While those with advanced training may readily recognize the land

While those with advanced training may readily recognize the landmarks, other research staff may have a difficult time accurately and reproducibly identifying the correct levels. The flexicurve ruler, gently pressed onto the back, adopts the thoracic and lumbar contours of the participant. The researcher then traces the ruler’s retained shape onto paper and calculates the kyphosis index (Fig. 1) [21]. One can also

calculate an inscribed angle of kyphosis from the tracing, using geometric formulae (Fig. 1) [14]. Fig. 1 Three PF299 chemical structure Methods of quantifying thoracic kyphosis angles are illustrated. The modified T4–T12 Cobb angle (dotted lines) measures the angle created by lines Crenigacestat ic50 drawn parallel to the limit vertebrae visualized on a lateral standing thoracolumbar radiograph. In this case, the limit vertebrae are pre-specified at T4 and T12. The Flexicurve kyphosis index and angle are computed using measurements taken from the flexicurve Bucladesine tracing of the thoracic curve, represented here by the solid dark curve posterior to the

thoracic vertebral bodies. To calculate the Flexicurve kyphosis index, the apex kyphosis height (E) is divided by the length of the entire thoracic curve (L). The Flexicurve kyphosis angle, Theta (θ), is calculated using lines drawn perpendicular to the short sides of the triangle inscribed by the thoracic curve. This triangle is demarcated by points a (Apex), b (at the cranial end of the curve), and c (at the caudal end). Theta equals arc tan (E/L1) + arc tan (E/L2) Although the non-radiological kyphosis measures minimize cost and obviate radiation, they have enjoyed limited adoption. One explanation may be that they are not calibrated to the Cobb angle, which limits their clinical interpretation. A metric that translates a non-radiological kyphosis result into an approximate Cobb angle would allow estimation of clinical severity from non-Cobb measures. Demonstrations of the reliability and validity of the non-radiological measures, especially in older persons, have been minimal,

a possible second reason for limited use [13, 20, 22–24]. Therefore, we designed this study to describe: (1) the intra-rater and inter-rater reliability of three non-radiological kyphosis Acetophenone measures, the Debrunner kyphosis angle, the flexicurve kyphosis index, and the flexicurve kyphosis angle; (2) the validity of each non-radiological measure using the modified Cobb angle as the criterion standard; and (3) a translational formula that provides an approximate Cobb angle based on results of the non-radiological measures. We used baseline data from the Yoga for Kyphosis trial, during which we performed standing lateral radiographs to assess modified Cobb angle as well as multiple, same-day, intra-rater and inter-rater measures of the non-radiological assessments. Methods Participants The analysis sample came from the Yoga for Kyphosis Trial, a single masked, randomized, controlled trial (RCT) of Yoga intended to improve thoracic hyperkyphosis [14].

Figure 4 Phenotypes

of exponentially growing double mutan

Figure 4 Phenotypes

of exponentially growing double mutant B. subtilis cells. A) dynA/floT double selleck mutant cells (note that membrane staining is highly heterogeneous between cells), white triangles indicate membrane abnormalities, B) mreB mutant cells grown in high magnesium medium, C-D) dynA/mreB double mutant cells growing in high magnesium medium. White or grey bars 2 μm. Figure 5 Growth curve of wild type cells (diamonds) or of dynA/ floT double mutant cells (squares) growing in S750 minimal glucose medium containing 0.1% casamino acids at 37° C. Data are means from four independently growing cultures. Based on its ability to tubulate membranes in vitro[11, 13], DynA may facilitate membrane invagination through a mechanical bending of the membrane, while FloT may be important to HDAC inhibitor generate a local environment favoring membrane curvature and/or recruitment of cell division proteins. In agreement with its function in lipid raft formation, a functional FloT-YFP fusion formed many discrete foci at the cell membrane [34] (Figure 3F). FloT-YFP was previously shown to move along random paths within or adjacent to the membrane [34]. These findings imply that due to the random movement, FloT would also be Wnt inhibitor frequently present at mid cell, which indeed was shown to be the case by colocalization of FloT-YFP with membrane

