Journal of Clinical Microbiology 2006,44(5):1859–1862 PubMedCross

Journal of Clinical Microbiology 2006,44(5):1859–1862.PubMedCrossRef Authors’ contributions RCLM: Study design, primers design, fieldwork and data collection, laboratory tests, data analysis, manuscript writing; ASR: Study design, primers design, laboratory tests, data analysis, manuscript writing; FFM: Primers

design, laboratory test, data analysis, manuscript writing; MASC: Fieldwork, data collection and analysis, manuscript writing; KDE: Fieldwork and data collection; ADF: Fieldwork and data collection; LMSM: Diagnostic laboratorial tests; MSL: Data interpretation and analysis, manuscript writing; BW: Coordination, study design, fieldwork and data collection, data analysis, manuscript writing. All authors read an approved the final draft.”
“Background The animal gastrointestinal NVP-AUY922 mw tract harbors a complex microbial network and its composition reflects the constant co-evolution of these microorganisms with their host environment [1]. Uncovering the taxonomic composition and functional capacity within the animal

gut microbial consortia is of great importance to understanding the roles they play in the host physiology and health. Since animal feces can harbor human pathogens, understanding the genetic composition check details of fecal microbial communities also has important implications for food and water safety. The structure and function of the gut microbial community has received significant attention for decades, although most of the work was restricted by the use of culture-based techniques. Recently, sequence Oxymatrine analysis of the 16S rRNA gene has shed new light on the diversity and composition of microbial communities within several animal gut systems [2]. While 16S rRNA gene-based techniques have revealed impressive microbial diversity within gut environments, this approach offers only limited information on the physiological role of microbial consortia within a given gut environment. Random sequencing of metagenomes has allowed scientists to reveal significant differences in metabolic potential within different environments [3], including microbial populations associated with host-microbial partnerships. Specifically,

the publicly available database IMG/M [4] contains 596 Mb of sequencing data, representing 1,424, 000 genes from 17 different gut microbiomes. Studying gut metagenomes has particularly helped in uncovering several important biological characteristics of these microbiomes. For example, when 13 human gut metagenomes were compared, Kurokawa et al [5] found that adult and infant type gut microbiomes have enriched gene families sharing little overlap, suggesting different core functions within the adult and infantile gut microbiota. This study also demonstrated the presence of hundreds of gene families exclusively found in the adult human gut, suggesting various strategies are employed by each type of microbiota to adapt to its intestinal environment [5].

Results obtained could help to better

define strategies f

Results obtained could help to better

define strategies for pathogenicity studies and control strategies in C. perfringens and can moreover be used to design focused wet-lab experiments. Table 1 Genomes and plasmids analyzed C.p. Strain Type (name) Sequencing Status N Genes Length (nt) Str. 13 G Finished 2905 3085740 ATCC 13124 G Finished 3066 3256683 ATCC 3626 G Draft 3427 3896305 C JGS1495 G Draft 3254 3661329 CPE F4969 G Draft 3118 3510272 D JGS1721 G Draft 3485 4045016 E JGS1987 G Draft 3729 4127102 SM101 G Finished 2748 2921996 C. perfringens P (pBCNF5603) Finished 36 36695 C. perfringens P (pCP8533etx) Finished 63 64753 F4969 P (pCPF5603) Finished 73 75268 F5603 P (pCW3) Finished 51 47263 F5603 P (pCPF4969) Finished 62 70480 SM101 P (1) Finished Belinostat 10 12397 SM101 P (2) Finished 11 12206 Str. 13 P (pCP13) Finished 63 54310 List of genomes and plasmids used in

this study. The Type column indicates if a sequence is a genome (G) or a plasmid (P) in that case we also indicate the name of the plasmid within round parentheses. C.p. stands for Clostridium perfringens. Results and Discussion Comparisons of C. perfringens strains As a preliminary analysis we studied the variability of the selected genomes using both standard check details phylogenetic techniques and a comparison of all intergenic sequences. The alignment of rrnA operons for a total of 4719 nt was used to build a Neighbor-Joining tree revealing that these strains are closely related [Additional file 1: panel a]. In agreement with a low differentiation on ribosomal operon sequences, bootstrap support for the branching pattern was quite low; in fact, 32 variable sites only were found in the alignment,

