Molecular identification of the swede midge (Diptera: Cecidomyiidae)

Early detection of pest infestation is a prerequisite for sustainable crop protection. However, many pest species are difficult to detect and thus infestation is diagnosed from damage observed on the respective crop. This diagnosis is often made too late for implementation of crop protection measures, and serious crop losses may result. The swede midge, Contarinia nasturtii Kieffer, is a major pest of Brassica L. (Brassicaceae) vegetables in Europe that has recently invaded North America. With its small size and short adult life-span, and the cryptic lifestyle of the larvae feeding at the growing points of its host plants, it is usually detected only after damage has already occurred. Furthermore, because field-trapped specimens are rarely fully intact, it is extremely difficult to identify. Therefore, we developed a species-specific molecular diagnostic method that enables reliable identification of swede midge from various sources such as alcohol or sticky glue traps. The method enables large-scale screening of field-trapped specimens and is used to evaluate the attractiveness and specificity of pheromone traps that are currently under developmen

Evaluating next-generation sequencing (NGS) methods for routine monitoring of wild bees: metabarcoding, mitogenomics or NGS barcoding

Implementing cost-effective monitoring programs for wild bees remains challenging due to the high costs of sampling and specimen identification. To reduce costs, next-generation sequencing (NGS)-based methods have lately been suggested as alternatives to morphology-based identifications. To provide a comprehensive presentation of the advantages and weaknesses of different NGS-based identification methods, we assessed three of the most promising ones, namely metabarcoding, mitogenomics and NGS barcoding. Using a regular monitoring data set (723 specimens identified using morphology), we found that NGS barcoding performed best for both species presence/absence and abundance data, producing only few false positives (3.4%) and no false negatives. In contrast, the proportion of false positives and false negatives was higher using metabarcoding and mitogenomics. Although strong correlations were found between biomass and read numbers, abundance estimates significantly skewed the communities' composition in these two techniques. NGS barcoding recovered the same ecological patterns as morphology. Ecological conclusions based on metabarcoding and mitogenomics were similar to those based on morphology when using presence/absence data, but different when using abundance data. In terms of workload and cost, we show that metabarcoding and NGS barcoding can compete with morphology, but not mitogenomics which was consistently more expensive. Based on these results, we advocate that NGS barcoding is currently the seemliest NGS method for monitoring of wild bees. Furthermore, this method has the advantage of potentially linking DNA sequences with preserved voucher specimens, which enable morphological re-examination and will thus produce verifiable records which can be fed into faunistic databases

An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics

Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations. Our strategy toward accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs (a modified six-frame translation considering alternative start codons) in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics data set against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and single amino acid variations (SAAVs) identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin. We release iPtgxDBs for B. henselae, Bradyrhizobium diazoefficiens and Escherichia coli and the software to generate both proteogenomics search databases and integrated annotation files that can be viewed in a genome browser for any prokaryote

MPS raw data

Barcodes and primer sequences are available in the mapping file formatted for QIIME

Absolute abundance community matrix

Community matrix for all identification methods

Global Transcriptomic Effects of Environmentally Relevant Concentrations of the Neonicotinoids Clothianidin, Imidacloprid, and Thiamethoxam in the Brain of Honey Bees (<i>Apis mellifera</i>)

Neonicotinoids are implicated in the decline of honey bees, but the molecular basis underlying adverse effects is poorly known. Here we describe global transcriptomic profiles in the brain of honey bee workers exposed for 48 h at one environmentally realistic and one sublethal concentration of 0.3 and 3.0 ng/bee clothianidin and imidacloprid, respectively, and 0.1 and 1.0 ng/bee thiamethoxam (1–30 ng/mL sucrose solution) by high-throughput RNA-sequencing (RNA-seq). All neonicotinoids led to significant alteration (mainly down-regulation) of gene expression, generally with a concentration-dependent effect. Among many others, genes related to metabolism and detoxification were differently expressed. Gene ontology (GO) enrichment analysis of biological processes revealed catabolic carbohydrate metabolism (regulation of enzyme activities such as amylase), lipid metabolism, and transport mechanisms as shared terms between all neonicotinoids at high concentrations. KEGG pathway analysis indicated that at least two neonicotinoids induced changes in expression of various metabolic pathways: pentose phosphate pathways, starch and sucrose metabolism, and sulfur metabolism, in which <i>glucose 1-dehydrogenase</i> and <i>alpha-amylase</i> were down-regulated and <i>3′(2′), 5′-bisphosphate nucleotidase</i> was up-regulated. RT-qPCR analysis confirmed the down-regulation of <i>major royal jelly proteins</i>, <i>hbg3</i>, and <i>cyp9e2</i> found by RNA-seq. Our study highlights the comparative molecular effects of neonicotinoid exposure to bees. Further studies should link these effects with physiological outcomes for a better understanding of effects of neonicotinoids

MB raw data

Barcodes and primer sequences are available in the mapping file formatted for QIIME1

Data from: Evaluating NGS methods for routine monitoring of wild bees: metabarcoding, mitogenomics or NGS barcoding

Implementing cost-effective monitoring programs for wild bees remains challenging due to the high costs of sampling and specimen identification. To reduce costs, next generation sequencing (NGS)-based methods have lately been suggested as alternatives to morphology-based identifications. To provide a comprehensive presentation of the advantages and weaknesses of different NGS-based identification methods, we assessed three of the most promising ones, namely metabarcoding, mitogenomics and NGS barcoding. Using a regular monitoring dataset (723 specimens identified using morphology), we found that NGS barcoding performed best for both species presence/absence and abundance data, producing only few false positives (3.4%) and none false negatives. In contrast, the proportion of false positive and false negative was higher using metabarcoding and mitogenomics. Although strong correlations were found between biomass and read numbers, abundance estimates significantly skewed the communities’ composition in these two techniques. NGS barcoding recovered the same ecological patterns as morphology. Ecological conclusions based on metabarcoding and mitogenomics were similar to those based on morphology when using presence/absence data, but different from when using abundance data. In terms of workload and cost, we show that metabarcoding and NGS barcoding can compete with morphology, but not mitogenomics which was consistently more expensive. Based on these results, we advocate that NGS barcoding is currently the seemliest NGS method for monitoring of wild bees. Furthermore, this method has the advantage of potentially linking DNA sequences with preserved voucher specimens, which enable morphological re-examination and will thus produce verifiable records which can be fed into faunistic databases