COVID-19 pathogenesis is associated with an exaggerated immune response. However, the specific cellular mediators and inflammatory components driving diverse clinical disease outcomes remain poorly understood. We undertook longitudinal immune profiling on both whole blood and peripheral blood mononuclear cells (PBMCs) of hospitalized patients during the peak of the COVID-19 pandemic in the UK. Here, we report key immune signatures present shortly after hospital admission that were associated with the severity of COVID-19. Immune signatures were related to shifts in neutrophil to T cell ratio, elevated serum IL-6, MCP-1 and IP-10, and most strikingly, modulation of CD14+ monocyte phenotype and function. Modified features of CD14+ monocytes included poor induction of the prostaglandin-producing enzyme, COX-2, as well as enhanced expression of the cell cycle marker Ki-67. Longitudinal analysis revealed reversion of some immune features back to the healthy median level in patients with a good eventual outcome. These findings identify previously unappreciated alterations in the innate immune compartment of COVID-19 patients and lend support to the idea that therapeutic strategies targeting release of myeloid cells from bone marrow should be considered in this disease. Moreover, they demonstrate that features of an exaggerated immune response are present early after hospital admission suggesting immune-modulating therapies would be most beneficial at early timepoints.

Previous studies have failed to identify mutations in the Wilson's disease gene ATP7B in a significant number of clinically diagnosed cases. This has led to concerns about genetic heterogeneity for this condition but also suggested the presence of unusual mutational mechanisms. We now present our findings in 181 patients from the United Kingdom with clinically and biochemically confirmed Wilson's disease. A total of 116 different ATP7B mutations were detected, 32 of which are novel. The overall mutation detection frequency was 98%. The likelihood of mutations in genes other than ATP7B causing a Wilson's disease phenotype is therefore very low. We report the first cases with Wilson's disease due to segmental uniparental isodisomy as well as three patients with three ATP7B mutations and three families with Wilson's disease in two consecutive generations. We determined the genetic prevalence of Wilson's disease in the United Kingdom by sequencing the entire coding region and adjacent splice sites of ATP7B in 1000 control subjects. The frequency of all single nucleotide variants with in silico evidence of pathogenicity (Class 1 variant) was 0.056 or 0.040 if only those single nucleotide variants that had previously been reported as mutations in patients with Wilson's disease were included in the analysis (Class 2 variant). The frequency of heterozygote, putative or definite disease-associated ATP7B mutations was therefore considerably higher than the previously reported occurrence of 1:90 (or 0.011) for heterozygote ATP7B mutation carriers in the general population (P < 2.2 × 10(-16) for Class 1 variants or P < 5 × 10(-11) for Class 2 variants only). Subsequent exclusion of four Class 2 variants without additional in silico evidence of pathogenicity led to a further reduction of the mutation frequency to 0.024. Using this most conservative approach, the calculated frequency of individuals predicted to carry two mutant pathogenic ATP7B alleles is 1:7026 and thus still considerably higher than the typically reported prevalence of Wilson's disease of 1:30 000 (P = 0.00093). Our study provides strong evidence for monogenic inheritance of Wilson's disease. It also has major implications for ATP7B analysis in clinical practice, namely the need to consider unusual genetic mechanisms such as uniparental disomy or the possible presence of three ATP7B mutations. The marked discrepancy between the genetic prevalence and the number of clinically diagnosed cases of Wilson's disease may be due to both reduced penetrance of ATP7B mutations and failure to diagnose patients with this eminently treatable disorder.

Motivation: High-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression (DE) estimation requires a probabilistic approach to properly account for ambiguity caused by shared exons and finite read sampling as well as the intrinsic biological variance of transcript expression.Results: We present Bayesian inference of transcripts from sequencing data (BitSeq), a Bayesian approach for estimation of transcript expression level from RNA-seq experiments. Inferred relative expression is represented by Markov chain Monte Carlo samples from the posterior probability distribution of a generative model of the read data. We propose a novel method for DE analysis across replicates which propagates uncertainty from the sample-level model while modelling biological variance using an expression-level-dependent prior. We demonstrate the advantages of our method using simulated data as well as an RNA-seq dataset with technical and biological replication for both studied conditions.Availability: The implementation of the transcriptome expression estimation and differential expression analysis, BitSeq, has been written in and . The software is available online from http://code.google.com/p/bitseq/, version 0.4 was used for generating results presented in this article.Contact: glaus@cs.man.ac.uk, antti.honkela@hiit.fi or m.rattray@sheffield.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.