Numbers distribution of protein-protein interactions was obtained by random simulation. 108 genes were randomly drawn from the genome 10,

SIS3 supplier 000 times, and the 10, 000 numbers of protein-protein interactions in the subgraph existing between theses genes were plotted. A vertical arrow indicates the observed value of 84 interactions with its significance. (PPT 92 KB) References 1. Mackenzie JS, Gubler DJ, Petersen LR: Emerging flaviviruses: the spread and resurgence of Japanese encephalitis, West Nile and dengue viruses. Nat Med 2004,10(12 Suppl):S98–109.PubMedCrossRef 2. C M, Fauquet MAM, Maniloff J, Desselberger U, Ball LA: Virus Taxonomy: VIIIth Report of the International Committee on Taxonomy of Viruses. 2005. 3. Melian EB, Hinzman E, Nagasaki T, Firth AE, Wills NM, Nouwens AS, Blitvich BJ, Leung J, Funk A, Atkins JF, et al.: NS1′ of flaviviruses in the Japanese encephalitis virus serogroup is a product of ribosomal frameshifting and plays a role in viral neuroinvasiveness. J Virol 2010,84(3):1641–1647.PubMedCrossRef 4. Luo D, Xu T, Watson RP, Scherer-Becker find more D, Sampath A, Jahnke W, Yeong SS, Wang CH, Lim SP, Strongin A, et al.: Insights into RNA unwinding and ATP hydrolysis by the flavivirus

NS3 protein. EMBO J 2008,27(23):3209–3219.PubMedCrossRef 5. Wang CC, Huang ZS, Chiang PL, Chen CT, Wu HN: Analysis of the nucleoside triphosphatase, RNA triphosphatase, and unwinding activities of the helicase domain of dengue virus NS3 protein. FEBS Lett 2009,583(4):691–696.PubMedCrossRef 6. Davidson AD: Chapter 2. New insights into flavivirus nonstructural protein 5. Adv Virus Res 2009, 74:41–101.PubMedCrossRef 7. Welsch S, Miller S, Romero-Brey I, Merz A, Bleck CK, Walther P, Fuller SD, Antony C, Krijnse-Locker J, Bartenschlager R: Composition and three-dimensional architecture of the dengue virus replication and assembly sites. Cell Host Microbe 2009,5(4):365–375.PubMedCrossRef 8. Lescar J, Luo D, Xu T, science Sampath A, Lim SP, Canard B, Vasudevan SG: Towards the design of antiviral inhibitors against flaviviruses: the case for the multifunctional

NS3 protein from Dengue virus as a target. Antiviral Res 2008,80(2):94–101.PubMedCrossRef 9. Sampath A, Padmanabhan R: Molecular targets for flavivirus drug discovery. Antiviral Res 2009,81(1):6–15.PubMedCrossRef 10. Uetz P, Dong YA, Zeretzke C, Atzler C, Baiker A, Berger B, Rajagopala SV, Roupelieva M, Rose D, Fossum E, et al.: Herpesviral protein networks and their interaction with the human proteome. Science 2006,311(5758):239–242.PubMedCrossRef 11. Calderwood MA, Venkatesan K, Xing L, Chase MR, Vazquez A, Holthaus AM, Ewence AE, Li N, Hirozane-Kishikawa T, Hill DE, et al.: Epstein-Barr virus and virus human protein interaction maps. Proc Natl Acad Sci USA 2007,104(18):7606–7611.PubMedCrossRef 12.

The microbial community at the top oxidizes the sulfide to corrosive H2SO4[39]. Consistent with

this observation, analysis of 16S rRNA gene clone libraries showed that the community structures differ, with a dominant presence in the BP of sulfate reducing bacteria (SRB) affiliated to Deltaproteobacteria. Specifically, there were 24 phylotypes represented by the genera Foretinib ic50 Desulfobacter Desulfobacterium Desulfobulbus Desulfomicrobium Desulforegula and Desulfovibrio (Additional file 1, Figure S 5). The predominant SRB phylotype (5.4%) in the clone libraries is closely related to Desulfobacter postgatei, a strict anaerobic chemoorganotroph that completely oxidizes acetate to CO2 and reduces sulfur compounds (e.g. sulfate, sulfite, or