stain FM4-64 [34].To obtain a better idea about the extent of colocalization of FloT with the septal membrane, we quantified the number of FloT-YFP foci between cells. Indeed, 26% of FloT-YFP foci colocalized with the septal/polar membrane (184 foci analysed), or in other words, 22% of the cells had FloT-YFP fluorescence at the septum (Figure 3F, green FloT-YFP foci, red membrane) (148 cells analysed from 3 independent

experiments), showing Phosphoglycerate kinase that FloT is present at sites of cell division in a large fraction of the cells; even more cells contained FloT-YFP foci close to the cell centre. To investigate if one protein affects the localization of the other, we localized DynA-YFP in delta floT (yuaG) mutant cells. The localization pattern was indistinguishable from that of wild type cells (Figure 3G). Conversely, the absence of DynA did not visibly alter the localization pattern and dynamics of FloT-YFP (Figure 3H), showing that the proteins do not affect each other’s localization within the cell membrane and that they are not functionally linked. Synthetic phenotype of a dynA mreB double mutant strain Because floT dynA double mutant cells had a highly disturbed cell shape, we investigated the effect of a dynA deletion in combination with an mreB deletion. MreB is essential for the maintenance of rod shape in many bacteria, and the depletion of MreB leads to the generation of round cells that eventually lyse [20, 35].

At the end of the experiment, the medium was discarded, and non-a

At the end of the experiment, the medium was discarded, and non-adherent bacteria were removed by three washes with sterile PBS. Quantification of bacterial adhesiveness and biofilm formation on polystyrene was assessed by a spectrophotometric method, as previously described by Christensen et al. [43], with minor modifications. Briefly, after washing, attached bacteria were fixed for 1 hour at 60°C and then stained with Hucker crystal violet solution for 5 minutes. After washing with water to remove the excess of stain, the plates were dried for 30 minutes at 37°C. The color produced by attached bacteria (indirect index of adhesiveness

or biofilm formation) was measured spectrophotometrically at OD492. A low cut-off corresponding to 3 standard deviations (SDs) above the mean of control wells not seeded with bacteria was chosen [43]. Co-infection assays Co-infection assays were performed using S. maltophilia strain click here OBGTC9 and P. aeruginosa strain PAO1. Briefly, confluent IB3-1 cell monolayers were first infected for 2 hours

at 37°C with P. aeruginosa PAO1 (MOI 1000). At that time, non-adherent bacteria were removed by three washes with PBS, and monolayers were then infected with S. maltophilia strain OBGTC9 (MOI 1000) and incubated for further 2 hours. At the end of the experiment infected IB3-1 cells were removed by a treatment with 0.25% check details trypsin/EDTA, vortexed, serially diluted and plated on MH agar to determine the number (cfu chamber-1) of the two bacteria bound to IB3-1 cells. P. aeruginosa PAO1 and S. maltophilia OBGTC10 colonies were easily differentiated on the basis of their AZD1390 colonial morphology. As controls we used IB3-1 cell monolayers infected separately with each of the two bacterial strains. Motility tests Swimming

motility assays were performed with single well-isolated colonies grown overnight on MH agar plates, according to a modification of the technique described by Rashid et al. [44]. Briefly, tryptone swim plates (1% tryptone, 0.5% NaCl, 0.3% agar; Oxoid) were Pregnenolone inoculated with bacteria at the surface by using a sterile needle. Plates were incubated for 24 hours at 37°C. Motility was assessed by calculating the diameter (mm) of the circular turbid zone formed by bacterial cells migrating away from the point of inoculation at the agar surface. Scanning electron microscopy Biofilm formation was assessed by scanning electron microscopy (SEM). Samples were air-dried, and fixed with a solution of 2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer for 90 minutes. After washing with buffer, samples were post-fixed in osmium tetroxide and then dehydrated in a series of aqueous ethanol solutions (30 to 70%). Specimens were mounted on aluminum stubs with conductive carbon cement, allowed to dry for 3 hours, and coated with 15-nm Au film with an agar automatic sputter coater.

