Temporal patterns of picoplankton abundance and metabolism on the western coast of the equatorial Atlantic Ocean

Picoplankton are central global carbon (C) cycling players and often dominate the ocean plankton communities, especially in low latitudes. Therefore, evaluating picoplankton temporal dynamics is critical to understanding microbial stocks and C fluxes in tropical oceans. However, the lack of studies on low-latitude picoplankton communities translates into a common conception that there is an absence of seasonality. Herein, we studied the temporal variation in abundance (measured by flow cytometry), and carbon flux (taking bacterial production and respiration as proxies) of the picoplanktonic community for the first time, as well as their environmental drivers in a low-latitude (05° 59’ 20.7″S 035° 05’ 14.6″W) Atlantic coastal station. We performed monthly samplings between February 2013 and August 2016 in a novel microbial observatory – hereafter called the Equatorial Atlantic Microbial Observatory – established on the northeastern Brazilian Atlantic coast. Our results revealed stability in temporal dynamics of picoplankton, despite a considerable inter-annual variation, with some related to the El Niño (ENSO) event in 2015. However, weak environmental relationships found were not enough to explain the variation in picoplankton’s abundance, which suggests that other factors such as biological interactions may lead to picoplankton abundance variation over time. Heterotrophic bacteria dominated picoplankton during the entire study period and between photosynthetic counterparts, and Synechococcus showed greater relative importance than picoeukaryotes. These results bring a novel perspective that picoplankton may exhibit more pronounced fluctuations in the tropical region when considering inter-annual intervals, and is increasing prokaryotic contribution to carbon cycling towards the equator.Fil: Menezes, Maiara. Universidade Federal do Rio Grande do Norte; BrasilFil: Junger, Pedro C.. Universidade Federal do São Carlos; BrasilFil: Kavagutti, Vinicius S.. Universidade Federal do São Carlos; BrasilFil: Wanderley, Bruno. Universidade Federal do Rio Grande do Norte; BrasilFil: Cabral, Anderson de Souza. Universidade Federal do Rio de Janeiro; BrasilFil: Paranhos, Rodolfo. Universidade Federal do Rio de Janeiro; BrasilFil: Unrein, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de San Martin. Instituto Tecnologico de Chascomus. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - la Plata. Instituto Tecnologico de Chascomus.; ArgentinaFil: Amado, André M.. Universidade Federal do Rio Grande do Norte; Brasil. Universidade Federal de Juiz de Fora; BrasilFil: Sarmento, Hugo. Universidade Federal do São Carlos; Brasi

Temporal patterns of picoplankton abundance and metabolism on the western coast of the equatorial Atlantic Ocean

Picoplankton are central global carbon (C) cycling players and often dominate the ocean plankton communities,especially in low latitudes. Therefore, evaluating picoplankton temporal dynamics is critical to understandingmicrobial stocks and C fluxes in tropical oceans. However, the lack of studies on low-latitude picoplanktoncommunities translates into a common conception that there is an absence of seasonality. Herein, we studied thetemporal variation in abundance (measured by flow cytometry), and carbon flux (taking bacterial production andrespiration as proxies) of the picoplanktonic community for the first time, as well as their environmental driversin a low-latitude (05° 59’ 20.7″S 035° 05’ 14.6″W) Atlantic coastal station. We performed monthly samplingsbetween February 2013 and August 2016 in a novel microbial observatory – hereafter called the Equatorial AtlanticMicrobial Observatory – established on the northeastern Brazilian Atlantic coast. Our results revealed stabilityin temporal dynamics of picoplankton, despite a considerable inter-annual variation, with some related to the ElNiño (ENSO) event in 2015. However, weak environmental relationships found were not enough to explain thevariation in picoplankton’s abundance, which suggests that other factors such as biological interactions may leadto picoplankton abundance variation over time. Heterotrophic bacteria dominated picoplankton during the entirestudy period and between photosynthetic counterparts, and Synechococcus showed greater relative importancethan picoeukaryotes. These results bring a novel perspective that picoplankton may exhibit more pronouncedfluctuations in the tropical region when considering inter-annual intervals, and is increasing prokaryotic contributionto carbon cycling towards the equator

