The phylogeny and evolutionary ecology of hoverflies (Diptera: Syrphidae) inferred from mitochondrial genomes

Hoverflies (Diptera: Syrphidae) are a diverse group of pollinators and a major research focus in ecology, but their phylogenetic relationships remain incompletely known. Using a genome skimming approach we generated mitochondrial genomes for 91 species, capturing a wide taxonomic diversity of the family. To reduce the required amount of input DNA and overall cost of the library construction, sequencing and assembly was conducted on mixtures of specimens, which raises the problem of chimera formation of mitogenomes. We present a novel chimera detection test based on gene tree incongruence, but identified only a single mitogenome of chimeric origin. Together with existing data for a final set of 127 taxa, phylogenetic analysis on nucleotide and amino acid sequences using Maximum Likelihood and Bayesian Inference revealed a basal split of Microdontinae from all other syrphids. The remainder consists of several deep clades assigned to the subfamily Eristalinae in the current classification, including a clade comprising the subfamily Syrphinae (plus Pipizinae). These findings call for a re-definition of subfamilies, but basal nodes had insufficient support to allow such action. Molecular-clock dating placed the origin of the Syrphidae crown group in the mid-Cretaceous while the Eristalinae-Syrphinae clade likely originated near the K/Pg boundary. Transformation of larval life history characters on the tree suggests that Syrphidae initially had sap feeding larvae, which diversified greatly in diet and habitat association during the Eocene and Oligocene, coinciding with the diversification of angiosperms and the evolution of various insect groups used as larval host, prey, or mimicry models. Mitogenomes proved to be a powerful phylogenetic marker for studies of Syrphidae at subfamily and tribe levels, allowing dense taxon sampling that provided insight into the great ecological diversity and rapid evolution of larval life history traits of the hoverflies.

Development and evaluation of a meat mitochondrial metagenomic (3MG) method for composition determination of meat from fifteen mammalian and avian species

BACKGROUND

Bioassessment and biomonitoring of meat products are aimed at identifying and quantifying adulterants and contaminants, such as meat from unexpected sources and microbes. Several methods for determining the biological composition of mixed samples have been used, including metabarcoding, metagenomics and mitochondrial metagenomics. In this study, we aimed to develop a method based on next-generation DNA sequencing to estimate samples that might contain meat from 15 mammalian and avian species that are commonly related to meat bioassessment and biomonitoring.

RESULTS

In this project, we found the meat composition from 15 species could not be identified with the metabarcoding approach because of the lack of universal primers or insufficient discrimination power. Consequently, we developed and evaluated a meat mitochondrial metagenomics (3MG) method. The 3MG method has four steps: (1) extraction of sequencing reads from mitochondrial genomes (mitogenomes); (2) assembly of mitogenomes; (3) mapping of mitochondrial reads to the assembled mitogenomes; and (4) biomass estimation based on the number of uniquely mapped reads. The method was implemented in a python script called 3MG. The analysis of simulated datasets showed that the method can determine contaminant composition at a proportion of 2% and the relative error was < 5%. To evaluate the performance of 3MG, we constructed and analysed mixed samples derived from 15 animal species in equal mass. Then, we constructed and analysed mixed samples derived from two animal species (pork and chicken) in different ratios. DNAs were extracted and used in constructing 21 libraries for next-generation sequencing. The analysis of the 15 species mix with the method showed the successful identification of 12 of the 15 (80%) animal species tested. The analysis of the mixed samples of the two species revealed correlation coefficients of 0.98 for pork and 0.98 for chicken between the number of uniquely mapped reads and the mass proportion.

CONCLUSION

To the best of our knowledge, this study is the first to demonstrate the potential of the non-targeted 3MG method as a tool for accurately estimating biomass in meat mix samples. The method has potential broad applications in meat product safety.