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

Optimization of anti-Trypanosoma cruzi oxadiazoles leads to identification of compounds with efficacy in infected mice

We recently showed that oxadiazoles have anti-Trypanosoma cruzi activity at micromolar concentrations. These compounds are easy to synthesize and show a number of clear and interpretable structure-activity relationships (SAR), features that make them attractive to pursue potency enhancement. We present here the structural design, synthesis, and anti-T. cruzi evaluation of new oxadiazoles denoted 5a-h and 6a-h. The design of these compounds was based on a previous model of computational docking of oxadiazoles on the T. cruzi protease cruzain. We tested the ability of these compounds to inhibit catalytic activity of cruzain, but we found no correlation between the enzyme inhibition and the antiparasitic activity of the compounds. However, we found reliable SAR data when we tested these compounds against the whole parasite. While none of these oxadiazoles showed toxicity for mammalian cells, oxadiazoles 6c (fluorine), 6d (chlorine), and 6e (bromine) reduced epimastigote proliferation and were cidal for trypomastigotes of T. cruzi Y strain. Oxadiazoles 6c and 6d have IC50 of 9.5 +/- 2.8 and 3.5 +/- 1.8 mu M for trypomastigotes, while Benznidazole, which is the currently used drug for Chagas disease treatment, showed an IC50 of 11.3 +/- 2.8 mu M. Compounds 6c and 6d impair trypomastigote development and invasion in macrophages, and also induce ultrastructural alterations in trypomastigotes. Finally, compound 6d given orally at 50 mg/kg substantially reduces the parasitemia in T. cruzi-infected BALB/c mice. Our drug design resulted in potency enhancement of oxadiazoles as anti-Chagas disease agents, and culminated with the identification of oxadiazole 6d, a trypanosomicidal compound in an animal model of infection. (C) 2012 Elsevier Ltd. All rights reserved.CNPqCNPqFAPESBFAPESBUniversidade Federal de Pernambuco (UFPE)Universidade Federal de Pernambuco (UFPE)Conselho Nacional de Pesquisas Brasileira (CNPq)Conselho Nacional de Pesquisas Brasileira (CNPq) [478454/2010-4]Fundacao de Amparo as Pesquisas do Estado da Bahia (FAPESB)Fundacao de Amparo as Pesquisas do Estado da Bahia (FAPESB) [6596

IGF-1-Overexpressing Mesenchymal Stem/Stromal Cells Promote Immunomodulatory and Proregenerative Effects in Chronic Experimental Chagas Disease

Submitted by Ana Maria Fiscina Sampaio ([email protected]) on 2018-12-14T12:54:42Z No. of bitstreams: 1 Silva DN IGF-1- Overexpressing Mesenchymal....2018.pdf: 19423799 bytes, checksum: c3702b567dcf37a96ef282cd8a66c8a2 (MD5)Approved for entry into archive by Ana Maria Fiscina Sampaio ([email protected]) on 2018-12-14T13:17:36Z (GMT) No. of bitstreams: 1 Silva DN IGF-1- Overexpressing Mesenchymal....2018.pdf: 19423799 bytes, checksum: c3702b567dcf37a96ef282cd8a66c8a2 (MD5)Made available in DSpace on 2018-12-14T13:17:36Z (GMT). No. of bitstreams: 1 Silva DN IGF-1- Overexpressing Mesenchymal....2018.pdf: 19423799 bytes, checksum: c3702b567dcf37a96ef282cd8a66c8a2 (MD5) Previous issue date: 2018FINEP, CNPq, and FAPESB for research funding.Hospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil.Hospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil / National Institute of Science and Technology for Regenerative Medicine. Rio de Janeiro, RJ, Brazil.Hospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil.Hospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil.Hospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, BrasilHospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, BrasilHospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, BrasilHospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, BrasilHospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, BrasilHospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, Brasil / National Institute of Science and Technology for Regenerative Medicine. Rio de Janeiro, RJ, Brazil.Hospital São Rafael. Centro de Biotecnologia e Terapia Celular. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil / National Institute of Science and Technology for Regenerative Medicine. Rio de Janeiro, RJ, Brazil.Mesenchymal stem/stromal cells (MSCs) have been investigated for the treatment of diseases that affect the cardiovascular system, including Chagas disease. MSCs are able to promote their beneficial actions through the secretion of proregenerative and immunomodulatory factors, including insulin-like growth factor-1 (IGF-1), which has proregenerative actions in the heart and skeletal muscle. Here, we evaluated the therapeutic potential of IGF-1-overexpressing MSCs (MSC_IGF-1) in a mouse model of chronic Chagas disease. C57BL/6 mice were infected with Colombian strain Trypanosoma cruzi and treated with MSCs, MSC_IGF-1, or vehicle (saline) six months after infection. RT-qPCR analysis confirmed the presence of transplanted cells in both the heart and skeletal muscle tissues. Transplantation of either MSCs or MSC_IGF-1 reduced the number of inflammatory cells in the heart when compared to saline controls. Moreover, treatment with MSCs or MSC_IGF-1 significantly reduced TNF-α, but only MSC treatment reduced IFN-γ production compared to the saline group. Skeletal muscle sections of both MSC- and MSC_IGF-1-treated mice showed a reduction in fibrosis compared to saline controls. Importantly, the myofiber area was reduced in T. cruzi-infected mice, and this was recovered after treatment with MSC_IGF-1. Gene expression analysis in the skeletal muscle showed a higher expression of pro- and anti-inflammatory molecules in MSC_IGF-1-treated mice compared to MSCs alone, which significantly reduced the expression of TNF-α and IL-1β. In conclusion, our results indicate the therapeutic potential of MSC_IGF-1, with combined immunomodulatory and proregenerative actions to the cardiac and skeletal muscles

