Phylogenetic patterns and conservation among North American members of the genus Agalinis (Orobanchaceae)

<p>Abstract</p> <p>Background</p> <p>North American <it>Agalinis </it>Raf. species represent a taxonomically challenging group and there have been extensive historical revisions at the species, section, and subsection levels of classification. The genus contains many rare species, including the federally listed endangered species <it>Agalinis acuta</it>. In addition to evaluating the degree to which historical classifications at the section and subsection levels are supported by molecular data sampled from 79 individuals representing 29 <it>Agalinis </it>species, we assessed the monophyly of 27 species by sampling multiple individuals representing different populations of those species. Twenty-one of these species are of conservation concern in at least some part of their range.</p> <p>Results</p> <p>Phylogenetic relationships estimated using maximum likelihood analyses of seven chloroplast DNA loci (aligned length = 11 076 base pairs (bp) and the nuclear ribosomal DNA ITS (internal transcribed spacer) locus (733 bp); indicated no support for the historically recognized sections except for Section Erectae. Our results suggest that North American members of the genus comprise six major lineages, however we were not able to resolve branching order among many of these lineages. Monophyly of 24 of the 29 sampled species was supported based on significant branch lengths of and high bootstrap support for subtending branches. However, there was no statistical support for the monophyly of <it>A. acuta </it>with respect to <it>Agalinis tenella </it>and <it>Agalinis decemloba</it>. Although most species were supported, deeper relationships among many species remain ambiguous.</p> <p>Conclusion</p> <p>The North American <it>Agalinis </it>species sampled form a well supported, monophyletic group within the family Orobanchaceae relative to the outgroups sampled. Most hypotheses regarding section- and subsection-level relationships based on morphology were not supported and taxonomic revisions are warranted. Lack of support for monophyly of <it>Agalinis acuta </it>leaves the important question regarding its taxonomic status unanswered. Lack of resolution is potentially due to incomplete lineage sorting of ancestral polymorphisms among recently diverged species; however the gene regions examined did distinguish among almost all other species in the genus. Due to the important policy implications of this finding we are further evaluating the evolutionary distinctiveness of <it>A. acuta </it>using morphological data and loci with higher mutation rates.</p

Elucidating the evolutionary history and expression patterns of nucleoside phosphorylase paralogs (vegetative storage proteins) in Populusand the plant kingdom

Nucleoside phosphorylases (NPs) have been extensively investigated in human and bacterial systems for their role in metabolic nucleotide salvaging and links to oncogenesis. In plants, NP-like proteins have not been comprehensively studied, likely because there is no evidence of a metabolic function in nucleoside salvage. However, in the forest trees genus Populus a family of NP-like proteins function as an important ecophysiological adaptation for inter- and intra-seasonal nitrogen storage and cycling. We conducted phylogenetic analyses to determine the distribution and evolution of NP-like proteins in plants. These analyses revealed two major clusters of NP-like proteins in plants. Group I proteins were encoded by genes across a wide range of plant taxa while proteins encoded by Group II genes were dominated by species belonging to the order Malpighiales and included the Populus Bark Storage Protein (BSP) and WIN4-like proteins. Additionally, we evaluated the NP-like genes in Populus by examining the transcript abundance of the 13 NP-like genes found in the Populus genome in various tissues of plants exposed to long-day (LD) and short-day (SD) photoperiods. We found that all 13 of the Populus NP-like genes belonging to either Group I or II are expressed in various tissues in both LD and SD conditions. Tests of natural selection and expression evolution analysis of the Populus genes suggests that divergence in gene expression may have occurred recently during the evolution of Populus, which supports the adaptive maintenance models. Lastly, in silico analysis of cis-regulatory elements in the promoters of the 13 NP-like genes in Populus revealed common regulatory elements known to be involved in light regulation, stress/pathogenesis and phytohormone responses. In Populus, the evolution of the NP-like protein and gene family has been shaped by duplication events and natural selection. Expression data suggest that previously uncharacterized NP-like proteins may function in nutrient sensing and/or signaling. These proteins are members of Group I NP-like proteins, which are widely distributed in many plant taxa. We conclude that NP-like proteins may function in plants, although this function is undefined.https://doi.org/10.1186/1471-2229-13-11

