The ability of anti-PLD antibodies to block PLD-mediated lipid raft rearrangement was investigated. In the absence of PLD, addition of pre-immune or anti-PLD serum did not significantly affect the number of punctate-staining cells compared to untreated HeLa cells (9.9%; negative control; Figure 2D). In the presence of PLD, 26.0% of HeLa cells displayed punctate staining (positive control; p < 0.05 compared to the negative control; Figure Luminespib 2D). In the presence of PLD, addition of pre-immune serum did not significantly affect the number of punctate staining cells as compared to the positive control (p = 0.25; Figure 2D).

In contrast, in the presence of PLD, the addition of anti-PLD antibodies significantly reduced the number of punctate staining

cells (p < 0.05 compared to the positive control; Figure 2D). Numbers of punctate staining cells under these conditions were not significantly different to untreated HeLa click here cells (p = 0.15; Figure 2D), indicating that the lipid raft rearrangement seen is specific to the action of PLD. Cholesterol sequestration by MβCD blocks lipid raft rearrangement by partially solubilizing GPI-anchored and transmembrane proteins [37] and preventing small lipid rafts from aggregating into larger, functional membrane platforms [20]. In the absence of PLD, only 9.9% of HeLa cells displayed punctate staining (untreated negative control; Figure 2D). Treatment of HeLa cells with 5 mM MβCD significantly reduced the amount of punctate staining cells to 7.4% (p < 0.05 compared with the negative control; Figure 2D), indicating that spontaneous lipid raft rearrangement was being inhibited. In the presence of PLD, 26.0% of HeLa cells displayed punctate staining (positive control; p < 0.05 compared to negative control; Figure 2D). Treatment of HeLa cells with MβCD significantly reduced the level of punctate staining to 10.5% (p < 0.05 compared with the positive control; Figure 2D). This value is similar to the amount of lipid raft rearrangement seen in negative control HeLa cells (9.9%; p = 0.54; Figure 2D). These

data indicate that PLD-mediated Montelukast Sodium lipid raft rearrangement can be inhibited by cholesterol sequestration. A. haemolyticum PLD is required for efficient bacterial adhesion to the host cell The ability of PLD to enhance lipid raft rearrangement may affect the interaction between the bacterium and the host cell. To test this hypothesis, wild type and pld mutant A. haemolyticum were assayed for their ability to adhere to HeLa cells. A pld mutant was constructed by allelic exchange and this mutant lost its hemolytic phenotype on TS agar containing 5% bovine blood and 10% Equi Factor. Hemolysis was restored to wild type levels upon check details complementation with pBJ61, carrying the pld gene (data not shown). The hemolytic phenotype was not affected by the introduction of the shuttle vector alone (data not shown). The ability of wild type or the pld mutants to adhere to HeLa cells was determined. Wild type A.

18 0.3058 1.59 0.2077 parasitic 0.06 0.9398 0.97 0.4072 1.63 0.1820 1.40 0.2122 0.99 0.4289 0.77 0.6458 5.75 0.0169 predatory 1.52 0.2190 2.57 0.0537 1.07 0.3628 1.30 0.2541 0.45 0.8420 0.68 0.7289 0.31 0.5761 Acari omnivorous

& parasitic 1.16 0.3141 3.76 0.0110 0.07 0.9743 0.41 0.8735 1.69 0.1220 0.61 0.7885 4.66 0.0315 Hymenoptera parasitic 2.13 0.1204 0.68 0.5659 4.76 0.0028 0.51 0.7970 0.73 0.6279 1.48 0.1518 0.59 0.4446 Araneae predatory 0.47 0.6260 1.95 0.1213 1.16 0.3255 0.64 0.6975 1.05 0.3911 0.93 0.5025 4.13 0.0429 Collembola detritivorous 0.97 0.3785 11.91 <0.0001 3.14 0.0253 2.68 0.0146 0.29 0.9404 Androgen Receptor Antagonist chemical structure 0.75 0.6660 10.39 0.0014 Coleoptera detritivorous 0.16 0.8514 23.63 <0.0001 3.10 0.0268 1.95 0.0716 0.31 0.9322 2.51 0.0084 0.07 0.7964 predatory 2.67 0.0708 18.81 <0.0001 1.28 0.2792 0.68 0.6669 1.60 0.1455 1.77 0.0730 2.85 0.0923 Table 3 The effects of endophyte status (E+ = endophyte infected, E- = endophyte-free, and manipulatively endophyte-free = ME-), water and nutrient treatments (C = control, N = nutrient, W = water, and WN = water + nutrient), plant origin (A = Åland, G = Gotland, and S = coastal Sweden; K = Tubastatin A cultivar “Kentucky 31”) and