which were evenly distributed between strains [Additional file 1: panel b]. However, the comparison of a large number of intergenic sequences extracted from the genomes revealed that some of them are quite variable between the different strains with respect to the very conserved rrnA operon (down to 82% with respect to C. perfringens Str. 13, [Additional file 1: panel Phosphoribosylglycinamide formyltransferase c]). Regulon prediction in sequenced C. perfringens strains The presence of VirR and VirS sequences was checked in all strains using blast and the functionally characterized sequences of Str. 13 as queries. We found that they are indeed both present in all strains and that they are moreover always organized in what resembles a bi-cistronic operon with the two genes often overlapped (data not shown). We scanned available C. perfringens genomes using the VirR position weight matrix (PWM) derived from experimental observations, following the procedure reported in figure 1 (see Methods for details). At the time we performed this analysis (April, 2009), the NCBI microbial genome database stored three different complete genomes for C.

This study shows very good results for ID of Enterobacteriaceae

This study shows very good results for ID of Enterobacteriaceae. Only two errors Selleck ICG-001 occurred with ID in this group. One strain was not identified and one strain of E. coli was misidentified as S. choleraesuis. Results of ID for Pseudomonas species were less reliable. Both errors in this group were P. aeruginosa strains that were identified

as P. fluorescens, a rare cause of bloodstream infections. These misidentifications did not lead to errors in interpretation of AST, but rare or unlikely results of ID should be dealt with carefully and be confirmed using additional tests. Other studies also showed that ID of non-fermenting GNR was less reliable than that of Enterobacteriaceae [18, 23]. This may be due to the lower growth rate of non-fermenters, which could result in weaker fluorescent biochemical reactions in the Phoenix ID panel. Errors in ID with the direct method could also be caused by traces of blood culture components in the ID broth. This however

seems less likely, since with Enterobacteriaceae, errors in ID were rare. Since the Phoenix system was not used for ID of GPC, ID by direct inoculation was not tested in this group. But since ID is required for interpretation of AST, in clinical practice, rapid AST will have to be combined with a rapid method of ID, such as PCR-based methods on whole blood, like LightCycler® SeptiFast Test MGRADE (Roche), VYOO Sepsis Test (SIRS-Lab), SepsiTest™ (Molzym), or MALDITOF-MS on positive blood cultures [24]. Some studies on direct methods for AST showed poor results for GPC [15, 16] or focus on GNR MAPK inhibitor only due to unfavorable results for GPC [17]. However, in this

study, direct AST for Staphylococcus species and Enterococcus species showed good agreement with conventional methods, comparable to results of the standard method, but with fewer very major errors. Lupetti et al. [19], who tested the direct Chloroambucil Phoenix method for GPC and compared their results with those of the Vitek 2, found an even higher agreement. They incubated a portion of the positive blood culture with saponin in order to harvest more bacteria from a positive blood culture through the release of intracellular bacteria. Other studies that presented results of direct methods for AST of GPC showed variable results [13–16, 25, 26], which makes comparison difficult. But our results were comparable to those of the routine Phoenix method. Moreover, categorical agreement for most tested antibiotics in this study, including oxacillin and vancomycin, were well over 90% and the percentage of major and very major errors is low, meeting the standards proposed by Jorgensen et al. [27]. Only erythromycin and trimethoprim-sulfamethoxazole showed lower agreements. The majority of errors for erythromycin were minor errors, but also some major errors occurred. Trimethoprim-sulfamethoxazole was the only antibiotic for both GPC and GNR showing very major errors.

Taking the view of metabolic responses to high protein diet, it c

Taking the view of metabolic responses to high protein diet, it can be presumed that excessive protein intake could lead negative health outcomes by metabolic changes. However, this study implied that resistance exercise with adequate mineral Mitomycin C manufacturer supplementation, such as potassium and calcium, could reduce or offset the negative effects of protein-generated metabolic changes. This study was based on a cross-sectional design with a relatively small sample size, so it is limited when inferring causal links. Because

of the study limitations, our results are mostly hypothesis-generated. Nevertheless, this study is constructive in providing preliminary information of metabolic responses to high protein intake in bodybuilders. Further studies would be required to determine the effects of the intensity of exercise and the level of mineral intakes, especially potassium and calcium, which have a role to maintain acid-base homeostasis, on protein metabolism in large population of bodybuilders. In addition, an experimental this website study to ascertain the safety and efficiency of protein intake in athlete group would be needed. References 1. McCall GE, Byrnes WC, Dickinson A, Pattany PM, Fleck SJ: Muscle fiber hypertrophy, hyperplasia, and capillary

