Human thymopoiesis produces polyspecific CD8+ α/β T cells responding to multiple viral antigens

T-cell receptors (TCRs) are formed by stochastic gene rearrangements, theoretically generating >10¹⁹ sequences. They are selected during thymopoiesis, which releases a repertoire of about 10⁸ unique TCRs per individual. How evolution shaped a process that produces TCRs that can effectively handle a countless and evolving set of infectious agents is a central question of immunology. The paradigm is that a diverse enough repertoire of TCRs should always provide a proper, though rare, specificity for any given need. Expansion of such rare T cells would provide enough fighters for an effective immune response and enough antigen-experienced cells for memory. We show here that human thymopoiesis releases a large population of clustered CD8⁺ T cells harboring α/β paired TCRs that (i) have high generation probabilities and (ii) a preferential usage of some V and J genes, (iii) which CDR3 are shared between individuals, and (iv) can each bind and be activated by multiple unrelated viral peptides, notably from EBV, CMV, and influenza. These polyspecific T cells may represent a first line of defense that is mobilized in response to infections before a more specific response subsequently ensures viral elimination. Our results support an evolutionary selection of polyspecific α/β TCRs for broad antiviral responses and heterologous immunity.

Definition of erythroid cell-positive blood transcriptome phenotypes associated with severe respiratory syncytial virus infection

Biomarkers to assess the risk of developing severe respiratory syncytial virus (RSV) infection are needed. We conducted a meta-analysis of 490 unique profiles from six public RSV blood transcriptome datasets. A repertoire of 382 well-characterized transcriptional modules was used to define dominant host responses to RSV infection. The consolidated RSV cohort was stratified according to four traits: "interferon response" (IFN), "neutrophil-driven inflammation" (Infl), "cell cycle" (CC), and "erythrocytes" (Ery). We identified eight prevalent blood transcriptome phenotypes, of which three Ery+ phenotypes comprised higher proportions of patients requiring intensive care. This finding confirms on a larger scale data from one of our earlier reports describing an association between an erythrocyte signature and RSV disease severity. Further contextual interpretation made it possible to associate this signature with immunosuppressive states (late stage cancer, pharmacological immunosuppression), and with a population of fetal glycophorin A+ erythroid precursors. Furthermore, we posit that this erythrocyte cell signature may be linked to a population of immunosuppressive erythroid cells previously described in the literature, and that overabundance of this cell population in RSV patients may underlie progression to severe disease. These findings outline potential priority areas for biomarker development and investigations into the immune biology of RSV infection. The approach that we developed and employed here should also permit to delineate prevalent blood transcriptome phenotypes in other settings.

microRNA profiles in urine by next-generation sequencing can stratify bladder cancer subtypes

Bladder cancer (BC) is the most frequent malignancy of the urinary tract with a high incidence in men and smokers. Currently, there are no non-invasive markers useful for BC diagnosis and subtypes classification that could overcome invasive procedures such as cystoscopy. Dysregulated miRNA profiles have been associated with numerous cancers, including BC. Cell-free miRNAs are abundantly present in a variety of biofluids including urine and make them promising candidates in cancer biomarker discovery.

In the present study, the identification of miRNA fingerprints associated with different BC status was performed by next-generation sequencing on urine samples from 66 BC and 48 controls. Three signatures based on dysregulated miRNAs have been identified by regression models, assessing the power to discriminate different BC subtypes. Altered miRNAs according to invasiveness and grade were validated by qPCR on 112 cases and 65 controls (among which 46 cases and 16 controls were an independent group of subjects while the rest were replica samples).

The area under the curve (AUC) computed including three miRNAs (miR-30a-5p, let-7c-5p and miR-486-5p) altered in all BC subtypes showed a significantly increased accuracy in the discrimination of cases and controls (AUC model = 0.70; p-value = 0.01).

In conclusions, the non-invasive detection in urine of a selected number of miRNAs altered in different BC subtypes could lead to an accurate early diagnosis of cancer and stratification of patients.

Peculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data sets

High-Throughput technologies provide genomic and trascriptomic data that are suitable for biomarker detection for classification purposes. However, the high dimension of the output of such technologies and the characteristics of the data sets analysed represent an issue for the classification task. Here we present a new feature selection method based on three steps to detect class-specific biomarkers in case of high-dimensional data sets. The first step detects the differentially expressed genes according to the experimental conditions tested in the experimental design, the second step filters out the features with low discriminative power and the third step detects the class-specific features and defines the final biomarker as the union of the class-specific features. The proposed procedure is tested on two microarray datasets, one characterized by a strong imbalance between the size of classes and the other one where the size of classes is perfectly balanced. We show that, using the proposed feature selection procedure, the classification performances of a Support Vector Machine on the imbalanced data set reach a 82% whereas other methods do not exceed 73%. Furthermore, in case of perfectly balanced dataset, the classification performances are comparable with other methods. Finally, the Gene Ontology enrichments performed on the signatures selected with the proposed pipeline, confirm the biological relevance of our methodology. The download of the package with the implementation of Peculiar Genes Selection, 'PGS', is available for R users at: http://github.com/mbeccuti/PGS.