Role of metformin and other metabolic drugs in the prevention and therapy of endocrine-related cancers

Metabolic syndrome is associated with chronic diseases, including type 2 diabetes, cardiovascular diseases, and cancer. This review summarizes the current evidence on the antitumor effects of some relevant drugs currently used to manage metabolic-related pathologies (i.e. insulin and its analogs, metformin, statins, etc.) in endocrine-related cancers including breast cancer, prostate cancer, pituitary cancer, ovarian cancer, and neuroendocrine neoplasms. Although current evidence does not provide a clear antitumor role of several of these drugs, metformin seems to be a promising chemopreventive and adjuvant agent in cancer management, modulating tumor cell metabolism and microenvironment, through both AMP-activated protein kinase–dependent and –independent mechanisms. Moreover, its combination with statins might represent a promising therapeutic strategy to tackle the progression of endocrine-related tumors. However, further studies are needed to endorse the clinical relevance of these drugs as adjuvants for cancer chemotherapy

Role of metformin and other metabolic drugs in the prevention and therapy of endocrine-related cancers

Metabolic syndrome is associated with chronic diseases, including type 2 diabetes, cardiovascular diseases, and cancer. This review summarizes the current evidence on the antitumor effects of some relevant drugs currently used to manage metabolic-related pathologies (i.e. insulin and its analogs, metformin, statins, etc.) in endocrine-related cancers including breast cancer, prostate cancer, pituitary cancer, ovarian cancer, and neuroendocrine neoplasms. Although current evidence does not provide a clear antitumor role of several of these drugs, metformin seems to be a promising chemopreventive and adjuvant agent in cancer management, modulating tumor cell metabolism and microenvironment, through both AMP-activated protein kinase-dependent and -independent mechanisms. Moreover, its combination with statins might represent a promising therapeutic strategy to tackle the progression of endocrine-related tumors. However, further studies are needed to endorse the clinical relevance of these drugs as adjuvants for cancer chemotherapy.Ministerio de Ciencia e Innovación de España. PID2019- 105564RB-I00/FPU16-05059Fondo Europeo de Desarrollo Regional (FEDER) y Fondo Social Europeo (FSE). PI20/01301Instituto de Salud Carlos III de España. SCIII-AES-2019/002525Junta de Andalucía. PI-0152-2019, PI-0094-2020, PI-0038/2019, RH-0084-2020 y BIO-013

The road to integrate climate change projections with regional land‐use–biodiversity models

Current approaches to project spatial biodiversity responses to climate change mainly focus on the direct effects of climate on species while regarding land use and land cover as constant or prescribed by global land-use scenarios. However, local land-use decisions are often affected by climate change and biodiversity on top of socioeconomic and policy drivers. To realistically understand and predict climate impacts on biodiversity, it is, therefore, necessary to integrate both direct and indirect effects (via climate-driven land-use change) of climate change on biodiversity.In this perspective paper, we outline how biodiversity models could be better integrated with regional, climate-driven land-use models. We initially provide a short, non-exhaustive review of empirical and modelling approaches to land-use and land-cover change (LU) and biodiversity (BD) change at regional scales, which forms the base for our perspective about improved integration of LU and BD models. We consider a diversity of approaches, with a special emphasis on mechanistic models. We also look at current levels of integration and at model properties, such as scales, inputs and outputs, to further identify integration challenges and opportunities.We find that LU integration in BD models is more frequent than the other way around and has been achieved at different levels: from overlapping predictions to simultaneously coupled simulations (i.e. bidirectional effects). Of the integrated LU-BD socio-ecological models, some studies included climate change effects on LU, but the relative contribution of direct vs. indirect effects of climate change on BD remains a key research challenge.Important research avenues include concerted efforts in harmonizing spatial and temporal resolution, disentangling direct and indirect effects of climate change on biodiversity, explicitly accounting for bidirectional feedbacks, and ultimately feeding socio-ecological systems back into climate predictions. These avenues can be navigated by matching models, plugins for format and resolution conversion, and increasing the land-use forecast horizon with adequate uncertainty. Recent developments of coupled models show that such integration is achievable and can lead to novel insights into climate–land use–biodiversity relations.info: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 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