We dissect the C4 trait into its components, and show two independent origins of the C4 phenotype via different anatomical modifications, and the use of distinct sets of genes. Further, inference of enzyme adaptation suggests that one of the two groups encompasses two transitions to a full C4 state from a common ancestor with an intermediate phenotype that had some C4 anatomical and biochemical components. Molecular dating of C4 genes confirms the introgression of two key C4 components between species, while the inheritance of all others matches the species tree. The number of origins consequently varies among C4 components, a scenario that could not have been inferred from analyses of the species tree alone. Our results highlight the power of studying individual components of complex traits to reconstruct trajectories toward novel adaptations.The origins of novel traits are often studied using species trees and modeling phenotypes as different states of the same character, an approach that cannot always distinguish multiple origins from fewer origins followed by reversals. We address this issue by studying the origins of C Inferences of transitions among character states along species phylogenies provide powerful tools to test specific hypotheses about the timing and rate of functional diversification, correlations among functional and ecological traits . We combined different approaches to investigate different components of the complex C4 trait. (i) Focusing on anatomical characters, we evaluate the most likely number of episodes of movement of chloroplasts to the bundle sheath, and expansion of this tissue. (ii) Using transcriptome analyses to estimate gene expression, we then determine the most likely number of origins of a C4 cycle via the upregulation of known C4 photosynthetic genes. (iii) The number of episodes of enzyme adaptation for the C4 cycle is estimated using positive selection analyses, with scenarios corresponding to episodes of adaptation along different sets of branches. (iv) Finally, we compare divergence times across genes, to detect potential introgression of C4 components, as suggested within this genus for two C4 genes  accession, one A. paniculata (C4) accession, two A. angusta (C4) accessions, and up to 10 different A. semialata accessions collected from separate populations that encompass the global genetic and photosynthetic diversity of this species , previously identified using plastid markers (GPWGII 4 in Alloteropsis.The different datasets were obtained from plants grown under controlled conditions (See Supporting Information Methods 1.1 for detailed description of growth conditions), including one s GPWGII . Using t3+C4 and C4 accessions. Co‐option of different tissues and distinct modifications among accessions would support independent origins, while a reversal should result in the leaves of C3 individuals having reverted to a state that retain traces of their past C4 state when compared to the ancestral condition .Leaf cross‐sections were analyzed to identify the leaf compartment being used for the segregation of Rubisco and the modifications that increased the proportion of bundle sheath tissue in CA. semialata accessions and A. angusta (Table S2), which supplemented previously published anatomical data for E. marginata, P. pygmaeum, A. cimicina, and A. paniculata , sectioning on a manual rotary microtome , staining with Toluidine Blue O , then photographing them with a camera mounted atop a microscope , as described in Lundgren et al. . The sheath co‐opted for the segregation of Rubisco was identified by a concentration of chloroplasts producing starch. We also recorded the presence of minor veins, and measured the following traits on one cross‐sectional image per accession, as described in Christin et al. , using I4 cycle, as those encoding C4‐related enzymes that reach high abundance in C4 leaves. Variation in the co‐opted loci would support multiple origins of a weak C4 cycle, while a reversal might lead to high expression of C4‐related genes in individuals without a C4 cycle or loss of functions of genes previously used for the C4 cycle.We use RNA‐Seq to identify the genes co‐opted by the different accessions performing a CFor RNA‐Seq, we sampled the highly photosynthetically active distal halves of fully expanded new leaves and fresh roots midway into the photoperiod, which were subsequently flash frozen. Two different photoperiods  were used to ensure that the identification of the most highly expressed genes did not differ among light regimes. Data from root libraries were only used in this study for transcriptome assembly, while all leaf samples were used for both assembly and quantification of transcript abundances. For a full list of individuals, conditions, and tissues sampled see Table S3.4‐related enzymes. For each gene family, all sequences descending from a single gene in the common ancestor of grasses via speciation and/or duplication were considered as the same gene lineage . These groups include potential lineage specific paralogs . When different Alloteropsis genes were identified within the same group of co‐orthologs through detailed phylogenetic analyses, the abundance of each group was estimated independently. In Alloteropsis, this is the case only for genes previously shown to have been acquired laterally from distantly related C4 lineages . For each assembled contig, the transcript abundance was calculated as reads per million of mapped reads (rpm). Using a previously developed phylogenetic annotation pipeline  and their co‐orthologs in other C3 and C4 grasses (see Supporting Information Methods 1.3 for a detailed description of phylogeny construction). The inferred gene trees were used to verify that C4‐related genes were placed as expected based on the species tree, as opposed to a position suggesting an acquisition from distant C4 relatives. In addition, the gene tree topologies were used for positive selection analyses to detect traces of past episodes of enzyme adaptation for the new catalytic context after the initial emergence of a C4 cycle  to test for adaptive evolution using codeml as implemented in PAML (Yang Alloteropsis (one round of enzyme adaptation), (2) both A. cimicina and A. angusta + A. semialata (two rounds of enzyme adaptation), and (3) A. cimicina, A. angusta, and A. semialata (three independent episodes of enzyme adaptation). Foreground branches for all models were specified as the branch leading to the identified node plus all descending branches . Models involving positive selection in only one of the C4 lineages were also considered . For each gene lineage, the best‐fit model was identified based on the corrected Akaike information criterion (AICc), selecting the model with the lowest AICc after checking that its ΔAICc score was at least 5.22 units below that of the M1a null model. An ΔAICc score = 5.22 corresponds to a P‐value threshold of 0.01 for a likelihood ratio test comparing these two models using 2 degrees of freedom (df). C4 species other than Alloteropsis were removed prior to analysis to avoid an influence of positive selection in these taxa affecting our conclusion. Analyses were repeated using only codons with fixed nucleotides within each lineage , to verify that short terminal branches with unfixed mutations did not significantly inflate the dN/dS ratio, and therefore alter our conclusion. Finally, to assess the effect of gene tree topology on our conclusions, we repeated the positive selection analyses using 100 bootstrap pseudoreplicate topologies.For each set of genes encoding core CAML Yang . The bes4 adaptations among species, we performed molecular dating of markers from across the transcriptomes, including those used for C4 by at least two Alloteropsis accession and their paralogs. The divergence times between species estimated from introgressed genes are expected to be younger than those estimated from other genes , two non‐Panicoideae grasses (Brachypodium distachyon and Oryza sativa), and four nongrass species . To ensure accurate annotation, we restricted the analysis to gene families that included at least one A. thaliana sequence. The coding sequences (CDS) from the above genomes were