Six DNA regions were evaluated as potential DNA barcodes for Fungi, the second largest kingdom of eukaryotic life, by a multinational, multilaboratory consortium. The region of the mitochondrial cytochrome c oxidase subunit 1 used as the animal barcode was excluded as a potential marker, because it is difficult to amplify in fungi, often includes large introns, and can be insufficiently variable. Three subunits from the nuclear ribosomal RNA cistron were compared together with regions of three representative protein-coding genes (largest subunit of RNA polymerase II, second largest subunit of RNA polymerase II, and minichromosome maintenance protein). Although the protein-coding gene regions often had a higher percent of correct identification compared with ribosomal markers, low PCR amplification and sequencing success eliminated them as candidates for a universal fungal barcode. Among the regions of the ribosomal cistron, the internal transcribed spacer (ITS) region has the highest probability of successful identification for the broadest range of fungi, with the most clearly defined barcode gap between inter- and intraspecific variation. The nuclear ribosomal large subunit, a popular phylogenetic marker in certain groups, had superior species resolution in some taxonomic groups, such as the early diverging lineages and the ascomycete yeasts, but was otherwise slightly inferior to the ITS. The nuclear ribosomal small subunit has poor species-level resolution in fungi. ITS will be formally proposed for adoption as the primary fungal barcode marker to the Consortium for the Barcode of Life, with the possibility that supplementary barcodes may be developed for particular narrowly circumscribed taxonomic groups.

/pdf/nuclear-ribosomal-internal-transcribed-spacer-its-region-as-9rhgb6gvih.pdf

Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi

Evolution of Protein Molecules

Within uncharacterized groups, DNA barcodes, short DNA sequences that are present in a wide range of species, can be used to assign organisms into species. We propose an automatic procedure that sorts the sequences into hypothetical species based on the barcode gap, which can be observed whenever the divergence among organisms belonging to the same species is smaller than divergence among organisms from different species. We use a range of prior intraspecific divergence to infer from the data a model-based one-sided confidence limit for intraspecific divergence. The method, called Automatic Barcode Gap Discovery (ABGD), then detects the barcode gap as the first significant gap beyond this limit and uses it to partition the data. Inference of the limit and gap detection are then recursively applied to previously obtained groups to get finer partitions until there is no further partitioning. Using six published data sets of metazoans, we show that ABGD is computationally efficient and performs well for standard prior maximum intraspecific divergences (a few per cent of divergence for the five data sets), except for one data set where less than three sequences per species were sampled. We further explore the theoretical limitations of ABGD through simulation of explicit speciation and population genetics scenarios. Our results emphasize in particular the sensitivity of the method to the presence of recent speciation events, via (unrealistically) high rates of speciation or large numbers of species. In conclusion, ABGD is fast, simple method to split a sequence alignment data set into candidate species that should be complemented with other evidence in an integrative taxonomic approach.

ABGD, Automatic Barcode Gap Discovery for primary species delimitation

DNA barcoding involves sequencing a standard region of DNA as a tool for species identification. However, there has been no agreement on which region(s) should be used for barcoding land plants. To provide a community recommendation on a standard plant barcode, we have compared the performance of 7 leading candidate plastid DNA regions (atpF–atpH spacer, matK gene, rbcL gene, rpoB gene, rpoC1 gene, psbK–psbI spacer, and trnH–psbA spacer). Based on assessments of recoverability, sequence quality, and levels of species discrimination, we recommend the 2-locus combination of rbcL+matK as the plant barcode. This core 2-locus barcode will provide a universal framework for the routine use of DNA sequence data to identify specimens and contribute toward the discovery of overlooked species of land plants.

/pdf/a-dna-barcode-for-land-plants-3fs5bo85qv.pdf

A DNA barcode for land plants.

https://www.ruhr-uni-bochum.de/imperia/md/content/geobot/2007/hibbett_2007-a_higher-level-phylogenetic.pdf

A higher-level phylogenetic classification of the Fungi

Selaginella apoda has many advantages as a model species, including a short life cycle. In order to work effectively with a lycophyte model species, it is important to be able to grow the gametophytes and sporophytes quickly. In the case of S. apoda, the gametophytes grow quickly and have high fertilization success in warm nutrient enriched water. Sporophytes at the three root stage can be easily transplanted to soil. They grow well in moist containers at a wide range of temperatures. Treatment with 0.15% Previcur®N prevents most fungal contaminations.

Growth and Care Instructions of a New Model Species—the Lycophyte Selaginella apoda

Devil’s claw is the vernacular name for a genus of medicinal plants that occur in the Kalahari Desert and Namibia Steppes. The genus comprises two distinct species: Harpagophytum procumbens and H. zeyheri. Although the European pharmacopeia considers the species interchangeable, recent studies have demonstrated that H. procumbens and H. zeyheri are chemically distinct and should not be treated as the same species. Further, the sale of H. zeyheri as an herbal supplement is not legal in the United States. Four markers were tested for their ability to distinguish H. procumbens from H. zeyheri: rbcL, matK, nrITS2, and psbA-trnH. Of these, only psbA-trnH was successful. A novel DNA mini-barcode assay that produces a 178-base amplicon in Harpagophytum (specificity = 1.00 [95% confidence interval = 0.80–1.00]; sensitivity = 1.00 [95% confidence interval = 0.75–1.00]) was used to estimate mislabeling frequency in a sample of 23 devil’s claw supplements purchased in the United States. PCR amplification failed in 13% of cases. Among the 20 fully-analyzable supplements: H. procumbens was not detected in 75%; 25% contained both H. procumbens and H. zeyheri; none contained only H. procumbens. We recommend this novel mini-barcode region as a standard method of quality control in the manufacture of devil’s claw supplements.

DNA Barcode Authentication of Devil's Claw Herbal Dietary Supplements.

The modest genomic and genetic resources for eggplant and most fruit crops, compared to oil and starch model crops, have not tamed the interest of researchers that seek to discover and dissect the regulation of nutrition-related traits. The health-promoting, multifunctional, and sometimes toxic or antinutritional chemistry found across the supergenus Solanum is also present in eggplant. The international research community has explored many of the relationships among genetic variation and primary metabolites, secondary metabolites, yield, marketability, and culturally important traits, using varietal diversity and gene pools of cultivated and wild relatives. Results have opened imaginative and lucrative breeding opportunities. Eggplant is emerging as model system to demonstrate the possibility of improving health-beneficial qualities, while preserving marketable traits, through targeted introgression from related species. This chapter first describes the progress to date and illustrates the kinds of comparative questions the eggplant research community is poised to ask. Then, it presents a case study that uses a multispecies panel to identify candidate genes directing the synthesis of phenylpropanoids that offer numerous nutritional benefits.

The Genetics of Eggplant Nutrition

Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all–important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine–grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half–Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half–Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images.

/pdf/the-herbarium-2021-half-earth-challenge-dataset-and-machine-3pfp9f7b.pdf

Damon P. Little

Papers

Growth and Care Instructions of a New Model Species—the Lycophyte Selaginella apoda

DNA Barcode Authentication of Devil's Claw Herbal Dietary Supplements.

The Genetics of Eggplant Nutrition

The Herbarium 2021 Half–Earth Challenge Dataset and Machine Learning Competition

Reproductive Phenology of Zamia L.: A Comparison Between Wild Cycads and Their Cultivated Counterparts