Immune evasion and the evolution of molecular mimicry in parasites

Molecular Mimicry as a Mechanism of Viral Immune Evasion and Autoimmunity

Mimicry of host protein structures ("molecular mimicry") is a common mechanism employed by viruses to evade the host's immune system. To date, studies have primarily evaluated molecular mimicry in the context of full protein structural mimics. However, recent work has demonstrated that short linear amino acid (AA) molecular mimics can elicit cross-reactive antibodies and T-cells from the host, which may contribute to development and progression of autoimmunity. Despite this, the prevalence of molecular mimics throughout the human virome has not been fully explored. In this study, we evaluate 134 human infecting viruses and find significant usage of linear mimicry across the virome, particularly those in the herpesviridae and poxviridae families. Furthermore, we identify that proteins involved in cellular replication and inflammation, those expressed from autosomes, the X chromosome, and in thymic cells are over-enriched in viral mimicry. Finally, we demonstrate that short linear mimicry from Epstein-Barr virus (EBV) is significantly higher in auto-antibodies found in multiple sclerosis patients to a greater degree than previously appreciated. Our results demonstrate that human-infecting viruses frequently leverage mimicry in the course of their infection, point to substantial evolutionary pressure for mimicry, and highlight mimicry's important role in human autoimmunity. Clinically, our findings could translate to development of novel therapeutic strategies that target viral infections linked to autoimmunity, with the goal of eliminating disease-associated latent viruses and preventing their reactivation.

A systematic bioinformatics approach for large-scale identification and characterization of host-pathogen shared sequences

Background

Biology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications.

Results

This study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions.

Conclusion

Mapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07657-4.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

When Appearance Misleads: The Role of the Entomopathogen Surface in the Relationship with Its Host

Currently, potentially harmful insects are controlled mainly by chemical synthetic insecticides, but environmental emergencies strongly require less invasive control techniques. The use of biological insecticides in the form of entomopathogenic organisms is undoubtedly a fundamental resource for the biological control of insect pests in the future. These infectious agents and endogenous parasites generally act by profoundly altering the host's physiology to death, but their success is closely related to the neutralization of the target insect's immune response. In general, entomopathogen parasites, entomopathogenic bacteria, and fungi can counteract immune processes through the effects of secretion/excretion products that interfere with and damage the cells and molecules typical of innate immunity. However, these effects are observed in the later stages of infection, whereas the risk of being recognized and neutralized occurs very early after penetration and involves the pathogen surface components and molecular architecture; therefore, their role becomes crucial, particularly in the earliest pathogenesis. In this review, we analyze the evasion/interference strategies that entomopathogens such as the bacterium Bacillus thuringiensis, fungi, nematocomplexes, and wasps implement in the initial stages of infection, i.e., the phases during which body or cell surfaces play a key role in the interaction with the host receptors responsible for the immunological discrimination between self and non-self. In this regard, these organisms demonstrate evasive abilities ascribed to their body surface and cell wall; it appears that the key process of these mechanisms is the capability to modify the surface, converting it into an immunocompatible structure, or interaction that is more or less specific to host factors.

Structures and Strategies of Anti-CRISPR-Mediated Immune Suppression

More than 50 protein families have been identified that inhibit CRISPR (clustered regularly interspaced short palindromic repeats)-Cas-mediated adaptive immune systems. Here, we analyze the available anti-CRISPR (Acr) structures and describe common themes and unique mechanisms of stoichiometric and enzymatic suppressors of CRISPR-Cas. Stoichiometric inhibitors often function as molecular decoys of protein-binding partners or nucleic acid targets, while enzymatic suppressors covalently modify Cas ribonucleoprotein complexes or degrade immune signaling molecules. We review mechanistic insights that have been revealed by structures of Acrs, discuss some of the trade-offs associated with each of these strategies, and highlight how Acrs are regulated and deployed in the race to overcome adaptive immunity.

Human babesiosis: Indication of a molecular mimicry between thrombospondin domains from a novel Babesia microti BmP53 protein and host platelets molecules

Human babesiosis is caused by the apicomplexan parasite Babesia microti, which is of major public health concern in the United States and elsewhere, resulting in malaise and fatigue, followed by a fever and hemolytic anemia. In this paper we focus on the characterization of a novel B. microti thrombospondin domain (TSP1)-containing protein (BmP53) from the new annotation of the B. microti genome (locus 'BmR1_04g09041'). This novel protein (BmP53) had a single TSP1 and a transmembrane domain, with a short cytoplasmic tail containing a sub-terminal glutamine residue, but no signal peptide and Von Willebrand factor type A domains (VWA), which are found in classical thrombospondin-related adhesive proteins (TRAP). Co-localization assays of BmP53 and Babesia microti secreted antigen 1 (BmSA1) suggested that BmP53 might be a non-secretory membranous protein. Molecular mimicry between the TSP1 domain from BmP53 and host platelets molecules was indicated through different measures of sequence homology, phylogenetic analysis, 3D structure and shared epitopes. Indeed, hamster isolated platelets cross-reacted with mouse anti-BmP53-TSP1. Molecular mimicry are used to help parasites to escape immune defenses, resulting in immune evasion or autoimmunity. Furthermore, specific host reactivity was also detected against the TSP1-free part of BmP53 in infected hamster sera. In conclusion, the TSP1 domain mimicry might help in studying the mechanisms of parasite-induced thrombocytopenia, with the TSP1-free truncate of the protein representing a potential safe candidate for future vaccine studies.

Unresolved issues in theories of autoimmune disease using myocarditis as a framework

Many theories of autoimmune disease have been proposed since the discovery that the immune system can attack the body. These theories include the hidden or cryptic antigen theory, modified antigen theory, T cell bypass, T cell-B cell mismatch, epitope spread or drift, the bystander effect, molecular mimicry, anti-idiotype theory, antigenic complementarity, and dual-affinity T cell receptors. We critically review these theories and relevant mathematical models as they apply to autoimmune myocarditis. All theories share the common assumption that autoimmune diseases are triggered by environmental factors such as infections or chemical exposure. Most, but not all, theories and mathematical models are unifactorial assuming single-agent causation of disease. Experimental and clinical evidence and mathematical models exist to support some aspects of most theories, but evidence/models that support one theory almost invariably supports other theories as well. More importantly, every theory (and every model) lacks the ability to account for some key autoimmune disease phenomena such as the fundamental roles of innate immunity, sex differences in disease susceptibility, the necessity for adjuvants in experimental animal models, and the often paradoxical effect of exposure timing and dose on disease induction. We argue that a more comprehensive and integrated theory of autoimmunity associated with new mathematical models is needed and suggest specific experimental and clinical tests for each major theory that might help to clarify how they relate to clinical disease and reveal how theories are related.

Pathogen evolution and the immunological niche

Host immunity is a major driver of pathogen evolution and thus a major determinant of pathogen diversity. Explanations for pathogen diversity traditionally assume simple interactions between pathogens and the immune system, a view encapsulated by the susceptible-infected-recovered (SIR) model. However, there is growing evidence that the complexity of many host-pathogen interactions is dynamically important. This revised perspective requires broadening the definition of a pathogen's immunological phenotype, or what can be thought of as its immunological niche. After reviewing evidence that interactions between pathogens and host immunity drive much of pathogen evolution, I introduce the concept of a pathogen's immunological phenotype. Models that depart from the SIR paradigm demonstrate the utility of this perspective and show that it is particularly useful in understanding vaccine-induced evolution. This paper highlights questions in immunology, evolution, and ecology that must be answered to advance theories of pathogen diversity.