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Journal Article•DOI•

Mutations in TUBG1 , DYNC1H1 , KIF5C and KIF2A cause malformations of cortical development and microcephaly

Karine Poirier1, Nicolas Lebrun1, Nicolas Lebrun2, Loic Broix1, Loic Broix2, Guoling Tian3, Yoann Saillour2, Yoann Saillour1, Cécile Boscheron4, Elena Parrini5, Stéphanie Valence2, Stéphanie Valence1, Benjamin Saint Pierre2, Benjamin Saint Pierre1, Madison Oger1, Madison Oger2, Didier Lacombe, David Geneviève6, Elena Fontana, Franscesca Darra, Claude Cances, Magalie Barth7, Dominique Bonneau7, Bernardo Dalla Bernadina, Sylvie Nguyen, Cyril Gitiaux1, Cyril Gitiaux8, Cyril Gitiaux2, Philippe Parent, Vincent des Portes9, Jean Michel Pedespan, Victoire Legrez, Laetitia Castelnau-Ptakine2, Laetitia Castelnau-Ptakine1, Patrick Nitschke10, Thierry Hieu10, Cécile Masson10, Diana Zelenika, Annie Andrieux4, Fiona Francis2, Fiona Francis11, Renzo Guerrini5, Nicholas J. Cowan3, Nadia Bahi-Buisson2, Nadia Bahi-Buisson1, Nadia Bahi-Buisson8, Jamel Chelly1, Jamel Chelly2 •
Centre national de la recherche scientifique1, French Institute of Health and Medical Research2, New York University3, Joseph Fourier University4, University of Florence5, University of Montpellier6, University of Angers7, Necker-Enfants Malades Hospital8, Lyon College9, Paris Descartes University10, Pierre-and-Marie-Curie University11
01 Jun 2013-Nature Genetics (Nature Publishing Group)-Vol. 45, Iss: 6, pp 639-647
TL;DR: The discovery of multiple pathogenic missense mutations in TUBG1, DYNC1H1 and KIF2A, as well as a single germline mosaic mutation in KIF5C, in subjects with MCD are reported, suggesting that microtubule-dependent mitotic and postmitotic processes are major contributors to the pathogenesis of MCD.
Abstract: The genetic causes of malformations of cortical development (MCD) remain largely unknown. Here we report the discovery of multiple pathogenic missense mutations in TUBG1, DYNC1H1 and KIF2A, as well as a single germline mosaic mutation in KIF5C, in subjects with MCD. We found a frequent recurrence of mutations in DYNC1H1, implying that this gene is a major locus for unexplained MCD. We further show that the mutations in KIF5C, KIF2A and DYNC1H1 affect ATP hydrolysis, productive protein folding and microtubule binding, respectively. In addition, we show that suppression of mouse Tubg1 expression in vivo interferes with proper neuronal migration, whereas expression of altered γ-tubulin proteins in Saccharomyces cerevisiae disrupts normal microtubule behavior. Our data reinforce the importance of centrosomal and microtubule-related proteins in cortical development and strongly suggest that microtubule-dependent mitotic and postmitotic processes are major contributors to the pathogenesis of MCD.

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Citations
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The journal of neuroscience : the official journal of the Society for Neuroscience.

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HighWire Press
01 Jan 1981

1,737 citations

Journal Article•DOI•
The Cell Biology of Neurogenesis: Toward an Understanding of the Development and Evolution of the Neocortex

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Elena Taverna1, Magdalena Götz, Wieland B. Huttner1•
Max Planck Society1
06 Oct 2014-Annual Review of Cell and Developmental Biology
TL;DR: The issue of cell polarity is discussed, as well as specific subcellular features of these cells that are relevant for their modes of division and daughter cell fate, which help gain insight into key developmental and evolutionary mechanisms underlying neocortex expansion.
Abstract: Neural stem and progenitor cells have a central role in the development and evolution of the mammalian neocortex. In this review, we first provide a set of criteria to classify the various types of cortical stem and progenitor cells. We then discuss the issue of cell polarity, as well as specific subcellular features of these cells that are relevant for their modes of division and daughter cell fate. In addition, cortical stem and progenitor cell behavior is placed into a tissue context, with consideration of extracellular signals and cell-cell interactions. Finally, the differences across species regarding cortical stem and progenitor cells are dissected to gain insight into key developmental and evolutionary mechanisms underlying neocortex expansion.

