Community structures representing by mcrA gene based on 12 clone libraries from nSCS showed separate clusters indicating niche specificity, while, Methanomicrobiales, Methanosarcinales clades 1,2, and Methanomassiliicoccus-like groups of methanogens were the most abundant groups in nSCS sediment samples. Novel clusters specific to the SCS were identified and the phylogeny of mcrA gene-harboring archaea was updated. Quantitative polymerase chain reaction was used to detect mcrA gene abundance in all samples: similar abundance of mcrA gene in the surface layers of mangrove (3.4∼3.9 × 106 copies per gram dry weight) and of intertidal mudflat (5.5∼5.8 × 106 copies per gram dry weight) was observed, but higher abundance (6.9 × 106 to 1.02 × 108 copies per gram dry weight) was found in subsurface samples of both sediment types. Aerobic MOB were more abundant in surface layers (6.7∼11.1 × 105 copies per gram dry weight) than the subsurface layers (1.2∼5.9 × 105 copies per gram dry weight) based on pmoA gene. Mangrove surface layers harbored more abundant pmoA gene than intertidal mudflat, but less pmoA genes in the subsurface layers. Meanwhile, it is also noted that in surface layers of all samples, more pmoA gene copies were detected than the subsurface layers. Reedbed rhizosphere exhibited the highest gene abundance of mcrA gene (8.51 × 108 copies per gram dry weight) and pmoA gene (1.56 × 107 copies per gram dry weight). This study investigated the prokaryotic communities responsible for methane cycling in both marine and coastal wetland ecosystems, showing the distribution characteristics of mcrA gene-harboring communities in nSCS and stratification of mcrA and pmoA gene diversity and abundance in the Mai Po Nature Reserve.Communities of methanogens, anaerobic methanotrophic archaea and aerobic methanotrophic bacteria (MOB) were compared by profiling polymerase chain reaction (PCR)-amplified products of  Methane is a significant greenhouse gas drawing increasingly attention from the public recently as its contribution to global warming effect accounts for nearly 20% according to the previous research  and it iIn terrestrial ecosystems, natural wetland is one of the important emission sources, supplying almost 20% of the total global methane emission , especiaMethanogenesis is an important process in the carbon cycling, serving as the final step of decomposition of organic matter under anaerobic conditions when most of the electron acceptors are limited . Methano2 reduction, methyl-containing compounds, and acetate, but all requiring the final step of methane synthesis catalyzed by the methyl-coenzyme M reductase  genes. Three major clusters are designated to be ANME-1, 2 and 3, in which ANME-1 has been viewed as a ubiquitous cluster because of its close association with SRB and large genetic distances from the other two groups  and the coastal Mai Po Nature Reserve for a comparative analysis. In the study, the transition of methanogen community structures in shallow marine surface sediments along the gradient from Pearl River Estuary to deep pristine nSCS was ascertained. Moreover, responses of community structures to niche specificity were delineated by analysis of mcrA gene-based communities from all the samples in this study.Naturally occurring community of methanogens and ANME assembly and function in wetland area, coastal intertidal zone, pristine ocean, and methane seeps has been reported before. Comprehensive studies on the Figures 1 and Table 1 according to previous research  strictly following the manufacturer’s instruction. The extracted genomic DNA was used to run agarose gel electrophoresis with Gel Red  to evaluate the integral quality of extraction and preliminary estimation of DNA concentration. Primers for er ME2r’ .2+ , 1.5 μl of dNTPs , 5 μl of each forward and reverse primers (20 μM), and 0.25 μl of GoTaq Flexi polymerase . Gradient PCR method was used to optimize the suitable annealing temperature and 59.5∘C was chosen as the suitable annealing temperature because it is the lowest temperature to avoid non-specific amplicons and capable of obtaining the highest yield. Double identical PCR reactions were conducted for each sample to obtain more amplicons from potential templates and also minimize arbitrary aberrations from one experiment.The PCR reaction mixture with a final volume of 50 μl contained: 2 μl of template DNA (30∼50 ng/μl), 1 μl of bovine serum albumin aiming to potentiate synthesizing accuracy , 10 μl of 5× GoTaq Flexi Buffer (Promega) and 4 μl of MgPolymerase chain reaction products of each case were purified with QiagenII Gel Extraction Kit  following the guidance of manufacturer’s instruction. Then, clones were constructed into PMD-18T vector . The insert fragment sequences were amplified by M13F and M13R primer pairs, and then delivered for sequencing on a 3730xl DNA analyzer .mcrA and pmoA gene, primer pairs ME3MF+ME3MF-e (250:1), ME2r’ and A189F: GGNGACTGGGACTTCTGG, mb661R: CCGGMGCAACGTCYTTACC  in each reaction with a total volume of 20 μl for q-PCR using methodology provided by its manufacturer. For TCYTTACC  were empp-distance model algorithm, bootstrap value 1000 times for resampling. A less related arbitrary sequence should be used as a criterion for out-group to generate rootless phylogenetic tree.Obtained sequences were firstly checked to filter out meaningless ones and chimeras. All qualified ones were aligned by Clustal W given by MEGA5.05 together with those from previously reported methanogens and ANME groups archaea retrieved from GenBank to build phylogenetic permutation. Subsequently, MEGA5.05 was used to construct phylogenetic trees as the following default settings: neighbor-joint criterion, http://fastgroup.sdsu.edu/; Operational taxonomic units (OTUs) divided in the whole assemblages were gained through implementations of online software Fastgroup II  and Redundancy analysis (RDA) were conducted by the software CANOCO 4.5. Pearson moment correlation analysis was also employed to depict the relationships between physiochemical factors and abundance of mcrA gene sequences into NCBI.Accession numbers were obtained from GenBank database from KF595310 to KF596336 after depositing Figure 1. E401B, E407B, and CF5B were collected from the subsurface close to Taiwan Island, Luzon Island and center position of nSCS, respectively. E702S, E704S, E706S, E707S, E708S, and E709S formed a gradient line with anthropogenic pollution from the Pearl River Estuary to the nSCS. E702S is regarded as a deep-sea sample and the rest are shallow sea surface samples. E201S is coastal surface sample located east to the Pearl River Delta and E510S is coastal sample near Hainan Island. 08CF7S is a surface sample in the surrounding part of Taiwan Strait. Temperature of different sampling sites in nSCS decreased when the depth increased as shown in Table 1.Three major groups of samples in this study can be categorized by the depths and sampling sites: deep South China Sea, shallow South China Sea and coastal Mai Po wetland. There are mangrove, mudflat and reedbed in the coastal wetland. The geographic locations of samples from nSCS are showed in The intertidal mudflat and mangrove samples were collected at Site 1 and Site 3 in Mai Po Nature Reserve Figure . SurfacemcrA gene of the rest samples and effective PCR products and mcrA gene clone libraries from all the 12 SCS samples were established .Two rounds of PCR were engaged to retrieve amplicons from sediments in E401B, E407B, and CF5B using shorter PCR primer pairs Table , successed Table . The abumcrA gene was evenly distributed between intertidal mudflat and mangrove samples at Mai Po wetland by qPCR amplification and quantification. It was 3.4∼3.9 × 106 copies per gram dry weight and 5.5∼5.8 × 106 copies per gram dry weight for Site 1 and Site 3 of both layers, respectively , but subsurface sediments of sample 1B-B contained higher abundance of mcrA gene than the others. Extremely high mcrA gene abundance was detected in reedbed sample (L1), consistent with the high organics and less oxygen available with low redox potential. Abundance in subsurface samples had generally 1–3 orders of magnitude higher mcrA gene abundance than those in surface samples.Abundance of pmoA gene distribution and abundance observed from qPCR showed a reverse pattern as that for mcrA gene. Abundance of pmoA gene was 1–3 orders of magnitude lower than mcrA gene in corresponding samples, indicating the dominant role of mcrA genes in all samples. Mangrove samples harbored fewer pmoA genes than intertidal mudflat samples when examined at the same depth, whereas mangrove surface samples harbored more pmoA genes than that of mudflat. The most abundant pmoA gene was detected at site L1 among all the samples. The ratio of subsurface gene abundance to surface (B/S ratio) of both mcrA gene and pmoA gene was mainly determined by gene abundance in subsurface samples because that of surface samples did not show any significant change .On the other hand, Methanopyrales and Methanococcales, indicative of rather diverse community in sediments from nSCS , Methanomicrobiales group occupied a large proportion in samples from Mai Po Nature Reserve and deep-sea sediments. Shallow sea sediments, E708S and E707S showed low diversity of site specificity, dominated by Methanosarcinales Clade 2. On the contrary, other shallow sea samples were dominated by Methanomicrobiales. The phylogeny of this clade showed rather heterogeneous without noticeable site-specific cluster except for two proposed SCS specific clusters (SCS Clusters 1 and 2) mainly occupied with South China Sea derived sequences.The other large branch of Methanosarcinales were divided into two parallel clusters. In cluster 1, ANME mcrA group f was included. Three kinds of samples all harbored sequences belonging to this anaerobic methanotrophs cluster, which was ANME 3 subtype  other than that based on 16S rRNA gene which falls into the Methanobacteriales order and their coccoid shapes other than rod shapes shared by the rest in the order, all makes it mysterious to address its taxonomic relatedness between those two orders above  and Jiulong River estuary  Methanosarcinales clade including previously identified ANME Group c to f, grouped with monophyletic RC-I cluster; (ii) Methanomicrobiales clade which buried Fen-like cluster inside; (iii) Order Methanobacteriales, Methanococcales, genus Methanopyrus, Methanomassiliicoccus, new proposed uncultured Thermoplasmatales as seventh order of methanoarchaea and ANME Group a-b, g-h together with unknown deeply branching Guaymas cluster. The consensus ratios distribution pattern of mcrA gene coding amino acid reflected certain proportions contributed more on diversification .Radical phylogenetic tree revealed subclade distance between major groups of methanoarchaea. Three parts of groups were dispersed when rooted by mcrA gene is presented in Figure 3 together with heatmap of the log-normalized percentage of each group in each sample.Sequences obtained in the clone libraries were grouped into 269 OTUs at nucleotide similarity threshold of 95%. Rarefaction curves indicated relatively high coverage indices among all samples of this study Figure . The comTables 1 and mcrA clone library . In the Mai Po Nature Reserve, the surface samples harbored more diverse and even mcrA communities than subsurface samples from both intertidal sediments and mangrove.Shannon–Wiener and Chao1 indices were given together with OTU numbers and valid sequence numbers . For E707S and E708S, they located along the PC2 axis separated from others. Their mcrA gene communities were dominated by Methanosarcinales Clade 2 and showed low diversity value and uneven distribution pattern. The Mai Po samples grouped less concentrated than the marine samples, indicative of relatively more diverse pattern showing in the coastal ecosystem. Pearl River Estuary sample was grouped with Mai Po samples in Jackknife samples cluster, suggesting closer relatedness, while