A longitudinal study to examine the influence of farming practices and environmental factors on pathogen prevalence using structural equation modeling

The contamination of fresh produce with foodborne pathogens has been an on-going concern with outbreaks linked to these commodities. Evaluation of farm practices, such as use of manure, irrigation water source, and other factors that could influence pathogen prevalence in the farming environment could lead to improved mitigation strategies to reduce the potential for contamination events. Soil, water, manure, and compost were sampled from farms in Ohio and Georgia to identify the prevalence of Salmonella, Listeria monocytogenes (Lm), Campylobacter, and Shiga-toxin-producing Escherichia coli (STEC), as well as Arcobacter, an emerging human pathogen. This study investigated agricultural practices to determine which influenced pathogen prevalence, i.e., the percent positive samples. These efforts identified a low prevalence of Salmonella, STEC, and Campylobacter in soil and water (&lt; 10%), preventing statistical modeling of these pathogens. However, Lm and Arcobacter were found in soil (13 and 7%, respectively), manure (49 and 32%, respectively), and water samples (18 and 39%, respectively) at a comparatively higher prevalence, suggesting different dynamics are involved in their survival in the farm environment. Lm and Arcobacter prevalence data, soil chemical characteristics, as well as farm practices and weather, were analyzed using structural equation modeling to identify which factors play a role, directly or indirectly, on the prevalence of these pathogens. These analyses identified an association between pathogen prevalence and weather, as well as biological soil amendments of animal origin. Increasing air temperature increased Arcobacter and decreased Lm. Lm prevalence was found to be inversely correlated with the use of surface water for irrigation, despite a high Lm prevalence in surface water suggesting other factors may play a role. Furthermore, Lm prevalence increased when the microbiome’s Simpson’s Diversity Index decreased, which occurred as soil fertility increased, leading to an indirect positive effect for soil fertility on Lm prevalence. These results suggest that pathogen, environment, and farm management practices, in addition to produce commodities, all need to be considered when developing mitigation strategies. The prevalence of Arcobacter and Lm versus the other pathogens suggests that multiple mitigation strategies may need to be employed to control these pathogens

Phylogenetic Analyses of Shigella and Enteroinvasive Escherichia coli for the Identification of Molecular Epidemiological Markers: Whole-Genome Comparative Analysis Does Not Support Distinct Genera Designation

As a leading cause of bacterial dysentery, Shigella represents a significant threat to public health and food safety. Related, but often overlooked, enteroinvasive Escherichia coli (EIEC) can also cause dysentery. Current typing methods have limited ability to identify and differentiate between these pathogens despite the need for rapid and accurate identification of pathogens for clinical treatment and outbreak response. We present a comprehensive phylogeny of Shigella and EIEC using whole genome sequencing of 169 samples, constituting unparalleled strain diversity, and observe a lack of monophyly between Shigella and EIEC and among Shigella taxonomic groups. The evolutionary relationships in the phylogeny are supported by analyses of population structure and hierarchical clustering patterns of translated gene homolog abundance. Lastly, we identified a panel of 254 single nucleotide polymorphism (SNP) markers specific to each phylogenetic cluster for more accurate identification of Shigella and EIEC. Our findings show that Shigella and EIEC are not distinct evolutionary groups within the E. coli genus and, thus, EIEC as a group is not the ancestor to Shigella. The multiple analyses presented provide evidence for reconsidering the taxonomic placement of Shigella. The SNP markers offer more discriminatory power to molecular epidemiological typing methods involving these bacterial pathogens

The Time to Most Recent Common Ancestor Does Not (Usually) Approximate the Date of Divergence.

