Plasma ACE2 predicts outcome of COVID-19 in hospitalized patients

AimsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to angiotensin converting enzyme 2 (ACE2) enabling entrance of the virus into cells and causing the infection termed coronavirus disease of 2019 (COVID-19). Here, we investigate associations between plasma ACE2 and outcome of COVID-19.Methods and resultsThis analysis used data from a large longitudinal study of 306 COVID-19 positive patients and 78 COVID-19 negative patients (MGH Emergency Department COVID-19 Cohort). Comprehensive clinical data were collected on this cohort, including 28-day outcomes. The samples were run on the Olink® Explore 1536 platform which includes measurement of the ACE2 protein. High admission plasma ACE2 in COVID-19 patients was associated with increased maximal illness severity within 28 days with OR = 1.8, 95%-CI: 1.4-2.3 (P ConclusionThis study suggests that measuring plasma ACE2 is potentially valuable in predicting COVID-19 outcomes. Further, ACE2 could be a link between COVID-19 illness severity and its established risk factors hypertension, pre-existing heart disease and pre-existing kidney disease

T-Cell Epitope Prediction: Rescaling Can Mask Biological Variation between MHC Molecules

Theoretical methods for predicting CD8+ T-cell epitopes are an important tool in vaccine design and for enhancing our understanding of the cellular immune system. The most popular methods currently available produce binding affinity predictions across a range of MHC molecules. In comparing results between these MHC molecules, it is common practice to apply a normalization procedure known as rescaling, to correct for possible discrepancies between the allelic predictors. Using two of the most popular prediction software packages, NetCTL and NetMHC, we tested the hypothesis that rescaling removes genuine biological variation from the predicted affinities when comparing predictions across a number of MHC molecules. We found that removing the condition of rescaling improved the prediction software's performance both qualitatively, in terms of ranking epitopes, and quantitatively, in the accuracy of their binding affinity predictions. We suggest that there is biologically significant variation among class 1 MHC molecules and find that retention of this variation leads to significantly more accurate epitope prediction

Identification of CD8+ T Cell Epitopes in the West Nile Virus Polyprotein by Reverse-Immunology Using NetCTL

West Nile virus (WNV) is a growing threat to public health and a greater understanding of the immune response raised against WNV is important for the development of prophylactic and therapeutic strategies.In a reverse-immunology approach, we used bioinformatics methods to predict WNV-specific CD8(+) T cell epitopes and selected a set of peptides that constitutes maximum coverage of 20 fully-sequenced WNV strains. We then tested these putative epitopes for cellular reactivity in a cohort of WNV-infected patients. We identified 26 new CD8(+) T cell epitopes, which we propose are restricted by 11 different HLA class I alleles. Aiming for optimal coverage of human populations, we suggest that 11 of these new WNV epitopes would be sufficient to cover from 48% to 93% of ethnic populations in various areas of the World.The 26 identified CD8(+) T cell epitopes contribute to our knowledge of the immune response against WNV infection and greatly extend the list of known WNV CD8(+) T cell epitopes. A polytope incorporating these and other epitopes could possibly serve as the basis for a WNV vaccine

A Comprehensive Genetic Analysis of Candidate Genes Regulating Response to Trypanosoma congolense Infection in Mice

About one-third of cattle in sub-Saharan Africa are at risk of contracting “Nagana”—a disease caused by Trypanosoma parasites similar to those that cause human “Sleeping Sickness.” Laboratory mice can also be infected by trypanosomes, and different mouse breeds show varying levels of susceptibility to infection, similar to what is seen between different breeds of cattle. Survival time after infection is controlled by the underlying genetics of the mouse breed, and previous studies have localised three genomic regions that regulate this trait. These three “Quantitative Trait Loci” (QTL), which have been called Tir1, Tir2 and Tir3 (for Trypanosoma Infection Response 1–3) are well defined, but nevertheless still contain over one thousand genes, any number of which may be influencing survival. This study has aimed to identify the specific differences associated with genes that are controlling mouse survival after T. congolense infection. We have applied a series of analyses to existing datasets, and combined them with novel sequencing, and other genetic data to create short lists of genes that share polymorphisms across susceptible mouse breeds, including two promising “candidate genes”: Pram1 at Tir1 and Cd244 at Tir3. These genes can now be tested to confirm their effect on response to trypanosome infection

Proteome Sampling by the HLA Class I Antigen Processing Pathway

The peptide repertoire that is presented by the set of HLA class I molecules of an individual is formed by the different players of the antigen processing pathway and the stringent binding environment of the HLA class I molecules. Peptide elution studies have shown that only a subset of the human proteome is sampled by the antigen processing machinery and represented on the cell surface. In our study, we quantified the role of each factor relevant in shaping the HLA class I peptide repertoire by combining peptide elution data, in silico predictions of antigen processing and presentation, and data on gene expression and protein abundance. Our results indicate that gene expression level, protein abundance, and rate of potential binding peptides per protein have a clear impact on sampling probability. Furthermore, once a protein is available for the antigen processing machinery in sufficient amounts, C-terminal processing efficiency and binding affinity to the HLA class I molecule determine the identity of the presented peptides. Having studied the impact of each of these factors separately, we subsequently combined all factors in a logistic regression model in order to quantify their relative impact. This model demonstrated the superiority of protein abundance over gene expression level in predicting sampling probability. Being able to discriminate between sampled and non-sampled proteins to a significant degree, our approach can potentially be used to predict the sampling probability of self proteins and of pathogen-derived proteins, which is of importance for the identification of autoimmune antigens and vaccination targets

Degenerate T-cell Recognition of Peptides on MHC Molecules Creates Large Holes in the T-cell Repertoire

The cellular immune system screens peptides presented by host cells on MHC molecules to assess if the cells are infected. In this study we examined whether the presented peptides contain enough information for a proper self/nonself assessment by comparing the presented human (self) and bacterial or viral (nonself) peptides on a large number of MHC molecules. For all MHC molecules tested, only a small fraction of the presented nonself peptides from 174 species of bacteria and 1000 viral proteomes (0.2%) is shown to be identical to a presented self peptide. Next, we use available data on T-cell receptor-peptide-MHC interactions to estimate how well T-cells distinguish between similar peptides. The recognition of a peptide-MHC by the T-cell receptor is flexible, and as a result, about one-third of the presented nonself peptides is expected to be indistinguishable (by T-cells) from presented self peptides. This suggests that T-cells are expected to remain tolerant for a large fraction of the presented nonself peptides, which provides an explanation for the “holes in the T-cell repertoire” that are found for a large fraction of foreign epitopes. Additionally, this overlap with self increases the need for efficient self tolerance, as many self-similar nonself peptides could initiate an autoimmune response. Degenerate recognition of peptide-MHC-I complexes by T-cells thus creates large and potentially dangerous overlaps between self and nonself

HLA Alleles Associated with Slow Progression to AIDS Truly Prefer to Present HIV-1 p24

Background: The mechanism behind the association between human leukocyte antigen (HLA) molecules and the rate of HIV-1 disease progression is still poorly understood. Recent data suggest that ‘‘protective’’ HLA molecules, i.e. those associated with a low HIV-1 viral load and relatively slow disease progression, tend to present epitopes from the Gag capsid protein. Although this suggests that preferential targeting of Gag delays disease progression, the apparent preference for Gag could also be a side-effect of the relatively high immunogenicity of the protein. Methods and Findings: To separate cause and effect, we predicted HIV-1 epitopes from the whole genome of HIV-1, and found that protective HLA alleles have a true preference for the p24 Gag protein, while non-protective HLA alleles preferentially target HIV-1 Nef. In line with this, we found a significant negative correlation between the predicted affinity of the best-binding p24 epitopes and the relative hazard of HIV-1 disease progression for a large number of HLA molecules. When the epitopes targeted by protective HLA alleles were mapped to the known p24 structure, we found that mutations in these epitopes are likely to disturb the p24 dimer structure, which is expected to severely reduce the fitness of the virus. Conclusions: Our results suggest that the intrinsic preference of different HLA molecules to present p24 peptides explains why some HLA molecules are more protective than others

Bacteria Modulate the CD8+ T Cell Epitope Repertoire of Host Cytosol-Exposed Proteins to Manipulate the Host Immune Response

The main adaptive immune response to bacteria is mediated by B cells and CD4+ T-cells. However, some bacterial proteins reach the cytosol of host cells and are exposed to the host CD8+ T-cells response. Both gram-negative and gram-positive bacteria can translocate proteins to the cytosol through type III and IV secretion and ESX-1 systems, respectively. The translocated proteins are often essential for the bacterium survival. Once injected, these proteins can be degraded and presented on MHC-I molecules to CD8+ T-cells. The CD8+ T-cells, in turn, can induce cell death and destroy the bacteria's habitat. In viruses, escape mutations arise to avoid this detection. The accumulation of escape mutations in bacteria has never been systematically studied. We show for the first time that such mutations are systematically present in most bacteria tested. We combine multiple bioinformatic algorithms to compute CD8+ T-cell epitope libraries of bacteria with secretion systems that translocate proteins to the host cytosol. In all bacteria tested, proteins not translocated to the cytosol show no escape mutations in their CD8+ T-cell epitopes. However, proteins translocated to the cytosol show clear escape mutations and have low epitope densities for most tested HLA alleles. The low epitope densities suggest that bacteria, like viruses, are evolutionarily selected to ensure their survival in the presence of CD8+ T-cells. In contrast with most other translocated proteins examined, Pseudomonas aeruginosa's ExoU, which ultimately induces host cell death, was found to have high epitope density. This finding suggests a novel mechanism for the manipulation of CD8+ T-cells by pathogens. The ExoU effector may have evolved to maintain high epitope density enabling it to efficiently induce CD8+ T-cell mediated cell death. These results were tested using multiple epitope prediction algorithms, and were found to be consistent for most proteins tested