then used to identify similar sequences in our transcriptomes using BLASTn with a minimum alignment length of 500 bp.Groups of genes descending from a single gene in the common ancestor of Panicoideae (Panicoideae co‐orthologs), the grass subfamily that includes Sorghum sequence was selected as the outgroup and the root of the tree was fixed to 31 Ma , based on estimates from Christin et al. . In total, 2,797 1:1 Panicoideae co‐ortholog datasets were used for subsequent molecular dating, as implemented in Beast version 1.5.4 , using the same parameters. To guarantee a consistent species sampling, the taxa included in the transcriptome‐wide analyses were retrieved from manually curated alignments for C4‐specific genes as well as other groups of orthologs from the same gene families, obtained as described above for C4‐specific forms. In addition, plastid genomes for the same species were retrieved from Lundgren et al. . In these species, the overall proportion of outer bundle sheath tissue within the leaf is increased via enlarged outer bundle sheath cells. Indeed, the outer sheath is 7.8‐fold larger than the inner sheath in C4A. cimicina and A. paniculata, compared to a 1.2‐ to 0.6‐fold differences in C4A. semialata and A. angusta (Table S2). This contrasts strongly with the anatomy of the C4A. semialata and A. angusta . Both of these species use the inner bundle sheath for Rubisco segregation and increase the overall proportion of this tissue via the proliferation of minor veins, and enlargement of the inner sheath cell size .Grasses ancestrally possess two concentric rings of bundle sheath cells and either can be co‐opted for Cis Brown . The clo3 and C3+C4A. semialata , which implies some Rubisco activity in these cells, confirming previous reports . In the C3 and C3+C4A. semialata, the outer bundle sheath is slightly larger than the inner one , while the C3 outgroup species P. pygmaeum and E. marginata have outer bundle sheaths that are considerably larger than their small inner sheaths .Staining by Toluidine Blue O indicates some starch production occurs in the inner bundle sheaths of both the C4A. cimicina and A. semialata/A. angusta use different tissues for Rubisco segregation and achieve high bundle sheath proportions via distinct modifications, supporting independent origins of C4 anatomical components in these two groups. Some Rubisco activity is suggested in the inner sheath of the C3A. semialata, which supports an early origin migration of chloroplasts to this tissue , the enzyme used for the initial fixation of atmospheric carbon into organic compounds in C4 plants. However, the gene lineage most highly expressed varies among accessions , supporting the previous conclusion that these genes were acquired by Alloteropsis via lateral gene transfer  are also highly expressed in the C3 outgroup and in other distantly related C3 taxa , analyses including all Alloteropsis accessions clearly point to changes in selective pressures specifically in the branch leading to A. cimicina , the model that assumes positive selection after the split of the two species was favored . Conclusions based on the selection tests were also supported using only codons with fixed nucleotides within a lineage , with the exception of nadpme‐1P4 for which no positive selection was inferred after removing the unfixed codons (Tables S5 and S6). Furthermore, gene tree topology had no effect on our conclusions, since all bootstrap replicates supported the same model, with the exception of 2% of nadpme‐1P4 bootstrap replicates (Tables S7 and S8).The codon models do not support positive selection on any genes involved in Cup Table . In two 4 context in each of the C4 groups , followed by a reversal to expression levels that resemble the ancestral condition in the C3 accessions . The genetic control of these features is unknown, preventing a comparison of the causal mutations. However, the distribution of anatomical characters among grasses indicates that the vast majority of C4 lineages that co‐opted the inner bundle sheath increased its proportion via the addition of minor veins  for each C4‐related gene and sample.Table S5. Results of positive selection analyses inferring the episodes of enzymatic adaptation in Alloteropsis using only fixed differences.Table S6. Results of positive selection analyses inferring the episodes of enzymatic adaptation in the A. angusta/A. semialata clade using only fixed differences.Methods 1.1. Plant growth conditions.Methods 1.2. RNA‐Seq protocol.Methods 1.3. Positive selection analysis.Methods 1.4. Alignment and filteringClick here for additional data file.Supplementary MaterialClick here for additional data file.


Three experiments were conducted separately to estimate the digestible Lys (dig. Lys) requirements of Cobb × Cobb 500 male broilers using different statistical models. For each experiment, 1,200 chicks were housed in 48 floor pens in a completely randomized design with 6 treatments and 8 replicates. Broilers were fed diets with increasing dig. Lys levels from 1 to 12 d (Exp. 1), from 12 to 28 d (Exp. 2), and 28 to 42 d (Exp. 3). Increasing dig. Lys levels were equally spaced from 0.97 to 1.37% in Exp. 1, 0.77 to 1.17% in Exp. 2, and 0.68 to 1.07% in Exp. 3. The lowest dig. Lys diets were not supplemented with L-Lysine and all other essential AA met or exceeded recommendations. In Exp. 3, six birds per pen were randomly selected from each replication to evaluate carcass and breast yields. Digestible Lys requirements were estimated by quadratic polynomial (QP), linear broken-line (LBL), quadratic broken-line (QBL), and exponential asymptotic (EA) models. Overall, dig. Lys requirements varied among response variables and statistical models. Increasing dietary dig. Lys had a positive effect on BW, carcass and breast yields. Levels of dig. Lys that optimized performance using QP, LBL, QBL, and EA models were 1.207, 1.036, 1.113, and 1.204% for BWG and 1.190, 1.027, 1.100, and 1.172% for FCR in Exp. 1; 1.019, 0.853, 0.944; 1.025% for BWG and 1.050, 0.879, 1.032, and 1.167% for FCR in Exp. 2; and 0.960, 0.835, 0.933, and 1.077% for BWG, 0.981, 0.857, 0.963, and 1.146% for FCR in Exp. 3. The QP, LBL, QBL, and EA also estimated dig. Lys requirements as 0.941, 0.846, 0.925, and 1.070% for breast meat yield in Exp. 3. In conclusion, Lys requirements vary greatly according to the statistical analysis utilized; therefore, the origin of requirement estimation must be taken into account in order to allow adequate comparisons between references. Lysine (Lys) is the second limiting amino acid (AA) in practical corn-soybean meal diets. Growth performance and proper muscle development of broiler chickens is highly dependent of an adequate dietary supply of Lys. Although all AA coded in the nucleic acid sequence are required for muscle protein synthesis, also depending on its frequency and dietary supply, Lys is the main essential AA required for muscle building for broilers . Lysine Nutrient recommendations for broilers intend to optimize growth performance as well as the proportions of individual carcass components, especially breast meat, to a point that best economic returns are obtained. It has been reported that Lys can increase carcass yields and alter its composition by increasing meat yield and reducing carcass fat ,4. This Pectoralis major, which is more sensitive to Lys than wings and thigh muscles [Dietary Lys also plays an important role in breast muscle protein turnover by modulating protein synthesis and breakdown rates . Further muscles . The bre muscles . In cont muscles .Usual determinations of AA requirements target the optimization of growth performance, feed conversion ratio (FCR), and breast meat yields; however, AA concentrations that optimize breast meat yields have shown to be higher than for the other responses ,9,10. ImDiverse regression methods have been used to estimate Lys requirements for broilers. Pesti et al. (2009)  tested dAn adequate statistical model is essential to correctly interpret the requirements ,14,17–19All procedures utilized in the present study were approved by the Ethics and Research Committee of the Federal University of Rio Grande do Sul, Porto