Enhanced phagocytosis of Corynebacterium pseudotuberculosis by monocyte-macrophage cells from goats naturally infected with caprine arthritis encephalitis virus

Sanches B.G.S., Souza F.N., Azedo M.R., Batista C.F., Bertagnon H.G., Blagitz M.G. & Della Libera A.M.M.P. 2012. [Enhanced phagocytosis of Corynebacterium pseudotuberculosis by monocyte-macrophage cells from goats naturally infected with caprine arthritis encephalitis virus.] Fagocitose intensificada de Corynebacterium pseudotuberculosis por celulas da serie monocito-macrofago de caprinos naturalmente infectados pelo virus da artrite encefalite. Pesquisa Veterinaria Brasileira 32(12):1225-1229. Departamento de Clinica Medica, Faculdade de Medicina Veterinaria e Zootecnia, Universidade de Sao Paulo, Avenida Prof. Dr. Orlando Marques de Paiva 87, Cidade Universitaria, Sao Paulo, SP 05508-270, Brazil. E-mail: [email protected] Caprine arthritis encephalitis (CAE) and caseous lymphadenitis (CL) have high incidence and transmissibility in small ruminants. Since both virus have tropism for macrophages and monocytes and affect the innate immune response, it is believed that CAE can predispose the animal to infection by Corynebacteruim pseudotuberculosis, the etiological agent of CL. To confirm this hypothesis, we evaluated phagocytosis from the monocyte-macrophage cells from 30 Saanen goats. Goats were uniformly divided in two groups according to results of agar gel immunodiffusion test for CAE virus (CAEV). Peripheral blood mononuclear cells were isolated by density gradient centrifugation and the monocyte-macrophage cells were isolated from the mononuclear cells by their adhesion properties in plaques. Afterwards, phagocytosis of C. psudotuberculosis was performed for two hours at 37 degrees C, 5% of CO2, and assessed by microscopic visualization. There was no difference in the percentage of monocyte-macrophage cells that phagocytozed C. bovis between groups (P = 0.41). However, when phagocytosis rates were classified according to the number of C. pseudotuberculosis phagocyted, the percentage of monocyte-macrophage cells that internalized more than 12 bacteria were higher in serologically CAEV positive animals compared to the serologically negative ones (P < 0.001). Furthermore, a positive and significant correlation (r = 0.488; P = 0.006) between the percentage of monocyte-macrophage cells that internalized more than 12 bacteria and the percentage of monocyte that were carrying out phagocytosis was also encountered in serologically CAEV positive goats, however the same were not observed in serologically negative ones. These results demonstrated an alteration in the intensity of C. pseudotuberculosis phagocytosis by monocytes-macrophages from goats infected by CAEV. Thus, these results indicated that goats infected with CAEV may be more susceptible to CL

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost