Simeone Marino

Bio: Simeone Marino is an academic researcher from University of Michigan. The author has contributed to research in topics: Mycobacterium tuberculosis & Tuberculosis. The author has an hindex of 29, co-authored 53 publications receiving 4269 citations. Previous affiliations of Simeone Marino include Sapienza University of Rome.

Microenvironments in tuberculous granulomas are delineated by distinct populations of macrophage subsets and expression of nitric oxide synthase and arginase isoforms

Abstract: Macrophages in granulomas are both antimycobacterial effector and host cell for Mycobacterium tuberculosis, yet basic aspects of macrophage diversity and function within the complex structures of granulomas remain poorly understood. To address this, we examined myeloid cell phenotypes and expression of enzymes correlated with host defense in macaque and human granulomas. Macaque granulomas had upregulated inducible and endothelial NO synthase (iNOS and eNOS) and arginase (Arg1 and Arg2) expression and enzyme activity compared with nongranulomatous tissue. Immunohistochemical analysis indicated macrophages adjacent to uninvolved normal tissue were more likely to express CD163, whereas epithelioid macrophages in regions where bacteria reside strongly expressed CD11c, CD68, and HAM56. Calprotectin-positive neutrophils were abundant in regions adjacent to caseum. iNOS, eNOS, Arg1, and Arg2 proteins were identified in macrophages and localized similarly in granulomas across species, with greater eNOS expression and ratio of iNOS/Arg1 expression in epithelioid macrophages as compared with cells in the lymphocyte cuff. iNOS, Arg1, and Arg2 expression in neutrophils was also identified. The combination of phenotypic and functional markers support that macrophages with anti-inflammatory phenotypes localized to outer regions of granulomas, whereas the inner regions were more likely to contain macrophages with proinflammatory, presumably bactericidal, phenotypes. Together, these data support the concept that granulomas have organized microenvironments that balance antimicrobial anti-inflammatory responses to limit pathology in the lungs.

Variability in tuberculosis granuloma T cell responses exists, but a balance of pro- and anti-inflammatory cytokines is associated with sterilization.

Abstract: Lung granulomas are the pathologic hallmark of tuberculosis (TB). T cells are a major cellular component of TB lung granulomas and are known to play an important role in containment of Mycobacterium tuberculosis (Mtb) infection. We used cynomolgus macaques, a non-human primate model that recapitulates human TB with clinically active disease, latent infection or early infection, to understand functional characteristics and dynamics of T cells in individual granulomas. We sought to correlate T cell cytokine response and bacterial burden of each granuloma, as well as granuloma and systemic responses in individual animals. Our results support that each granuloma within an individual host is independent with respect to total cell numbers, proportion of T cells, pattern of cytokine response, and bacterial burden. The spectrum of these components overlaps greatly amongst animals with different clinical status, indicating that a diversity of granulomas exists within an individual host. On average only about 8% of T cells from granulomas respond with cytokine production after stimulation with Mtb specific antigens, and few “multi-functional” T cells were observed. However, granulomas were found to be “multi-functional” with respect to the combinations of functional T cells that were identified among lesions from individual animals. Although the responses generally overlapped, sterile granulomas had modestly higher frequencies of T cells making IL-17, TNF and any of T-1 (IFN-γ, IL-2, or TNF) and/or T-17 (IL-17) cytokines than non-sterile granulomas. An inverse correlation was observed between bacterial burden with TNF and T-1/T-17 responses in individual granulomas, and a combinatorial analysis of pair-wise cytokine responses indicated that granulomas with T cells producing both pro- and anti-inflammatory cytokines (e.g. IL-10 and IL-17) were associated with clearance of Mtb. Preliminary evaluation suggests that systemic responses in the blood do not accurately reflect local T cell responses within granulomas.

