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Decock, A.

Publications and source records attributed to Decock, A..

4 recordsLinked to original sources

A systemically delivered AAV-CFTR gene therapy for cystic fibrosis

Cystic fibrosis (CF) is the most common monogenic lung disease and results from mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR). There have been over 2000 variants identified in patients that result in loss of function of the CFTR protein leading to systemic disease and respiratory failure in adolescence. While some variants encode proteins with residual activity that can be corrected or potentiated by CFTR modulators, at least 10% of CF individuals cannot tolerate the modulators or have nonsense mutations which fail to make any protein. For all people with CF, a mutation agnostic gene replacement strategy could provide a cure for CF lung disease. Here, we propose using a systemic route of administration to deliver a functional CFTR minigene cargo with a lung tropic AAV capsid. This would serve to reach multiple organs, most importantly the lung epithelium, and would provide a functional CFTR transgene that could be expressed in any cell type with a ubiquitous promoter. To achieve this, we generated the smallest CFTR minigene tested in an AAV delivery to date. We demonstrate its expression and function following transfection in cell-based assays and restoration of function in primary CF airway cells after viral delivery. Furthermore, we identify an AAV capsid that can transduce alveolar and airway epithelium with systemic delivery in non-human primates. These data provide tools for delivering a functional CFTR minigene that fits within the packaging capacity of an AAV and demonstrate lung transduction with an AAV following systemic delivery in a large animal model. This strategy first and foremost can reach target airway cells by circumventing the strong mucosal barrier in CF airways but may also provide a method by which to restore CFTR function in additional CF affected organs.

genetics↗

Performance of RNA purification kits and blood collection tubes in the Extracellular RNA Quality Control (exRNAQC) study

The use of blood-based extracellular RNA (cell-free RNA; exRNA) as clinical biomarker requires the implementation of a validated procedure for sample collection, processing, and profiling. So far, no study has systematically addressed the pre-analytical variables affecting transcriptome analysis of exRNAs. In the exRNAQC study, we evaluated ten blood collection tubes, three time intervals between blood draw and downstream processing, and eight RNA purification methods using the supplier-specified minimum and maximum biofluid input volumes. The impact of these pre-analytics on deep transcriptome profiling of both small and messenger RNA from healthy donors plasma or serum was assessed for each pre-analytical variable separately and for interactions between a selected set of pre-analytics, resulting in 456 extracellular transcriptomes. Making use of 189 synthetic spike-in RNAs, the processing and analysis workflow was controlled. When comparing blood collection tubes, so-called preservation tubes do not stabilize exRNA well, and result in variable RNA concentration and sensitivity (i.e., the number of detected RNAs) over time, as well as compromised reproducibility. We also document large differences in RNA purification kit performance in terms of sensitivity, reproducibility, and observed transcriptome complexity, and demonstrate interactions between specific blood collection tubes, purification kits and time intervals. Our results are summarized in 11 performance metrics that enable an informed selection of the most optimal sample processing workflow for a given experiment. In conclusion, we put forward robust quality control metrics for exRNA quantification methods with validated standard operating procedures (SOPs), representing paramount groundwork for future exRNA-based precision medicine applications.

genomics↗

Performance assessment of total RNA sequencing of human biofluids and extracellular vesicles

RNA profiling has emerged as a powerful tool to investigate the biomarker potential of human biofluids. However, despite enormous interest in extracellular nucleic acids, RNA sequencing methods to quantify the total RNA content outside cells are rare. Here, we evaluate the performance of the SMARTer Stranded Total RNA-Seq method in human platelet-rich plasma, platelet-free plasma, urine, conditioned medium, and extracellular vesicles (EVs) from these biofluids. We found the method to be accurate, precise, compatible with low-input volumes and able to quantify a few thousand genes. We picked up distinct classes of RNA molecules, including mRNA, lncRNA, circRNA, miscRNA and pseudogenes. Notably, the read distribution and gene content drastically differ among biofluids. In conclusion, we are the first to show that the SMARTer method can be used for unbiased unraveling of the complete transcriptome of a wide range of biofluids and their extracellular vesicles.

molecular biology↗

Circulating microRNA biomarkers for metastatic disease in neuroblastoma patients

In this study, the circulating miRNome from diagnostic neuroblastoma serum was assessed for identification of non-invasive biomarkers with potential in monitoring metastatic disease. After determining the circulating neuroblastoma miRNome, 743 miRNAs were screened in two independent cohorts of 131 and 54 patients. Evaluation of serum miRNA variance in a model testing for tumor stage, MYCN status, age at diagnosis and overall survival, revealed tumor stage as the most significant factor impacting miRNA abundance in neuroblastoma serum. Differential expression analysis between patients with metastatic and localized disease revealed 9 miRNAs strongly associated with metastatic stage 4 disease in both patient cohorts. Increasing levels of these miRNAs were also observed in serum from xenografted mice bearing human neuroblastoma tumors. Moreover, murine serum miRNA levels were strongly associated with tumor volume, suggesting this miRNA signature may be applied to monitor disease burden.

cancer biology↗