thiosulfate) to H2S [40]. In the TP sample, most SOB phylotypes (i.e., 39 of 45) are affiliated to the genus Thiobacillus (Betaproteobacteria) ( Additional file 1, Figure S6), further supporting the importance of this group in concrete corrosion [41]. During the concrete corrosion process it has been shown that Thiobacillus thioparus T. novellus T. neapolitanus, and T. intermedius are involved in the initial and intermediate stages of colonization, while T. thiooxidans dominate in the final stage when the pH reaches values <3 [3]. In our study the majority of the Thiobacillus-like sequences were closely related to uncultured sulfur-oxidizing bacteria clones. Interestingly, two of the dominant clones in our libraries were identified as neutrophilic T. thioparus and T. plumbophilus (>98.5% sequence check details identity) (Additional file 1, Figure S 6). T. thioparus oxidizes sulfur and thiosulfate, reducing the medium between pH 3.5 and 5 [3]. T. plumbophilus grows by oxidation of H2S and H2 at pH 4 and 6.5 [42]. There were also sequences with a high sequence

homology (>99%) to representatives of the Thiomonas intermedia and Acidiphilium acidophilum, members of the Beta- and Alphaproteobacteria class, respectively. T. intermedia is an obligate aerobe and facultative chemolithoautotroph that produces sulfuric acid at an optimum pH between 5 and 7 [43]. Thiomonas species are Metformin molecular weight unable to denitrify or oxidize ferrous iron. In contrast, A. acidophilum is able to grow autotrophically or mixotrophically using sulfur or reduced inorganic sulfur compounds, as well as heterotrophically using various organic compounds and is capable of reducing iron [44]. Wastewater concrete corrosion involves the interaction of multiple groups and the establishment of these groups are driven by factors, such as the pH of the concrete, and the temporal dynamics of sulfur compounds [41]. The data from different studies conducted thus far suggest that the composition of species involved in concrete corrosion may vary within different wastewater systems. For instance, our study did not find any hyper-acidophilic SOB sequences (e.g. T.

ORs less than unity indicated a treatment effect learn more that favored the study agent. Pooled, weighted ORs and their respective 95% CIs were then estimated separately per each outcome for each meta-analysis. In the RCTs that

have reported the severity of complications classified according to the Radiation Therapy Oncology Group (RTOG) or other score systems were combined when possible. Subgroup analyses for each outcome were performed by recalculating the ORs and 95% CIs, based on the clinical stage of the disease. We evaluated heterogeneity across trials using the I2 statistics, which describes the percentage of total variation across studies that are due to heterogeneity rather than chance [18]. The interpretation of I2 depends on the magnitude and direction of effects, as well as the strength

of evidence for heterogeneity (e.g. P value from the chi-squared test, or a confidence interval for I2) [19]. We used the following classification based on the value of I2 [17, 18]: 0–30 = low; 30–60 = moderate and worthy of investigation; 60–90 = severe and worthy LY2603618 of understanding; 90–100 = allowing aggregation only with major caution. Publication bias is a common concern in meta-analysis, which is related to the tendency of journals to favor the publication of large and positive studies. Quality of the evidence has been assessed using the grade four-category system (high, moderate, low and very low quality) (Table 1). Factors that are considered in classifying evidence are: the study design and rigor of its execution, the consistency of results and how well the Phenylethanolamine N-methyltransferase evidence can be directly applied to patients, interventions, outcomes and comparator. Other important factors

are whether the data are sparse or imprecise and whether there is potential for reporting bias. Using this approach, assessments of the quality of evidence for each important outcome take into account the study design, limitations of the studies, consistency of the evidence across studies, the directness of the evidence, and the precision of the estimate [20, 21]. Table 1 Quality of the quality evidence, definitions and underlying methodology Grade Definition Underlying Methodology High Further research is very unlikely to change our confidence in the estimate of effect RCT or meta-analysis Moderate Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate Downgraded RCTs or upgraded observational studies Low have an important impact on our confidence in the estimate of effect an its likely to change the estimate Well-done observational studies with control groups Very low Any estimate of effect is very uncertain Others (e.g., case reports or case series) For each intervention considered, we formulated a consensus recommendation based on our judgments, regarding the balance between the benefits, harms (adverse effects), costs, and values and preferences of the intervention.