Alegre, Brazil. The Ethics Committee approved this study .2, 25 birds per pen). Experimental pens had rice hulls as bedding, one 15 kg capacity tube feeder, and 3 nipple drinkers. Mash feeds and water were available for ad libitum consumption. Mortality was recorded daily. Initial temperature was set to 32°C being reduced to maintain bird comfort throughout the studies. A continuous lighting schedule was used until 7 d-of-age, whereas a 20L:4D cycle with constant intensity was used thereafter.Three experiments (Exp.) were conducted using 1,200 one-d-old male, slow-feathering Cobb 500 x Cobb broilers. Chicks were vaccinated for Marek’s and infectious bursal diseases at the hatchery  and then randomly distributed into 48 pens of 1.65 × 1.65 m  were selected within the upper and lower 5% of each pen average at 43 d of age for processing to carcass and breast yields evaluation. Birds were fasted for 6 h, individually weighed before electrical stunning (45 V for 3 s), bled for 3 min after carotid and jugular veins cut, scalded at 60°C for 45 s, and mechanically defeathered. Carcasses were manually eviscerated and then statically chilled in slush ice for 3 h before processing. Breast fillets were manually deboned from the carcasses. Carcass yield was expressed as a percentage of live weight and breast yield was expressed as a percentage of the eviscerated carcass weight.Dietary treatments in Exp. 1 were provided from 1 to 12 d-of-age, in Exp. 2 from 12 to 28 d-of-age and in Exp. 3 from 28 to 42 d-of-age. Diets in the experiments were based on corn, soybean meal, and corn gluten meal . Basal dTreatments were structured with the addition of increasing levels of dig. Lys in 0.08% increments from 0.97% to 1.37% in Exp. 1, from 0.77 to 1.17% in Exp. 2, and from 0.68 to 1.07% in Exp. 3 by adding L-Lysine HCl at the expense of an inert filler (kaolin). Standard corn-SBM-based broiler diets were provided to all treatments in the periods after  the experimental phase of Exp. 1, before (starter diet from 1 to 12 d-of-age) and after (finisher diet from 28 to 35 d-of-age) experimental phases of Exp. 2, and before  the experimental phase of Exp. 3 . Before 0 + β1 × X + β2 × X2, where Y is the dependent variable, X is the dietary Lys concentration, and β0 is the intercept, β1 and β2 are the linear and quadratic coefficients, respectively; maximum response concentration was obtained by:—β1 ÷ (2 × β2). The LBL model was expressed as Y = β0 + β1 × (β2—X), where (β2—X) = 0 for X > β2, Y is the dependent variable, X is the dietary Lys concentration, β0 is the value at the plateau, β1 is the slope and β2 is the Lys concentration at the break point. Quadratic broken-line model is Y = β0 + β1 × (β2—X)2, where (β2—X) = 0 for X > β2, Y is the dependent variable, X is the dietary Lys concentration, β0 is the value at the plateau, β1 is the slope and β2 is the Lys concentration at the break point. The EA was expressed as Y = β0 + β1 × (1 –EXP(– β2 × (X– β3), where Y is the dependent variable, X is the dietary Lys concentration, β0 is the response for the dependent variable estimated for the feed with the lower Lys, β1 is the difference estimated between the minimum and maximum response obtained by the increasing Lys, β2 is the slope of the exponential curve, β3 is the Lys at the lower level; requirement were estimated by calculating (ln(0.05) ÷ – β2) + β3 for 95% of the requirement and (ln(0.01) ÷ – β2) + β3 for 99%.The experiments had a gradient treatment structure in a completely randomized design. Treatments had 8 replications in every experiment. Four mathematical models were used to estimate the optimal level of dig. Lys inclusion to maximize broiler responses based on a gradient treatment structure ,25–27. BP > 0.05). Overal means for the period mortality in the 3 studies were 0.37%, 0.64% and 0.52% in Exp. 1, 2, and 3, respectively.Overall, there were no statistical differences between % mortality in any of the experiments (P ≤ 0.01) for dig. Lys intake and quadratic responses (P ≤ 0.01) for BWG and FCR (2 was the highest (0.88 for BWG and 0.74 for FCR).Feeding Cobb × Cobb 500 birds with increasing levels of dig. Lys resulted in a positive linear response ( and FCR . DigestiP ≤ 0.01) were observed for BWG, FCR, feed intake, and dig. Lys intake in the period from 12 to 28 d (Significant quadratic responses ( to 28 d . DigestiP ≤ 0.01) for dig. Lys intake, and quadratic responses (P ≤ 0.05) for BWG, FCR, feed intake, dig. Lys intake, carcass yield, and breast meat yield from 28 to 42 d. Digestible Lys requirements estimates based on 100, 99%, and 95% of optimal responses were calculated and are represented in Growth performance and broiler processing data from 28 to 42 d are presented in Basal experimental diets utilized in the present research were based on corn, soybean meal, and corn gluten meal, and were formulated to contain CP and energy levels comparable to usual commercial diets in Brazil and US. It is crucial that requirement studies use a basal diet that is deficient in the tested AA to produce accurate estimates. Amino acid analysis determined that the basal diets in Exp. 1, 2, and 3 contained 1.12, 0.85, and 0.77% total Lys, respectively. Moreover, in these 3 experiments, broilers fed the basal diets presented poor performance compared with broilers fed the dose-response diets with increasing Lys levels, demonstrating that Lys was deficient in the basal diets.In the present research, dig. Lys requirements varied depending upon the response criterion. Requirements for FCR was higher than BWG in Exp. 2 and 3, similar as observed in previous researches –14,16,29In parallel to results from the present study, previous research with broilers fed diets with progressive additions of dig. Lys had have shown quadratic increases in BWG and decreases in FCR in all studied ages ,14,29,30Dozier et al. (2009b)  estimateDozier et al. (2010)  estimateIn the present study, 4 statistical models were generated in order to estimate dig. Lys requirements. Comparisons with previous research are valid; however, as breast muscles consistently increase as proportions of the total broiler due to genetic selection, the demand for Lys consumed also is expected to increase. Since genetic improvements also affect body weight gain and, therefore, feed intake, it could be questioned that the increased feed intake with the modern broiler would provide the increased Lys to sustain maximum breast yield. However, the breast muscle mass of the Ross 708 of 2009 broiler was 18% whereas the one from a heritage chicken of 1962 was 9% of the total body mass at 5 weeks . HavenstThe QP, LBL, QBL, and EA models have been thoroughly reviewed by Pesti et al. (2009) . Accordi2 and the sum of the square of residuals. Since the latter is a linear function of R2 values, the order of fit is the same. In the present research, all models tested were statistically significant (P ≤ 0.01). The model that provided the best fit was the QBL for BWG and FCR and the LBL for carcass and breast meat yield. The LBL model can underestimate requirements because response of broilers to increasing Lys levels is clearly nonlinear decreasing as the AA approaches its requirement what is popularly referred as the law of diminishing returns [It is possible to compare models through their R returns ,27. The  returns . The modThe comparisons between the present study with previous research has to take in consideration differences in studied variables such as strain, sex, age, type of diet, statistical model, and method . Overall, requirement estimates observed in the present research are notably higher than the recommended by the NRC (1994) . The higIn conclusion, estimates of Lys requirements vary among response variables and statistical models; therefore, influencing recommendations to optimize economic performances. Throughout the 3 studies the requirement estimates for FCR were higher than BWG. Overall, the QBL model provided the best fit for growth performance variables and seemed adequate in both mathematical and biological backgrounds. Regardless of the model of choice, researchers should be familiar with the characteristics of each model to obtain precise and coherent requirements estimates.


