Cyanidiophyceae (Rhodophyta) Tolerance to Precious Metals: Metabolic Response to Palladium and Gold

Polyextremophilic red algae, which belong to the class Cyanidiophyceae, are adapted to live in geothermal and volcanic sites. These sites often have very high concentrations of heavy and precious metals. In this study, we assessed the capacity of three strains of Galdieria (G. maxima, G. sulphuraria, and G. phlegrea) and one strain of Cyanidiumcaldarium to tolerate different concentrations of precious metals, such as palladium (Cl4K2Pd) and gold (AuCl4K) by monitoring algal growths in cultures exposed to metals, and we investigated the algae potential oxidative stress induced by the metals. This work provides further understanding of metals responses in the Cyanidiophyceae, as this taxonomic class is developed as a biological refinement tool.

NLP-MeTaxa: A Natural Language Processing approach for Metagenomic Taxonomic Binning based on deep learning

Background: Metagenomics is the study of genomic content in mass from an environment of interest such as the human gut or soil. Taxonomy is one of the most important fields of metagenomics, which is the science of defining and naming groups of microbial organisms that share the same characteristics. The problem of taxonomy classification is the identification and quantification of microbial species or higher-level taxa sampled by high throughput sequencing. Objective: Although many methods exist to deal with the taxonomic classification problem, assignment to low taxonomic ranks remains an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. Methods: In this paper, we introduce NLP-MeTaxa, a novel composition-based method for taxonomic binning, which relies on the use of words embeddings and deep learning architecture. The new proposed approach is word-based, where the metagenomic DNA fragments are processed as a set of overlapping words by using the word2vec model to vectorize them in order to feed the deep learning model. NLP-MeTaxa output is visualized as NCBI taxonomy tree, this representation helps to show the connection between the predicted taxonomic identifiers. NLP-MeTaxa was trained on large-scale data from the NCBI RefSeq, more than 14,000 complete microbial genomes. The NLP-MeTaxa code is available at the website: https://github.com/padriba/NLP_MeTaxa/ Results: We evaluated NLP-MeTaxa with a real and simulated metagenomic dataset and compared our results to other tools' results. The experimental results have shown that our method outperforms the other methods especially for the classification of low-ranking taxonomic class such as species and genus. Conclusion: In summary, our new method might provide novel insight for understanding the microbial community through the identification of the organisms it might contain.

Vertebrate Mitochondrial Genome Organization: Evidence of Convergent Evolution

The evolution of the vertebrate mitochondrial genome has been the focus of numerous genetic and evolutionary studies over the last several decades. Initially, sampling was heavily biased toward taxonomic orders of greatest economic or health importance, but recent advances in DNA sequencing technology have facilitated a much broader phylogenetic sampling from which we can clarify general evolutionary trends such as patterns of gene rearrangement. Toward this end, we performed a comparative genomic analysis of the 2,831 vertebrate mitochondrial genomes representing 12 classes that are available in the NCBI database. Using a combination of bioinformatics methods, we determined that there is a great variation in the proportion of rearrangement by gene and by taxonomic class, with higher rates being observed in Reptilia, Amphibia, Petromyzonti, Mammalia, and Actinopteri. Further, within each class, there is large variation in proportion of reorganization among different orders or even taxonomic families. Eleven events of convergence in the genic order among different taxonomic orders were determined, most of them not previously reported.

Proteobacteria Contain Diverse flg22 Epitopes That Elicit Varying Immune Responses in Arabidopsis thaliana

Bacterial flagellin protein is a potent microbe-associated molecular pattern. Immune responses are triggered by a 22-amino-acid epitope derived from flagellin, known as flg22, upon detection by the pattern recognition receptor FLAGELLIN-SENSING2 (FLS2) in multiple plant species. However, increasing evidence suggests that flg22 epitopes of several bacterial species are not universally immunogenic to plants. We investigated whether flg22 immunogenicity systematically differs between classes of the phylum Proteobacteria, using a dataset of 2,470 flg22 sequences. To predict which species encode highly immunogenic flg22 epitopes, we queried a custom motif (11[ST]xx[DN][DN]xAGxxI21) in the flg22 sequences, followed by sequence conservation analysis and protein structural modeling. These data led us to hypothesize that most flg22 epitopes of the γ- and β-Proteobacteria are highly immunogenic, whereas most flg22 epitopes of the α-, δ-, and ε-Proteobacteria are weakly to moderately immunogenic. To test this hypothesis, we generated synthetic peptides representative of the flg22 epitopes of each proteobacterial class, and we monitored their ability to elicit an immune response in Arabidopsis thaliana. The flg22 peptides of γ- and β-Proteobacteria triggered strong oxidative bursts, whereas peptides from the ε-, δ-, and α-Proteobacteria triggered moderate, weak, or no response, respectively. These data suggest flg22 immunogenicity is not highly conserved across the phylum Proteobacteria. We postulate that sequence divergence of each taxonomic class was present prior to the evolution of FLS2, and that the ligand specificity of A. thaliana FLS2 was driven by the flg22 epitopes of the γ- and β-Proteobacteria, a monophyletic group containing many common phytopathogens. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Body mass and latitude predict the presence of multiple stressors in global vertebrate populations

