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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01098-8.

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.