573 citations

Journal Article•DOI•
Axonal transport: cargo-specific mechanisms of motility and regulation

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Sandra Maday1, Alison E. Twelvetrees1, Armen J. Moughamian1, Erika L.F. Holzbaur1•
University of Pennsylvania1
22 Oct 2014-Neuron
TL;DR: An overview of axonal transport pathways is provided and their role in neuronal function is discussed and Retrograde transport, which plays a major role in neurotrophic and injury response signaling, is discussed.

538 citations

Cites background from "Mutations in TUBG1 , DYNC1H1 , KIF5..."

  • ...Neurodevelopmental and Neurodegenerative Diseases Caused by Mutations in the Axonal Transport Machinery Protein(s) Gene(s) with Known Mutation Disease(s) References Motor Proteins Dynein DYNC1H1 CMT, SMA-LED, ID, MCD (Epilepsy) Weedon et al., 2011; Tsurusaki et al., 2012; Harms et al., 2012; Willemsen et al., 2012; Poirier et al., 2013; Fiorillo et al., 2014 Kinesin-1 KIF5A, KIF5C HSP (SPG10), ID, MCD Ebbing et al., 2008; de Ligt et al., 2012; Poirier et al., 2013 Kinesin-13 KIF2A CDCBM3/MCD Poirier et al., 2013 Kinesin-3 KIF1A, KIF1B, KIF1C HSP (SPG30), CMT2A, HSN, MR, SPAX Erlich et al., 2011; Zhao et al., 2001; Rivière et al., 2011; Hamdan et al., 2011; Klebe et al., 2012; Dor et al., 2014; Novarino et al., 2014 Kinesin-4 KIF21A CFEOM Yamada et al., 2003 Motor Adaptors and Regulators Dynactin DCTN1 Perry syndrome, MND Puls et al., 2003; Farrer et al., 2009; Caroppo et al., 2014; Araki et al., 2014 BICD2 BICD2 SMA, HSP Neveling et al., 2013; Peeters et al., 2013; Oates et al., 2013 Huntingtin HTT HD HDCRG, 1993 Lis-1 PAFAH1B1 Lissencephaly Dobyns et al., 1993; Reiner et al., 1993 NDE1 NDE1 Microcephaly, MHAC Alkuraya et al., 2011; Bakircioglu et al., 2011; Paciorkowski et al., 2013 Rab7 RAB7A CMT2B Verhoeven et al., 2003 Cytoskeleton and Associated Proteins (e.g., MAPs) CLIP-170 CLIP1 ARID Larti et al., 2014 Doublecortin DCX Lissencephaly des Portes et al., 1998a, 1998b; Gleeson et al., 1998 Microtubules TUBA1A, TUBA8, TUBG1, TUBB3, TUBB2B Lissencephaly, MCD, microcephaly, polymicrogyria, CFEOM Keays et al., 2007; Poirier et al., 2007, 2010, 2013; Jaglin et al., 2009; Abdollahi et al., 2009; Tischfield et al., 2010; Chew et al., 2013 Neurofilaments NEFL CMT Mersiyanova et al., 2000 Spastin SPAST HSP (SPG4) Hazan et al., 1999 Tau MAPT FTD, Pick disease, AD Hutton et al., 1998; Murrell et al., 1999 Abbreviations are as follows: AD, Alzheimer’s disease; ARID, autosomal recessive intellectual disability; CDCBM3, complex cortical dysplasia with other brain malformations-3; CFEOM, congenital fibrosis of the extraocular muscles; CMT, Charcot-Marie-Tooth disease; FTD, frontotemporal dementia; HD, Huntington’s disease; HMN, hereditary motor neuropathy; HSN, hereditary sensory neuropathy; HSP, hereditary spastic paraplegia; ID, intellectual disability; MCD,malformations of cortical development; MHAC,microhydranencephaly; MND,motor neuron disease; MR,mental retardation; SMA, spinal muscular atrophy; SMA-LED, SMA-lower extremity dominant; and SPAX, spastic ataxia. stabilization; because of the high expression levels of MAPs in neurons, microtubules are generally more stable in these cells than in other cell types....

    [...]

  • ...…Proteins Dynein DYNC1H1 CMT, SMA-LED, ID, MCD (Epilepsy) Weedon et al., 2011; Tsurusaki et al., 2012; Harms et al., 2012; Willemsen et al., 2012; Poirier et al., 2013; Fiorillo et al., 2014 Kinesin-1 KIF5A, KIF5C HSP (SPG10), ID, MCD Ebbing et al., 2008; de Ligt et al., 2012; Poirier et al.,…...

