Complete Genome Sequences of Paenibacillus Larvae Phages BN12, Dragolir, Kiel007, Leyra, Likha, Pagassa, PBL1c, and Tadhana

We present here the complete genomes of eight phages that infect Paenibacillus larvae, the causative agent of American foulbrood in honeybees. Phage PBL1c was originally isolated in 1984 from a P. larvae lysogen, while the remaining phages were isolated in 2014 from bee debris, honeycomb, and lysogens from three states in the USA

Hypersaline lakes harbor more active bacterial communities

ABSTRACT Extremophiles employ a diverse array of resistance strategies to thrive under harsh 18 environmental conditions but maintaining these adaptations comes at an energetic cost. If energy reserves to drop too low, extremophiles may enter a dormant state of reduced 20 metabolic activity to survive. Dormancy is frequently offered as a plausible explanation for the persistence of bacteria under suboptimal environmental conditions with the 22 prevalence of this mechanism only expected to rise as stressful conditions intensify. We estimated dormancy in ten hypersaline and freshwater lakes across the Western United 24 States. To our surprise, we found that extreme environmental conditions did not induce higher levels of bacterial dormancy. Based on our approach using rRNA:rDNA gene 26 ratios to estimate activity, halophilic and halotolerant bacteria were classified as inactive at a similar percentage as freshwater bacteria, and the proportion of the community 28 exhibiting dormancy was considerably lower (16%) in hypersaline than freshwater lakes across a range of cutoffs estimating activity. Of the multiple chemical characteristics we 30 evaluated, salinity and, to a lesser extent, total phosphorus concentrations influenced activity. But instead of dormancy being more common as stressful conditions intensified, 32 the percentage of the community residing in an inactive state decreased with increasing salinity in freshwater and hypersaline lakes, suggesting that salinity acts as a strong 34 environmental filter selecting for bacteria that persist and thrive under saltier conditions. Within the compositionally distinct and less diverse hypersaline communities, abundant 36 taxa were disproportionately active and localized in families Microbacteriacea

Phage cluster relationships identified through single gene analysis

Abstract Background Phylogenetic comparison of bacteriophages requires whole genome approaches such as dotplot analysis, genome pairwise maps, and gene content analysis. Currently mycobacteriophages, a highly studied phage group, are categorized into related clusters based on the comparative analysis of whole genome sequences. With the recent explosion of phage isolation, a simple method for phage cluster prediction would facilitate analysis of crude or complex samples without whole genome isolation and sequencing. The hypothesis of this study was that mycobacteriophage-cluster prediction is possible using comparison of a single, ubiquitous, semi-conserved gene. Tape Measure Protein (TMP) was selected to test the hypothesis because it is typically the longest gene in mycobacteriophage genomes and because regions within the TMP gene are conserved. Results A single gene, TMP, identified the known Mycobacteriophage clusters and subclusters using a Gepard dotplot comparison or a phylogenetic tree constructed from global alignment and maximum likelihood comparisons. Gepard analysis of 247 mycobacteriophage TMP sequences appropriately recovered 98.8% of the subcluster assignments that were made by whole-genome comparison. Subcluster-specific primers within TMP allow for PCR determination of the mycobacteriophage subcluster from DNA samples. Using the single-gene comparison approach for siphovirus coliphages, phage groupings by TMP comparison reflected relationships observed in a whole genome dotplot comparison and confirm the potential utility of this approach to another widely studied group of phages. Conclusions TMP sequence comparison and PCR results support the hypothesis that a single gene can be used for distinguishing phage cluster and subcluster assignments. TMP single-gene analysis can quickly and accurately aid in mycobacteriophage classification

VOLUME FOUR Using the Primary Literature in an Allied Health Microbiology Course

A strategy was adapted for using the primary literature to foster active learning in an allied health microbiology course. Recent journal articles were selected that underscored the fundamental microbiological principles to be learned in each course unit. At the beginning of the semester, students were taught the relationship between the layout of scientific articles and the scientific method. During the rest of the semester, students were oriented to the topic of each paper by viewing videos from Unseen Life on Earth: an Introduction to Microbiology, reading assigned pages from the text, and participating in mini-lectures and discussions. After all preparatory material was completed, a paper was read and discussed in small groups and as a class. Students were assessed using daily reading quizzes and end-of-unit concept quizzes. While reading quizzes averaged approximately 93%, concept quiz grades averaged approximately 82%. Student recognition of the terms used in each unit’s scientific article was assesse

