MUC22, HLA-A, and HLA-DOB variants and COVID-19 in resilient super-agers from Brazil.

Background: Although aging correlates with a worse prognosis for Covid-19, super elderly still unvaccinated individuals presenting mild or no symptoms have been reported worldwide. Most of the reported genetic variants responsible for increased disease susceptibility are associated with immune response, involving type I IFN immunity and modulation; HLA cluster genes; inflammasome activation; genes of interleukins; and chemokines receptors. On the other hand, little is known about the resistance mechanisms against SARS-CoV-2 infection. Here, we addressed polymorphisms in the MHC region associated with Covid-19 outcome in super elderly resilient patients as compared to younger patients with a severe outcome. Methods: SARS-CoV-2 infection was confirmed by RT-PCR test. Aiming to identify candidate genes associated with host resistance, we investigated 87 individuals older than 90 years who recovered from Covid-19 with mild symptoms or who remained asymptomatic following positive test for SARS-CoV-2 as compared to 55 individuals younger than 60 years who had a severe disease or died due to Covid-19, as well as to the general elderly population from the same city. Whole-exome sequencing and an in-depth analysis of the MHC region was performed. All samples were collected in early 2020 and before the local vaccination programs started. Results: We found that the resilient super elderly group displayed a higher frequency of some missense variants in the MUC22 gene (a member of the mucins' family) as one of the strongest signals in the MHC region as compared to the severe Covid-19 group and the general elderly control population. For example, the missense variant rs62399430 at MUC22 is two times more frequent among the resilient super elderly (p = 0.00002, OR = 2.24). Conclusion: Since the pro-inflammatory basal state in the elderly may enhance the susceptibility to severe Covid-19, we hypothesized that MUC22 might play an important protective role against severe Covid-19, by reducing overactive immune responses in the senior population.

Synthesizing the connections between environmental disturbances and zoonotic spillover.

Zoonotic spillover is a phenomenon characterized by the transfer of pathogens between different animal species. Most human emerging infectious diseases originate from non-human animals, and human-related environmental disturbances are the driving forces of the emergence of new human pathogens. Synthesizing the sequence of basic events involved in the emergence of new human pathogens is important for guiding the understanding, identification, and description of key aspects of human activities that can be changed to prevent new outbreaks, epidemics, and pandemics. This review synthesizes the connections between environmental disturbances and increased risk of spillover events based on the One Health perspective. Anthropogenic disturbances in the environment (e.g., deforestation, habitat fragmentation, biodiversity loss, wildlife exploitation) lead to changes in ecological niches, reduction of the dilution effect, increased contact between humans and other animals, changes in the incidence and load of pathogens in animal populations, and alterations in the abiotic factors of landscapes. These phenomena can increase the risk of spillover events and, potentially, facilitate new infectious disease outbreaks. Using Brazil as a study model, this review brings a discussion concerning anthropogenic activities in the Amazon region and their potential impacts on spillover risk and spread of emerging diseases in this region.

Unveiling the influence of factor VIII physicochemical properties on hemophilia A phenotype through an in silico methodology.

BACKGROUND AND OBJECTIVES: Hemophilia A (HA) is an X-linked blood disorder. It is caused by pathogenic F8 gene variants, among which missense mutations are the most prevalent. The resulting amino acid substitutions may have different impacts on physicochemical properties and, consequently, on protein functionality. Regular prediction tools do not include structural elements and their physiological significance, which hampers our ability to functionally link variants to disease phenotype, opening an ample field for investigation. The present study aims to elucidate how physicochemical changes generated by substitutions in different protein domains relate to HA, and which of these features are more consequential to protein function and its impact on HA phenotype. METHODS: An in silico evaluation of 71 F8 variants found in patients with different HA phenotypes (mild, moderate, severe) was performed to understand protein modifications and functional impact. Homology modeling was used for the structural analysis of physicochemical changes including electrostatic potential, hydrophobicity, solvent-accessible/excluded surface areas, disulfide disruptions, and substitutions indexes. These variants and properties were analyzed by hierarchical clustering analysis (HCA) and principal component analysis (PCA), independently and in combination, to investigate their relative contribution. RESULTS: About 69% of variants show electrostatic changes, and almost all show hydrophobicity and surface area modifications. HCA combining all physicochemical properties analyzed was better in reflecting the impact of different variants in disease severity, more so than the single feature analysis. On the other hand, PCA led to the identification of prominent properties involved in the clustering results for variants of different domains. CONCLUSIONS: The methodology developed here enables the assessment of structural features not available in other prediction tools (e.g., surface distribution of electrostatic potential), evaluating what kind of physicochemical changes are involved in FVIII functional disruption. HCA results allow distinguishing substitutions according to their properties, and yielded clusters which were more homogeneous in phenotype. All evaluated properties are involved in determining disease severity. The nature, as well as the position of the variants in the protein, were shown to be relevant for physicochemical changes, demonstrating that all these aspects must be collectively considered to fine-tune an approach to predict HA severity.

pMHC Structural Comparisons as a Pivotal Element to Detect and Validate T-Cell Targets for Vaccine Development and Immunotherapy-A New Methodological Proposal.

