Our study referred to the MDRI and NSOR and we compared both raw data and data normalized for body weight. Hematology and blood chemistry

Blood samples representing nutritional status were collected at three time points (0, 4, 6 months) and the following components were analyzed: hemoglobin (Hgb), iron, transferrin, ferritin, folic acid, vitamin B12, and calcium. Approximately 30 cc’s of venous blood samples were obtained by antecubital venipuncture into tubes (BD Vacutainer; Becton, Dickinson and Company®, 2002 BD) containing the appropriate anticoagulant. All samples were taken during the morning hours (0600-0700 h), in a sitting Cell Cycle inhibitor position after an overnight fast (6-10 h) and no exercise. The samples were placed in ice and sent within four hours to be processed and analyzed at the Sheba Medical Center Laboratories (Hematology and Biochemistry). Blood counts were performed on fresh blood using an automated analyzer (Cell-Dyn® 3000; Abbott Diagnostics, Abbott Park, IL) for measuring Hgb values. Serum ferritin was measured using an electrochemiluminescence immunoassay (Roche Elecsys®,

Roche Diagnostics GmbH, Mannheim, Germany, reference: of 16-293 ng/ml). Serum iron was measured see more with a commercial kit on Olympus (AU2700, Oligomycin A reference ranges 60-170 μg/dL). Vitamin B12 and folic acid levels were determined with an automated analyzer (Architect Abbott Diagnostics). Serum transferrin

was measured with an immunoturbidimetric assay (Tina-quant® with Roche Diagnostics GmbH, Mannheim, Germany, reference ranges 193-378 mg/dL). Transferrin saturation was calculated according to the following formula: transferrin saturation (%) = serum iron/serum transferrin. Blood calcium was measured using a commercial kit on Olympus (AU2700, reference values: 8.5-10.9 mg/dl). Radioimmunoassay (RIA) was used to measure 25(OH)D levels (DiaSorin, Stillwater, MN, reference range 30.0-74.0 ng/ml). Parathyroid hormone (PTH) was measured by immunoassay with chemiluminescent detection on the Immulite 2000 (Diagnostics Products Corporation, Los Angeles, CA, reference range 12.0-72.0 ng/L). Hematological deficiencies were established as follows: anemia was diagnosed at Hgb levels of less than 14 g/dl, and ferritin levels (< 20 ng/ml). for Nutrition provided to recruits The recruits had 3 main meals and 3 snacks a day. Breakfast (7-8 am, about 2 h after awakening), which included porridge, bread, 1-2 eggs, cream cheese, vegetables, olives, jam and additional savory spread for the bread (avocado, chickpea etc, depending on the season). A chocolate drink (200 ml milk) and milk desserts were also served. Dinner (12-1 pm) included soup, a meat/chicken/fish portion (200 grams) 2 salads and a cooked vegetable, a starch (potatoes/rice/macaroni), bread and a fruit desert.

In randomised clinical trials, these treatments can reduce fracture incidence by up to 50%. However, in routine care, these see more treatment benefits may be compromised by poor

adherence to treatment, with around 50% of women discontinuing treatment within 1 year [10, 11]. Suboptimal adherence to antiresorptive treatment has been shown to be associated with an increased risk of fracture [12–14]. Barriers to better adherence to osteoporosis treatment include the constraints associated with the administration of some of these agents, side-effects, the treatment regimen, the lack of a visible “read-out” of treatment benefit and inappropriate patient expectations and perceptions [15–17].

