Multi-omic approach characterises the neuroprotective role of retromer in regulating lysosomal health.

Retromer controls cellular homeostasis through regulating integral membrane protein sorting and transport and by controlling maturation of the endo-lysosomal network. Retromer dysfunction, which is linked to neurodegenerative disorders including Parkinson's and Alzheimer's diseases, manifests in complex cellular phenotypes, though the precise nature of this dysfunction, and its relation to neurodegeneration, remain unclear. Here, we perform an integrated multi-omics approach to provide precise insight into the impact of Retromer dysfunction on endo-lysosomal health and homeostasis within a human neuroglioma cell model. We quantify widespread changes to the lysosomal proteome, indicative of broad lysosomal dysfunction and inefficient autophagic lysosome reformation, coupled with a reconfigured cell surface proteome and secretome reflective of increased lysosomal exocytosis. Through this global proteomic approach and parallel transcriptomic analysis, we provide a holistic view of Retromer function in regulating lysosomal homeostasis and emphasise its role in neuroprotection.

3D ECM-rich environment sustains the identity of naive human iPSCs.

The establishment of in vitro naive human pluripotent stem cell cultures opened new perspectives for the study of early events in human development. The role of several transcription factors and signaling pathways have been characterized during maintenance of human naive pluripotency. However, little is known about the role exerted by the extracellular matrix (ECM) and its three-dimensional (3D) organization. Here, using an unbiased and integrated approach combining microfluidic cultures with transcriptional, proteomic, and secretome analyses, we found that naive, but not primed, hiPSC colonies are characterized by a self-organized ECM-rich microenvironment. Based on this, we developed a 3D culture system that supports robust long-term feeder-free self-renewal of naive hiPSCs and also allows direct and timely developmental morphogenesis simply by modulating the signaling environment. Our study opens new perspectives for future applications of naive hiPSCs to study critical stages of human development in 3D starting from a single cell.

Transcriptome Analysis Reveals Altered Expression of Genes Involved in Hypoxia, Inflammation and Immune Regulation in Pdcd10-Depleted Mouse Endothelial Cells.

Cerebral cavernous malformations (CCM) are capillary malformations affecting the central nervous system and commonly present with headaches, epilepsy and stroke. Treatment of CCM is symptomatic, and its prevention is limited. CCM are often sporadic but sometimes may be multifocal and/or affect multiple family members. Heterozygous pathogenic variants in PDCD10 cause the rarest and apparently most severe genetic variant of familial CCM. We carried out an RNA-Seq and a Q-PCR validation analysis in Pdcd10-silenced and wild-type mouse endothelial cells in order to better elucidate CCM molecular pathogenesis. Ninety-four differentially expressed genes presented an FDR-corrected p-value < 0.05. A functionally clustered dendrogram showed that differentially expressed genes cluster in cell proliferation, oxidative stress, vascular processes and immune response gene-ontology functions. Among differentially expressed genes, the major cluster fell in signaling related to inflammation and pathogen recognition, including HIF1α and Nos2 signaling and immune regulation. Validation analysis performed on wild-type, Pdcd10-null and Pdcd10-null reconstituted cell lines was consistent with RNA-Seq data. This work confirmed previous mouse transcriptomic data in endothelial cells, which are recognized as a critical tissue for CCM formation and expands the potential molecular signatures of PDCD10-related familial CCM to alterations in inflammation and pathogen recognition pathways.

Therapeutic homology-independent targeted integration in retina and liver.

Challenges to the widespread application of gene therapy with adeno-associated viral (AAV) vectors include dominant conditions due to gain-of-function mutations which require allele-specific knockout, as well as long-term transgene expression from proliferating tissues, which is hampered by AAV DNA episomal status. To overcome these challenges, we used CRISPR/Cas9-mediated homology-independent targeted integration (HITI) in retina and liver as paradigmatic target tissues. We show that AAV-HITI targets photoreceptors of both mouse and pig retina, and this results in significant improvements to retinal morphology and function in mice with autosomal dominant retinitis pigmentosa. In addition, we show that neonatal systemic AAV-HITI delivery achieves stable liver transgene expression and phenotypic improvement in a mouse model of a severe lysosomal storage disease. We also show that HITI applications predominantly result in on-target editing. These results lay the groundwork for the application of AAV-HITI for the treatment of diseases affecting various organs.

SARS-CoV-2 infection and replication in human gastric organoids.

COVID-19 typically manifests as a respiratory illness, but several clinical reports have described gastrointestinal symptoms. This is particularly true in children in whom gastrointestinal symptoms are frequent and viral shedding outlasts viral clearance from the respiratory system. These observations raise the question of whether the virus can replicate within the stomach. Here we generate gastric organoids from fetal, pediatric, and adult biopsies as in vitro models of SARS-CoV-2 infection. To facilitate infection, we induce reverse polarity in the gastric organoids. We find that the pediatric and late fetal gastric organoids are susceptible to infection with SARS-CoV-2, while viral replication is significantly lower in undifferentiated organoids of early fetal and adult origin. We demonstrate that adult gastric organoids are more susceptible to infection following differentiation. We perform transcriptomic analysis to reveal a moderate innate antiviral response and a lack of differentially expressed genes belonging to the interferon family. Collectively, we show that the virus can efficiently infect the gastric epithelium, suggesting that the stomach might have an active role in fecal-oral SARS-CoV-2 transmission.

MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning.

BACKGROUND: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. METHODS: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purposes we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. RESULTS: The neural networks had a Met exon 14 skipping detection rate greater than 94% when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interestingly they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1 (Long Interspersed Nuclear Element 1)-MET (Mesenchymal Epithelial Transition receptor tyrosine kinase) fusion. CONCLUSIONS: Taken together, our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events, and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool.

The Microfluidic Environment Reveals a Hidden Role of Self-Organizing Extracellular Matrix in Hepatic Commitment and Organoid Formation of hiPSCs.

The specification of the hepatic identity during human liver development is strictly controlled by extrinsic signals, yet it is still not clear how cells respond to these exogenous signals by activating secretory cascades, which are extremely relevant, especially in 3D self-organizing systems. Here, we investigate how the proteins secreted by human pluripotent stem cells (hPSCs) in response to developmental exogenous signals affect the progression from endoderm to the hepatic lineage, including their competence to generate nascent hepatic organoids. By using microfluidic confined environment and stable isotope labeling with amino acids in cell culture-coupled mass spectrometry (SILAC-MS) quantitative proteomic analysis, we find high abundancy of extracellular matrix (ECM)-associated proteins. Hepatic progenitor cells either derived in microfluidics or exposed to exogenous ECM stimuli show a significantly higher potential of forming hepatic organoids that can be rapidly expanded for several passages and further differentiated into functional hepatocytes. These results prove an additional control over the efficiency of hepatic organoid formation and differentiation for downstream applications.

HiCeekR: A Novel Shiny App for Hi-C Data Analysis.

The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (https://github.com/lucidif/HiCeekR), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset.