer C
    3. Secombe J

    (2014) KDM5 interacts with foxo to modulate cellular levels of oxidative stress

    PLOS Genetics 10:e1004676.

    https://doi.org/10.1371/journal.pgen.1004676

    • PubMed
    • Google Scholar
47. 1. Liu X
    2. Shen J
    3. Xie L
    4. Wei Z
    5. Wong C
    6. Li Y
    7. Zheng X
    8. Li P
    9. Song Y

    (2020) Mitotic implantation of the transcription factor Prospero via phase separation drives terminal neuronal differentiation

    Developmental Cell 52:277–293.

    https://doi.org/10.1016/j.devcel.2019.11.019

    • PubMed
    • Google Scholar
48. 1. Liu X
    2. Secombe J

    (2015) The histone demethylase KDM5 activates gene expression by recognizing chromatin context through its PHD reader motif

    Cell Reports 13:2219–2231.

    https://doi.org/10.1016/j.celrep.2015.11.007

    • Google Scholar
49. 1. Lloret-Llinares M
    2. Pérez-Lluch S
    3. Rossell D
    4. Morán T
    5. Ponsa-Cobas J
    6. Auer H
    7. Corominas M
    8. Azorín F

    (2012) dKDM5/LID regulates H3K4me3 dynamics at the transcription-start site (TSS) of actively transcribed developmental genes

    Nucleic Acids Research 40:9493–9505.

    https://doi.org/10.1093/nar/gks773

    • PubMed
    • Google Scholar
50. 1. Lopez-Bigas N
    2. Kisiel TA
    3. Dewaal DC
    4. Holmes KB
    5. Volkert TL
    6. Gupta S
    7. Love J
    8. Murray HL
    9. Young RA
    10. Benevolenskaya EV

    (2008) Genome-wide analysis of the H3K4 histone demethylase RBP2 reveals a transcriptional program controlling differentiation

    Molecular Cell 31:520–530.

    https://doi.org/10.1016/j.molcel.2008.08.004

    • PubMed
    • Google Scholar
51. 1. Lussi YC
    2. Mariani L
    3. Friis C
    4. Peltonen J
    5. Myers TR
    6. Krag C
    7. Wong G
    8. Salcini AE

    (2016) Impaired removal of H3K4 methylation affects cell fate determination and gene transcription

    Development 143:3751–3762.

    https://doi.org/10.1242/dev.139139

    • Google Scholar
52. 1. Marchetti G
    2. Tavosanis G

    (2017) Steroid hormone ecdysone signaling specifies mushroom body neuron sequential fate via chinmo

    Current Biology 27:3017–3024.

    https://doi.org/10.1016/j.cub.2017.08.037

    • PubMed
    • Google Scholar
53. 1. Mariani L
    2. Lussi YC
    3. Vandamme J
    4. Riveiro A
    5. Salcini AE

    (2016) The H3K4me3/2 histone demethylase RBR-2 controls axon guidance by repressing the actin-remodeling gene wsp-1

    Development 143:851–863.

    https://doi.org/10.1242/dev.132985

    • PubMed
    • Google Scholar
54. Software

    1. Marshall OJ
    2. Brand AH
    3. Southall TD
    4. Gold KS
    5. Egger B
    6. Davidson CM
    7. Caygill EE

    (2013) polii.gene.call, version ff3417d

    GitHub.

    https://github.com/owenjm/polii.gene.call
55. 1. Marshall OJ
    2. Southall TD
    3. Cheetham SW
    4. Brand AH

    (2016a) Cell-type-specific profiling of protein-DNA interactions without cell isolation using targeted DamID with next-generation sequencing

    Nature Protocols 11:1586–1598.

    https://doi.org/10.1038/nprot.2016.084

    • PubMed
    • Google Scholar
56. Software

    1. Marshall OJ
    2. Southall TD
    3. Cheetham SW
    4. Brand AH

    (2016b) find_peaks, version 2259915

    GitHub.

    https://github.com/owenjm/find_peaks
57. 1. Marshall OJ
    2. Brand AH

    (2015a) damidseq_pipeline: an automated pipeline for processing DamID sequencing datasets

    Bioinformatics 31:3371–3373.

    https://doi.org/10.1093/bioinformatics/btv386

    • PubMed
    • Google Scholar
58. Software

    1. Marshall OJ
    2. Brand AH

    (2015b) damidseq_pipeline, version 21fb45f

    GitHub.

