De novo mutations in MSL3 cause an X-linked syndrome marked by impaired histone H4 lysine 16 acetylation

Basilicata, M. Felicia; Bruel, Ange‐Line; Semplicio, Giuseppe; Valsecchi, Claudia Isabelle Keller; Aktaş, Tuğçe; Duffourd, Yannis; Rumpf, Tobias; Morton, Jenny; Bache, Iben; Szymanski, Witold G.; Gilissen, Christian; Vanakker, Olivier; Õunap, Katrin; Mittler, Gerhard; Burgt, Ineke van der; Chehadeh, Salima El; Cho, Megan T.; Pfundt, Rolph; Tan, Tiong Yang; Kirchhoff, Maria; Menten, Björn; Vergult, Sarah; Lindstrom, Kristin; Reis, André; Johnson, Diana; Fryer, Alan; McKay, Victoria; Fisher, Richard; Thauvin‐Robinet, Christel; Francis, David; Roscioli, Tony; Pajusalu, Sander; Radtke, Kelly; Ganesh, Jaya; Brunner, Han G.; Wilson, Meredith; Faivre, Laurence; Kalscheuer, Vera M.; Thévenon, Julien; Akhtar, Asifa

doi:10.1038/s41588-018-0220-y

Cited by 35 publications

(50 citation statements)

References 71 publications

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“…Comparing the KANSL1-mediated Koolen de Vries syndrome and the MSL3 syndrome is particularly interesting, as KANSL1 and MSL3 belong to two different complexes but use MOF as their catalytic subunit 10 . While patients with mutations in either gene display intellectual disability, the facial dysmorphisms in each syndrome are unique 155,157 . Furthermore, patients with Koolen de Vries syndrome display general muscle weakness 157 , whereas patients with MSL3 syndrome display a progressive disturbance in gait 155 .…”

Section: Diencephalonmentioning

confidence: 99%

“…While patients with mutations in either gene display intellectual disability, the facial dysmorphisms in each syndrome are unique 155,157 . Furthermore, patients with Koolen de Vries syndrome display general muscle weakness 157 , whereas patients with MSL3 syndrome display a progressive disturbance in gait 155 . One characteristic feature of Koolen de Vries syndrome is the friendly and sociable behaviour of patients 157 , which has not been reported for patients with the MSL3 syndrome.…”

Section: Diencephalonmentioning

confidence: 99%

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The many lives of KATs — detectors, integrators and modulators of the cellular environment

2018

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Research over the past three decades has firmly established lysine acetyltransferases (KATs) as central players in regulating transcription. Recent advances in genomic sequencing, metabolomics, animal models and mass spectrometry technologies have uncovered unexpected new roles for KATs at the nexus between the environment and transcriptional regulation. Thousands of reversible acetylation sites have been mapped in the proteome that respond dynamically to the cellular milieu and maintain major processes such as metabolism, autophagy and stress response. Concurrently , researchers are continuously uncovering how deregulation of KAT activity drives disease, including cancer and developmental syndromes characterized by severe intellectual disability. These novel findings are reshaping our view of KATs away from mere modulators of chromatin to detectors of the cellular environment and integrators of diverse signalling pathways with the ability to modify cellular phenotype.

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Section: Diencephalonmentioning

confidence: 99%

Section: Diencephalonmentioning

confidence: 99%

The many lives of KATs — detectors, integrators and modulators of the cellular environment

2018

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“…The Basilicata et al histone dataset [22] consists of 94 mass spectrometry acquisitions, each analyzing a histone-enriched SDS-PAGE gel band from various patient derived fibroblasts using a Q-Exactive mass spectrometer. The raw data files were downloaded from PRIDE Archive (ID: PXD009317) and converted to mzML format using ThermoRawFileParser [23], with vendor peak-picking enabled.…”