Colonies were counted and CFU/mL calculated (CFU/mL = (number of

Colonies were counted and CFU/mL calculated (CFU/mL = (number of colonies × 10D)/0.02). The values

were plotted from the average of the samples with the error bars representing the standard deviation of the data. Samples were assayed in triplicate. For cells from the biofilm CRT0066101 cell line lifestyle; using the same plate as for the planktonic CFU/mL assay, the residual liquid was drained and the attached cells were washed three times with 200 μL of LB broth. After washing, 100 μL of fresh BHI media broth added into each well. The cells are detached by sonication for 3 seconds (Soniclean sonicating waterbath, a protocol established to disrupt bacterial attachment and aggregation), followed by removal of 20 μL from each well and a serial dilutions from 10-1 to H 89 in vitro 10-8 and plating onto BHI agar plates. Biofilm cells grow with an altered metabolism and it should be noted that

the colonies on the plate appear different (generally smaller), but colony numbers are representative of live cell numbers within the system. CFU/mL are once again calculated using the formula; CFU/mL = (number of colonies × 10D)/0.02. The values were plotted from the average of the samples and the error bars represented the standard deviation of the data. Transcriptomic analysis The selected strains; R3264 and Eagan were grown until late log-phase (16 hours) in 10 mL BHI liquid media and then cultured in BHI media broth in pH 6.8 and 8.0 for 3.5 hours before the collecting selleck inhibitor the cells for RNA extraction. To prevent RNA from degradation and preserved the RNA within the cells, cells were directly added to Phenol/Ethanol solution. The composition of phenol/ethanol solution is; 5% v/v Phenol (pH 4.3) and 95% v/v ethanol. The ratio used is 2/5 of the total cell culture volume: phenol/ethanol. This

was left on ice for 2 hours before being centrifuged for 5 min. (4˚C/4000×g) and the supernatant discarded. The cell pellet was kept at -80˚C until RNA extraction. RNA is extracted using RNAeasy Mini kit according to RNAeasy mini standard protocol Histone demethylase (QIAGEN). The RNA quality of the samples were checked with the Agilent Bioanalyzer (according to Agilent RNA 6000 Nano kit standard protocol; samples were loaded into RNA Nano chip and run using Agilent 2100 Bioanalyser machine). For each sample three biological replicates of cell growth, harvesting and RNA extraction was performed. The RNA was pooled. RNA was provided to the Adelaide Cancer Genomic Research Facility (Adelaide Australia) for library preparation and sequencing (RNAseq) using the Ion Proton platform (Life Technologies). The analysis pipeline used Bowtie2 [55] align reads from both samples to the H. influenzae RdKW20 reference genome (Genbank: NC_000907), followed by processing with SAMtools and BEDTools to generate a mapped read count for the reference genes from each sample. Differential expression analysis was performed using R program within the package edgeR and DESeq.

In Week 0 and Week 16, intake was below 2/3 of the RDA in 42 9% o

In Week 0 and Week 16, intake was below 2/3 of the RDA in 42.9% of the participants [29]. Mean carbohydrate intake was below the RDA [28] at all time points, whereas fat and protein intakes were above 100% of the RDA [28]. Table 3 AZD1390 mouse recommended daily allowance covered for energy, macronutrients and folic acid at three time points Nutrient ≤ 2/3 RDA > 2/3 RDA ≤ RDA > RDA Macronutrients (%) Protein Week 0 – - 100 Week 8 – - 100 Week 16 – - 100 Carbohydrate Week 0 35.7 64.3 – Week 8 – 92.9 7.1 Week 16 – 100 – Fat Week