The anti-inflammatory and immunomodulatory potential of braylin: Pharmacological properties and mechanisms by <i>in silico</i>, <i>in vitro</i> and <i>in vivo</i> approaches - Fig 5

<p>Docking solutions showing the main interactions for (A) RU486 (pink), (B) dexamethasone (orange) and (C) braylin (cyan) superimposed in GR (pdb 1NHZ).</p

Effects of braylin on tail flick and hot plate tests in mice.

<p>Panels representing the latency in seconds in the tail flick (panel A) and hot plate (panel B) tests, after ip injection of braylin (BRA; 100 mg/kg), vehicle (50% propylene glycol in saline; control group) or morphine (5 mg/kg; reference drug). Data are reported as means ± SEM; <i>n</i> = 6 mice per group. * Significantly different from the control group (<i>p</i> < 0.05). Two-way ANOVA followed by the Bonferroni’s test.</p

Effects of braylin on cytokines paw levels during CFA-induced inflammation.

<p>Mice were injected with braylin (BRA; 50 mg/kg), vehicle (50% propylene glycol in saline; control group) or dexamethasone (Dexa; 2 mg/kg; reference drug) by ip route 40 minutes before CFA (injected at time zero). The naïve group consists of mice that did not receive any experimental manipulation. Panels shows the paw levels of (A) interleukin-1β (IL-1β), (B) tumor necrosis factor-α (TNF-α), (C) interleukin-6 (IL-6), (D) interleukin-13 (IL-13), (E) interleukin-10 (IL-10) and (F) transforming growth factor-β (TGF-β), determined in skin tissues samples by ELISA, 3 hours after the CFA injection. The results are expressed as picograms of cytokine per milligram of protein. Data are expressed as means ± SEM; <i>n</i> = 6 mice per group. * Significantly different from the vehicle group in the same time (<i>p</i> < 0.05); ^# significantly different from the naive group (<i>p</i> < 0.05). ANOVA followed by Tukey´s multiple comparison test.</p

Cytotoxic effect of braylin and its modulation of nitric oxide production on macrophages.

<p>Panels A and C: J774 cells (A) or peritoneal exudate macrophages (C) were incubated with vehicle (50% propylene glycol in saline, Ct, control group) or different concentrations of braylin (BRA; 10, 20, 40 or 80 μM) for 72 hours and cell viability was determined by Alamar Blue assay. Gentian violet (GV) was used as positive control. Data are expressed as means ± SEM; <i>n</i> = 9 determinations per group. *Significantly different from the vehicle treated cultures (<i>p</i> < 0.05). ANOVA followed by Tukey´s multiple comparison test. Panels B and D: Concentrations of nitrite were determined in J774 macrophages (B) or peritoneal exudate macrophages (D) treated with vehicle (50% propylene glycol in saline, Ct+, control group), braylin (BRA; 10, 20 or 40 μM) or dexamethasone (Dexa; 40 μM) in the presence of LPS (500 ng/mL) + IFN-γ (5 ng/mL). Cell-free supernatants were collected 24 hours after treatments for nitrite quantification by the Griess method. Ct- shows concentrations of nitrite in unstimulated cells. Data are expressed as means ± SEM; <i>n</i> = 9 determinations per group. *Significantly different from the vehicle treated cultures stimulated with LPS + IFN-γ (<i>p</i>< 0.05). ANOVA followed by Tukey´s multiple comparison test.</p

Effect of braylin on cytokine production by activated macrophages.

<p>Concentrations of TNF-α, IL-1β and IL-6 were determined in cultures of J774 macrophages (panels A, C and E) or peritoneal exudate macrophages (panels B, D and F) treated with vehicle (50% propylene glycol in saline, Ct+, control group), braylin (BRA; 10, 20 or 40 μM) or dexamethasone (Dexa; 40 μM) in the presence of LPS (500 ng/mL) plus IFN-γ (5 ng/mL). Cell-free supernatants were collected 4 hours (for TNF-α measurement) and 24 hours (for IL-1β and IL-6) after treatments for ELISA assay. Ct- shows cytokine concentrations in unstimulated cells. Data are expressed as means ± SEM; <i>n</i> = 10 determinations per group. *Significantly different from the vehicle treated cultures stimulated with LPS + IFN-γ (<i>p</i> < 0.05). ANOVA followed by Tukey´s multiple comparison test.</p