Peterson RL, Massicotte

HB: Exploring structural definitions of mycorrhizas, with emphasis on nutrient-exchange interfaces. Can J Bot-Rev Can Bot 2004,82(8):1074–1088.CrossRef 47. Bucking H, Heyser W: Uptake and transfer of nutrients in ectomycorrhizal associations: interactions between photosynthesis and phosphate nutrition. Mycorrhiza 2003,13(2):59–68.CrossRefPubMed 48. Harrison MJ: Signaling in the arbuscular mycorrhizal symbiosis. Annual Review of Microbiology 2005, 59:19–42.CrossRefPubMed 49. Williamson VM, Gleason CA: Plant-nematode learn more interactions. Current Opinion in Plant Biology 2003,6(4):327–333.CrossRefPubMed 50. Gheysen G, Fenoll C: Gene expression in nematode feeding sites. Annual Review of Phytopathology 2002, 40:191–219.CrossRefPubMed 51. Vanholme B, De Meutter J, Tytgat T, Van Montagu M, Coomans A, Gheysen G: Secretions of plant-parasitic nematodes: a molecular update. Gene 2004, 332:13–27.CrossRefPubMed 52. Lilley CJ, Atkinson HJ, Urwin PE: Molecular aspects of cyst nematodes. Molecular Plant Pathology 2005,6(6):577–588.CrossRefPubMed 53. Bianciotto V, Bandi C, Minerdi D, Sironi M, Tichy HV, Bonfante P: An obligately endosymbiotic mycorrhizal fungus itself harbors obligately intracellular bacteria. Applied and Environmental Microbiology 1996,62(8):3005–3010.PubMed 54. Lindsay DB: Ruminant metabolism in the last 100 years. A-1155463 price J Agric Sci 2006, 144:205–219.CrossRef 55. Escobar MA, Dandekar AM:Agrobacterium tumefaciens

as an agent of disease. Trends in Plant Science 2003,8(8):380–386.CrossRefPubMed 56. James EK, Reis VM, Olivares FL, Baldani JI, Dobereiner J: Infection of sugar cane by the nitrogen-fixing bacterium Acetobacter diazotrophicus. Journal of Experimental Botany 1994,45(275):757–766.CrossRef

57. Ruby EG, McFall-Ngai MJ: Oxygen-utilizing reactions and symbiotic colonization of the squid light organ by Vibrio fischeri. Trends in Microbiology 1999,7(10):414–420.CrossRefPubMed 58. Visick KL, Ruby EG:Vibrio fischeri and its host: it takes two to tango. Curr Opin Microbiol 2006,9(6):632–638.CrossRefPubMed 59. Deising HB, Werner S, Wernitz M: The role of fungal appressoria in plant infection. Microbes and Infection 2000,2(13):1631–1641.CrossRefPubMed 60. Choquer M, Fournier E, Kunz C, Levis C, Pradier J-M, Simon A, Viaud M:Botrytis cinerea virulence factors: Glutathione peroxidase new insights into a necrotrophic and polyphageous pathogen. FEMS Microbiology Letters 2007,277(1):1–10.CrossRefPubMed 61. Zuppini A, Navazio L, Sella L, Castiglioni C, Favaron F, Mariani P: An endopolygalacturonase from Sclerotinia sclerotiorum induces calcium-mediated signaling and programmed cell death in soybean cells. Molecular Plant-Microbe Interactions 2005,18(8):849–855.CrossRefPubMed 62. Torto-Alalibo T, Tian MY, Gajendran K, Waugh ME, van West P, Kamoun S: Expressed sequence tags from the oomycete fish pathogen Saprolegnia parasitica reveal putative virulence factors. BMC Microbiology 2005, 5:13.

