Shigella sonnei

Shigella sonnei is a rod-shaped, Gram-negative facultative intracellular pathogen. It was named ‘Sonne’s bacillus’ after Carl Olaf Sonne who described it as a causative agent of bacillary dysentery. S. sonnei is distributed worldwide and represents the most common cause of shigellosis in industrialized regions in Europe, North America, and Australia. It is currently undergoing expansion in middle-income countries across Asia, Latin America, and the Middle East. S. sonnei evolved from Escherichia coli to specialize in intracellular infection of the human gut epithelium, and its genome comprises a 4.99 Mbp circular chromosome and a 216 kbp invasion plasmid (pINV) required for virulence. The chromosome is ~6% smaller than other E. coli and is punctuated by >300 copies of insertion sequence (IS) elements, whose expansion has degraded the genome through disruption and deletion of genes. Here we describe the key and disease facts allowing bacteria to evade host immune defences and to establish infection

Benchmarking of long-read assemblers for prokaryote whole genome sequencing.

Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled - one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of eight long-read assemblers (Canu, Flye, Miniasm/Minipolish, NECAT, NextDenovo/NextPolish, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v2.1 produced reliable assemblies and was good with plasmids, but it performed poorly with circularisation and had the longest runtimes of all assemblers tested. Flye v2.8 was also reliable and made the smallest sequence errors, though it used the most RAM. Miniasm/Minipolish v0.3/v0.1.3 was the most likely to produce clean contig circularisation. NECAT v20200803 was reliable and good at circularisation but tended to make larger sequence errors. NextDenovo/NextPolish v2.3.1/v1.3.1 was reliable with chromosome assembly but bad with plasmid assembly. Raven v1.3.0 was reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.7.0 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish, NextDenovo/NextPolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms

ModuleFinder and CoReg: alternative tools for linking gene expression modules with promoter sequences motifs to uncover gene regulation mechanisms in plants

BACKGROUND: Uncovering the key sequence elements in gene promoters that regulate the expression of plant genomes is a huge task that will require a series of complementary methods for prediction, substantial innovations in experimental validation and a much greater understanding of the role of combinatorial control in the regulation of plant gene expression. RESULTS: To add to this larger process and to provide alternatives to existing prediction methods, we have developed several tools in the statistical package R. ModuleFinder identifies sets of genes and treatments that we have found to form valuable sets for analysis of the mechanisms underlying gene co-expression. CoReg then links the hierarchical clustering of these co-expressed sets with frequency tables of promoter elements. These promoter elements can be drawn from known elements or all possible combinations of nucleotides in an element of various lengths. These sets of promoter elements represent putative cis-acting regulatory elements common to sets of co-expressed genes and can be prioritised for experimental testing. We have used these new tools to analyze the response of transcripts for nuclear genes encoding mitochondrial proteins in Arabidopsis to a range of chemical stresses. ModuleFinder provided a subset of co-expressed gene modules that are more logically related to biological functions than did subsets derived from traditional hierarchical clustering techniques. Importantly ModuleFinder linked responses in transcripts for electron transport chain components, carbon metabolism enzymes and solute transporter proteins. CoReg identified several promoter motifs that helped to explain the patterns of expression observed. CONCLUSION: ModuleFinder identifies sets of genes and treatments that form useful sets for analysis of the mechanisms behind co-expression. CoReg links the clustering tree of expression-based relationships in these sets with frequency tables of promoter elements. These sets of promoter elements represent putative cis-acting regulatory elements for sets of genes, and can then be tested experimentally. We consider these tools, both built on an open source software product to provide valuable, alternative tools for the prioritisation of promoter elements for experimental analysis

A Comparison of High School Physical Education and Junior Reserve Officer Training Corps

Physical education is recommended for its potential to promote and provide physical activity. However, in high school settings students can commonly participate in other specified physical activity related programs in lieu of physical education and these programs are referred to as physical education waivers. Junior Reserve Officer Training Corps (JROTC) is a common physical education waiver program. Although the criteria used to establish JROTC as a waiver program for physical education is unclear, anecdotally similar accrual of physical activity appears to be a main rationale. The primary purpose of this study was to examine student physical activity levels, lesson contexts, and the promotion of physical activity outside of class time in physical education and JROTC. The secondary purpose was to describe curricular goals and objectives in physical education and JROTC. Forty high school physical education lessons and 40 JROTC lessons from four high schools were systematically observed using the System for Observing Fitness Instruction Time (SOFIT). Data were analyzed using descriptive statistics to describe physical activity levels, lesson contexts, and the time teachers spent promoting physical activity outside of class time in physical education and JROTC lessons. Results showed that students were engaged in moderate to vigorous physical activity (MVPA) 60% of time in physical education and 24% of the time in JROTC. Additionally, promotion of physical activity outside of class time was minimal in both settings but much greater in JROTC (23 times) than in physical education (3 times). Furthermore, physical education and JROTC programs contrasted greatly relative to course syllabi goals, objectives and policies. Physical education and JROTC had more differences than program similarities. The most critical difference was that students in physical education were engaged in MVPA three times as much than students in JROTC. The fee structure in physical education was fairly consistent, around $20.00, and the fees in JROTC ranged from$15.00 to $50.00 with additional fees that would occur throughout the course of the school year. Program cost differences may default lower SES students to enrollment in physical education while, higher SES students have opportunity to choose waiver options. Policy requiring annual program evaluation and teacher professional development in physical education and JROTC would likely promote optimal PA outcomes