Bioinformatics evaluation of the possibility of heat shock proteins as autoantigens in multiple sclerosis based on molecular mimicry hypothesis

Mining Autoimmune-Disorder-Linked Molecular-Mimicry Candidates in Clostridioides difficile and Prospects of Mimic-Based Vaccine Design: An In Silico Approach

Molecular mimicry, a phenomenon in which microbial or environmental antigens resemble host antigens, has been proposed as a potential trigger for autoimmune responses. In this study, we employed a bioinformatics approach to investigate the role of molecular mimicry in Clostridioides difficile-caused infections and the induction of autoimmune disorders due to this phenomenon. Comparing proteomes of host and pathogen, we identified 23 proteins that exhibited significant sequence homology and were linked to autoimmune disorders. The disorders included rheumatoid arthritis, psoriasis, Alzheimer's disease, etc., while infections included viral and bacterial infections like HIV, HCV, and tuberculosis. The structure of the homologous proteins was superposed, and RMSD was calculated to find the maximum deviation, while accounting for rigid and flexible regions. Two sequence mimics (antigenic, non-allergenic, and immunogenic) of ≥10 amino acids from these proteins were used to design a vaccine construct to explore the possibility of eliciting an immune response. Docking analysis of the top vaccine construct C2 showed favorable interactions with HLA and TLR-4 receptor, indicating potential efficacy. The B-cell and T-helper cell activity was also simulated, showing promising results for effective immunization against C. difficile infections. This study highlights the potential of C. difficile to trigger autoimmunity through molecular mimicry and vaccine design based on sequence mimics that trigger a defensive response.

A Novel In Silico Method for Molecular Mimicry Detection Finds a Formin with the Potential to Manipulate the Maize Cell Cytoskeleton

Molecular mimicry is one of the evolutionary strategies that parasites use to manipulate the host metabolism and perform an effective infection. This phenomenon has been observed in several animal and plant pathosystems. Despite the relevance of this mechanism in pathogenesis, little is known about it in fungus-plant interactions. For that reason, we performed an in silico method to select plausible mimicry candidates for the Ustilago maydis-maize interaction. Our methodology used a tripartite sequence comparison between the parasite, the host, and nonparasitic organisms' genomes. Furthermore, we used RNA sequencing information to identify gene coexpression, and we determined subcellular localization to detect potential cases of colocalization in the imitator-imitated pairs. With these approximations, we found a putative extracellular formin in U. maydis with the potential to rearrange the host cell cytoskeleton. In parallel, we detected at least two maize genes involved in the cytoskeleton rearrangement differentially expressed under U. maydis infection; thus, this find increases the expectation for the potential mimicry role of the fungal protein. The use of several sources of data led us to develop a strict and replicable in silico methodology to detect molecular mimicry in pathosystems with enough information available. Furthermore, this is the first time that a genomewide search has been performed to detect molecular mimicry in a U. maydis-maize system. Additionally, to allow the reproducibility of this experiment and the use of this pipeline, we created a Web server called Molecular Mimicry Finder.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Heat Exposure and Multiple Sclerosis-A Regional and Temporal Analysis

Multiple sclerosis (MS) is a neurological disorder that progressively distorts the myelination of axons within the central nervous system (CNS). Increased core body temperature or metabolism as a result of exercise are common causes of short-term exacerbations of neurological symptoms in MS. About 60–80% of patients with MS experience a worsening of their symptoms when exposed to heat. In comparison, less data are available on the relationship between ambient meteorological conditions (e.g., temperature and relative humidity (RH)) and fluctuations in such variables in relation to MS symptoms. Thus, this study examined associations between time-lagged exposure to meteorological conditions and risk of a clinic visit due to MS among US veterans between 2010 and 2013. This study leveraged data from the Veterans Affairs (VA) and National Climactic Data Center (NCDC) for the continental US, partitioned into eight climate zones. We used a case crossover design to assess the risk of a MS clinic visit with respect to several meteorological conditions. Location-specific time-lagged daily (ambient) exposure to temperature, RH, and temperature variations (standard deviation (SD) of temperature) were computed (up to 30 days) for each case (i.e., day of MS visit) and control (a randomly assigned date ± 90–270 days prior to visit). Statistical analyses were conducted to examine independent associations between the selected meteorological conditions and risk of MS visits at the national and regional levels. A total of 533,066 patient visits received a MS diagnosis (International Classifications of Diseases (ICD)-9 code = 340). The Northeast (NE) and Upper Midwest (UMW) regions reported the highest frequency of clinic visits due to MS. Clinic visits were 9% more likely to occur in the spring, summer, and fall months (March–October) than in the winter (OR = 1.089; 95% CI = 1.076–1.103; p < 0.01). In the univariate analyses, the SD of temperature, temperature, and temperature–RH interaction were positively associated with an elevated risk of a MS clinic visit, while the RH was negatively associated with the risk for a clinic visit. In multivariate analyses, the strongest association of a MS clinic visit was observed with the SD of the temperature (OR = 1.012; 95% CI 1.008–1.017; p < 0.01). These associations between MS clinic visits and meteorological conditions varied across climate regions, with the strongest associations being observed in the LMW, UMW, DSW, and NE zones. The SD of the temperature was again the strongest associated predictor when examined regionally. Temperature variations and temperature–RH interactions (a proxy of the heat index) showed significant associations with MS clinic visits. These associations varied across climate regions when examined geographically. Our findings have implications for the management of MS in severe or recurrent cases, especially considering the impending changes in the daily temperature variations and intensity of the heatwaves expected with the intensification of global warming.