Ultra‐Conserved Elements and morphology reciprocally illuminate conflicting phylogenetic hypotheses in Chalcididae (Hymenoptera,Chalcidoidea)

Recent technical advances combined with novel computational approaches have promised the acceleration of our understanding of the tree of life. However, when it comes to hyperdiverse and poorly known groups of invertebrates, studies are still scarce. As published phylogenies will be rarely challenged by future taxonomists, careful attention must be paid to potential analytical bias. We present the first molecular phylogenetic hypothesis for the family Chalcididae, a group of parasitoid wasps, with a representative sampling (144 ingroups and seven outgroups) that covers all described subfamilies and tribes, and 82% of the known genera. Analyses of 538 Ultra‐Conserved Elements (UCEs) with supermatrix (RAx ML and IQTREE) and gene tree reconciliation approaches (ASTRAL, ASTRID) resulted in highly supported topologies in overall agreement with morphology but reveal conflicting topologies for some of the deepest nodes. To resolve these conflicts, we explored the phylogenetic tree space with clustering and gene genealogy interrogation methods, analyzed marker and taxon properties that could bias inferences and performed a thorough morphological analysis (130 characters encoded for 40 taxa representative of the diversity). This joint analysis reveals that UCEs enable attainment of resolution between ancestry and convergent/divergent evolution when morphology is not informative enough, but also shows that a systematic exploration of bias with different analytical methods and a careful analysis of morphological features is required to prevent publication of artifactual results. We highlight a GC content bias for maximum‐likelihood approaches, an artifactual mid‐point rooting of the ASTRAL tree and a deleterious effect of high percentage of missing data (>85% missing UCEs) on gene tree reconciliation methods. Based on the results we propose a new classification of the family into eight subfamilies and ten tribes that lay the foundation for future studies on the evolutionary history of Chalcididae.     

Teaching life cycle assessment in higher education

The International Journal of Life Cycle Assessment - Scientific Life Cycle Assessment (LCA) literature provides some examples of LCA teaching in higher education, but not a structured overview of...     

Differentiation of the binding of two ligands to a tetrameric protein with a single symmetric active site by 19F NMR

R67 dihydrofolate reductase (R67 DHFR) is a plasmid‐encoded enzyme that confers resistance to the antibacterial drug trimethoprim. R67 DHFR is a tetramer with a single active site that is unusual as both cofactor and substrate are recognized by symmetry‐related residues. Such promiscuity has limited our previous efforts to differentiate ligand binding by NMR. To address this problem, we incorporated fluorine at positions 4, 5, 6, or 7 of the indole rings of tryptophans 38 and 45 and characterized the spectra to determine which probe was optimal for studying ligand binding. Two resonances were observed for all apo proteins. Unexpectedly, the W45 resonance appeared broad, and truncation of the disordered N‐termini resulted in the appearance of one sharp W45 resonance. These results are consistent with interaction of the N‐terminus with W45. Binding of the cofactor broadened W38 for all fluorine probes, whereas substrate, dihydrofolate, binding resulted in the appearance of three new resonances for 4‐ and 5‐fluoroindole labeled protein and severe line broadening for 6‐ and 7‐fluoroindole R67 DHFR. W45 became slightly broader upon ligand binding. With only two peaks in the ¹⁹F NMR spectra, our data were able to differentiate cofactor and substrate binding to the single, symmetric active site of R67 DHFR and yield binding affinities.     

Correction to: Whole-Genome sequencing and comparative genomics of Mycobacterium spp. from farmed Atlantic and coho salmon in Chile

Antonie van Leeuwenhoek -     

Role of HRTPT in kidney proximal epithelial cell regeneration: Integrative differential expression and pathway analyses using microarray and scRNA-seq

