Characteristics of SARS-CoV-2 variants of concern B.1.1.7, B.1.351 or P.1: data from seven EU/EEA countries, weeks 38/2020 to 10/2021.

We compared 19,207 cases of SARS-CoV-2 variant B.1.1.7/S gene target failure (SGTF), 436 B.1.351 and 352 P.1 to non-variant cases reported by seven European countries. COVID-19 cases with these variants had significantly higher adjusted odds ratios for hospitalisation (B.1.1.7/SGTF: 1.7, 95% confidence interval (CI): 1.0-2.9; B.1.351: 3.6, 95% CI: 2.1-6.2; P.1: 2.6, 95% CI: 1.4-4.8) and B.1.1.7/SGTF and P.1 cases also for intensive care admission (B.1.1.7/SGTF: 2.3, 95% CI: 1.4-3.5; P.1: 2.2, 95% CI: 1.7-2.8).

Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients.

BACKGROUND: The amount of publicly available cancer-related "omics" data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. METHODS: Here we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA) - an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency. RESULTS: By applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible. CONCLUSIONS: We present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival.

Extraction and Analysis of RNA Isolated from Pure Bacteria-Derived Outer Membrane Vesicles.

Outer membrane vesicles (OMVs) are released by commensal as well as pathogenic Gram-negative bacteria. These vesicles contain numerous bacterial components, such as proteins, peptidoglycans, lipopolysaccharides, DNA, and RNA. To examine if OMV-associated RNA molecules are bacterial degradation products and/or are functionally active, it is necessary to extract RNA from pure OMVs for subsequent analysis. Therefore, we describe here an isolation method of ultrapure OMVs and the subsequent extraction of RNA and basic steps of RNA-Seq analysis. Bacterial culture, extracellular supernatant concentration, OMV purification, and the subsequent RNA extraction out of OMVs are described. Specific pitfalls within the protocol and RNA contamination sources are highlighted.