    [...]

  • ...…et al., 2014 Kinesin-1 KIF5A, KIF5C HSP (SPG10), ID, MCD Ebbing et al., 2008; de Ligt et al., 2012; Poirier et al., 2013 Kinesin-13 KIF2A CDCBM3/MCD Poirier et al., 2013 Kinesin-3 KIF1A, KIF1B, KIF1C HSP (SPG30), CMT2A, HSN, MR, SPAX Erlich et al., 2011; Zhao et al., 2001; Rivière et al., 2011;…...

    [...]

  • ...…et al., 2012; Poirier et al., 2013; Fiorillo et al., 2014 Kinesin-1 KIF5A, KIF5C HSP (SPG10), ID, MCD Ebbing et al., 2008; de Ligt et al., 2012; Poirier et al., 2013 Kinesin-13 KIF2A CDCBM3/MCD Poirier et al., 2013 Kinesin-3 KIF1A, KIF1B, KIF1C HSP (SPG30), CMT2A, HSN, MR, SPAX Erlich et al.,…...

    [...]

  • ...…Gleeson et al., 1998 Microtubules TUBA1A, TUBA8, TUBG1, TUBB3, TUBB2B Lissencephaly, MCD, microcephaly, polymicrogyria, CFEOM Keays et al., 2007; Poirier et al., 2007, 2010, 2013; Jaglin et al., 2009; Abdollahi et al., 2009; Tischfield et al., 2010; Chew et al., 2013 Neurofilaments NEFL CMT…...

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Journal Article•DOI•
Malformations of cortical development: clinical features and genetic causes

[...]

Renzo Guerrini1, William B. Dobyns2, William B. Dobyns3•
University of Florence1, Seattle Children's Research Institute2, University of Washington3
01 Jul 2014-Lancet Neurology
TL;DR: Because of substantial genotypic and phenotypic heterogeneity for most of these genes, a comprehensive analysis of clinical, imaging, and genetic data is needed to properly define these disorders.
Abstract: Malformations of cortical development are common causes of developmental delay and epilepsy. Some patients have early, severe neurological impairment, but others have epilepsy or unexpected deficits that are detectable only by screening. The rapid evolution of molecular biology, genetics, and imaging has resulted in a substantial increase in knowledge about the development of the cerebral cortex and the number and types of malformations reported. Genetic studies have identified several genes that might disrupt each of the main stages of cell proliferation and specification, neuronal migration, and late cortical organisation. Many of these malformations are caused by de-novo dominant or X-linked mutations occurring in sporadic cases. Genetic testing needs accurate assessment of imaging features, and familial distribution, if any, and can be straightforward in some disorders but requires a complex diagnostic algorithm in others. Because of substantial genotypic and phenotypic heterogeneity for most of these genes, a comprehensive analysis of clinical, imaging, and genetic data is needed to properly define these disorders. Exome sequencing and high-field MRI are rapidly modifying the classification of these disorders.

340 citations

Journal Article•DOI•
Mechanisms and functions of lysosome positioning

[...]

Jing Pu1, Carlos M. Guardia1, Tal Keren-Kaplan1, Juan S. Bonifacino1•
National Institutes of Health1
01 Dec 2016-Journal of Cell Science
TL;DR: This work reviews recent findings on the mechanisms that mediate the motility and positioning of lysosomes, and the importance ofLysosome dynamics for cell physiology and pathology.
Abstract: Lysosomes have been classically considered terminal degradative organelles, but in recent years they have been found to participate in many other cellular processes, including killing of intracellular pathogens, antigen presentation, plasma membrane repair, cell adhesion and migration, tumor invasion and metastasis, apoptotic cell death, metabolic signaling and gene regulation In addition, lysosome dysfunction has been shown to underlie not only rare lysosome storage disorders but also more common diseases, such as cancer and neurodegeneration The involvement of lysosomes in most of these processes is now known to depend on the ability of lysosomes to move throughout the cytoplasm Here, we review recent findings on the mechanisms that mediate the motility and positioning of lysosomes, and the importance of lysosome dynamics for cell physiology and pathology

336 citations

Cites background from "Mutations in TUBG1 , DYNC1H1 , KIF5..."

  • ...…Rab7 in Charcot–Marie–Tooth disease type 2B (Verhoeven et al., 2003), KIF1B in Charcot–Marie–Tooth disease type 2A (Zhao et al., 2001), KIF5A in hereditary spastic paraplegia type 10 (Reid et al., 2002) and KIF5C in cortical dysplasia with other brain malformations type 2 (Poirier et al., 2013)....

