Covid-19 in cystic fibrosis patients compared to the general population: Severity and virus-host cell interactions.

BACKGROUND: People with cystic fibrosis (pwCF) are considered at risk of developing severe forms of respiratory viral infections. We studied the consequences of COVID-19 and virus-host cell interactions in CF vs. non-CF individuals. METHODS: We enrolled CF and non-CF individuals, with /without COVID-like symptoms, who underwent nasopharyngeal swab for detection of SARS-CoV-2. Gene expression was evaluated by RNA sequencing on the same nasopharyngeal swabs. Criteria for COVID-19 severity were hospitalization and requirement or increased need of oxygen therapy. RESULTS: The study included 171 patients (65 pwCF and 106 non-CF individuals). Among them, 10 pwCF (15.4 %) and 43 people without CF (40.6 %) tested positive at RT-PCR. Symptomatic infections were observed in 8 pwCF (with 2 requiring hospitalization) and in 11 individuals without CF (6 requiring hospitalization). Host transcriptomic analysis revealed that genes involved in protein translation, particularly ribosomal components, were downregulated in CF samples irrespective of SARS-CoV-2 status. In SARS-CoV-2 negative individuals, we found a significant difference in genes involved with motile cilia expression and function, which were upregulated in CF samples. Pathway enrichment analysis indicated that interferon signaling in response to SARS-CoV-2 infection was upregulated in both pwCF and non-CF subjects. CONCLUSIONS: COVID-19 does not seem to be more severe in CF, possibly due to factors intrinsic to this population: the lower expression of ribosomal genes may downregulate the protein translation machinery, thus creating an unfavorable environment for viral replication.

Cellular population dynamics shape the route to human pluripotency.

Human cellular reprogramming to induced pluripotency is still an inefficient process, which has hindered studying the role of critical intermediate stages. Here we take advantage of high efficiency reprogramming in microfluidics and temporal multi-omics to identify and resolve distinct sub-populations and their interactions. We perform secretome analysis and single-cell transcriptomics to show functional extrinsic pathways of protein communication between reprogramming sub-populations and the re-shaping of a permissive extracellular environment. We pinpoint the HGF/MET/STAT3 axis as a potent enhancer of reprogramming, which acts via HGF accumulation within the confined system of microfluidics, and in conventional dishes needs to be supplied exogenously to enhance efficiency. Our data suggest that human cellular reprogramming is a transcription factor-driven process that it is deeply dependent on extracellular context and cell population determinants.

Improved SARS-CoV-2 sequencing surveillance allows the identification of new variants and signatures in infected patients.

BACKGROUND: Genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the only approach to rapidly monitor and tackle emerging variants of concern (VOC) of the COVID-19 pandemic. Such scrutiny is crucial to limit the spread of VOC that might escape the immune protection conferred by vaccination strategies or previous virus exposure. It is also becoming clear now that efficient genomic surveillance would require monitoring of the host gene expression to identify prognostic biomarkers of treatment efficacy and disease progression. Here we propose an integrative workflow to both generate thousands of SARS-CoV-2 genome sequences per week and analyze host gene expression upon infection. METHODS: In this study we applied an integrated workflow for RNA extracted from nasal swabs to obtain in parallel the full genome of SARS-CoV-2 and transcriptome of host respiratory epithelium. The RNA extracted from each sample was reverse transcribed and the viral genome was specifically enriched through an amplicon-based approach. The very same RNA was then used for patient transcriptome analysis. Samples were collected in the Campania region, Italy, for viral genome sequencing. Patient transcriptome analysis was performed on about 700 samples divided into two cohorts of patients, depending on the viral variant detected (B.1 or delta). RESULTS: We sequenced over 20,000 viral genomes since the beginning of the pandemic, producing the highest number of sequences in Italy. We thus reconstructed the pandemic dynamics in the regional territory from March 2020 to December 2021. In addition, we have matured and applied novel proof-of-principle approaches to prioritize possible gain-of-function mutations by leveraging patients' metadata and isolated patient-specific signatures of SARS-CoV-2 infection. This allowed us to (i) identify three new viral variants that specifically originated in the Campania region, (ii) map SARS-CoV-2 intrahost variability during long-term infections and in one case identify an increase in the number of mutations in the viral genome, and (iii) identify host gene expression signatures correlated with viral load in upper respiratory ways. CONCLUSION: In conclusion, we have successfully generated an optimized and cost-effective strategy to monitor SARS-CoV-2 genetic variability, without the need of automation. Thus, our approach is suitable for any lab with a benchtop sequencer and a limited budget, allowing an integrated genomic surveillance on premises. Finally, we have also identified a gene expression signature defining SARS-CoV-2 infection in real-world patients' upper respiratory ways.