plant biomass on abundances of herbivores, detritivores and predators     Herbivores Detritivores Omnivores Parasitoids Predators df F p F p F p F p F p Endophyte status (E) 2 0.35 0.7036 0.80 0.4484 0.29 0.8330 2.14 0.1192 2.31 0.1007 Treatment (TRT) 3 3.10 0.0268 15.05 <0.0001 0.71 0.5471 0.63 0.5987 15.38 <0.0001 Plant origin (PO) 3 1.61 0.1870 3.99 0.0080 0.52 0.5932 4.59 0.0036 1.04 0.3730 E * TRT 6 2.62 0.0169 2.63 0.0165 0.50 0.8089 0.55 0.7674 0.68 0.6681 E * PO 6 0.74 0.6199 0.26 0.9565 0.87 0.5156 0.75 0.6119 1.04 0.3987 TRT * PO 9 1.94 0.0449 0.72 0.6885 0.44 0.9142 1.46 0.1591 1.45 0.1662 Plant biomass 1 9.67 0.0020 10.28 0.0015 0.04 0.8338 0.78 0.3781 Orotidine 5′-phosphate decarboxylase 3.22 0.0734 Table 4 Means and standard errors (SE) of taxonomic groups of invertebrates showing statistically significant (a) interactive effects of water and nutrient treatments (C = control, N = nutrient, W = water, and WN = water + nutrient) and endophyte status (E+ = endophyte infected,

E- = endophyte-free, and manipulatively endophyte-free = ME-), (b) effects of plant origin (A = Åland, G = Gotland, and S = coastal Sweden; K = cultivar “Kentucky 31”) and (c) interactive effects of water and endophyte status (see Table 2)         Taxon a       selleck Herbivorous Diptera Omnivorous Diptera Collembola Treatment Endophyte status n mean SE mean SE mean SE C E+ 39 2.7 2.7 1.2 0.37 9.4 1.76 E- 39 3.4 3.4 0.5 0.14 10.2 2.03 ME- 40 3.7 3.7 0.6 0.12 11.7 2.54 W E+ 39 3.2 3.2 0.7 0.15 20.7 3.27 E- 40 2.6 2.6 0.6 0.13 14.3 2.31 ME- 39 2.1 2.1 0.8 0.25 11.4 1.81 N E+ 32 2.4 2.4 0.6 0.14 21.8 3.36 E- 37 2.4 2.4 0.5 0.13 28.7 5.10 ME- 34 3.6 3.6 0.6 0.13 25.9 3.66 WN E+ 38 3.9 3.9 0.7 0.18 33.7 6.22 E- 34 4.6 4.6 1.6 0.36 18.8 3.87 ME- 34 3.3 3.3 0.5 0.14 22.0 3.

Analysis of gene sequence similarity and phylogeny Sequence data were edited and assembled in Omiga 2.0 and EMBOSS GUI (European Molecular Biology Open Software Suite [56] and gene alignments were manually checked and optimized using BioEdit v.7.0.9

[57] and MEGA 4 [58]. GC content and the location of polymorphic sites were analyzed using Omiga 2.0 and FaBOX [59] (http://​www.​birc.​au.​dk/​software/​fabox). All seven see more genes (flaA, recA, pyrH, ppnK, dnaN, era, and radC) were concatenated using Se-Al ver.2.0a11 [60], giving a final alignment of 6,780 nucleotides (including gaps). The range of intraspecific sequence similarity (%) for each gene was calculated using the sequence identity matrix program implemented in BioEdit. Nucleotide polymorphism in each gene was evaluated by quantifying the nucleotide diversity per site (Pi) using DNA Sequence Polymorphism software (DnaSP 5.10) [61].

