Arsenic is toxic to most living cells. The two soluble inorganic forms of arsenic are arsenite (+3) and arsenate (+5), with arsenite the more toxic. Prokaryotic metabolism of arsenic has been reported in both thermal and moderate environments and has been shown to be involved in the redox cycling of arsenic. No arsenic metabolism (either dissimilatory arsenate reduction or arsenite oxidation) has ever been reported in cold environments (i.e. < 10°C).Our study site is located 512 kilometres south of the Arctic Circle in the Northwest Territories, Canada in an inactive gold mine which contains mine waste water in excess of 50 mM arsenic. Several thousand tonnes of arsenic trioxide dust are stored in underground chambers and microbial biofilms grow on the chamber walls below seepage points rich in arsenite-containing solutions. We compared the arsenite oxidisers in two subsamples (which differed in arsenite concentration) collected from one biofilm. 'Species' (sequence) richness did not differ between subsamples, but the relative importance of the three identifiable clades did. An arsenite-oxidising bacterium (designated GM1) was isolated, and was shown to oxidise arsenite in the early exponential growth phase and to grow at a broad range of temperatures (4-25°C). Its arsenite oxidase was constitutively expressed and functioned over a broad temperature range.The diversity of arsenite oxidisers does not significantly differ from two subsamples of a microbial biofilm that vary in arsenite concentrations. GM1 is the first psychrotolerant arsenite oxidiser to be isolated with the ability to grow below 10°C. This ability to grow at low temperatures could be harnessed for arsenic bioremediation in moderate to cold climates. Arsenic's toxic and medicinal properties have been appreciated for more than two millennia . Its twoProkaryotic arsenic metabolism has been detected in hydrothermal and temperate environments and has been shown to be involved in the redox cycling of arsenic -10. The Although arsenic metabolism has been detected in both moderate and high-temperature environments, and mesophilic and thermophilic arsenite oxidisers have been isolated, no arsenic metabolism (either dissimilatory arsenate reduction or arsenite oxidation) has ever been detected in cold environments (i.e. < 10°C). One such environment with high concentrations of arsenic is the Giant Mine, one of Canada's oldest and largest gold mines. It is located a few kilometres north of Yellowknife, Northwest Territories, 62° north of the equator and 512 kilometres south of the Arctic Circle. Gold was produced from 1948 to 1999 by roasting arsenopyrite (FeAsS)-bearing ore. The mine now contains approximately 300,000 tonnes of arsenic trioxide, stored in underground chambers . TemperaHere we report the detection, isolation and characterisation of an aerobic psychrotolerant arsenite-oxidising bacterium from a subterranean biofilm in the Giant Mine. Unlike other characterised arsenite oxidisers, this organism is capable of growing below 10°C and is the first heterotrophic organism to oxidise arsenite in the early exponential phase of growth. We also compare the diversity of arsenite oxidisers in two subsamples of the biofilm that vary in arsenite concentrations.ca. 4°C). Water taken from the top of the biofilm in 2006 contained 14.01 mM total soluble arsenic and 2.56 mM arsenite. Samples taken in 2007 from the top and bottom of the biofilm contained 9.57 mM total soluble arsenic and 9.22 mM arsenite (top) and 9.16 mM total soluble arsenic and 6.01 mM arsenite (bottom). The concentration of arsenite in the 2006 sample was substantially lower than that of the equivalent top sample from 2007. The reason for this was probably microbial arsenite oxidation during storage as the liquid was not extracted from the 2006 sample until 18 days after collection whereas the liquid was extracted immediately from the 2007 samples. SEM examination of the biofilm revealed the presence of threadlike extracellular polymeric substances and distinct microorganisms  to Polaromonas sp. JS666, a cis-dichloroethene-degrading bacterium isolated from granular activated carbon from Dortmund, Germany  [2O3) or manganarsite (Mn3As2O4(OH)4)].The 2006 biofilm sample was used for mineral characterisation. Mineral precipitates were characterised using beamline X26A at the National Synchrotron Light Source. MicroXANES (at the arsenic K edge) and microXRD followed methods similar to those described previously . The XAN24.3H2O]   using a Perkin Elmer - Analyst 300.Cultures were analysed for total arsenic and arsenite using a JY Ultima 2C ICP-OES using the methods described previously -25.Samples from the top and bottom of the 2007 microbial biofilm were examined using a Jeol JSM-6480LV high-performance, variable pressure analytical scanning electron microscope (SEM) operating in low-vacuum mode using 7-11 kV accelerating voltage and a spot size of 29 nm. Prior to examination, samples were mounted on 12.5-mm pin stubs with sticky carbon discs, freeze-dried in liquid nitrogen using a MODULO 4 k instrument for 30 minutes, and gold coated using a Polaron E5000 instrument.3 as carbon source] and heterotrophic (containing 0.04% (w/v) yeast extract) growth with arsenite .Genomic DNA was extracted using the Wizardeviously , with Pheviously . [GM1 16® Genomic DNA purification kit (Promega) and from the top and bottom biofilm samples using the PowerSoil DNA isolation kit (MoBio Laboratories). The degenerate oligonucleotides used to amplify a portion of the aroA gene were primer set #2 as described previously [aroA PCR products from GM1 and the two biofilm samples were cloned into pBluescript II KS+ (Stratagene). Both strands of the cloned GM1 aroA gene and 50 individual aroA clones from each library were sequenced using the T7 and T3 promoter primers at the WIBR (UCL). Database searches were performed using BLASTP [aroA sequence GenBank accession number: EU106602. The TOP and BOT aroA library sequences GenBank accession numbers: FJ151018-FJ151051].Genomic DNA was extracted from GM1 using the Wizardeviously  using Phg BLASTP . [GM1 paSequences were aligned with CLUSTALX 2.0  using deAchromobacter sp. str. SY8 (ABP63660), Aeropynum pernix (NP_148692), Agrobacterium tumefaciens str. 5A (ABB51928), 'Alcaligenes faecalis' (AAQ19838), Burkholderia multivorans (YP_001585661), Chlorobium limicola (ZP_00512468), Chlorobium phaeobacteroides (ZP_00530522), Chloroflexus aurantiacus (YP_001634827), Herminiimonas arsenicoxydans (YP_001098817), Nitrobacter hamburgensis (YP_571843), NT-26 (AAR05656), Ochrobacterum tritici (ACK38267), Pseudomonas sp. str. TS44 (ACB05943), Pyrobaculum calidifontis (YP_001056256), Rhodoferax ferrireducens (YP_524325), Roseovarius sp. 217 (ZP_01034989), Thermus thermophilus str. HB8 (YP_145366), Thiomonas sp. 3As (CAM58792), Sulfolobus tokodaii str. 7 (NP_378391) and Xanthobacter autotrophicus Py2 (YP_001418831).Accession numbers of reference sequences used in AroA phylogenetic analysis are given in parentheses following the organism name: http://www.uga.edu/~strata/software/index.html which uses the rarefaction calculations given by Hulbert [http://toolkit.tuebingen.mpg.de/blastclust# based on a 99% identity threshold over 100% of the sequence length to create operating taxonomic units.Rarefaction calculations were performed to compare the DNA sequence diversity of the TOP and BOT libraries, and to assess whether full coverage of sequence diversity was obtained. This was performed with the program ANALYTICAL RAREFACTION 1.3  Hulbert  and Tipp Hulbert . SequencTHO performed the majority of the experiments . HEJ collected the samples from Giant Mine and oversaw the mineral characterisation. KAH-E did the arsenic analyses for the growth experiments. SRW performed the mineral characterisation of the biofilm. DKN oversaw the chemical analyses of the biofilm samples. SAW advised on the statistical analyses and edited the manuscript. JMS isolated GM1 and the DNA from the biofilm, conceived and coordinated the study. All authors read and approved the final version of the manuscript.


TP53 mutations have demonstrated a defective AR compared with normal controls. We have also previously demonstrated that AR is reduced as individuals age. Results from the current study on 108 twins aged 18–80 years confirm these earlier findings that the AR of lymphoid cells to DNA damage is significantly reduced with increasing age. In addition this twin study shows, for the first time, that DNA-damage-induced AR has a strong degree of heritability of 81% . The vital role of DNA-damage-induced apoptosis in maintaining genetic stability, its relationship with age and its strong heritability underline the importance of this area of biology and suggest areas for further study.Apoptosis is a physiological form of cell death important in normal processes such as morphogenesis and the functioning of the immune system. In addition, defects in the apoptotic process play a major role in a number of important areas of disease, such as autoimmune diseases and cancer. DNA-damage-induced apoptosis plays a vital role in the maintenance of genomic stability by the removal of damaged cells. Previous studies of the apoptotic response (AR) to radiation-induced DNA damage of lymphoid cells from individuals carrying germline  TP53, which in its wild-type form, is able to induce apoptosis when damage to DNA is too great to be repaired. The importance of this gene is emphasised by the fact that the TP53 gene is mutated in more than half of all cancers and inactivated in many more (TP53 mutations have a defective apoptotic response (AR) when subjected to gamma radiation .In the present study, we wished to test whether the DNA-damage-induced AR had a significant genetic basis and to confirm in normal individuals with a wide range of ages that AR is indeed related to age. To do this we measured AR in 54 pairs of twins (28 monozygotic and 26 dizygotic) from the TwinsUK adult twin registry in London consisting of unselected twin volunteers developed to study the genetics of age-related disease (www.twinsuk.ac.uk). These twins have been shown to be similar to age-matched singletons in the population in terms of disease-related and lifestyle characteristics  from the TwinsUK adult twin registry, which consists of volunteers unselected for any disease  and the cells were cultured for 70 h and either irradiated or mock-treated. Irradiation was carried out using a Gammacell 1000 Elite  containing a caesium 137 source and with a dose rate of 858 cGy min−1. Cells were subjected to a dose of 4 Gy. Following a further 24 h culture, cells were split into two aliquots and fixed in 70% ethanol. After removal of ethanol, 2 × 106 cell aliquots were subjected to treatment with 0.1 M HCl at 37°C for 12 min to extract low molecular weight DNA. After washes, cells were stained for DNA content by addition of propidium iodide  in a volume of 1 ml. Cells were stained for a minimum of 30 min before measurement of red fluorescence (PI), forward and 90° light scatter on a FACSCalibur flow cytometer . At least 10 000 cells per sample were scanned and data stored in list mode prior to analysis using CellQuest software. Doublet discrimination using pulse area/width analysis on the PI signal was used to remove cell clumps from the analysis. Measurement of the extent of apoptosis was performed by assessment of cells appearing in a sub-G1 peak on DNA profiles. The AR to radiation was defined as the increase in apoptosis seen when comparing the irradiated with the unirradiated sample (% apoptosis after 4 Gy – % apoptosis after 0 Gy). Examples of the apoptotic data for a high and low AR twin pair are illustrated in Peripheral blood lymphocytes were separated from 10 ml of blood by centrifugation on Histopaque and 5 × 10A=81; 95% confidence interval is 67–89%) and unique environmental effect specific to the individual . As it has been suggested that there may be a spreading of data with age , this was also assessed. The difference between AR for each twin pair was computed and this variable was called apoptotic difference. Subsequently, apoptotic difference was regressed on age.Apoptotic response was shown to be normally distributed and standard regression techniques were used to assess associations between age and AR, utilising the regression cluster option in P-value=0.648). Thus, there is no evidence that with ageing there is a ‘spreading’ of results which would tend to make twin pairs discordant with age.P=0.007), with a reduction in AR of 3% per decade of life, which is similar to that found in earlier studies (The mean age of the twins was 57 years (range 18–78) for the monozygotic pairs and 56 years (range 29–80) for the dizygotic pairs. The mean ARs in the two zygosity groups were also very close, namely 50% (range 27–73) for the MZ twins and 49% (range 30–69) for the DZ pairs. Given these findings the two zygosity groups were analysed together in terms of the relationship between age and AR. As illustrated in vs DZ pairs . This lack of correlation between telomere length and AR suggests that AR may not be associated with the same risk factors as TRF but with other, as yet unknown, lifestyle and disease-related factors. It would also be consistent with the idea that AR is controlled by different genes and processes to those that control telomere length.The fact that AR appears highly heritable in humans is consistent with a body of research in rodents that demonstrates significant strain differences in DNA-damage-induced apoptosis in various tissues (for a review see Apoptosis plays a central role in the maintenance of genetic stability and apoptotic defects are involved in various common diseases. Given these facts and the demonstration that we can measure the phenotype accurately in populations, a large scale family based or twin study is now needed to unravel the strong underlying genetic basis of the AR to DNA damage and to investigate environmental influences on this process.


Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers. Protein biomarkers in the blood are urgently needed for the diagnosis of a wide variety of diseases to improve health care. We aim to find a fast and cost-effective strategy to discover diagnostic protein biomarkers. Hundreds of diseases have already been investigated using microarray technology, measuring the mRNA expression of all genes in the disease-damaged tissues. We analyzed biopsy-based microarray data for 41 diseases in the public repository, identified genes with dysregulated mRNA expressions and detectable-protein abundance in the blood, and predicted them as candidate diagnostic protein biomarkers. We found that clinically and preclinically validated diagnostic protein biomarkers were significantly enriched in our predicted protein candidates for 22 diseases. We then measured the concentrations of ten predicted protein biomarkers in the serum samples from 39 renal transplant patients. Three of them were confirmed to be diagnostic of acute rejection after renal transplantation. All three proteins were further confirmed to be diagnostic of acute rejection in 63 cardiac transplant recipients. Our results show that publically available genome-wide gene expression data on disease-damaged tissues can be effectively translated into diagnostic protein biomarkers. The utility of serum and plasma proteomic techniques to find diagnostic biomarkers has received considerable attention and investment in recent years. However, the limited sensitivity of mass spectrometers, the dynamic range of protein concentrations, and the presence of high abundance proteins in blood samples are major challenges in the identification and verification of potential protein biomarkers in peripheral blood Since the development of gene expression microarrays more than a decade ago We have previously developed a methodology to determine gene expression signatures across 238 diseases from GEO. We have found that the molecular signature of disease-specific RNA across tissues is more prominent than the signature of tissue-specific expression patterns One field in urgent need of non- or minimally invasive protein biomarkers is solid-organ transplantation At the same time, a previous study also showed that there are similarities in the biology of the processes involved in the rejection of different transplanted solid organs Our first goal was to test the hypothesis that blood- and urine-detectable protein biomarker candidates could be identified by using tissue-based gene expression microarray data. Using previously described methods We applied our IRDDP methodology to each disease. First, we calculated a set of differentially expressed genes for each disease using the RankProd meta-analysis package at a percentage of false prediction (pfp) ≤5% For each disease, we then compared our candidate biomarkers with known diagnostic protein biomarkers in the GVK BIO Online Biomarker Database (GOBIOM). GIOBIOM is an independent manually curated knowledge base taken from global clinical trials, annual meetings, and journal articles We then applied IRDDP to the specific problem of finding serum biomarkers for the diagnosis of transplant acute rejection (AR). We integrated three biopsy-based gene expression microarray studies from pediatric renal, adult renal We combined the results of this study with data from two other transplant studies that we retrieved from GEO. One study compared biopsy samples from 13 AR patients with 19 STA samples after adult kidney transplant . Furthermore, 23 common-AR genes were involved in cell-mediated immune responses, . Finally, 23 common-AR genes were involved in a single pathway associated with inflammatory responses, antimicrobial responses, and cellular movement regulated by STAT-1 . The patients were from a pediatric and young adult renal transplant study. No patients were positive for BK virus infection, and no patient samples in the ELISA study were matched with samples used in the microarray study. The AR/STA samples were matched for recipient and donor gender, age, type of immunosuppression, time post-transplant, race, and type of end stage renal disease .