Retinal gene therapy with adeno-associated viral (AAV) vectors holds promises for treating inherited and noninherited diseases of the eye. Although clinical data suggest that retinal gene therapy is safe and effective, delivery of large genes is hindered by the limited AAV cargo capacity. Protein trans-splicing mediated by split inteins is used by single-cell organisms to reconstitute proteins. Here, we show that delivery of multiple AAV vectors each encoding one of the fragments of target proteins flanked by short split inteins results in protein trans-splicing and full-length protein reconstitution in the retina of mice and pigs and in human retinal organoids. The reconstitution of large therapeutic proteins using this approach improved the phenotype of two mouse models of inherited retinal diseases. Our data support the use of split intein–mediated protein trans-splicing in combination with AAV subretinal delivery for gene therapy of inherited blindness due to mutations in large genes.

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MicroRNAs (miRNAs) are attractive therapeutic targets and promising candidates as molecular biomarkers for various therapy-resistant tumors. However, the association between miRNAs and drug resistance in melanoma remains to be elucidated. We used an integrative genomic analysis to comprehensively study the miRNA expression profiles of drug-resistant melanoma patients and cell lines. MicroRNA-181a and -181b (miR181a/b) were identified as the most significantly down-regulated miRNAs in resistant melanoma patients and cell lines. Re-establishment of miR-181a/b expression reverses the resistance of melanoma cells to the BRAF inhibitor dabrafenib. Introduction of miR-181 mimics markedly decreases the expression of TFAM in A375 melanoma cells resistant to BRAF inhibitors. Furthermore, melanoma growth was inhibited in A375 and M14 resistant melanoma cells transfected with miR-181a/b mimics, while miR-181a/b depletion enhanced resistance in sensitive cell lines. Collectively, our study demonstrated that miR-181a/b could reverse the resistance to BRAF inhibitors in dabrafenib resistant melanoma cell lines. In addition, miR-181a and -181b are strongly down-regulated in tumor samples from patients before and after the development of resistance to targeted therapies. Finally, melanoma tissues with high miR-181a and -181b expression presented favorable outcomes in terms of Progression Free Survival, suggesting that miR-181 is a clinically relevant candidate for therapeutic development or biomarker-based therapy selection.

Background Mutations of the mitochondrial protein paraplegin cause hereditary spastic paraplegia type 7 (SPG7), a so-far untreatable degenerative disease of the upper motoneuron with still undefined pathomechanism. The intermittent mitochondrial permeability transition pore (mPTP) opening, called flickering, is an essential process that operates to maintain mitochondrial homeostasis by reducing intra-matrix Ca 2+ and reactive oxygen species (ROS) concentration, and is critical for efficient synaptic function. Methods We use a fluorescence-based approach to measure mPTP flickering in living cells and biochemical and molecular biology techniques to dissect the pathogenic mechanism of SPG7. In the SPG7 animal model we evaluate the potential improvement of the motor defect, neuroinflammation and neurodegeneration by means of an mPTP inducer, the benzodiazepine Bz-423. Findings We demonstrate that paraplegin is required for efficient transient opening of the mPTP, that is impaired in both SPG7 patients-derived fibroblasts and primary neurons from Spg7 −/− mice. We show that dysregulation of mPTP opening at the pre-synaptic terminal impairs neurotransmitter release leading to ineffective synaptic transmission. Lack of paraplegin impairs mPTP flickering by a mechanism involving increased expression and activity of sirtuin3, which promotes deacetylation of cyclophilin D, thus hampering mPTP opening. Pharmacological treatment with Bz-423, which bypasses the activity of CypD, normalizes synaptic transmission and rescues the motor impairment of the SPG7 mouse model. Interpretation mPTP targeting opens a new avenue for the potential therapy of this form of spastic paraplegia. Funding Telethon Foundation grant (TGMGCSBX16TT); Dept. of Defense, US Army, grant W81XWH-18–1–0001

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks.