Clinical and Functional Characterization of the Recurrent TUBA1A p.(Arg2His) Mutation

TTLL12 is required for primary ciliary axoneme formation in polarized epithelial cells

The primary cilium is a critical sensory organelle that is built of axonemal microtubules ensheathed by a ciliary membrane. In polarized epithelial cells, primary cilia reside on the apical surface and must extend these microtubules directly into the extracellular space and remain a stable structure. However, the factors regulating cross-talk between ciliation and cell polarization, as well as axonemal microtubule growth and stabilization in polarized epithelia, are not fully understood. In this study, we find TTLL12, a previously uncharacterized member of the Tubulin Tyrosine Ligase-Like (TTLL) family, localizes to the base of primary cilia and is required for cilia formation in polarized renal epithelial cells. We also show that TTLL12 directly binds to the α/β-tubulin heterodimer in vitro and regulates microtubule dynamics, stability, and post-translational modifications (PTMs). While all other TTLLs catalyze the addition of glutamate or glycine to microtubule C-terminal tails, TTLL12 uniquely affects tubulin PTMs by promoting both microtubule lysine acetylation and arginine methylation. Together, this work identifies a novel microtubule regulator and provides insight into the requirements for apical extracellular axoneme formation.

TTLL12 is required for primary ciliary axoneme formation in polarized epithelial cells

The primary cilium is a critical sensory organelle that is built of axonemal microtubules ensheathed by a ciliary membrane. In polarized epithelial cells, primary cilia reside on the apical surface and must extend these microtubules directly into the extracellular space and remain a stable structure. However, the factors regulating cross-talk between ciliation and cell polarization, as well as, axonemal microtubule growth and stabilization in polarized epithelia are not fully understood. In this study, we find TTLL12, a previously uncharacterized member of the Tubulin Tyrosine Ligase-Like (TTLL) family, localizes to the base of primary cilia and is required for cilia formation in polarized renal epithelial cells. We also show that TTLL12 directly binds to the α/β-tubulin heterodimer in vitro and regulates microtubule dynamics, stability, and post-translational modifications (PTMs). While all other TTLLs catalyze the addition of glutamate or glycine to microtubule C-terminal tails, TTLL12 uniquely affects tubulin PTMs by promoting both microtubule lysine acetylation and arginine methylation. Together, this work identifies a novel microtubule regulator and provides insight into the requirements for apical extracellular axoneme formation.

Mechanism of ribosome-associated mRNA degradation during tubulin autoregulation

Microtubules play crucial roles in cellular architecture, intracellular transport, and mitosis. The availability of free tubulin subunits affects polymerization dynamics and microtubule function. When cells sense excess free tubulin, they trigger degradation of the encoding mRNAs, which requires recognition of the nascent polypeptide by the tubulin-specific ribosome-binding factor TTC5. How TTC5 initiates the decay of tubulin mRNAs is unknown. Here, our biochemical and structural analysis reveals that TTC5 recruits the poorly studied protein SCAPER to the ribosome. SCAPER, in turn, engages the CCR4-NOT deadenylase complex through its CNOT11 subunit to trigger tubulin mRNA decay. SCAPER mutants that cause intellectual disability and retinitis pigmentosa in humans are impaired in CCR4-NOT recruitment, tubulin mRNA degradation, and microtubule-dependent chromosome segregation. Our findings demonstrate how recognition of a nascent polypeptide on the ribosome is physically linked to mRNA decay factors via a relay of protein-protein interactions, providing a paradigm for specificity in cytoplasmic gene regulation.

KGLRR: A low-rank representation K-means with graph regularization constraint method for Single-cell type identification

Single-cell RNA sequencing technology provides a tremendous opportunity for studying disease mechanisms at the single-cell level. Cell type identification is a key step in the research of disease mechanisms. Many clustering algorithms have been proposed to identify cell types. Most clustering algorithms perform similarity calculation before cell clustering. Because clustering and similarity calculation are independent, a low-rank matrix obtained only by similarity calculation may be unable to fully reveal the patterns in single-cell data. In this study, to capture accurate single-cell clustering information, we propose a novel method based on a low-rank representation model, called KGLRR, that combines the low-rank representation approach with K-means clustering. The cluster centroid is updated as the cell dimension decreases to better from new clusters and improve the quality of clustering information. In addition, the low-rank representation model ignores local geometric information, so the graph regularization constraint is introduced. KGLRR is tested on both simulated and real single-cell datasets to validate the effectiveness of the new method. The experimental results show that KGLRR is more robust and accurate in cell type identification than other advanced algorithms.

Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis

In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

Understanding molecular mechanisms and predicting phenotypic effects of pathogenic tubulin mutations

Cells rely heavily on microtubules for several processes, including cell division and molecular trafficking. Mutations in the different tubulin-α and -β proteins that comprise microtubules have been associated with various diseases and are often dominant, sporadic and congenital. While the earliest reported tubulin mutations affect neurodevelopment, mutations are also associated with other disorders such as bleeding disorders and infertility. We performed a systematic survey of tubulin mutations across all isotypes in order to improve our understanding of how they cause disease, and increase our ability to predict their phenotypic effects. Both protein structural analyses and computational variant effect predictors were very limited in their utility for differentiating between pathogenic and benign mutations. This was even worse for those genes associated with non-neurodevelopmental disorders. We selected tubulin-α and -β disease mutations that were most poorly predicted for experimental characterisation. These mutants co-localise to the mitotic spindle in HeLa cells, suggesting they may exert dominant-negative effects by altering microtubule properties. Our results show that tubulin mutations represent a blind spot for current computational approaches, being much more poorly predicted than mutations in most human disease genes. We suggest that this is likely due to their strong association with dominant-negative and gain-of-function mechanisms.

Broadening the phenotypic spectrum of TUBA1A tubulinopathy to syndromic arthrogryposis multiplex congenita

The recent finding that some patients with fetal akinesia deformation sequence (FADS) carry variants in the TUBB2B gene has prompted us to add to the existing literature a first description of two fetal FADS cases carrying TUBA1A variants. Hitherto, only isolated cortical malformations have been described with TUBA1A mutation, including microlissencephaly, lissencephaly, central pachygyria and polymicrogyria-like cortical dysplasia, generalized polymicrogyria cortical dysplasia, and/or the "simplified" gyral pattern. The neuropathology of our fetal cases shows several common features of tubulinopathies, in particular, the dysmorphism of the basal ganglia, as the most pathognomonic sign. The cortical ribbon anomalies were extremely severe and concordant with the complex cortical malformation. In conclusion, we broaden the phenotypic spectrum of TUBA1A variants, to include FADS.

Single-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework

The rapid development of single-cell RNA sequencing (scRNA-seq)technology reveals the gene expression status and gene structure of individual cells, reflecting the heterogeneity and diversity of cells. The traditional methods of scRNA-seq data analysis treat data as the same subspace, and hide structural information in other subspaces. In this paper, we propose a low-rank subspace ensemble clustering framework (LRSEC)to analyze scRNA-seq data. Assuming that the scRNA-seq data exist in multiple subspaces, the low-rank model is used to find the lowest rank representation of the data in the subspace. It is worth noting that the penalty factor of the low-rank kernel function is uncertain, and different penalty factors correspond to different low-rank structures. Moreover, the single cluster model is difficult to find the cellular structure of all datasets. To strengthen the correlation between model solutions, we construct a new ensemble clustering framework LRSEC by using the low-rank model as the basic learner. The LRSEC framework captures the global structure of data through low-rank subspaces, which has better clustering performance than a single clustering model. We validate the performance of the LRSEC framework on seven small datasets and one large dataset and obtain satisfactory results.

Phenotypes and genotypes in non-consanguineous and consanguineous primary microcephaly: High incidence of epilepsy

BACKGROUND

Primary microcephaly (PM) is defined as a significant reduction in occipitofrontal circumference (OFC) of prenatal onset. Clinical and genetic heterogeneity of PM represents a diagnostic challenge.

METHODS

We performed detailed phenotypic and genomic analyses in a large cohort (n = 169) of patients referred for PM and could establish a molecular diagnosis in 38 patients.

RESULTS

Pathogenic variants in ASPM and WDR62 were the most frequent causes in non-consanguineous patients in our cohort. In consanguineous patients, microarray and targeted gene panel analyses reached a diagnostic yield of 67%, which contrasts with a much lower rate in non-consanguineous patients (9%). Our series includes 11 novel pathogenic variants and we identify novel candidate genes including IGF2BP3 and DNAH2. We confirm the progression of microcephaly over time in affected children. Epilepsy was an important associated feature in our PM cohort, affecting 34% of patients with a molecular confirmation of the PM diagnosis, with various degrees of severity and seizure types.

CONCLUSION

Our findings will help to prioritize genomic investigations, accelerate molecular diagnoses, and improve the management of PM patients.

Neuropathology of genetically defined malformations of cortical development-A systematic literature review

AIMS

Malformations of cortical development (MCD) include a heterogeneous spectrum of clinical, imaging, molecular and histopathological entities. While the understanding of genetic causes of MCD has improved with the availability of next-generation sequencing modalities, genotype-histopathological correlations remain limited. This is the first systematic review of molecular and neuropathological findings in patients with MCD to provide a comprehensive overview of the literature.

METHODS

A systematic review was performed between November 2019 and February 2020. A MEDLINE search was conducted for 132 genes previously linked to MCD in order to identify studies reporting macroscopic and/or microscopic findings in patients with a confirmed genetic cause.

