Late-onset hereditary ataxias with dementia

PURPOSE OF REVIEW

Late-onset genetic cerebellar ataxias are clinically heterogenous with variable phenotypes. Several of these conditions are commonly associated with dementia. Recognition of the relationship between ataxia and dementia can guide clinical genetic evaluation.

RECENT FINDINGS

Spinocerebellar ataxias often present with variable phenotypes that may include dementia. Genomic studies have begun to identify links between incomplete penetrance and such variable phenotypes in certain hereditary ataxias. Recent studies evaluating the interaction of TBP repeat expansions and STUB1 sequence variants provide a framework to understand how genetic interactions influence disease penetrance and dementia risk in spinocerebellar ataxia types 17 and 48. Further advances in next generation sequencing methods will continue to improve diagnosis and create new insights into the expressivity of existing disorders.

SUMMARY

The late-onset hereditary ataxias are a clinically heterogenous group of disorders with complex presentations that can include cognitive impairment and/or dementia. Genetic evaluation of late-onset ataxia patients with dementia follows a systemic testing approach that often utilizes repeat expansion testing followed by next-generation sequencing. Advances in bioinformatics and genomics is improving both diagnostic evaluation and establishing a basis for phenotypic variability. Whole genome sequencing will likely replace exome sequencing as a more comprehensive means of routine testing.

The splicing regulator PTBP1 controls the activity of the transcription factor Pbx1 during neuronal differentiation

The RNA-binding proteins PTBP1 and PTBP2 control programs of alternative splicing during neuronal development. PTBP2 was found to maintain embryonic splicing patterns of many synaptic and cytoskeletal proteins during differentiation of neuronal progenitor cells (NPCs) into early neurons. However, the role of the earlier PTBP1 program in embryonic stem cells (ESCs) and NPCs was not clear. We show that PTBP1 controls a program of neuronal gene expression that includes the transcription factor Pbx1. We identify exons specifically regulated by PTBP1 and not PTBP2 as mouse ESCs differentiate into NPCs. We find that PTBP1 represses Pbx1 exon 7 and the expression of the neuronal Pbx1a isoform in ESCs. Using CRISPR-Cas9 to delete regulatory elements for exon 7, we induce Pbx1a expression in ESCs, finding that this activates transcription of neuronal genes. Thus, PTBP1 controls the activity of Pbx1 to suppress its neuronal transcriptional program prior to induction of NPC development.

DOI:http://dx.doi.org/10.7554/eLife.09268.001

The neurons that transmit information around the nervous system develop in several stages. Embryonic stem cells specialize to form neuronal progenitor cells, which then develop into neurons. These cell types have different characteristics, in part because they make different proteins or different versions of the same proteins.

To make a protein, the DNA sequence of a gene is used to build a molecule of ribonucleic acid (RNA) that acts as a template for the protein. However, not all of this sequence codes for the protein. The non-coding regions must be removed from the RNA, and the remaining “exons” joined together to form the final “mRNA” template. Not all of the exons are necessarily included in the final mRNA molecule. By joining together different combinations of exons, several different versions of a protein can be produced from a single gene. This process is known as alternative splicing.

One way that alternative splicing is controlled is through proteins that bind to RNA and determine which exons are included or excluded from the final mRNA molecule. PTBP1 is an RNA-binding protein that controls alternative splicing in embryonic stem cells and neuronal progenitor cells. Embryonic stem cells have the ability to develop into all the cells of the body. In contrast, neuronal progenitor cells are restricted in their development and only give rise to specialized cells of the nervous system. The role of PTBP1 in these properties was not clear.

Linares et al. have now used a range of techniques to study the RNA molecules produced in these two cell types and how these RNAs change when PTBP1 is removed. This identified many RNAs whose splicing is regulated by PTBP1, including mRNAs of the gene that produces a protein called Pbx1, which is an important regulator of neuronal development.

Further investigation revealed that PTBP1 prevents a particular exon being included in the mRNA template for Pbx1. This creates an embryonic stem cell form of Pbx1 that does not affect neuronal genes. Removal of PTBP1 allows splicing of the Pbx1 exon and produces a version of Pbx1 that is found in neuronal progenitor cells and which turns on neuronal genes. Thus, through its action on Pbx1, one role of PTBP1 is to enable stem cells to maintain their non-neuronal properties and prevent their premature development into neuronal progenitor cells.