−3, 1×10−4, and 5×10−3, respectively. Receiver Operating Characteristics (ROC) curves showed the ability of each individual protein to distinguish AR from STA , 0.019 (CXCL9), and 4×10−3 (CD44)  . The areWe evaluated the performance of a combined panel of PECAM1 and CXCL9 using a three-fold cross-validation. We randomly selected two thirds of the samples, trained a multinomial logistic regression model, and calculated the predictive performance on the remaining one third of samples. After repeating the process 1000 times, the average ROC curves showed an improvement on cardiac AR diagnosis and no additional improvement on renal AR diagnosis , suggestWe performed an immunohistochemistry study on our best-performing marker, PECAM1. The goal of the study was to compare its protein expression in AR and STA samples from renal, hepatic and cardiac allograft biopsies . In STA −5, Mann-Whitney U test) after renal transplantation . These results suggest that PECAM1 is a serum marker specific for allograft acute rejection, irrespective of the transplanted organ.Furthermore, our studies showed that PECAM1 protein was also significantly upregulated in the serum samples from AR patients compared with samples from patients with BK virus infection  and chronic allograft injury  method, which exploits the link between RNA changes in disease-affected tissue with serum detectable proteins coded by those RNA, yielding candidate proteins diagnostic for those diseases. We have demonstrated that this approach could be used to suggest candidate protein biomarkers for 22 diseases, and have shown the enrichment of known clinically and pre-clinically validated protein biomarkers in these candidate biomarkers. We applied our method to new and publicly-available microarray measurements on solid-organ transplantation, and identified and validated three cross-organ serum protein biomarkers for transplant rejection. Our results demonstrate that the integration of gene expression microarray measurements from disease samples, and even publicly-available data sets, can be a powerful, fast, and cost-effective strategy for discovering diagnostic serum protein biomarkers.We found that PECAM1, CXCL9 and CD44 proteins were significantly upregulated in the serum/plasma samples of both renal and heart transplant patients with acute rejection compared with patients with stable graft function. The abundance of CXCL9 in urine We found that the likelihood of finding protein biomarkers indicative for a disease increases with the number of available gene expression datasets. Many meta-analysis methods have been shown to improve the identification of differentially expressed genes Future work will involve taking markers validated in our pilot studies of cross-organ AR and testing their clinical utility in blinded prospective studies. These studies might also elucidate the prognostic value of these markers. Given that hundreds of thousands of microarray measurements are now publicly available and that this number is growing, RNA data-driven proteomics could provide hundreds of serum and urine biomarkers for other diseases.This study was approved by the Stanford University Institutional Review Board. Written informed consent was obtained from all the subjects.As previously described We have previously constructed a human biofluid proteome database Biochemical in Nature and Diagnosis in Application, and limited the retrieval to those with valid Entrez Gene IDs annotated as Protein in Chemical Nature. We mapped clinical indications in this database to disease concepts represented in the Unified Medical Language System (UMLS) We downloaded all diagnostic protein biomarkers from the GVK BIO Online Biomarker Database (GOBIOM) We collected 18 acute rejection (AR) and 18 stable (STA) biopsy samples from pediatric renal allograft recipients at the Stanford Hospitals, and measured gene expression profiles by microarrays. AR and STA samples were matched for recipient and donor gender, age, donor source, race, time post-transplant, HLA matches. Furthermore, all patients were under the same double (Tacrolimus and MMF) or triple immunosuppression protocols , and all had received Daclizumab induction therapy For ELISA experiments on renal transplant serum samples, we used previously collected serum samples from 19 AR and 20 STA patients who were not infected with BK virus. All serum samples were obtained within 24 hours of a clinically indicated or protocol graft biopsy, and each sample was matched with the patient's biopsy. AR samples were biopsy-proven according to the Banff classification . For specificity testing, an additional 10 samples were collected from patients with chronic allograft injury, who were defined as having an IFTA score ≥1 in vitro transcription reaction using a GeneChip IVT Labeling Kit (Affymetrix). Ten micrograms of fragmented cRNA was used for hybridization on Affymetrix Human Genome U133 Plus 2.0 microarrays according to the manufacturer's instructions. The raw and processed data have been deposited into GEO .Total RNA used for first-strand cDNA synthesis using a T7 promoter-linked oligo(dT) primer following the standard protocol for the Affymetrix One-Cycle cDNA Synthesis Kit . After second strand cDNA synthesis, biotin-labeled cRNA was prepared in an All three datasets  were normalized by the quantile-quantile method using dChip softwareThe number of heart AR genes was significantly less than those of kidney AR genes due to different platforms and organs. Publicly available heart AR data came from studies that used a 70mer spotted array from NIH/NIAID (GEO accession numbers GPL1053 and GSE4470). The array contained 8972 probes that corresponded to 8437 Entrez Gene Ids. This array was smaller than the Affymetrix U133 plus 2.0 array used for the pediatric renal study and the Affymetrix U95 array used for the publicly available adult renal study (GEO accession numbers GPL91 & GDS724).To make the number of AR genes comparable between pediatric and adult renal studies, we added an extra filter. We included only genes with a fold change ≥2 in the pediatric renal study. We obtained 2,805 pediatric renal AR genes, 2,316 adult renal AR genes, and 283 heart AR genes . When weTen proteins in serum were measured by using commercial ELISA kits. ELISA kits for PECAM1 (Cat. No. ab45910), CD44 (Cat. No. ab45912), and SELL (Cat. No. ab45917) were purchased from ABCam Inc ; an ELISA kit for SA100A4 (Cat. No. CY-8059) was purchased from MBL International ; ELISA kits for CCL4 (Cat. No. DMB00), CXCL11 (cat. No. DCX110) and CXCL9 (cat. No. DCX900) were purchased from R&D Systems . An ELISA kit for STAT-1 (cat. CBA034) was purchased from Calbiochem ; an ELISA Kit for BIRC5/Survivin (Cat. No. 900-111) was purchased from Assay Designs , and an ELISA assay for CCL8 was developed using the DuoSet ELISA Development System for human CCL8/MCP-2 from R&D Systems (Cat. No. DY281).Sample, reagent, and buffer preparation were done according to manufacturer manuals, and the assay was performed by following manual instructions exactly. Microwell plates were read by a SPECTRAMax 190 microplate reader . Protein concentrations were determined from a standard curve generated from standards supplied with the kits. Protein concentrations of PECAM1, CXCL9 and CD44 in the plasma samples of heart transplant patients were also measured with the ELISA kits specified above.Immunohistochemical staining was performed on 4 µm sections obtained from formalin-fixed paraffin embedded tissues using mouse monoclonal anti-human antibodies directed against PECAM-1 . Heat induced antigen retrieval was performed with Ventana Benchmark Autostainer. The staining was optimized using appropriate positive and negative controls.T-tests and chi-square tests were used to compare continuous and categorical clinical variables in patient demographics using SAS 9.1.3 . Protein concentration data from ELISAs were compared between AR and STA using the Mann-Whitney U test in R. P-values ≤0.05 were considered statistically significant. The enrichment of known protein biomarkers in differentially expressed genes was calculated using Fisher's exact test in R.Dataset S1Predicted diagnostics protein biomarkers on 22 diseases.(1.39 MB XLS)Click here for additional data file.Figure S1Histogram of overlapping genes in three transplant rejection microarray datasets after shuffling gene labels. We shuffled the gene labels in the three pediatric renal, adult renal and cardiac transplant rejection gene expression data sets, calculated differentially expressed AR genes in common. After repeating the processed 100,000 times, we plotted the distribution of the number of overlapping genes (blue histogram). The probability of getting 17 or more common genes by random is less than 1% and the probability of getting 24 or more common genes is less than 1×10-5 (red curve).(0.95 MB PDF)Click here for additional data file.Figure S2Shared pathway for AR across solid-organ transplantation. Among 45 genes that were upregulated in the AR compared with stable biopsy samples across transplanted organs, 23 of them were involved in a single pro-inflammatory pathway regulated by STAT-1. We tested 5 proteins (circled) from the pathway by ELISA, and three of them (red circle) were validated as cross-organ serum protein biomarkers for transplant rejection. The 18 untested AR proteins from the 45 are highlighted in the pathway, providing promising leads for further validation. Five of them (red star) were known to have detectable levels of protein expression in the normal serum or urine according to our human biofluid proteome database. Seven of them (blue star) were studied in knock-out mouse models, confirming their involvement in the immune system.(0.59 MB PDF)Click here for additional data file.Figure S3ROC curves predicting renal and cardiac AR using PECAM1+CXCL9. ROC curves showed three-fold cross-validation results on predicting renal (solid curve) and cardiac (dotted curve) transplant rejection (AR) from stable graft function using a combined panel of PECAM1 and CXCL9 proteins in serum  and plasma (cardiac). The true positive rates were showed as mean ± standard error across 1,000 three-fold cross-validation. It showed an improvement over individual proteins on cardiac AR and no improvement on renal AR.(0.89 MB PDF)Click here for additional data file.Figure S4Serum PECAM1 protein was significantly upregulated in AR than BK virus infection, chronic allograft injury, and stable graft function after renal transplant. The protein concentrations of PECAM1 was statistically significantly higher in the serum samples of 18 patients with acute rejection (AR) than 10 patients with BK virus infection (BKV), 10 patients with chronic allograft injury (CAN) and 20 patients with stable graft function (STA) serum samples after renal transplantation.(1.40 MB PDF)Click here for additional data file.Table S1Patient demographics of AR versus STA allograft biopsies in pediatric renal transplant microarray study.(0.05 MB DOC)Click here for additional data file.Table S2Forty-five AR genes commonly upregulated in biopsy-based gene expression studies across solid-organ transplantation.(0.09 MB DOC)Click here for additional data file.Table S3Patient demographics of renal transplant in ELISA study.(0.05 MB DOC)Click here for additional data file.Table S4Patient demographics of cardiac transplant in ELISA study.(0.05 MB DOC)Click here for additional data file.


