The mitochondrial genome of Iberobaenia (Coleoptera: Iberobaeniidae): first rearrangement of protein-coding genes in the beetles

International audienceThe complete mitochondrial genome of the recently discovered beetle family Iberobaeniidae is described and compared with known coleopteran mitogenomes. The mitochondrial sequence was obtained by shotgun metagenomic sequencing using the Illumina Miseq technology and resulted in an average coverage of 130 × and a minimum coverage of 35×. The mitochondrial genome of Iberobaeniidae includes 13 protein-coding genes, 2 rRNAs, 22 tRNAs genes, and 1 putative control region, and showed a unique rearrangement of protein-coding genes. This is the first rearrangement affecting the relative position of protein-coding and ribosomal genes reported for the order Coleoptera

Monitoring soil biodiversity in nature reserves in England - a role for metabarcoding

Tullgren extracts of soil mesofauna are proving challenging to identify using trained volunteers. Could metabarcoding be a rapid, cost-effective approach for monitoring soil mesofauna and characterising their communities

Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study

Background Iatrogenic bile duct injury (IBDI) is a challenging surgical complication. IBDI management can be guided by artificial intelligence models. Our study identified the factors associated with successful initial repair of IBDI and predicted the success of definitive repair based on patient risk levels. Methods This is a retrospective multi-institution cohort of patients with IBDI after cholecystectomy conducted between 1990 and 2020. We implemented a decision tree analysis to determine the factors that contribute to successful initial repair and developed a risk-scoring model based on the Comprehensive Complication Index. Results We analyzed 748 patients across 22 hospitals. Our decision tree model was 82.8% accurate in predicting the success of the initial repair. Non-type E (p < 0.01), treatment in specialized centers (p < 0.01), and surgical repair (p < 0.001) were associated with better prognosis. The risk-scoring model was 82.3% (79.0-85.3%, 95% confidence interval [CI]) and 71.7% (63.8-78.7%, 95% CI) accurate in predicting success in the development and validation cohorts, respectively. Surgical repair, successful initial repair, and repair between 2 and 6 weeks were associated with better outcomes. Discussion Machine learning algorithms for IBDI are a novel tool may help to improve the decision-making process and guide management of these patients

Molecular systematics and evolution of the subgenus Mesocarabus Thomson, 1875 (Coleoptera: Carabidae: Carabus), based on mitochondrial and nuclear DNA

International audienc

Data from: Metabarcoding of freshwater invertebrates to detect the effects of a pesticide spill

Biomonitoring underpins the environmental assessment of freshwater ecosystems and guides management and conservation. Current methodology for surveys of (macro)invertebrates uses coarse taxonomic identification where species-level resolution is difficult to obtain. Next-generation sequencing of entire assemblages (metabarcoding) provides a new approach for species detection, but requires further validation. We used metabarcoding of invertebrate assemblages with two fragments of the cox1 "barcode" and partial nuclear ribosomal (SSU) genes, to assess the effects of a pesticide spill in the River Kennet (Southern England). Operational Taxonomic Unit (OTU) recovery was tested under72 parameters (read denoising, filtering, pair merging and clustering). Similar taxonomic profiles were obtained under a broad range of parameters. The SSU marker recovered Platyhelminthes and Nematoda, missed by cox1,while Rotifera were only amplified with cox1. A reference set was created from all available barcode entries for Arthropoda in the BOLD database and clustered into OTUs. The River Kennet metabarcoding produced matches to 207 of these reference OTUs, five times the number of species recognised with morphological monitoring. The increase was due to: greater taxonomic resolution (e.g. splitting a single morphotaxon ‘Chironomidae’ into 55 named OTUs); splitting of binomial species names into multiple molecular OTUs in species complexes; and the use of a filtration-flotation protocol for extraction of minute specimens (meiofauna). Community analyses revealed strong differences between "impacted" vs. "control" samples, detectable with each gene marker, for each major taxonomic group, and for meio- and macro-faunal samples separately. Thus, highly resolved taxonomic data can be extracted at a fraction of the time and cost of traditional non-molecular methods, opening new avenues for freshwater invertebrate biodiversity monitoring and molecular ecology

Raw metabarcode data Slot 1 (12 libraries)

Raw metabarcode data Slot 1 (12 libraries). Inlcudes R1 and R2 files for macrofauna samples from the 3 impacted and 3 control sites, each sampled at two times after pesticide spill. More info on paper

Raw_metabarcode_data_Slot_2_(12_libraries)

Raw metabarcode data Slot 2 (12 libraries). Inlcudes R1 and R2 files for meiofauna samples from the 3 impacted and 3 control sites, each sampled at two times after pesticide spill. More info on paper

A validated workflow for rapid taxonomic assignment and monitoring of a national fauna of bees (Apiformes) using high throughput DNA barcoding

Improved taxonomic methods are needed to quantify declining populations of insect pollinators. This study devises a high‐throughput DNA barcoding protocol for a regional fauna (United Kingdom) of bees (Apiformes), consisting of reference library construction, a proof‐of‐concept monitoring scheme, and the deep barcoding of individuals to assess potential artefacts and organismal associations. A reference database of cytochrome oxidase c subunit 1 (cox1) sequences including 92.4% of 278 bee species known from the UK showed high congruence with morphological taxon concepts, but molecular species delimitations resulted in numerous split and (fewer) lumped entities within the Linnaean species. Double tagging permitted deep Illumina sequencing of 762 separate individuals of bees from a UK‐wide survey. Extracting the target barcode from the amplicon mix required a new protocol employing read abundance and phylogenetic position, which revealed 180 molecular entities of Apiformes identifiable to species. An additional 72 entities were ascribed to nuclear pseudogenes based on patterns of read abundance and phylogenetic relatedness to the reference set. Clustering of reads revealed a range of secondary operational taxonomic units (OTUs) in almost all samples, resulting from traces of insect species caught in the same traps, organisms associated with the insects including a known mite parasite of bees, and the common detection of human DNA, besides evidence for low‐level cross‐contamination in pan traps and laboratory procedures. Custom scripts were generated to conduct critical steps of the bioinformatics protocol. The resources built here will greatly aid DNA‐based monitoring to inform management and conservation policies for the protection of pollinators