Mathematical modeling of primary succession of murine intestinal microbiota

Abstract: Understanding the nature of interpopulation interactions in host-associated microbial communities is critical to understanding gut colonization, responses to perturbations, and transitions between health and disease. Characterizing these interactions is complicated by the complexity of these communities and the observation that even if populations can be cultured, their in vitro and in vivo phenotypes differ significantly. Dynamic models are the cornerstone of computational systems biology and a key objective of computational systems biologists is the reconstruction of biological networks (i.e., network inference) from high-throughput data. When such computational models reflect biology, they provide an opportunity to generate testable hypotheses as well as to perform experiments that are impractical or not feasible in vivo or in vitro. We modeled time-series data for murine microbial communities using statistical approaches and systems of ordinary differential equations. To obtain the dense time-series data, we sequenced the 16S ribosomal RNA (rRNA) gene from DNA isolated from the fecal material of germfree mice colonized with cecal contents of conventionally raised animals. The modeling results suggested a lack of mutualistic interactions within the community. Among the members of the Bacteroidetes, there was evidence for closely related pairs of populations to exhibit parasitic interactions. Among the Firmicutes, the interactions were all competitive. These results suggest future animal and in silico experiments. Our modeling approach can be applied to other systems to provide a greater understanding of the dynamics of communities associated with health and disease.

Macrophage plasticity, polarization, and function in health and disease.

Abstract: Macrophages are heterogeneous and their phenotype and functions are regulated by the surrounding micro-environment. Macrophages commonly exist in two distinct subsets: 1) Classically activated or M1 macrophages, which are pro-inflammatory and polarized by lipopolysaccharide (LPS) either alone or in association with Th1 cytokines such as IFN-γ, GM-CSF, and produce pro-inflammatory cytokines such as interleukin-1β (IL-1β), IL-6, IL-12, IL-23, and TNF-α; and 2) Alternatively activated or M2 macrophages, which are anti-inflammatory and immunoregulatory and polarized by Th2 cytokines such as IL-4 and IL-13 and produce anti-inflammatory cytokines such as IL-10 and TGF-β. M1 and M2 macrophages have different functions and transcriptional profiles. They have unique abilities by destroying pathogens or repair the inflammation-associated injury. It is known that M1/M2 macrophage balance polarization governs the fate of an organ in inflammation or injury. When the infection or inflammation is severe enough to affect an organ, macrophages first exhibit the M1 phenotype to release TNF-α, IL-1β, IL-12, and IL-23 against the stimulus. But, if M1 phase continues, it can cause tissue damage. Therefore, M2 macrophages secrete high amounts of IL-10 and TGF-β to suppress the inflammation, contribute to tissue repair, remodeling, vasculogenesis, and retain homeostasis. In this review, we first discuss the basic biology of macrophages including origin, differentiation and activation, tissue distribution, plasticity and polarization, migration, antigen presentation capacity, cytokine and chemokine production, metabolism, and involvement of microRNAs in macrophage polarization and function. Secondly, we discuss the protective and pathogenic role of the macrophage subsets in normal and pathological pregnancy, anti-microbial defense, anti-tumor immunity, metabolic disease and obesity, asthma and allergy, atherosclerosis, fibrosis, wound healing, and autoimmunity.

The spectrum of latent tuberculosis: rethinking the biology and intervention strategies

Abstract: Immunological tests provide evidence of latent tuberculosis in one third of the global population, which corresponds to more than two billion individuals. Latent tuberculosis is defined by the absence of clinical symptoms but carries a risk of subsequent progression to clinical disease, particularly in the context of co-infection with HIV. In this Review we discuss the biology of latent tuberculosis as part of a broad range of responses that occur following infection with Mycobacterium tuberculosis, which result in the formation of physiologically distinct granulomatous lesions that provide microenvironments with differential ability to support or suppress the persistence of viable bacteria. We then show how this model can be used to develop a rational programme to discover effective drugs for the eradication of M. tuberculosis infection.

The Immune Response in Tuberculosis

Abstract: There are 9 million cases of active tuberculosis reported annually; however, an estimated one-third of the world's population is infected with Mycobacterium tuberculosis and remains asymptomatic. Of these latent individuals, only 5–10% will develop active tuberculosis disease in their lifetime. CD4+ T cells, as well as the cytokines IL-12, IFN-γ, and TNF, are critical in the control of Mycobacterium tuberculosis infection, but the host factors that determine why some individuals are protected from infection while others go on to develop disease are unclear. Genetic factors of the host and of the pathogen itself may be associated with an increased risk of patients developing active tuberculosis. This review aims to summarize what we know about the immune response in tuberculosis, in human disease, and in a range of experimental models, all of which are essential to advancing our mechanistic knowledge base of the host-pathogen interactions that influence disease outcome.

Sparse and Compositionally Robust Inference of Microbial Ecological Networks

Abstract: 16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.