Maximum Likelihood (ML) and Bayesian methods were used to analyze both individual genes, and concatenated gene sequence datasets. The optimal substitution model and gamma rate heterogeneity for XAV-939 concentration individual genes and combined dataset were determined using the Akaike Information Criterion (AIC) in MrModeltest ver. 2.2 [62]. Maximum likelihood (ML) trees were generated using GARLI ver. 0.96 [63] with support calculated from 100 bootstrap replicates. Bootstrap support (BS) values ≥ 70% were considered to have strong support. Partitioned Bayesian analyses (BA) were conducted using MrBayes v.3.1.2 [64], with two independent runs of Metropolis-coupled Markov chain Monte Carlo (MCMCMC) analyses, each with 4 chains and 1 million generations, with trees sampled every 100 generations. The level of convergence was assessed by checking the average standard deviation of split frequencies (<0.005). Convergence of the runs was also checked visually in Tracer ver. 1.5 [65], ensuring the effective sample sizes (ESS) were all above 200. Bayesian posterior probabilities (PP) were calculated by generating a 50% majority-rule consensus tree from the remaining sampled trees after discarding the burn-in (10%). PP values ≥ 0.95 indicate statistical

support. Thalidomide Detection of recombination and natural selection A codon-based approach implemented in HYPHY 2.0 [41] was used to analyze selection pressures within the seven individual CBL0137 chemical structure protein-encoding genes, using a neighbor-joining model. Genetic algorithm recombination detection (GARD) was first used to identify any possible recombination breakpoints within each gene. Single likelihood ancestor counting (SLAC) was employed to calculate the global nonsynonymous (d N) and synonymous (d S) nucleotide substitution rate ratios (ω = d N/d S), with 95% confidence intervals; and to test the selection of variable codon sites based on the most appropriate nucleotide substitution model and tree topology, with a critical p-value of 0.05.

These data indicate that the expression of GDF3 increase the number of CD24 and CD44 double-positive cells during tumorigenesis. Expression levels of GDF3 in implant tumor cells We finally confirmed RSL3 solubility dmso that GDF3-transfected F1 and F10 cells continued to express GDF3 in implant tumors. RT-PCR analyses of excised tumors suggested that the transfected F1/F10 cells expressed the mRNA of GDF3 10 days after implantation although the levels of GDF3 mRNA decreased after 10 days compared to day 0 (Figure 6A). A negative control Soxl5 and a positive control β-actin were not affected

by GDF3 transfection. Protein expression of GDF3 in F1 and F10 cells was examined by Western blotting using antibody against GDF3. A representative blotting profile is shown in Figure 6B. The protein as well as mRNA Barasertib clinical trial amounts of GDF3 were similar in F1 and F10 cells (Figure 6A,B). The results infer that the GDF3 message is translated into functional protein in these tumor cells and forced expression of GDF3 are still minimally expressed 10 days after transfection in these cells. Figure 6 (A) RT-PCR analysis of the GDF3 message in F1/F10 cells. F1/F10 cells were transfected with the plasmid for expression of GDF3 (upper panel). Cells just before inoculation (indicated as 0 day) and cells isolated from tumors on day 10 after inoculation (indicated as day 10) were ITF2357 mw prepared and

adjust the cell numbers. These cells were lysed and total RNA was extracted from the lysates. RT-PCR was performed to detect GDF3 as well as Soxl5 (nagative control, center panel) and PIK3C2G β-actin (positive control, lower panel). PCR cycles are 32 rounds, 3 times less in those shown in Fig. 2C,D (B) Cell lysate (day 0) was subjected to SDS-PAGE (left 10% gel, right 8% gel) followed by immunoblotting. Lower panel-

Commassie brilliant blue (CBB) staining of the blot. Upper panel- blots GDF3 band visualized by treating with anti-GDF3 mAb and then HRP-labeled 2nd Ab. No relevant band of GDF3 was detected by CBB staining. Discussion We have shown that GDF3 mRNA increased during tumorigenesis in mouse melanoma B16-F1 and B16-F10 cells. Although the genotypic and phenotypic differences of these sublines of the same cell line origin was described earlier [32], genes responsible for their tumorigenic difference have not been fully elucidated. We found that GDF3 overexpression promotes tumorigenesis of mouse melanoma by B16-F1 and B16-F-10 cells but not hepatoma by G1 or G5 cells. Moreover, ectopic expression of GDF3 increased CD24 expression in both B16-F1 and B16-F10 cells. Human GDF3 is primarily expressed in embryonal carcinomas, testicular germ cell tumors, seminomas, and breast carcinomas. However, the role of GDF3 in tumorigenesis has not been shown yet. This is the first report that establishes a positive role of GDF3 in tumorigenesis.