density in college men after resistance training. J Appl Physiol 1996,81(5):2004–2012.PubMed 2. Phillips SM, Tipton KD, Ferrando AA, Wolfe RR: Resistance training reduces the acute exercise-induced increase in muscle protein turnover. Am J Physiol 1999,276(1 Pt 1):E118–124.PubMed Nintedanib (BIBF 1120) 3. Kimball SR, Farrell PA, Jefferson LS: Role of insulin in translational control of protein synthesis in skeletal muscle by amino acids or exercise. J Appl Physiol 2002,93(3):1168–1180.PubMed 4. Hornberger TA, Esser KA: Mechanotransduction and the regulation of protein synthesis in skeletal muscle. Proc Nutr Soc 2004,63(2):331–335.PubMedCrossRef 5. Meredith CN, Frontera WR, O’Reilly KP, Evans WJ: Body composition in elderly men: effect of dietary modification during strength training. J Am Geriatr Soc 1992,40(2):155–162.PubMed 6. Tipton KD, Wolfe RR: Exercise, protein metabolism, and muscle growth.

Int J Sport Nutr Exerc Metab 2001,11(1):109–132.PubMed 7. Tarnopolsky MA, MacDougall JD, Atkinson SA: Influence of protein intake and training status on nitrogen balance and lean body mass. J Appl Physiol 1988,64(1):187–193.PubMed 8. Lemon PW, Tarnopolsky MA, Atkinson SA: Protein requirements and muscle mass/strength changes during intensive training in novice body builders. J Appl Physiol 1992,73(2):767–775.PubMed 9. Lambert CP, Frank LL, Evans WJ: Macronutrient considerations for the sport of bodybuilding. Sports Med 2004,34(5):317–327.PubMedCrossRef 10. Lee SIG, Lee HS, Choue R: Study on nutritional knowledge, use of nutritional supplements and nutrient intakes in Korean elite bodybuilders. Kor J Exer Nutr 2009,13(2):101–107. 11.

Such microorganisms have adapted their vital cellular processes t

Such microorganisms have adapted their vital cellular processes to thrive in cold environments [4]. They make essential contributions to nutrient recycling and organic matter mineralization, via a special class of extracellular enzymes known as “cold-adapted” or “cold-active” enzymes [5]. Because these

enzymes have a higher catalytic efficiency than their mesophilic counterparts at temperatures below 20°C and display unusual substrate specificities, they are attractive candidates for industrial processes requiring high enzymatic activity at low temperatures. Cold-adapted enzymes include amylase, cellulase, invertase, inulinase, protease, lipase and isomerase, which are used in the food, biofuel LBH589 mw and detergent industries [6]. Largely

because of their potential in biotechnological applications, cold-adapted microorganisms have become increasingly studied in recent years, yet remain poorly understood. Of the microorganisms most isolated and studied from cold environments, the majority are bacteria, while yeasts constitute a minor proportion [1]. Antarctica is considered the coldest and driest terrestrial habitat on Earth. It is covered almost totally with ice and snow, and receives high levels of solar radiation [7]. The Sub-Antarctic region, including the Shetland South Archipelago, has warmer temperatures, the soils close to the sea are free of snow/ice and receive significant quantities of organic material from marine animals; however, they are subject to continuous and rapid free-thaw cycles, which are stressful and INCB024360 order restrictive to life [8]. Although the first report of Antarctic yeasts was

published 50 years ago [9] current reports next have focused on cold-tolerant Bacteria and Archaea, with yeasts receiving less attention. Yeasts dwelling in Antarctic and Sub-Antarctic maritime and terrestrial habitats belong mainly to the Cryptococcus, Mrakia, Candida and Rhodotorula genera [10–12]. In a recent work, 43 % of Antarctic yeast isolates were assigned to undescribed species [13], reflecting the lack of knowledge regarding cultivable yeasts that colonize the Antarctic soils. Yet these organisms constitute a valuable resource for ecological and applied studies. This work describes the isolation of yeasts from terrestrial habitats of King George Island, the major island of the Shetland South archipelago. The yeast isolates were characterized physiologically and identified at the molecular level using the D1/D2 and ITS1-5.8S-ITS2 regions of rDNA. In addition, the ability of the yeasts to degrade simple or complex carbon sources was evaluated by analyzing their extracellular hydrolytic enzyme activities. Characterizing these enzyme activities may enhance the potential of the yeasts in industrial applications.