Section: Methodsmentioning

confidence: 99%

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

Fondrie

Noble

2019

Preprint

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1Machine learning methods have proven invaluable for increasing the sensitivity of peptide de-2 tection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, 3 use semi-supervised algorithms to learn models directly from the datasets that they analyze. 4 Although these methods are effective for many proteomics experiments, we suspected that they 5 may be suboptimal for experiments of smaller scale. In this work, we found that the power and 6 consistency of Percolator results was reduced as the size of the experiment was decreased. As 7 an alternative, we propose a different operating mode for Percolator: learn a model with Per-8 colator from a large dataset and use the learned model to evaluate the small-scale experiment. 9 We call this a "static modeling" approach, in contrast to Percolator's usual "dynamic model" 10 that is trained anew for each dataset. We applied this static modeling approach to two settings: 11 small, gel-based experiments and single-cell proteomics. In both cases, static models increased 12 the yield of detected peptides and eliminated the model-induced variability of the standard dy-13 namic approach. These results suggest that static models are a powerful tool for bringing the 14 full benefits of Percolator and other semi-supervised algorithms to small-scale experiments. 15 1 Introduction 16The assignment of peptide sequences to tandem mass spectra is a fundamental task in any pro-17 teomics experiment [1]. This task is most often performed by a database search algorithm, which 18 was first introduced with the SEQUEST search engine in 1994 [2]. Database search algorithms score 19 the theoretical mass spectra of peptides from a selected sequence database against the acquired 20 mass spectra from the experiment, yielding a set of peptide-spectrum matches (PSMs). Although 21 the score functions of individual search engines may differ greatly, the scores reported by each are 22 intended to reflect the quality of PSMs. 23 2 Machine learning strategies to re-score PSMs were first introduced due to their ability to inte-24 grate multiple, orthogonal scores and features from database search engines, thereby improving the 25 sensitivity of peptide detection. Two such methods were proposed simultaneously: one based on 26 linear discriminant analysis [3] and another that used support vector machine (SVM) models to a 27 similar end [4]. Critically, both methods were examples of supervised learning: they relied on fully 28 labeled datasets-where the correct and incorrect PSMs could be determined a priori -to train 29 their respective models. These trained models were then used to predict a new score for the PSMs 30 of a new dataset. We refer to the models used by these methods as static, meaning their parameters 31 do not change when the dataset is changed. Critically, the success of these static models depended 32 on the quality of the labeled dataset that was used for training and how well it reflected the datasets 33 that were subsequently analyzed. The origina...

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“…Recent work has established that the MSL complex drives H4K16ac and transcription of highly conserved developmental genes in D. melanogaster and mouse . Consistently, loss of just one allele of MSL3 leads to human developmental disorders typified by intellectual disability and developmental delay . While the recruitment mechanisms for the MSL complex to the D. melanogaster male X‐chromosome are well studied, future studies are needed to determine how the MOF‐MSL complex is recruited to developmentally important genes on autosomes.…”

Section: Two Independent Mof Complexes—msl and Nslmentioning

confidence: 99%

The non‐specific lethal ( NSL ) complex at the crossroads of transcriptional control and cellular homeostasis

2019

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The functionality of chromatin is tightly regulated by post‐translational modifications that modulate transcriptional output from target loci. Among the post‐translational modifications of chromatin, reversible ε‐lysine acetylation of histone proteins is prominent at transcriptionally active genes. Lysine acetylation is catalyzed by lysine acetyltransferases (KATs), which utilize the central cellular metabolite acetyl‐CoA as their substrate. Among the KATs that mediate lysine acetylation, males absent on the first (MOF/KAT8) is particularly notable for its ability to acetylate histone 4 lysine 16 (H4K16ac), a modification that decompacts chromatin structure. MOF and its non‐specific lethal (NSL) complex members have been shown to localize to gene promoters and enhancers in the nucleus, as well as to microtubules and mitochondria to regulate key cellular processes. Highlighting their importance, mutations or deregulation of NSL complex members has been reported in both human neurodevelopmental disorders and cancer. Based on insight gained from studies in human, mouse, and Drosophila model systems, this review discusses the role of NSL‐mediated lysine acetylation in a myriad of cellular functions in both health and disease. Through these studies, the importance of the NSL complex in regulating core transcriptional and signaling networks required for normal development and cellular homeostasis is beginning to emerge.

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De novo mutations in MSL3 cause an X-linked syndrome marked by impaired histone H4 lysine 16 acetylation

Cited by 35 publications

References 71 publications

The many lives of KATs — detectors, integrators and modulators of the cellular environment

The many lives of KATs — detectors, integrators and modulators of the cellular environment

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

The non‐specific lethal ( NSL ) complex at the crossroads of transcriptional control and cellular homeostasis

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