0 – - 100 Week 8 – - 100 Week 16 – - 100 Vitamins (%) Folic acid mTOR inhibitor Week 0 42.9 42.9 14.3 Week 8 – - 100 Week 16 42.9 50.0 7.1 RDA, recommended daily allowance. Training profile The results in Figure 1 show the training loads recorded during the study period. Training load is reported here as training time, RPE and distribution among three levels of intensity during the intervention (STp) and post-intervention periods (NSTp). There were no statistically significant differences in training time

between STp and NSTp. Figure 1 Comparison of training variables throughout the experimental trial. *Statistically significant difference (P < 0.05) STp vs NSTp. Overall BMN 673 concentration RPE during STp was significantly lower (P < 0.05) than during NSTp. With regard to the durations of different RHR levels (training intensity), a significant difference (P < 0.05) was found for the 60%–80% range, which accounted for 30.35% of the total training time during STp, and for 35.87% of the training time during the NSTp. There were no significant differences for training intensity levels in the <60% range or the >80% range. Bivariate analysis to calculate Pearson’s correlation coefficient detected statistically significant correlations (P < 0.01) between overall RPE and training intensity levels of 60%–80% RHR (r = 0.64) and >80% RHR (r = 0.76). Biochemical assays The results

of biochemical analyses are shown in Table 4. There were no significant changes in plasma folic acid at any time point, and all values were within the normal PAK5 range for the healthy population. However, plasma concentrations of Hcy increased significantly (P < 0.05) to above the normal range of values during the Week 8 and Week 16 periods compared to baseline values in Week 0. Regarding the relationship between plasma concentrations of Hcy and folic acid and training intensity, we found that both plasma concentrations showed a significant negative correlation (r = −0.75) (P < 0.01) with the level of intensity of <60% RHR. Bivariate analysis disclosed a significant negative correlation (P < 0.01) between Hcy and folic acid concentrations (r = −0.84) in Week 8. Table 4 Biochemical values of clinical and nutritional parameters at three time points N = 14 Study period Biochemical parameters Reference value Week 0 Week 8 Week 16     Mean SD Mean SD Mean SD Transferrin (mg/dl) 200 – 360 261.21 27.82 261.71 33.00 265.50 28.67 Prealbumin (mg/dl) 20 – 40 26.76 3.53 27.

Consequences and limits J Clin Densitom 2:37–44CrossRef 16 Kanis

Consequences and limits J Clin Densitom 2:37–44CrossRef 16. Kanis JA,

Johnell O, Oden A et al (2008) FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int 19:385–97PubMedCrossRef 17. WHO Collaborating Centre for Metabolic Bone Diseases (2008) FRAX WHO fracture risk assessment tool. Available at: http://​www.​shef.​ac.​uk/​FRAX/​. Accessed 27 April 2011 18. Briot K, CBL-0137 order Tremollieres F, Thomas T et al (2007) How long should patients take medications for postmenopausal P5091 solubility dmso osteoporosis? Joint Bone Spine 74:24–31PubMedCrossRef 19. Bone HG, Hosking D, Devogelaer JP et al (2004) Ten years’ experience with alendronate for osteoporosis in postmenopausal women. N Engl J Med 350:1189–99PubMedCrossRef 20. Kanis JA, Johansson SB-715992 solubility dmso H, Oden A et al. (2011) A meta-analysis of the effect of strontium ranelate on the risk of vertebral and non-vertebral fracture in postmenopausal osteoporosis and the interaction with FRAX((R)). Osteoporos Int In press 21. McCloskey E, Johansson H, Oden A et al. (2011) Denosumab reduces the risk of clinical osteoporotic fractures in postmenopausal women, particularly in those with moderate to high fracture risk as assessed

with FRAX. Abstract OC15. Osteoporos Int 22 (suppl 1):S103 22. McCloskey EV, Johansson H, Oden A et al (2009) Ten-year fracture probability identifies women who will benefit from clodronate therapy—additional results from a double-blind,