Tocochromanols are organic compounds mostly produced by photosynthetic organisms that exhibit vitamin E activity in animals. They result from the condensation of homogentisate with four different polyprenyl side chains derived all from geranylgeranyl pyrophosphate. The core tocochromanol biosynthesis has been investigated in several photosynthetic organisms and is now well-characterized. In contrast, our current knowledge of the biosynthesis and transport of tocochromanol biosynthetic precursors is much more limited. While tocochromanol synthesis occurs in plastids, converging genetic data in Arabidopsis and soybean demonstrate that the synthesis of the polar precursor homogentisate is located in the cytoplasm. These data implies that tocochromanol synthesis involves several plastidic membrane transporter(s) that remain to be identified. In addition, the metabolic origin of the lipophilic isoprenoid precursor is not fully elucidated. While some genetic data exclusively attribute the synthesis of the prenyl component of tocochromanols to the plastidic methyl erythritol phosphate pathway, multiple lines of evidence provided by feeding experiments and metabolic engineering studies indicate that it might partially originate from the cytoplasmic mevalonate pathway. Although this question is still open, these data demonstrate the existence of membrane transporter(s) capable of importing cytosolic polyprenyl pyrophosphate such as farnesyl pyrophosphate into plastids. Since the availability of both homogentisate and polyprenyl pyrophosphates are currently accepted as major mechanisms controlling the type and amount of tocochromanols produced in plant tissues, we summarized our current knowledge and research gaps concerning the biosynthesis, metabolic origins and transport of tocochromanol biosynthetic precursors in plant cells. Figure 1). To date, four types of tocochromanols, namely tocopherols, tocotrienols, plastochromanol-8 (PC-8) and tocomonoenols, have been identified in higher plants. These organic compounds drew the attention because tocopherols and tocotrienols exhibit vitamin E activity that is notably essential for animal reproduction . Following its synthesis by the plastidic shikimate pathway, tyr is converted into 4-hydroxyphenylpruvate (HPP) by tyrosine aminotransferases (TATs), a class of enzymes that catalyze the transamination between tyr and 2-oxoglutarate, and HPP and L-glutamate . The TAT isoform(s) involved in seed tocochromanol synthesis remains to be identified.Homogentisate is an aromatic compound resulting from the degradation of lutamate . Based os genome . Howeverd AtTAT2 . AtTAT1 d AtTAT2 . The othd AtTAT2 . It was d AtTAT2 . This daSpinacia oleracea and Lemna gibba localized the HPPD activity in chloroplasts  gene degrading HGA into 4-maleyl acetoacetate, overaccumulated free HGA and tocochromanols in seeds . These data further support the existence of a mechanism transferring cytoplasmic HGA into plastids where it is notably used for tocochromanol synthesis. To date, the identity of this membrane transporter(s) remains to be identified. Interestingly, the Arabidopsis HGO gene is also predicted to be localized in the cytoplasm (TAIR10).Hydroxyphenylpruvate is converted into HGA by the 4-hydroxyphenylpyruvate dioxygenase (HPPD), an Fe(II)-containing non-heme oxygenase encoded by a single gene in Arabidopsis . Severalroplasts . In toma peptide . In maizroplasts . In controplasts . Besidesytoplasm . The recin seeds . InteresFigure 2). Among the 10 GGPPS identified in Arabidopsis, seven are predicted to be localized in plastids, notably GGPPS11 that is the main paralog responsible for the synthesis of GGPP used for tocochromanol production  derive from GGPP produced by GERANYLGERANYL PYROPHOSPHATE SYNTHASEs . Seed and leaf tocopherols of vte5 mutants are reduced by 80 and 65%, respectively, while vte6 leaves fully lack tocopherols  or dimethylallyl pyrophosphate (DMAPP) showed that both enzymes poorly utilize geranyl pyrophosphate and DMAPP  1 and 2 . In vitrnd DMAPP , 2005. Ia and GGPP into phytylated chlorophyll a and PPP, respectively  3:1 and 3:2 that both stabilize GGR . To date, this transporter(s) remains to be identified.Most articles on plant tocochromanol metabolism attribute the origin of the prenyl component of tocochromanols exclusively to the plastidic methylerythritol phosphate (MEP) pathway. These references cite two significant review articles published right after the discovery of this non-mevalonate isoprenoid pathway that did not support the exchange of isoprenoid precursor(s) between the plastidic MEP and cytoplasmic mevalonate (MVA) pathways at that time , 1999. Rcopherol . This sue chains . These r feeding . Since t. This suggests that MVA pathway-derived radiolabeled IPP is concatenated in the cytoplasm into radiolabeled FPP that is imported into plastids where it is condensed with non-labeled MEP pathway-derived IPP. This biosynthetic model is supported by data showing the import of cytosolic FPP into plastids and its incorporation into the phytyl side chain of chlorophylls and into β-carotene .Recent feeding experiments with radiolabeled MVA combined to NMR analysis further refined our understanding of the import of cytoplasmic MVA pathway-derived isoprenoid precursors into plastids. It was independently shown that three quarters of the prenyl units composing the phytyl side chain of chlorophylls, β-carotene or other plastidic isoprenoids produced after [2-carotene , 1997. Mcarotene . CollectBrassica juncea 3-HYDROXY-3-METHYLGLUTARYL-COA SYNTHASE1, an MVA pathway biosynthetic gene, did not only increase the accumulation of cytoplasmic phytosterols in transgenic tomato fruits, but also strongly increased the synthesis of plastidic isoprenoids such as α-tocopherol (fivefold) and carotenoids . Thus, if both pathways are not fully functional at a given physiological stage, they cannot complement the one disrupted by the mutation, and this, despite the possible exchange of isoprenoid precursor(s) showed by feeding experiments and metabolic engineering. Importantly, there is currently no report in the literature demonstrating the dual origin of the prenyl side chain of tocochromanols in wild-type plants.Several metabolic engineering studies overexpressing cytoplasmic isoprenoid biosynthetic genes further support the utilization of cytoplasmic MVA-derived isoprenoid precursors for the synthesis of plastidic isoprenoids. It was shown for instance that overexpression of twofold; . Overexp pathway . Similar pathway . TogetheFrom the cloning of the first tocopherol biosynthetic genes to the recent identification of the tocomonoenol ones, substantial progress has been made in understanding the core tocochromanol biosynthetic pathways in plants . In contSP and LM-S designed and wrote the manuscript.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.


