Additional file 1: of Development of a genus-specific next generation sequencing approach for sensitive and quantitative determination of the Legionella microbiome in freshwater systems

The additional file provides supplementary material contained in Figures S1 to S9 and Tables S1 to S6. Figure S1. Comparison of cumulative 16S rRNA gene V3-V4 sequences abundance with sequence identity to L. pneumophila ATCC 33152T using KAPA HiFi and HotStarTaq DNA polymerases. Figure S2. Error rate profiling with KAPA HiFi and HotStarTaq DNA polymerases. Figure S3. Hypervariable regions within the 16S rRNA gene in the genus Legionella. Figure S4. Phylogenetic resolution of the 16S rRNA gene V3-V4 region for the genus Legionella, amplified by primer pair Lgsp17F/Lgsp28R. Figure S5. Sequence identity of Legionella 16S rRNA gene V3-V4 sequences to the sequences of L. pneumophila ATCC 33152T. Figure S6. Rarefaction curves of Legionella OTUs diversity for 7 water samples using the genus-specific NGS approach. Figure S7. Within-sample and inter-sample distinctiveness of Legionella microbiome structure. Figure S8. Rarefaction curves of bacterial OTUs diversity for 7 water samples using the pan-bacterial NGS approach. Figure S9. Sensitive quantitative determination of L. pneumophila by the genus-specific and pan-bacterial NGS approach. Table S1a. Nucleotide sequences of Legionella genus-specific NGS primers, targeting 16S rRNA gene, used in the first amplification step (target-specific) of the library preparation for Illumina MiSeq Sequencing. Table S1b. Nucleotide sequences of primers, targeting 16S rRNA gene, used in the second amplification step (multiplexing) of the library preparation for Illumina MiSeq Sequencing. Table S2. Alpha-diversity of Legionella community between replicates (n = 3) within each of the 7 water samples analysed. Table S3. Bray-Curtis similarity (BC) and Spearman rank correlation (rs) of Legionella community between replicates (n = 3) within each of the 7 water samples analysed. Table S4. Taxonomic assignment of 16S rRNA gene sequences affiliated to genus Legionella. Table S5. Relative abundance (%) of Legionella phylotypes in the 7 freshwater samples analysed. Table S6. Quantification of Legionella spp. and L. pneumophila by NGS. (DOCX 998 kb

JCoast – A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes-0

N the middle, and the 'Observation Browser' at the bottom. The 'Genome Browser' provides a graphical representation of genes on the genomic or metagenomic contigs under investigation. The 'Table Browser' displays different types of regions (CDS, contig, tRNA and rRNA) belonging to a project. A button panel implements rapid switching between regions. The 'Observation Browser' at the bottom displays the different similarity search results for a CDS.<p><b>Copyright information:</b></p><p>Taken from "JCoast – A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes"</p><p>http://www.biomedcentral.com/1471-2105/9/177</p><p>BMC Bioinformatics 2008;9():177-177.</p><p>Published online 1 Apr 2008</p><p>PMCID:PMC2311307.</p><p></p

JCoast – A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes-5

<p><b>Copyright information:</b></p><p>Taken from "JCoast – A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes"</p><p>http://www.biomedcentral.com/1471-2105/9/177</p><p>BMC Bioinformatics 2008;9():177-177.</p><p>Published online 1 Apr 2008</p><p>PMCID:PMC2311307.</p><p></p

JCoast – A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes-4

Ts. For each CDS such a chart can be calculated on the fly, based on different taxonomic levels e.g. phylum, class, order, family or species. In addition also contextual information can be used for this calculation.<p><b>Copyright information:</b></p><p>Taken from "JCoast – A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes"</p><p>http://www.biomedcentral.com/1471-2105/9/177</p><p>BMC Bioinformatics 2008;9():177-177.</p><p>Published online 1 Apr 2008</p><p>PMCID:PMC2311307.</p><p></p

JCoast – A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes-2

<p><b>Copyright information:</b></p><p>Taken from "JCoast – A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes"</p><p>http://www.biomedcentral.com/1471-2105/9/177</p><p>BMC Bioinformatics 2008;9():177-177.</p><p>Published online 1 Apr 2008</p><p>PMCID:PMC2311307.</p><p></p

