De Novo Missense Variants in FBXW11 Cause Diverse Developmental Phenotypes Including Brain, Eye, and Digit Anomalies

Holt, R. J.; Young, Rodrigo; Crespo, Berta; Ceroni, Fabiola; Curry, Cynthia J.; Bellacchio, Emanuele; Bax, Dorine A.; Ciolfi, Andrea; Simon, Marleen; Fagerberg, Christina; Binsbergen, Ellen van; Luca, Alessandro De; Memo, Luigi; Dobyns, William B.; Mohammed, Alaa Afif; Clokie, Samuel; Seco, Celia Zazo; Jiang, Yong; Sørensen, Kristina Pilekær; Andersen, Helle; Sullivan, Jennifer; Powis, Zöe; Chassevent, Anna; Smith‐Hicks, Constance; Petrovski, Steve; Antoniadi, Thalia; Shashi, Vandana; Gelb, Bruce D.; Wilson, Stephen W.; Gerrelli, Dianne; Tartaglia, Marco; Chassaing, Nicolas; Calvas, Patrick; Ragge, Nicola

doi:10.1016/j.ajhg.2019.07.005

Cited by 33 publications

(30 citation statements)

References 81 publications

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“…Although computational stability predictors have not been specifically designed to identify pathogenic mutations, they are very commonly used when assessing candidate disease mutations. For example, publications reporting novel variants will often include the output of stability predictors as evidence in support of pathogenicity 24 – 27 . This relies essentially upon the assumption that the molecular mechanism underlying many or most pathogenic mutations is directly related to the structural destabilisation of protein folding or interactions 28 – 31 .…”

Section: Introductionmentioning

confidence: 99%

Identification of pathogenic missense mutations using protein stability predictors

Gerasimavicius

Liu

Marsh

2020

Sci Rep

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Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 13 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, significantly outperforms all other predictors in the identification of disease variants. Moreover, we demonstrate that employing predicted absolute energy change scores improves performance of nearly all predictors in distinguishing pathogenic from benign variants. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.

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Section: Introductionmentioning

confidence: 99%

Identification of pathogenic missense mutations using protein stability predictors

Gerasimavicius

Liu

Marsh

2020

Sci Rep

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“…Although computational stability predictors have not been specifically designed to identify pathogenic mutations, they are very commonly used when assessing candidate disease mutations. For example, publications reporting novel variants will often include the output of stability predictors as evidence in support of pathogenicity [22][23][24][25] . This relies essentially upon the assumption that the molecular mechanism underlying many or most pathogenic mutations is directly related to the structural destabilisation of protein folding or interactions [26][27][28][29] .…”

Section: Introductionmentioning

confidence: 99%

Identification of pathogenic missense mutations using protein stability predictors

Gerasimavicius

Liu

Marsh

2020

Preprint

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Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 12 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, considerably outperforms all others in the identification of disease variants. Moreover, we demonstrate that employing absolute energy change scores improves performance of nearly all predictors. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all are inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.