RESULTS

Eighty-one studies were included in this review reporting neuropathological features associated with pathogenic variants in 46 genes (46/132 genes, 34.8%). Four groups emerged, consisting of (1) 13 genes with well-defined histological-genotype correlations, (2) 27 genes for which neuropathological reports were limited, (3) 5 genes with conflicting neuropathological features, and (4) 87 genes for which no histological data were available. Lissencephaly and polymicrogyria were reported most frequently. Associated brain malformations were variably present, with abnormalities of the corpus callosum as most common associated feature.

CONCLUSIONS

Neuropathological data in patients with MCD with a defined genetic cause are available only for a small number of genes. As each genetic cause might lead to unique histopathological features of MCD, standardised thorough neuropathological assessment and reporting should be encouraged. Histological features can help improve the understanding of the pathogenesis of MCD and generate hypotheses with impact on further research directions.

Loss-of-Function Plays a Major Role in Early Neurogenesis of Tubulin α-1 A (TUBA1A) Mutation-Related Brain Malformations

Tubulin α-1 A (TUBA1A) mutations cause a wide spectrum of brain abnormalities. Although many mutations have been identified and functionally verified, there are clearly many more, and the relationship between TUBA1A mutations and brain malformations remains unclear. The aim of this study was to identify a TUBA1A mutation in a fetus with severe brain abnormalities, verify it functionally, and determine the mechanism of the mutation-related pathogenesis. A de novo missense mutation of the TUBA1A gene, c.167C>G p.T56R/P.THR56Arg, was identified by exon sequencing. Computer simulations showed that the mutation results in a disruption of lateral interactions between the microtubules. Transfection of 293T cells with TUBA1A p.T56R showed that the mutated protein is only partially incorporated into the microtubule network, resulting in a decrease in the rate of microtubule re-integration in comparison with the wild-type protein. The mechanism of pathological changes induced by the mutant gene was determined by knockdown and overexpression. It was found that knockdown of TUBA1A reduced the generation of neural progenitor cells, while overexpression of wild-type or mutant TUBA1A promoted neurogenesis. Our identification and functional verification of the novel TUBA1A mutation extends the TUBA1A gene-phenotype database. Loss-of-function of TUBA1A was shown to play an important role in early neurogenesis of TUBA1A mutation-related brain malformations.

[TUBB1 mutation in children with congenital hypothyroidism and thyroid dysgenesis in Shandong, China]

OBJECTIVE

To study the types and characteristics of TUBB1 mutation in children with congenital hypothyroidism (CH) and thyroid dysgenesis (TD) in Shandong, China.

METHODS

Mutations of the whole coding region of the TUBB1 gene were analyzed for 289 children with CH and TD in Shandong. Whole-genome DNA was extracted from peripheral blood leukocytes. PCR multiplication was performed for the whole coding region of the TUBB1 gene. Sanger sequencing was performed for the PCR products, and a biological information analysis was performed.

RESULTS

Among the 289 children with CH and TD, 4 (1.4%) were found to have a c.952C>T(p.R318W) heterozygous mutation in the TUBB1 gene, resulting in the change of tryptophan into arginine at codon 318 of TUBB1 protein. This mutation was evaluated as "potentially pathogenic" based on the classification criteria and guidelines for genetic variation by American College of Medical Genetics and Genomics.

CONCLUSIONS

A novel mutation is detected in the exon of the TUBB1 gene in children with CH and TD in Shandong, suggesting that the TUBB1 gene may be a candidate pathogenic gene for CH children with TD.

Epilepsy in Tubulinopathy: Personal Series and Literature Review

Mutations in tubulin genes are responsible for a large spectrum of brain malformations secondary to abnormal neuronal migration, organization, differentiation and axon guidance and maintenance. Motor impairment, intellectual disability and epilepsy are the main clinical symptoms. In the present study 15 patients from a personal cohort and 75 from 21 published studies carrying mutations in TUBA1A, TUBB2B and TUBB3 tubulin genes were evaluated with the aim to define a clinical and electrophysiological associated pattern. Epilepsy shows a wide range of severity without a specific pattern. Mutations in TUBA1A (60%) and TUBB2B (74%) and TUBB3 (25%) genes are associated with epilepsy. The accurate analysis of the Electroencephalogram (EEG) pattern in wakefulness and sleep in our series allows us to detect significant abnormalities of the background activity in 100% of patients. The involvement of white matter and of the inter-hemispheric connection structures typically observed in tubulinopathies is evidenced by the high percentage of asynchronisms in the organization of sleep activity recorded. In addition to asymmetries of the background activity, excess of slowing, low amplitude and Magnetic Resonance (MR) imaging confirm the presence of extensive brain malformations involving subcortical and midline structures. In conclusion, epilepsy in tubulinopathies when present has a favorable evolution over time suggesting a not particularly aggressive therapeutic approach.

Editorial for Brain Sciences Special Issue: "Diagnosis of Neurogenetic Disorders: Contribution of Next-Generation Sequencing and Deep Phenotyping"

In this Special Issue we bring together papers demonstrating the need for both detailed genomic and phenotypic studies to aid our scientific and clinical understanding of neurogenetic disorders [...].