The gene for Pbx1 is only one of many genes controlled by PTBP1 at the level of splicing. One challenge for the future will be to understand how these genes work together in a common program that determines the properties of stem cells. Another question regards how the different Pbx1 proteins in stem cells and in neuronal progenitors can exert different effects in the cells where they are made.

DOI:http://dx.doi.org/10.7554/eLife.09268.002

De novo prediction of PTBP1 binding and splicing targets reveals unexpected features of its RNA recognition and function

The splicing regulator Polypyrimidine Tract Binding Protein (PTBP1) has four RNA binding domains that each binds a short pyrimidine element, allowing recognition of diverse pyrimidine-rich sequences. This variation makes it difficult to evaluate PTBP1 binding to particular sites based on sequence alone and thus to identify target RNAs. Conversely, transcriptome-wide binding assays such as CLIP identify many in vivo targets, but do not provide a quantitative assessment of binding and are informative only for the cells where the analysis is performed. A general method of predicting PTBP1 binding and possible targets in any cell type is needed. We developed computational models that predict the binding and splicing targets of PTBP1. A Hidden Markov Model (HMM), trained on CLIP-seq data, was used to score probable PTBP1 binding sites. Scores from this model are highly correlated (ρ = −0.9) with experimentally determined dissociation constants. Notably, we find that the protein is not strictly pyrimidine specific, as interspersed Guanosine residues are well tolerated within PTBP1 binding sites. This model identifies many previously unrecognized PTBP1 binding sites, and can score PTBP1 binding across the transcriptome in the absence of CLIP data. Using this model to examine the placement of PTBP1 binding sites in controlling splicing, we trained a multinomial logistic model on sets of PTBP1 regulated and unregulated exons. Applying this model to rank exons across the mouse transcriptome identifies known PTBP1 targets and many new exons that were confirmed as PTBP1-repressed by RT-PCR and RNA-seq after PTBP1 depletion. We find that PTBP1 dependent exons are diverse in structure and do not all fit previous descriptions of the placement of PTBP1 binding sites. Our study uncovers new features of RNA recognition and splicing regulation by PTBP1. This approach can be applied to other multi-RRM domain proteins to assess binding site degeneracy and multifactorial splicing regulation.

A key step in the regulation of mammalian genes is the splicing of the messenger RNA precursor to produce a mature mRNA that can be translated into a particular protein needed by the cell. Through the process of alternative splicing, mRNAs encoding different proteins can be derived from the same primary gene transcript. The regulation of this process plays essential roles in the development of differentiated tissues and is mediated by special pre-mRNA binding proteins. To understand how these proteins control gene expression, one must characterize what they recognize in RNA and identify these binding sites across the genome in order to predict their targets. Models that allow this prediction are essential to understanding developmental regulatory programs and their perturbation by disease causing mutations. In this study, we use statistical methods to build models of RNA recognition by the important splicing regulator PTBP1 and then apply these models to predict PTBP1 regulation of new gene transcripts. We show that PTBP1 has different specificity for RNA than was previously recognized and that its target exons are more diverse than was known before. There are many similar splicing regulators in mammalian cells, and these analyses provide a general framework for the computational analysis of their RNA binding and target identification.

A simple model of backbone flexibility improves modeling of side-chain conformational variability

The considerable flexibility of side-chains in folded proteins is important for protein stability and function, and may have a role in mediating allosteric interactions. While sampling side-chain degrees of freedom has been an integral part of several successful computational protein design methods, the predictions of these approaches have not been directly compared to experimental measurements of side-chain motional amplitudes. In addition, protein design methods frequently keep the backbone fixed, an approximation that may substantially limit the ability to accurately model side-chain flexibility. Here, we describe a Monte Carlo approach to modeling side-chain conformational variability and validate our method against a large dataset of methyl relaxation order parameters derived from nuclear magnetic resonance (NMR) experiments (17 proteins and a total of 530 data points). We also evaluate a model of backbone flexibility based on Backrub motions, a type of conformational change frequently observed in ultra-high-resolution X-ray structures that accounts for correlated side-chain backbone movements. The fixed-backbone model performs reasonably well with an overall rmsd between computed and predicted side-chain order parameters of 0.26. Notably, including backbone flexibility leads to significant improvements in modeling side-chain order parameters for ten of the 17 proteins in the set. Greater accuracy of the flexible backbone model results from both increases and decreases in side-chain flexibility relative to the fixed-backbone model. This simple flexible-backbone model should be useful for a variety of protein design applications, including improved modeling of protein-protein interactions, design of proteins with desired flexibility or rigidity, and prediction of correlated motions within proteins.