Innate Immune Responses in Leprosy

Pinheiro, Roberta Olmo; Schmitz, Verônica; Silva, Bruno Jorge de Andrade; Dias, André Alves; Souza, Beatriz Junqueira de; Barbosa, Mayara Garcia de Mattos; Esquenazi, Danuza; Pessolani, Maria Cristina Vidal; Sarno, Euzenir Nunes

doi:10.3389/fimmu.2018.00518

Cited by 76 publications

(88 citation statements)

References 182 publications

(197 reference statements)

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“…For biopsies obtained from normal skin and leprosy, we performed MACS enrichments from the epidermal section and loaded Langerhans cells as 5% of the total amount to increase recovery. When we directly compared Langerhans cells from leprosy and normal skin, we observed elevated expression of IDO1 , STAT1 , HCAR3 and MHC class I molecules ( HLA-A , HLA-B and HLA-F ) in Langerhans cells in leprosy infection, which may suggest a role for Langerhans cells in priming CD8 + T cell responses in this disease ( Figure 4E ) (Hunger et al, 2004; Pinheiro et al, 2018).…”

Section: Resultsmentioning

confidence: 96%

Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology

Tk¹,

Mh²,

Tm³

et al. 2019

Preprint

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1The development of high-throughput single-cell RNA-sequencing (scRNA-Seq) methodologies 2 has empowered the characterization of complex biological samples by dramatically increasing the 3 number of constituent cells that can be examined concurrently. Nevertheless, these approaches 4 typically recover substantially less information per-cell as compared to lower-throughput microtiter 5 plate-based strategies. To uncover critical phenotypic differences among cells and effectively link 6 scRNA-Seq observations to legacy datasets, reliable detection of phenotype-defining transcripts 7 -such as transcription factors, affinity receptors, and signaling molecules -by these methods is 8 essential. Here, we describe a substantially improved massively-parallel scRNA-Seq protocol we 9 term Seq-Well S^3 ("Second-Strand Synthesis") that increases the efficiency of transcript capture 10 and gene detection by up to 10-and 5-fold, respectively, relative to previous iterations, surpassing 11 best-in-class commercial analogs. We first characterized the performance of Seq-Well S^3 in cell 12 lines and PBMCs, and then examined five different inflammatory skin diseases, illustrative of 13 distinct types of inflammation, to explore the breadth of potential immune and parenchymal cell 14 states. Our work presents an essential methodological advance as well as a valuable resource 15 for studying the cellular and molecular features that inform human skin inflammation. 109Mann-Whitney U Test & Linear Regression; Figure 1B-C). To confirm that these overall 110 improvements were not driven by changes in the relative frequencies of different cell types 111 captured by each technology, we also examined each subset independently (Figure S2A-B). For 112 each cell type detected, we observed significant increases in the numbers of transcripts captured 113 and genes detected using S^3 for each pairwise comparison between techniques (P < 0.05, 114 Mann-Whitney U Test; CD4 + T cells, Seq-Well V1: 1,044 ± 62.3 UMIs/cell; 10x v2: 7,671 ± 103.9 115 UMIs/cell; Seq-Well S^3: 13,390 ± 253.4 UMIs/cell; Mean ± Standard Error of the Median (SEM); 116 Figure S2). Both Seq-Well S^3 and 10x v2 displayed increased sensitivity for transcripts and 117 genes relative to Seq-Well v1, but Seq-Well S^3 showed the greatest efficiency (defined as genes 118 recovered at matched read depth) to detect genes for each cell type (Figure 1D-E; Figure S2). 119We sought to further understand whether these improvements resulted in enhanced 120 detection of biologically relevant genes typically under-represented in high-throughput single-cell 121 sequencing libraries (Tabula Muris Consortium et al., 2018). Importantly, genes that were 122 differentially detected (i.e., higher in S^3) within each cell type include numerous transcription 123 factors, cytokines and cell-surface receptors (Figure 1D-E). For example, among CD4 + T cells, 124we observe significantly increased detection of cytokines (e.g., TGFB1 and TNF), surface 125 receptors (e.g., TGFBR and CCR4) and transcription fact...

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Section: Resultsmentioning

confidence: 96%

Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology

Tk¹,

Mh²,

Tm³

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Thus, as described for leprosy, the clinical evolution of leishmaniasis depends on the interaction of the infectious agent and M1 or M2 macrophages, with the M2 subpopulation related to the process of disease progression. 302,303 Lastly, in rats experimentally infected with Toxoplasma gondii, peritoneal macrophages in the pre-gestational phase were primarily composed of M1 macrophages, whereas in the intra-gestational phase, the M2 phenotype dominated. This is related to the immune response patterns mediated by Th2 lymphocytes, which are predominant during gestation.…”

Section: Autophagymentioning

confidence: 99%

<p>Functional aspects, phenotypic heterogeneity, and tissue immune response of macrophages in infectious diseases</p>

Sousa¹,

Vasconcelos²,

Quaresma³

2019

IDR

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Macrophages are a functionally heterogeneous group of cells with specialized functions depending not only on their subgroup but also on the function of the organ or tissue in which the cells are located. The concept of macrophage phenotypic heterogeneity has been investigated since the 1980s, and more recent studies have identified a diverse spectrum of phenotypic subpopulations. Several types of macrophages play a central role in the response to infectious agents and, along with other components of the immune system, determine the clinical outcome of major infectious diseases. Here, we review the functions of various macrophage phenotypic subpopulations, the concept of macrophage polarization, and the influence of these cells on the evolution of infections. In addition, we emphasize their role in the immune response in vivo and in situ, as well as the molecular effectors and signaling mechanisms used by these cells. Furthermore, we highlight the mechanisms of immune evasion triggered by infectious agents to counter the actions of macrophages and their consequences. Our aim here is to provide an overview of the role of macrophages in the pathogenesis of critical transmissible diseases and discuss how elucidation of this relationship could enhance our understanding of the host–pathogen association in organ-specific immune responses.

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“…The first is that the overexpression of this transcription factor occurs in the dermal nerve fiber, which could lead to its deterioration (10). Our second hypothesis is that the increase in the transcription of Runx-1 is a consequence of its canonical expression in immune cells such as macrophages (22,23), cells that have a marked activity in situ against M. leprae in the skin of MB patients (24).…”

Section: Differences In the Expression Of Some Components Of The Notcmentioning

confidence: 98%

Notch Signaling Pathway Expression in the Skin of Leprosy Patients: Association With Skin and Neural Damage

et al. 2020

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Introduction: Leprosy is an infectious disease caused by Mycobacterium leprae, a debilitating disease that affects the skin and peripheral nerves. It is possible that tissue changes during infection with leprosy are related to alterations in the activity of the Notch signaling pathway, an innate signaling pathway in the physiology of the skin and peripheral nerves. Methods: This is a descriptive observational study. Thirty skin biopsies from leprosy patients and 15 from individuals with no history of this disease were evaluated. In these samples, gene expressions of cellular components associated with the Notch signaling pathway, Hes-1, Hey-1, Runx-1 Jagged-1, Notch-1, and Numb, were evaluated using q-PCR, and protein expression was evaluated using immunohistochemistry of Runx-1 and Hes-1. Results: Changes were observed in the transcription of Notch signaling pathway components; Hes-1 was downregulated and Runx-1 upregulated in the skin of infected patients. These results were confirmed by immunohistochemistry, where reduction of Hes-1 expression was found in the epidermis, eccrine glands, and hair follicles. Increased expression of Runx-1 was found in inflammatory cells in the dermis of infected patients; however, it is not related to tissue changes. With these results, a multivariate analysis was performed to determine the causes of transcription factor Hes-1 reduction. It was concluded that tissue inflammation was the main cause. Conclusions: The tissue changes found in the skin of infected patients could be associated with a reduction in the expression of Hes-1, a situation that would promote the survival and proliferation of M. leprae in this tissue.

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Innate Immune Responses in Leprosy

Cited by 76 publications

References 182 publications

Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology

Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology

<p>Functional aspects, phenotypic heterogeneity, and tissue immune response of macrophages in infectious diseases</p>

Notch Signaling Pathway Expression in the Skin of Leprosy Patients: Association With Skin and Neural Damage

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