Mol Med 2002, 8: 725–32.PubMed 26. Galligan L, Longley DB, McEwan M, Wilson TR, McLaughlin K, Johnston PG: Chemotherapy and TRAIL-mediated colon cancer cell death: the roles of p53, TRAIL receptors, and c-FLIP. Mol Cancer Ther 2005, 4: 2026–36.CrossRefPubMed 27. Longley DB, Wilson TR, McEwan M, Allen WL, McDermott U, Galligan L, Johnston PG: c-FLIP inhibits chemotherapy-induced colorectal cancer cell death. Oncogene 2006, 25: 838–48.CrossRefPubMed Competing interests The authors declare that they selleck products have no competing interests. Authors’ contributions XD and GB participated in the preparation of the tissue sections and the construction of the siRNA vectors, and helped in coordinating

the work. GB also participated in data analysis and interpretation and in BI 10773 in vitro manuscript preparation. XH and QQ have been involved in western blot analysis, PCR assays and IHC and ICC assays of c-FLIP expression. HZ and FY participated

in cell culture and cellular work. JL participated in study design and critical revision of the manuscript. QM participated in the study design and coordination and helped to revise the manuscript. All authors read and approved the final manuscript.”
“Introduction A number of genes for apoptosis play an important role in tumorigenesis [1]. Several gene abnormalities were reported as prognostic Inhibitor Library price markers of non-small cell lung cancer, such as p53 [2]; however, these processes are complex and remain unclear. The abnormal expression of p53 is frequently reported in a variety of cancers [3]. p53 mutations are generally more common in Calpain smokers than in nonsmokers and an excess of G to T transversions of p53 has been described as a molecular signature of tobacco smoke mutagens in smoking-associated lung cancers. There are also mutational

hotspots (codons 157, 158, 245, 248, and 273) in the p53 gene in lung cancer [4]. Several reports have shown that p53 expression is a prognostic marker in non-small cell lung cancer [2]. p53 protein is a tumor suppressor gene and mediates cell cycle arrest or programmed cell death [5, 6]. These p53-mediated events were triggered through the transactivation of specific genes, including p21, GADD45, cyclin G1, Bax, and fas [7, 8]. Recently, we reported that p53AIP1, which is a new potential mediator of p53-dependent apoptosis, is associated with prognosis in non-small cell lung cancer [9]. p53AIP1 is not normally expressed in any tissues except the thymus, but is induced when Ser-46 of p53 was phosphorylated after severe DNA damage [10, 11]. Only a few papers have reported p53AIP1 function in cancer biology and it has not been well investigated [9, 12]. On the other hand, survivin is a member of the IAP gene family, which has been implicated in both the inhibition of apoptosis and mitosis regulation [13].

Given that perfectly complete genome sequences are rare and as the price for genome sequencing decreases, there are likely to be more and more species sequenced by those interested in the allure of new

datasets rather than the complete genome per se. As eukaryote taxa begin to be included in truly genome-level analyses (as distinct from simply mining genomes for individual genes and loci), there are also likely to be more missing data and parts of genomes that cannot necessarily be easily compared and homologized (e.g. junk DNA; although this has yet to be determined if it is SBE-��-CD clinical trial indeed problematic). The 44-taxon phylogenetic analysis presented here thus represents the future of phylogenomic analyses in scope and complexity. The presence

of two chromosomes in all species Vibrionaceae has been of interest and investigated by many selleck workers, but the origin and purpose of the second, smaller chromosome is subject to speculation e.g.[11]. While the total number of genes for species of Vibrionaceae is very similar to the total number of genes for those related bacteria with a single chromosome (e.g. Shewanellaceae), the second chromosome is not of similar composition to the first chromosome. It is smaller and more size variable [1]. It is considered a chromosome and not a plasmid, however. Chromosomes are distinguished from plasmids by the presence of “essential” genes required under all circumstances (i.e. not only when certain stresses are present) and in that the timing of replication of chromosomes occurs once

