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Summary: By providing the scientific community with uniform and standardized resources of consistent quality, plasmid repositories play an important role in enabling scientific reproducibility. Plasmids containing insertion sequence elements (IS elements) represent a challenge from this perspective, as they can change the plasmid structure and function. In this study, we conducted a systematic analysis of a subset of plasmid stocks distributed by plasmid repositories (The Arabidopsis Biological Resource Center and Addgene) which carry unintended integrations of bacterial mobile genetic elements. The integration of insertion sequences was most often found in, but not limited to, pBR322‐derived vectors, and did not affect the function of the specific plasmids. In certain cases, the entire stock was affected, but the majority of the stocks tested contained a mixture of the wild‐type and the mutated plasmids, suggesting that the acquisition of IS elements likely occurred after the plasmids were acquired by the repositories. However, comparison of the sequencing results of the original samples revealed that some plasmids already carried insertion mutations at the time of donation. While an extensive BLAST analysis of 47 877 plasmids sequenced from the Addgene repository uncovered IS elements in only 1.12%, suggesting that IS contamination is not widespread, further tests showed that plasmid integration of IS elements can propagate in conventional Escherichia coli hosts over a few tens of generations. Use of IS‐free E. coli hosts prevented the emergence of IS insertions as well as that of small indels, suggesting that the use of IS‐free hosts by donors and repositories could help limit unexpected and unwanted IS integrations into plasmids. [ABSTRACT FROM AUTHOR]

1. The use of biomarkers (e.g., genetic, microchemical and morphometric characteristics) to discriminate among and assign individuals to a population can benefit species conservation and management by facilitating our ability to understand population structure and demography. 2. Tools that can evaluate the reliability of large genomic datasets for population discrimination and assignment, as well as allow their integration with non-genetic markers for the same purpose, are lacking. Our r package, assignPOP, provides both functions in a supervised machine-learning framework. 3. assignPOP uses Monte-Carlo and K-fold cross-validation procedures, as well as principal component analysis, to estimate assignment accuracy and membership probabilities, using training (i.e., baseline source population) and test (i.e., validation) datasets that are independent. A user then can build a specified predictive model based on the relative sizes of these datasets and classification functions, including linear discriminant analysis, support vector machine, naïve Bayes, decision tree and random forest. 4. assignPOP can benefit any researcher who seeks to use genetic or non-genetic data to infer population structure and membership of individuals. assignPOP is a freely available r package under the GPL license, and can be downloaded from CRAN or at https://github.com/alexkychen/assignPOP. A comprehensive tutorial can also be found at https://alexkychen.github.io/assignPOP/. [ABSTRACT FROM AUTHOR]

An increase in studies using restriction site-associated DNA sequencing ( RADseq) methods has led to a need for both the development and assessment of novel bioinformatic tools that aid in the generation and analysis of these data. Here, we report the availability of Aftr RAD, a bioinformatic pipeline that efficiently assembles and genotypes RADseq data, and outputs these data in various formats for downstream analyses. We use simulated and experimental data sets to evaluate Aftr RAD's ability to perform accurate de novo assembly of loci, and we compare its performance with two other commonly used programs, stacks and pyrad. We demonstrate that Aftr RAD is able to accurately assemble loci, while accounting for indel variation among alleles, in a more computationally efficient manner than currently available programs. Aftr RAD run times are not strongly affected by the number of samples in the data set, making this program a useful tool when multicore systems are not available for parallel processing, or when data sets include large numbers of samples. [ABSTRACT FROM AUTHOR]

A long-standing question in evolutionary studies of snake venoms is the extent to which phylogenetic divergence and diet can account for between-species differences in venom composition. Here we apply phylogeny-based comparative methods to address this question. We use data on venom variation generated using proteomic techniques for all members of a small clade of rattlesnakes (Sistrurus sp.) and two outgroups for which phylogenetic and diet information is available. We first complete the characterization of venom variation for all members of this clade with a “venomic” analysis of pooled venoms from two members of this genus, S. milarius streckeri and S. m. milarius. These venoms exhibit the same general classes of proteins as those found in other Sistrurus species but differ in their relative abundances of specific protein families. We then test whether there is significant phylogenetic signal in the relative abundances of major venom proteins across species and if diet (measured as percent mammals and lizards among all prey consumed) covaries with venom composition after phylogenetic divergence is accounted for. We found no evidence for significant phylogenetic signal in venom variation: K values for seven snake venom proteins and two composite venom variables [PC 1 and 2]) were all nonsignificant and lower (mean = 0.11+0.06 sd) than mean K values (>0.35) previously reported for a wide range of morphological, life history, physiological and behavioral traits from other species. Finally, analyses based on Phylogenetic Generalized Least Squares (PGLS) methods reveal that variation in abundance of some venom proteins, most strongly CRISP is significantly related to snake diet. Our results demonstrate that venom variation in these snakes is evolutionarily a highly labile trait even among very closely-related taxa and that natural selection acting through diet variation may play a role in molding the relative abundance of specific venom proteins. [ABSTRACT FROM AUTHOR]