    https://github.com/owenjm/damidseq_pipeline
59. 1. Marshall OJ
    2. Brand AH

    (2017) Chromatin state changes during neural development revealed by in vivo cell-type specific profiling

    Nature Communications 8:1–9.

    https://doi.org/10.1038/s41467-017-02385-4

    • Google Scholar
60. 1. Mi H
    2. Muruganujan A
    3. Casagrande JT
    4. Thomas PD

    (2013) Large-scale gene function analysis with the panther classification system

    Nature Protocols 8:1551–1566.

    https://doi.org/10.1038/nprot.2013.092

    • PubMed
    • Google Scholar
61. 1. Michel CI
    2. Kraft R
    3. Restifo LL

    (2004) Defective neuronal development in the mushroom bodies of Drosophila fragile X mental retardation 1 mutants

    Journal of Neuroscience 24:5798–5809.

    https://doi.org/10.1523/JNEUROSCI.1102-04.2004

    • PubMed
    • Google Scholar
62. 1. Miyoshi G
    2. Young A
    3. Petros T
    4. Karayannis T
    5. McKenzie Chang M
    6. Lavado A
    7. Iwano T
    8. Nakajima M
    9. Taniguchi H
    10. Huang ZJ
    11. Heintz N
    12. Oliver G
    13. Matsuzaki F
    14. Machold RP
    15. Fishell G

    (2015) Prox1 regulates the Subtype-Specific development of caudal ganglionic Eminence-Derived GABAergic cortical interneurons

    Journal of Neuroscience 35:12869–12889.

    https://doi.org/10.1523/JNEUROSCI.1164-15.2015

    • PubMed
    • Google Scholar
63. 1. Morris JH
    2. Apeltsin L
    3. Newman AM
    4. Baumbach J
    5. Wittkop T
    6. Su G
    7. Bader GD
    8. Ferrin TE

    (2011) clusterMaker: a multi-algorithm clustering Plugin for cytoscape

    BMC Bioinformatics 12:1–14.

    https://doi.org/10.1186/1471-2105-12-436

    • PubMed
    • Google Scholar
64. 1. Navarro-Costa P
    2. McCarthy A
    3. Prudêncio P
    4. Greer C
    5. Guilgur LG
    6. Becker JD
    7. Secombe J
    8. Rangan P
    9. Martinho RG

    (2016) Early programming of the oocyte epigenome temporally controls late prophase I transcription and chromatin remodelling

    Nature Communications 7:12331.

    https://doi.org/10.1038/ncomms12331

    • PubMed
    • Google Scholar
65. 1. Ni JQ
    2. Liu LP
    3. Binari R
    4. Hardy R
    5. Shim HS
    6. Cavallaro A
    7. Booker M
    8. Pfeiffer BD
    9. Markstein M
    10. Wang H
    11. Villalta C
    12. Laverty TR
    13. Perkins LA
    14. Perrimon N

    (2009) A Drosophila resource of transgenic RNAi lines for neurogenetics

    Genetics 182:1089–1189.

    https://doi.org/10.1534/genetics.109.103630

    • PubMed
    • Google Scholar
66. 1. Nishibuchi G
    2. Shibata Y
    3. Hayakawa T
    4. Hayakawa N
    5. Ohtani Y
    6. Sinmyozu K
    7. Tagami H
    8. Nakayama J

    (2014) Physical and functional interactions between the histone H3K4 demethylase KDM5A and the nucleosome remodeling and deacetylase (NuRD) complex

    Journal of Biological Chemistry 289:28956–28970.

    https://doi.org/10.1074/jbc.M114.573725

    • PubMed
    • Google Scholar
67. 1. Park S
    2. Kim GW
    3. Kwon SH
    4. Lee JS

    (2020) Broad domains of histone H3 lysine 4 trimethylation in transcriptional regulation and disease

    The FEBS Journal 287:2891–2902.

    https://doi.org/10.1111/febs.15219

    • PubMed
    • Google Scholar
68. 1. Parkel S
    2. Lopez-Atalaya JP
    3. Barco A

    (2013) Histone H3 lysine methylation in cognition and intellectual disability disorders

    Learning & Memory 20:570–579.

    https://doi.org/10.1101/lm.029363.112

    • PubMed
    • Google Scholar
69. 1. Pfeiffer BD
    2. Jenett A
    3. Hammonds AS
    4. Ngo TT
    5. Misra S
    6. Murphy C
    7. Scully A
    8. Carlson JW
    9. Wan KH
    10. Laverty TR
    11. Mungall C
    12. Svirskas R
    13. Kadonaga JT
    14. Doe CQ
    15. Eisen MB
    16. Celniker SE
    17. Rubin GM