Additional file 1: of Genome sequencing of Rhinorhipus Lawrence exposes an early branch of the Coleoptera

Text the morphology-based classifications of Rhinorhipidae. Table S1. The list of taxa included in the LSU rRNA, SSU rRNA, rrnL, and cox1 mitochondrial DNA dataset with GenBank accession and voucher ID numbers. Table S2. The list of taxa included in the mitogenomic analysis with GenBank accession numbers. Table S3. The list of taxa included in the LSU rRNA, SSU rRNA, and six nuclear protein coding genes. Table S4. The list of taxa included in the 65-gene dataset. Table S5. The list of markers in the 95-gene dataset with information on multi-copy genes. Table S6. The list of taxa included in the phylotranscriptomic dataset and the number of sequences available for each taxon. Table S7. Overview of official gene sets of six reference species used for transcript ortholog assessment, including the source, version and number of genes. Table S8. Gene descriptions for the 4220 ortholog groups (OGs) as present in. OrthoDB 9.1. Each OG contains one gene of each of the 6 reference species. Table S9. Success of transcript assignment to ortholog groups (OGs) of Rhinorhipus, published beetles transcriptomes and genomes. Table S10. The models and partition selections recovered with ModelFinder for the maximum likelihood analysis of the LSU rRNA, SSU rRNA, rrnL mtDNA, and cox1 mtDNA dataset. Table S11. Identification of the best partition scheme and models for the mitochondrial DNA dataset. Table S12. The LSU rRNA, SSU rRNA, and six nuclear protein coding genes dataset: characteristics, partition scheme and models of DNA evolution. Table S13. The transcriptomic supermatrix 3: partition scheme and models of DNA evolution (amino acid dataset, 4220 orthologs). Table S14. The transcriptomic supermatrix 4: partition scheme and models of DNA evolution (amino acid dataset, 943 orthologs). Figure S1. Maximum likelihood tree for Rhinorhipus, 517 Elateriformia and 46 outgroups recovered from the LSU rRNA, SSU rRNA, rrnL mtDNA and cox1 mtDNA dataset. Figure S2. Maximum likelihood tree for 83 species of beetles recovered from 15 mitochondrial genes. Figure S3. Maximum likelihood tree for 139 species of beetles recovered from the. LSU rRNA, SSU rRNA and six nuclear protein coding genes. Figure S4. Bayesian tree for 139 species of beetles recovered from the LSU rRNA, SSU rRNA and six nuclear protein coding genes. Figure S5. Maximum likelihood (RaxML) tree for 372 species of beetles and for outgroups recovered from the 66-gene amino acid dataset. Figure S6. Maximum likelihood (iQ) tree for 372 species of beetles and for outgroups recovered from the 66-gene amino acid dataset. Figure S7. Maximum likelihood (iQ) tree for 372 species of beetles and for outgroups recovered from the 66-gene nucleotide dataset. Figure S8. Tree network obtained from the separate maximum likelihood analyses of 968 orthologs 590. Figure S9. Dated phylogenetic tree of beetle relationships inferred from the Bayesian analysis of mitogenomic dataset using maximum likelihood topology. Figure S10. Dated phylogenetic tree of beetle relationships inferred from the Bayesian analysis of mitogenomic dataset using Bayesian topology. Figure S11. Dated phylogenetic tree of beetle relationships inferred from the Bayesian analysis of eight-gene dataset using constrained Bayesian topology and two calibration points (A, B) and verified by mapping of nineteen fossil records reported by Toussaint et al. (2016). The bottom diagram shows accumulation of the number of extant beetle families (red dots on the tree). Time line relates the tree to extinction events and geologic periods. Red bars designate the origin of Rhinorhipidae. (PDF 30160 kb

Vascular injury during cholecystectomy: A multicenter critical analysis behind the drama

Background: The management of a vascular injury during cholecystectomy is still very complicated, especially in centers not specialized in complex hepatobiliary surgery. Methods: This was a multi-institutional retrospective study in patients with vascular injuries during cholecystectomy from 18 centers in 4 countries. The aim of the study was to analyze the management of vascular injuries focusing on referral, time to perform the repair, and different treatments options outcomes. Results: A total of 104 patients were included. Twenty-nine patients underwent vascular repair (27.9%), 13 (12.5%) liver resection, and 1 liver transplant as a first treatment. Eighty-four (80.4%) vascular and biliary injuries occurred in nonspecialized centers and 45 (53.6%) were immediately transferred. Intraoperative diagnosed injuries were rare in referred patients (18% vs 84%, P = .001). The patients managed at the hospital where the injury occurred had a higher number of reoperations (64% vs 20%, P ˂ .001). The need for vascular reconstruction was associated with higher mortality (P = .04). Two of the 4 patients transplanted died. Conclusion: Vascular lesions during cholecystectomy are a potentially life-threatening complication. Management of referral to specialized centers to perform multiple complex multidisciplinary procedures should be mandatory. Late vascular repair has not shown to be associated with worse results