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  • ..., 2002) and KIF5C in cortical dysplasia with other brain malformations type 2 (Poirier et al., 2013)....

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References
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Journal Article•DOI•
The Sequence Alignment/Map format and SAMtools

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Heng Li1, Bob Handsaker2, Alec Wysoker2, T. J. Fennell2, Jue Ruan3, Nils Homer2, Gabor T. Marth4, Gonçalo R. Abecasis2, Richard Durbin1 •
Wellcome Trust Sanger Institute1, University of California, Los Angeles2, Chinese Academy of Sciences3, Boston College4
01 Aug 2009-Bioinformatics
TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Abstract: Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: [email protected]

45,957 citations

Journal Article•DOI•
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Aaron McKenna1, Matthew Hanna, Eric Banks, Andrey Sivachenko, Kristian Cibulskis, Andrew Kernytsky, Kiran V. Garimella, David Altshuler, Stacey Gabriel, Mark J. Daly, Mark A. DePristo •
Broad Institute1
01 Sep 2010-Genome Research
TL;DR: The GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

20,557 citations

Journal Article•DOI•
A framework for variation discovery and genotyping using next-generation DNA sequencing data

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Mark A. DePristo1, Eric Banks1, Ryan Poplin1, Kiran V. Garimella1, Jared Maguire1, Christopher Hartl1, Anthony A. Philippakis1, Anthony A. Philippakis2, Anthony A. Philippakis3, Guillermo del Angel1, Manuel A. Rivas1, Manuel A. Rivas2, Matt Hanna1, Aaron McKenna1, Timothy Fennell1, Andrew Kernytsky1, Andrey Sivachenko1, Kristian Cibulskis1, Stacey Gabriel1, David Altshuler1, David Altshuler2, Mark J. Daly1, Mark J. Daly2 •
Broad Institute1, Harvard University2, Brigham and Women's Hospital3
01 May 2011-Nature Genetics
TL;DR: A unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs is presented.
Abstract: Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.

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Use of T7 RNA polymerase to direct expression of cloned genes.

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F W Studier, A H Rosenberg, John J. Dunn, J W Dubendorff
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Journal Article•DOI•
Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.

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Priyank Kumar1, Steven Henikoff2, Steven Henikoff3, Pauline C. Ng1, Pauline C. Ng2 •
J. Craig Venter Institute1, Fred Hutchinson Cancer Research Center2, Howard Hughes Medical Institute3
25 Jun 2009-Nature Protocols
TL;DR: This protocol describes the use of the 'Sorting Tolerant From Intolerant' (SIFT) algorithm in predicting whether an AAS affects protein function.
Abstract: The effect of genetic mutation on phenotype is of significant interest in genetics. The type of genetic mutation that causes a single amino acid substitution (AAS) in a protein sequence is called a non-synonymous single nucleotide polymorphism (nsSNP). An nsSNP could potentially affect the function of the protein, subsequently altering the carrier's phenotype. This protocol describes the use of the 'Sorting Tolerant From Intolerant' (SIFT) algorithm in predicting whether an AAS affects protein function. To assess the effect of a substitution, SIFT assumes that important positions in a protein sequence have been conserved throughout evolution and therefore substitutions at these positions may affect protein function. Thus, by using sequence homology, SIFT predicts the effects of all possible substitutions at each position in the protein sequence. The protocol typically takes 5–20 min, depending on the input. SIFT is available as an online tool ( http://sift-dna.org ).

6,154 citations

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Exome Sequencing Identifies a DYNC1H1 Mutation in a Large Pedigree with Dominant Axonal Charcot-Marie-Tooth Disease

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12 Aug 2011-American Journal of Human Genetics
Michael N. Weedon, Robert Hastings, Richard Caswell, Weijia Xie, Konrad Paszkiewicz, Thalia Antoniadi, Maggie Williams, Cath King, Lynn Greenhalgh, Ruth Newbury-Ecob, Sian Ellard 
Mutations in the [beta]-tubulin gene TUBB2B result in asymmetrical polymicrogyria

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Trending Questions (1)
What are the mechanisms by which mutations in DYNC1H1 contribute to the development of SMA?

The provided paper does not mention anything about the development of SMA or the mechanisms by which mutations in DYNC1H1 contribute to it. The paper focuses on the genetic causes of malformations of cortical development (MCD) and microcephaly.

See answers from 4 other papers