mcrA gene community in Pearl River Estuary represented a transition between Mai Po and nSCS area. Reedbed rhizosphere sample was separated from other Mai Po samples and its community structure was classified more closely to marine samples on account of both weighted PCoA and Jackknife sample clustering analysis.The Unifrac-based PCoA was carried out to delineate any differences of environmental heterogeneity by analyzing mcrA genes containing archaeal group composition  and environment variables . And axis 1 together with axis 2 accounted for 77.9% cumulative variance percentage of mcrA containing archaeal groups and environments. Subsurface samples and surface samples from both mangrove and intertidal mudflat were relatively apart and formed two assemblages according along axis 1. Coordinates of subsurface samples showed in a compact manner, while surface samples scatter along axis 2. L1 sample was separately located indicating different distribution pattern against other sites. Depth and pH values were positively correlated with axis 1, serving as dominating variables which influenced subsurface microbial community. Surface sediment would be more susceptible to perturbations of natural and anthropogenic activities, resulting in alterative physiochemical environmental conditions which alter microbial community. Redox potential values were negatively correlated with depth (showing –0.44 correlative coefficient), and pH values changes were concomitant with the increasing of depth (showing 0.48 correlative coefficient). The three parameters mentioned above showed closed relationship with community structures of these two assemblages, suggesting the stratified environmental condition between the subsurface and surface largely influenced the community composition. L1 was collected from reedbed with abundant organic matter and anaerobic condition possessing both methanogenesis and methane-utilization. Its methanogenic community obtained unique composition and abundance pattern distinguished from mangrove and intertidal zones according to this research. No noticeable parameters in this study were found to account for its distribution in the CCA ordination plots, whereas, considerably large humic compounds, anoxic and low redox potential would be certain factors affecting its richness and uniqueness of methane cycling microorganisms. More comprehensive physiochemical parameters detecting and analyzing model should be elaborated to tell the accurate shaping effects under different environmental conditions.Two CCA dimensions represented two major axes which yielded the most variance explanatory percentage (32.6%) between Figure 6B), depth values were the most influential effects on the abundance of mcrA gene abundances. All surface samples from different locations obtained low abundance. Additionally, Pearson moment correlation analysis indicated that OTU numbers and abundance were positively correlated with depth significantly , consistent with their similarity in major community compositions . Subsurface samples were grouped together despite of their different geographic locations. E407B with a relatively shallow depth below sea floor separated slightly from the other two subsurface samples, but close to surface samples, representing an intermediate between two sampling depths. This might contribute to evidence on methanoarchaea distribution affected by depth below seafloor.Three subsurface samples from nSCS  belonging to the deep sea were clustered together according to Jackknife cluster analysis . This could interpret higher methanoarchaea community diversity values observed in the surface samples both in mangrove and intertidal mudflat samples. And on the other hand, subsurface layer samples usually acquired low oxygen and low redox potential, especially in the reedbed rhizosphere sample, representing much less diversity patterns.Surface samples were the top layer and influenced by variable environments, such as those imposed by intertidal ecology and occasional weather change and bioturbation. CCA showed that surface samples represented a more dispersed pattern resulting from relatively more variable environmental conditions in the surface . As for the lower layer mangrove samples, they were sampled from rhizosphere of mangroves. Exudates and detritus from mangrove roots could supply substrates for methane generation and relatively high oxygen condition reversely limited the proliferation of methanoarchaea. As a result, it could be more dynamic in terms of the mcrA abundance in mangrove rhizosphere.With respect to coccales , howeverpmoA abundance information, depth and sediment site both affected the amount. Mangrove samples harbored more pmoA gene copies than intertidal mudflat, and surface samples harbored more pmoA gene copies than subsurface samples. These patterns were shared, irrespective of sampling sites. It should be noted that subsurface samples harbored fewer pmoA gene copies, but the surface samples had more abundant pmoA gene at the same site. This could be the first report in consideration of abundance distribution of the pmoA gene in coastal wetland in relation to depth as far as the up-to-date references concerned. Methane eﬄux and uptake should be in accordance to flux transport from subsurface to surface, and the total net methane flux differences could be relatively insignificant in mangrove and intertidal mudflat on the basis of former study  group was the major ANME group based on retrieved clone numbers and semi-quantitative PCR  while small proportion of ANME f (ANME-3) was also detected, implying multiple phylotypes of ANME participating in AOM process. On the other hand, ANME f (ANME-3) sequences were detected from shallow and deep marine samples from nSCS with few clones, directly verifying the qPCR results of ANME f group distribution in nSCS . First mzer mats . Other szer mats  and sporzer mats .mcrA genes within methanogen community, so that more detail analysis could be delineated  genes, community and diversity patterns in nSCS marine sediments and Mai Po Nature Reserve have been revealed and a comparison to other environments was made. In general, Mai Po wetland sediments have higher diversity than marine sediments, but the community diversity was not significantly correlated with the depth of the marine samples. In Mai Po wetland, the surface layer showed higher diversity but lower abundance than the subsurface layer. Additionally, conditions at different layers exercise effects on the mcrA gene diversities and community composition. We also measured the quantity of aerobic methanotrophs by means of detection of pmoA gene abundance, and found that mangrove samples harbors more pmoA gene copies than intertidal mudflat samples in the surface layer while fewer pmoA gene copies in the subsurface layer. Besides, the total pmoA gene abundance was relatively stable when adding surface and subsurface samples together. However, due to the possible presence of mcrA and pmoA genes could be inactive in methane cycling microorganisms, analysis on transcript abundance of each gene in the future should be carried out to reflect their real activity (Based on methyl-coenzyme M reductase alpha subunit (activity .in situ physiochemical parameters and community composition and geographic distribution still needs higher resolution measurement technique and strategies to unravel. However, our results are indicative of substrate compounds and depth distinction may effectively lead to structural and functional differences.To summarize, the marine and coastal wetland served as energetic and variable environmental habitats for activity of methanoarchaea with different distribution patterns and functional groups. Their relationship between The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two ( In particular, Carvill and colleagues CHD2 and SYNGAP1 as novel Epileptic Encephalopathy genes. Another study employed a whole exome sequencing ‘trio design’ of 264 probands with Epileptic Encephalopathies and their parents in search of de novo variants GABRB3 and ALG13 to be novel Epileptic Encephalopathy genes. These four genes join KCNT1GRIN2AMassively parallel sequencing has recently accelerated gene discovery, revealed unexpected genetic heterogeneity and cemented the role played by Along with discovering new ‘definite’ Epileptic Encephalopathy genes, large cohort studies, such as the Epi4K and EPGP Consortia There are currently at least 29 ‘definite’ Epileptic Encephalopathy genes. The observation that a number of these known genes are biologically associated , make it reasonable to hypothesize that true candidate Epileptic Encephalopathy genes will form part of the same or related biological networks. These biological networks are identified and described by looking for evidence of association between genes. This evidence can be gathered using diverse resources including publicly available data such as gene expression, protein-protein interaction (PPI) networks or even literature based searches . Gene networks can be examined and analyzed in their own right to identify modules of co-expression, or as sub-networks, as well as overarching networks with the aid of gene ontology annotation. Another application is their use in an approach known as “guilt by association”, which can be used to prioritize candidate genes according to their level of association with known disease-causing genes (reference set). Alternatively, a genome-wide approach has the potential advantage of identifying novel gene networks, however, it will lack the power gained by utilizing a reference set.The concept of candidate gene prioritization is well-established and the “guilt by association” principle widely applied The Allen Human Brain Atlas (AHBA) has generated large-scale brain expression data that has been carefully curated and processed, allowing downloading of normalized gene expression data, ready for analysis We explore and compare the application of both the AHBA and Celsius expression data to known and candidate Epileptic Encephalopathy gene sets. We use Pearson's and Spearman's correlation coefficient to summarize the linear relationship between gene pairs based on their gene expression data. We also know that negatively correlated genes play an important role in neurological gene networks Using methods analogous to leave-one-out cross-validation we show that the known set of 29 Epileptic Encephalopathy genes are substantially co-expressed as a network. We exploit this to prioritize 182 candidate Epileptic Encephalopathy genes resulting from the recent Epi4K and EPGP Consortia study http://www.r-project.org/) making use of the specific packages gtools http://bioinf.wehi.edu.au/software/BrainGEP.Statistical analysis and visualization were performed in the statistical programming language R (http://www.brain-map.org/) for all six adult and four developing brains. Gene expression data was generated using a custom-made Agilent 8×60K array with 58,692 probes covering 20,782 genes. The design of the array, the normalization procedure and selection of brain samples and dissection protocols are described in whitepapers available from the AHBA website. The probe with the highest median expression value for each gene was chosen to represent the gene expression value for that gene.We downloaded the normalized microarray gene expression data from the AHBA website (http://genome.ucla.edu/projects/UGET/) to explore the Celsius gene expression data available for the HG-U133_Plus_2 array design: the largest human dataset with 5,954 CEL files. Approximately 25% of these arrays are of “nervous system origin”, presumably from brain We employed UGET ; BE3A see .de novo variants in their Epileptic Encephalopathy cohort Candidate genes were selected from a list of genes reported by the Epi4K & EPGP Consortia typically with ‘single hit’ The pairwise Pearson's correlation coefficient (r) for any two genes determined their level of co-expression. We performed these statistical