Podospora anserina genome shows highly dynamic evolution since its divergence from Neurospora crassa.A 10X draft sequence of  Podospora anserina is a model used to study various aspects of eukaryotic and fungal biology, such as ageing, prions and sexual development.The dung-inhabiting ascomycete fungus P. anserina genome, linked to the sequences of a large expressed sequence tag collection. Similar to higher eukaryotes, the P. anserina transcription/splicing machinery generates numerous non-conventional transcripts. Comparison of the P. anserina genome and orthologous gene set with the one of its close relatives, Neurospora crassa, shows that synteny is poorly conserved, the main result of evolution being gene shuffling in the same chromosome. The P. anserina genome contains fewer repeated sequences and has evolved new genes by duplication since its separation from N. crassa, despite the presence of the repeat induced point mutation mechanism that mutates duplicated sequences. We also provide evidence that frequent gene loss took place in the lineages leading to P. anserina and N. crassa. P. anserina contains a large and highly specialized set of genes involved in utilization of natural carbon sources commonly found in its natural biotope. It includes genes potentially involved in lignin degradation and efficient cellulose breakdown.We present a 10X draft sequence of P. anserina genome indicate a highly dynamic evolution since the divergence of P. anserina and N. crassa, leading to the ability of the former to use specific complex carbon sources that match its needs in its natural biotope.The features of the  With one billion years of evolution , probablEumycete fungi have a huge impact on the global carbon cycle in terrestrial biotopes. Some species associate with plant and algae, helping them to scavenge mineral nutrients and to cope with various stresses, such as poor soils, desiccation, parasites and herbivore damage. These mutualistic relationships lead to better carbon dioxide fixation. In contrast, many species parasitize plants and algae, resulting in reduced carbon fixation , as wellSaccharomyces cerevisiae or Neurospora crassa, have been exploited as research tools to make fundamental biological discoveries. In recent years, a number of genome initiatives have been launched to further knowledge of the biology and evolution of these organisms. Presently, a large effort is dedicated to saccharomycotina yeasts (formerly hemiascomycetes) [Magnaporthe grisea, a rice pathogen, Fusarium graminearum, a wheat pathogen, Ustilago maydis, a maize pathogen, Cryptococcus neorformans and Aspergillus fumigatus, two human pathogens, have been published [Phanerochaete chrysosporium [N. crassa [Schizosaccharomyces pombe [A. nidulans, A. oryzae and A. niger [In addition to these global effects, eumycetes impact their biotope and humans in many ways. Indeed, humans have been using them for thousands of years as food, to process other plant or animal materials and to produce compounds of medicinal interest. A few species degrade human artifacts, causing permanent damage to irreplaceable items. Furthermore, due to their ease of handling, some species, such as mycetes) . Other eublished -14. In aosporium , of the . crassa  and Schies pombe , and thrA. niger -20, are Podospora anserina . Based on the sequence divergence between the P. anserina and N. crassa 18S rRNA, the split between the two species has been estimated to have occurred at least 75 million years ago [P. anserina to adapt to its biotope and permit this fungus to efficiently complete its life cycle, we have undertaken to determine the genome sequence of P. anserina and have compared it to that of N. crassa, its closest relative for which the genome sequence is already known. We started with a pilot project of about 500 kb (about 1.5% of the genome) [Because of its ease of culture and the speed of its sexual cycle, which is completed within a week, the saprobic filamentous ascomycete a Figure  has longa Figure . P. anseears ago . Howeverears ago , the samears ago , which dears ago ,27. It i genome)  and in tThe genome of the laboratory reference S mat+ strain was sequenced using a whole-genome shotgun approach . Ten-fold coverage permitted complete assembly of the mitochondrial genome as a single circular contig of about 95 kb and most of the nuclear genome Table . The latP. anserina genome and the main features of the CDSs are indicated in Table N. crassa are 9,826 CDSs at the Broad Institute [P. anserina is real or due to the differences in strategies used to annotate the genomes of these fungi. We have searched for orthologous genes between P. anserina, N. crassa, M. grisea and A. nidulans by the best reciprocal hit method and found that these four fungi share a common core of 2,876 genes , including two inteins . 5S rRNAnstitute  and 9,35nstitute . It remas Figure . Comparis Figure  indicateP. anserina life cycle (Table P. anserina. As seen in Table P. anserina proteome might be more complex than concluded from the present annotation. Of interest is the presence of 668 transcribed regions without obvious protein-coding capacity (designated here as 'non-coding transcripts'). Some of these produce ESTs that are spliced, poly-adenylated or present in multiple copies, suggesting that they originate from true transcription units. Although some genes may have been miss-called during annotation, these transcription units may correspond to transcriptional noise, code for catalytic/regulatory RNA or reflect polycistronic units coding for small peptides as described recently [S. cerevisiae PHO5 gene [Fusarium verticilloides [S. cerevisiae [In addition to genomic DNA sequencing, a collection of 51,759 cDNAs was sequenced. These originate from libraries constructed at different stages of the le Table . The resrecently ,34. FinaHO5 gene . In largilloides  and S. crevisiae  ESTs, sirevisiae .P. anserina and shown to strongly impact physiology [P. anserina genome, excluding a role in the observed phenotypes of the non-conventional translation insertion of this amino acid, which takes place at specific UGA stop codons [During the manual annotation of the genome, we detected 14 genes possibly requiring a frame-shift or a read-through to be properly expressed . In all cases, sequencing errors were discounted. In addition, ESTs covering putative read-through or frame-shift sites confirm six of them. Some of the putative frame-shifts and read-throughs detected could correspond to first mutations that will lead to pseudogene formation. However, four sites seem conserved during evolution, arguing for a physiological role. One of the putative -1 frame-shift sequences is located in the Yeti retrotransposon, a classic feature of this type of element. The 13 others affect genes coding for cellular proteins. Factors involved in the control of translation fidelity and affecting rates of frame-shift and read-through have been studied in ysiology -42. To dp codons . SimilarP. anserina and N. crassa genomes  yeasts [P. anserina and N. crassa. This discrepancy of genome evolution between the three groups of fungi might stem from the fact that P. anserina and N. crassa have likely had a long history of heterothallism, whereas Aspergilli and saccharomycotina yeasts are either homothallic, undergo a parasexual cycle or switch mating types. In heterothallics, the presence of interchromosomic translocation results in chromosome breakage during meiosis and, hence, reduced fertility. On the contrary, homothallism, parasexualilty or mating-type switching may allow translocation to be present in both partners during sexual reproduction and, therefore, have fewer consequences on fertility. Additionally, meiotic silencing of unpaired DNA (MSUD), an epigenetic gene silencing mechanism operating in N. crassa [We have explored in more detail the synteny between orthologous genes in the  Figures  and 4. S Figures . For exa) yeasts . The siz) yeasts , suggest) yeasts . However) yeasts ,47, unli. crassa , abolishP. anserina and N. crassa, with 37 orthologous genes, encompasses the mating type, a region involved in sexual incompatibility. A similar trend in conserved synteny in the mating-type region has been observed in the genus Aspergillus [P. anserina and N. crassa, the mating-type regions are known to display peculiar properties. In P. anserina, meiotic recombination is severely repressed around the mating-type locus [Neurospora tetrasperma [N. crassa, MSUD is inhibited in the mat region [de novo insertion or, alternatively, these species-specific genes have been lost in the other species. This lends credit to the hypothesis put forward to explain the mating-type region of Cryptococcus neoformans [P. anserina with that of M. grisea or A. nidulans is absent or limited to very few genes.Interestingly, the largest synteny block between ergillus . This supe locus , as alsorasperma . In N. ct region . Howeveroformans , in whicP. anserina [P. anserina genome (omitting the rDNA cluster). They can be divided into four categories: RNA genes , although one is 20 kb large. They occupy about 1.5% of the genome. An interesting feature of all these repeated sequences (except for the 5S RNA and tRNA genes) is that they are nested together , differ by 30 polymorphic sites. Twenty-five of them (83%) were C●G versus T●A polymorphisms and may, therefore, be accounted for by RIP, while the five others (17%) cannot. A reciprocal ratio was observed in other instances as seen for the largest segmental triplication with two copies present on