Personal microbiome analysis improves student engagement and interest in Immunology, Molecular Biology, and Genomics undergraduate courses

<div><p>A critical area of emphasis for science educators is the identification of effective means of teaching and engaging undergraduate students. Personal microbiome analysis is a means of identifying the microbial communities found on or in our body. We hypothesized the use of personal microbiome analysis in the classroom could improve science education by making courses more applied and engaging for undergraduate students. We determined to test this prediction in three Brigham Young University undergraduate courses: Immunology, Advanced Molecular Biology Laboratory, and Genomics. These three courses have a two-week microbiome unit and students during the 2016 semester students could submit their own personal microbiome kit or use the demo data, whereas during the 2017 semester students were given access to microbiome data from an anonymous individual. The students were surveyed before, during, and after the human microbiome unit to determine whether analyzing their own personal microbiome data, compared to analyzing demo microbiome data, impacted student engagement and interest. We found that personal microbiome analysis significantly enhanced the engagement and interest of students while completing microbiome assignments, the self-reported time students spent researching the microbiome during the two week microbiome unit, and the attitudes of students regarding the course overall. Thus, we found that integrating personal microbiome analysis in the classroom was a powerful means of improving student engagement and interest in undergraduate science courses.</p></div

Online microbiome module assignment #1 evaluating health effects of the most abundant microbiome phylum.

<p>Measurement of student responses regarding their agreement with questions about the online microbiome module assignment #1. Students who were given a kit evaluated their own microbiome data whereas those without a kit evaluated demo data. (A) Level of agreement for how engaged the students were during an internet search examining how bacterial phylum influences health. Statistical analysis for (A) performed using a Mann-Whitney U-test and all values are mean ± SEM with n = 65 for kit (blue) and n = 80 for no kit (black) (* = p<0.05). (B) Self-reported time spent searching showed equivalent levels of time between groups whereas the students analyzing their own microbiome data had significantly higher number of website visits (C) and quantity of evidence collected (D). Statistical analysis for (B-D) were performed using a chi-square test and all values are shown as stacked bars (percentages of the categories chosen) with n = 65 for kit and n = 80 for no kit (* = p<0.05).</p

Online microbiome module assignment #4 determining the taxonomy of the most abundant species in the microbiome data.

<p>Quantification of student responses regarding their agreement with questions regarding online microbiome module assignment #4. Students who were given a kit evaluated their own microbiome data whereas those without a kit evaluated demo data. (A) Level of agreement for how engaged the students were during an internet search regarding how a microbial species in the sample could affect health. Statistical analysis for (A) performed using a Mann-Whitney U-test and all values are mean ± SEM with n = 65 for kit (blue) and n = 80 for no kit (black) (*** = p<0.001). (B) Students analyzing their own microbiome data had significantly higher self-reported search time and number of website visits (C) whereas the quantity of evidence collected was equivalent between groups (D). Statistical analysis for (B-D) were performed using a chi-square test and all values are shown as stacked bars (percentages of the categories chosen) with n = 65 for kit and n = 80 for no kit (* = p<0.05).</p

Overall experience with the online microbiome module.

<p>Measurement of student responses regarding their agreement with questions regarding their experience overall with the online microbiome module. (A) Students who were given a kit (i.e. evaluated their own microbiome data) reported significantly higher levels of engagement, enjoyment, interest, and learning. Statistical analysis for (A) was performed using a Mann-Whitney U-test and all values are mean ± SEM with n = 65 for kit (blue) and n = 80 for no kit (black) (*** = p<0.001; **** = p<0.0001). (B) Students self-reported equivalent overall time spent in regards to their experience with the online microbiome modules. Statistical analysis for (B) was performed using a chi-square test and all values are shown as stacked bars (percentages of the categories chosen) with n = 65 for kit and n = 80 for no kit.</p