To keep iron in a reduced state we also performed experiments in the presence of 5 mM sodium

ascorbate. Data in Figure 7 show that transcription from the PP0903 promoter can be induced both by ferrous and ferric sulphate. However, considering that sodium ascorbate can suppress the responses elicited by either metal salt, we deduce that ferric iron is the signal sensed by ColS. This conclusion was further supported by the finding that the same amount of sodium ascorbate could not affect the zinc-promoted activation of ColS (data not shown). Figure 7 ColS responds to ferric iron. β-galactosidase activities measured in P. putida wild-type PaW85 strain carrying the transcriptional fusion of the PP0903 promoter with lacZ in the plasmid p9TTBlacZ. Bacteria were grown in LB medium and in LB containing 0.15 mM FeSO4 or 0.075 mM C646 Fe2(SO4)3 with and without 0.5 mM Na-ascorbate. Data (means with 95% confidence intervals) of at least six independent experiments are presented. Asterisks indicate a statistically significant difference (p < 0.05, two-way ANOVA with post-hoc Bonferroni’s

multiple comparison test) between values obtained in media containing no Na-ascorbate and URMC-099 clinical trial media supplemented with Na-ascorbate. Discussion The controversial nature of biologically important transition metals requires constant monitoring of their concentrations to avoid potential toxic effects of metals. In this study, we demonstrate that the ColRS two-component system acts as a sentinel for external levels of zinc, iron, manganese, and cadmium. Metal-promoted signaling of ColRS system results in the activation of the ColR regulon, which contributes to metal tolerance of P. putida. The finding that the ColRS system is involved in metal tolerance is consistent with previous reports as the ColRS system has been shown to promote heavy metal tolerance of P. putida CD2 [43], cadmium tolerance of Xanthomonas campestris [42], and copper tolerance of X. citri [34]. Thymidine kinase Comparison of our metal tolerance data for P. putida PaW85 with those previously

published for P. putida CD2 [43] revealed that the absence of the ColRS system results in different outcomes in these two strains. While the disruption of ColRS signaling in P. putida PaW85 increases the sensitivity of bacteria only to the excess of zinc, iron, manganese and cadmium, the ColRS-deficient P. putida CD2 also displays higher susceptibility to copper, cobalt and nickel. However, one should consider that P. putida CD2 was isolated from sewage sludge as a cadmium-resistant bacterium [43] and this strain is substantially more tolerant to metals than P. putida PaW85. Therefore, it is not surprising that these two P. putida strains behave somewhat differently from each other although their colRS operons are almost identical. The ColRS systems of X. campestris and X. citri are distantly related orthologs of the ColRS of P. putida, as judged by the 57% identity of ColR and only about 26-27% identity of ColS proteins.

4 13.9 20.0 13.0 9.4 14.2 14.5 15.6 14.3 ± 2.9    Total Energy Expenditure 46.0 44.0 54.3 35.1 49.5 39.7 36.0 38.4 42.9 ± 6.8* Energy Deficit (MJ) -28.2 -24.7 -38.2 -18.3 -6.9 -16.6 -7.9 -20.2 -20.1 ± 10.4 EI:EE b 0.39 0.44 0.30 0.48 0.86 0.58 0.76 0.47 0.54 ± 0.19 a Energy intake b Ratio between energy intake and energy expenditure. * Statistical difference Proteases inhibitor (P < 0.05) between total energy intake and energy expenditure during the event. Correlation between nutritional data and performance during the event The main performance variables such as distance covered and speed did not correlate to the main nutritional variables such as calories, carbohydrates, fluids and caffeine (P <

0.05). In addition, other dietary variables such as intake of proteins, fats and sodium were also not related to performance variables. The strongest correlation was found between cycling speed and total fluid intake (r = 0.71; P = 0.074). When we compared data between the first and the second half of the event, the strongest correlations were

found between the total fluid intake in mL/h (r = -0.66; P = 0.073) and mL of racing time SB431542 manufacturer (r = -0.66; P = 0.077) with % of speed decrease during the last 12 hours (0700 – 1900 h). Discussion In contrast to our first hypothesis, this study shows that athletes were able to consume amounts of carbohydrates which were in accordance with the current recommendations for longer events [6, 7]. However, despite of this fact, these athletes did not meet their energy requirements during the event resulting in a higher energy deficit. The huge workload performed by athletes (TRIMP > 800), Cediranib (AZD2171) which was significantly above to data reported in elite cyclists during high mountain stages of the Tour de France (~ 600 TRIMP) [25], induced a higher energy expenditure. Thus, these results confirmed partially our preliminary hypotheses and were in agreement with two previous investigations showing