chromosome 5 and one on chromosome 1. The three members share a common region of about 9 kb. In this region they differ by numerous indels and in about 20% of their nucleotides. More precisely, in the 4,000 nucleotide-long core region where the three sequences can unambiguously be aligned, there are 1,341 polymorphic sites in which at least one sequence differs from the others. For 418 of them (31%), two members have a C●G polymorphism whereas the other has a T●A polymorphism, strongly suggesting that these polymorphisms may originate from RIP, whereas for the remaining 923 (69%), the variations are small indels or single nucleotide variations not accounted for by RIP. Therefore, in the case of rainette, RIP polymorphisms are foremost, whereas for the triplication, non-RIP polymorphisms are more frequent. This is compatible with a model in which RIP occurs first and is then followed by accumulation of other types of mutations.Nearly all copies of these repeated elements differ by polymorphisms, many of which appear to be caused by repeat induced point mutation (RIP). RIP is a transcriptional gene silencing and mutagenic process that occurs during the sexual dikaryotic stage of many pezizomycotina . P. anse process ,55. It rP. anserina has experienced a fairly complex history of transposition and duplications, although it has not accumulated as many repeats as N. crassa. P. anserina possesses all the orthologues of N. crassa factors necessary for gene silencing , including RIP, meiotic MSUD [P. anserina, despite the construction of numerous transgenic strains since transformation was first performed [N. crassa are present in the genome of P. anserina. Although the P. anserina orthologues of these two proteins seem functional based on the analysis of the conserved catalytic motifs, no cytosine methylation has been reported to occur in this fungus [P. anserina questions their true impact on genome evolution, especially since this fungus contains less repeated sequences than N. crassa. Maybe the life strategy of P. anserina makes it less exposed to incoming selfish DNA elements, therefore diminishing the requirement of highly efficient gene silencing mechanisms. Supporting this assumption is the fact that, although heterothallic, formation of ascospores makes P. anserina pseudo-homothallic .The detection of segmental duplications raised the question of whether new genes evolved through duplication in the lineage that gave rise to possible . On the ications . In addiications . We prev. crassa , showingP. anserina, N. crassa, M. grisea and additional fungal CDSs. In some instances, the trees confirmed a recent duplication event in the P. anserina lineage after the split between P. anserina and N. crassa, because the phylogenetic analysis clustered the P. anserina paralogues with high statistical confidence. Figure Cochliobolus carbonum  genes. Seven PKSs were reported for N. crassa [M. grisea has 23 [P. anserina. A comparison of all these PKSs (data not shown) indicates a complex evolution process in which N. crassa has probably lost most of its PKSs and the two other fungi present several duplications yielding very different copies. Again, this does not permit us to establish whether the duplications are ancient or recent but followed by intense divergence. See also below for additional examples of losses and amplifications of genes involved in carbon source degradation.The phylogenetic analyses of the multigene families suggest that gene loss may also have occurred during fungal evolution. The putative chitinases related to the killer toxin of s Figure  suggests. crassa , while Ma has 23 , and we P. anserina, M. grisea and A. nidulans share 1,624 genes that seem to be lacking in N. crassa , even though M. grisea and A. nidulans are more distantly related to P. anserina than is N. crassa  and a comparison with the genome sequence of other fungi confirmed the adaptation capacity of P. anserina to growth on lignocellulose. The total number of putative glycoside hydrolases (GHs), glycoside transferases, polysaccharide lyases (PLs) and carbohydrate esterases (CEs) are similar to those of other ascomycetes, such as A. niger [M. grisea [P. anserina has the highest number of carbohydrate-binding modules (CBMs) of all the fungal genomes sequenced to date. Despite possessing similar numbers of putative enzymes, the distribution of the possible enzyme functions related to plant cell wall degradation  with 33 members, two-fold higher than the phytopathogen ascomycete M. grisea and the white rot basidiomycete P. chrysosporium. Similar patterns are visible for other cellulose-degrading families  and in the high number of CBM1 (possibly cellulose-binding) modules found, which are only equivalent to the sets of P. chrysosporium and M. grisea.As mentioned above, A. niger  and M. g. grisea , but P. P. anserina also has an increased potential for xylan degradation, with abundant enzyme sets in families GH10 and GH11, together with a relative abundance of exo-acting enzymes in families GH3 and GH43. Interestingly, no α-fucosidases of families GH29 and GH95 are found, suggesting a depletion of xyloglucan prior to growth of P. anserina. During the stage at which P. anserina grows in dung, significant amounts of cellulose, but also xylan, are still available. Xylan can be cross-linked to lignin through ferulic acid [P. anserina for lignin degradation, it is conceivable that this fungus particularly consumes lignin-linked xylan that could not be degraded by 'earlier' growing organisms that lack a lignin-degradation system. The relatively high number of putative CE1 acetyl xylan and feruloyl esterases found in P. anserina by comparison with other fungi correlates with this hypothesis.Strikingly, lic acid  or 4-O-mlic acid . In lighP. anserina. No members of GH28 (containing pectin hydrolases) were detected in the genome and only a single α-rhamnosidase (GH78). In comparison, A. niger contains 21 GH28 members and 8 GH78 members [A. niger. The auxiliary activities of GH88 and GH105, likely to act on pectin lyase degradation products, are equally absent from P. anserina while present in all pectin-degrading organisms .P. anserina to degrade plant polymeric substrates, growth was monitored on minimal medium plates containing lignin, cellulose, beech wood xylan, apple pectin, inulin and 25 mM sucrose, D-glucose, D-fructose or D-xylose cellulose. Growth on cellulose, xylan and D-xylose was significantly faster than on pectin, which agrees with the enzymatic potential based on the genome sequence as described above. No growth was observed on inulin or sucrose, while efficient growth was observed on D-fructose and D-glucose. This is in agreement with the absence of genes required to degrade sucrose and inulin from the genome of P. anserina. Overall, these data suggest that P. anserina has all the enzymatic complement necessary to efficiently scavenge the carbohydrates it encounters in its natural biotope. Selection has in fact evolved its genome to deal efficiently with these carbon sources, first by duplicating genes involved in cellulose degradation, as shown by the high number of GH61 CDSs, and second by deleting genes required to use carbon sources not commonly encountered . This demonstrates the high environmental pressure on evolution as well as the high level of specialization that occurs in the fungal kingdom.To evaluate whether the enzymatic potential reflects the ability of e Figure . P. anseP. anserina, a saprophytic model ascomycete, provides new insights into the genomic evolution of fungi. EST analysis indicates that similar to other eukaryotes, the transcription machinery generates a large array of RNAs with potential regulatory roles. Functional characterization of these RNAs might be one of the most interesting perspectives of this study. Strikingly, in addition to abundant inversions of chromosome segments and gene losses, substantial gene duplications were uncovered. Since this fungus displays a mild RIP, these findings allow us to ask whether the RIP process, when relatively inefficient, might be more of a genome evolution tool rather than a genome defense mechanism.Our analysis of the genome sequence of P. anserina. The polymorphic markers designed to plot scaffolds onto the genetic map are now successfully used for positional cloning. Gene deletion is facilitated thanks to the availability of the PaKu70 mutant strain, which greatly enhanced homologous recombination [N. crassa, mus-51 [PaPKS1 gene by a candidate gene approach permits us to envision the design of new genetic tools based on mycelium or ascospore color [Moreover, availability of the genome sequence has also already permitted the development of new tools that will bolster research in bination , similar, mus-51 . The idere color . The desP. anserina genome sequence has opened new avenues in the comprehensive study of a variety of biological processes. Of importance is the novel discovery of a large array of P. anserina genes potentially involved in lignin and cellulose degradation, some of which may be used for biotechnology applications. It also demonstrates how P. anserina is well adapted at the genome level to its natural environment, which was confirmed by the analysis of growth profiles. This result emphasizes the necessity to study several less well-tracked organisms in addition to those well known in the scientific community, as these may yield unexpected new insights into biological phenomena of general interest.As for other saprophytic fungi, the Podospora anserina Genome Project web site [The sequenced strain is the S mat+ homokaryotic strain . Cultureweb site .Nuclear genomic DNA was extracted and separated from mitochondrial DNA as described . Residua+ RNA was purified twice on oligo (dT)-cellulose columns . Anchored dT25 primers were used to obtain double-stranded DNA . Three cDNA libraries, corresponding to three ranges of molecular weight cDNA  were cloned using BstX1 adaptators in the pFL61 vector between the 5' (promoter) and 3' (terminator) sequences of the S. cerevisiae pgk1 gene as described previously [Two strategies were used to construct the cDNA libraries. First, a mycelium library was constructed in the yeast expression vector pFL61 . Total Reviously .