SARS-CoV-2 variants: what have we learnt so far? Commentary.

Besides the timely detection of different SARS-CoV-2 variants through surveillance systems, functional and modelling studies are essential to better inform public health response and preparedness. Here, the knowledge available so far on SARS-CoV-2 variants is discussed from different perspectives, in order to highlight the relevance of a multidisciplinary approach in countering the threat posed by this insidious virus.

Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields' immaturity, it may also encumber a newcomer to blend in.In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field.

Drosophila Mitf regulates the V-ATPase and the lysosomal-autophagic pathway.

An evolutionarily conserved gene network regulates the expression of genes involved in lysosome biogenesis, autophagy, and lipid metabolism. In mammals, TFEB and other members of the MiTF-TFE family of transcription factors control this network. Here we report that the lysosomal-autophagy pathway is controlled by Mitf gene in Drosophila melanogaster. Mitf is the single MiTF-TFE family member in Drosophila and prior to this work was known only for its function in eye development. We show that Mitf regulates the expression of genes encoding V-ATPase subunits as well as many additional genes involved in the lysosomal-autophagy pathway. Reduction of Mitf function leads to abnormal lysosomes and impairs autophagosome fusion and lipid breakdown during the response to starvation. In contrast, elevated Mitf levels increase the number of lysosomes, autophagosomes and autolysosomes, and decrease the size of lipid droplets. Inhibition of Drosophila MTORC1 induces Mitf translocation to the nucleus, underscoring conserved regulatory mechanisms between Drosophila and mammalian systems. Furthermore, we show Mitf-mediated clearance of cytosolic and nuclear expanded ATXN1 (ataxin 1) in a cellular model of spinocerebellar ataxia type 1 (SCA1). This remarkable observation illustrates the potential of the lysosomal-autophagy system to prevent toxic protein aggregation in both the cytoplasmic and nuclear compartments. We anticipate that the genetics of the Drosophila model and the absence of redundant MIT transcription factors will be exploited to investigate the regulation and function of the lysosomal-autophagy gene network.

Transcriptional activation of lysosomal exocytosis promotes cellular clearance.

Lysosomes are cellular organelles primarily involved in degradation and recycling processes. During lysosomal exocytosis, a Ca²âº-regulated process, lysosomes are docked to the cell surface and fuse with the plasma membrane (PM), emptying their content outside the cell. This process has an important role in secretion and PM repair. Here we show that the transcription factor EB (TFEB) regulates lysosomal exocytosis. TFEB increases the pool of lysosomes in the proximity of the PM and promotes their fusion with PM by raising intracellular Ca²âº levels through the activation of the lysosomal Ca²âº channel MCOLN1. Induction of lysosomal exocytosis by TFEB overexpression rescued pathologic storage and restored normal cellular morphology both in vitro and in vivo in lysosomal storage diseases (LSDs). Our data indicate that lysosomal exocytosis may directly modulate cellular clearance and suggest an alternative therapeutic strategy for disorders associated with intracellular storage.

Multiple sulfatase deficiency is due to hypomorphic mutations of the SUMF1 gene.

Sulfatases catalyze the hydrolysis of sulfate ester bonds from a wide variety of substrates and are implicated in several human inherited diseases. Multiple sulfatase deficiency (MSD) is a rare autosomal recessive disorder characterized by the simultaneous deficiency of all known sulfatases. MSD is caused by mutations in the Sulfatase Modifying Factor 1 (SUMF1) gene encoding the alpha-formylglycine generating enzyme (FGE), which is responsible for the post-translational modification of sulfatases. In all MSD patients, residual sulfatase activities are detectable, at variable levels. To correlate the nature of the residual sulfatase activities detected in MSD patients with residual FGE activity, four FGE mutants (i.e. p.S155P, p.R224W, p.R345C, p.R349W) found in homozygosis in MSD patients were analyzed. Using viral-mediated gene delivery, these mutants were over-expressed in mouse embryonic fibroblasts (MEFs) from a recently developed Sumf1 KO mouse line which is completely devoid of all sulfatase activities. The results obtained indicate that mutant SUMF1 cDNAs encode stable SUMF1 proteins which are of the appropriate molecular weight and are properly localized in the endoplasmic reticulum. Expression of these cDNAs in Sumf1-/- MEFs results in partial rescue of sulfatase activities. These data indicate that MSD is due to hypomorphic SUMF1 mutations and suggest that complete loss of SUMF1 function is likely to be lethal in humans.