With the advent of more sophisticated models and increase in computational power, an ever-growing amount of information can be extracted from DNA sequence data. In particular, recent advances have allowed researchers to estimate the date of historical events for a group of interest including time to most recent common ancestor (TMRCA), dates of specific nodes in a phylogeny, and the date of divergence or speciation date. Here I use coalescent simulations and re-analyze an empirical dataset to illustrate the importance of taxon sampling, in particular, on correctly estimating such dates. I show that TMRCA of representatives of a single taxon is often not the same as divergence date due to issues such as incomplete lineage sorting. Of critical importance is when estimating divergence or speciation dates a representative from a different taxonomic lineage must be included in the analysis. Without considering these issues, studies may incorrectly estimate the times at which historical events occurred, which has profound impacts within both research and applied (e.g., those related to public health) settings

Metadata for the five samples of Salmonella enterica ssp. enterica Serovar included in the empirical analyses.

<p>Metadata for the five samples of Salmonella enterica ssp. enterica Serovar included in the empirical analyses.</p

Ten randomly selected genealogies from the coalescent simulations of two taxa X (N = 10) and Y (N = 1) using the ms [6] program under the a) early divergence scenario where the TMRCA is much more recent than divergence, b) the late divergence scenario where TMRCA and divergence data overlap.

<p>Visualization was performed using DensiTree v2.2.1 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128407#pone.0128407.ref014" target="_blank">14</a>]. The red line and blue line approximate the range of estimates for divergence time and TMRCA, respectively. Histograms of the distribution of estimates of the date of divergence between X and Y and TMRCA of X run under the c) early divergence and d) late divergence scenarios. In panel (d) bars are offset (“dodge”) to illustrate that the distributions for each parameter are indistinguishable.</p

Genealogies under the different BEAST analyses where a) only Agona samples were included and analyzed with the best fitting model identified in Zhou et al. [5], b) with the outgroup analyzed with a strict molecular clock (note that the scale is in 10³ ybp) and, c) with the outgroup Soerenga and analyzed under best fitting model from the publication and, d) a phylogeny inferred with MrBayes [12].

<p>Genealogies under the different BEAST analyses where a) only Agona samples were included and analyzed with the best fitting model identified in Zhou et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128407#pone.0128407.ref005" target="_blank">5</a>], b) with the outgroup analyzed with a strict molecular clock (note that the scale is in 10³ ybp) and, c) with the outgroup Soerenga and analyzed under best fitting model from the publication and, d) a phylogeny inferred with MrBayes [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128407#pone.0128407.ref012" target="_blank">12</a>].</p

Segal’s Law, 16S rRNA gene sequencing, and the perils of foodborne pathogen detection within the American Gut Project

Obtaining human population level estimates of the prevalence of foodborne pathogens is critical for understanding outbreaks and ameliorating such threats to public health. Estimates are difficult to obtain due to logistic and financial constraints, but citizen science initiatives like that of the American Gut Project (AGP) represent a potential source of information concerning enteric pathogens. With an emphasis on genera Listeria and Salmonella, we sought to document the prevalence of those two taxa within the AGP samples. The results provided by AGP suggest a surprising 14% and 2% of samples contained Salmonella and Listeria, respectively. However, a reanalysis of those AGP sequences described here indicated that results depend greatly on the algorithm for assigning taxonomy and differences persisted across both a range of parameter settings and different reference databases (i.e., Greengenes and HITdb). These results are perhaps to be expected given that AGP sequenced the V4 region of 16S rRNA gene, which may not provide good resolution at the lower taxonomic levels (e.g., species), but it was surprising how often methods differ in classifying reads—even at higher taxonomic ranks (e.g., family). This highlights the misleading conclusions that can be reached when relying on a single method that is not a gold standard; this is the essence of Segal’s Law: an individual with one watch knows what time it is but an individual with two is never sure. Our results point to the need for an appropriate molecular marker for the taxonomic resolution of interest, and calls for the development of more conservative classification methods that are fit for purpose. Thus, with 16S rRNA gene datasets, one must be cautious regarding the detection of taxonomic groups of public health interest (e.g., culture independent identification of foodborne pathogens or taxa associated with a given phenotype)