Autophagy Compound Library datasheet per cell cycle while plasmids could possibly replicate more than once during a cell cycle or not at all [12]. When the first Vibrionaceae (Vibrio cholerae) genome sequence was completed [11], there were found to be few “housekeeping” and mostly “hypothetical” genes present on the small chromosome compared to the larger chromosome. From this, the authors hypothesized that absorption and expansion of an unrelated plasmid was the most likely source of the small chromosome. Vibrio gazogenes, Salinivibrio costicola, and Aliivibrio logei were chosen as candidates for genome sequencing because the bulk of previous genome sequencing has focused Meloxicam on pathogenic species and strains. While Vibrio gazogenes has been classified in the genus Vibrio and yet in previous study of the Vibrionaceae family [9], it was placed within Photobacterium. There is little else in the literature regarding its phylogenetic placement, so it seemed to be a good candidate for genome sequencing. It is generally found in salt marshes and other marshy areas and produces red-pigmented colonies [13]. Salinivibrio costicola, is part of a clade of lesser-known species of Vibrionaceae, which also includes the species that were members of Enterovibrio and Grimontia.

The accumulation of neutrophils in the skin lesions, similar with Sweet syndrome (acute febrile neutrophilic dermatosis) supporting the inclusion of PG within the spectrum of neutrophilic dermatoses [3]. The frequency of pathergy (development of new lesions or aggravation of existing ones following local injuries) suggests altered inflammatory responses to nonspecific stimuli. The widely accepted hypothesis is that PG has a complex and multifactorial pathogenesis, including genetic predisposition, paraneoplastic check details or para-immune phenomena, and undefined infectious agents [4, 5]. The most common clinical classification includes

four major types: ulcerative, pustular, bullous, and vegetative [6, 7]. Other particular forms have also been described: peristomal, genital, mucosal, extracutaneous, and postoperative [8–11]. Herein, the authors present a patient with postoperative PG in association with renal cell carcinoma and chronic lymphocytic leukemia. Case Report A 62-year-old male patient presented with renal carcinoma. The tumor was removed by partial nephrectomy in cold ischemia without undesirable events. Histology confirmed a well-differentiated

renal cell carcinoma with histologically negative margins. The patient also suffered from stable chronic lymphocytic leukemia treated with rituximab and hypothyroidism under substitution with l-thyroxine. Five days after nephrectomy, a progressive painful

ulceration developed rapidly at the site of incision. Selleckchem S3I-201 The lesion was deep and had an overhanging violaceous border. The left lumbar area was indurated and erythematous (Fig. 1a). Fig. 1 Pyoderma gangrenosum: a extensive ulceration at the site of incision with violaceous borders at the periphery; b the ulceration after 12 days of corticotherapy The patient aminophylline became febrile and his white blood cells (WBC) rose from 6,100 to 56,000/mm3. C-reactive protein (CRP) levels increased from 1.4 to 259 mg/L. At this point, a wound selleck kinase inhibitor infection was suspected. He was empirically treated with antibiotics (ciprofloxacin, then imipenem and doxycycline) but its condition progressed relentlessly. Ultrasound and computer tomography scans failed to identify an abscess. Surgical wound revision did not identify any sign of bacterial infection. Preoperative, intraoperative, and postoperative wound culture remained negative. However, blood culture was positive for Staphylococcus haemolyticus, and imipenem was changed for vancomycin. Despite broad-spectrum antibiotics, there was a sustained expansion of the skin lesion. PG was suspected and the patient was referred to a dermatologist. A biopsy specimen of the edge of the ulceration showed a phlegmonous nonspecific inflammation without being able to differentiate between a necrotizing wound infection and PG. Microbiology of the skin specimen was negative.

It is notable that

the PTS/glycosidase systems seem to be present in gut/commensal bacteria and others such as Clostridium difficile that can colonise the gut. Therefore, it would appear that adaptation to the intestinal niche seems to be associated with the presence of substantially higher numbers of genes encoding glycosidase enzymes, particularly those involved in the hydrolysis of disaccharides and oligosaccharides of plant origin. Genes for the metabolism of sugars other than lactose are almost entirely absent from the more nutritionally CX-4945 fastidious dairy strains. Another interesting observation was that the degree of similarity between the genes/protein sequences from Lb. helveticus DPC4571 and Lb. acidophilus NCFM was generally much higher than between Lb. acidophilus NCFM and any of the other strains. While Lb. acidophilus NCFM and the other gut and multi-environment strains had very similar complements of glycosidase genes, the sequence