that, like solo events, a high energy deficit is common in a team relay format events despite that athletes have considerable time to recover between bouts of exercise [4, 26]. One explanation for this effect has been related with appetite suppression since it is known that longer exercise induces a suppression of acylated ghrelin in humans [27]. Ghrelin is an amino acid peptide hormone secreted primarily from cells within the stomach and it has been suggested to have an orexigenic function (i.e. appetite stimulating) [27]. Macronutrients intake The recommended amount of carbohydrate intake during longer exercise to optimize oxidation rates have been reported as between 1.0 to 1.5 g/min [15]. This recommendation could be also useful to improve glycogen replenishment during the first 4 hours after exercise [28]. In the current study, the mean carbohydrate intake in relation to total racing time (2.61 ± 0.62 g/min) was substantially above these values. Moreover, the relative amount of carbohydrate intake by cyclists was equivalent to 13.1 ± 4.

Representation of the clonal relatedness of STs. Figure S1 – Clonal complex for the four multilocus genotypes found in the Mexican Typhimurium population. The eBURST diagram show the genetic relationships for 66 Typhimurium strains based on the MLST data. ST 19 was unambiguously (100% bootstrap support) predicted as the founder genotype, with STs 213, 302 and 429 related as single locus variants of ST19. The size of the circles is proportional to the number of

isolates belonging to each ST. (PPT 12 KB) Additional file 2: Table S1 – Complete list of strains and results. The complete list of strains, sampling information and results of the Alisertib ic50 genotypic and phenotypic characterization is presented. Table S1 – Complete list of strains and results. The complete list of strains, sampling information and results of the genotypic and phenotypic characterization is presented. (DOC 646 KB) Additional file 3: Table S2 – Primers used in this study. The primer sequences, amplification sizes, annealing temperatures

and references are listed. Table S2 – Primers used in this study. The primer sequences, amplification sizes, annealing temperatures and references are listed. (DOC 88 KB) References 1. Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R: The microbial pan-genome. Curr Opin Genet Dev 2005, 15:589–594.CrossRefPubMed 2. Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, Angiuoli SV, Crabtree J, Jones AL, Durkin AS, et al.: Genome analysis of multiple Orotic acid pathogenic isolates of Streptococcus agalactiae : implications learn more for the microbial “”pan-genome”". Proc Natl Acad Sci USA 2005, 102:13950–13955.CrossRefPubMed 3. Young JP, Crossman LC, Johnston AW, Thomson NR, Ghazoui ZF, Hull KH, Wexler M, Curson AR, Todd JD, Poole PS, et al.:

The genome of Rhizobium leguminosarum has recognizable core and accessory components. Genome Biol 2006, 7:R34.CrossRefPubMed 4. Levin BR, Bergstrom CT: Bacteria are different: observations, interpretations, speculations, and opinions about the mechanisms of adaptive evolution in prokaryotes. Proc Natl Acad Sci USA 2000, 97:6981–6985.CrossRefPubMed 5. Feil EJ: Small change: keeping pace with microevolution. Nat Rev Microbiol 2004, 2:483–495.CrossRefPubMed 6. Maynard-Smith J, Smith NH, O’Rourke M, Spratt BG: How clonal are bacteria? Proc Natl Acad Sci USA 1993, 90:4384–4388.CrossRef 7. Selander RK, Li J, Nelson K: Evolutionary genetics of Salmonella enterica. Escherichia coli and Salmonella: Celular and Molecular Biology (Edited by: Neidhardt FC, Curtiss III R, Ingraham JL, Lin ECC, Low KB, Magasanik B, Reznikoff WS, Riley M, Schaechter M, Umbarger HE). Washington, DC: American Society of Microbiology 1996, 2691–2707. 8. Spratt BG, Maiden MC: Bacterial population genetics, evolution and epidemiology. Philos Trans R Soc Lond B Biol Sci 1999, 354:701–710.CrossRefPubMed 9.