The focus of this study is to profile changes in DNA methylation and small RNA expression occurring with increased age in almond breeding germplasm to identify possible biomarkers of age that can be used to assess the potential of individuals to develop aging-related disorders. To profile DNA methylation in almond germplasm, 70 methylomes were generated from almond individuals representing three age cohorts (11, 7, and 2 years old) using an enzymatic methyl-seq approach followed by analysis to call differentially methylated regions (DMRs) within these cohorts. Small RNA (sRNA) expression was profiled in three breeding selections, each from two age cohorts (1 and 6 years old), using sRNA-Seq followed by differential expression analysis. Weighted chromosome-level methylation analysis reveals hypermethylation in 11-year-old almond breeding selections when compared to 2-year-old selections in the CG and CHH contexts. Seventeen consensus DMRs were identified in all age contrasts. sRNA expression differed significantly between the two age cohorts tested, with significantly decreased expression in sRNAs in the 6-year-old selections compared to the 1-year-old. Almond shows a pattern of hypermethylation and decreased sRNA expression with increased age. Identified DMRs and differentially expressed sRNAs could function as putative biomarkers of age following validation in additional age groups. • Aging is poorly understood in trees regardless of its implications for human society • Tree aging has implications for conservation, recovery, breeding, and production • Almond can serve as a model to understand tree aging since it exhibits an aging disorder • Differential DNA methylation is found to be associated with aging in the almond genome • DNA-methylation and small RNA expression may be used as aging biomarkers in almond [ABSTRACT FROM AUTHOR]

A long-standing question in evolutionary studies of snake venoms is the extent to which phylogenetic divergence and diet can account for between-species differences in venom composition. Here we apply phylogeny-based comparative methods to address this question. We use data on venom variation generated using proteomic techniques for all members of a small clade of rattlesnakes (Sistrurus sp.) and two outgroups for which phylogenetic and diet information is available. We first complete the characterization of venom variation for all members of this clade with a “venomic” analysis of pooled venoms from two members of this genus, S. milarius streckeri and S. m. milarius. These venoms exhibit the same general classes of proteins as those found in other Sistrurus species but differ in their relative abundances of specific protein families. We then test whether there is significant phylogenetic signal in the relative abundances of major venom proteins across species and if diet (measured as percent mammals and lizards among all prey consumed) covaries with venom composition after phylogenetic divergence is accounted for. We found no evidence for significant phylogenetic signal in venom variation: K values for seven snake venom proteins and two composite venom variables [PC 1 and 2]) were all nonsignificant and lower (mean = 0.11+0.06 sd) than mean K values (>0.35) previously reported for a wide range of morphological, life history, physiological and behavioral traits from other species. Finally, analyses based on Phylogenetic Generalized Least Squares (PGLS) methods reveal that variation in abundance of some venom proteins, most strongly CRISP is significantly related to snake diet. Our results demonstrate that venom variation in these snakes is evolutionarily a highly labile trait even among very closely-related taxa and that natural selection acting through diet variation may play a role in molding the relative abundance of specific venom proteins. [ABSTRACT FROM AUTHOR]

Context: Prior to the COVID-19 pandemic, wastewater influent monitoring for tracking disease burden in sewered communities was not performed in Ohio, and this field was only on the periphery of the state academic research community., Program: Because of the urgency of the pandemic and extensive state-level support for this new technology to detect levels of community infection to aid in public health response, the Ohio Water Resources Center established relationships and support of various stakeholders. This enabled Ohio to develop a statewide wastewater SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) monitoring network in 2 months starting in July 2020., Implementation: The current Ohio Coronavirus Wastewater Monitoring Network (OCWMN) monitors more than 70 unique locations twice per week, and publicly available data are updated weekly on the public dashboard., Evaluation: This article describes the process and decisions that were made during network initiation, the network progression, and data applications, which can inform ongoing and future pandemic response and wastewater monitoring., Discussion: Overall, the OCWMN established wastewater monitoring infrastructure and provided a useful tool for public health professionals responding to the pandemic., Competing Interests: The authors declare that they have no conflicts of interests to disclose., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)