Gene duplication and paleopolyploidy in soybean and the implications for whole genome sequencing

<p>Abstract</p> <p>Background</p> <p>Soybean, <it>Glycine max </it>(L.) Merr., is a well documented paleopolyploid. What remains relatively under characterized is the level of sequence identity in retained homeologous regions of the genome. Recently, the Department of Energy Joint Genome Institute and United States Department of Agriculture jointly announced the sequencing of the soybean genome. One of the initial concerns is to what extent sequence identity in homeologous regions would have on whole genome shotgun sequence assembly.</p> <p>Results</p> <p>Seventeen BACs representing ~2.03 Mb were sequenced as representative potential homeologous regions from the soybean genome. Genetic mapping of each BAC shows that 11 of the 20 chromosomes are represented. Sequence comparisons between homeologous BACs shows that the soybean genome is a mosaic of retained paleopolyploid regions. Some regions appear to be highly conserved while other regions have diverged significantly. Large-scale "batch" reassembly of all 17 BACs combined showed that even the most homeologous BACs with upwards of 95% sequence identity resolve into their respective homeologous sequences. Potential assembly errors were generated by tandemly duplicated pentatricopeptide repeat containing genes and long simple sequence repeats. Analysis of a whole-genome shotgun assembly of 80,000 randomly chosen JGI-DOE sequence traces reveals some new soybean-specific repeat sequences.</p> <p>Conclusion</p> <p>This analysis investigated both the structure of the paleopolyploid soybean genome and the potential effects retained homeology will have on assembling the whole genome shotgun sequence. Based upon these results, homeologous regions similar to those characterized here will not cause major assembly issues.</p

The Global Alliance for Infections in Surgery : defining a model for antimicrobial stewardship-results from an international cross-sectional survey

Background: Antimicrobial Stewardship Programs (ASPs) have been promoted to optimize antimicrobial usage and patient outcomes, and to reduce the emergence of antimicrobial-resistant organisms. However, the best strategies for an ASP are not definitively established and are likely to vary based on local culture, policy, and routine clinical practice, and probably limited resources in middle-income countries. The aim of this study is to evaluate structures and resources of antimicrobial stewardship teams (ASTs) in surgical departments from different regions of the world. Methods: A cross-sectional web-based survey was conducted in 2016 on 173 physicians who participated in the AGORA (Antimicrobials: A Global Alliance for Optimizing their Rational Use in Intra-Abdominal Infections) project and on 658 international experts in the fields of ASPs, infection control, and infections in surgery. Results: The response rate was 19.4%. One hundred fifty-six (98.7%) participants stated their hospital had a multidisciplinary AST. The median number of physicians working inside the team was five [interquartile range 4-6]. An infectious disease specialist, a microbiologist and an infection control specialist were, respectively, present in 80.1, 76.3, and 67.9% of the ASTs. A surgeon was a component in 59.0% of cases and was significantly more likely to be present in university hospitals (89.5%, p <0.05) compared to community teaching (83.3%) and community hospitals (66.7%). Protocols for pre-operative prophylaxis and for antimicrobial treatment of surgical infections were respectively implemented in 96.2 and 82.3% of the hospitals. The majority of the surgical departments implemented both persuasive and restrictive interventions (72.8%). The most common types of interventions in surgical departments were dissemination of educational materials (62.5%), expert approval (61.0%), audit and feedback (55.1%), educational outreach (53.7%), and compulsory order forms (51.5%). Conclusion: The survey showed a heterogeneous organization of ASPs worldwide, demonstrating the necessity of a multidisciplinary and collaborative approach in the battle against antimicrobial resistance in surgical infections, and the importance of educational efforts towards this goal.Peer reviewe

The nucleosome saturation on IE, E, and late genes is statistically similar.

<p><b>A</b>. The predicted nucleosomes for each gene kinetic class (IE, E, L), and <b>B</b>. sub-class (late, leaky, true) were compared to the maximum number of nucleosomes to give the nucleosome saturation, which is expressed as a %. The average % saturation of IE genes was not significantly different than the average saturation of the E genes or that of the Late genes (57 of 66 analyzed) using a non-parametric Wilcoxon rank sum test, although the IE genes did trend lower. Error bars represents the standard deviation.</p

Array Design and Nucleosome Counting.

<p>SY5Y cells were infected with HSV-1 at a moi of 5. At 6h post infection cells were harvested and nuclei prepared. Nuclei were digested to completion with MNase and the DNA fragments purified. These were separated by gel electrophoresis and the 150bp band eluted and labeled with Cy3. The array slide consisted of 8 wells containing approximately 12k x 50mer oligonucleotides. The 50mers covered the entire HSV-1 genome and were tilled with a 25nt overlap. In each well there were 3 sets of 3046 oligos covering the HSV genome plus some alien (control) oligos. Eight labeled samples were hybridized on the array slide and subsequently analyzed for the position of nucleosomes on the HSV genome as described in the Methods.</p

Nucleosomes on Immediate Early genes.