Damage to proximal tubules due to exposure to toxicants can lead to conditions such as acute kidney injury (AKI), chronic kidney disease (CKD) and ultimately end-stage renal failure (ESRF). Studies have shown that kidney proximal epithelial cells can regenerate particularly after acute injury. In the previous study, we utilized an immortalized in vitro model of human renal proximal tubule epithelial cells, RPTEC/TERT1, to isolate HRTPT cell line that co-expresses stem cell markers CD133 and CD24, and HREC24T cell line that expresses only CD24. HRTPT cells showed most of the key characteristics of stem/progenitor cells; however, HREC24T cells did not show any of these characteristics. The goal of this study was to further characterize and understand the global gene expression differences, upregulated pathways and gene interaction using scRNA-seq in HRTPT cells. Affymetrix microarray analysis identified common gene sets and pathways specific to HRTPT and HREC24T cells analysed using DAVID, Reactome and Ingenuity software. Gene sets of HRTPT cells, in comparison with publicly available data set for CD133+ infant kidney, urine-derived renal progenitor cells and human kidney-derived epithelial proximal tubule cells showed substantial similarity in organization and interactions of the apical membrane. Single-cell analysis of HRTPT cells identified unique gene clusters associated with CD133 and the 92 common gene sets from three data sets. In conclusion, the gene expression analysis identified a unique gene set for HRTPT cells and narrowed the co-expressed gene set compared with other human renal–derived cell lines expressing CD133, which may provide deeper understanding in their role as progenitor/stem cells that participate in renal repair.     

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.     

Breathing space: deoxygenation of aquatic environments can drive differential ecological impacts across biological invasion stages

The influence of climate change on the ecological impacts of invasive alien species (IAS) remains understudied, with deoxygenation of aquatic environments often-overlooked as a consequence of climate change. Here, we therefore assessed how oxygen saturation affects the ecological impact of a predatory invasive fish, the Ponto-Caspian round goby (Neogobius melanostomus), relative to a co-occurring endangered European native analogue, the bullhead (Cottus gobio) experiencing decline in the presence of the IAS. In individual trials and mesocosms, we assessed the effect of high, medium and low (90%, 60% and 30%) oxygen saturation on: (1) functional responses (FRs) of the IAS and native, i.e. per capita feeding rates; (2) the impact on prey populations exerted; and (3) how combined impacts of both fishes change over invasion stages (Pre-invasion, Arrival, Replacement, Proliferation). Both species showed Type II potentially destabilising FRs, but at low oxygen saturation, the invader had a significantly higher feeding rate than the native. Relative Impact Potential, combining fish per capita effects and population abundances, revealed that low oxygen saturation exacerbates the high relative impact of the invader. The Relative Total Impact Potential (RTIP), modelling both consumer species’ impacts on prey populations in a system, was consistently higher at low oxygen saturation and especially high during invader Proliferation. In the mesocosm experiment, low oxygen lowered RTIP where both species were present, but again the IAS retained high relative impact during Replacement and Proliferation stages at low oxygen. We also found evidence of multiple predator effects, principally antagonism. We highlight the threat posed to native communities by IAS alongside climate-related stressors, but note that solutions may be available to remedy hypoxia and potentially mitigate impacts across invasion stages.

     

Arabidopsis ERG28 Tethers the Sterol C4-Demethylation Complex to Prevent Accumulation of a Biosynthetic Intermediate That Interferes with Polar Auxin Transport

Sterols are vital for cellular functions and eukaryotic development because of their essential role as membrane constituents. Sterol biosynthetic intermediates (SBIs) represent a potential reservoir of signaling molecules in mammals and fungi, but little is known about their functions in plants. SBIs are derived from the sterol C4-demethylation enzyme complex that is tethered to the membrane by Ergosterol biosynthetic protein28 (ERG28). Here, using nonlethal loss-of-function strategies focused on Arabidopsis thaliana ERG28, we found that the previously undetected SBI 4-carboxy-4-methyl-24-methylenecycloartanol (CMMC) inhibits polar auxin transport (PAT), a key mechanism by which the phytohormone auxin regulates several aspects of plant growth, including development and responses to environmental factors. The induced accumulation of CMMC in Arabidopsis erg28 plants was associated with diagnostic hallmarks of altered PAT, including the differentiation of pin-like inflorescence, loss of apical dominance, leaf fusion, and reduced root growth. PAT inhibition by CMMC occurs in a brassinosteroid-independent manner. The data presented show that ERG28 is required for PAT in plants. Furthermore, it is accumulation of an atypical SBI that may act to negatively regulate PAT in plants. Hence, the sterol pathway offers further prospects for mining new target molecules that could regulate plant development.