placebo-controlled randomised study. Osteoporos Int 20:811–7PubMedCrossRef 23. Cummings SR, Black DM, Thompson DE et al (1998) Effect of alendronate on risk of fracture in women with low bone density but without vertebral fractures: results from the Fracture Intervention Trial. JAMA 280:2077–82PubMedCrossRef 24. Vittinghoff Tobramycin E, McCulloch CE, Woo C et al (2010) Estimating long-term effects of treatment from placebo-controlled trials with an extension period, using virtual twins. Stat Med 29:1127–36PubMed”
“Introduction Osteoarthritis (OA) and osteoporosis (OP) are two common, age-related disorders that are associated with considerable morbidity. The relationship between OA and OP has been examined in both community studies and case series. Studies of adult twins have shown an association between birth weight and bone mineral density (BMD) [1]. The twin studies have also shown that lumbar degenerative disc disease is similar in many ways to OA with evidence that degenerative disc disease is associated with a higher BMD at the hip and lumbar spine [2]. Data from Finland have shown that persons with poor height gain during childhood have an increase in their risk of hip fracture several decades later [3]. It has been suggested that the presence of OA protects against osteoporosis-related fractures [4–7], and that there is an inverse relationship between the two conditions [8–11].

It has recently been proposed as the official primary barcoding m

It has recently been proposed as the official primary barcoding marker for fungi (Deliberation of 37 mycologists from 12 countries at the Smithsonian’s Conservation and Research Centre, Front Royal, Virginia, May 2007). More than 100 000 fungal ITS sequences generated by conventional Sanger sequencing are deposited in the International Nucleotide Sequence Databases and/or

other databases [11], providing a large reference material for identification of fungal taxa. However, these data are to some extent hampered by misidentifications or technical errors such as mixing of DNA templates or sequencing errors [12]. Furthermore, a large amount of partial ITS sequences generated by next-generation sequencing has Proteasome inhibitor recently been deposited in public sequence databases. The ITS region includes the ITS1 and ITS2 regions, separated by the 5.8S gene, and is situated between the 18S (SSU) and 28S (LSU) genes in the nrDNA repeat unit (Figure 1). The large number of ITS copies per cell (up to 250; [13]) makes the region an appealing target for sequencing environmental substrates where the quantity of DNA

present is low. The entire ITS region has commonly been targeted with traditional Sanger sequencing approaches and typically ranges between 450 and 700 bp. Either the ITS1 or the ITS2 region have been targeted in recent high-throughput sequencing FG-4592 molecular weight studies [14–17], because the entire ITS region is still too long for 454 sequencing or other high-throughput sequencing methods. Using high-throughput sequencing, thousands of sequences can be analysed from a single environmental sample, enabling in-depth analysis of the fungal diversity. Various primers Aldol condensation are used for amplifying the entire or parts of the ITS region (Figure 1). The most commonly used primers were published

early in the 1990′s (e.g. [18, 19] when only a small fraction of the molecular PF-04929113 mouse variation in the nrDNA repeat across the fungal kingdom was known. Several other ITS primers have been published more recently [20] but have not been used extensively compared to the earlier published primers. However, little is actually known about the potential biases that commonly used ITS primers introduce during PCR amplification. Especially during high-throughput sequencing, where quantification (or semi-quantification) of species abundances is also possible to a certain degree (although hampered by factors like copy-number variation), primer mismatches might potentially introduce large biases in the results because some taxonomic groups are favoured during PCR. Our main focus in this study is on the two dominating taxonomic groups of fungi in the Dikarya, Ascomycota and Basidiomycota.