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“…Illustration: © www.gograph.com/ Eraxion. Genes (protein if different), names of diseases and references (in alphabetical order): ASB10, Glaucoma (Pasutto et al, 2012); CCNF (cyclinF), Amyotrophic Lateral Sclerosis and Frontotemporal dementia (Williams et al, 2016); CRBN, mental retardation (MRT2A; Higgins et al, 2004); CUL4B, mental retardation (MRXS15; Zou et al, 2007); DCAF8, Charcot-Marie-Tooth disease (CMT2; Klein et al, 2014); FBXL7, Hennekam syndrome (Boone et al, 2020); FBXO7, Parkinson syndrome (PARK15; Shojaee et al, 2008); FBXO31, mental retardation (MRT45; Mir et al, 2014); FBXO38, distal hereditary motor neuropathy (HMN2D; Sumner et al, 2013); FBXW11, neurodevelopmental syndrome (Holt et al, 2019); GAN (Gigaxonin), Giant Axonal Neuropathy (Bomont et al, 2000); HACE1, neurodevelopmental syndrome (SPPRS;Hollstein et al, 2015); HECW2, neurodevelopmental syndrome (NDHSAL; Berko et al, 2017); HERC1, neurodevelopmental syndrome (MDFPMR;Nguyen et al, 2016); HERC2, mixed with mental retardation (MRT38; Puffenberger et al, 2012); HUWE1, mental retardation (MRXST; Froyen et al, 2008); ITCH (Itch), multi-system autoimmune disease with neurodevelopmental defects (Lohr et al, 2010); KLHL7, retinitis pigmentosa (RP42; Friedman et al, 2009); KLHL15, mixed with mental (Continued) retardation (MRX103;Mignon-Ravix et al, 2014); LRSAM1, CMT2P (Guernsey et al, 2010); MID1, Opitz G/BBB syndrome 1 (GBBB1; Quaderi et al, 1997); MID2, mental retardation (MRX101; Geetha et al, 2014); NEDD4L, Periventricular nodular heterotopia 7 (PVNH7; Broix et al, 2016); NHLRC1 (Malin), Lafora disease (Chan et al, 2003); PARK2 (Parkin), Parkinson disease 2 (PARK2; Kitada et al, 1998); PEX10, Zellweger syndrome (Okumoto et al, 1998;Warren et al, 1998); RANBP2* (RanBP2), acute necrotizing encephalopathy…”

Section: The Gan Gene: Transmission and Mutationsmentioning

confidence: 99%

E3 Ubiquitin Ligases in Neurological Diseases: Focus on Gigaxonin and Autophagy

Lescouzères

Bomont

2020

Front. Physiol.

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Ubiquitination is a dynamic post-translational modification that regulates the fate of proteins and therefore modulates a myriad of cellular functions. At the last step of this sophisticated enzymatic cascade, E3 ubiquitin ligases selectively direct ubiquitin attachment to specific substrates. Altogether, the ∼800 distinct E3 ligases, combined to the exquisite variety of ubiquitin chains and types that can be formed at multiple sites on thousands of different substrates confer to ubiquitination versatility and infinite possibilities to control biological functions. E3 ubiquitin ligases have been shown to regulate behaviors of proteins, from their activation, trafficking, subcellular distribution, interaction with other proteins, to their final degradation. Largely known for tagging proteins for their degradation by the proteasome, E3 ligases also direct ubiquitinated proteins and more largely cellular content (organelles, ribosomes, etc.) to destruction by autophagy. This multi-step machinery involves the creation of double membrane autophagosomes in which engulfed material is degraded after fusion with lysosomes. Cooperating in sustaining homeostasis, actors of ubiquitination, proteasome and autophagy pathways are impaired or mutated in wide range of human diseases. From initial discovery of pathogenic mutations in the E3 ligase encoding for E6-AP in Angelman syndrome and Parkin in juvenile forms of Parkinson disease, the number of E3 ligases identified as causal gene for neurological diseases has considerably increased within the last years. In this review, we provide an overview of these diseases, by classifying the E3 ubiquitin ligase types and categorizing the neurological signs. We focus on the Gigaxonin-E3 ligase, mutated in giant axonal neuropathy and present a comprehensive analysis of the spectrum of mutations and the recent biological models that permitted to uncover novel mechanisms of action. Then, we discuss the common functions shared by Gigaxonin and the other E3 ligases in cytoskeleton architecture, cell signaling and autophagy. In particular, we emphasize their pivotal roles in controlling multiple steps of the autophagy pathway. In light of the various targets and extending functions sustained by a single E3 ligase, we finally discuss the challenge in understanding the complex pathological cascade underlying disease and in designing therapeutic approaches that can apprehend this complexity.

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De Novo Missense Variants in FBXW11 Cause Diverse Developmental Phenotypes Including Brain, Eye, and Digit Anomalies

Cited by 33 publications

References 81 publications

Identification of pathogenic missense mutations using protein stability predictors

Identification of pathogenic missense mutations using protein stability predictors

Identification of pathogenic missense mutations using protein stability predictors

E3 Ubiquitin Ligases in Neurological Diseases: Focus on Gigaxonin and Autophagy

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