analyses for both AHBA time periods (developing and adult) in R. These analyses were all repeated using Spearman's sample correlation coefficient. We did not have direct access to the Celsius expression data; however, using UGET we were able to generate pairwise Pearson's correlation coefficients for gene pairs.Pairwise Pearson sample correlation coefficients |r| were calculated for each known Epileptic Encephalopathy gene pair (n = 406). For the AHBA resource, the correlation coefficients were found within each individual, using all brain samples, and then combined within their respective time periods  using a weighting scheme based on the sample variance of the correlation coefficients derived from the gene expression data within each individual. Details can be found in Supporting Information see .We generated three gene expression correlation matrices  for all possible gene pairs in the reference set of 29 Epileptic Encephalopathy genes. These results were compared to a list of 1,000 randomly chosen genes , representing the null distribution, by comparing empirical cumulative distribution functions (ECDFs).The gene expression correlation matrices for the reference sets were visualized with the corrplot R package A discrete (K*) and continuous (K) connectivity score was generated for each candidate gene see . The conSignificant correlation networks were generated using the qgraph package th percentile for intolerance, 2) review of the current literature for a gene's prior association with neurological disease, 3) PolyPhen-2 results for gene variants We compared connectivity scores between candidates predicted to be pathogenic versus those that were not according to four alternative resources. The chosen resources are a combination of gene and variant-based approaches where we classified results as supporting pathogenicity for each as follows: 1) genic intolerance score (GIT) For each of the predictive resources of GIT, prior neurological evidence, PolyPhen-2 and CADD predictions, the connectivity scores for candidate genes with evidence for pathogenicity versus those without were compared using one-sided, two-sample Mann-Whitney rank sum tests with continuity correction. P-values were determined using both reference tables and permutation tests where the group membership labels were permuted 1000 times. This was performed for results from the three gene expression resources, and for both the discrete (K*) and continuous (K) connectivity scores.Our method prioritizes candidate genes based on their connectivity scores with known Epileptic Encephalopathy genes. We applied a false discovery rate in silico prioritization approach called Endeavour (http://homes.esat.kuleuven.be/~bioiuser/endeavour/index.php) Finally we compared our prioritization results for the candidate genes to those obtained by an established The 29 reference Epileptic Encephalopathy genes were all represented on the AHBA gene expression array and in the Celsius resource accessed via UGET.C15orf-AP3S2, TNNI3K, WHSCIL1 were not present on the AHBA array, nor could alternative gene names be found for them, hence they were excluded from the analysis, leaving 179 candidate Epileptic Encephalopathy genes.Of the 182 candidate genes, C15orf-AP3S2, LCE1A, LDLRAD1, MSANTD1, OR10S1, SGK223, SLCO1B7, TNNI3K, TPTE2, WHSC1L1 in the Celsius gene expression data (Affymetrix HG-U133_Plus_2 array), leaving 172 candidate Epileptic Encephalopathy genes to explore in this resource.Similarly we were unable to identify transcripts representing candidate genes Results for Pearson's and Spearman's sample correlation coefficients were very similar but with a loss of power for Spearman's due to the rank transformation. We thus focus on the Pearson's correlation coefficient results, using the Spearman's results as a test for robustness.The weightings used for each individual to derive both Pearson's and Spearman's correlations for the AHBA data are given in The adult AHBA gene expression data showed a greater shift to the right in the ECDF compared to the developing AHBA data suggesting that the reference Epileptic Encephalopathy genes co-express more strongly as a network in adulthood. However, the Celsius resource showed the greatest shift in the ECDF overall. This is reflected in The ordered correlation matrices revealed some striking patterns .HNRNPU, SLC2A1, CHD2, ALG13, PNPO and MECP2, connected by negative co-expression with the larger cluster containing 23 genes. The average correlation for cluster one is 0.445 and the average correlation for cluster two is 0.380, with an average inter-cluster correlation of −0.100. There are two genes on the fringe of both clusters, PNKP and SPTAN1, showing weak co-expression with the majority of Epileptic Encephalopathy genes. It is interesting then to note that these genes are both involved in DNA repair. This shared and distinct biological role perhaps can explain their co-expression together and overall relative isolation from the other Epileptic Encephalopathy genes.For the adult AHBA data  we obserThe ordered correlation matrix for the developing AHBA data  also shoMECP2, was not detected, suggesting that this is a brain specific signature. Nonetheless both the adult AHBA and Celsius correlation matrices contained clusters of the same co-regulated genes, demonstrating biological replication between two independent gene expression resources. This supports our hypothesis that these 29 known genes show genetic network structure and allows the application of “guilt by association” in prioritizing true Epileptic Encephalopathy genes based on their co-expression with one or more of the genes in this network.The major adult AHBA cluster of positively correlated genes was recapitulated using the correlation matrix derived from the large, generic gene expression Celsius dataset , again cConnectivity scores were generated for each candidate gene. We were interested to compare connectivity scores between those candidates predicted to be pathogenic versus those that were not according to three alternative predictive resources. The permutation test values and standard distribution P-values were very similar for the Mann-Whitney tests, indicating that the lack of independence between data points was not adversely affecting the null distribution. Thus we report the P-values based on the Mann-Whitney distribution.Discrete (K*) and continuous (K) connectivity scores were significantly higher for candidate genes with gene intolerance scores GNAO1, RALGPS1, ANK3, GRIN1 and MAST1, with PLXNA1 prioritized by the developing and adult AHBA resources and GRIN2B by the adult AHBA and Celsius.A total of 19 genes were prioritized using Pearson's correlation coefficient; seven of these genes were prioritized by more than one of the three gene expression resources utilized. The adult AHBA, developing AHBA and Celsius resource prioritized 4, 10 and 12 candidate genes respectively. These candidates attained the empirical false discovery rate (eFDR) of 0.25 with thresholds of 8.4 (adult AHBA), 7.4 (developing AHBA) and 10.6 (Celsius) using the continuous connectivity measure K only , Table 2KCNB1, being prioritized (n = 1) and two fewer genes were prioritized with the developing AHBA data (n = 8); six in common with Pearson's  and two differing (CEP55 and SMURF1). We were unable to apply Spearman's correlation coefficient to the Celsius resource since the data is provided as Pearson's sample correlation coefficients by UGET.The results were very similar for Spearman's correlation coefficient. Again we applied an eFDR of 0.25 to determine a connectivity score threshold for the two AHBA gene expression resources. For the adult AHBA resource, this cutoff resulted in only the top candidate, For almost all of these prioritized candidate genes (18 out of 19) there is additional evidence from other sources of information that also implicates them as true Epileptic Encephalopathy genes, such as being predicted damaging by Polyphen-2 or CADD, having a high Gene Intolerance Score or already known to play a role in other neurological disorders such as intellectual disability, autism and malformations of cortical development .Interestingly, the developing AHBA had much greater specificity (more genes prioritized for the same eFDR level chosen) than the adult AHBA, and the Celsius resource in turn shows greater specificity than the developing AHBA with the most number of genes prioritized. This highlights the power of large sample sizes in reducing biological and technical variability and thus detecting signal with a lower false positive rate. See Networks of the known and prioritized Epileptic Encephalopathy genes show how highly connected the candidate genes are, commensurate with their prioritization . Exceeding an eFDR of 0.25 , the ninGNAO1GRIN2BGNAO1 with rank 1 in the developing AHBA and rank 2 in the Celsius resource and GRIN2B was ranked 2 with the adult AHBA data and 11 using the Celsius resource  or genes  in disease causality. Confirmation that our prioritization approach works came with two recent publications reporting resource .in silico gene expression analysis providing an even more powerful way of finding relationships between genes. Refinement of the in silico prioritization analysis methods should enable better use of smaller sub-networks such as the smaller of the two clusters discovered in the adult AHBA resource. Our analysis is based on a weighted sum of correlation coefficients to derive connectivity, which would benefit from an approach based on principal components, rather than the direct gene-gene correlations This study has highlighted the strengths and weaknesses of two large-scale gene expression resources, based on markedly different study designs. The Allen Human Brain Atlas (AHBA) is a highly curated, carefully designed study, and although it has many data points , these are sampled from only 10 individuals. The resulting data has lower technical variability (reduced artifacts) at the cost of biological variability. Thus this data set represents a lower level of biological and technical variation than that represented by Celsius. However, it is clear from our results that larger sample sizes, such as the thousands of arrays from Celsius, can be beneficial for teasing out true biological signals from technical effects. The advantage of using a brain specific resource, such as the AHBA, is the detection of brain specific signatures that can be incorporated into the Through further analysis of data from the AHBA, we observed for the first time that gene correlation networks for known Epileptic Encephalopathy genes have distinctive brain co-expression patterns at the two very different time periods available (15–20 post-conception weeks versus adulthood). The adult brain shows stronger co-expression signals in comparison to the developing brain, yet prioritizes a smaller number of candidate Epileptic Encephalopathy genes at an equivalent false discovery rate. This suggests much greater variability in the adult brains . The adult brain shows more structured gene co-expression with two gene co-expression modules emerging , in compInterestingly we have also examined gene expression networks in the AHBA for genes known to be involved in Malformations of Cortical Development and for these we see the developing brain data showing stronger correlation patterns compared to the adult brain (data not shown) representing greater sensitivity. This is consistent with our understanding of when these genes are likely to be of importance  and again suggests that disease and time specific resources do add important signal and information.In silico gene prioritization approaches have been promising with many already used in practice. However, the results are variable and real applications with tailored analysis  are missing. Popular resource databases such as STRING http://string-db.org/) show publication-age bias (data not shown), indicative of a strong reliance on text-mining. Addressing this limitation, in silico prioritization methods now often include multiple data sources  but in comparison only 155 (85%) candidates were available for prioritization by Endeavour, an important limitation for researchers to be aware of when considering in silico prioritization methods.oritized , whereasin silico prioritization methods have been applied to the epilepsy field Several in silico prioritization, potentially giving an edge to laboratories who can only explore one or a few “best” candidate genes. We have applied in silico analysis to putative Epileptic Encephalopathy variant discovery results from recent large-scale studies. Whilst able to show evidence that our prioritization has yielded highly plausible results it is important to note that our findings do not mean that those candidate genes not prioritized should be discounted. It is possible that they represent the first Epileptic Encephalopathy genes in entirely new pathways that would not be discovered using an approach based on known genes. Pragmatically, however, the gathering of functional evidence in support of candidate gene pathogenicity remains costly Large cohort, massively parallel sequencing studies provide an ideal resource in which to apply Methods S1Extended description of methods for weighted correlation matrices and connectivity measures.