Domestication of Peach Palm in Southwestern Amazonia

The peach palm (Bactris gasipaes Kunth) is the only Neotropical palm domesticated by Native Americans. Its place of origin as a crop (B. gasipaes var. gasipaes) has been debated for more than a century, with three hypotheses currently in discussion: southwestern Amazonia; northwestern South America; or multiple origins in the distribution of the wild relatives (B. gasipaes var. chichagui). The small amount of archaeological data available supports the second hypothesis, but they contrast dramatically with the molecular-genetic analyses that support the first or the third, depending on how they are interpreted. On morphological grounds, two of the three types of var. chichagui are plausible candidates for wild ancestral populations. All the molecular-genetic analyses have identified a deep division between the landraces of cultivated peach palm in western Amazonia to Central America and those in southwestern to eastern Amazonia. The first analysis using isoenzymes linked the Tembe population (Bolivia) with the Pará landrace (eastern Amazonia), and these were distant from the western landraces. Multiple RAPD and SSR analyses identified the same deep division, which was interpreted by the group of researchers in Brazil as a single domestication in southwestern Amazonia with two dispersals, while another group working in Costa Rica interpreted it as three domestication events. Analysis with nuclear markers does not allow discrimination among the hypotheses, because gene flow may occur via pollen and seed. A new analysis with two sequences from the chloroplast genome, which has maternal inheritance and is therefore more appropriate to test the hypothesis, suggests that the cultivated peach palm was domesticated once in southwestern Amazonia, with two dispersals. One dispersal started in the upper Ucayali River basin, in southeastern Peru, and then throughout western Amazonia, northwestern South America and southern Central America. Another dispersal started in the upper Madeira River basin and then along the Madeira River into eastern Amazonia. New explorations in southwestern Amazonia are critical to identify the exact location of the original events

Annona coriacea Mart. fractions promote cell cycle arrest and inhibit autophagic flux in human cervical cancer cell lines

Plant-based compounds are an option to explore and perhaps overcome the limitations of current antitumor treatments. Annona coriacea Mart. is a plant with a broad spectrum of biological activities, but its antitumor activity is still unclear. The purpose of our study was to determine the effects of A. coriacea fractions on a panel of cervical cancer cell lines and a normal keratinocyte cell line. The antitumor effect was investigated in vitro by viability assays, cell cycle, apoptosis, migration, and invasion assays. Intracellular signaling was assessed by Western blot, and major compounds were identified by mass spectrometry. All fractions exhibited a cytotoxic effect on cisplatin-resistant cell lines, SiHa and HeLa. C3 and C5 were significantly more cytotoxic and selective than cisplatin in SiHa and Hela cells. However, in CaSki, a cisplatin-sensitive cell line, the compounds did not demonstrate higher cytotoxicity when compared with cisplatin. Alkaloids and acetogenins were the main compounds identified in the fractions. These fractions also markedly decreased cell proliferation with p21 increase and cell cycle arrest in G2/M. These effects were accompanied by an increase of H2AX phosphorylation levels and DNA damage index. In addition, fractions C3 and C5 promoted p62 accumulation and decrease of LC3II, as well as acid vesicle levels, indicating the inhibition of autophagic flow. These findings suggest that A. coriacea fractions may become effective antineoplastic drugs and highlight the autophagy inhibition properties of these fractions in sensitizing cervical cancer cells to treatment.e FINEP (MCTI/FINEP/MS/SCTIE/DECIT-01/ 2013—FP XII-BIOPLAT), Barretos Cancer Hospital, CAPES, CNPq, FAPEMIG, UFSJ. RMR is a recipient of CNPq Productivity Gran

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