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The Flavivirus genus is made up of viruses that are either mosquito-borne or tick-borne and other viruses transmitted by unknown vectors. Flaviviruses present a significant threat to global health and infect up to 400 million of people annually. As the climate continues to change throughout the world, these viruses have become prominent infections, with increasing number of infections being detected beyond tropical borders. These include dengue virus (DENV), West Nile virus (WNV), Japanese encephalitis virus (JEV), and Zika virus (ZIKV). Several highly conserved epitopes of flaviviruses had been identified and reported to interact with antibodies, which lead to cross-reactivity results. The major interest of this review paper is mainly focused on the serological cross-reactivity between DENV serotypes, ZIKV, WNV, and JEV. Direct and molecular techniques are required in the diagnosis of Flavivirus -associated human disease. In this review, the serological assays such as neutralization tests, enzyme-linked immunosorbent assay, hemagglutination-inhibition test, Western blot test, and immunofluorescence test will be discussed. Serological assays that have been developed are able to detect different immunoglobulin isotypes (IgM, IgG, and IgA); however, it is challenging when interpreting the serological results due to the broad antigenic cross-reactivity of antibodies to these viruses. However, the neutralization tests are still considered as the gold standard to differentiate these flaviviruses.

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.

Embracing cross-reactivity allows antibodies to function similarly to selective arrays, enabling the detection of a range of analytes, including proteins, and improving diagnostics and surveillance.Moving beyond the traditional focus on high specificity can unlock greater flexibility in sensing applications, expanding the role of antibodies beyond their conventional use.To harness the full potential of cross-reactivity of antibodies, we must develop new selection criteria and quantification methods to measure and utilize cross-reactivity effectively to enhance sensors, diagnostics, and separation techniques. Cross-reactivity is viewed as a highly undesirable property of antibodies, with efforts focused on achieving high specificity and minimal crosstalk. Yet, in selective arrays, cross-reactivity can be a strength. The ability to bind to a range of antigens can be powerful if used strategically to distinguish complex samples. Chemometric and chemolfactory arrays rely on cross-reactivity to deliver powerful assays with capabilities that often outperform those of specific assays. Here, we argue that there is a unique opportunity to exploit the cross-reactivity of antibodies by using them in array similar to the mode of chemolfactory arrays. Embracing this shift could transform biosensing, offering scalable tools for multiplexed detection, especially in settings where speed, cost, and adaptability are critical. Cross-reactivity is viewed as a highly undesirable property of antibodies, with efforts focused on achieving high specificity and minimal crosstalk. Yet, in selective arrays, cross-reactivity can be a strength. The ability to bind to a range of antigens can be powerful if used strategically to distinguish complex samples. Chemometric and chemolfactory arrays rely on cross-reactivity to deliver powerful assays with capabilities that often outperform those of specific assays. Here, we argue that there is a unique opportunity to exploit the cross-reactivity of antibodies by using them in array similar to the mode of chemolfactory arrays. Embracing this shift could transform biosensing, offering scalable tools for multiplexed detection, especially in settings where speed, cost, and adaptability are critical.

Preexisting immunity to Zika virus (ZIKV) or dengue virus (DENV) may alter the course of their infection, and here we use robust mouse models to examine pathological outcomes following passive immunization, sequential cross-infection, or vaccination with inactivated virus. DENV infection was enhanced (through antibody-dependent enhancement [ADE]) or was suppressed by both DENV and ZIKV immunity. Notably, inactivated ZIKV vaccination enhanced dengue disease severity, although it was highly protective against ZIKV infection. On the other hand, ADE was not observed upon ZIKV infection in mice that were passively immunized or preinfected with DENV. Surprisingly, however, we found that vaccination with inactivated DENV enhanced ZIKV infection, mainly in the mesenteric lymph node, indicating the potential for DENV immunity to cause ADE in vivo. Collectively, our data call for greater attention to detail in the design of ZIKV or DENV vaccines.

N6-methyladenosine (m6A) is a widespread RNA modification that influences nearly every aspect of the messenger RNA lifecycle. Our understanding of m6A has been facilitated by the development of global m6A mapping methods, which use antibodies to immunoprecipitate methylated RNA. However, these methods have several limitations, including high input RNA requirements and cross-reactivity to other RNA modifications. Here, we present DART-seq (deamination adjacent to RNA modification targets), an antibody-free method for detecting m6A sites. In DART-seq, the cytidine deaminase APOBEC1 is fused to the m6A-binding YTH domain. APOBEC1-YTH expression in cells induces C-to-U deamination at sites adjacent to m6A residues, which are detected using standard RNA-seq. DART-seq identifies thousands of m6A sites in cells from as little as 10 ng of total RNA and can detect m6A accumulation in cells over time. Additionally, we use long-read DART-seq to gain insights into m6A distribution along the length of individual transcripts.