(DOCX)Click here for additional data file.Figure S1eFDR estimates as a function (A) of the continuous connectivity (K) for all three gene expression data sets and (B) of Endeavour's 1-Rank scores. Dotted red line indicates an eFDR = 0.25 with dots near the eFDR plots near 0 to 0.05 indicating the observed connectivities for the top ranked candidate Epileptic Encephalopathy genes for each dataset. The number of discovered variants for each dataset for an eFDR = 0.25 is the number of dots that have been plotted.(TIFF)Click here for additional data file.Figure S2Adult AHBA gene co-expression network and correlation matrix for known and prioritized Epileptic Encephalopathy genes. Gene co-expression networks for the known Epileptic Encephalopathy genes that are involved in any of the top 5% of overall connections of the adult AHBA along with the 4 candidate Epileptic Encephalopathy genes (shown in blue) as determined by the connectivity measures that are estimated to have an eFDR = 0.25 (thresholded r) using qgraph (A) or represented as an ordered sample correlation  matrix (B), with ordering based on angular distance.(TIFF)Click here for additional data file.Figure S3Celsius gene co-expression network and correlation matrix for known and prioritized Epileptic Encephalopathy genes. Gene co-expression networks for the known Epileptic Encephalopathy genes that are involved in any of the top 5% of overall connections of the Celsius resource along with the 12 candidate Epileptic Encephalopathy genes (shown in blue) as determined by the connectivity measures that are estimated to have an eFDR = 0.25 (thresholded r) using qgraph (A) or represented as an ordered sample correlation  matrix (B), with ordering based on angular distance.(TIFF)Click here for additional data file.Table S1List of known Epileptic Encephalopathy genes chosen from the literature with relevant reference details.(DOCX)Click here for additional data file.Table S2List of 182 candidate Epileptic Encephalopathy genes for prioritization.(DOCX)Click here for additional data file.Table S3Weights derived for each individual AHBA brain for both Pearson's and Spearman's correlation coefficient. The sum of the weights add to one within each of the two time periods.(DOCX)Click here for additional data file.Table S4Extended Mann-Whitney results . P-values in brackets are those derived using a permutation test with 1000 permutations.(DOCX)Click here for additional data file.Table S5The connectivity (K) and rank position for 179 candidate genes according to the developing AHBA, adult AHBA and Celsius expression data resources and the Endeavour approach. Sheet one shows the results based on Pearson's correlation coefficient and sheet two has Spearman's.(XLS)Click here for additional data file.


The discovery of circulating fetal DNA in maternal blood led to the discovery of new strategies to perform noninvasive testing for prenatal diagnosis.The purpose of the study was to detect fetal aneuploidy at chromosomes 13, 18, 21, X, and Y by analysis of fetal cell-free DNA from maternal blood, without endangering pregnancy.This retrospective study has been performed in Bucharest at Medlife Maternal and Fetal Medicine Department between 2013-2014. In total 201 women were offered noninvasive prenatal test. Maternal plasma samples were collected from women at greater than 9 weeks of gestation after informed consent and genetics counseling.From 201 patients; 28 (13.93%) had screening test with high risk for trisomy 21, 116 (57.71%) had advanced maternal age, 1 (0.49%) had second trimester ultrasound markers and the remaining 56 patients (27.86%) performed the test on request. Of those patients, 189 (94.02%) had a “low risk” result . Of those who had a low risk result, 2 continued on to have amniocentesis with normal results.Five patients (2.48%) received “high risk” results (>99% risk) all for trisomy 21 (T21). T21 was confirmed by amniocentesis in 1 patient and the other 4 patients declined confirmation. The 7 remaining patients (3.48%) had a low fetal fraction of DNA. It is probably that prenatal diagnosis using fetal DNA in maternal blood would play an increasingly role in the future practice of prenatal testing because of high accuracy. Prenatal screening and diagnosis are frequently used in antenatal care and are considered to be a very useful tool for physicians and women to take informed decision about the continuation of pregnancies affected by genetics diseases. Diagnostic testing requires removal of a sample of fetal cells from chorionic villus (between 11-13 weeks) or amniotic fluid (after 16 weeks of gestation). These invasive procedures have a risk of miscarriage between 0.5-2% , advanced maternal age, abnormal ultrasound markers  and to those who request a discussion of prenatal testing options including invasive procedures. On week 9 the test was performed by request of the patients. Patients receive genetics counseling which contains information about the fetal cell-free DNA technology, and specifies the screening nature of this testing and limitation of screening to aneuploidy involving chromosomes 21, 18, and 13. In total 201 women were offered non-invasive prenatal test. Maternal plasma samples were collected from patients at greater than 9 weeks of gestation after informed consent.The method targets 19500 single-nucleotide polymorphisms (SNPs) in a single multiplex PCR reaction, thus can differentiate between maternal and fetal genotypes and uses the Next-generation Aneuploidy Test Using SNPs (NATUS) algorithm (Patented by Natera Laboratory) to analyze data. Panorama test utilizes the mother’s white blood cells to isolate and identify her DNA, and then uses this information to “extract” the maternal genotype, resulting in a more accurate fetal genotype. Panorama test has a unique bioinformatics approach, results in greater quality control capabilities.The goal of this report is to share our experience, highlighting the clinical utility and limitations of this novel screening test.Informed consent was obtained from all human adult participants. The study was approved by the Ethics Committee of Life Memorial hospital.Statistical analysisDescriptive data are presented in numbers and percentages for categorical variables using PROC FREQ software.Demographic maternal characteristics of women undergoing the test are presented in The test was accepted by 201 patients (99.5%). From 201 patients who accepted the test 28 (13.93%) had screening test with high risk for trisomy 21 (cut off >1/250), 116 (57.71%) had advanced maternal age (over 35 years), 1 (0.49%) had second trimester ultrasound markers and the remaining 56 patients (27.86%) performed the test on request  . Of thosFive patients (2.48%) received “high risk” results (>99% risk) all for trisomy 21 (T21). T21 was confirmed by amniocentesis in 1 patient and the other 4 patients declined confirmation but are using this information for delivery management (made abortion). The 7 remaining patients (3.48%) received “no result” reports because of a low fetal fraction of DNA. In group with “no result” maternal weight was higher but the difference was not significant  .The 1997 discovery of free fetal DNA in maternal plasma initiated clinical researchers’ efforts to discover a precise method for non- invasive prenatal testing for fetal genetics conditions. Different methods have recently been developed as highly accurate noninvasive screening tools for common fetal chromosomal aneuploidies. Incorporating these noninvasive techniques into clinical practice will impact the current prenatal screening algorithm for fetal aneuploidy, in which genetics counseling plays an integral role.As shown in our study, the majority of pregnancies identified by the combined test as being at high risk for trisomy 21, 18, or 13 are euploid. The use of fetal cell-free DNA testing would reduce the number of unnecessary invasive test (amniocentesis and chorionic villi sampling) and eliminate their associated risk of miscarriage. These data are consistent with those obtained by Nicolaides and colleagues . Given nWe find that non-invasive prenatal testing is chosen by many women in place of diagnostic testing. It is essential that patients have genetics counseling to understand the limitation of this technology which is a screening test. Patients who have other factors suggestive of a chromosomal abnormality  should receive genetics counseling and have the option of conventional confirmatory diagnostic testing, regardless of non-invasive test results, because this test does not screen for all chromosomal or genetics conditions and maternal abnormalities, such as maternal mosaicism. In addition it can contribute to false positive results with NIPT tests that cannot distinguish between fetal and maternal DNA. In fact, maternal mosaicism led to 5-9% of false positive results when screening sex chromosomes with NIPT tests .The primary source of non-maternal cell free DNA in the maternal circulation is thought to be apoptosis of placental cells (syncytiotrophoblast), while maternal hematopoietic cells are the source of most maternal cell free DNA. Somatic mosaicism, placental mosaicism and maternal CNV changes can modify the accuracy of cell free DNA testing in maternal blood .Approximately 5% of cases failed to meet quality control metrics and a portion of cell free DNA samples will not return a result because of a low fetal fraction. Recent data suggests that this “no-call” group is at increased risk of chromosomal aneuploidy . DiagnosCounseling regarding the limitations of cell free fetal DNA testing should include a discussion that the screening test provides information regarding only trisomy 21, trisomy 18 and trisomy 13. Other limitations of cell free fetal DNA includes the lack of outcome data for low-risk populations; therefore, cell free fetal DNA testing is not currently recommended for low-risk women. Preliminary data available on twins demonstrate accuracy in a very small cohort, but more information is needed before use of this test can be recommended in multiple gestations It is possible that non-invasive prenatal diagnosis using fetal DNA in maternal blood would play an increasingly important role in the future practice of prenatal testing. However it is important to address the ethical, legal and social issues surrounding such developments. The positive side of non-invasive testing is the avoidance to harm the fetus that would be associated with invasive testing. It does not replace the precision obtained with diagnostic tests, such as chorionic villus sampling (CVS) or amniocentesis, and currently does not offer other genetics information.