Improving RepSox adherence to osteoporosis treatment thus represents an important public health issue. Achieving this requires appropriate tools to measure adherence which KU-57788 order can be used to monitor improvements due to public health interventions. The notion of adherence involves a number of inter-related aspects. With regard to osteoporosis, an expert consensus recently described adherence as a general term encompassing both compliance and persistence [18]. Compliance was defined as the extent to which a patient acts in accordance with the prescribed interval and dose of a given treatment regimen, whereas persistence was defined as the cumulative time from initiation to discontinuation of therapy. Currently, three principal types of adherence measure have been developed, prescription follow-up or pharmacy claims to determine medication consumption over time, direct medication use measures (for example, pill counts, electronic measures or canister weights) or patient reports. Direct medication use measures are not particularly useful for naturalistic studies, since they may lead to bias due to potential

modification of adherence behaviour by implementation of the reporting measure. Of the prescription follow-up methods, the medication possession ratio (MPR) [19, 20] has Gemcitabine manufacturer been widely used. A number of patient-reported measures of treatment adherence have been developed and validated, including the Morisky Medication Adherence Scale (MMAS) [21], the Medication Adherence Report Scale [22], the Adherence to Refills and Medications Scale [23], the ASK-20 [24] and the Hypertension Compliance Questionnaire [25]. However, none of these instruments were designed specifically with osteoporosis in mind, and it would therefore be of interest to develop a disease-specific adherence measure which would focus on adherence issues that are pertinent to osteoporosis and its treatment and may be more discriminating and sensitive to change than non-specific measures.

Statistical methods All results were analysed with SPSS-statistics program (PASW statistics

17). Means ± SDs selleck inhibitor were calculated and the Wilcoxon Signed Rank Test was used to evaluate the differences between the means. A nonparametric test was chosen because the data was not normally distributed LCZ696 clinical trial tested with the Shapiro-Wilk test. Statistical comparisons were considered significant when p values were < 0.05. Results Subjects reported no side effects related to SB intake, but symptoms of paraesthesia was experienced by all subjects consuming BA. Swimming times There were no significant differences in the time of the first 100-m sprint between the groups. In the second 100-m swim, the increase in time of the second

versus the first 100-m swimming time was 1.5 s less (p < 0.05) in the SB group compared to the PL group (Figure 2). No significant differences were noted between the first or second sprint in either BA + SB or BA + PL. Figure 2 Swimming times (mean ± SD) in the supplemented groups. PL = placebo, SB = sodium bicarbonate, BA + PL = beta-alanine and placebo, BA + SB = beta-alanine and sodium bicarbonate, *Indicates a significant click here difference (p < 0.05) compared to PL. Blood variables Lactate, pH There were no significant differences between the groups although lactates in measurements III and IV tended (p < 0.08-0.09) to be greater in SB supplemented groups (Figure 3A). Blood pH values (Figure 3B) were significantly (p <

0.05) greater in the SB and in the BA + SB combination group 2 min before the first swim and in all measurement points following swimming compared to the PL measurement values. Figure 3 Blood lactate and pH values (mean ± SD) in the supplemented groups in different measurement time points. A) Blood lactate (B-Lactate), B) pH (B-pH), PL = placebo, SB = sodiumbicarbonate, BA + PL = beta-alanine and placebo, BA + SB = beta-alanine and sodium bicarbonate, pre 1 = 60 min before swimming, pre 2 = 2 min before swimming the first 100 m, I and III 2 min after both 100 m swimming, II and IV 8 min after both 100 m swimming, * Indicates a significant (p < 0.05) difference Resveratrol compared to PL. Sodium, potassium Significantly (p < 0.05) greater increases in plasma sodium concentrations were observed in SB and in BA + SB at every measurement point (except pre 1) compared to the PL values. A significant decrease in sodium concentrations was seen at BA + PL compared with PL during IV (Figure 4A). Significantly (p < 0.05) smaller plasma potassium concentrations were observed in SB and in the SB + BA groups at Pre 2, II and III compared to the PL values (Figure 4B). Figure 4 Blood sodium and potassium values (mean ± SD) in the supplemented groups in different measurement time points.