similarity was much lower (with the exception of a few Lb. johnsonii genes) than between the NCFM/DPC4571 sequences, even though there were substantial differences in glycosidase gene content between Lb. acidophilus NCFM and Lb. helveticus DPC4571. The loss of a significant number of glycosidase genes together with the high degree of similarity between the remaining genes suggests that Lb. helveticus DPC4571 this website has undergone a relatively recent loss of sugar metabolism capacity relative to its divergence from Lb. acidophilus NCFM. Of the sugar metabolism genes analysed, only one (lba_1689) can be used in our barcode as

a gut organism indicator. Bile Salt Hydrolases Intestinal bacteria can experience a wide number of stresses in the intestinal tract including Dichloromethane dehalogenase those caused by low pH and presence of bile. In this respect, bile salt tolerance is thought to be an important aspect of survival for bacteria which inhabit the intestinal tract. Most intestinal isolates of lactobacilli and some lactobacilli involved in food fermentations exhibit bile salt hydrolase activity [22, 23]. These enzymes catalyze the hydrolysis of conjugated bile acids, which enter the small bowel in bile and are important for the emulsification, digestion and absorption of dietary lipids present in the proximal small bowel [24]. It has been suggested that deconjugation of bile acids is a detoxification method and protects the cells from conjugated bile. Conversely, negative effects of bile salt hydrolase activity have also been reported including cases of contaminated small bowel syndrome, impaired lipid absorption, EX 527 solubility dmso gallstone formation, and increased risk of colon cancer [25]. In Lactobacillus-free mice, bile salt hydrolase activity was reduced by 87%, revealing that lactobacilli are the main contributors to bile salt hydrolysis [23].

The samples from aCO2 and eCO2 were well separated by the first axis of RDA with 19.4% explained

by the first axis and a total of 47.6% explained with microbial communities (p = 0.047). Similar RDA results were obtained for subsets of functional genes, with 48.1% of the total variance explained for the C check details cycling genes (p = 0.037) and 48.2% of the total variance explained for the N cycling genes (p = 0.044). Within these variables, all detected functional genes and subsets of those genes were significantly different between CO2 treatments (p = 0.001). Figure 6 Biplot of redundancy analysis (RDA) of entire functional gene communities of soil samples from aCO 2 and eCO 2 conditions. Open circles represent samples PF-6463922 collected from aCO2, whereas solid circles represent samples

collected from eCO2. Four soil variables: soil N% at the depth of 0–10 ( SN0-10) and MK-4827 10–20 cm (SN10-20), soil C and N ratio at the depth of 10–20 cm (SCNR10-20) and soil pH (pH), and five plant variables: biomass of C4 plant species Andropogon gerardi (BAG) and Bouteloua gracilis (BBG), biomass of legume plant species Lupinus perennis (BLP), below ground plant C percentage (BPC), and the number of plant functional groups (PFG), were selected by forward selection based variance inflation factor (VIF) with 999 Monte Carlo permutations. To better understand the relationships between the functional structure of soil microbial communities and the plant and soil variables, variation partitioning analysis (VPA) was performed. After accounting for the effects of the CO2 treatment, the nine environmental variables could explain 42.2%, 42.8% and 42.8% of the total variation for all detected genes (p = 0.098), C cycling genes (p = 0.072), and N cycling genes (p = 0.087), respectively (Table 1). clonidine These five selected plant variables could significantly explain

24.7% (p = 0.010) of the variance for all detected genes, 24.6% (p = 0.022) for detected C cycling genes, and 25.1% (p = 0.014) for detected N cycling genes (Table 1). For the soil variables, these four selected variables also could explain 19.4% (p = 0.053) of the variance for all detected genes, 19.0% (p = 0.146) for detected C cycling genes, and 19.7% (p = 0.067) for detected N cycling genes (Table 1). Within these nine selected parameters, distinct differences were observed between the samples from aCO2 and eCO2 (p values ranged from 0.023 to 0.092), and the variance explained by four of the important variables, including pH (r = 0.411, p = 0.046), BLP (r = 0.378, p = 0.069), BPC (r = −0.345, p = 0.098), and PFG (r = 0.385, p = 0.063). Table 1 The relationships of microbial community functional structure to plant and soil characteristics by RDA and VPA a     All genes detected C cycling genes N cycling genes With nine selected variables First axis explanation (%) 19.

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