Detailed analysis identified a total of 524 highly significant MTAs  with wide phenotypic variance (PV) range (5.81–90.09%) for 36 traits. These MTAs after validation may be deployed in improving biotic resistance, oil/ seed/ nutritional quality, drought tolerance related traits, and yield/ yield components.Peanut is an important and nutritious agricultural commodity and a livelihood of many small-holder farmers in the semi-arid tropics (SAT) of world which are facing serious production threats. Integration of genomics tools with on-going genetic improvement approaches is expected to facilitate accelerated development of improved cultivars. Therefore, high-resolution genotyping and multiple season phenotyping data for 50 important agronomic, disease and quality traits were generated on the ‘reference set’ of peanut. This study reports comprehensive analyses of allelic diversity, population structure, linkage disequilibrium (LD) decay and marker-trait association (MTA) in peanut. Distinctness of all the genotypes can be established by using either an unique allele detected by a single SSR or a combination of unique alleles by two or more than two SSR markers. As expected, DArT features  showed lower allele frequency and polymorphic information content (PIC) than SSRs . Both marker types clearly differentiated the genotypes of diploids from tetraploids. Multi-allelic SSRs identified three sub-groups (K = 3) while the LD simulation trend line based on squared-allele frequency correlations (r Arachis hypogaea L., 2n = 4x = 40) is the mainstay to livelihood of millions of small-holder farmers residing in semi-arid tropic (SAT) regions of the world. This crop is cultivated in 24.6 million ha with the total production of 41.3 million tons and productivity of 1676 kg/ha during 2012. Asia with 11.6 million ha (47.15%) and Africa with 11.7 million ha (47.56%) hold maximum global area. The productivity of Asia (2217 kg/ha) and Africa (929 kg/ha) remained very poor as compared to Americas (3632 kg/ha) Peanut or groundnut  in cultivated peanut, very few informative and good quality single nucleotide polymorphisms (SNPs) are available in peanut Resource-poor farmers of SAT region with small land holdings need a strong support from genomics and breeding for sustaining their livelihood through increase in their profitability and better health. Limited success could be achieved through genetic improvement approaches in developing improved cultivars performing better under adverse climatic and soil conditions. This situation is going to be further worsened due to fast changing environmental conditions, soil health, limited water and land resources. In addition, inability to identify desirable alleles from germplasm collections and limited use of unique/ rare alleles in breeding programmes led to narrow genetic base in modern cultivars and varieties The peanut ‘mini core collection’ (184 genotypes) or the ‘reference set’ (300 genotypes) represent the global diversity of about 14, 000 accessions conserved in ICRISAT genebank . This geLarge scale genotyping  of the ‘reference set’ provided a great insight in genetic relatedness, identification of most informative markers and other features such as allele number, gene diversity and observed heterozygosity. A total of 9,194 alleles were identified at 4,597 polymorphic DArT loci , while 1Unique molecular IDs for the different accessions are useful in effective germplasm maintenance and plant variety protection. Therefore, multi-allelic data for SSR markers were used to assign unique molecular IDs to all the accessions . It is iMulti-allelic SSR markers have always been more effective in revealing the genetic structure of a natural population consisting of diverse genotypes 2) was found to decay with the genetic distance of 15 cM  with large LD blocks. Similar large LD blocks were also been detected in many self-pollinated crops such as durum wheat The present study provided most comprehensive insight on population structure and LD decay in a large germplasm set in peanut. Mapping positions for 139 SSR loci mapped onto 20 linkage groups of peanut genome of 15 cM . A complA total of 524 highly significant MTAs for 36 agronomically important traits were identified using most accepted MTA analysis method (P3D mixed linear model with optimum compression) and most stringent multiple test correction method (Bonferroni correction) to filter the false positives . PhenotyAspergillus flavus (aflatoxin contamination), early leaf spot (ELS), late leaf spot (LLS), rust and groundnut rosette disease (GRD) are among the most devastating diseases in several parts of Asia, Africa and parts of Americas. GRD is endemic in Africa and does not occur in the other continents. A total of 24 season data was collected for these five important diseases at seven locations in 6 countries including India , Malawi, Mali, Senegal, Tanzania and Vietnam (Aspergillus (24.69% PV), 31 MTAs for GRD (10.25–39.29% PV), six MTAs for ELS (9.18–10.99% PV), and single MTA for LLS (18.10% PV). The marker associated to one MTA for LLS (Seq1B09) identified in the present study was different than the markers identified in earlier study using biparental populations Aspergillus flavus Vietnam . Associa Vietnam . Of the Role of high oil content in increasing profitability along with increased awareness towards health benefits of improved oil and nutritional quality have gained much importance in recent years among producers, consumers and traders. MTA analysis in present study included several oil and nutritional quality traits such as oil content (OC), oleic acid (OLE), oleic / linoleic acid ratio (OLR), protein content (PC), zinc content (ZC), iron content (IC) and sound mature kernels % (SMK%). A total of 32 seasons data on five important traits at six locations in India  and Vietnam  were genA total of 25 MTAs were detected for OC for which PV ranged from 5.84% (gnPt-714399) to 40.37% (TC4G10) . FurtherA maiden attempt was made here for identifying MTAs for three important nutritional quality traits which resulted in detection of 11 MTAs (11.63–36.1% PV) for PC and single MTA for ZC (15.63% PV) while no MTA for IC . In addi13C, harvest index (HI), haulm weight (HLMWT), leaf dry weight (LDWT), leaf area (LA), leaf length (LLN), leaf weight (LWT), leaf width (LWD), root / shoot ratio (RSR), rate of water loss (RWL), root length (RTL), root volume (RTVOL), root weight (RWT), shelling percentage (ShP), shoot length (SLN), shoot weight (SWT), specific leaf area (SLA), total leaf area (TLA), total leaf weight (TLWT), SPAD chlorophyll meter reading (SCMR), total dry matter (TDM), TDM/LA, days to flowering (DF), days to maturity (DM), emergence (EMR) and first flowering (FFL). A total of 208 seasons data on these 26 important physiological traits characterised in four countries including India , Niger (ICRISAT Sahelian center), Thailand and Vietnam , SLA , TLA , TLWT , SCMR , RTVOL , RWL , ShWT , HLMWT , HI  and ShP  . So far Yield and yield component traits have been the prime target of improvement in all the breeding programmes. Total 12 yield component traits  were analysed. A total of 50 MTAs could be identified for four yield component traits with PV ranging from 9.74% (PYLD) to 37.6% (PYLD). Significant MTAs could be identified only for SDL , SDWD , SDWT  and pod yield  . One of Aspergillus infection resulting into aflatoxin contamination of the produce and thus making it unsafe for consumption. Small-holder farmers of SAT regions have been among the most affected with drought stress and hence, continuously posing threat to their health, income and sustainable livelihood. With an objective to detect MTAs for physiological traits affecting drought tolerance significantly for use in GAB, an experiment was conducted under both the conditions (well watered-WW and drought stress-DS) to understand the genetic mechanism and common genomic regions controlling these physiological traits.Drought stress has been the most devastating abiotic stress resulting in huge yield loss along with high level of A total of 152 MTAs were detected under both the conditions (WW and DS) for six traits namely LA, LDWT, SCMR, HLMWT, HI and SDWT . A totalIn the past, only few studies have been conducted on trait mapping for economically important traits in peanut and therefore, very limited information is available on the markers linked to the traits analysed in this study. Furthermore, use of different kind/set of markers in these studies don't allow a possibility to compare results of this study with the previous studies. Nevertheless, some SSR markers used in the present study were also used in the previous linkage mapping based marker-association studies The main objective of this study was to identify MTAs for agronomical traits of complex nature using diverse panel of genotypes. As a result, a total of 134 MTAs were identified with PV >20% for 15 important traits. A total of 30 significant allele effects for these 15 traits were identified associated with 24 markers showing significant impact on these traits while nine markers were found to be associated with multiple traits . Fifty nIn view of making peanut crop more resilient to stresses with high pod and oil yield and improved oil and nutritional quality, this study is the timeliest and most comprehensive marker-trait association study conducted so far in peanut using thousands of markers and multiple season phenotyping data generated on wide range of economically important traits. Thus, several MTAs detected for many disease resistance, oil content and quality, drought tolerance related  traits, yield components and yield in the present study based on multiple season phenotyping data will facilitate their improvement through GAB. To achieve this, these MTAs upon validation may be deployed in marker-assisted improvement of peanut leading to development of improved cultivars with higher resilience to drought tolerance and disease resistance, increased yield and, improved oil and nutritional quality. Such improved cultivars will ensure sustainable livelihood to the farmers of SAT regions of Africa and Asia, and better nutritional supply to the consumers' worldwide.The peanut ‘reference set’ (300 genotypes) along with four additional elite genotypes was genotyped with 154 SSRs (genomic/genic) spanning complete peanut genome . The ‘re2, 1X amplification buffer and 0.1 U of Taq DNA polymerase . Primers were amplified using touchdown PCR amplification profile which had initial denaturation step for 3 min at 94°C followed by first 5 cycles of 94°C for 20 sec, 65°C for 20 sec and 72°C for 30 sec, with 1°C decrease in temperature for each cycle, followed by 35 cycles of 94°C for 20 sec with constant annealing temperature (59°C) for 20 sec and 72°C for 30 sec, followed by a final extension for 20 min at 72°C. After getting the amplified PCR products, agarose gel (1.2%) was used for checking the amplification of markers. The PCR products with good amplification were then used for estimating fragment size.A total of 154 SSR markers were used in the present study and details of these markers have been provided in For estimating amplicon length size, amplified PCR products were diluted to 60-100 folds in order to use them for multiplexing SSRs based on their fluorescent labels and amplicon length. SSRs with different labels and allele size ranges were considered together to get good multiplexes. The PCR products (1 µl) with GeneScan 500 LIZ standard (Applied Biosystems) from all the SSRs of a single multiplex were then mixed with formamide (1 µl) in each well. Capillary electrophoresis  was then used to analyse amplified products. Result files were then transferred to computer to do allele sizing using GENEMAPPER v4.0 software (Applied Biosystems). In addition, few SSRs which were not amenable to ABI genotyping, PCR products of these markers were analysed on 6% non-denaturing polyacrylamide gels (PAGE) (29∶1 acrylamide/bisacrylamide) and visualized by silver staining.http://www.diversityarrays.com/molecularprincip.html), however, the method is briefly described below. DArT technology consists of several steps such as complexity reduction of the DNA of interest, library creation, microarraying libraries onto glass slides, hybridisation of fluoro-labelled DNA onto slides, scanning of slides for hybridisation signal and data extraction for analysis. The complexity of a DNA sample was reduced to obtain a ‘representation’ of that sample and then variation for that representation is determined which reflect sequence variation. DArT markers detect variations of its presence vs. absence in a genomic ‘representation’ through hybridisation to DArT array consisting of a library from peanut. In this case, earlier a library for peanut representing mixture of genomic ‘representations’ from a pool of individuals covering the genetic diversity of the species is amplified. These fragments were then cloned into a vector that was introduced into E. coli to form a library and each colony contained one of the fragments from the genomic ‘representation’. Currently the high-throughput capability of DArT is based on a microarray platform and, selections of clones from the library are arranged into a plate format  after library creation. The fragments within the library were amplified and spotted onto glass slides using a microarrayer to form a genotyping array. After washing and processing of these hybridised slides to remove unbound labelled DNA, the slides are then scanned using a scanner to detect fluorescent signal emitted from the hybridised fragments. Finally, the result from each fluorescent channel is recorded and the data from the scanned images was extracted and analysed using the DArTsoft software and the information is managed by the DArTdb Laboratory Information Management System.DArT arrays in peanut have been developed by DArT Pty Ltd, Australia in collaboration with ICRISAT (India). The peanut ‘reference set’ has been genotyped with a DArT array consisting of 15,360 features. A total of 4,597 markers/features were polymorphic which were used for population diversity and association study. The detailed method of genotyping is available in the website of , oil and nutritional quality , physiological traits , yield and its components traits . Evaluation and characterization was done in a total of 467 environments at 157 locations . These 14 locations from eight countries included Patancheru, Bangalore, Dharwad, Raichur, Jalgaon, Durgapura and Coimbatore in India, Malawi, Mali, Senegal, Tanzania, Niger (ICRISAT Sahelian Centre), Vietnam and Thailand. The details on standard procedure for phenotyping are explained in several earlier published literature The peanut ‘mini core collection’ is a subset of the ‘reference set’ and either complete ‘reference set’ or ‘mini core collection’ was characterised for a total of 50 agronomic traits . These tThe polymorphism information content (PIC), major allele frequency, number for observations, availability and gene diversity were calculated using the software PowerMarker ver. 3.25 The genetic structure and number of subgroups of this germplasm set was estimated using the model-based Bayesian clustering method implemented in STRUCTURE software version 2.1 Estimated likelihood values [LnP(D)], log likelihood of the observed genotype distribution in K clusters obtained from STRUCTURE runs against K was used to predict the most probable number of subgroups in the population. The delta-K value best describes the population structure based on the criteria of maximizing the log probability of data or in other words the value at which LnP(D) reaches a plateau and hence, delta-K was calculated in order to have appropriate subgroups in this population.2) between the pairs of loci were used for calculating LD 2 values were plotted against the genetic distance (cM) between markers.Linkage disequilibrium (LD), which provides an estimate for number of markers required for conducting genetic/QTL mapping and GWAS, sometimes influenced by population structure and subgrouping derived based on the demographic and breeding history of the accessions included in the germplasm set. Genomewide LD in the present set was estimated by pair-wise comparisons among the genome anchored 139 SSR markers. Squared allele frequency correlations (r2) by the marker is reported In order to conduct precise marker-trait association analysis, population structure and Q values from the software STRUCTURE while principal components (PCs) obtained from TASSEL were used as covariates during MLM analysis. Further among different options available within MLM, the widely adapted approach called “optimum levels of compression in combination with P3D” for variance component estimation was used for association analysis. For MLM analysis, marker-based kinship matrix (K) obtained using TASSEL was used along with the Q matrix to correct for both family and population structure and the phenotypic variation explained (r−6 at a significance level of 1% after Bonferroni multiple test correction (0.01/4751). The denominator in the Bonferroni correction is the total number of markers tested.Of the several methods suggested to correct false positive in association analysis even keeping stringent p-value benchmark, the most stringent correction method called “Bonferroni Correction” was used in the present analysis. The threshold was found to be 2.1×10Table S1Details on the peanut ‘reference set’ in terms of genome type, origin and biological status. This table contains list of genotypes used in this study. It also contains information on species, market types (runner or bunch), genome information, geographical region and biological status for all the genotypes.(XLS)Click here for additional data file.Table S2Summary on phenotyping data collected on the peanut ‘reference set’. This table contains information on names of locations where phenotyping experiment was conducted. It also contains information for each trait about number of environments and locations.(XLS)Click here for additional data file.Table S3Major allele frequency, gene diversity and PIC content of 4597 DArT loci assayed on the peanut ‘reference set’. This table contains information on major allele frequencies estimated, number of alleles scored, gene diversity calculated and polymorphic information content (PIC) calculated for each polymorphic DArT marker.(XLS)Click here for additional data file.Table S4Major allele frequency, gene diversity and PIC content of 154 SSR loci screened on the peanut ‘reference set’. This table contains information on major allele frequencies estimated, number of alleles scored, gene diversity calculated, heterozygosity observed and polymorphic information content (PIC) calculated for each polymorphic SSR marker. The information on sequence and source of development of these SSRs have also been provided in this table.(XLS)Click here for additional data file.Table S5Marker allele unique to the genotype identified for all the genotypes of the peanut ‘reference set’. This table contains information on unique allele identified for selected SSR markers which can differentiate each genotype from the other genotypes. Majority of the genotypes could be identified by unique allele detected by a single marker and the remaining genotypes could be discriminated by using a combination of unique alleles for two or more than two SSR markers. Alleles highlighted with green colour indicate uniqueness to a single genotype. Dark orange colour indicates that this allele is present only in two genotypes; blue colour alleles are present in three genotypes while purple colour indicates presence in four or five genotypes.(XLSX)Click here for additional data file.Table S6List of significant marker trait association (MTA) identified after Bonferroni correction for different agronomical traits. This table contains information on the entire 524 marker trait associations (MTAs) identified for different traits. Information on linked marker, F Value, p-value, phenotypic variance (PV) explained, genetic variance, residual variance and likelihood have been provided for all the MTAs.(XLS)Click here for additional data file.


Ppt1-/-) mice, a model of the infantile disease subtype, exhibit progressive axonal degeneration in the optic nerve and loss of retinal ganglion cells, faithfully reflecting disease severity in the CNS. Here we performed spectral domain optical coherence tomography (OCT) in Ppt1-/- and ceroid lipofuscinosis neuronal 3 deficient (Cln3-/-) mice, which are models of infantile and juvenile neuronal ceroid lipofuscinosis, respectively, in order to establish a non-invasive method to assess retinal alterations and monitor disease severity in vivo.The neuronal ceroid lipofuscinoses constitute a group of fatal inherited lysosomal storage diseases that manifest in profound neurodegeneration in the CNS. Visual impairment usually is an early symptom and selective degeneration of retinal neurons has been described in patients suffering from distinct disease subtypes. We have previously demonstrated that palmitoyl protein thioesterase 1 deficient (Ppt1-/- and of 16-month-old Cln3-/- mice in comparison with age-matched control littermates. Additionally, optical coherence tomography demonstrated reduced thickness of retinae in knockout mice in comparison with age-matched control littermates. High resolution scans and manual measurements allowed for separation of different retinal composite layers and revealed a thinning of layers in the inner retinae of both mouse models at distinct ages. OCT measurements correlated well with subsequent histological analysis of the same retinae.Blue laser autofluorescence imaging revealed increased accumulation of autofluorescent storage material in the inner retinae of 7-month-old These results demonstrate the feasibility of OCT to assess neurodegenerative disease severity in mouse models of neuronal ceroid lipofuscinosis and might have important implications for diagnostic evaluation of disease progression and therapeutic efficacy in patients. Moreover, the non-invasive method allows for longitudinal studies in experimental models, reducing the number of animals used for research. The assessment of neuronal perturbation and neurodegeneration is of critical importance in many diseases of the central nervous system (CNS), as it strongly correlates with permanent neurological disability. Therefore, minimally- or non-invasive techniques for unbiased determination of neuronal perturbation are not only useful for diagnosis, but even more so for monitoring disease progression and efficacy of therapeutic interventions.An ideal CNS-related target to monitor these issues is the eye with its easy accessibility. Consequently, optical coherence tomography (OCT) has emerged as an important diagnostic tool not only to monitor ophthalmological disorders but also other neurological diseases of the CNS including multiple sclerosis, Alzheimer’s and Parkinson’s disease –3. The nCLN genes encoding proteins important for lysosomal function and cellular homeostasis [The different CLN subtypes are caused by mutations in distinct eostasis . Deficieeostasis , 6.Deficiencies in the soluble lysosomal enzyme palmitoyl protein thioesterase 1 (PPT1) result in the devastating infantile form of the disease (CLN1 disease) and mutations in ceroid lipofuscinosis neuronal 3 (CLN3), a transmembrane protein of uncertain function, are cause of the most common juvenile subtype (CLN3 disease) . In bothPpt1 and Cln3 mutant mice have demonstrated that deficits mainly occur in the optic nerve and inner retinal layers at distinct ages [Mouse models of CLN1 and CLN3 disease have been generated using different genetic strategies and authentically mimic human disease aspects –16. Retinct ages –23. Acconct ages , 24.Ppt1-/- and Cln3-/- mice. By combining confocal laser fundus imaging and cross-sectional OCT analysis with high resolution, we demonstrate an accumulation of autofluorescent storage material and a thinning of distinct retinal layers indicative of neurodegeneration. We show that neuron loss in both mouse models predominantly affects the inner retina and confirm that these features occur later in Cln3-/- mice than in Ppt1-/- mice.In order to monitor retinal degeneration in a non-invasive manner, we performed spectral domain optical coherence tomography (OCT) in In general, the presented method can be used to perform non-invasive and longitudinal studies in mouse models of neurological diseases and might have important implications for diagnosis and monitoring of disease progression and therapeutic interventions.Mice were kept at the animal facility of the Department of Neurology, University of Wuerzburg, under barrier conditions and at a constant cycle of 12 h in the light (< 300 lux) and 12 h in the dark. All animal experiments were approved by the Government of Lower Franconia, Germany.Ppt1-deficient (Ppt1-/-) mice with disruption of exon 9 [Ppt1+/+) littermates were on a uniform C57BL/6 genetic background. Cln3-deficient (Cln3-/-) mice with disruption of exons 2-6 and most of exon 1 [Cln3+/+) were on a Sv/129 genetic background. Genotypes were determined by conventional PCR using isolated DNA from tail biopsies following previously published protocols [Rd8 mutation in the Crb1 gene as described [f exon 9  and age-f exon 1  and age-rotocols , 16. Micescribed . Both moWe did not detect significant differences in the measured parameters between 18-month-old C57BL/6 and Sv/129 wildtype controls (not shown).