News

Just as DNA analysis regularly overturns seemingly solid eyewitness identifications in crime investigations, routine DNA analysis can also help biologists avoid blunders. In 28 August 2007 Mol Ecol, researchers from University of Colorado, New Mexico State University, Pisces Molecular, and Brigham Young University report that over 20 years of restocking efforts in western US aimed at restoring native populations of endangered greenback cutthroat trout Oncorhynchus clarkii stomias have mostly been restocking a non-native, non-endangered subspecies, Colorado River cutthroat trout O. c. pleuriticus.  They trace the confusion to repeated introductions beginning in the late 1800s of Colorado River cutthroat trout throughout the native range of greenback cutthroat trout. The authors analyzed mitochondrial (COI, ND2) and nuclear (microsatellites, AFLP) DNA from 365 individuals from 15 locations in 3 major river drainage systems in Colorado and surrounding states. Distinct mtDNA lineages corresponding to each subspecies were corroborated by nuclear microsatellite and AFLP data.  For another cautionary tale of repeated misidentification of a widely studied organism, see Siddall and colleagues’ entertaining June 2007 Proc R Soc paper scrutinizing commercially available medicinal leeches sold as Hirudo medicinalis. 

How might the future look with routine application of DNA ID as quality control? Incorporating DNA barcode analysis into Tree of Life studies is one useful approach, exemplified by two recent large-scale evolutionary studies published in January and April 2008 Syst Entomol, one on phylogenetic relationships in Saturnid silkmoths, and one on higher-level relationships among 12 families in ‘bombycoid complex’ of Lepidoptera. Both studies analyze COI barcodes of all specimens, “allowing confirmination of their identification for species present in the BOLD reference library and enabling future identifications of organisms whose identity is still pending.”

On February 27, 2008, Encyclopedia of Life (EOL), “a web page for every species” officially launched, with over 30,000 species pages, mostly fish so far, and a diversity of links to internet resources including Biodiversity Heritage Library (2.9 million pages digitized). In case you missed it, there is a thrilling, award-winning video.  Following Wikipedia model, EOL users are invited to become “curators” for one or more species pages, and later this year all are invited to submit content (photos, drawing, text, video, for example) for review. For an entertaining brief history of Wikipedia and why it keeps getting better see Nicholson Baker’s review of John Broughton’s Wikipedia: The Missing Manual in March 20, 2008 New York Review of Books.

Most near-sun comets are now discovered by amateurs, using images downloaded from the Solar and Heliospheric Observatory (SOHO), a satellite launched December 2, 1995 as part of international collaboration between European Space Agency (ESA) and National Aeronautics and Space Administration (NASA). I expect that EOL and other open-access databases will lead to many more persons contributing to biodiversity science.

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What limits mitochondrial variation within species?  In January 2008 PLoS Biology researchers from Karolinksa Institute, Sweden, and University of Newcastle upon Tyne, United Kingdom, report on an ingenious mouse model that shows strong purifying selection acting within a single generation, or even earlier, during embryogenesis. Stewart and colleagues employed “mtDNA mutator” mice which are homozygous defective for a nuclear gene which encodes a proof-reading subunit of mtDNA polymerase. These mice have increased levels of mtDNA mutations in all tissues, with mutations evenly distributed along all codon positions in mtDNA protein genes, accelerated senescence and “a number of phenotypes associated with mitochondrial diseases.” mtDNA mutator mice were backcrossed to wild-type mice to produce offspring that inherited defective mitochondria but whose nuclear genome is homozygous normal at the mtDNA polymerase locus.  They then sequenced entire mitochondrial genomes from 190 of these progeny individuals in N2 to N6 generations (N2 is the first backcross that is homozygous normal at mtDNA mutator locus). To skip to the conclusion, most of the non-synonomous mitochondrial mutations were eliminated, leaving a pattern of  excess synonymous mutations similar to that seen in human populations (which are the largest dataset so far for mitochondrial variation). The authors conclude that the mitochondrial population bottleneck known to occur at oogenesis, which deposits just one or few mitochondrial genomes per oocyte, means each mitochondrial genome must stand on its own so to speak, with the result that those eggs, embryos, or offspring harboring defective mitochondria will fail to survive. My figure at right tries to illustrate part of this process. 

In the same issue, David Rand, Brown University, provides a lucid commentary on Stewart et al’s research putting it in the context of mitochondrial and evolutionary biology, and suggesting next steps. Among others, he notes “the new mouse study also begs new questions about positive selection on mtDNA. …it is interesting that no signature of a selective sweep leading to fixation of a novel mtDNA variant was evident in the data”.