At different time points postinfection, mice were sacrificed and

At different time points postinfection, mice were sacrificed and the spleen, stomach, and cecum were harvested. The numbers of bacteria in these three organs were determined. No bacteria were found in the stomach at 12–24 hours postinfection, consistent with the fact that the systemicSalmonellainfection does not spread to the organ or is cleared at this early time point (data not shown). The expression of the tagged proteins in the bacterial strains isolated from the spleen and cecum of infected mice was detected using Western analysis

with an anti-FLAG antibody and normalized Emricasan cost using the expression of bacterial protein DnaK as the internal control (Figure6A–B). Normalization of samples was also carried out by loading total protein extracted from the same CFU (e.g. 5 × 107CFU) of bacteria in each lane. The protein level of DnaK did not appear to be significantly different in bacteria from the spleen and cecum as similar amount of the DnaK protein was

detected from 5 × 107CFU of each bacterial strain regardless of infection route (intraperitoneally or intragastrically) or time point postinfection (12–24 hours or 5–7 days) (data not shown). Figure 6 Western analyses of the expression of the tagged proteins from the internalized bacterial strains T-prgI (lane 1), T-sipA (lane 2), T-sptP (lane 3), T-spaO (lane 4), T-sopE2 (lane 5), and T-sipB (lane 6) recovered from spleens. BALB/c mice were intraperitoneally heptaminol infected with 1 × Doramapimod concentration 105CFU of the tagged strains, and internalized bacteria were Selleck MK-8931 recovered from the spleens at 5 days

post inoculation. The expression of bacterial DnaK was used as the internal control (B). Protein samples were reacted with antibodies against the FLAG sequence (A) and DnaK (B). Each lane was loaded with material from 5 × 107CFU bacteria. Salmonellastrains isolated from both the spleen and cecum at 18 hours postinfection continued to express PrgI, SpoE2, SipB, and SipA. In contrast, a substantial level of SpaO was detected inSalmonellaisolated from the cecum but not the spleen, while that of SptP was observed inSalmonellarecovered from the spleen but not the cecum (Figure7A–B). These results suggest that SpaO and SptP are differentially expressed bySalmonellawhen they colonize specific organs and tissues. Figure 7 Level of the tagged proteins from the internalized bacterial strains T-prgI, T-sipA, T-sptP, T-spaO, T-sopE2, and T-sipB recovered from the spleen (A) and cecum (B). BALB/c mice were intraperitoneally infected with 1 × 107or 1 × 105CFU of the tagged strains, and internalized bacteria were recovered from the spleen at 18 hours or 5 days post inoculation, respectively.

In the present study, despite its selectivity, plate cultivation

In the present study, despite its selectivity, plate cultivation was partly successful in reflecting increased fungal diversity and/or detecting major indicator fungi arising from building material sources in settled dust samples. This was not, however, consistent PI3K Inhibitor Library concentration across all samples, as the masking effect of certain

species occurring in very high concentrations was considerable. ERMI is an index derived from a set of qPCR assays used to describe the indoor fungal burden [20]. Here, the ERMI values were below 5, i.e. relatively low compared to US homes. Vesper et al. reported ERMI values greater than 5 for the highest quartile of randomly selected US homes, whereas over 75% of homes with asthmatic children were above this value [54]. However, no similar data are available in Finland. In the present study, the ERMI index was observed to reflect the overall level of diversity. In our sample material, the group 1 members A. pullulans and Eurotium spp. occurred in significant concentrations in all studied dust samples and in similar concentrations in the index and reference buildings. This suggests that the placement of these species in the indicator group may not be appropriate. Conclusions The present study is the first to assess the effect of water damage and

its remediation on indoor mycobiota using universal culture-independent community characterization 4EGI-1 research buy methods, and also the first study to compare nucITS sequencing results with an extensive panel of mold specific qPCR assays. Observations were made from a small number of buildings, and thus the findings are descriptive and need to be studied further with larger data sets. In the studied buildings, we found selleck chemical indications of elevated fungal diversity, as well as the presence of fungi attributable to building growth to be associated with water damage. The community variation between Ilomastat supplier buildings was significant,