<p><b>A</b> and <b>B</b>. The nucleosome signal is shown for the region covering two immediate early genes, which are diploid in the HSV-1 genome. The two gene copies have been inverted and aligned with each other to show that the array signal is similar but not identical. Spaces in the graph lines are “poorly performing” oligos where no signal could be measured. Between the array signal information we have mapped the transcribed gene (green). The red balls indicate the predicted position of nucleosomes with 5 consecutive positive hybridizing oligos. The orange balls indicate where we have inferred a nucleosome from 4 positive oligos and assumed there is a fifth positive oligo that is not recorded because of a “poorly performing” oligo, or some other technical problem. Gene features from the NCIB database are also shown. <b>C</b> To confirm our nucleosome positioning we used ChIP and Q-PCR (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117471#sec002" target="_blank">Methods</a>), selecting primers to amplify regions of ICP0 that we designated as having a probable nucleosome (green), or NFR (red). <b>D</b>. Nucleosome Saturation for each Immediate Early gene. The predicted number of nucleosomes for each IE gene were compared to the maximum number of nucleosomes possible (assuming a standard cell 200nt/nucleosome repeat pattern) to give the nucleosome saturation, which is expressed as a %.</p

Nucleosomes on Early genes.

<p><b>A. and B</b> The nucleosome signal is shown in the region covering the gene for DNA polymerase (UL30) and its processivity factor (UL41). There is one copy of these genes located in the unique long segment of the viral gene. Below the array signal information we have mapped the transcribed gene and the red or orange balls indicate the predicted position of nucleosomes. Gene features from the NCIB database are also shown. <b>C</b>. Nucleosome Saturation for each E gene. The predicted nucleosomes for each E gene were compared to the maximum number of nucleosomes (assuming a 200nt/nucleosome repeat pattern) to give the nucleosome saturation, which is expressed as a %.</p

Nucleosome on Late Genes.

<p><b>A</b>. The nucleosome signal is shown in the region covering the late gene VP16 (UL48) a tegument protein and transactivating factor for transcription of IE genes, VP19 (UL38) a capsid protein, and gB (UL27) a glycoprotein found in the viral membrane. Spaces in the graph lines are “poorly performing” oligos. Below the array signal information we have mapped the transcribed gene and the orange balls indicate the predicted position of nucleosomes. Gene features from the NCIB database are also shown. <b>B</b>. Nucleosome Saturation for select L genes. The predicted nucleosomes for each L gene were compared to the maximum number of nucleosomes (assuming a 200nt/nucleosome repeat pattern) to give the nucleosome saturation, which is expressed as a %. Late genes were sub-grouped into true late genes (which are not expressed prior to DNA replication; B1) leaky late genes (which are expressed at a low level prior to DNA replication; B.2) and Late genes (which were not sub-characterized; B3).</p

Array Validation with virion DNA, Box plot, and Heat Map Hierarchical Cluster Analysis.

<p><b>A</b>. Section of array data with virion DNA probe is shown to illustrate the increase in signal with increasing viral DNA. Arrays were hybridized with samples of HSV-1 DNA purified from virion particles. Each sample consisted of 500ng cell DNA to which was added 0ng (control), 0.1ng, 0.5ng, 1ng, 5ng, 10ng of HSV-1 DNA. The samples were sheared to 500 nt size (ave). A small section of the array is graphed to illustrate the results. Array Oligos that did not hybridize (poorly performing oligos) were marked by black arrows. <b>B</b>. The signal box plot for the array samples are shown. Clearly the signal from the standard samples increases with increasing amount of HSV-1 virion DNA. The box plots represent data from the whole genome. The signal for 3 independent 6h PI samples is also shown. <b>C</b>. Global profile of microarray experiments by cluster analysis. The columns are samples and the rows are probes. Two groups of microarray samples resolved as major clusters in the heat-map plot. The three infected cell samples (6 hr P.I.) containing viral chromatin clearly segregate from the mock infected (cont.) and virion DNA samples (Std) (which contain no histone but increasing amounts of HSV-1 virion DNA).</p

Gene duplication and paleopolyploidy in soybean and the implications for whole genome sequencing-3

<p><b>Copyright information:</b></p><p>Taken from "Gene duplication and paleopolyploidy in soybean and the implications for whole genome sequencing"</p><p>http://www.biomedcentral.com/1471-2164/8/330</p><p>BMC Genomics 2007;8():330-330.</p><p>Published online 19 Sep 2007</p><p>PMCID:PMC2077340.</p><p></p>me shotgun trace files. BAC corresponds to any contig that showed greatest identity to already assembled soybean BAC sequence. Mdh refers to a previously sequenced region of soybean containing repetitive sequence. No hit means that there was no blast-based match to the nonredundant database. Other was a best match to a sequence (BAC or genomic) from another organism that was not characterized. Satellite refers to known Sb92 or Str120 centromeric repeat sequences. The rest of the categories are as described in the figure legend

Gene duplication and paleopolyploidy in soybean and the implications for whole genome sequencing-2

<p><b>Copyright information:</b></p><p>Taken from "Gene duplication and paleopolyploidy in soybean and the implications for whole genome sequencing"</p><p>http://www.biomedcentral.com/1471-2164/8/330</p><p>BMC Genomics 2007;8():330-330.</p><p>Published online 19 Sep 2007</p><p>PMCID:PMC2077340.</p><p></p>e11. Predicted gene structures are shown as green boxes and arrows, with the boxes representing exons and lines being introns. Black tick marks on a gene show the start position of a repeated PPR domains within the gene. The blue boxes show the repetitive sequences identified by Vmatch. Orange gene alignments reflect the realignment of predicted gene structures back to the genomics sequence