The kingdom Fungi is estimated to include 1.5 million or more species, playing key roles as decomposers, mutualists, and parasites in every biome on the earth. To comprehensively understand the diversity and ecology of this huge kingdom, DNA barcoding targeting the internal transcribed spacer (ITS) region of the nuclear ribosomal repeat has been regarded as a prerequisite procedure. By extensively surveying ITS sequences in public databases, we designed new ITS primers with improved coverage across diverse taxonomic groups of fungi compared to existing primers. An in silico analysis based on public sequence databases indicated that the newly designed primers matched 99% of ascomycete and basidiomycete ITS taxa (species, subspecies or varieties), causing little taxonomic bias toward either fungal group. Two of the newly designed primers could inhibit the amplification of plant sequences and would enable the selective investigation of fungal communities in mycorrhizal associations, soil, and other types of environmental samples. Optimal PCR conditions for the primers were explored in an in vitro investigation. The new primers developed in this study will provide a basis for ecological studies on the diversity and community structures of fungi in the era of massive DNA sequencing.
In an era of ecosystem degradation and climate change, maximizing microbial functions in agroecosystems has become a prerequisite for the future of global agriculture. However, managing species-rich communities of plant-associated microbiomes remains a major challenge. Here, we propose interdisciplinary research strategies to optimize microbiome functions in agroecosystems. Informatics now allows us to identify members and characteristics of 'core microbiomes', which may be deployed to organize otherwise uncontrollable dynamics of resident microbiomes. Integration of microfluidics, robotics and machine learning provides novel ways to capitalize on core microbiomes for increasing resource-efficiency and stress-resistance of agroecosystems.
Taxonomic identification of biological specimens based on DNA sequence information (a.k.a. DNA barcoding) is becoming increasingly common in biodiversity science. Although several methods have been proposed, many of them are not universally applicable due to the need for prerequisite phylogenetic/machine-learning analyses, the need for huge computational resources, or the lack of a firm theoretical background. Here, we propose two new computational methods of DNA barcoding and show a benchmark for bacterial/archeal 16S, animal COX1, fungal internal transcribed spacer, and three plant chloroplast (rbcL, matK, and trnH-psbA) barcode loci that can be used to compare the performance of existing and new methods. The benchmark was performed under two alternative situations: query sequences were available in the corresponding reference sequence databases in one, but were not available in the other. In the former situation, the commonly used “1-nearest-neighbor” (1-NN) method, which assigns the taxonomic information of the most similar sequences in a reference database (i.e., BLAST-top-hit reference sequence) to a query, displays the highest rate and highest precision of successful taxonomic identification. However, in the latter situation, the 1-NN method produced extremely high rates of misidentification for all the barcode loci examined. In contrast, one of our new methods, the query-centric auto-k-nearest-neighbor (QCauto) method, consistently produced low rates of misidentification for all the loci examined in both situations. These results indicate that the 1-NN method is most suitable if the reference sequences of all potentially observable species are available in databases; otherwise, the QCauto method returns the most reliable identification results. The benchmark results also indicated that the taxon coverage of reference sequences is far from complete for genus or species level identification in all the barcode loci examined. Therefore, we need to accelerate the registration of reference barcode sequences to apply high-throughput DNA barcoding to genus or species level identification in biodiversity research.
The escalation of defensive/offensive arms is ubiquitous in prey-predator evolutionary interactions. However, there may be a geographically varying imbalance in the armaments of participating species that affects the outcome of local interactions. In a system involving the Japanese camellia (Camellia japonica) and its obligate seed predator, the camellia weevil (Curculio camelliae), we investigated the geographic variation in physical defensive/offensive traits and that in natural selection on the plant's defense among 17 populations over a 700-km-wide area in Japan. The sizes of the plant defensive apparatus (pericarp thickness) and the weevil offensive apparatus (rostrum length) clearly correlated with each other across populations. Nevertheless, the balance in armaments between the two species was geographically structured. In the populations for which the balance was relatively advantageous for the plant's defense, natural selection on the trait was stronger because in the other populations, most plant individuals were too vulnerable to resist the attacks of the weevil, and their seeds were infested independent of pericarp thickness. We also found that the imbalance between the defensive/offensive armaments and the intensity of natural selection showed clear latitudinal clines. Overall, our results suggest that the imbalance of armament between sympatric prey and predator could determine the strength of local selection and that climatic conditions could affect the local and overall trajectory of coevolutionary arms races.
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