Recent genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with risk of esophageal cancer (EC). However, investigation of genetic basis from the perspective of systematic biology and integrative genomics remains scarce.In this study, we explored genetic basis of EC based on GWAS data and implemented a series of bioinformatics methods including functional annotation, expression quantitative trait loci (eQTL) analysis, pathway enrichment analysis and pathway grouped network analysis.Two hundred and thirteen risk SNPs were identified, in which 44 SNPs were found to have significantly differential gene expression in esophageal tissues by eQTL analysis. By pathway enrichment analysis, 170 risk genes mapped by risk SNPs were enriched into 38 significant GO terms and 17 significant KEGG pathways, which were significantly grouped into 9 sub-networks by pathway grouped network analysis. The 9 groups of interconnected pathways were mainly involved with muscle cell proliferation, cellular response to interleukin-6, cell adhesion molecules, and ethanol oxidation, which might participate in the development of EC.Our findings provide genetic evidence and new insight for exploring the molecular mechanisms of EC. Meanwhile, fold enrichment of risk genes in each significant pathway were all larger than 1.5, demonstrating risk genes were significantly enriched in these pathways. The details of significant pathways including pathway ID, P-values, involved genes, and fold enrichment were shown in Table As shown in ADH4, ADH1C) were identified to have significantly differential gene expression levels under different genotypes on esophageal tissues including esophagus muscularis and esophagus mucosa, as alcohol drinking has been considered as an important risk factor of EC [In this study, we employed an integrative genomics approach to investigate genetic risk factors and biological functions of EC. By systematic data analysis, evidence from large-scale GWAS, eQTL, pathway and network were obtained. As shown in or of EC , and preor of EC . Our eQTFGFR2 and FOXP1 involved. In accordance with our results, have shown FGFR2 are able to promote tumor development and progression in esophageal carcinoma [FOXP1, as a member of Forkhead-box (FOX) family genes, was reported to be associated with poor prognosis of multi-cancer [IL-6 on the development of EC [By functional annotation with genome information, 211 risk SNPs were mapped into 170 genes, which were enriched into 38 significant GO terms and 17 significant KEGG pathways by pathway enrichment analysis. Then these EC related pathways were significantly grouped into 9 sub-networks according to shared risk genes among pathways. Two pathway groups related to muscle cell proliferation were identified, with genes such as nt of EC . MoreoveResults from pathway grouped network analysis demonstrated some pathways were shared among different groups, such as immune related pathways including T-helper cell differentiation, alpha-beta T cell activation and positive regulation of interferon-gamma production; as well as muscle development related pathways such as regulation of muscle organ development and smooth muscle cell proliferation, indicating EC related genes and pathways did not function independently, but functioned in the form of interacting with each other. Therefore, results from our study revealed the multi-gene effect on genetic basis of EC, supporting the view indicating that combined effect of multi genetic variants or genes with modest effect were also involved in genetic basis of complex disorders such as EC .In conclusion, in this study, we explored genetic basis of EC by comprehensive data mining and systematical data analysis based on GWAS data, evidence from SNP, gene, gene expressions, pathway and network were identified, which might provide new insight for exploring the molecular mechanisms of EC.https://www.genome.gov/gwastudies/), which collected all currently published GWAS of various traits. Besides, we also searched public database of Pubmed to collect recently published GWAS of esophageal cancer. Information of GWAS studies including sample size, genotyping platform, ethnic groups, reported SNPs and their P-values were collected. Due to the polygenic risk of complex disorders, individual genetic variants may only accounted for a very small amount of the genetic risk and heritability of complex disorders such as esophageal cancer [http://biostats.usc.edu/Quanto.html) [In order to identify SNPs associated with risk of esophageal cancer, GWAS of esophageal cancer were collected from GWAS catalog  . To compTo identify genes of SNPs and candidate regulatory SNPs at disease-associated loci, we annotated genome information to SNPs including related chromosome, genome position, allele changes, mapped genes by using data from 1000 Genomes Project  and ENCOMeanwhile, some GWAS identified SNPs had regulatory functions by causing differential gene expressions with different genotypes and understanding the functional consequence of genetic variants was essential for biological interpretation on genetic etiology of disease . ExpressTo investigate whether risk genes of esophageal cancer identified from GWAS were enriched in functional pathways, we performed pathway enrichment analysis. Information from Kyoto encyclopedia of genes and genomes (KEGG) database  and GeneTo investigate whether identified pathways were biologically interconnected, we constructed a pathway grouped network of risk genes of esophageal cancer by using a Cytoscape plug-in called “ClueGO” . The rel


Systems biology is an important field for understanding whole biological mechanisms composed of interactions between biological components. One approach for understanding complex and diverse mechanisms is to analyze biological pathways. However, because these pathways consist of important interactions and information on these interactions is disseminated in a large number of biomedical reports, text-mining techniques are essential for extracting these relationships automatically.In this study, we applied node2vec, an algorithmic framework for feature learning in networks, for relationship extraction. To this end, we extracted genes from paper abstracts using pkde4j, a text-mining tool for detecting entities and relationships. Using the extracted genes, a co-occurrence network was constructed and node2vec was used with the network to generate a latent representation. To demonstrate the efficacy of node2vec in extracting relationships between genes, performance was evaluated for gene-gene interactions involved in a type 2 diabetes pathway. Moreover, we compared the results of node2vec to those of baseline methods such as co-occurrence and DeepWalk.Node2vec outperformed existing methods in detecting relationships in the type 2 diabetes pathway, demonstrating that this method is appropriate for capturing the relatedness between pairs of biological entities involved in biological pathways. The results demonstrated that node2vec is useful for automatic pathway construction. In the field of biology, biological pathway analysis is important for gaining insight into the underlying phenomenon of complex interactions between biological components –3. BioloNumerous text mining techniques for relationship extraction have been proposed, ranging from a simple but flexible method such as co-occurrence-based relationship extraction , 10 to cTo tackle the training data issue, distantly/weakly supervised learning methods have been introduced –28. SpecSelf-supervised learning is a type of supervised learning used for learning representations entirely from unlabeled data such as autoencoders , Word2VeAs a result, in this