17-AAG-induced activation of the autophagic pathway in Leishmania is associated with parasite death

The heat shock protein 90 (Hsp90) is thought to be an excellent drug target against parasitic diseases. The leishmanicidal effect of an Hsp90 inhibitor, 17-N-allylamino-17-demethoxygeldanamycin (17-AAG), was previously demonstrated in both in vitro and in vivo models of cutaneous leishmaniasis. Parasite death was shown to occur in association with severe ultrastructural alterations in Leishmania, suggestive of autophagic activation. We hypothesized that 17-AAG treatment results in the abnormal activation of the autophagic pathway, leading to parasite death. To elucidate this process, experiments were performed using transgenic parasites with GFP-ATG8-labelled autophagosomes. Mutant parasites treated with 17-AAG exhibited autophagosomes that did not entrap cargo, such as glycosomes, or fuse with lysosomes. ATG5-knockout (Δatg5) parasites, which are incapable of forming autophagosomes, demonstrated lower sensitivity to 17-AAG-induced cell death when compared to wild-type (WT) Leishmania, further supporting the role of autophagy in 17-AAG-induced cell death. In addition, Hsp90 inhibition resulted in greater accumulation of ubiquitylated proteins in both WT- and Δatg5-treated parasites compared to controls, in the absence of proteasome overload. In conjunction with previously described ultrastructural alterations, herein we present evidence that treatment with 17-AAG causes abnormal activation of the autophagic pathway, resulting in the formation of immature autophagosomes and, consequently, incidental parasite death

Autophagic Induction Greatly Enhances Leishmania major Intracellular Survival Compared to Leishmania amazonensis in CBA/j-Infected Macrophages

CBA mouse macrophages control Leishmania major infection yet are permissive to Leishmania amazonensis. Few studies have been conducted to assess the role played by autophagy in Leishmania infection. Therefore, we assessed whether the autophagic response of infected macrophages may account for the differential behavior of these two parasite strains. After 24 h of infection, the LC3-II/Act ratio increased in both L. amazonensis- and L. major-infected macrophages compared to uninfected controls, but less than in chloroquine-treated cells. This suggests that L. amazonensis and L. major activate autophagy in infected macrophages, without altering the autophagic flux. Furthermore, L. major-infected cells exhibited higher percentages of DQ-BSA-labeled parasitophorous vacuoles (50%) than those infected by L. amazonensis (25%). However, L. major- and L. amazonensis-induced parasitophorous vacuoles accumulated LysoTracker similarly, indicating that the acidity in both compartment was equivalent. At as early as 30 min, endogenous LC3 was recruited to both L. amazonensis- and L. major-induced parasitophorous vacuoles, while after 24 h a greater percentage of LC3 positive vacuoles was observed in L. amazonensis-infected cells (42.36%) compared to those infected by L. major (18.10%). Noteworthy, principal component analysis (PCA) and an hierarchical cluster analysis completely discriminated L. major-infected macrophages from L. amazonensis-infected cells accordingly to infection intensity and autophagic features of parasite-induced vacuoles. Then, we evaluated whether the modulation of autophagy exerted an influence on parasite infection in macrophages. No significant changes were observed in both infection rate or parasite load in macrophages treated with the autophagic inhibitors wortmannin, chloroquine or VPS34-IN1, as well as with the autophagic inducers rapamycin or physiological starvation, in comparison to untreated control cells. Interestingly, both autophagic inducers enhanced intracellular L. amazonensis and L. major viability, while the pharmacological inhibition of autophagy exerted no effects on intracellular parasite viability. We also demonstrated that autophagy induction reduced NO production by L. amazonensis- and L. major-infected macrophages but not alters arginase activity. These findings provide evidence that although L. amazonensis-induced parasitophorous vacuoles recruit LC3 more markedly, L. amazonensis and L. major similarly activate the autophagic pathway in CBA macrophages. Interestingly, the exogenous induction of autophagy favors L. major intracellular viability to a greater extent than L. amazonensis related to a reduction in the levels of NO

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

Liposomes encapsulating <i>L</i>. <i>amazonensis</i> promastigotes.

<p>The liposome membrane is composed of a EPC-PEG lipid mixture. Five million of <i>L</i>. <i>amazonensis</i> promastigotes per ml were used for the encapsulation process. (A) Liposomes containing multiple parasites. (B) Liposome with a single parasite with its flagellum at the right part of the parasite. Bright field images.</p

<i>L</i>. <i>amazonensis</i> viability in liposomes.

<p>Parasites in RPMI-dextran were encapsulated in liposomes and the living parasites were counted as function of time. (A) Average percentage of living encapsulated parasites. (B) Average number of living parasites per liposome after 24, 48 and 72h at pH 7.5 at 24°C. (C,D) Same parameters as in A and B after 24 and 48h at pH 5.5 at 37°C.</p

Picture of the homemade PDMS chambers for liposome production.

<p>The chamber size is 8 mm in diameter and 10mm in height). PDMS firmly adheres on a glass slide.</p