Comparative molecular cytogenetics in Melipona Illiger species (Hymenoptera, Apidae)

Cytogenetic studies in Melipona are scarce with only 24 species analyzed cytogenetically. Of these, six species had the rDNA sites physically mapped and characterized by Fluorescent in situ Hybridization (fish). The aim of this study was to perform karyotype analyzes on Melipona species from different regions of Brazil, with a greater sampling representative of the Amazonian fauna and using conventional, fluorochrome staining and FISH with heterologous rDNA probes. The predominant chromosome number was 2n = 18, however, the subspecies M. seminigra abunensis and M. s. pernigra showed 2n = 22 chromosomes. The karyotypes were symmetrical, however M. bicolor, M. quadrifasciata, M. flavolineata, M. fuscopilosa, M. nebulosa presented the first pair heteromorphic in length. CMA3+ blocks also exhibited heteromorphism of size and in almost all cases coincided with rDNA sites, except for M. crinita and M. nebulosa, which presented additional non-coincident CMA3+ blocks. The CMA/ rDNA sites were terminal and interstitial in species with high heterochromatic content, and pericentromeric in those species with low heterochromatic content. In addition to pointing out cytogenetic features of cytotaxonomic importance, the reorganization of the genome in Melipona is discussed

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

Functional categorization and cell location of identified differentially expressed proteins in infected and non-infected sweet orange variety “Westin” subjects.

<p><b>A,</b> Functional categorization of significantly different proteins. <b>B,</b> Cell location of differentially expressed proteins.</p

Distribution of differentially-expressed proteins of sweet orange variety “Westin” in response to <i>Citrus tristeza virus</i> (CTV), according to their expression levels.

<p>The gray segment of the bar corresponds to up-accumulated proteins and in light gray, the down-accumulated ones.</p

Peroxidase activity in “Westin” sweet orange infected and non-infected with CTV, with the use of ascorbic acid and guaiacol as electron donors.

<p><b>A, B, and C</b> AsA—exhaustion times in the reaction at 10 mmol.L^-1, 20 mmol.L^-1 and 30 mmol.L^-1, respectively. The arrows indicate the times at which GPX activity was started.</p

Amplification of CTV viral particles through RT-PCR in order to confirm the presence of the virus in infected samples, and to confirm its absence in non-infected samples.

<p><b>M,</b> molecular weight marker; <b>NIC-</b> and <b>IC-,</b> negative controls of the reaction for each of the primers (1 and 2). <b>NI1</b> and <b>I1</b>, non-infected and infected samples (primer 1—CN487/489), respectively. <b>NI2</b> e <b>I2</b>, non-infected and infected samples (primer 2—CN488/491), respectively. The arrows show the amplified bands as per their expected sizes. The reaction was confirmed in a 1% agarose gel.</p

Differentially expressed proteins among infected and non-infected samples of “Westin” sweet orange, identified through Mass Spectrometry (ms/ms).

<p>* Exclusives Spots from Infected Samples</p><p>The peptides were sequenced through ms/ms</p><p>Score corresponding to the coverage value, as calculated by Mascot.</p><p>Differentially expressed proteins among infected and non-infected samples of “Westin” sweet orange, identified through Mass Spectrometry (ms/ms).</p