Infect Immun 1997,65(1):298–304.PubMed 14. Schaible UE, Winau F, Sieling PA, Fischer K, Collins HL, Hagens K, Modlin RL, Brinkmann V, Kaufmann SHE: Apoptosis facilitates antigen presentation to T lymphocytes through MHC-I and CD1 in tuberculosis. Nat Med 2003,9(8):1039–1046.PubMedCrossRef 15. Winau F, Weber S, Sad S, de Diego J, Hoops SL, Breiden B, Sandhoff K, Brinkmann V, Kaufmann SHE, Schaible UE: Apoptotic vesicles crossprime CD8 T cells and protect against tuberculosis. Immunity 2006,24(1):105–117.PubMedCrossRef 16. Montes-Worboys A, Brown S, Regev D, Bellew BF, Mohammed KA, Faruqi I, Sharma P, Moudgil B, Antony VB: Targeted delivery

of amikacin into granuloma. Am J Respir Crit Care Med 2010,182(12):1546–1553.PubMedCrossRef 17. McShane H, Behboudi S, Goonetilleke N, Brookes R, Hill AVS: QNZ molecular weight Protective immunity against Mycobacterium tuberculosis induced by dendritic cells pulsed with both CD8 + INK1197 price – and CD4 + -T-cell epitopes from antigen 85A. Infect Immun 2002,70(3):1623–1626.PubMedCrossRef 18. Badovinac VP, Messingham KAN, Jabbari A, Haring JS, Harty JT: Accelerated CD8 + T-cell memory and prime-boost response after dendritic-cell

vaccination. Nat Med 2005,11(7):748–756.PubMedCrossRef 19. Kong CU, Ng LG, Nambiar JK, Spratt JM, Weninger W, Triccas JA: Targeted induction of antigen expression within dendritic cells modulates antigen-specific immunity afforded by recombinant BCG. Vaccine 2011,29(7):1374–1381.PubMedCrossRef 20. Kerr JFR, Wyllie AH, Currie AR: Apoptosis: A basic biological phenomenon with wide-ranging implications in tissue kinetics. British Journal of Cancer 1972,26(4):239–257.PubMedCrossRef 21. Mevorach D, Trahtemberg U, Krispin A, Attalah M, Zazoun J, Tabib A, Grau A, Verbovetski-Reiner I: What do we

mean when we write “”senescence,”"”"apoptosis,”"”"necrosis,”" Inositol monophosphatase 1 or “”clearance of dying cells”"? Annals of the New York NVP-HSP990 nmr Academy of Sciences 2010,1209(1):1–9.PubMedCrossRef 22. Kroemer G, Galluzzi L, Vandenabeele P, Abrams J, Alnemri ES, Baehrecke EH, Blagosklonny MV, El-Deiry WS, Golstein P, Green DR, Hengartner M, Knight RA, Kumar S, Lipton SA, Malorni W, Nuñez G, Peter ME, Tschopp J, Yuan J, Piacentini M, Zhivotovsky B, Melino G: Classification of cell death: recommendations of the Nomenclature Committee on Cell Death 2009. Cell Death Differentiation 2009,16(1):3–11.CrossRef 23. Caserta TM, Smith AN, Gultice AD, Reedy MA, Brown TL: Q-VD-OPh, a broad spectrum caspase inhibitor with potent antiapoptotic properties. Apoptosis 2003,8(4):345–352.PubMedCrossRef 24. Kroemer G, Martin SJ: Caspase-independent cell death. Nat Med 2005,11(7):725–730.PubMedCrossRef 25. Molloy A, Laochumroonvorapong P, Kaplan G: Apoptosis, but not necrosis, of infected monocytes is coupled with killing of intracellular bacillus Calmette-Guérin. The Journal of Experimental Medicine 1994,180(4):1499–1509.PubMedCrossRef 26. Laochumroonvorapong P, Paul S, Elkon K, Kaplan G: H 2 O 2 induces monocyte apoptosis and reduces viability of Mycobacterium avium-M.

