SummaryGroup A Streptococcus (GAS, Streptococcus pyogenes) is a Gram-positive human pathogen responsible for several acute diseases and autoimmune sequelae that account for half a million deaths worldwide every year. GAS infections require the capacity of the pathogen to adhere to host tissues and assemble in cell aggregates. Furthermore, a role for biofilms in GAS pathogenesis has recently been proposed. Here we investigated the role of GAS pili in biofilm formation. We demonstrated that GAS pilusnegative mutants, in which the genes encoding either the pilus backbone structural protein or the sortase C1 have been deleted, showed an impaired capacity to attach to a pharyngeal cell line. The same mutants were much less efficient in forming cellular aggregates in liquid culture and microcolonies on human cells. Furthermore, mutant strains were incapable of producing the typical three-dimensional layer with bacterial microcolonies embedded in a carbohydrate polymeric matrix. Complemented mutants had an adhesion and aggregation phenotype similar to the wild-type strain. Finally, in vivo expression of pili was indirectly confirmed by demonstrating that most of the sera from human patients affected by GASmediated pharyngitis recognized recombinant pili proteins. These data support the role of pili in GAS adherence and colonization and suggest a general role of pili in all pathogenic streptococci.

We propose an experimental strategy for highly accurate selection of candidates for bacterial vaccines without using in vitro and/or in vivo protection assays. Starting from the observation that efficacious vaccines are constituted by conserved, surface-associated and/or secreted components, the strategy contemplates the parallel application of three high throughput technologies, i.e. mass spectrometry-based proteomics, protein array, and flow-cytometry analysis, to identify this category of proteins, and is based on the assumption that the antigens identified by all three technologies are the protective ones. When we tested this strategy for Group A Streptococcus, we selected a total of 40 proteins, of which only six identified by all three approaches. When the 40 proteins were tested in a mouse model, only six were found to be protective and five of these belonged to the group of antigens in common to the three technologies. Finally, a combination of three protective antigens conferred broad protection against a panel of four different Group A Streptococcus strains. This approach may find general application as an accelerated and highly accurate path to bacterial vaccine discovery.

We investigated here the potential role of Toll-like receptors (TLR) and the adaptor protein MyD88 in innate immunity responses to Cryptococcus neoformans, a pathogenic encapsulated yeast. Peritoneal macrophages from MyD88 -/-or TLR2 -/-mice released significantly less TNF-a, compared with wild-type controls, after in vitro stimulation with whole yeasts. In contrast, no differences in TNF-a release were noted between macrophages from C3H/HeJ mice, which have a loss of function mutation in TLR4, relative to C3H/HeN controls. When MyD88-or TLR2-deficient mice were infected with low doses of the H99 serotype A strain, all of the control animals, but none of MyD88 -/-and only 38% of the TLR2 -/-animals survived, in association with higher fungal burden in the mutant mice. Both MyD88 -/-and TLR2 -/-animals showed decreased TNF-a, IL12p40 and/or IFN-c expression in various organs during infection. No difference in susceptibility to experimental cryptococcosis was found between C3H/HeJ mice and C3H/HeN controls. In conclusion, our data indicate that TLR2 and MyD88, but not TLR4, critically contribute to anti-cryptococcal defenses through the induction of increased TNF-a, IL-12 and IFN-c expression.

To understand how a protective immune response against SARS-CoV-2 develops over time, we integrated phenotypic, transcriptional and repertoire analyses on PBMCs from mild and severe COVID-19 patients during and after infection, and compared them to healthy donors (HD). A type I IFN-response signature marked all the immune populations from severe patients during the infection. Humoral immunity was dominated by IgG production primarily against the RBD and N proteins, with neutralizing antibody titers increasing post infection and with disease severity. Memory B cells, including an atypical FCRL5+ T-BET+ memory subset, increased during the infection, especially in patients with mild disease. A significant reduction of effector memory, CD8+ T cells frequency characterized patients with severe disease. Despite such impairment, we observed robust clonal expansion of CD8+ T lymphocytes, while CD4+ T cells were less expanded and skewed toward TCM and TH2-like phenotypes. MAIT cells were also expanded, but only in patients with mild disease. Terminally differentiated CD8+ GZMB+ effector cells were clonally expanded both during the infection and post-infection, while CD8+ GZMK+ lymphocytes were more expanded post-infection and represented bona fide memory precursor effector cells. TCR repertoire analysis revealed that only highly proliferating T cell clonotypes, which included SARS-CoV-2-specific cells, were maintained post-infection and shared between the CD8+ GZMB+ and GZMK+ subsets. Overall, this study describes the development of immunity against SARS-CoV-2 and identifies an effector CD8+ T cell population with memory precursor-like features.

Diagnostic accuracy can be improved considerably by combining multiple markers, whose performance in identifying diseased subjects is usually assessed via receiver operating characteristic (ROC) curves. The selection of multimarker signatures is a complicated process that requires integration of data signatures with sophisticated statistical methods. We developed a user-friendly tool, called CombiROC, to help researchers accurately determine optimal markers combinations from diverse omics methods. With CombiROC data from different domains, such as proteomics and transcriptomics, can be analyzed using sensitivity/specificity filters: the number of candidate marker panels rising from combinatorial analysis is easily optimized bypassing limitations imposed by the nature of different experimental approaches. Leaving to the user full control on initial selection stringency, CombiROC computes sensitivity and specificity for all markers combinations, performances of best combinations and ROC curves for automatic comparisons, all visualized in a graphic interface. CombiROC was designed without hard-coded thresholds, allowing a custom fit to each specific data: this dramatically reduces the computational burden and lowers the false negative rates given by fixed thresholds. The application was validated with published data, confirming the marker combination already originally described or even finding new ones. CombiROC is a novel tool for the scientific community freely available at http://CombiROC.eu.