Toxoplasma gondii down modulates cadherin expression in skeletal muscle cells inhibiting myogenesis

<p>Abstract</p> <p>Background</p> <p><it>Toxoplasma gondii </it>belongs to a large and diverse group of obligate intracellular parasitic protozoa. Primary culture of mice skeletal muscle cells (SkMC) was employed as a model for experimental toxoplasmosis studies. The myogenesis of SkMC was reproduced <it>in vitro </it>and the ability of <it>T. gondii </it>tachyzoite forms to infect myoblasts and myotubes and its influence on SkMC myogenesis were analyzed.</p> <p>Results</p> <p>In this study we show that, after 24 h of interaction, myoblasts (61%) were more infected with <it>T. gondii </it>than myotubes (38%) and inhibition of myogenesis was about 75%. The role of adhesion molecules such as cadherin in this event was investigated. First, we demonstrate that cadherin localization was restricted to the contact areas between myocytes/myocytes and myocytes/myotubes during the myogenesis process. Immunofluorescence and immunoblotting analysis of parasite-host cell interaction showed a 54% reduction in cadherin expression at 24 h of infection. Concomitantly, a reduction in M-cadherin mRNA levels was observed after 3 and 24 h of <it>T. gondii-</it>host cell interaction.</p> <p>Conclusions</p> <p>These data suggest that <it>T. gondii </it>is able to down regulate M-cadherin expression, leading to molecular modifications in the host cell surface that interfere with membrane fusion and consequently affect the myogenesis process.</p

Litterfall Chemistry Is Modulated by Wet-Dry Seasonality and Leaf Phenology of Dominant Species in the Tropics

Litterfall has a large influence on carbon and nutrient cycling of ecosystems, particularly in light-limited forested streams, as most nutrients return in the form of litter. Although recent evidence points to the prevalence of seasonal litterfall in species-rich and evergreen tropical riparian forests, there is a limited understanding of how riparian plant diversity intersects with stream and riparian ecosystem functions. To explore this question, we investigate litterfall chemistry across wet and dry seasons and the congruence between litter traits and plant species composition of litterfall in the wet-dry tropics. Using generalized additive models, we observed consistent seasonal patterns of litterfall chemistry over 2 years, mostly influenced by dominant species in litterfall. While drier seasons showed litter lower in nutrients and structural compounds and higher in polyphenols, litter from wetter seasons were nutrient rich but lower in polyphenols. We also found contrasting seasonal patterns in litterfall chemistry, one showing that litterfall nutrient, structural compounds, and secondary metabolite concentrations declined in drier seasons while the other showed that mass-based litterfall inputs increased markedly in drier seasons. Our findings suggest that litterfall chemistry may be altered by shifts in the identity of dominant plant species and seasonality, possibly leading to changes in carbon and nutrient fluxes in tropical riparian ecosystems

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