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Additional file 1: Figure S1. A) Boxplots showing the percentage of samples with 100x genome coverage (left), million reads produced (centre) and mapped (right), divided by each tested solution. Only samples with average Ct value < 33 were considered. Figure S2. A) Histogram representing the cohort of patients of this study. Age (x axis) and sex (colors) are reported. B) Barcharts showing the distribution, in time, of the samples assigned to variants of concern and of interest identified in the study. C) Distribution of Ct values, in time, for all the samples collected during the pandemic in Campania. The trend line (red) and 95% confidence interval (light gray) are shown. Figure S3. A) Donut charts representing the amount of analyzed genomes presenting some mutation of concern, namely Spike L18F, S477N and P681H, divided by lineage. The definition of Expected lineage is described in the Methods. B) Phylogeny of the proposed lineage from Fig. 3A, the proposed lineage is in green. Bootstrap values for each node are shown as node points. C) Results from the clustering analysis for samples collected until May 2022, displayed as line plots of frequency over time (trends). The arrow indicates the investigated cluster in Fig. 3D. D) Phylogeny of the proposed lineage from Fig. 3D, the proposed lineage is in green. Bootstrap values for each node are shown as node points. E) Results from the clustering analysis for samples collected until January 2022, displayed as line plots of frequency over time (trends). The arrow indicates the investigated cluster F) Phylogeny of the proposed BA.1 sublineage, the proposed new variant is in green. Bootstrap values for each node are shown as node points. Figure S4. A) Schematic representation of RNA-seq data structure, pre- and post-filtering. B) Principal Component Analysis plots of B.1 and Delta datasets, colored by SARS-COV-2 infection positivity. C) Correlation analysis between CTs and gene expression of Delta patients, performed on 5525 genes, is shown as a barplot. For each gene (x axis), its correlation value (y axis) and significance (p-value < 0.0001, red) is reported. Bottom: highlight of the significant results. (16 genes). D) Pathway and gene set enrichment analysis performed for different databases using the gene signature previously identified. Each barplot shows the significance (x axis) and the percentage of overlap (fill color) between the input signature and the tested public genesets. E) Heatmap of z-scored, log2-transformed and normalized gene counts for the 16 significantly correlated genes from the analysis of Delta dataset. Values have been averaged in 3 groups of samples depending on the CT (x axis) or whether they were negative.

Additional file 3. Laboratory protocol adopted in this work for SARS-CoV-2 WGS library generation (“Solution B”).

Prostate health index (PHI) and, more recently, Proclarix have been proposed as serum biomarkers for prostate cancer (PCa). In this study, we aimed to evaluate Proclarix and PHI for predicting clinically significant prostate cancer (csPCa).Proclarix and PHI were measured using samples of 344 men from two different centers. All patients underwent prostate biopsy, and among those, 188 men with PCa on biopsy had an additional radical prostatectomy (RP). All men had a prostate-specific antigen (PSA) between 2 and 10 ng/ml. Evaluation of area under the curve (AUC) and performance at predefined cut-offs of Proclarix and PHI risk scores as well as the linear combination thereof was performed to predict csPCa. PSA density was used as an independent comparator.The cohort median age and PSA were 65 (interquartile range [IQR]: 60-71) and 5.6 (IQR: 4.3-7.2) ng/ml, respectively. CsPCa was diagnosed in 161 (47%) men based on the RP specimen. ROC analysis showed that Proclarix and PHI accurately predicted csPCa with no significant difference (AUC of 0.79 and 0.76, p = 0.378) but significantly better when compared to PSA density (AUC of 0.66, p 0.001). When using specific cut-offs, Proclarix (cut-off 10) revealed higher specificity and positive predictive value than PHI (cut-off 27) at similar sensitivities. The combination of Proclarix and PHI provided a significant increase in the AUC (p ≤ 0.007) compared to the individual tests alone and the highest clinical benefit was achieved.Results of this study show that both Proclarix and PHI accurately detect the presence of csPCa. The model combining Proclarix and PHI revealed the synergistic effect and improved the diagnostic performance of the individual tests.

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.