The experiment was repeated at least three times with similar results. Vancomycin susceptibility assay For the growth experiments, overnight cultures of S. aureus were diluted to 1.0 × 107 colony-forming units (CFU)/ml in Mueller-Hinton (MH) broth medium (BD) with or without vancomycin, and inoculated into 50 ml flasks in a final volume of 10 ml. The flasks were

incubated at 37°C with constant shaking (220 rpm). The growth was monitored each hour by measuring the OD600 using a spectrophotometer (DU 730, Beckman Coulter, Brea, CA, USA). For the plate sensitivity assays, overnight cultures were collected by centrifugation and adjusted to 1.0 × 107 CFU/ml with MH. Each culture followed 4 tenfold serial dilutions, and 1 μl of each sample was spotted onto a MH agar plate that contained 0 or 0.6 μg/ml of vancomycin. All the plates and cultures

were incubated at 37°C for 24 hours MCC950 mouse before the colonies were counted. These assays were repeated at least three times with similar results. Total RNA isolation, real-time RT PCR, and microarray processing For the total RNA isolation, Anlotinib ic50 the overnight cultures of S. aureus were diluted 1:100 in TSB and then grown to the exponential phase until collected. The cells were processed with 1 ml TRIzol (TaKaRa, Kyoto, Japan) in combination with 0.1-mm-diameter-silica beads in a FastPrep-24 Automated system (MP Biomedicals Solon, OH, USA), and residual DNA was removed with RNase free DNaseI (TaKaRa, Kyoto, Japan). For the CYTH4 reverse transcription, the cDNAs were synthesized using a PrimeScript 1st Strand cDNA Synthesis Kit (TaKaRa). The real-time PCR was performed with SYBR Premix Ex Taq (TaKaRa) using the StepOne Real-Time PCR System (Applied Biosystems, Carlsbad, CA, USA). The

quantity of cDNA measured using real-time PCR was normalized to the abundance of pta cDNA [26]. The real-time PCR assays were repeated at least three times. The microarray processing and data analysis were conducted by the Biochip Company of Shanghai, China. The microarray data was uploaded to Gene Expression Omnibus (GEO) with accession number: GSE51197. Purification of AirR and AirS 6-His-tagged AirR was cloned and purified using standard procedures. The full-length airR ORF was amplified by PCR with the e-airR-f and e-airR-r primers from S. aureus NCTC8325 genomic DNA, cloned into the expression vector pET28a (+) (Novagen, Merck, Darmstadt, Germany), and transformed into E. coli BL21 (DE3). The transformant was grown in LB at 37°C to an OD600 of 0.4 and induced with 0.5 mM isopropyl-β-D-1-thiogalactopyranoside (IPTG) at 37°C for an additional three hours. The cells were harvested and lysed by sonication in a lysis buffer (20 mM Tris–HCl, pH 8.0, 200 mM NaCl). The 6-His-tagged AirR protein was purified with a nickel-nitrilotriacetic acid agarose solution (Qiagen, Valencia, CA, USA) following the manufacturer’s recommendation.

Austral Ecol 28:287–304CrossRef Stork NE (1988) Insect diversity−facts, fiction and speculation. Biol J Linn Soc 35:321–337CrossRef Ter Braak CJF, Šmilauer P (1998) CANOCO Reference Manual and User’s Guide to CANOCO for Windows: Epacadostat chemical structure Software for Canonical Ordination (version 4). Microcomputer Power, Ithaca Uehara-Prado M, Fernandes