® Fluid; Bausch & Lomb) and a custom-made polymethylmethacrylate hard contact lens . Mouse eyes were subjected to OCT imaging with a commercially available device  reaching a digital resolution of 3.9 μm and a measurable thickness change of 1 μm [® (Bausch & Lomb) until awakening of the mice. Resultant image files were used for analysis in the proprietary software or exported and processed using Photoshop CS3 (Adobe Systems). The analyzed retinal area was estimated based on previous observations [Mice were anesthetized by intraperitoneal injection of ketamine and xylazine, and their pupils were dilated by topical administration of 1 drop of 0.5% tropicamide eye drops  before image acquisition. Air-corneal interface refraction and corneal dehydration were prevented by applying artificial tears  nerve fiber layer (NFL), ganglion cell layer (GCL) and inner plexiform layer (IPL); 2) inner nuclear layer (INL); 3) outer plexiform layer (OPL), outer nuclear layer (ONL) and inner/outer segments of photoreceptors (IOS); 4) retinal pigment epithelium (RPE) and choriocapillary complex (CC). Additionally, a combination of NFL, GCL, IPL and INL was defined as inner retina and a combination of OPL, ONL, IOS, RPE and CC was defined as outer retina. Five to ten measurements of each layer were performed along the width of the circle scan, avoiding measurements on top of major blood vessels.Mice were euthanized and transcardially perfused with PBS containing heparin and subsequently with 2% paraformaldehyde (PFA) in PBS. Optic nerves and eyes were postfixed in the same solution for 1 h, dehydrated in 30% sucrose and frozen in OCT medium. 10 μm-thick cryo-sections of optic nerves and retinae were prepared and stored at -20°C until use.Eyes were enucleated and postfixed in 4% PFA in PBS for 15 minutes and retinal flat mounts were prepared. Cresyl violet staining and quantification of Nissl-positive retinal ganglion cells was performed according to previously published protocols .Immunohistochemical labeling of NeuN, Brn3a and non-phosphorylated neurofilaments in cryo-sections was performed as previously described .For electron microscopy and correlation of OCT and histology, eyes with attached optic nerves were enucleated and postfixed in 4% PFA and 2% glutaraldehyde in cacodylate buffer over night. Tissue was osmicated, dehydrated and embedded in Spurr’s medium as described . Semi-tht-test. Correlation of thickness measurements by OCT versus histology was assessed by Pearson’s correlation analysis.Statistical analysis was performed using PASW Statistics 18  software. Shapiro-Wilk test was used to check for normal distribution of data. Parametric comparisons between thickness values of age-matched control mice and knockout mice were made by unpaired two-tailed Student’s Ppt1+/+ mouse  and a +25 diopter add-on lens (Heidelberg Engineering) were used to adjust for optical qualities of the mouse eye Figure A. A custPpt1-/- mice and age-matched Ppt1+/+ control littermates. At this age, our previous histological examination of retinal flat mounts demonstrated significant loss of retinal ganglion cells [Ppt1-/- mice compared with controls  and a non-significant tendency for layer 3  close to 1  retinal neurons has also been indicated by electroretinographic analysis of Ppt1 and Cln3 mutant mice, as mostly b-wave amplitudes were affected [Cln6 mutant mice, a predominant degeneration of the outer retina has been demonstrated [These findings display some common features in retinae of affected , 21, 23.nstrated . Similarnstrated . It remanstrated .Cln3-/- mice, as retinal alterations (this study and unpublished observations) and regional brain atrophy [Rag1 deficiency regarding retinal ganglion cell loss in Ppt1-/- mice [The feasibility of OCT for non-invasive imaging of storage material and neurodegeneration has important implications for longitudinal imaging in animal studies. Our previous observations have shown that loss of retinal ganglion cells is a reliable indicator of neuron loss in other brain regions and that the retina can be used as a surrogate tissue to monitor disease severity at least in a model of CLN1 disease . This al atrophy  occur on-/- mice , demonstMoreover, the presented method might be of high relevance for studies or trials in patients. Non-invasive imaging techniques revealing typical alterations associated with CLN diseases  might not only guide in diagnostics, but are also important to determine disease status and severity and control efficacy of therapeutic interventions. As the perspectives for such treatment approaches become increasingly more promising the demand for efficient non-invasive techniques to judge their efficacy is constantly growing , 33.Finally, non-invasive assessment of specific retinal layers appears to emerge as a powerful tool to monitor disease severity also in other more common neurological disorders and their corresponding models. For example, in line with our findings, thinning of inner retinal layers is also associated with a loss of retinal ganglion cells in a mouse model of multiple sclerosis  and is ain vivo. Reflecting accompanying conventional histological investigations, both accumulation of autofluorescent storage material and retinal thinning indicative of neurodegeneration mainly occur in the inner retina of the analyzed mice. The non-invasive method allows for longitudinal studies in experimental models, reducing the number of animals used for research. Moreover, it might have important implications for diagnostic evaluation of disease progression and therapeutic efficacy in patients. The presented method might emerge as an important tool to monitor neuronal alterations not only in CLN diseases, but in many neurological disorders characterized by widespread axonal perturbation in the CNS.We demonstrate the feasibility of OCT to assess distinct alterations related to disease severity in the retina of two mouse models of neuronal ceroid lipofuscinosis


Saccharomyces cerevisiae through RAS2/cAMP signaling pathway. Adiponectin, produced in adipose tissues of mammals, is an insulin-sensitizing hormone. Strangely, osmotin acts like the mammalian hormone adiponectin in various in vitro and in vivo models. Adiponectin and osmotin, the two receptor binding proteins do not share sequence similarity at the amino acid level, but interestingly they have a similar structural and functional properties. In experimental mice, adiponectin inhibits endothelial cell proliferation and migration, primary tumor growth, and reduces atherosclerosis. This retrospective work examines the vital role of osmotin in plant defense and as a potential targeted therapeutic drug for humans.Osmotin is a stress responsive antifungal protein belonging to the pathogenesis-related (PR)-5 family that confers tolerance to both biotic and abiotic stresses in plants. Protective efforts of osmotin in plants range from high temperature to cold and salt to drought. It lyses the plasma membrane of the pathogens. It is widely distributed in fruits and vegetables. It is a differentially expressed and developmentally regulated protein that protects the cells from osmotic stress and invading pathogens as well, by structural or metabolic alterations. During stress conditions, osmotin helps in the accumulation of the osmolyte proline, which quenches reactive oxygen species and free radicals. Osmotin expression results in the accumulation of storage reserves and increases the shelf-life of fruits. It binds to a seven-transmembrane-domain receptor-like protein and induces programmed cell death in  Glycine max OLP (GmOLP) is an acidic protein (Thaumatococcus danielli (Plants are subjected to various kinds of biotic  and abio protein . PR-5 prdanielli . In spitdanielli . Contrardanielli . Osmotindanielli . Adiponedanielli . HusainiTables 1 and 2). Osmotin is induced in Petunia when treated with Penicillium funiculosum, Erwinia stewartii, Pseudomonas syringae, aspirin, wounding, and salicylic acid  and CaOXR1/CaRAV1 (Capsicum annum related to ABI3/VP1) in pepper leaves when treated with NaCl or mannitol  which bind to two GCC boxes in osmotin promoter are essential for ethylene response (Osmotin expression was influenced by the type of promoter used. Osmotin-specific promoter showed higher expression levels compared to CaMV35S promoter in sugar beet cells (osmotin promoter binding protein 1), an apetala2/ethylene responsive transcription factor of tobacco displayed salt and disease tolerance in rice (Gossypium barbadense Ethylene Response Factor) induces osmotin expression by activating the GCC box  protein also  showed domain I with 12-β sheets, an α-helical domain II, and domain III with 2-β sheets, contrarily PcOSM1 exhibited a deformed, unidentical domain III and loss of 4-β sheets in domain I. But, the acidic cleft responsible for antifungal activity was present in both the isoforms , a MAPK in tobacco showed upregulation of osmotin with enhanced tolerance to salt and drought, which suggests that GhMPK2 has a role in signal transduction  is involved in signal transduction during wound stress . The mitn attack . Overexpsduction . Based og motifs .53 is overexpressed in cells treated with toxins and the balance is influenced by hormone or toxin. P53 acts as a link between cell cycle and PCD. Osmotin decreases the pathogenesis by interacting with the cell cycle machinery and overexpresses the cell cycle components which inhibit the cell death pathway components like P53. Brassica juncea calli overexpressing osmotin showed delayed symptoms when treated with Alternaria toxin, suppressed the expression of P53 and the activity of caspase I was not affected which shows that osmotin is involved in P53-mediated PCD pathway (Saccharomyces cerevisiae BWG7a cells when treated with different concentrations of osmotin due to suppression of transcription of the stress responsive genes with the accumulation of ROS (Hypersensitive reaction (HR) is a consequence of disease resistance in plants and cell death occurs either by apoptosis or programmed cell death (PCD). PCD plays a crucial role in plant development and host interaction, which is activated by cell wall components or toxins or proteins secreted from pathogens . P53 is  pathway . PCD wasn of ROS .