The search for epitopes that will effectively trigger an immune response remains the "El Dorado" for immunologists. The development of promising immunotherapeutic approaches requires the appropriate targets to elicit a proper immune response. Considering the high degree of HLA/TCR diversity, as well as the heterogeneity of viral and tumor proteins, this number will invariably be higher than ideal to test. It is known that the recognition of a peptide-MHC (pMHC) by the T-cell receptor is performed entirely in a structural fashion, where the atomic interactions of both structures, pMHC and TCR, dictate the fate of the process. However, epitopes with a similar composition of amino acids can produce dissimilar surfaces. Conversely, sequences with no conspicuous similarities can exhibit similar TCR interaction surfaces. In the last decade, our group developed a database and in silico structural methods to extract molecular fingerprints that trigger T-cell immune responses, mainly referring to physicochemical similarities, which could explain the immunogenic differences presented by different pMHC-I complexes. Here, we propose an immunoinformatic approach that considers a structural level of information, combined with an experimental technology that simulates the presentation of epitopes for a T cell, to improve vaccine production and immunotherapy efficacy.

A new in silico approach to investigate molecular aspects of factor IX missense causative mutations and their impact on the hemophilia B severity.

Factor IX (encoded by F9) is a protein in the coagulation process, where its lack or deficiency leads to hemophilia B. This condition has been much less studied than hemophilia A, especially in Latin America. We analyzed the structural and functional impact of 54 missense mutations (18 reported by us previously, and 36 other mutations from the Factor IX database) through molecular modeling approaches. To accomplish this task, we examine the electrostatic patterns, hydrophobicity/hydrophilicity, disulfide, and H-bond differences of the Factor IX structures harboring the missense mutations found, correlating them with their clinical effects. The 54 mutated sequences were modeled and their physicochemical features were determined and used as input in clusterization tools. The electrostatic pattern seems to influence in disease severity, especially for mutations investigated in epidermal growth factors 1 and 2 (EGF1/2) domains. The combined use of all physicochemical information improved the clustering of structures associated to similar phenotypes, especially for mutations from GLA and EGF1-2 domains. The effect of mutations in the disease phenotype severity seems to be a complex interplay of molecular features, each one contributing to different impacts. This highlights that previous studies and tools analyzing individually single features for single mutations are missing elements that fulfill the whole picture.

Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy.

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient's own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide-ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide-MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC "hot-spots" for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.

Structural in silico analysis of cross-genotype-reactivity among naturally occurring HCV NS3-1073-variants in the context of HLA-A*02:01 allele.

Cellular immune response plays a central role in outcome of Hepatitis C Virus (HCV) infection. While specific T-cell responses are related to viral clearance, impaired responses can lead to chronic infection, turning HCV variability into a major obstacle for vaccine development. In a recent work, Fytili et al. (2008) studied the cross reactive potential of HCV specific CD8+ T-cells and observed a large variation in immunogenicity among 28 naturally occurring NS3(1073) variants. In this work, we intend to evaluate this immunogenic variation at molecular level, through bioinformatics approaches. The D1-EM-D2 strategy was used to build in silico MHC:peptide complexes (pMHC) of these HCV-derived peptides in the context of HLA-A*02:01 allele. The TCR-interacting surface of these complexes were evaluated using the GRASP2 program. Structural analysis indicated a sharing of topological and electrostatic features among complexes that induced strong response in vitro. Besides, complexes that induced low response presented an important positively charged spot in the center of TCR-interacting area. This spot was seen even in complexes with conservative amino acid changes and is consistent with the impairment of recognition by wild-type-specific T-cells, observed in vitro. Furthermore, the most remarkable difference in electrostatic potential was seen precisely in the only complex unable to induce in vitro stimulation. All these observations were confirmed by Principal Component Analysis (PCA) and this approach was also applied to a set of 45 non-related immunogenic viral epitopes, indicating possible new targets for cross-reactivity studies. Our results suggest structural in silico analysis of pMHC complexes as a reliable tool for vaccine development, affording to predict the impact of viral escape mutations and selection of epitopes with potential to induce cross-reactive immune responses.

Structural allele-specific patterns adopted by epitopes in the MHC-I cleft and reconstruction of MHC:peptide complexes to cross-reactivity assessment.

The immune system is engaged in a constant antigenic surveillance through the Major Histocompatibility Complex (MHC) class I antigen presentation pathway. This is an efficient mechanism for detection of intracellular infections, especially viral ones. In this work we describe conformational patterns shared by epitopes presented by a given MHC allele and use these features to develop a docking approach that simulates the peptide loading into the MHC cleft. Our strategy, to construct in silico MHC:peptide complexes, was successfully tested by reproducing four different crystal structures of MHC-I molecules available at the Protein Data Bank (PDB). An in silico study of cross-reactivity potential was also performed between the wild-type complex HLA-A2-NS31073 and nine MHC:peptide complexes presenting alanine exchange peptides. This indicates that structural similarities among the complexes can give us important clues about cross reactivity. The approach used in this work allows the selection of epitopes with potential to induce cross-reactive immune responses, providing useful tools for studies in autoimmunity and to the development of more comprehensive vaccines.