Brief Research Report: Expression of PD-1 and CTLA-4 in T Lymphocytes and Their Relationship With the Periparturient Period and the Endometrial Cytology of Dairy Cows During the Postpartum Period

The present study sought to evaluate the expression of PD-1 and CTLA-4 in blood T lymphocytes during the periparturient period and their relationship with uterine health in dairy cows, as determined by endometrial cytology and serum concentrations of β-hydroxybutyrate (BHB) and non-esterified fatty acids (NEFAs), which are indicators of a negative energy balance. The second objective of this study was to investigate whether the expression of PD-1 and CTLA-4 in T lymphocytes is associated with the serum acute phase-protein haptoglobin concentration during the periparturient period. To address these objectives, 26 clinically healthy dairy cows were used. Peripheral blood was collected 14 days prepartum (T-14), at calving (T0), and 30 days postpartum (T30) to measure the expression of PD-1 and CTLA-4 in blood T lymphocytes by flow cytometry. In addition, we collected blood at T0, 10 days after parturition (T10), and T30 to obtain serum and determine the serum concentrations of NEFA, BHB, and Hp. Endometrial cytology was performed at T10, 20 days after parturition (T20), and T30. In the present study, we observed higher expression of CTLA-4 and PD-1 in T lymphocytes at parturition and in the prepartum period, which could indicate a relationship between these immune checkpoints and immunological tolerance during gestation in dairy cattle. In addition, a negative association between the expression of these immune checkpoints prepartum or at parturition and endometrial cytology at T20 and T30 was observed, indicating the negative implications of these immune response regulators in susceptibility to infections. This finding was further corroborated by the relationship between the serum concentration of haptoglobin and the expression of CTLA-4 and PD-1 by T lymphocytes. However, we did not observe a relationship between the indicators of negative energy balance, evaluated by the serum concentrations of BHB and NEFA, and the expression of the immune checkpoint markers studied. Thus, our findings represent an initial step that paves the way for the development of new therapeutic alternatives directed by the host with the objective of increasing the resistance of dairy cattle to infections in this critical period of life

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

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