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Reconsideration of species boundaries and proposed DNA barcodes for Calonectria.

ABSTRACT: Calonectria represents a genus of phytopathogenic ascomycetous fungi with a worldwide distribution. In recent years, there has been an increase in the number of taxonomic studies on these fungi. Currently, there are 169 described species of Calonectria based on comparisons of DNA sequence data, combined with morphological characteristics. However, for some of these species, the sequence data utilised at the time of their description were relatively limited. This has justified an urgent need to reconsider the species boundaries for Calonectria based on robust genus-wide phylogenetic analyses. In this study, we utilised 240 available isolates including the ex-types of 128 Calonectria species, and re-sequenced eight gene regions (act, cmdA, his3, ITS, LSU, rpb2, tef1 and tub2) for them. Sequences for 44 Calonectria species, for which cultures could not be obtained, were downloaded from GenBank. DNA sequence data of all the 169 Calonectria species were then used to determine their phylogenetic relationships. As a consequence, 51 species were reduced to synonymy, two new species were identified, and the name Ca. lauri was validated. This resulted in the acceptance of 120 clearly defined Calonectria spp. The overall data revealed that the genus includes 11 species complexes, distributed across the Prolate and Sphaero-Naviculate Groups known to divide Calonectria. The results also made it possible to develop a robust set of DNA barcodes for Calonectria spp. To accomplish this goal, we evaluated the outcomes of each of the eight candidate DNA barcodes for the genus, as well as for each of the 11 species complexes. No single gene region provided a clear identity for all Calonectria species. Sequences of the tef1 and tub2 genes were the most reliable markers; those for the cmdA, his3, rpb2 and act gene regions also provided a relatively effective resolution for Calonectria spp., while the ITS and LSU failed to produce useful barcodes for species discrimination. At the species complex level, results showed that the most informative barcodes were inconsistent, but that a combination of six candidate barcodes (tef1, tub2, cmdA, his3, rpb2 and act) provided stable and reliable resolution for all 11 species complexes. A six-gene combined phylogeny resolved all 120 Calonectria species, and revealed that tef1, tub2, cmdA, his3, rpb2 and act gene regions are effective DNA barcodes for Calonectria.

SUBMITTER: Liu QL

PROVIDER: S-EPMC8295567 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Recent estimates suggest that the global insect fauna includes fewer than six million species, but this projection is very uncertain because taxonomic work has been limited on some highly diverse groups. Validation of current estimates minimally requires the investigation of all lineages that are diverse enough to have a substantial impact on the final species count. This study represents a first step in this direction; it employs DNA barcoding to evaluate patterns of species richness in 27 orders of Canadian insects. The analysis of over one million specimens revealed species counts congruent with earlier results for most orders. However, Diptera and Hymenoptera were unexpectedly diverse, representing two-thirds of the 46 937 barcode index numbers (=species) detected. Correspondence checks between known species and barcoded taxa showed that sampling was incomplete, a result confirmed by extrapolations from the barcode results which suggest the occurrence of at least 94 000 species of insects in Canada, a near doubling from the prior estimate of 54 000 species. One dipteran family, the Cecidomyiidae, was extraordinarily diverse with an estimated 16 000 species, a 10-fold increase from its predicted diversity. If Canada possesses about 1% of the global fauna, as it does for known taxa, the results of this study suggest the presence of 10 million insect species with about 1.8 million of these taxa in the Cecidomyiidae. If so, the global species count for this fly family may exceed the combined total for all 142 beetle families. If extended to more geographical regions and to all hyperdiverse groups, DNA barcoding can rapidly resolve the current uncertainty surrounding a species count for the animal kingdom. A newly detailed understanding of species diversity may illuminate processes important in speciation, as suggested by the discovery that the most diverse insect lineages in Canada employ an unusual mode of reproduction, haplodiploidy.This article is part of the themed issue 'From DNA barcodes to biomes'.

Project description:BackgroundSpecific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms.MethodsIn this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised classification methods, is adopted to address the task of DNA Barcode analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the classification results are compared with respect to ad-hoc and well-established DNA Barcode classification methods.ResultsA software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the classification procedure. The analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable classification model. Rule-based methods have slightly inferior classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior classification performances with respect to the traditional DNA Barcode classification methods. On empirical data their classification performances are at a comparable level to the other methods.ConclusionsThe classification analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community.

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Reconsideration of species boundaries and proposed DNA barcodes for Calonectria.

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