study, we predicted whether two biological entities can be connected in a network using the node2vec algorithm. A series of experiments showed that the network embedding technique is well-suited for relationship extraction between genes in a biological pathway.The type 2 diabetes mellitus pathway consists of 25 genes, 14 other biological components, such as disease and molecular function, and their direct/indirect relationships. Figure Among the 25 relationships, we evaluated 18 gene-gene interactions in the type 2 diabetes pathway where the relationships between genes are made up of the most part. The 18 pairs of genes and relationship types (direct/undirect) are shown in Table Accordingly, we expanded indirect interactions involved in the type 2 diabetes pathway, if two entities in a given indirect relationship participate in another pathway, to capture more detailed information on the process of interactions. Among the 18 gene-gene interactions, 9 were found to be indirect and pathway information in which each pair of genes participate in the type 2 diabetes pathway is described in Table Therefore, these 9 indirect paths were expanded to identify direct relationships. Table Each entity participating in the direct interactions was selected in sequence as a starting node to discover its closest terms. With each starting node, the 100 most similar genes were extracted by calculating cosine similarity between a given starting gene and other gene vectors. Next, the starting gene was paired with each of the 100 extracted genes and the newly generated relationships were ranked by cosine similarity. We evaluated the performance of node2vec by examining whether a given path in the type 2 diabetes pathway was ranked high in the results. For instance, when INS was a starting node, genes with high similarity to INS were extracted and the ranking of INSR was examined. If a direct path was not shown in the 100 newly created relationships, we considered that node2vec did not capture the path.The 30 direct links and ranking of each relationship are described in Table According to a previous study , insulinThe results listed in Table However, the ranking of the 5 direct paths is not included in the top 100. These results show that node2vec cannot capture the similarity between two entities belonging to these paths because biological entities and relationships among them were not observed in type 2 diabetes–related papers. This issue will be further described in the Discussion section.Moreover, we compared our results with those generated by the baseline methods, co-occurrence and DeepWalk. To compare the 3 different techniques, node2vec, co-occurrence, and DeepWalk, we extracted 100 co-occurring gene pairs as well as DeepWalk-generated pairs with every starting node of the 30 direct links. First, co-occurring links were ranked by their co-occurrence counts. For example, in the case of direct link no. 1, genes frequently co-occurring with INS were extracted such as INS-GLP-1(co-occurrence frequency: 3959) and INS-TNF-alpha (co-occurrence frequency: 3145). The co-occurrence link, INS-INSR (co-occurrence frequency: 1819), was ranked 9th. Moreover, DeepWalk-generated gene paths were ranked as paths generated by node2vec.The results are listed in Table In addition, 23 DeepWalk-generated paths are ranked within the top 100, revealing that 2 additional direct paths (direct relationships no. 1 and 16) were captured by node2vec. For the ranking of the 30 direct relationships, only 1 DeepWalk path (direct relationship no. 4) ranked higher than the node2vec-generated path. In contrast, the rankings of 8 paths generated by node2vec were higher than those by DeepWalk. The remaining 14 paths showed the same rank. These results demonstrate that node2vec performs better than co-occurrence and DeepWalk in capturing the relatedness of two genes in the extended type 2 diabetes pathway.In the study, we applied the node2vec algorithm to extract direct paths in a biological pathway. The results revealed the possibility of its application in automated pathway extraction. We further examined if node2vec can capture the directions between pairs of biological components in the pathway. It is essential to extract these directions because biological reactions in the pathway flow from reactants to products, in reverse, or both , 45.The directions were expressed from starting nodes to target nodes, which means that the biological reaction between a given gene pair flows from the starting entity to the target entity. To investigate whether node2vec reflects the directions in the ranking, we changed the position of two entities in the 5 pairs such as INSR-IRS1/IRS, AMPKK-AMPK, Raf-MEK1/2, MEK1/2-ERK1/2, and PKCZ-GLUT4. Next, we set the original target genes  as starting entities, and the top 100 closest genes were extracted and ranked by similarity between pairs of entities.The results are presented in Table In addition to the flow directions, in a pathway network, hub nodes exist showing the highest degree , 47. HubThe extended network consists of 29 genes and 35 edges between these entities. The thickness of the edges represents directed or undirected links among genes and 5 edges in the thin lines are undirected links . Nodes with a high degree of centrality indicate hub genes in the extended network. Specifically, IRS1/IRS showed the highest degree centrality (8), demonstrating that IRS1/IRS is a hub gene in the network. Genes connected directly to IRS1/IRS are INSR, PI3K, SOCS, IKK, JNK, PKCZ, PKCD/E, and GRB2. These 8 direct links were ranked within the top 10 in the node2vec results, showing that node2vec is an appropriate technique for extracting important relationships in the network, which is essential for pathway construction.Moreover, the node2vec model was generated based on the adjacency matrix of biological components extracted from the text. Therefore, if entities and relationships are not extracted from the text and not included in the adjacency matrix, it is less likely that node2vec can capture the relatedness of two entities. For direct relationships such as TNFR1-TRADD, TRADD-TNFR2, TNFR2-TRAF2, GRB2-SOS, and SOS-Ras, similarities between these gene pairs is not captured by node2vec. This is because the number of abstracts including each pair of genes is zero, as shown in Table Specifically, 5 gene pairs in Table In the study, we applied node2vec to extract relationships between biological entities in the extended type 2 diabetes pathway. We showed that node2vec successfully extracted a high percentage of gene pairs belonging to the expanded pathway. Moreover, it outperformed existing techniques such as co-occurrence and DeepWalk. In addition, we demonstrated that node2vec captured the direction flow between two genes, which is essential because reaction flow exists in biological pathways. Accordingly, it has been shown that node2vec is a suitable technique for extracting relationships between entity pairs in pathways.However, we evaluated our results based on the existing relationships in the pathway for path prediction and thus the relationships extracted using node2vec that have not been verified should be further examined, which is the main theme of our follow-up study. Moreover, several direct paths detected in our extended pathway were not captured by node2vec. As described in the Discussion section, two entity names belonging to the paths did not appear in abstracts but rather in full-text articles. Accordingly, if we use full-text articles, it will be possible to construct a more exquisite co-occurrence network, which ultimately increases node2vec performance in extracting relationships of biological pathways. This is