JD, Bello AD, Machado G, Santos AJ, Vaz-de-Mello FZ, Freitas AVL (2009) Selecting terrestrial arthropods as indicators of small-scale disturbance: a first approach in the Brazilian Atlantic Forest. Biol Conserv 142:1220–1228CrossRef Unwin DM (1988) A key to the families of British beetles. Field Studies Council, Taunton, UK Van der Meijden R (2005) Heukels’ Flora van Nederland, 23rd edn. Wolters Noordhoff, Groningen Verdonschot PMF (2006) Data composition and taxonomic resolution in macro-invertebrate stream typology. Hydrobiologia 566:59–74CrossRef Vincent A, Clarke A (1995) Diversity in the click here marine environment. Trends Ecol Evol 10:55–56CrossRef Ward JV, Tockner K, Arscott DB, Claret C (2002) Riverine landscape diversity. Freshwater Biol 47:517–539CrossRef Warwick RM (1988) The level of taxonomic discrimination required to detect pollution

effects on marine benthic communities. Mar Pollut Bull 19:259–268CrossRef Williams PH, Gaston KJ (1994) Measuring more of biodiversity: can higher-taxon richness predict wholesale species richness? Biol Conserv 67:211–217CrossRef”
“Introduction Over the last 50 years, ecologists have generated an immense amount of knowledge about how natural systems work and how to protect and restore them. Just as modern science has revolutionized medicine, there is now an effort to shift the management of natural resources from an experience-based approach to an evidence-based approach (Pullin and Knight 2001; Salafsky et al. 2002). A major challenge in developing evidence-based management is identifying the most effective ways to incorporate scientific knowledge Pembrolizumab into the decision-making process (Pullin and Knight 2005;

Pyke et al. 2007). There are many sources of information that can help land managers and policy makers incorporate scientific evidence into the decision making process (Alexander et al. 2009). These sources include a wide variety of printed documents and computer-based sources of information that help decision makers understand how different choices will influence the natural resources they manage. In the peer-reviewed literature, papers that emphasize the management implications of ecological research can be used for decision support. Outside of the peer-reviewed literature, documents that synthesize large amounts of ecological information into a single resource are becoming more abundant. Examples of such documents include the habitat conservation plans developed by Partners in Flight (Bonney et al. 1999; Alexander et al.

We then used these hits as edges in a homology graph, and identified clusters of highly conserved paralogs as connected components. Finally, we removed hits within a cluster if the pairwise distance differed significantly from the mean distance within the cluster. In the second step, we grouped detected homologous clusters across species

using OMA alignments, BMS202 chemical structure but this time with a score cut-off of 180 and minimum sequence identity of ≥50%. We further required that ≥0.8·n i ·n j of hits between any pair of clusters i and j be present in order to be considered, where n i n j is the number of genes in clusters i and j, respectively. If a cluster in one genome grouped with several clusters in another genome, we chose the one with www.selleckchem.com/products/LY2603618-IC-83.html the lowest average pairwise distance. Again, homologous groups were extracted as connected components from the resulting graph. Finally, single orthologs from the OMA orthologous matrix (i.e, with no detected multiple copies within their originating genome) were matched and added to corresponding homologous groups. We tested whether a correlation between cell differentiation and copy numbers could be observed for the identified genes. To do this,

we devided cyanobacterial species into four different groups of cell differentiation (G0-G3; see results). Five strains belong to G0, 12 taxa belong to G1, Tricodesmium is the only genus in G2, and four species belong to G3. For 16S rRNA genes additional data could be obtained from rrndb-database [45] (Additional file 3). Adding these data resulted in a taxon set of 16S rRNA gene sequences as follows: five strains belonging to G0, 12 strains Lck representing G1, Trichodesmium as the only species in G2 and 11 species in G3. Spearman’s rank and Pearson’s correlation coefficients were applied in order to estimate associations between conserved copy numbers and morphological groups

(G0-G3), using R-software. Correlations with a p-value<0.01 were considered to be significant. Phylogenetic analyses We conducted separate phylogenetic analyses of 16S rRNA gene sequences of cyanobacteria (Table 1) and four different eubacterial phyla (Additional file 10). For all taxa included in the phylogenetic trees, full genome sequences were available. All sequences were downloaded from GenBank [61]. For cyanobacteria two phylogenetic trees were reconstructed. One including a single 16S rRNA sequence per taxon and another including all 16S rRNA copies per taxon. Final taxon sets included 22 sequences in the first case and 48 sequences in the latter. The datasets were aligned using Clustal-X software with default settings [62] (1,325nt incl. gaps). Gaps were excluded from the analysis. Phylogenetic reconstructions were done using Bayesian analysis as implemented in MrBayes software [63].