In earlier studies, phosphoglycerate kinase was reported on the s

In earlier studies, phosphoglycerate kinase was reported on the surface of S. pneumoniae, was antigenic in humans, and elicited protective immune responses in mouse model [33] [see Additional file 6]. Also in Schistosoma mansoni, phosphoglycerate kinase has been identified as a protective antigen [34]. Another surface protein, EF-G, identified in this study was found to be immuno-reactive against sera from broiler

chicken immune to necrotic entritis [30]. The protein was secreted into the culture supernatant and unique to virulent C. perfringens strain CP4 causing necrotic entritis. Notably, EF-G is regulated Selleckchem BGJ398 by the VirR-VirS virulence regulon of C. perfringens [35]. Moreover, EF-G has been demonstrated as an immunogenic protein and was identified in both cell surface and extracellular fraction

of B. anthracis [9, 29]. Further, choloylglycine hydrolase family protein, cell wall-associated serine proteinase, and rhomboid family protein can be excellent surface protein markers for specific GSK1120212 detection of C. perfringens from environment and food as they share very low percent amino acid sequence identity with there nearest homologs (<50%) and are conserved among the C. perfringens strains [see Additional file 6]. Some of the surface proteins from C. perfringens ATCC13124 showed metabolic functions that would typically place them in the cytoplasm. Moreover, except for N-acetylmuramoyl-L-alanine amidase and cell wall-associated serine proteinase, these proteins have no N-terminal signal peptide and do not possess the canonical gram-positive anchor motif LPXTG [see Additional file 7]. Several surface-associated cytoplasmic proteins reported in this study were also detected on the bacterial surface in previous proteomic analysis [see Additional file 6]. For example, phosphoglycerate kinase was reported on the surface of S. pneumoniae [33], S. agalactiae [24], S. pyogenes [25], and S. oralis [see Additional file 6] and also as secreted protein in B. anthracis [29]. Increasing number of reports have shown presence of proteins on the surface of Gram positive bacteria or secreted into the medium that one would otherwise

expect to be cytoplasmic [25, 29, 36, 37]. In a previous study, the culture supernatant of C. perfringens at the late exponential Wilson disease protein growth phase was shown to contain intracellular proteins that had no putative signal sequences, such as ribokinase, β-hydroxybutyryl-coenzyme A dehydrogenase, fructosebisphosphate aldolase, and elongation factor G [36]. In other studies also, a significant number of cytoplasmic proteins have been identified as cell-wall associated proteins/immunogens [25, 37]. In spite of a growing list of cytoplasmic proteins identified on the bacterial surface, the mechanism of their surface localization and attachment to the bacterial envelope remain unclear. Internal signal sequences, posttranslational acylation, or an association with a secreted protein are hypothesized as possible means [38].

Thus, there is an urgent need and a great clinical interest to be

Thus, there is an urgent need and a great clinical interest to better understand the molecular mechanisms responsible for gastric cancer metastasis in order to improve the outcome of gastric cancer patients. To this end, our recent research on gastric cancer has focused on microRNAs (miRNAs), which are small, single-stranded noncoding RNA molecules of 19–23 nucleotides in length

Tanespimycin molecular weight that are able to post-transcriptionally regulate target gene expression [6]. So far, several hundred miRNAs have been identified in plants, animals, and even viral RNA genomes. In humans, miRNAs regulate many cellular processes through binding to 3′-untranslated regions (UTRs) and other regions of protein-coding mRNA sequences of their target mRNAs to cause mRNA degradation or inhibit its translation [7]. Thus, altered miRNA expression plays a role in tumor development and progression, such as tumor cell proliferation, invasion,

and metastasis [8]; in addition, certain miRNAs also can predict the prognosis of various cancers, including gastric, breast, lung, and prostate cancers [9, 10]. In gastric cancer, aberrant expression of miRNAs has been linked to tumor metastasis; for example, plasma levels of miR-223, miR-21, miR-218, and miR-25 have been linked to gastric cancer metastasis [11, 12]. Furthermore, elevated miR-21 expression is associated with lymph node metastasis Buparlisib of gastric cancer [13]. Thus, these miRNAs could be useful as biomarkers to predict gastric cancer lymph node metastasis. In addition, miR-625 expression is significantly downregulated