Pre-existing cross-reactive immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins in infection-naive subjects have been described by several studies. In particular, regions of high homology between SARS-CoV-2 and common cold coronaviruses have been highlighted as a likely source of this cross-reactivity. However, the role of such cross-reactive responses in the outcome of SARS-CoV-2 infection and vaccination is currently unclear. Here, we review evidence regarding the impact of pre-existing humoral and T cell immune responses to outcomes of SARS-CoV-2 infection and vaccination. Furthermore, we discuss the importance of conserved coronavirus epitopes for the rational design of pan-coronavirus vaccines and consider cross-reactivity of immune responses to ancestral SARS-CoV-2 and SARS-CoV-2 variants, as well as their impact on COVID-19 vaccination.This Review discusses the evidence for pre-existing cross-reactive immune responses to SARS-CoV-2, which are mainly due to infections with common cold coronaviruses, and how such cross-reactivity affects adaptive immune responses. Furthermore, it explores cross-reactivity in the context of SARS-CoV-2 variants of concern and its implications for vaccine development.

The therapeutic targeting of the immune system, for example in vaccinology and cancer treatment, is a challenging task and the subject of active research. Several in silico tools used for predicting immunogenicity are based on the analysis of peptide sequences binding to the Major Histocompatibility Complex (pMHC). However, few of these bioinformatics tools take into account the pMHC three-dimensional structure. Here, we describe a new bioinformatics tool, MatchTope, developed for predicting peptide similarity, which can trigger cross-reactivity events, by computing and analyzing the electrostatic potentials of pMHC complexes. We validated MatchTope by using previously published data from in vitro assays. We thereby demonstrate the strength of MatchTope for similarity prediction between targets derived from several pathogens as well as for indicating possible cross responses between self and tumor peptides. Our results suggest that MatchTope can enhance and speed up future studies in the fields of vaccinology and cancer immunotherapy.

Understanding and predicting an individual's clinical cross-reactivity to related allergens is a key to better management, treatment and progression of novel therapeutics for food allergy. In food allergy, clinical cross-reactivity is observed in patients reacting to unexpected allergen sources containing the same allergenic protein or antibody binding patches (epitopes), often resulting in severe allergic reactions. Shellfish allergy affects up to 2% of the world population and persists for life in most patients. The diagnosis of shellfish allergy is however often challenging due to reported clinical cross-reactivity to other invertebrates including mites and cockroaches. Prediction of cross-reactivity can be achieved utilizing an in-depth analysis of a few selected IgE-antibody binding epitopes. We combined available experimentally proven IgE-binding epitopes with informatics-based cross-reactivity prediction modeling to assist in the identification of clinical cross-reactive biomarkers on shellfish allergens. This knowledge can be translated into prevention and treatment of allergic diseases. To overcome the problem of predicting IgE cross-reactivity of shellfish allergens we developed an epitope conservation model using IgE binding epitopes available in the Immune Epitope Database and Analysis Resource (http://www.iedb.org/). We applied this method to a set of four different shrimp allergens, and successfully identified several non-cross-reactive as well as cross-reactive epitopes, which have been experimentally established to cross-react. Based on these findings we suggest that this method can be used for advanced component-resolved-diagnosis to identify patients sensitized to a specific shellfish group and distinguish from patients with extensive cross-reactivity to ingested and inhaled allergens from invertebrate sources.

Cellular processes are carried out by many genes, and their study and optimization requires multiple levers by which they can be independently controlled. The most common method is via a genetically encoded sensor that responds to a small molecule. However, these sensors are often suboptimal, exhibiting high background expression and low dynamic range. Further, using multiple sensors in one cell is limited by cross-talk and the taxing of cellular resources. Here, we have developed a directed evolution strategy to simultaneously select for lower background, high dynamic range, increased sensitivity, and low cross-talk. This is applied to generate a set of 12 high-performance sensors that exhibit >100-fold induction with low background and cross-reactivity. These are combined to build a single “sensor array” in the genomes of E. coli MG1655 (wild-type), DH10B (cloning), and BL21 (protein expression). These “Marionette” strains allow for the independent control of gene expression using 12 small-molecule inducers. A directed evolution approach was applied to optimize a set of 12 small-molecule-responsive biosensors, which led to the engineering of “Marionette” strains of Escherichia coli incorporating these sensors for biotechnological applications.