another principal topic of our follow-up study.To demonstrate the efficacy of node2vec for relation extraction, we selected ‘type 2 diabetes’ as a case study. The methodology used in this study is described below.Type 2 diabetes-related data were collected from PubMed, which contains over 26 million references to journal articles in life sciences on biomedicine. We used the keyword ‘type 2 diabetes’ to retrieve all papers indexed with this search term. Only articles including the term in the titles and abstracts were collected. PubMed XML records were retrieved using EFetch API . As a reFor entity extraction, PKDE4J , a biomeFor relationship extraction, two biological components were linked when the entities were mentioned together in the same sentence. The assumption behind this approach is that frequently co-occurring entities in the same sentence are more likely to be related than those occurring together in the same abstract. A co-occurrence network in which nodes and edges represent biological entities and co-occurrence relationships, respectively, was constructed and used as an input for node2vec.Node2vec is “an algorithmic framework for learning continuous feature representations for nodes in the networks” . It can p = 1 and q = 0.5.Specifically, in the random walks process, if the return parameter is high ), the walk is less inclined to visit already visited nodes. In contrast, if p is low ), the search is restricted to nearby nodes, which is essential for ascertaining structure equivalence . For theIn addition to the p and q parameters, we set other parameters involved in node2vec as d = 128, r = l = 10, and k = 10 where d, r, l, and k denote embedding dimensions, walk per node, walk length, and context size, respectively. Parameter values were selected based on the parameter-sensitive part of the original paper  for the The constructed co-occurrence network was used as input for node2vec and DeepWalk to learn rich feature representation for every node in the network. Node2vec extends the Skip-gram architecture  to netwoe1, ve2).With the embedding matrix, the relatedness between each pair of biological entities  shown in the biological pathway, was identified by computing the cosine similarity of their corresponding transformed vectors (vTo evaluate node2vec performance for predicting relationships between biological components, pathway-based analysis was conducted. Specifically, the type 2 diabetes mellitus pathway sourced from the KEGG PATHWAY database was used for the evaluation task. The pathway map in the KEGG PATHWAY provides knowledge regarding diverse molecular networks composed of nodes such as orthologs, genes, small molecules, and their reactions and interactions . As such


Left atrial enlargement and persistent atrial fibrillation (AF) are well-known predictors for arrhythmia recurrence after AF catheter ablation (LRAF). In this study, by using pathway enrichment analysis of GWAS data, we tested the hypothesis that genetic pathways associated with these phenotypes are also associated with LRAF.Samples from 660 patients with paroxysmal (n = 370) or persistent AF (n = 290) undergoing de-novo AF catheter ablation were genotyped for ~1,000,000 SNPs. SNPs found to be significantly associated with left atrial diameter (LAD) or AF type were used for gene-based association tests in a systematic biological Knowledge-based mining system for Genome-wide Genetic studies (KGG). Associated genes were tested for pathway enrichment using WEB-based Gene SeT AnaLysis Toolkit , the Gene Annotation Tool to Help Explain Relationships (GATHER) and the databases provided by Kyoto Encyclopedia of Genes and Genomes (KEGG). In a second step, the association of consistently enriched pathways and LRAF was tested.By using sequential 7-day Holter ECGs, LRAF between 3 and 12 months was observed in 48% and was associated with LAD  and persistent AF . WebGestalt (adj. p = 2.7E-22) and GATHER (adj. p = 5.2E-3) identified the calcium signaling pathway (hsa04020) as the only consistently enriched pathway for LAD, while the extracellular matrix (ECM) -receptor interaction pathway (hsa04512) was the only consistently enriched pathway for AF type . Both calcium signaling  and ECM-receptor interaction  were significantly associated with LRAF.Calcium signaling and ECM-receptor interaction pathways are associated with LAD and AF type and, in turn, with LRAF. Future and larger studies are necessary to replicate and apply these findings. Genetic studies have revealed diverse mechanisms of atrial fibrillation (AF), the most common cardiac arrhythmia . This hePathway-based analysis of GWAS data is a powerful tool to detect subtle but systematic patterns in the genome that underpin complex diseases, natural disease progression and responses to therapy. For instance, this approach has been successfully applied to identify novel regulatory pathways in different phenotypes such as body mass index , colorecHere, for the first time, we use pathway enrichment analysis of GWAS data to test the hypothesis that genetically-modulated pathways associated with left atrial enlargement and persistent AF also associate with arrhythmia recurrence following AF catheter ablation.Six hundred-and-sixty AF patients undergoing de-novo radiofrequency AF catheter ablation between 2008 and 2013 were enrolled in the Leipzig Heart Center AF ablation registry. Paroxysmal AF was defined as self-terminating episodes of AF within 7 days after onset documented by ECG or an ambulatory ECG monitor. Persistent AF was defined as an AF episode either lasting longer than 7 days or requiring drug or direct current cardioversion for termination.In all patients, transthoracic and transesophageal echocardiography was performed prior to catheter ablation. Left atrial diameter (LAD) and left ventricular ejection fraction were determined using standard measurements and a left atrial thrombus was excluded. All class I or III antiarrhythmic medications with the exception of amiodarone were discontinued at least 5 half-lives before the procedure.The study protocol was approved by the Ethics Committee of the Leipzig University Medical Faculty. All patients signed written informed consent for study participation. All methods were performed in accordance with the relevant guidelines and regulations.Left atrial catheter ablation was performed using a previously described approach . In brieAfter circumferential line placement, voltage and pace mapping along the ablation line were used to identify and close gaps. The isolation of all pulmonary veins with bidirectional block was verified with a multipolar circular mapping catheter and was defined as the procedural endpoint.After ablation, class I and III antiarrhythmic drugs were not reinitiated. Oral anticoagulation was prescribed for 6 months, and proton pump inhibitors were added for 4 weeks. All patients were followed in the outpatient clinic for 12 months after the ablation. During this follow-up period, 7-day Holter ECG recordings were performed 3, 6 and 12 months after the ablation. Additional ECGs and Holter ECG recordings were obtained when patients’ symptoms were suggestive of AF. AF recurrence was defined as a documented atrial arrhythmia episode  lasting longer than 30 seconds between 3 and 12 months after the ablation . All patients with sustained early recurring AF underwent direct cardioversion. Additional drug administration was left to the discretion of the treating physician.Blood samples were obtained in EDTA test tubes in fasting state prior ablation. Genomic DNA was isolated using a commercial kit according to the manufacturer’s recommendations . Genotyping was performed using HumanOmniExpressExome-8-v1.2 