+ mRNAs were extracted with the 'Oligotex mRNA Maxi Kit' (Qiagen), reverse transcribed and cloned with the 'cloneMiner cDNA library construction Kit' into plasmid pDONR222 .Second, total RNA obtained under various physiological conditions Table  was extrP. anserina was sequenced using a 'whole genome shotgun and assembly' strategy. We generated 510,886 individual sequences from two plasmid libraries of 3.3 and 12 kb insert sizes, and from one BAC library of about 90 kb insert size. This corresponds to genome coverage of 9.7-fold. The reads were automatically assembled using Arachne [The genome of  Arachne , and theTo connect the genome sequence with the genetic map , two appCU633438; CU633901; CU633867; CU633899; CU633445; CU633897. Chromosome 2: CU633446; CU640366; CU633447. Chromosome 3: CU633448; CU633447; CU633453. Chromosome 4: CU633454; CU633455; CU633456; CU633895. Chromosome 5: CU633457; CU633458; CU633459; CU633866; CU633871; CU607053; CU633461, CU633870, CU633865, CU633876. Chromosome 6: CU633898; CU638744; CU633463, CU633872. Chromosome 7: CU633900; CU633464; CU633873.Chromosome 1: P. anserina open reading frames longer than 20 codons that are evolutionary conserved in N. crassa were retrieved by TBLASTN analysis. Candidates with an e-value lower than 10-18 were conserved as hypothetical exons. Exons separated by less than 200 nucleotides were merged into putative CDSs and putative introns were predicted thanks to the P. anserina consensus sequences defined in the pilot project [N. crassa region. Candidates with an e-value lower than 10-5 were conserved and added to the putative CDSs. CDS and intron predictions were edited with Artemis [ab initio prediction with GeneID [N. crassa and Chaetomium globosum parameter files were performed on regions devoid of annotated features. Manual verification was then applied to improve prediction. This resulted in the definition of 10,545 putative CDSs.CDSs were annotated by a combination of semi-automatic procedures. First,  project . Then, 5 Artemis  and manuh GeneID  using thN. crassa 5S genes. They are encoded by a set of 87 genes, including 72 full-length copies dispersed in the genome. tRNAs were identified with tRNAscan [-4 was retained, producing a list of 28 annotated non-coding RNA genes or elements, including 12 spliceosomal RNAs, 15 snoRNAs (mostly of the C/D box class) and one thiamine pyrophosphateriboswitch.A canonic rDNA unit was assembled. A junction sequence between the left arm of chromosome 3 and an rDNA unit was observed, confirming the position of the cluster on this chromosome based on pulse field electrophoresis data . On the tRNAscan . A totaltRNAscan , Blast [tRNAscan  and YasstRNAscan  programstRNAscan  and ncRNtRNAscan  databaseP. anserina genome. As a first step, BLAST [A two-step strategy was used to align the EST sequences on the p, BLAST  served tp, BLAST   and their ancillary CBMs in fungi were searched by comparison with a library of modules derived from all entries of the Carbohydrate-Active enZymes (CAZy) database . Each prP. anserina were compared to the content and distribution of CAZymes in several fungal species  in order to identify singularities in the families' distributions and sizes per genome (data not shown). This allowed the identification of significant expansions and reductions of specific CAZyme families in P. anserina.Many of the sequence similarity-based families present in CAZy do not coincide with a single substrate or product specificity and, therefore, they are susceptible to grouping proteins with different Enzyme Commission (EC) numbers. Similarly to what has been provided for other genome annotation efforts, we aimed at producing annotations for each protein model that will survive experimental validation, avoiding over-interpretation. A strong similarity to an enzyme with a characterized activity allows annotation as 'candidate activity', but often for a safe prediction of substrate specificity, annotation such as 'candidate α- or β-glycosidase' may be provided, as the stereochemistry of the α- or β-glycosidic bond is more conserved than the nature of the sugar itself. Each protein model was compared to the manually curated CAZy database, and a functional annotation was assigned according to the relevance. All uncharacterized protein models were thus annotated as 'candidates' or 'related to' or 'distantly related to' their characterized match as a function of their similarity. The overall results of the annotation of the set of CAZymes from 2PO4, 0.3 g K2HPO4, 0.25 g MgSO4·7H2O, 0.5 g urea, 0.05 mg thiamine, 0.25 μg biotine and trace elements [2PO4. Standard M2 contains also 5.5 g/l dextrine, which was replaced by the other tested carbon sources. Sucrose, D-glucose, D-fructose, D-xylose, inulin, Apple pectin, carboxymethyl cellulose and Birchwood xylan were from Sigma-Aldrich  and were added before autoclaving. P. anserina was grown for 7 days at 25°C.M2 minimal medium contained per liter: 0.25 g KHelements , 12.5 g BAC, bacterial artificial chromosome; CAZymes, carbohydrate-active enzymes; CBM, carbohydrate binding module; CDS, coding sequence; CE, carbohydrate esterase; EST, expressed sequence tag; GH, glycoside hydrolase; MSUD, meiotic silencing of unpaired DNA; PKS, polyketide synthase; PL, polysaccharide lyase; RIP, repeat induced point mutation.RD and PS initiated the project. Funding was secured thanks to Genoscope and CNRS. PS coordinated the project. AC, JMA, BS, JP, VA, PW and JW acquired and assembled the sequence. FM, EE, PS, ASC, AB, HK, EC, MDC, MP, VC, SA, AB and CHS contributed to the assembly and juxtaposition of the sequence with the genetic map. BPL, RD and CHS constructed the cDNA libraries. SG and OL developed the bioinformatic tools. MP and CDS analyzed the EST database. OJ and RD performed the synteny analysis. DG identified the non-coding RNA. EE, OL and PS analyzed the repeated sequences and the gain/loss of genes. EE, OL, FM, VBL, RPdV, EB, PMC, EGJD, BH, REK and PS analyzed the genome content. EE, OL, FM, CDS, PW, RPdV, PC, VB, AS, RD and PS contributed to writing the manuscript. All authors read and approved the final manuscript.P. anserina genome map as defined by classic genetic markers and molecular markers, mainly microsatellites, that are polymorphic between strains S and T. Additional data file P. anserina genome. Additional data file P. anserina genome. Additional data file The following additional data are available with the online version of this paper. Additional data file P. anserina genome map as defined by classic genetic markers and molecular markers, mainly microsatellites, that are polymorphic between strains S and T.The Click here for fileCDSs potentially expressed through frame-shift and read-through.Click here for fileP. anserina genome.Transposons and transposon-like elements of the Click here for fileP. anserina genome.Segmental duplications in the Click here for fileCDSs putatively involved in genome protection mechanisms.Click here for fileCDSs involved in (hemi-)cellulose and pectin degradation.Click here for file


PR) genes. Arabidopsis non-expressor of PR1 (NPR1) is a regulatory gene of the SA signal pathway [Colletotrichum trancatum that causes anthracnose disease. We investigated if SAR in soybean is regulated by a pathway, similar to the one characterized in Arabidopsis.Systemic acquired resistance (SAR) is induced in non-inoculated leaves following infection with certain pathogenic strains. SAR is effective against many pathogens. Salicylic acid (SA) is a signaling molecule of the SAR pathway. The development of SAR is associated with the induction of pathogenesis related  or infection with the oomycete pathogen, Phytophthora sojae. In P. sojae-infected plants, SAR was induced against the bacterial pathogen, Pseudomonas syringae pv. glycinea. Soybean GmNPR1-1 and GmNPR1-2 genes showed high identities to Arabidopsis NPR1. They showed similar expression patterns among the organs, studied in this investigation. GmNPR1-1 and GmNPR1-2 are the only soybean homologues of NPR1and are located in homoeologous regions. In GmNPR1-1 and GmNPR1-2 transformed Arabidopsis npr1-1 mutant plants, SAR markers: (i) PR-1 was induced following INA treatment and (ii) BGL2 following infection with Pseudomonas syringae pv. tomato (Pst), and SAR was induced following Pst infection. Of the five cysteine residues, Cys82, Cys150, Cys155, Cys160, and Cys216 involved in oligomer-monomer transition in NPR1, Cys216 in GmNPR1-1 and GmNPR1-2 proteins was substituted to Ser and Leu, respectively.Pathogenesis-related gene npr1-1 mutants revealed that homoeologous GmNPR1-1 and GmNPR1-2 genes are orthologous to Arabidopsis NPR1. Therefore, SAR pathway in soybean is most likely regulated by GmNPR1 genes. Substitution of Cys216 residue, essential for oligomer-monomer transition of Arabidopsis NPR1, with Ser and Leu residues in