    (2008) Tools for neuroanatomy and neurogenetics in Drosophila

    PNAS 105:9715–9720.

    https://doi.org/10.1073/pnas.0803697105

    • PubMed
    • Google Scholar
70. 1. Quinlan AR
    2. Hall IM

    (2010) BEDTools: a flexible suite of utilities for comparing genomic features

    Bioinformatics 26:841–842.

    https://doi.org/10.1093/bioinformatics/btq033

    • PubMed
    • Google Scholar
71. 1. Ropers HH

    (2010) Genetics of early onset cognitive impairment

    Annual Review of Genomics and Human Genetics 11:161–187.

    https://doi.org/10.1146/annurev-genom-082509-141640

    • PubMed
    • Google Scholar
72. 1. Scandaglia M
    2. Lopez-Atalaya JP
    3. Medrano-Fernandez A
    4. Lopez-Cascales MT
    5. Del Blanco B
    6. Lipinski M
    7. Benito E
    8. Olivares R
    9. Iwase S
    10. Shi Y
    11. Barco A

    (2017) Loss of Kdm5c causes spurious transcription and prevents the Fine-Tuning of Activity-Regulated enhancers in neurons

    Cell Reports 21:47–59.

    https://doi.org/10.1016/j.celrep.2017.09.014

    • PubMed
    • Google Scholar
73. 1. Secombe J
    2. Li L
    3. Carlos L
    4. Eisenman RN

    (2007) The trithorax group protein lid is a trimethyl histone H3K4 demethylase required for dMyc-induced cell growth

    Genes & Development 21:537–551.

    https://doi.org/10.1101/gad.1523007

    • PubMed
    • Google Scholar
74. 1. Shannon P
    2. Markiel A
    3. Ozier O
    4. Baliga NS
    5. Wang JT
    6. Ramage D
    7. Amin N
    8. Schwikowski B
    9. Ideker T

    (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks

    Genome Research 13:2498–2504.

    https://doi.org/10.1101/gr.1239303

    • PubMed
    • Google Scholar
75. 1. Southall TD
    2. Gold KS
    3. Egger B
    4. Davidson CM
    5. Caygill EE
    6. Marshall OJ
    7. Brand AH

    (2013) Cell-type-specific profiling of gene expression and chromatin binding without cell isolation: assaying RNA pol II occupancy in neural stem cells

    Developmental Cell 26:101–112.

    https://doi.org/10.1016/j.devcel.2013.05.020

    • PubMed
    • Google Scholar
76. 1. Stempor P
    2. Ahringer J

    (2016) SeqPlots - Interactive software for exploratory data analyses, pattern discovery and visualization in genomics

    Wellcome Open Research 1:14.

    https://doi.org/10.12688/wellcomeopenres.10004.1

    • PubMed
    • Google Scholar
77. 1. Syed MH
    2. Mark B
    3. Doe CQ

    (2017) Steroid hormone induction of temporal gene expression in Drosophila brain neuroblasts generates neuronal and glial diversity

    eLife 6:e26287.

    https://doi.org/10.7554/eLife.26287

    • PubMed
    • Google Scholar
78. 1. Tarazona S
    2. García-Alcalde F
    3. Dopazo J
    4. Ferrer A
    5. Conesa A

    (2011) Differential expression in RNA-seq: a matter of depth

    Genome Research 21:2213–2223.

    https://doi.org/10.1101/gr.124321.111

    • PubMed
    • Google Scholar
79. 1. Tea JS
    2. Chihara T
    3. Luo L

    (2010) Histone deacetylase Rpd3 regulates olfactory projection neuron dendrite targeting via the transcription factor Prospero

    Journal of Neuroscience 30:9939–9946.

    https://doi.org/10.1523/JNEUROSCI.1643-10.2010

    • PubMed
    • Google Scholar
80. 1. Technau G
    2. Heisenberg M

    (1982) Neural reorganization during metamorphosis of the corpora pedunculata in Drosophila melanogaster

    Nature 295:405–407.

    https://doi.org/10.1038/295405a0

    • PubMed
    • Google Scholar
81. 1. The Gene Ontology Consortium

    (2000) Gene ontology: tool for the unification of biology

    Gene Expression 25:25–29.

