Summary• The aims of this study were to investigate the appearance of strigolactones in the green lineage and to determine the primitive function of these molecules.• We measured the strigolactone content of several isolated liverworts, mosses, charophyte and chlorophyte green algae using a sensitive biological assay and LC-MS ⁄ MS analyses. In parallel, sequence comparison of strigolactone-related genes and phylogenetic analyses were performed using available genomic data and newly sequenced expressed sequence tags. The primitive function of strigolactones was determined by exogenous application of the synthetic strigolactone analog, GR24, and by mutant phenotyping.• Liverworts, the most basal Embryophytes and Charales, one of the closest green algal relatives to Embryophytes, produce strigolactones, whereas several other species of green algae do not. We showed that GR24 stimulates rhizoid elongation of Charales, liverworts and mosses, and rescues the phenotype of the strigolactone-deficient Ppccd8 mutant of Physcomitrella patens.• These findings demonstrate that the first function of strigolactones was not to promote arbuscular mycorrhizal symbiosis. Rather, they suggest that the strigolactones appeared earlier in the streptophyte lineage to control rhizoid elongation. They may have been conserved in basal Embryophytes for this role and then recruited for the stimulation of colonization by glomeromycotan fungi.

Listeria monocytogenes (Lm) causes severe foodborne illness (listeriosis). Previous molecular subtyping methods, such as pulsed-field gel electrophoresis (PFGE), were critical in detecting outbreaks that led to food safety improvements and declining incidence, but PFGE provides limited genetic resolution. A multiagency collaboration began performing real-time, whole-genome sequencing (WGS) on all US Lm isolates from patients, food, and the environment in September 2013, posting sequencing data into a public repository. Compared with the year before the project began, WGS, combined with epidemiologic and product trace-back data, detected more listeriosis clusters and solved more outbreaks (2 outbreaks in pre-WGS year, 5 in WGS year 1, and 9 in year 2). Wholegenome multilocus sequence typing and single nucleotide polymorphism analyses provided equivalent phylogenetic relationships relevant to investigations; results were most useful when interpreted in context of epidemiological data. WGS has transformed listeriosis outbreak surveillance and is being implemented for other foodborne pathogens.

The FDA has created a United States-based open-source whole-genome sequencing network of state, federal, international, and commercial partners. The GenomeTrakr network represents a first-of-its-kind distributed genomic food shield for characterizing and tracing foodborne outbreak pathogens back to their sources. The GenomeTrakr network is leading investigations of outbreaks of foodborne illnesses and compliance actions with more accurate and rapid recalls of contaminated foods as well as more effective monitoring of preventive controls for food manufacturing environments. An expanded network would serve to provide an international rapid surveillance system for pathogen traceback, which is critical to support an effective public health response to bacterial outbreaks. R ecent devastating outbreaks associated with the consumption of fresh-cut produce have reinforced the notion that foodborne disease remains a substantial global challenge to public health. In the United States alone, one in six or an estimated 48 million people fall prey to foodborne pathogens, yielding 128,000 hospitalizations and 3,000 deaths per year (http://www.cdc.gov /foodborneburden). Economic burdens are estimated cumulatively at $152 billion dollars annually, $39 billion of which is attributed directly to the contamination of fresh, canned, and processed produce (see the Produce Safety Project, http://www .pewtrusts.org/en/about/news-room/press-releases/0001/01/01 /foodborne-illness-costs-nation-$152-billion-annually-nearly -$39-billion-loss-attributed-to-produce). Mitigating foodborne illness, at times, seems to be an intractable challenge.One longstanding problem is the ability to rapidly identify the food source of the contamination. Despite the best efforts of food safety experts, the previous technology, pulsed-field gel electrophoresis (PFGE), often lacks the resolution to effectively pinpoint the source of an outbreak. The promise of whole-genome sequencing (WGS) came in 2012 when scientists with the U.S. Food and Drug Administration's Center for Food Safety and Applied Nutrition (FDA-CFSAN) performed a retrospective outbreak study on a 2012 Salmonella outbreak that was linked to spicy tuna sushi rolls by PFGE. The clinical isolates, food isolates, and historical isolates of the same PFGE pattern were all sequenced on the Illumina MiSeq. In contrast to the PFGE results, where isolates from the current outbreak looked exactly the same as unrelated historical isolates, WGS uncovered a surprising level of resolution distinguishing all of the isolates. Moreover, the isolates from the outbreak were most closely related to a 5-year-old historical isolate that was linked to a processing facility only 8 km away from the source of the outbreak (1). This isolate was collected at the port of entry from an earlier inspection of contaminated seafood and, like many others, was saved in the freezer collection of the FDA-CFSAN. The idea that the FDA's historical isolates could all be sequenced, providing investigators with geographic clues from a ...

Phylogenetic networks model the evolutionary history of sets of organisms when events such as hybrid speciation and horizontal gene transfer occur. In spite of their widely acknowledged importance in evolutionary biology, phylogenetic networks have so far been studied mostly for specific data sets. We present a general definition of phylogenetic networks in terms of directed acyclic graphs (DAGs) and a set of conditions. Further, we distinguish between model networks and reconstructible ones and characterize the effect of extinction and taxon sampling on the reconstructibility of the network. Simulation studies are a standard technique for assessing the performance of phylogenetic methods. A main step in such studies entails quantifying the topological error between the model and inferred phylogenies. While many measures of tree topological accuracy have been proposed, none exist for phylogenetic networks. Previously, we proposed the first such measure, which applied only to a restricted class of networks. In this paper, we extend that measure to apply to all networks, and prove that it is a metric on the space of phylogenetic networks. Our results allow for the systematic study of existing network methods, and for the design of new accurate ones.

The tremendous diversity of land plants all descended from a single charophyte green alga that colonized the land somewhere between 430 and 470 million years ago. Six orders of charophyte green algae, in addition to embryophytes, comprise the Streptophyta s.l. Previous studies have focused on reconstructing the phylogeny of organisms tied to this key colonization event, but wildly conflicting results have sparked a contentious debate over which lineage gave rise to land plants. The dominant view has been that ‘stoneworts,’ or Charales, are the sister lineage, but an alternative hypothesis supports the Zygnematales (often referred to as “pond scum”) as the sister lineage. In this paper, we provide a well-supported, 160-nuclear-gene phylogenomic analysis supporting the Zygnematales as the closest living relative to land plants. Our study makes two key contributions to the field: 1) the use of an unbiased method to collect a large set of orthologs from deeply diverging species and 2) the use of these data in determining the sister lineage to land plants. We anticipate this updated phylogeny not only will hugely impact lesson plans in introductory biology courses, but also will provide a solid phylogenetic tree for future green-lineage research, whether it be related to plants or green algae.