GmNPR1-1 and GmNPR1-2, respectively, suggested that there may be differences between the regulatory mechanisms of GmNPR1 and Arabidopsis NPR proteins.Complementation analyses in Arabidopsis  Plants use a series of physical, preformed chemical and inducible defense mechanisms to protect themselves from pathogen attack. One of the most common inducible defense mechanisms is systemic acquired resistance (SAR). SAR can be triggered by infection with certain pathogenic strains. The induced resistance is typically effective against a wide range of pathogens including those taxonomically unrelated to the SAR inducing organism .R-gene mediated local disease resistance mechanisms [Salicylic acid (SA) is a signaling molecule of the SAR pathway ,5. Exogechanisms .PR) gene expression [PR gene expression and enhanced disease resistance [nahG) gene cannot accumulate SA and fail to express SAR development [PR genes, known as the SAR markers, have been identified from several plant species including tobacco and Arabidopsis [PR1 homolog, GmPR1 is induced by both SA treatment and infection of soybean leaves with soybean mosaic virus (SMV) [The development of SAR is associated with the induction of pathogenesis related  .non-expressor of PR1 (NPR1) is a regulatory gene of the SA signal pathway [NPR1 is also known as non-inducible immunity 1 (NIM1) [salicylic acid insensitive 1 (SAI1)[NPR1 gene encodes a protein containing a bipartite nuclear localization sequence and two protein-protein interactive domains, a multiple ankyrin repeat domain and a BTB/POZ domain [PR) genes [npr1 mutants with mutations in NPR1 are sensitive to SA toxicity. In the npr1 mutant plants, induction of PR genes and pathogen resistance by SA are abolished. In spite of their ability to accumulate SA, mutant plants are unable to induce SAR indicating that NPR1 is required for the SAR signal transduction pathway [ pathway . NPR1 is1 (NIM1)  or salic 1 (SAI1). The NPRZ domain -16. BothR) genes . The NPRR) genes ,18. Both pathway .NPR1 or its orthologues in transgenic plants has been shown to induce broad-spectrum resistance. For example, overexpression of NPR1 led to development of constitutive enhanced resistance against the bacterial pathogen Pseudomonas syringae and the oomycete pathogen Hyaloperonospora parasitica in Arabidopsis [NPR1 and the rice homolog of NPR1, NH1 resulted in enhanced resistance against the blast pathogen, Xanthomonas oryzae pv. oryzae in transgenic rice [NPR1 gene resulted in an enhanced level of resistance to bacterial and Fusarium wilts and a moderate level of resistance against gray leaf spot and bacterial spot diseases [NPR1 resulted in enhanced resistance against Fusarium graminearum that causes Fusarium head blight in wheat and barley [NPR1 or its orthologues can create broad-spectrum resistance in crop plants, and therefore, could be a suitable strategy in improving crop plants for disease resistance [SAR inducers have been used in various field studies on several crop plants to reduce disease incidence . In all bidopsis . Overexpnic rice ,22. In tdiseases . Similard barley . These ssistance .Colletotrichum trancatum that causes anthracnose disease [C. trancatum infection was noted in epicotyls, when cotyledons were pre-injected with C. trancatum and C. lagenarium spore suspensions [NPR1 copies in soybean. Non conservation of the Arabidopsis Cys216 residue in GmNPR1s suggests that either conserved Cys82, Cys150, Cys155, Cys160 residues are sufficient for GmNPR1s' monomerization or some other soybean cysteine residue(s) complements the Arabidopsis Cys216 function.In the United States, soybean suffers annual yield losses valued at more than 2.6 billion dollars from various pathogenic diseases . SAR in  disease . A signipensions . We invePR1 homolog, GmPR1 was shown to be induced by both SA treatment and infection of soybean leaves with SMV [GmPR1. We determined if GmPR1 is systemically induced in leaves following feeding of soybean roots with INA, a functional analog of SA.Earlier a soybean with SMV . It has GmPR1. We investigated the time course accumulation of GmPR1 transcripts in response to INA treatment and the data are presented in Figure GmPR1 transcripts were detected as early as 36 h following INA treatment; and thereafter, GmPR1 expression levels continued to increase during rest of the time course. These results confirmed earlier observation of SA-mediated GmPR1 expression in soybean leaves [We used INA, a functional analog of SA, to induce n leaves .GmPR1, a SAR marker, was induced in response to SMV infection, it has not been shown if GmPR1 is systemically induced in non-inoculated systemic leaves [GmPR1 gene, a SAR marker, is needed in non-inoculated systemic tissue to provide resistance against secondary infection. To determine if pathogenic infection can also lead to GmPR1 expression in systemic tissues, hypocotyls of young soybean seedlings were inoculated with an avirulent P. sojae race and GmPR1 expression was monitored at the site of infection and in non-inoculated systemic leaves. Induction of GmPR1 at infection sites was observed as early as on day 1 with a peak on day 2 post inoculation; and thereafter, induction continued until day 9 following inoculation . These results suggested that SAR pathway is active in soybean.Although it was demonstrated earlier that c leaves . For SARP. sojae and subsequently at 9, 13, 17 and 21 days after the inoculation leaves were infected with a virulent bacterial pathogen, P. syringae pv. glycinea (Psg). Four days following Psg inoculation colony forming units (cfu) of the pathogen in infected leaves were determined. Bacterial counts were comparable to that in agar control when leaves were inoculated with the bacterium nine days following P. sojae-infection  and GmNPR1-2 (Accession No. FJ418597). GmNPR1-1 and GmNPR1-2 cDNAs share 96% amino acid identity. Both GmNPR1-1 and GmNPR1-2 shared 40% amino acid identity with Arabidopsis NPR1 (AAC49611)  (Accession No. FJ418594) and p101F23E1-2 (GmNPR1-2) (Accession No. FJ418596). Data obtained from DNA blot analysis and characterization of BAC and cDNA clones strongly indicated that the diploidized tetraploid soybean contained two NPR1-like sequences. In order to confirm this conclusion, we conducted nucleotide sequence comparison of the GmNPR1 genes with the soybean genome sequence . GmNPR1 genes were identified in two scaffolds (scaffolds_159 and _213) of the soybean genome sequence. GmNPR1-1 is located in Scaffold_159 and GmNPR1-2 in Scaffold_213. Flanking regions of the two genes were compared for possible microcolinearity. High conservation of gene sequences between the two genomic regions suggested that the two GmNPR1 genes are homoeologous and were evolved during the polyploidization event  with 58% amino acid identity to GmNPR1-1. Duplicated copies of this sequence, GmNPR1-1-like-1 and GmNPR1-1-like-2, were identified from Scaffolds_15 and _90 of the soybean genome sequence . These two genes are located in homoeologous regions suggesting that they were also duplicated during polyploidization event  cDNA templates for GmNPR1 genes. GmNPR1-1 and GmNPR1-2 were constitutively expressed in all soybean organs investigated  DC3000 containing AvrRpt2. Three days after inoculation, we inoculated two young non-inoculated leaves with a virulent strain, Pst DC3000 and extent of SAR induction in these leaves was determined. Arabidopsis transformants carrying either of the GmNPR1 genes showed induction of SAR in response to infection with the avirulent strain, Pst DC3000 carrying AvrRpt2. There was about 9.5-fold reduction in the number of colony forming units (cfu) of Pst in GmNPR1-1-complemented plants, when preinoculated with the avirulent strain as compared to the MgCl2 control  SAR inducer, INA and (ii) P. sojae infection  SAR markers PR1 and BGL2 are induced following INA treatment and infection, respectively and (ii) SAR following infection  may co-operate with Cys82, Cys150, Cys155, and Cys160 for oligomerization of GmNPR1s in soybean or in the GmNPR1 complemented npr1-1 plants.In  Figures  and 8. Nt Figure . These r156 is shown to play important role in oligomerization of NPR1 in Arabidopsis [156 was mutated to Asp156, the efficiency of oligomer formation was reduced as compared to the wild type protein [156 was mutated to alanine, both GmNPR1 proteins complemented NPR1 function in the npr1-1 mutant , at 9, 13, 17 and 21 days after the inoculation with P. sojae race 4 mycellia or agar-with no mycelia. Psg cell suspensions (107 cells/ml) were prepared from 2-day old cell cultures grown in King's B liquid medium [7 cells/ml) or 10 mM magnesium chloride were used to inoculate the youngest trifoliate. Leaves infected with Psg were detached 4 days after inoculation. To estimate the size of bacterial population in the inoculated leaves, infected leaves harvested from three different plants per treatment per replication were homogenized in 3 mL 0.9% sodium chloride solution with pestle and mortar. Glycerol stocks were prepared from the homogenized samples and stored at -80°C until use. Different dilutions were plated on King's B medium, grown for 2 days at 27°C and colonies were counted to determine the number of colony forming units in each treatment. Experiment was performed with three biological replications. ANOVA was used to compare different treatments. To determine which of the eight treatments differ from each other, Fisher's least significant difference (LSD) comparisons were performed at P value of 0.05.The green hypocotyls of 7-day-old light-grown