Gene-targeted therapy for neurofibromatosis and schwannomatosis: The path to clinical trials.

Numerous successful gene-targeted therapies are arising for the treatment of a variety of rare diseases. At the same time, current treatment options for neurofibromatosis 1 and schwannomatosis are limited and do not directly address loss of gene/protein function. In addition, treatments have mostly focused on symptomatic tumors, but have failed to address multisystem involvement in these conditions. Gene-targeted therapies hold promise to address these limitations. However, despite intense interest over decades, multiple preclinical and clinical issues need to be resolved before they become a reality. The optimal approaches to gene-, mRNA-, or protein restoration and to delivery to the appropriate cell types remain elusive. Preclinical models that recapitulate manifestations of neurofibromatosis 1 and schwannomatosis need to be refined. The development of validated assays for measuring neurofibromin and merlin activity in animal and human tissues will be critical for early-stage trials, as will the selection of appropriate patients, based on their individual genotypes and risk/benefit balance. Once the safety of gene-targeted therapy for symptomatic tumors has been established, the possibility of addressing a wide range of symptoms, including non-tumor manifestations, should be explored. As preclinical efforts are underway, it will be essential to educate both clinicians and those affected by neurofibromatosis 1/schwannomatosis about the risks and benefits of gene-targeted therapy for these conditions.

Targeted exon skipping of NF1 exon 17 as a therapeutic for neurofibromatosis type I.

We investigated the feasibility of utilizing an exon-skipping approach as a genotype-dependent therapeutic for neurofibromatosis type 1 (NF1) by determining which NF1 exons might be skipped while maintaining neurofibromin protein expression and GTPase activating protein (GAP)-related domain (GRD) function. Initial in silico analysis predicted exons that can be skipped with minimal loss of neurofibromin function, which was confirmed by in vitro assessments utilizing an Nf1 cDNA-based functional screening system. Skipping of exons 17 or 52 fit our criteria, as minimal effects on protein expression and GRD activity were noted. Antisense phosphorodiamidate morpholino oligomers (PMOs) were utilized to skip exon 17 in human cell lines with patient-specific pathogenic variants in exon 17, c.1885G>A, and c.1929delG. PMOs restored functional neurofibromin expression. To determine the in vivo significance of exon 17 skipping, we generated a homozygous deletion of exon 17 in a novel mouse model. Mice were viable and exhibited a normal lifespan. Initial studies did not reveal the presence of tumor development; however, altered nesting behavior and systemic lymphoid hyperplasia was noted in peripheral lymphoid organs. Alterations in T and B cell frequencies in the thymus and spleen were identified. Hence, exon skipping should be further investigated as a therapeutic approach for NF1 patients with pathogenic variants in exon 17, as homozygous deletion of exon 17 is consistent with at least partial function of neurofibromin.

Mutation-Directed Therapeutics for Neurofibromatosis Type I.

Significant advances in biotechnology have led to the development of a number of different mutation-directed therapies. Some of these techniques have matured to a level that has allowed testing in clinical trials, but few have made it to approval by drug-regulatory bodies for the treatment of specific diseases. While there are still various hurdles to be overcome, recent success stories have proven the potential power of mutation-directed therapies and have fueled the hope of finding therapeutics for other genetic disorders. In this review, we summarize the state-of-the-art of various therapeutic approaches and assess their applicability to the genetic disorder neurofibromatosis type I (NF1). NF1 is caused by the loss of function of neurofibromin, a tumor suppressor and downregulator of the Ras signaling pathway. The condition is characterized by a variety of phenotypes and includes symptoms such as skin spots, nervous system tumors, skeletal dysplasia, and others. Hence, depending on the patient, therapeutics may need to target different tissues and cell types. While we also discuss the delivery of therapeutics, in particular via viral vectors and nanoparticles, our main focus is on therapeutic techniques that reconstitute functional neurofibromin, most notably cDNA replacement, CRISPR-based DNA repair, RNA repair, antisense oligonucleotide therapeutics including exon skipping, and nonsense suppression.

PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins.

MOTIVATION: Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, 'non-classical' secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins; however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of 'non-classical' secreted proteins from sequence data. RESULTS: In this work, we first constructed a high-quality dataset of experimentally verified 'non-classical' secreted proteins, which we then used to create benchmark datasets. Using these benchmark datasets, we comprehensively analyzed a wide range of features and assessed their individual performance. Subsequently, we developed a two-layer Light Gradient Boosting Machine (LightGBM) ensemble model that integrates several single feature-based models into an overall prediction framework. At this stage, LightGBM, a gradient boosting machine, was used as a machine learning approach and the necessary parameter optimization was performed by a particle swarm optimization strategy. All single feature-based LightGBM models were then integrated into a unified ensemble model to further improve the predictive performance. Consequently, the final ensemble model achieved a superior performance with an accuracy of 0.900, an F-value of 0.903, Matthew's correlation coefficient of 0.803 and an area under the curve value of 0.963, and outperforming previous state-of-the-art predictors on the independent test. Based on our proposed optimal ensemble model, we further developed an accessible online predictor, PeNGaRoo, to serve users' demands. We believe this online web server, together with our proposed methodology, will expedite the discovery of non-classically secreted effector proteins in Gram-positive bacteria and further inspire the development of next-generation predictors. AVAILABILITY AND IMPLEMENTATION: http://pengaroo.erc.monash.edu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

Motivation: Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis. Results: In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation. Availability and implementation: The Quokka webserver and datasets are freely available at http://quokka.erc.monash.edu/. Supplementary information: Supplementary data are available at Bioinformatics online.

iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences.

Summary: Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. Availability and implementation: http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/. Supplementary information: Supplementary data are available at Bioinformatics online.

Integrative physical oncology.

Cancer is arguably the ultimate complex biological system. Solid tumors are microstructured soft matter that evolves as a consequence of spatio-temporal events at the intracellular (e.g., signaling pathways, macromolecular trafficking), intercellular (e.g., cell-cell adhesion/communication), and tissue (e.g., cell-extracellular matrix interactions, mechanical forces) scales. To gain insight, tumor and developmental biologists have gathered a wealth of molecular, cellular, and genetic data, including immunohistochemical measurements of cell type-specific division and death rates, lineage tracing, and gain-of-function/loss-of-function mutational analyses. These data are empirically extrapolated to a diagnosis/prognosis of tissue-scale behavior, e.g., for clinical decision. Integrative physical oncology (IPO) is the science that develops physically consistent mathematical approaches to address the significant challenge of bridging the nano (nm)-micro (µm) to macro (mm, cm) scales with respect to tumor development and progression. In the current literature, such approaches are referred to as multiscale modeling. In the present article, we attempt to assess recent modeling approaches on each separate scale and critically evaluate the current 'hybrid-multiscale' models used to investigate tumor growth in the context of brain and breast cancers. Finally, we provide our perspective on the further development and the impact of IPO.