    https://doi.org/10.1038/75556

    • Google Scholar
82. 1. The Gene Ontology Consortium

    (2019) The gene ontology resource: 20 years and still GOing strong

    Nucleic Acids Research 47:D330–D338.

    https://doi.org/10.1093/nar/gky1055

    • PubMed
    • Google Scholar
83. 1. Thurman RE
    2. Rynes E
    3. Humbert R
    4. Vierstra J
    5. Maurano MT
    6. Haugen E
    7. Sheffield NC
    8. Stergachis AB
    9. Wang H
    10. Vernot B
    11. Garg K
    12. John S
    13. Sandstrom R
    14. Bates D
    15. Boatman L
    16. Canfield TK
    17. Diegel M
    18. Dunn D
    19. Ebersol AK
    20. Frum T
    21. Giste E
    22. Johnson AK
    23. Johnson EM
    24. Kutyavin T
    25. Lajoie B
    26. Lee BK
    27. Lee K
    28. London D
    29. Lotakis D
    30. Neph S
    31. Neri F
    32. Nguyen ED
    33. Qu H
    34. Reynolds AP
    35. Roach V
    36. Safi A
    37. Sanchez ME
    38. Sanyal A
    39. Shafer A
    40. Simon JM
    41. Song L
    42. Vong S
    43. Weaver M
    44. Yan Y
    45. Zhang Z
    46. Zhang Z
    47. Lenhard B
    48. Tewari M
    49. Dorschner MO
    50. Hansen RS
    51. Navas PA
    52. Stamatoyannopoulos G
    53. Iyer VR
    54. Lieb JD
    55. Sunyaev SR
    56. Akey JM
    57. Sabo PJ
    58. Kaul R
    59. Furey TS
    60. Dekker J
    61. Crawford GE
    62. Stamatoyannopoulos JA

    (2012) The accessible chromatin landscape of the human genome

    Nature 489:75–82.

    https://doi.org/10.1038/nature11232

    • PubMed
    • Google Scholar
84. 1. Truman JW
    2. Bate M

    (1988) Spatial and temporal patterns of neurogenesis in the central nervous system of Drosophila melanogaster

    Developmental Biology 125:145–157.

    https://doi.org/10.1016/0012-1606(88)90067-X

    • PubMed
    • Google Scholar
85. 1. Vaessin H
    2. Grell E
    3. Wolff E
    4. Bier E
    5. Jan LY
    6. Jan YN

    (1991) Prospero is expressed in neuronal precursors and encodes a nuclear protein that is involved in the control of axonal outgrowth in Drosophila

    Cell 67:941–953.

    https://doi.org/10.1016/0092-8674(91)90367-8

    • PubMed
    • Google Scholar
86. 1. Vallianatos CN
    2. Farrehi C
    3. Friez MJ
    4. Burmeister M
    5. Keegan CE
    6. Iwase S

    (2018) Altered Gene-Regulatory function of KDM5C by a novel mutation associated with autism and intellectual disability

    Frontiers in Molecular Neuroscience 11:1–12.

    https://doi.org/10.3389/fnmol.2018.00104

    • PubMed
    • Google Scholar
87. 1. Vallianatos CN
    2. Raines B
    3. Porter RS
    4. Bonefas KM
    5. Wu MC
    6. Garay PM
    7. Collette KM
    8. Seo YA
    9. Dou Y
    10. Keegan CE
    11. Tronson NC
    12. Iwase S

    (2020) Mutually suppressive roles of KMT2A and KDM5C in behaviour, neuronal structure, and histone H3K4 methylation

    Communications Biology 3:278.

    https://doi.org/10.1038/s42003-020-1001-6

    • PubMed
    • Google Scholar
88. 1. Vallianatos CN
    2. Iwase S

    (2015) Disrupted intricacy of histone H3K4 methylation in neurodevelopmental disorders

    Epigenomics 7:503–519.

    https://doi.org/10.2217/epi.15.1

    • PubMed
    • Google Scholar
89. 1. van Bokhoven H

    (2011) Genetic and epigenetic networks in intellectual disabilities

    Annual Review of Genetics 45:81–104.

    https://doi.org/10.1146/annurev-genet-110410-132512

    • PubMed
    • Google Scholar
90. 1. van Oevelen C
    2. Wang J
    3. Asp P
    4. Yan Q
    5. Kaelin WG
    6. Kluger Y
    7. Dynlacht BD

    (2008) A role for mammalian Sin3 in permanent gene silencing

    Molecular Cell 32:359–370.