and inversely associated with lymph node metastasis of gastric cancer [14]. Therefore, in the present study, we first performed miRNA array analysis to profile differentially expressed miRNAs between primary and secondary gastric cancer tissues. We found that the expression of hsa-miR-134 and hsa-miR-337-3p was significantly less in metastatic lymph node tissues than in primary tumors of gastric cancer. Next, we Gemcitabine investigated the effects of hsa-miR-134 or hsa-miR-337-3p on the inhibition of gastric cancer cell growth and invasion. The results of this study may be useful to find potential therapeutic agents to inhibit gastric cancer metastasis. Methods Tissue samples In this study, samples of human primary gastric cancer and the corresponding metastatic lymph node tissues were collected from 19 patients and stored in liquid nitrogen until use. The demographic data of these patients are shown in Table 1. The institutional review board of the First Affiliated Hospital of Bengbu Medical College approved our protocol, and the patients signed a consent form to participate in this study.

She also constructed the plasmids, participated in the study desi

She also constructed the plasmids, participated in the study design PLX4032 molecular weight and interpretation of data, and in drafting of the manuscript. MK and LH carried out the bioinformatics analysis of DNA sequence data, participated in the study design and in revising the manuscript critically. BWW coordinated the

DNA sequencing, had the main responsibility for the study design, data interpretation and manuscript writing. All authors read and approved the final manuscript.”
“Background The cagA gene encoded CagA protein is a well-known virulent factor of Helicobacter pylori, which is associated with an increased risk of peptic ulcer or even gastric cancer [1–4]. The CagA protein can be tyrosine phosphorylated in the gastric epithelial cells via the type Crizotinib price IV secretion system translocation [5]. The phosphorylated-CagA (p-CagA) mediates interleukin-8 secretion, enhances gastric inflammation, and clinical diseases [5–8]. As shown in the Mongolian gerbil models, H. pylori isolates with functional type IV secretion system could induce more CagA phosphorylation and severer gastric inflammation and intestinal metaplasia (IM) [9, 10]. However, there is no adequate clinical evidence in a setting to support

the relationship between CagA phosphorylation intensity and the risk of gastric carcinogenesis. In the western countries, about 70% or less of clinical H. pylori strains are cagA-genopositive [11, 12]. In contrast, in the eastern countries, such as in Taiwan, there is a nearly 100% prevalence of cagA-vacA-babA2 Pregnenolone triple-positive H. pylori strains [13–15]. Moreover, most strains in East-Asia, and also Taiwan, encoded CagA contain EPIYA-ABD motif [16–18]. Our previous data supported 100% positive of some genes

which are encoded from cag pathogenicity island (PAI), such as cagC, cagE, cagF, cagN, and cagT [19]. Accordingly, because of the universal presence of genes in cag-PAI in Taiwan, this region should be suitable to answer whether different p-CagA intensity are related to different clinicopathologic outcomes of H. pylori infections. The study is highly original to illustrate the p-CagA intensity could be diverse among the cagA-positive H. pylori isolates, and to support H. pylori with stronger p-CagA intensity can increase the risk of gastric carcinogenesis. Methods Patients and study design Patients with recurrent dyspepsia symptoms, who received upper gastrointestinal endoscopy, were consecutively enrolled, once they were proven to have a H. pylori infection defined by a positive result of culture. None of them had a previous history of anti-H. pylori therapy. For each patient, the gastric biopsies were obtained during the endoscopy for H. pylori culture and histological analysis.

0), and the DNA was precipitated with 2 5 M ammonium acetate in e

0), and the DNA was precipitated with 2.5 M ammonium acetate in ethanol. After two washes with 80% (v/v) ethanol, the DNA pellet was dried and resuspended in 10 μl, 0.2 μl filtrated, double-distilled water. Following the manufacturer’s descriptions the cloning was done by using a Zero blunt TOPO cloning kit (Invitrogen Corporation). Fifty to hundred colonies from each cloning were

