128 research outputs found

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

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    <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

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    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

    Dynamics and determinants of SARS-CoV-2 RT-PCR testing on symptomatic individuals attending healthcare centers during 2020 in Bahia, Brazil

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    RT-PCR testing data provides opportunities to explore regional and individual determinants of test positivity and surveillance infrastructure. Using Generalized Additive Models, we explored 222,515 tests of a random sample of individuals with COVID-19 compatible symptoms in the Brazilian state of Bahia during 2020. We found that age and male gender were the most significant determinants of test positivity. There was evidence of an unequal impact among socio-demographic strata, with higher positivity among those living in areas with low education levels during the first epidemic wave, followed by those living in areas with higher education levels in the second wave. Our estimated probability of testing positive after symptom onset corroborates previous reports that the probability decreases with time, more than halving by about two weeks and converging to zero by three weeks. Test positivity rates generally followed state-level reported cases, and while a single laboratory performed ~90% of tests covering ~99% of the state's area, test turn-around time generally remained below four days. This testing effort is a testimony to the Bahian surveillance capacity during public health emergencies, as previously witnessed during the recent Zika and Yellow Fever outbreaks

    Pervasive gaps in Amazonian ecological research

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    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

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