Geographic patterns of Lucanus (Coleoptera: Lucanidae) species diversity and environmental determinants in China

Clarifying the geographic patterns of species diversity and the determinant factors can provide essential information for species conservation and management. Stag beetles (Coleoptera: Lucanidae) of Lucanus are important saproxylic insects and can be used for biomonitoring forests. Most of Lucanus species are facing conservation concerns due to their limited distribution and fragmented habitats, particularly in China, which has the richest species diversity of this genus. The distribution patterns of species diversity of Lucanus at large spatial scales remain portly understood. We studied the distribution patterns of Lucanus and its environmental and geographic determinants in China. Distribution data for 72 species and subspecies were examined. All these species are distributed in southern China except for Lucanus maculifemoratus dybowskyi, which is mainly distributed in north China. The hotspot for Lucanus in China is southeastern Tibet. Our study indicated that the species richness of Lucanus in China was shaped by the precipitation of the wettest and driest month, net primary productivity, digital elevation model, and latitude at a large scale. These variables collectively explained 56.2% of the variation in species richness; precipitation contributed the most (44.1%). Our results provide valuable insights to improve the conservation of Lucanus and can contribute to furthering our understanding of the biogeography of stag beetles in China.

We describe the diversity of the stag beetle species and further explore its formation. The result indicated that the variables of geography vegetation, temperature, and precipitation collectively shaped the distributed pattern, and geographical variables are most important.     

Targeted and passive environmental DNA approaches outperform established methods for detection of quagga mussels,Dreissena rostriformis bugensis in flowing water

1. The early detection of invasive non‐native species (INNS) is important for informing management actions. Established monitoring methods require the collection or observation of specimens, which is unlikely at the beginning of an invasion when densities are likely to be low. Environmental DNA (eDNA) analysis is a highly promising technique for the detection of INNS—particularly during the early stages of an invasion.
2. Here, we compared the use of traditional kick‐net sampling with two eDNA approaches (targeted detection using both conventional and quantitative PCR and passive detection via metabarcoding with conserved primers) for detection of quagga mussel, Dreissena rostriformis bugensis, a high priority INNS, along a density gradient on the River Wraysbury, UK.
3. All three molecular tools outperformed traditional sampling in terms of detection. Conventional PCR and qPCR both had 100% detection rate in all samples and outperformed metabarcoding when the target species was at low densities. Additionally, quagga mussel DNA copy number (qPCR) and relative read count (metabarcoding) were significantly influenced by both mussel density and distance from source population, with distance being the most significant predictor.
4. Synthesis and application. All three molecular approaches were more sensitive than traditional kick‐net sampling for the detection of the quagga mussel in flowing water, and both qPCR and metabarcoding enabled estimates of relative abundance. Targeted approaches were more sensitive than metabarcoding, but metabarcoding has the advantage of providing information on the wider community and consequently the impacts of INNS.

     

A Novel Strategy for Gene Selection of Microarray Data Based on Gene-to-Class Sensitivity Information

To obtain predictive genes with lower redundancy and better interpretability, a hybrid gene selection method encoding prior information is proposed in this paper. To begin with, the prior information referred to as gene-to-class sensitivity (GCS) of all genes from microarray data is exploited by a single hidden layered feedforward neural network (SLFN). Then, to select more representative and lower redundant genes, all genes are grouped into some clusters by K-means method, and some low sensitive genes are filtered out according to their GCS values. Finally, a modified binary particle swarm optimization (BPSO) encoding the GCS information is proposed to perform further gene selection from the remainder genes. For considering the GCS information, the proposed method selects those genes highly correlated to sample classes. Thus, the low redundant gene subsets obtained by the proposed method also contribute to improve classification accuracy on microarray data. The experiments results on some open microarray data verify the effectiveness and efficiency of the proposed approach.     

Differential expression of microRNAs associated with neurodegenerative diseases and diabetic nephropathy in protein l-isoaspartyl methyltransferase-deficient mice

Protein l -isoaspartyl methyltransferase (PIMT/PCMT1), an enzyme repairing isoaspartate residues in peptides and proteins that result from the spontaneous decomposition of normal l -aspartyl and l -asparaginyl residues during aging, has been revealed to be involved in neurodegenerative diseases (NDDs) and diabetes. However, the molecular mechanisms for a putative association of PIMT dysfunction with these diseases have not been clarified. Our study aimed to identify differentially expressed microRNAs (miRNAs) in the brain and kidneys of PIMT-deficient mice and uncover the epigenetic mechanism of PIMT-involved NDDs and diabetic nephropathy (DN). Differentially expressed miRNAs by sequencing underwent target prediction and enrichment analysis in the brain and kidney of PIMT knockout (KO) mice and age-matched wild-type (WT) littermates. Sequence analysis revealed 40 differentially expressed miRNAs in the PIMT KO mouse brain including 25 upregulated miRNAs and 15 downregulated miRNAs. In the PIMT KO mouse kidney, there were 80 differentially expressed miRNAs including 40 upregulated miRNAs and 40 downregulated miRNAs. Enrichment analysis and a systematic literature review of differentially expressed miRNAs indicated the involvement of PIMT deficiency in the pathogenesis in NDDs and DN. Some overlapped differentially expressed miRNAs between the brain and kidney were quantitatively assessed in the brain, kidney, and serum-derived exosomes, respectively. Despite being preliminary, these results may aid in investigating the pathological hallmarks and identify the potential therapeutic targets and biomarkers for PIMT dysfunction-related NDDs and DN.     

A Polydnavirus ANK Protein Acts as Virulence Factor by Disrupting the Function of Prothoracic Gland Steroidogenic Cells

Polydnaviruses are obligate symbionts integrated as proviruses in the genome of some ichneumonoid wasps that parasitize lepidopteran larvae. Polydnavirus free viral particles, which are injected into the host at oviposition, express virulence factors that impair immunity and development. To date, most studies have focused on the molecular mechanisms underpinning immunosuppression, whereas how viral genes disrupt the endocrine balance remains largely uninvestigated. Using Drosophila as a model system, the present report analyzes the function of a member of the ankyrin gene family of the bracovirus associated with Toxoneuron nigriceps, a larval parasitoid of the noctuid moth Heliothis virescens. We found that the TnBVank1 expression in the Drosophila prothoracic gland blocks the larval-pupal molt. This phenotype can be rescued by feeding the larvae with 20-hydroxyecdysone. The localization of the TnBVANK1 is restricted to the cytoplasm where it interacts with Hrs and Alix marked endosomes. Collectively, our data demonstrate that the TnBVANK1 protein acts as a virulence factor that causes the disruption of ecdysone biosynthesis and developmental arrest by impairing the vesicular traffic of ecdysteroid precursors in the prothoracic gland steroidogenic cells.