Multiple stressors are recognised as a key threat to biodiversity, but our understanding of what might predispose species to multiple stressors remains limited. Here we analyse a global dataset of over 7000 marine, freshwater, and terrestrial vertebrate populations, alongside species-specific trait data, to identify factors which influence the number of stressors a species is subjected to at the population level. We find that body mass and latitude can both influence the number of stressors a population is subjected to across ecosystems, with large-bodied species tending to be more threatened, except terrestrial amphibians which show the opposite trend. Latitudinal forecasts predict higher stressor numbers between 20°N and 40°N, and towards the poles. Global stressor distributions suggest a link between human population centres and stressor frequency generally impacting larger-bodied species. Latitude and body mass hence provide key predictive tools to identify which vertebrate populations are likely to be highly threatened, despite the strength of these trends differing between ecological system and taxonomic class.

Potential of soil resources of Coconut Research Station, Aliyarnagar, Tamil Nadu, India for agro-technology generation

Potential of soil resources of Coconut Research Station, Aliyarnagar of Tamil Nadu Agricultural University and one of the Centers of ICAR-AICRP (Palms), was assessed by soil profile examination and spatial variability mapping. Three soil profiles were examined, one each in A, B and C blocks of the farm, soils were studied horizon wise samples were collected, and fertility parameters were analyzed. Spatial variability of primary nutrients was mapped employing GIS techniques. Soil profile examination revealed the presence of canker nodules in the lower horizons and the depth of the soil was not a constraint for the cultivation of perennial crops. The texture of the soil varied from loamy sand to sandy clay loam. pH was alkaline and electrical conductivity was less than 2 dSm-1. The content of KMnO4-N was low, and Olsen P, NNNH4OAc-K and organic carbon were medium. Land capability class was IIIew and was highly suitable (S1) for coconut, moderately suitable (S2) for cocoa and marginally suitable (S3) for pepper. The soil taxonomic class is fine-loamy mixed, isohyperthermic Fluventic/Typic Haplustepts. Rock outcrops were noticed over 5 per cent of the area. Top soil erosion and seepage problems resulting in temporary water logging are the major fertility constraints associated with this farm. Scrupulous application of organic manures, split application of fertilizers, providing trenches in areas of water logging, etc., are the strategies to overcome the constraints, which are existing in the farm.

Proteobacteria encode diverse flg22 peptides that elicit varying immune responses in Arabidopsis thaliana

ABSTRACTBacterial flagellin protein is a potent microbe-associated molecular pattern. Immune responses are triggered by a 22 amino acid epitope derived from flagellin, known as flg22, upon detection by the pattern recognition receptor FLAGELLIN-SENSING2 (FLS2) in multiple plant species. However, increasing evidence suggests that flg22 epitopes of several bacterial species are not universally immunogenic to plants. We investigated whether flg22 immunogenicity systematically differs between classes of the phylum Proteobacteria, using a dataset of 2,470 flg22 sequences. To predict which species encode highly immunogenic flg22 epitopes, we queried a custom motif (11[ST]xx[DN][DN]xAGxxI21) in the flg22 sequences, followed by sequence conservation analysis and protein structural modelling. These data led us to hypothesize that most flg22 epitopes of the γ- and β-Proteobacteria are highly immunogenic, whereas most flg22 epitopes of the α-, δ-, and ε-Proteobacteria are weakly to moderately immunogenic. To test this hypothesis, we generated synthetic peptides representative of the flg22 epitopes of each proteobacterial class, and we monitored their ability to elicit an immune response in Arabidopsis thaliana. Flg22 peptides of the γ- and β-Proteobacteria triggered strong oxidative bursts, whereas peptides from the ε-, δ-, and α-Proteobacteria triggered moderate, weak, or no response, respectively. These data suggest flg22 immunogenicity is not highly conserved across the phylum Proteobacteria. We postulate that sequence divergence of each taxonomic class was present prior to the evolution of FLS2, and that the ligand specificity of A. thaliana FLS2 was driven by the flg22 epitopes of the γ- and β-proteobacteria, a monophyletic group containing many common phytopathogens.