soybean cultivar Williams 82 seedlings were slit open for a length of 1.0 cm and e wounds . In contd medium . To facid medium . Ten micNPR1 was used to develop a primer pair . The soybean NPR1-like sequence, GmNPR1 was amplified from Williams 82 genomic DNA by conducting PCR at initial DNA denaturation temperature 94°C for 2 min followed by five cycles of 94°C for 30 sec, 65°C for 30 sec with an increment of -1°C per cycle, 72°C for 1 min; then thirty-five cycles of 94°C for 30 sec, 60°C for 30 sec, 72°C for 1 min, followed by a 10 min DNA extension at 72°C. The amplified products were sequenced to confirm the identity of GmNPR1 and used as a probe to screen a soybean Williams 82 BAC library and conduct DNA blot analyses [A soybean EST (Gm-c1004-4231) showing high identity to Arabidopsis analyses .BclI, EcoRI, HindIII, and PstI). Membranes were probed with the 32P-radiolabeled GmNPR1 sequence [DNA gel blot analysis was conducted as described previously . DNA wassequence .EcoRI, SstI, and XbaI DNA fragments of two individual BAC clones containing unique GmNPR1 sequences were cloned into the binary vector, pTF101.1 in E. coli DH10Bα and colonies were screened for DNA fragments containing GmNPR1 genes [GmNPR1-1 and GmNPR1-2 genes, respectively, under the regulation of their respective native promoters, were selected for further investigation.R1 genes . ResultaGmNPR1-1 and GmNPR1-2, respectively, were sequenced by sub-cloning restriction fragments in the pBluescript II KS (+) vector in E. coli DH10Bα. Sequencing was accomplished at the DNA Facility, Iowa State University. Sequence contigs were constructed using ContigExpress™ of the Vector NTI Suite program . A primer walking approach was applied in filling the gaps of sequence contigs. GmNPR1-1, GmNPR1-2 and Arabidopsis NPR1 (AAC49611) were compared using ClustalW program (European Bioinformatic Institute). Protein domains were identified by searching the conserved domain database (rpsblast).Inserts of p143K5Xb1-2.1 and p101F23E1-2 plasmids containing +) RNAs for the cDNA library were prepared from P. sojae-infected hypocotyl tissues of Williams 82 by using the polyAtract mRNA isolation system III . The library was constructed in EcoRI – XhoI sites of the plasmid vector pB42AD . Over 106 colony forming units (cfu) of the cDNA library were grown on 55 LB agar plates (150 mm × 15 mm) containing ampicillin. cDNAs of the bacterial colonies were blotted onto nylon membranes [GmNPR1 probe. Positive colonies were rescreened to identify pure colonies containing single GmNPR1 cDNA molecules. Two near full length GmNPR1 cDNAs representing both GmNPR1 genes were sequenced. Sequences were assembled by ContigExpress™ of the Vector NTI Suite program .A soybean cDNA library was constructed using the pBluescript II XR cDNA library construction kit . Poly. Reverse transcription (RT) was conducted using an oligo-dT primer (TTTTTTTTTTTTTTTTT) and M-MLV reverse transcriptase . A touchdown program used for PCR amplification of GmNPR1 was used in RT-PCR analyses. Following five touchdown cycles for primer annealing temperature from 65°C to 60°C, 25 cycles with annealing temperature at 60°C were applied in RT-PCR analyses. PCR products were electrophoresed in 2% agarose gels containing ethidium bromide (0.5 g/mL). The gels were run in 0.5× TBE buffer [Leaf, stem, flower, young pod, and root tissues were collected from Williams 82. Leaf, stem, and root tissues were harvested from three-week old plants. Tissues were frozen quickly in liquid nitrogen and stored at -80°C until their use for RNA isolation. Total RNA was isolated from individual samples using the Qiagen RNeasy Plant Mini kit . RNA concentration was determined using a Unico UV-2000 spectrophotometer . Gene-specific primers were designed for RT-PCR analyses  under continuous fluorescent light. Plants were fertilized weekly with the Miracle-Grow Excel water-soluble fertilizer 15-5-15 . Plasmids p143K5Xb1-2.1 and p101F23E1-2 containing GmNPR1-1 and GmNPR1-2 genes, respectively, in the binary vector pTF101.1 were transformed into Agrobacterium tumefaciens EHA101 by electroporation using a Cell-Porator Escherichia coli Pulser . npr1-1 mutant was transformed with either p143K5Xb1-2.1 or p101F23E1-2 [Seeds of Arabidopsis 0 seedlings were sprayed three times with 200 μM BASTA starting at 15 days after sowing, at a three day interval. The survivors were transferred into new soil. T1 seedlings were sprayed three times with 300 μM BASTA at a three day interval starting 21 days following sowing. BASTA resistant plants were used for GUS assays and SAR induction experiments.The Tnpr1-1 mutant, and npr1-1 mutant transformed with either GmNPR1-1 or GmNPR1-2 were selected for investigating the complementation of NPR1 function for SAR activity in the npr1-1 mutant background. Arabidopsis plants were sown in Sunshine LC1 mix  under 9 h light and 15 h dark regimen at 22°C with 55% humidity. After two weeks, seedlings were transplanted. Four weeks following planting fully developed two leaves (leaf number 3 and 4) were inoculated with 10 mM MgCl2 or an avirulent strain Pst DC3000 containing AvrRpt2. Leaves were inoculated with a syringe containing bacterial cells grown for 48 h in NYG medium containing rifampicin (50 μg/ml) and kanamycin (25 μg/ml) [2 to an optical density 0.2 at A600, which corresponds to ~108 cfu/ml. Bacterial suspensions were diluted to 107 cfu/ml in 10 mM MgCl2[7cfu/ml) was infiltrated in each leaf. Three days after inoculation, two younger systemic leaves (leaf number 5 and 6) were inoculated with the virulent strain Pst DC3000. Pst DC3000 contains empty vector with the kanamycin resistance gene. Bacterial cells for inoculation were collected by centrifugation and then resuspended in 10 mM MgCl2 to an optical density 0.001 at A600. After incubation for 3 days at 22°C, the inoculated leaves were harvested. Same size leaf disc was taken from each leaf and was washed twice in sterile water and homogenized in 1 ml 0.9% NaCl. The samples were vortexed and serial dilutions prepared in 0.9% NaCl were plated on NYGA solid medium containing rifampicin (50 μg/ml) and kanamycin (25 μg/ml), and viable colonies were counted after 2 d of growth at 28°C. The study was conducted with four biological replications. Two factor ANOVA was used to compare different treatments. To determine which of the eight treatments differ from each other, Fisher's least significant difference (LSD) comparisons were performed at P value of 0.05.Ecotype Columbia, 5 μg/ml) . Bacteri mM MgCl2. Two leaPst DC3000 and Pst DC3000 carrying the AvrRpt2 gene were used for inoculation experiments. The pathogen was grown in NYGA liquid medium containing rifampicin (50 μg/ml) and kanamycin (25 μg/ml) as described above. The leaves of (i) the Arabidopsis npr1-1 mutant carrying the BGL2-GUS fusion gene or (ii) the npr1-1 mutant plants carrying the BGL2-GUS and transformed with either GmNPR1-1 or GmNPR1-2 were infiltrated with either Pst DC3000 or Pst DC3000 carrying the AvrRpt2 gene (105 cfu/mL (OD600 = 0.002). The inoculated leaves were harvested three days after infiltration and stained with X-gluc to localize the GUS activity [activity .npr1-1 (BGL2-GUS) mutant or npr1-1 (BGL2-GUS) transgenic plants containing either GmNPR1-1 or GmNPR1-2 were uprooted from the soil and washed in water. Roots were dipped in 20 mL ddH2O in a 100 × 15 mm Petri dish for 24 h and then water was replaced with 0.5 mM INA for 24 h [Three-week old Arabidopsis for 24 h . FollowiP. sojae race 4 [P. sojae-infected tissues were harvested at 0, 1, 2, 3, 4, 5, 6, 9, and 14 days post inoculation, frozen in liquid nitrogen and stored at -80°C until their use for RNA isolation.Williams 82 seedlings were grown in trays containing soil. One-week old seedlings were stem-inoculated with the mycelia of e race 4 . Unifoli® GT nylon membranes  as described earlier [PR-1 gene, GmPR1 (AI930866) [PR1 probe (NM_127025) was PCR amplified from Arabidopsis genomic DNA for northern analyses of Arabidopsis RNA samples. The probes were labeled with α-32P (dATP) [Approximately 30 μg total RNAs per sample were fractionated by electrophoresis in 1% formaldehyde-agarose gels and blotted onto Zeta-Probe earlier . A soybeI930866)  was usedP (dATP) . HybridiGmNPR1 genes into a binary vector, transformed GmNPR1 genes into Arabidopsis, conducted sequence alignment and complementation analyses in Arabidopsis and, participated in drafting the initial manuscript and designing experiments. RF participated in SAR experiment in Arabidopsis and GUS assay. MKB participated in designing and coordinating research activities and drafting and finalizing the manuscript. All authors read and approved the final manuscript.DS carried out SAR experiment in soybean and Arabidopsis, isolated cDNA clones, conducted GUS assays and sequence alignments, participated in designing experiments and drafting the manuscript. IMT carried out DNA and RNA gel blot analyses, screened BAC library, cloned GmNPR1-like sequencesMicro-colinearity between homoeologous regions containing . mVISTA  programClick here for fileGmNPR1-1-like sequencesMicro-colinearity between homoeologous regions containing . mVISTA  programClick here for fileNPR1-like sequences with Arabidopsis NPR1Comparison of Soybean . GmNPR1-1-like sequence (BE801977.1) was used to identify two GmNPR1-like peptides, GmNPR1-like-1  and GmNPR1-like-2  from Scaffold_15 and Scaffold_90 of the soybean genome sequence, respectively . ClustalW analysis  essential for oligomerization of NPR1 (NP_176610.1).lysis  revealeClick here for file