picked and sequenced by pyrosequencing. A PYROMark Q96 ID was used to short DNA sequencing of the approximately 40-60 bp clone insert using the recommended protocol (Biotage AB, Uppsala, Sweden) as described previously using the primer PyroBact64f [19]. The sequences (tags) were imported into the software BioNumerics 4.61 and manually checked, aligned and filtered for high quality sequences. Sanger sequencing with an Applied Biosystem Apoptosis Compound Library screening 3130 Genetic Analyzer (Foster City, CA, USA) was used to check consensus tags for the pyrosequencing accuracy. The Sequence match analysis tool in the Ribosomal database project 10 http://​rdp.​cme.​msu.​edu/​ was used to assign the Phylogenetic position of each consensus tag. The search criteria were for both type and non-type strains, both environmental (uncultured) sequences and isolates, near-full-length

sequences (>1200 bases) of good quality. If there was a consensus at the genus level the tag was assigned this taxonomic classification. If no such consensus was found, the classification proceeded up one level to family and again if no taxonomic affiliation could be assigned the tag continued to be proceeded up the tree as described by Huse et al., [36]. In some cases it was not possible to assign a domain and these sequences might represent new novel organisms or the sequences might be biased, Obeticholic Acid price in these cases the tags were excluded from the dataset. In total 364 sequences were finally included in the alignment. The

phylogenetic analysis was done by downloading 16S rRNA gene sequences longer than 1,200 base pair from the RDP database of the Ralstonia type strains http://​rdp.​cme.​msu.​edu. The RDP alignment was used and a phylogenetic tree was constructed by using the Ward algorithm in the software Bionumerics. Burkholderia cepacia (GenBank accession no. AF097530) was used as an out-group. Statistics The statistical analysis was done in two steps: First, the association between one predictor at a time and the NEC score was analysed by robust least squares methodology adjusting for gestational age. This is equivalent to a normal linear GEE modal with working independence correlation structure on child level. For each predictor the estimated change in expected NEC score is reported with Wald 95% confidence limits in parentheses. The overall association between the predictor and the NEC score is evaluated by a robust score-test. Second, we formulate a normal linear GEE model including gestational age and all predictors with a robust score-test p-value below 0.1 in the above analyse.

BLAST results were parsed and filtered using a custom Perl script

BLAST results were parsed and filtered using a custom Perl script with the above criteria. The Perl script also mapped the hits to the corresponding COG category, reporting the category or categories for each query sequence. Each set was analysed 1,000 times randomly sampling 75% of the query sequences to calculate the Standard Deviation (SD; Figure 1). For the characterization of OGs, each comprising one gene per genome, only genes present in the genome of X. euvesicatoria str. 85-10 were used as representative

of the OG. Taxonomical distribution of homologous sequences BLAST searches against the non-redundant protein database of the NCBI (NR) [87] were performed in order to

identify click here the homologs of one Selleck MLN8237 or more genes in other organisms, with default parameters and Expect value below 10-10. The BLAST result was subsequently parsed with a custom Perl script to extract the organisms, subsequently building a cumulative counts table and mapping these organisms to any fixed taxonomical level using the NCBI’s Taxonomy database [87]. Acknowledgements This project was funded by the Colombian administrative department of Science, Technology and Innovation (Colciencias) and the Vice-chancellor’s Office of Research at the Universidad de Los Andes. We would like to thank Andrew Crawford, Ralf Koebnik and two anonymous reviewers for critical reading of the manuscript. We also thank Boris Szurek, Valérie Verdier, Kostantinos Konstantinidis, Catalina Arévalo and Camilo López for comments and discussion Calpain on the conception

and development of this study. Electronic supplementary material Additional file 1: COG distribution of different taxonomical ranges. Raw data graphically presented in Figure 2. Each row corresponds to one COG functional category. Each taxonomical range is represented in two columns, the average and the standard deviation. (PDF 23 KB) Additional file 2: Concatenated sequence alignment and partitions. ZIP file containing the input alignment in Phylip format (Suppl_file_2.phylip) and the coordinates of the partitions (Suppl_file_2.raxcoords) as employed for the ML phylogenetic analysis in RAxML. Unus automatically generated these files. (ZIP 2 MB) Additional file 3: Leaf and ancestral nodes in the GenoPlast events matrix. Each row corresponds to one node, and each column corresponds to a pattern of regions, as defined by Mauve developers’ tools. The first two additional columns contain the node identifier and the node content. (CSV 598 KB) Additional file 4: Species counts in similar sequences of cluster 1. Species counts within the BLAST hits in NCBI’s NR using the genes of Xeu8 in the cluster as query. (PDF 25 KB) Additional file 5: Species counts in similar sequences of cluster 2.