Asteroid taxonomy using artificial neural networks

<p>Asteroids are classified into different taxonomic groups according to their spectral reflectance properties in the visual and near-infrared (Vis-NIR) wavelengths. The spectral properties of the asteroid surfaces can be related to the material of the surface. There are a few taxonomic systems for asteroids, the most recent being the so-called Bus-DeMeo taxonomy (B-DM, DeMeo et al., Icarus 202, 2009). Usually, the exact wavelengths used in the taxonomic system are tied up with the particular survey data that was used to create the taxonomy. With the B-DM taxonomy, it is the SMASSII survey extended to near-infrared with the NASA IRTF telescope observations, and the wavelengths are 0.45–2.45 µm.</p> <p>The ESA space observatory Gaia will produce a significant number of low-resolution asteroid spectra over the 0.33–1.05 µm wavelength range in the Data Release 3 and the final data releases. There is an evident need for evaluating the surface properties of these asteroids using the Gaia data. For example, the list of taxonomic classifications for asteroids, maintained in the NASA Planetary Data System, has 2,600 asteroids with some taxonomic class (Neese, C., Ed., NASA Planetary Data System, 2010), but the Gaia data will eventually contain about 100,000 asteroid spectra.</p> <p>There is a plan to provide a new asteroid taxonomy using the Gaia observations (Delbo et al., PSS 73, 2012). However, a link to existing taxonomic systems such as B-DM would be highly valuable. In this work, we study the possibility to use feed-forward artificial neural network for asteroid spectral classification. We show that the classification accuracy can remain on a reasonably good level even if the B-DM classification is done with the neural network that is trained to use only data having wavelengths in the 0.45–1.05 µm range, which is the overlapping region with the Gaia and the original B-DM systems. This tool can provide the B-DM taxonomic classification for all the asteroids with Gaia spectroscopy.</p> <p><strong>Acknowledgements:</strong> Research supported, in part, by the Academy of Finland (project 325805).</p>

Risky Business: Live Non-CITES Wildlife UK Imports and the Potential for Infectious Diseases

International wildlife trade is recognised as a major transmission pathway for the movement of pathogenic organisms around the world. The UK is an active consumer of non-native live wild animals and is therefore subject to the risks posed by pathogen pollution from imported wildlife. Here, we characterise a key yet overlooked portion of the UK wildlife import market. We evaluate the trade in live non-CITES (Convention on International Trade in Endangered Species) wild terrestrial animals entering the UK over a 5-year period using data reported by the Animal and Plant Health Agency (APHA). Between 2014 and 2018, over 48 million individual animals, across five taxonomic classes and 24 taxonomic orders, were imported into the UK from 90 countries across nine global regions. The largest volumes of wild animals were imported from North America and Asia, and most of the import records were from Europe and Africa. Excluding Columbiformes (pigeons) and Galliformes (‘game birds’), amphibians were the most imported taxonomic class (73%), followed by reptiles (17%), mammals (4%), birds (3%), and arachnids (<1%). The records described herein provide insight into the scope and scale of non-CITES listed wildlife imported in to the UK. We describe the potential for pathogen pollution from these vast and varied wildlife imports and highlight the potential threats they pose to public health. We also draw attention to the lack of detail in the UK wildlife import records, which limits its ability to help prevent and manage introduced infectious diseases. We recommend that improved record keeping and reporting could prove beneficial in this regard.

The Pull of the Recent revisited: negligible species-level effect in a regional marine fossil record

Quantifying true patterns of biodiversity change over the Cenozoic has major implications for all of biology and paleontology but is still a source of significant debate. The problem centers on the magnitude and nature of several well-known sampling effects and analytical biases in the fossil record, including the Pull of the Recent. We test the effect of the Pull of the Recent at both generic and species levels on the exemplary New Zealand Cenozoic marine mollusk fossil record. We examine several biological traits of species to determine whether particular attributes of taxa control their likely presence or absence in the youngest fossil record (<2.4 Ma). We demonstrate that, for a tectonically active region, the Pull of the Recent does not exert a strong effect on apparent diversity patterns of genera and species over the Cenozoic at temporal scales typically used in global and regional biodiversity analyses. This result agrees with previous studies quantifying the effect of the Pull of the Recent in the marine and terrestrial realms at the genus level. The effect of the Pull of the Recent, although small, is greatest for the youngest fossil record (<2.4 Ma), particularly for species. This increase cannot easily be explained by effects related to shell mineralogical composition, size, habitat, taxonomic class, or lithification. The small effect that the Pull of the Recent exerts on the New Zealand molluscan fossil record implies that the apparent rise in regional marine diversity during the Cenozoic represents a true biological signal and/or reflects other confounding effects not considered here.