Measles Immunity Status of Greek Population after the Outbreak in 2017-2018: Results from a Seroprevalence National Survey.

Accurate data on susceptibility rates against measles in the general population of Greece are scarce. Many studies have estimated the vaccination coverage, but none have calculated the nationwide immunity rate, including all age groups, against the measles virus. The purpose of our study was to determine the measles immunity status, especially after the latest outbreak in 2017-2018. In total, 3972 leftover blood samples were obtained during 2020-2021. They were collected from a nationwide laboratory network using a geographically stratified sampling strategy and were tested for the presence of measles-specific IgG antibodies. The overall crude seroprevalence was calculated to be 89.6% and the adjusted was 89.8% (95% CI: 88.8-90.8%). There was no statistically significant difference in seropositivity between sexes (p = 0.783). Higher immunity rates and antibody concentrations were found in older age groups ≥41 years old (94.9%, 95% CI: 93.7-95.9%, and 730.0 mIU/mL) in comparison with younger individuals aged 1-40 years old (83.4%, 95% CI: 81.6-85.7%, and 616.5 mIU/mL). Comparing the seroprevalence among the Nomenclature of Territorial Units for Statistics (NUTS 2), a statistically significant difference was estimated among them (<0.001). The two regions where higher measles incidence was observed during the 2017-2018 outbreak, Eastern Macedonia and Thrace, and Western Greece, were among the four regions with lower seropositivity (84.6%, 95% CI: 79.9-89.4%, and 85.9%, 95% CI: 81.4-90.4%, respectively). Our study showed a measles immunity gap that affects the younger age groups and makes a new measles outbreak likely. The enforcement of vaccination campaigns and addressing vaccine hesitancy could bridge it and achieve the required target of herd immunity.

Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases.

During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community's focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS study, provides thousands of TCRs that can specifically recognise SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML)-assisted approach for analysing TCR-Seq data from the antigens' point of view, with the ability to unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was tested by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.

Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning.

Detection of alloreactive anti-HLA antibodies is a frequent and mandatory test before and after organ transplantation to determine the antigenic targets of the antibodies. Nowadays, this test involves the measurement of fluorescent signals generated through antibody-antigen reactions on multi-beads flow cytometers. In this study, in a cohort of 1,066 patients from one country, anti-HLA class I responses were analyzed on a panel of 98 different antigens. Knowing that the immune system responds typically to "shared" antigenic targets, we studied the clustering patterns of antibody responses against HLA class I antigens without any a priori hypothesis, applying two unsupervised machine learning approaches. At first, the principal component analysis (PCA) projections of intra-locus specific responses showed that anti-HLA-A and anti-HLA-C were the most distantly projected responses in the population with the anti-HLA-B responses to be projected between them. When PCA was applied on the responses against antigens belonging to a single locus, some already known groupings were confirmed while several new cross-reactive patterns of alloreactivity were detected. Anti-HLA-A responses projected through PCA suggested that three cross-reactive groups accounted for about 70% of the variance observed in the population, while anti-HLA-B responses were mainly characterized by a distinction between previously described Bw4 and Bw6 cross-reactive groups followed by several yet undocumented or poorly described ones. Furthermore, anti-HLA-C responses could be explained by two major cross-reactive groups completely overlapping with previously described C1 and C2 allelic groups. A second feature-based analysis of all antigenic specificities, projected as a dendrogram, generated a robust measure of allelic antigenic distances depicting bead-array defined cross reactive groups. Finally, amino acid combinations explaining major population specific cross-reactive groups were described. The interpretation of the results was based on the current knowledge of the antigenic targets of the antibodies as they have been characterized either experimentally or computationally and appear at the HLA epitope registry.

Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms.

Allele specific antibody response against the polymorphic system of HLA is the allogeneic response marker determining the immunological risk for graft acceptance before and after organ transplantation and therefore routinely studied during the patient's workup. Experimentally, bead bound antigen- antibody reactions are detected using a special multicolor flow cytometer (Luminex). Routinely for each sample, antibody responses against 96 different HLA antigen groups are measured simultaneously and a 96-dimensional immune response vector is created. Under a common experimental protocol, using unsupervised clustering algorithms, we analyzed these immune intensity vectors of anti HLA class II responses from a dataset of 1,748 patients before or after renal transplantation residing in a single country. Each patient contributes only one serum sample in the analysis. A population view of linear correlations of hierarchically ordered fluorescence intensities reveals patterns in human immune responses with striking similarities with the previously described CREGs but also brings new information on the antigenic properties of class II HLA molecules. The same analysis affirms that "public" anti-DP antigenic responses are not correlated to anti DR and anti DQ responses which tend to cluster together. Principal Component Analysis (PCA) projections also demonstrate ordering patterns clearly differentiating anti DP responses from anti DR and DQ on several orthogonal planes. We conclude that a computer vision of human alloresponse by use of several dimensionality reduction algorithms rediscovers proven patterns of immune reactivity without any a priori assumption and might prove helpful for a more accurate definition of public immunogenic antigenic structures of HLA molecules. Furthermore, the use of Eigen decomposition on the Immune Response generates new hypotheses that may guide the design of more effective patient monitoring tests.