+ ions and compartmentalizing them into vacuoles and intercellular spaces. The association of tobacco osmotin protein with tonoplast  gene to sequester Na+ ions or how it is able to perform this functions is not yet clear. It is also not clear if osmotin has a direct role to play or it stimulates other proteins that are downstream. Transgenics overexpressing osmotin gene exhibited salt tolerance in potato  was lower in transgenics overexpressing osmotin than the corresponding controls when treated with different NaCl concentrations implying that osmotin helps in controlling its overaccumulation. Transgenic Capsicum annum L. overexpressing osmotin when treated with salt, showed higher activity of ascorbate peroxidase (APX) and superoxide dismutase (SOD) to detoxify the accumulated H2O2 than the untransformed controls under identical conditions. But, how osmotin activates the APX and SOD activities is not clear. Low levels of malondialdehyde, an indicator of lipid peroxidation levels in transgenics overexpressing osmotin compared to controls, suggests less damage of cell membrane in transformants compared to controls  results in enhanced resistance to osmotin and deletion results in sensitivity to osmotin in resistant strains  βS (GDPβS) that blocks G-protein. Mutation in FadA (α-subunit) and deletion in SfaD (β subunit) of G-protein blocks the osmotin binding and displays increased resistance to osmotin. These mutants showed enhanced chitin content with decreased sensitivity to osmotin (Sachharomyces cerevisiae D1 (SSD1) regulates the deposition of glucans and PIR glycoproteins in the cell wall, a major determinant of osmotin resistance in yeast and susceptibility to osmotin in Saccharomyces cerevisiae is encoded by Fusarium osmotin resistance3 (FOR3), a homolog of SSD1. Δfor3 mutants showed high sensitivity to osmotin similar to Δssd1 mutants  of osmotin resembles the structural homolog of β-barrel domain of adiponectin. Like adiponectin, osmotin binds to AdipoRs and induces AMP kinase phosphorylation in mammalian C2C12 myocytes (in vitro and animal models. When administered intravenously in experimental rats, it acts as adiponectin agonist in obesity and type-II diabetes, and also displayed antiatherosclerotic activity (in vitro cultured human synovial fibroblasts (in silico analysis suggests that both osmotin and adiponectin interact with the same drugs.Osmotin, a naturally occurring plant protein mimics human adiponectin. Osmotin shares structural and functional homology with adiponectin and not sequence similarity . Osmotinmyocytes . Osmotinactivity ,b. Like roblasts . Like adroblasts . Subcutaroblasts . Osmotinroblasts , 2013a. via AdipoRs, but also binds to the AdipoR1 by activating the same signaling path of adiponectin. Osmotin and adiponectin involve in antitumor activity by inhibiting p53 and suppressing caspase activity. In vitro and animal model studies suggest that, like AdipoRon and pioglitazone, osmotin acts as agonist for adiponectin. Due to the multiple activities of osmotin, it can be explored as an attractive option as agonist for adiponectin in treating adiponectin deficiency diseases in humans besides its function in biotic and abiotic stress tolerance in crop plants.Osmotin, a multifaceted plant protein confers tolerance to both biotic and abiotic stresses. Adiponectin, an antidiabetic and antiatherosclerotic protein is reduced in obese patients and leads to several diseases including coronary artery disease, inflammation, and liver diseases. Osmotin shows homology with human hormone adiponectin given that osmotin not only induces AMP kinase phosphorylation in mammalian C2C12 myocytes All the authors of the manuscript meet the essential criteria of the publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Risk stratification of patients with systolic chronic heart failure (HF) is critical to better identify those who may benefit from invasive therapeutic strategies such as cardiac transplantation. Proteomics has been used to provide prognostic information in various diseases. Our aim was to investigate the potential value of plasma proteomic profiling for risk stratification in HF. A proteomic profiling using surface enhanced laser desorption ionization - time of flight - mass spectrometry was performed in a case/control discovery population of 198 patients with systolic HF (left ventricular ejection fraction <45%): 99 patients who died from cardiovascular cause within 3 years and 99 patients alive at 3 years. Proteomic scores predicting cardiovascular death were developed using 3 regression methods: support vector machine, sparse partial least square discriminant analysis, and lasso logistic regression. Forty two ion m/z peaks were differentially intense between cases and controls in the discovery population and were used to develop proteomic scores. In the validation population, score levels were higher in patients who subsequently died within 3 years. Similar areas under the curves (0.66 – 0.68) were observed for the 3 methods. After adjustment on confounders, proteomic scores remained significantly associated with cardiovascular mortality. Use of the proteomic scores allowed a significant improvement in discrimination of HF patients as determined by integrated discrimination improvement and net reclassification improvement indexes. In conclusion, proteomic analysis of plasma proteins may help to improve risk prediction in HF patients. IThere is a need for novel prognostic markers that may help to better stratify the risk of major cardiac events in HF patients. Recently, a systematic review of 55 papers dealing with risk prediction model accuracy has only shown a moderate succesfulness for prediction of mortality in HF patients using “classic” prognostic evaluation and emphasized the need for models using a systematic biology approach . Due to The aim of the present study was to investigate the potential value of plasma proteomic profiling for risk stratification in HF. Proteomic scores predictive of cardiovascular mortality were developed in a discovery population of chronic HF patients; then, their performances were tested in a validation cohort and challenged against established prognostic indicators.All patients referred for evaluation of systolic HF (LVEF <45%) in our institution between November 1998 and May 2010 have been included in a prospective cohort on prognostic indicators ,12,13,14The flow chart of the present study is shown in Detailed process is described in supplemental methods see . At inclProteomic analyses were performed on CPLL-treated plasma samples in both populations using the SELDI-TOF-MS technique. CPLL-treated plasma samples were profiled with eight-spot format CM10 (Weak Cation Exchanger) and H50 (Reverse Phase) ProteinChip arrays (Bio-Rad Laboratories). To obtain ion m/z peak intensities, all arrays were read in an automated PBS 4000 SELDI-TOF-MS (Bio-Rad Laboratories) as previously described . Samples2 values and 2 BNP values were imputed with their respective medians.All statistical analyses were performed using R Statistical Package version 3.0. Continuous variables are presented as mean ± standard deviation (SD) and were compared using Student’s t-test. Categorical variables are expressed as absolute number and/or percentages and were compared using the χ2 test or the Fisher test as appropriate. Single imputation was used for clinical and proteomic missing data. In the discovery set, missing proteomic data (1 patient) were imputed with the median of the corresponding ion m/z peak intensity. In the validation set, 9 peak VO2. Finally, the incremental value of the proteomic scores to predict the cardiovascular mortality risk, when added to models with established prognostic indicators, was estimated with the continuous net reclassification improvement (NRI), and the integrated discrimination improvement (IDI).Proteomic variables were standardized before further analyses by subtracting the mean then dividing by the SD to have a mean of 0 and a SD of 1. Detailed analysis is described in the supplemental methods see . The mea2 as compared to patients alive at 3 years. A total of 203 different ion m/z peaks was detected and analyzed by SELDI-TOF-MS in the CPLL-treated plasma of these patients , the proteomic scores were still significantly associated with cardiovascular death in the validation population whatever the statistical method used . Other variables kept into the models were NYHA class and peak VO2. Finally, continuous NRI and IDI demonstrated that the proteomic scores calculated with the three methods significantly improved the prediction of cardiovascular death in HF patients  were excluded from the analysis. The remaining 266 patients with no event were compared to the 43 patients with cardiovascular death during the 3-year follow-up period. As shown in who died . ROC curpatients .It has been emphasized by recent international guidelines that the assessment of prognosis is an important step for the management of chronic systolic HF, particularly when counselling patients about devices and cardiac transplantation . The aim2), previously identified in the literature [We paid close attention to the phenotyping of the study populations. It should be underlined that the patients were well treated regarding their HF status with more than 90% receiving ACE inhibitors and betablockers, reflecting a modern practice in CHF. In addition, all the main predictors of cardiovascular mortality . Incremental improvement in model performance with the proteomic scores was also demonstrated.The proteomic scores were built using plasma proteomic biomarker-classifier based on 42 ion m/z peaks differentially intense in the discovery cohort; this strategy of discriminatory patterns for disease detection has already been successful in previous studies . To selem/z peak identification and/or low rate of new analytes going from discovery to accurate measurement by specific assays in routine clinical practice. In addition, pre-analytic treatment of the samples is time-consuming and this technology will not be commercially available in the next years. Nevertheless it should also be emphasized that emerging proteomic technologies may provide easier access to the global information contained in patient plasma proteome in the next future [Our results thus suggest that a multimarker strategy based on plasma proteomics has the potential to be clinically useful for the risk stratification in HF patients. In practice however, it should be acknowledged that the SELDI-TOF-MS technique has some limitations including difficulties in ion t future ,27. Thest future .S1 Methods(DOC)Click here for additional data file.S2 Methods(DOC)Click here for additional data file.S1 Tablem/z peak detected is named using the initial “p” followed by its m/z value, the type of array on which it was detected (H50 or CM10) and then the laser intensity: low mass (LM) or high mass (HM). Ion m/z peak intensities are expressed as mean ± SD. The 42 ion m/z peaks that reach a significant p value after Bonferroni correction in the discovery population are highlighted in blue and were used to calculate the proteomic scores.Each ion (DOC)Click here for additional data file.S2 TableEach ion peak detected is named using the initial “p” followed by its m/z value, the type of array on which it was detected (H50 or CM10) and then the laser intensity: low mass (LM) or high mass (HM).(DOC)Click here for additional data file.S3 TableThe proteomic scores were developed using the support vector machine (SVM), the sparse partial least square discriminant analysis (sPLS-DA) and the lasso logistic regression (LASSO).(DOC)Click here for additional data file.S1 FigThe upper mass spectrum was obtained from the H50 proteinchip array using low-mass (LM) parameter settings and the lower mass spectrum was obtained from the CM10 proteinchip array using high-mass (HM) parameter settings.(TIF)Click here for additional data file.