Estudio de la implicación de los genes de síntesis de la pared celular en el mecanismo de acción de las avermectinas frente a micobacterias

La aparición de cepas de Mycobacterium tuberculosis resistentes al tratamiento convencional de primera línea genera la necesidad de desarrollar nuevos antimicrobianos para combatir la enfermedad. Una estrategia, para reducir tiempos y costes, puede provenir del reposicionamiento farmacológico como en el caso de la selamectina. Para ello, se debe determinar su mecanismo de acción empleando un microorganismo modelo como Mycobacterium smegmatis. En un estudio previo del Grupo de Investigación, se identificó por ensayo en tablero de ajedrez (checkerboard) la existencia de interacción sinérgica entre selamectina y etambutol.En este trabajo se ha observado la ausencia de interacción con otros inhibidores de la síntesis de la pared celular como PBTZ-169, lo que parece indicar que existe una relación entre las dianas del etambutol y el modo de acción de la selamectina, que se ha estudiado en mayor profundidad. Se han construido mutantes de sobreexpresión en plásmido y knock-out por recombineering de estas dianas y se han iniciado los pasos para realizar un análisis de la respuesta transcripcional de algunos genes de síntesis de la pared celular ante el tratamiento con ambos fármacos.También se ha caracterizado la actividad del compuesto frente a un inóculo mayor que el estandarizado por ensayos de determinación de las concentraciones mínimas inhibitoria y bactericida y cinética de muerte, llegando a establecer el perfil de acción de los compuestos y la dependencia del inóculo y las condiciones óptimas de la extracción de RNA.Sin embargo, la pandemia de la COVID-19 no ha permitido finalizar estas dos líneas, que requieren estudios futuros de susceptibilidad a los antibióticos en los mutantes construidos y caracterizar la respuesta transcripcional al tratamiento con selamectina y etambutol en las condiciones óptimas establecidas.<br /

Single-cell immune repertoire sequencing of B and T cells in murine models of infection and autoimmunity

Adaptive immune repertoires are composed by the ensemble of B and T cell receptors (BCR, TCR) within an individual and reflect both past and current immune responses. Recent advances in single-cell sequencing enable recovery of the complete adaptive immune receptor sequences in addition to transcriptional information. Such high-dimensional datasets enable the molecular quantification of clonal selection of B and T cells across a wide variety of conditions such as infection and disease. Due to costs, time required for the analysis and current practices of academic publishing, small-scale sequencing studies are often not made publicly available, despite having informative potential to elucidate immunological principles and guide future-studies. Here, we performed single-cell sequencing of B and T cells to profile clonal selection across murine models of viral infection and autoimmune disease. Specifically, we recovered transcriptome and immune repertoire information for polyclonal T follicular helper cells following acute and chronic viral infection, CD8+ T cells with binding specificity restricted to two distinct peptides of lymphocytic choriomeningitis virus, and B and T cells isolated from the nervous system in the context of experimental autoimmune encephalomyelitis. We could relate repertoire features such as clonal expansion, germline gene usage, and clonal convergence to cell phenotypes spanning activation, memory, naive, antibody secretion, T cell inflation, and regulation. Together, this dataset provides a resource for experimental and computational immunologists that can be integrated with future single-cell immune repertoire and transcriptome sequencing datasets

Single-cell immune repertoire sequencing of B and T cells in murine models of infection and autoimmunity

Adaptive immune repertoires are composed by the ensemble of B and T cell receptors (BCR, TCR) within an individual and reflect both past and current immune responses. Recent advances in single-cell sequencing enable recovery of the complete adaptive immune receptor sequences in addition to transcriptional information. Such high-dimensional datasets enable the molecular quantification of clonal selection of B and T cells across a wide variety of conditions such as infection and disease. Due to costs, time required for the analysis and current practices of academic publishing, small-scale sequencing studies are often not made publicly available, despite having informative potential to elucidate immunological principles and guide future-studies. Here, we performed single-cell sequencing of B and T cells to profile clonal selection across murine models of viral infection and autoimmune disease. Specifically, we recovered transcriptome and immune repertoire information for polyclonal T follicular helper cells following acute and chronic viral infection, CD8+ T cells with binding specificity restricted to two distinct peptides of lymphocytic choriomeningitis virus, and B and T cells isolated from the nervous system in the context of experimental autoimmune encephalomyelitis. We could relate repertoire features such as clonal expansion, germline gene usage, and clonal convergence to cell phenotypes spanning activation, memory, naive, antibody secretion, T cell inflation, and regulation. Together, this dataset provides a resource for experimental and computational immunologists that can be integrated with future single-cell immune repertoire and transcriptome sequencing datasets

Persistent virus-specific and clonally expanded antibody-secreting cells respond to induced self-antigen in the CNS

B cells contribute to the pathogenesis of both cellular- and humoral-mediated central nervous system (CNS) inflammatory diseases through a variety of mechanisms. In such conditions, B cells may enter the CNS parenchyma and contribute to local tissue destruction. It remains unexplored, however, how infection and autoimmunity drive transcriptional phenotypes, repertoire features, and antibody functionality. Here, we profiled B cells from the CNS of murine models of intracranial (i.c.) viral infections and autoimmunity. We identified a population of clonally expanded, antibody-secreting cells (ASCs) that had undergone class-switch recombination and extensive somatic hypermutation following i.c. infection with attenuated lymphocytic choriomeningitis virus (rLCMV). Recombinant expression and characterisation of these antibodies revealed specificity to viral antigens (LCMV glycoprotein GP), correlating with ASC persistence in the brain weeks after resolved infection. Furthermore, these virus-specific ASCs upregulated proliferation and expansion programs in response to the conditional and transient induction of the LCMV GP as a neo-self antigen by astrocytes. This class-switched, clonally expanded, and mutated population persisted and was even more pronounced when peripheral B cells were depleted prior to autoantigen induction in the CNS. In contrast, the most expanded B cell clones in mice with persistent expression of LCMV GP in the CNS did not exhibit neo-self antigen specificity, potentially a consequence of local tolerance induction. Finally, a comparable population of clonally expanded, class-switched, and proliferating ASCs was detected in the cerebrospinal fluid of relapsing multiple sclerosis (RMS) patients. Taken together, our findings support the existence of B cells that populate the CNS and are capable of responding to locally encountered autoantigens

ePlatypus: an ecosystem for computational analysis of immunogenomics data

Motivation: The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. Results: Here, we present the ePlatypus computational immunology ecosystem for immunogenomics data analysis, with a focus on adaptive immune repertoires and single-cell sequencing. ePlatypus is an open-source web-based platform and provides programming tutorials and an integrative database that helps elucidate signatures of B and T cell clonal selection. Furthermore, the ecosystem links novel and established bioinformatics pipelines relevant for single-cell immune repertoires and other aspects of computational immunology such as predicting ligand-receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics, and phylogenetics. The ePlatypus ecosystem helps extract deeper insight in computational immunology and immunogenomics and promote open science. Availability and implementation: Platypus code used in this manuscript can be found at github.com/alexyermanos/Platypus.ISSN:1367-4803ISSN:1460-205