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

Antigenicity, Immunogenicity and Protective Efficacy of Three Proteins Expressed in the Promastigote and Amastigote Stages of Leishmania infantum against Visceral Leishmaniasis.

In the present study, two Leishmania infantum hypothetical proteins present in the amastigote stage, LiHyp1 and LiHyp6, were combined with a promastigote protein, IgE-dependent histamine-releasing factor (HRF); to compose a polyproteins vaccine to be evaluated against L. infantum infection. Also, the antigenicity of the three proteins was analyzed, and their use for the serodiagnosis of canine visceral leishmaniasis (CVL) was evaluated. The LiHyp1, LiHyp6, and HRF DNA coding sequences were cloned in prokaryotic expression vectors and the recombinant proteins were purified. When employed in ELISA assays, all proteins were recognized by sera from visceral leishmaniasis (VL) dogs, and presented no cross-reactivity with either sera from dogs vaccinated with a Brazilian commercial vaccine, or sera of Trypanosoma cruzi-infected or Ehrlichia canis-infected animals. In addition, the antigens were not recognized by antibodies from non-infected animals living in endemic or non-endemic areas for leishmaniasis. The immunogenicity and protective efficacy of the three proteins administered in the presence of saponin, individually or in combination (composing a polyproteins vaccine), were evaluated in a VL murine model: BALB/c mice infected with L. infantum. Spleen cells from mice inoculated with the individual proteins or with the polyproteins vaccine plus saponin showed a protein-specific production of IFN-γ, IL-12, and GM-CSF after an in vitro stimulation, which was maintained after infection. These animals presented significant reductions in the parasite burden in different evaluated organs, when compared to mice inoculated with saline or saponin. The decrease in parasite burden was associated with an IL-12-dependent production of IFN-γ against parasite total extracts (produced mainly by CD4+ T cells), correlated to the induction of parasite proteins-driven NO production. Mice inoculated with the recombinant protein-based vaccines showed also high levels of parasite-specific IgG2a antibodies. The polyproteins vaccine administration induced a more pronounced Th1 response before and after challenge infection than individual vaccines, which was correlated to a higher control of parasite dissemination to internal organs