Evol Syst 6:87–104 Backer CA (1954) Myricaceae. Flora Malesiana, series 1, 4:277–279 Berg CC, Corner EJH (2005) Stem Cells inhibitor Moraceae. Flora Malesiana, series 1, 17(2):1–730 Brummitt RK (2001) Plant taxonomic database standards No. 2, 2nd edn. World geographical scheme for recording plant distributions, 15 (ed 2), 137, 17 maps Cannon CH, Manos PS (2003) Phylogeography of the Southeast Asian stone oaks (Lithocarpus). J Biogeogr 30:211–226CrossRef Cannon CH, Summers M, Harting JR, Keßler PJA (2007) Developing conservation priorities based on forest type, condition, and threats in a poorly known ecoregion: Sulawesi, Indonesia. Biotropica 39:747–759CrossRef Colwell RK (2006) EstimateS: statistical

estimation of species Milciclib richness and shared species from samples (software and user’s guide), version 8. http://​viceroy.​eeb.​uconn.​edu/​estimates. Accessed 6 January 2008 Corlett RT (2007) What’s so special about Asian tropical forests? Curr Sci 93:1551–1557 Pifithrin-�� concentration Corlett RT (2009) Seed dispersal distances and plant migration potential in tropical East Asia. Biotropica 41:592–598CrossRef Culmsee H (2008) Dysoxylum quadrangulatum, and notes on Meliaceae in Sulawesi. Blumea 53:602–606 Culmsee H, Pitopang R (2009) Tree diversity in sub-montane and lower montane

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Since then, results from YAP-TEAD Inhibitor 1 in vivo several studies suggest that planctomycetes favor a biofilm

lifestyle, adhering to surfaces in aquatic environments including marine sediments [6] (among others), diatom cells [7], seaweeds and other aquatic macrophytes [8, 9]. Rhodopirellula baltica is an Idasanutlin in vitro extensively studied marine particle-attached planctomycete. Its genome sequence reveals a large number of genes involved in the breakdown of sulfated polysaccharides [10], a carbon source found in marine photosynthetic organisms such as microalgae and seaweeds, who’s detrital material is thought to generate marine snow. Such genes are also encountered in other planctomycete genomes and planctomycete-derived metagenomic fosmid libraries see more from seawater collected in upwelling zones [11]. The overrepresentation of such genes, and the association of R. baltica and other planctomycetes with marine snow has led to the hypothesis that heterotrophic planctomycetes are specialized degraders of sulfated polymeric carbon, for example in marine snow [10, 11]. Given the significance of marine snow as part of the so-called “”biological pump”" of carbon in the oceans [12, 13], planctomycetes may thereby be playing a crucial role in global carbon turnover [11]. Still, quantitative data on the distribution of planctomycetes in the marine environment and elsewhere is still scarce,

and very little Chlormezanone is known about the yet uncultured planctomycete lineages that are assumed to carry out the bulk of these globally critical processes. Kelps are large brown seaweeds of the order Laminariales. They often form dense stands along rocky coastlines that are referred to as kelp forests. Kelp forest ecosystems are some of the most productive ecosystems in the world [14]. Their immense importance for coastal biodiversity, productivity

and human economy has long been recognized in temperate regions of the world and is only beginning to be understood in the tropics [15]. Kelp forests along the Atlantic coasts of Europe are dominated by the large kelp Laminaria hyperborea. Bacteria associated to kelp are believed to be important in the carbon and nitrogen turnover in kelp forest food webs [16, 17], but it is still not known what types of bacteria are involved in these processes. Recently, the seasonal dynamics of the cell density and bacterial community composition in biofilms on L. hyperborea were addressed. In this study, planctomycetes were frequently detected throughout the year but their abundance and phylogenetic relationships were not considered [18]. In order to address the importance of this group of bacteria in kelp forests, we therefore aimed to take an in-depth look at the abundance and phylogenetic diversity of planctomycete communities inhabiting L. hyperborea surface biofilms.