JCoast – A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes-3

N the middle, and the 'Observation Browser' at the bottom. The 'Genome Browser' provides a graphical representation of genes on the genomic or metagenomic contigs under investigation. The 'Table Browser' displays different types of regions (CDS, contig, tRNA and rRNA) belonging to a project. A button panel implements rapid switching between regions. The 'Observation Browser' at the bottom displays the different similarity search results for a CDS.<p><b>Copyright information:</b></p><p>Taken from "JCoast – A biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes"</p><p>http://www.biomedcentral.com/1471-2105/9/177</p><p>BMC Bioinformatics 2008;9():177-177.</p><p>Published online 1 Apr 2008</p><p>PMCID:PMC2311307.</p><p></p

Additional file 1: Figure S1. of Statin therapy causes gut dysbiosis in mice through a PXR-dependent mechanism

Effect of statin therapy and diet on body weight and glucose metabolism. Figure S2. Changes in the gut microbiome composition in response to statins of mice fed with ND. Figure S3. Changes in the gut microbiome composition in response to high fat diet. Figure S4. Statin therapy does not potentiate the diet-induced intestinal dysbiosis. Figure S5. Variation of LBP levels in serum in response to statin therapy and diet. Figure S6. Metagenome prediction based on the community composition of the gut microbiota of wild type mice treated with statins and normal diet. Figure S7. Metagenome prediction based on the community composition of the gut microbiota of wild type mice treated with statins and high fat diet. Figure S8. Metagenome prediction based on the community composition of the gut microbiota of wild type mice treated with statins and high fat diet. Figure S9. Effect of statin therapy and diet on body weight and glucose metabolism in Pxr-/- mice. Figure S10. Effect of statin therapy on the gut microbiota of Pxr-/- mice. Figure S11. Changes in the gut microbial community in response to statins differ based on the activity of PXR. Figure S12. Variation of LBP levels in serum of Pxr-/- mice in response to statin therapy. Figure S13. Metagenome prediction based on the community composition of the gut microbiota of Pxr-/- mice treated with statins. Figure S14. Production of short chain fatty acid by the gut microbiota of Pxr-/- mice treated with statins. Figure S15. PXR modulates the changes in gene expression induced by statins. (ZIP 5 mb

Intracellular enzyme (glycosyl hydrolase) activities associated to bacterial enzyme extracts isolated from two wild Iberian lynx fecal samples and rumen content from four rumen-fistulated and non-lactating Holstein cows.

<p>(A) Average potential hydrolysis rates (n = 2, ± standard deviation in three technical replicates) in protein extracts from two wild lynxes (Eva and Granadilla) captured in the same area and under the same protocols. (B) Average potential hydrolysis rates (n = 4, ± standard deviation in three technical replicates) in protein extracts from rumen content from four rumen-fistulated and non-lactating Holstein cows. (C) Comparative average glycosidase activity for lynx gut and cow rumen protein extracts; the fold difference is specifically shown based on data provided in panels A and B. In all cases, enzyme activity was quantified using a BioTek Synergy HT spectrophotometer by measuring release of <i>p</i>-nitrophenol (<i>p</i>NP) using a protein amount of 6.34 µg (for Eva), 7.74 µg (for Granadilla), 15.83 µg (for SRF) and 15.42 µg (for LAB), and [substrate] of 1 mg ml⁻¹ (from a 10 mg ml⁻¹ stock solution) in 20 mM glycine buffer, pH 9.0, <i>T</i> = 30 °C, in a final volume of 50 µl. The different substrates used as specifically shown. Note: activity against <i>p</i>NP derivatives of GalNAc or other mucus-associated sugars could not be determined because they are not commercially available.</p

Model representing the potential sugar uptake system feeding the central lynx metabolism through the hydrolysis of carbohydrate-containing substrates derived directly from animal tissues or indirectly from plant/sugar biomass most likely derived from prey nutrition sources.

<p>Model representing the potential sugar uptake system feeding the central lynx metabolism through the hydrolysis of carbohydrate-containing substrates derived directly from animal tissues or indirectly from plant/sugar biomass most likely derived from prey nutrition sources.</p

Heat map and clustering of predicted genes encoding GHs in the lynx, termite, bovine rumen, panda, wallaby and canine gastrointestinal metagenomes.

<p>The heat map colors represent the relative percentages of GH families within each sample. Hierarchical clustering based on relative percentage of predicted genes encoding GHs is specifically shown.</p