and calls for the analysis of larger data sets in order to understand the dynamics of microbial communities between building structures, surfaces and dust. Our results demonstrate that culture-based methods used to characterize indoor mycobiota provide an underestimate of the total diversity, and that many unknown or unsequenced fungal species are present in dust. Despite this, the majority of abundant phylotypes in nucITS clone libraries were affiliated with previously recognized indoor taxa, indicating that culture-dependent and independent methods agree on the dominant indoor taxa. Clone library sequencing was seen as an effective means to characterize indoor communities, and proves extremely useful when attempting to answer research questions on ‘real’ fungal diversity in a given environment.

Interestingly, many transposases and phage related


Interestingly, many transposases and phage related

genes were present in 8 strains (Figure  1A). The heterogeneous nature of the 18 kb region and the extremely high conserved 15 kb region found in our study are largely in agreement with earlier results. These proposed to separate the locus into a Sg1 specific and a L. Selleckchem PRIMA-1MET pneumophila specific region [34, 35]. Microarray analysis of Sg1 and non-Sg1 strains have identified 3-Methyladenine clinical trial a 13 kb region (ORF 16–28) which is present in all L. pneumophila strains and a 20 kb region (ORF 1–15) generally found in all Sg1 strains [34]. The two regions were defined based on the LPS-biosynthesis loci of the Sg1 strain Paris [30]. To determine the putative breakpoint between both regions is difficult. However, based on our analysis of the structural composition we would rather separate the LPS biosynthesis locus between lpg0763 (ORF 13) and wecA (ORF 14). This is in agreement with recent data, since the genes wecA (ORF 14) and galE (ORF 15) were demonstrated to be present in non-Sg1 strains with lower amino acid similarities when compared to Sg1 strains (55-61%) [35]. The initially mentioned ORF 13 is located next to the breakpoint region. In total, four different types of ORFs were found in the analyzed region of Sg1 strains

here named ORF 13-a, -b, -c and –A. In each of the strains Lens, 130b, HL 06041035 and Görlitz 6543 two ORFs were found. These strains carried a putative VX-661 in vivo conserved protein of unknown function (here referred to as ORF 13-A). A transposase-disrupted ORF 13-A was present in strain 130b (Figure  1A). Additionally,

the strains carried an ORF which shared features of the radical S-adenosylmethionine (SAM) superfamily (CDD: cd01335) named ORF 13-c (Additional file 1: Table S2). Interestingly, all these strains lacked the ORF 12. However, even though the strain Lorraine lacked ORF 12 as well, it carried only a single ORF 13-A variant. A distinct selleck screening library ORF of unknown function with amino acid similarity to ORF 13-A of only 38%, here named ORF 13-a, was present in the remaining strains with the exceptions of a truncated form in strains RC1, Philadelphia 1 and Paris. Philadelphia 1 and Paris shared high similarities with ORF 13-a but a deletion led to a frame shift resulting into three smaller fragments (pooled as ORF 13-b) (Table  3). Table 3 Amino acid similarity of the L. pneumophila Sg1 specific LPS-biosynthesis region from lpg0769-lpg0761 (ORF 1 – ORF 15) of strain Philadelphia 1 to other Sg1 strains Amino acid similarity [%]* Gene name of L. pneumophila Philadelphia# Knoxville# Benidorm# Bellingham# Allentown# OLDA# Camperdown# Heysham# Sg1 strain Philadelphia 1 Paris 2300/99 Alcoy Corby Uppsala 3 Ulm 130b Lens HL 0604 1035 Görlitz 6543 Lorraine RC1 Camperdown 1 Heysham1 lpg0761 (galE) ORF 15 100 100 100 100 100 97.1 96.0 99.8 99.8 99.8 100 100 100 lpg0762 (wecA) ORF 14 100 99.5 99.5 99.5 99.5 93.4 93.1 93.7 93.