Figure 4 Potential methanotrophic genera detected. Shown is the proportion of reads assigned to methanotrophic genera at the genus level in MEGAN for each metagenome. In the left section known aerobic methanotrophic genera are presented. In

the middle section known taxa involved in anaerobic methane oxidation are presented. In the right section known genera of sulphate reducing bacteria are presented. The archaeal sulphate reducing genus Archaeoglobus is also included in this section. The 0-4 cm metagenome is presented in red. The 10-15 cm metagenome is presented in blue. Numbers are given as log(10) percentage of total reads in each metagenome. ANME groups were the predominant anaerobic methanotrophs in the sediments. Since selleck kinase inhibitor taxonomic classification of reads in MEGAN was based on the NCBI taxonomy, the ANME clades

Danusertib concentration were not recognized as independent taxa. The artificial taxon “”Archaeal environmental samples”" was however represented (Additional file 3, Table S3). Inspection of the reads assigned to this taxon revealed their assignment to ANME-1 and ANME-2 S63845 mw fosmids isolated from Eel River [11] or to “”uncultured archaeon”". Further inspection of the best hits for the reads assigned to “”uncultured archaeon”" (mean bit score 146.8) showed that most of these reads were associated to ANME as well, while a few reads were assigned to fosmids isolated from methane seeps offshore Japan [12, 27–29] (Table 2). Table 2 “”Archaeal environmental samples”"- reads assigned to ANME-sequences Clade 0-4 cm metagenome 10-15 cm metagenome   Reads assigned Percent of reads Reads assigned Percent

of reads ANME-1, Eel River [11, 27] 27 0.01 3532 1.82 ANME-1, Black Sea [12] 177 0.07 12752 6.56 ANME-1b, Black Sea [28] 8 0.00 429 0.22 Total ANME-1 212 0.08 16713 8.60 ANME-2, Eel River [11] 20 0.01 534 0.27 ANME-2a [28] 11 0.00 14 0.01 ANME-2c [28] 2 0.00 12 0.01 Total ANME-2 33 0.01 560 0.29 ANME-3, Hydrate Ridge [28] 0 0.00 6 0.00 Total ANME-3 0 0.00 6 0.00 Total ANME 245 0.09 17279 8.89 The table presents Chloroambucil reads assigned to Archaeal environmental samples”" further classified as ANME. All percentages are given as the percentage of total reads for each filtered metagenome. The ANME-1 clade was by far the anaerobic methanotroph with most assigned reads, although ANME-2 and ANME-3 also were present in the 10-15 cm metagenome (Figure 4). ANME-1 and ANME-2 were detected with low abundance in the 0-4 cm metagenome. The high abundance of ANME in the 10-15 cm metagenome indicates that AOM caused the high methane oxidation rates measured at this depth. ANME are assumed to live in syntrophy with SRB. The most abundant genera of SRB in the metagenomes from the Tonya seep were Desulfococcus, Desulfobacterium and Desulfatibacillum (Figure 4). These genera were abundant in both metagenomes, and Desulfococcus, a common partner of ANME [7, 9, 10], especially so in the 10-15 cm metagenome (Additional file 2, Table S2).

3   Kidney disease patients Normal Total number 147 20 Positive number 133 2 Positive rate 90.5% 10.0% Most of the patients were positive for proteinuria with a substantial amount of

urine proteins; the IgA–uromodulin complex was found at various amounts, sometimes at high levels even though they were not diagnosed as IgAN (Table 1A). On the other hand, the ratio of the IgA–uromodulin complex compared to total urine protein was only high in cases of IgAN and not in other cases. In detail, the concentration of the urine protein of the specimen material that showed measurements higher than the cut-off value in urine was measured by the pyrogallol red method [19]. With the exception of one sample in which the concentration of the urine protein was below the detection limit, the amount of the IgA–uromodulin complex that had been obtained by the above-mentioned method was divided by the urine protein concentration, and the value of the complex for MK-2206 clinical trial each urine protein amount was calculated. In other words, the concentration of the IgA–uromodulin complex adjusted for urinary creatinine was divided by a urine protein concentration adjusted for urinary creatinine; the results are shown in Figure 5. Samples from eighty-five IgAN patients and from 47 kidney disease patients (other than IgAN) were able to be clearly distinguished