    https://doi.org/10.1016/j.molcel.2008.10.015

    • PubMed
    • Google Scholar
91. 1. Vogel MJ
    2. Peric-Hupkes D
    3. van Steensel B

    (2007) Detection of in vivo protein-DNA interactions using DamID in mammalian cells

    Nature Protocols 2:1467–1478.

    https://doi.org/10.1038/nprot.2007.148

    • PubMed
    • Google Scholar
92. 1. Wei G
    2. Deng X
    3. Agarwal S
    4. Iwase S
    5. Disteche C
    6. Xu J

    (2016) Patient mutations of the intellectual disability gene KDM5C downregulate netrin G2 and suppress neurite growth in Neuro2a cells

    Journal of Molecular Neuroscience 60:33–45.

    https://doi.org/10.1007/s12031-016-0770-3

    • PubMed
    • Google Scholar
93. 1. Witteveen JS
    2. Willemsen MH
    3. Dombroski TCD
    4. van Bakel NHM
    5. Nillesen WM
    6. van Hulten JA
    7. Jansen EJR
    8. Verkaik D
    9. Veenstra-Knol HE
    10. van Ravenswaaij-Arts CMA
    11. Wassink-Ruiter JSK
    12. Vincent M
    13. David A
    14. Le Caignec C
    15. Schieving J
    16. Gilissen C
    17. Foulds N
    18. Rump P
    19. Strom T
    20. Cremer K
    21. Zink AM
    22. Engels H
    23. de Munnik SA
    24. Visser JE
    25. Brunner HG
    26. Martens GJM
    27. Pfundt R
    28. Kleefstra T
    29. Kolk SM

    (2016) Haploinsufficiency of MeCP2-interacting transcriptional co-repressor SIN3A causes mild intellectual disability by affecting the development of cortical integrity

    Nature Genetics 48:877–887.

    https://doi.org/10.1038/ng.3619

    • Google Scholar
94. 1. Wolfram V
    2. Southall TD
    3. Brand AH
    4. Baines RA

    (2012) The LIM-homeodomain protein islet dictates motor neuron electrical properties by regulating K(+) channel expression

    Neuron 75:663–674.

    https://doi.org/10.1016/j.neuron.2012.06.015

    • PubMed
    • Google Scholar
95. 1. Yuan L
    2. Hu S
    3. Okray Z
    4. Ren X
    5. De Geest N
    6. Claeys A
    7. Yan J
    8. Bellefroid E
    9. Hassan BA
    10. Quan XJ

    (2016) The Drosophila neurogenin tap functionally interacts with the Wnt-PCP pathway to regulate neuronal extension and guidance

    Development 143:2760–2766.

    https://doi.org/10.1242/dev.134155

    • PubMed
    • Google Scholar
96. 1. Zamurrad S
    2. Hatch HAM
    3. Drelon C
    4. Belalcazar HM
    5. Secombe J

    (2018) A Drosophila model of intellectual disability caused by mutations in the histone demethylase KDM5

    Cell Reports 22:2359–2369.

    https://doi.org/10.1016/j.celrep.2018.02.018

    • PubMed
    • Google Scholar
97. 1. Zhan XL
    2. Clemens JC
    3. Neves G
    4. Hattori D
    5. Flanagan JJ
    6. Hummel T
    7. Vasconcelos ML
    8. Chess A
    9. Zipursky SL

    (2004) Analysis of dscam diversity in regulating axon guidance in Drosophila mushroom bodies

    Neuron 43:673–686.

    https://doi.org/10.1016/j.neuron.2004.07.020

    • PubMed
    • Google Scholar

Author details

1. Hayden AM Hatch

   Dominick P. Purpura Department of Neuroscience Albert Einstein College of Medicine, Bronx, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5922-7291

2. Helen M Belalcazar

   Department of Genetics Albert Einstein College of Medicine, Bronx, United States

   Contribution

   Resources, Validation, Writing - review and editing

   Competing interests

   No competing interests declared

3. Owen J Marshall

   Menzies Institute for Medical Research University of Tasmania, Hobart, Australia

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Julie Secombe

   1. Dominick P. Purpura Department of Neuroscience Albert Einstein College of Medicine, Bronx, United States
   2. Department of Genetics Albert Einstein College of Medicine, Bronx, United States

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Writing - review and editing

   For correspondence

   julie.secombe@einsteinmed.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5826-7547

• 1,682

  views

• 203

  downloads

• 20

  citations

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