Purifying selection against deleterious mutations enabled by an embryonic bottleneck may save mtDNA from “mutational meltdown”. Now we need to understand more about the positive selection on mtDNA that presumably occurs when species adapt to new environments or diverge. I believe that growing mtDNA databases in the form of COI barcodes from a diversity of organisms with varying size, lifespan, population size, and reproductive strategy, in a diversity of environments including marine, terrestrial, temperature, and tropical regions will help solve this puzzle.

Plants challenge DNA barcoding. It has been difficult to identify candidate barcode regions that amplify readily and also distinguish among closely-related species. In 7 February 2008 PNAS (open access) researchers from University of Johannesburg; University of Costa Rica; Royal Botanic Gardens, Kew; and Imperial College, London, analyze potential barcode regions on specimens collected in plant biodiversity hotspots in Kruger National Park, South Africa, and Costa Rica. They initially tested eight candidate regions identified in earlier studies (coding regions accD, rpoC1, rpoB, ndhJ, ycf5, and matK, and non-coding trnH-psbA). Amplification was done according to earlier studies except that a different set of matK primers was used which appeared to be more effective. All eight regions were examined in 101 specimens representing 32 species of trees, shrubs, and achlorophyllous parasites from South Africa, and on 71 specimens representing 48 species of Costa Rican orchids (in all, 44 species with 2-7 specimens per species, and 36 species with one sample). Based on their analysis, the coding region matK with the new primer set and the non-coding region trnH-psbA were >90% effective in species identification. For reasons I do not understand, the authors favor unweighted pair group method with arithmetic mean (UPGMA) for analyzing genetic clustering, although they tested neighbor-joining, maximum likelihood, maximum parsimony, and Bayesian methods. Given the presumed advantages of a coding region barcode (ease of alignment, greater higher-level phylogenetic signal), Lahaye et al propose 5′ region of plastid gene matK as a first-pass standard barcode for plants.  

The authors then analyzed the 5′ matK barcode in a much larger sample of orchids: 1,566 specimens representing 1,084 Mesoamerican species. It is exciting that this is the largest test of candidate barcode variation within species for plants to date. They report 212 genetic clusters in UPGMA tree, of which “86 fully matched previously recognized species and a further 25 partially matched taxonomic species…an examination of these clusters reveals cryptic species, which need further taxonomic work”. I am unsure from this short report what “partially matched taxonomic species” are and how many possible cryptic species were identified. I look forward to a more detailed report on the DNA barcodes, morphology, and range distribution of this very large sample of Mesoamerican orchids.  A DNA-based method for identifying non-flowering orchids and other plants could help protect many threatened species. 

Approximately 8,000 – 15,000 species of bivalves (clams, mussels, scallops, oysters, and relatives) are known. According to BOLD Taxonomy Browser www.barcodinglife.org, 620 bivalve species have COI barcode records so far, so this group is relatively unexplored genetically. In September 2007 Zoologica Scripta researchers from University of Bergen, Norway, analyze COI barcode region sequences of 62 deepwater clams dredged in a single offshore region at 69 m to 567 m, morphologically identified as 12 species from 4 genera (Thyasira, Ennucula, Nucula, Yoldiella) representing 3 subclasses of Bivalvia. The COI barcode region was amplified with broad-range primers (Folmer et al 1994). Mean differences within species collected in this single area were small, 0.0 – 0.48%, similar to results in other animal groups, suggesting assignment of specimens to species will be straightforward. This will be helpful in environmental surveys for example, as some species “are infamous for being difficult to determine to species from morphology” and some “remain difficult to identify for the non-expert.” As one example, some Thyasira species are distinguished only by sperm and egg morphology, which is impractical in most circumstances.

mtDNA differences among these bivalves are remarkably large, even among species in the same genus. The differences among congeneric species in this sample (average 22%, range 12-42%) are larger than differences among entire class Aves (according to my analysis with BOLD software, COI differences among birds in different orders, such as penguins and hummingbirds for example, average 20%, with range 14-28%).

Blastn GenBank searches with these divergent mtDNA sequences showed very limited identity to anything, and the closest matches were short stretches (100-150 nucleotides of the 678 full-length barcode sequence) to COI sequences of species outside the phylum Mollusca (I obtained similar results submitting Thyasira sequences for example to the public BOLD Identification Engine at www.barcodinglife.org.)  It will be helpful if Mikkelsen et al deposit their sequences along with associated collecting data (voucher specimen information, images, collection locations) to the BOLD database. I look forward to learning more about these bivalves, and whether their remarkably deep differences in mtDNA are associated with deep physiological, ecological, or other biological differences.