by comparing the value of the complex in the urine protein. Moreover, the ROC analysis of the samples from the 47 kidney disease patients (other than IgAN) and the samples from the 85 IgAN patients created the ROC curve shown in Fig. 6. The cut-off value calculated from BAY 11-7082 mouse the ROC curve was 2.45. The result of the positive rates of the 47 kidney disease patient samples (other than IgAN) and the 85 IgAN patient samples from the cut-off value is shown in Table 4. Seventy-nine samples of the 85 IgAN patient samples were positive (92.9%) and 20 samples of the 47 kidney disease patients were positive (42.6%) as shown in Table 4, and both were able to be distinguished clearly. Sensitivity at that time was 92.9%, specificity was

57.4%, and diagnosis efficiency was 80.3%. Fig. 5 Distribution chart of the value of measurements that detect the IgA–uromodulin complex in urine by ELISA for each amount of urine GPX6 protein. Cut-off line is drawn by ROC analysis in Fig. 6. 132 samples (133 ELISA-positive kidney disease samples except for one sample below the detection limit of pyrogallol red method) were analyzed. They included 17 MN, 5 SLE, 4 FGS, 3 MCNS, 5 DMN, 13 other kidney ARN-509 datasheet diseases and 85 IgAN Fig. 6 Result of the ROC analysis of the value of measurements that detect the IgA–uromodulin complex in urine by ELISA for each amount of urine protein in Fig. 5 Table 4 Positive rate of IgAN and other kidney diseases by ELISA for the IgA–uromodulin complex for each amount of urine protein in Fig. 5   IgAN Other kidney diseases Total number 85 47 Positive number 79 20 Positive rate 92.9% 42.

The least inhibited fungus in these bioassays was Piloderma croceum, closely related to the mycorrhizal fungus Piloderma sp., the fungus which dominated in the Norway spruce mycorrhizal roots used for isolations. This suggests the potential of such a niche-related community for protecting Norway spruce-Piloderma mycorrhizas from fungal and bacterial parasites without incurring harm to the host fungus. The production of secondary

metabolites by mycorrhiza associated streptomycetes After many years of intensive screening of actinomycetes, the frequency of discovering structurally new compounds is apparently decreasing [27]. Since the current strategies for addressing AZD5582 the urgent need for new

antibiotics are not ON-01910 cost efficient enough, another approach might be to examine new niches, or sources, for microbial resources that produce novel compounds [28]. To search for compounds that affect fungal growth we performed HPLC analyses coupled with UV/Vis detection and mass spectrometry with five selected mycorrhiza-associated streptomycetes, possessing different activities in Streptomyces-fungus bioassays. Typically, only a limited number of metabolites are produced selleck in synthetic media [27], and to promote production of diverse metabolites two different culture media were employed. The five strains produced diffusible secondary metabolites, of which only seven could be identified using the HPLC-UV–vis database containing 960 reference compounds [29], NIST database, and MS analyses. The identified metabolites included antifungal and antimicrobial substances as well as siderophores. The fungal inhibitory strain Streptomyces AcM11 produced the most characterized metabolites, the antibiotics Acta 2930 B1, actiphenol, cycloheximide and the siderophore ferulic acid. This indicates that function based screening, e.g. selection of isolates that are highly inhibitory towards fungi for biocontrol applications, may create a bias towards strains producing Anacetrapib known

compounds. Based on spectral measurements and MS analyses, a total of twenty one compounds were produced by the five isolates, suggesting an abundance of yet unreported, putatively bioactive compounds. Nevertheless, at least 7000 secondary metabolites have been discovered from streptomycetes [27], and the genome sequences of Streptomyces spp. commonly contain 20-30 gene clusters for secondary metabolite synthesis, of which approximately 30% may encode biochemical pathways for antibiotics production [30]. Thus, to conclusively determine the novelty of such substances both structural and chemical elucidation as well as the use of comprehensive substance databases is indispensable.

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