arrays comprising about one million Single Nucleotide Polymorphisms (SNPs) according to established protocols .Raw data was compiled using GenomeStudio (Illumina) software and exported to PLINK GWAs analysis package . Using PAssociation of genotypes with LAD was detected using linear regression with adjustment for age, gender and AF type. Association of genotypes with AF type (persistent AF) and arrhythmia recurrence was detected using logistic regression analysis with adjustment for age and gender. All variants identified by this approach were included in the analysis under an additive genetic model without testing for recessive or dominant genetic effects.Illumina specific “exmSNPs” were assigned to their corresponding dbSNP rs IDs. The resulting SNP lists were used for gene enrichment. This was done with Knowledge-based mining system for Genome-wide Genetic studies (KGG) . R-squarFor pathway enrichment analysis we used the Gene Annotation Tool to Help Explain Relationships (GATHER)  and WEB-We applied a two-stage analysis plan. First, we identified consistently enriched KEGG pathways in LAD and AF type present in both enrichment tools. Second, association of those identified pathway(s) with arrhythmia recurrence was tested with both enrichment tools .Clinical variables are presented as mean +/- one standard deviation or percentages. They were compared between patients with and without AF recurrence using chi-square or Student’s t-test.The study population included 660 patients with a history of paroxysmal (n = 370) or persistent AF n = 290, . ArrhythGenotyping call rate in all subjects was > 95% except in three samples (<85%) that were excluded from further analysis.28.062 SNPs were associated with LAD and were annotated to 10.252 genes while 27.400 SNPs were associated with AF type and were annotated to 10.180 genes. Of those, 1.778 SNPs and 3.834 genes were found in both phenotypes.In WebGestalt, 101 KEGG pathways were associated with LAD and 91 with persistent AF  Tables, Both calcium signaling  and ECM-receptor interaction  were significantly associated with rhythm outcome , Fig 2.Within the calcium signaling pathway, there were 55 genes that were associated with LAD and LRAF while there were 26 genes in ECM-receptor interaction pathway that were associated with persistent AF and LRAF Tables  and 4.This study is the first to explore shared genetic pathways of predictive clinical variables such as left atrial enlargement and AF type and response to AF catheter ablation. In a two-stage association study, we first identified calcium signaling and ECM-receptor interaction as common regulatory pathways for LAD and persistent AF in two enrichment tools. In a second, candidate-based step, those pathways were found to also associate with rhythm outcome of AF catheter ablation in both tools. This analysis approach is novel and the identification of the genomic background of those pathways and its link to clinical variables and response to therapy is a relevant finding.ACE), [AGT) [IL6R) [While left atrial enlargement and AF persistence have been linked with AF ablation outcome , atrial ACE),  the angi), [AGT)  or the i) [IL6R)  have alsIn this study, we associated calcium signaling and ECM-receptor interaction pathways with left atrial dilatation and AF persistence and subsequently with rhythm outcome.RyR2 have been shown to predispose to catecholaminergic polymorphic ventricular tachycardia and AF by enhanced propensity for spontaneous Ca(2+) release [CACNA1C) [JPH2) resulting in defective RyR2-mediated sarcoplasmatic reticulum Ca(2+) release [Interestingly, some of the calcium signaling and ECM-receptor interaction pathway genes found in our study Tables  and 4 ha release . In addiCACNA1C)  or junct release . Abnorma release , the hal release . Angiote release .ITGA9 encodes an alpha integrin, an integral membrane glycoprotein that mediates diverse functions including cell–cell and cell–matrix adhesion, proliferation, and apoptosis and is associated with the PR interval on the surface ECG [face ECG . PR inteface ECG .vWF gene [Very recently, thrombin has been found to cause pro-fibrotic and pro-inflammatory responses in adult atrial fibroblasts and promote the development of a substrate for AF . ThrombivWF gene .In summary, these data together with our findings strongly suggest the involvement of calcium signaling and ECM-receptor interaction pathway genes in AF-remodeling associated phenotypes such as left atrial enlargement and AF persistence which then in turn may serve as markers for AF ablation rhythm outcome.Our study is based on small sample size. We addressed this by using well-defined intermediate AF phenotypes that are known markers of AF ablation outcome and different bioinformatics tools. Moreover, we required the genotype—phenotype correlation to be present in two different pathway enrichment tools and we used a two-stage analysis approach; (1) unselected identification of consistently enriched pathways and (2) selected “candidate-”pathway analysis for association with outcome. Single SNPs were not in the center of the study rather we focused on the most significant candidate genes from enrichment analysis. Consequently, candidate genes and pathways with lower significance levels or failing our stringent identification process could have been overlooked by this approach. In addition, we did not consider dominant or recessive SNP effects that should be done in future studies. Single pathway components were not assessed in detail that was beyond the scope of this study. Finally, ablation approaches in persistent AF are evolving and linear lesion sets have lessened in popularity due to no incremental benefit and even pro-arrhythmia. This may impact on type of arrhythmia recurrence and should be considered when assessing recurrence rates and comparing this to other studies.Calcium signaling and ECM-receptor interaction pathways are associated with LAD and AF type and, in turn, with LRAF. Future and larger studies are necessary to replicate and apply these findings.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.


In recent years, the integration of ‘omics’ technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc.qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds.In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds’ treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound’s efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds , which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound’s treatment efficacy In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.The online version of this article (10.1186/s12918-017-0501-6) contains supplementary material, which is available to authorized users. In recent years, molecular and cellular biology has significantly improved our understanding of cancers in human, however, the discovery and use of therapeutic drugs still faces major challenges . ParticuHttp://www.lincsproject.org/) was established, aiming to create a network-based understanding of biology by cataloging molecular changes in gene expression and signal transduction occurring when cells were exposed to various genetic or drug perturbations [Recently, the public LINCS genomic database , to infer cell-specific signaling pathway network and predict the response of cells to new compounds Fig. . The TerThe P100 phosphoproteomic data for MCF7 breast cancer cell line was considered, which was treated by 15 small molecule chemical compounds (see Table ρ) in P100 dataset was represented as log2 ratio of treatment to control (un-treatment). Therefore, the raw data of P100 were normalized to ternary values  with threshold 1.2, where 1 corresponds to up-regulation (ρ ≥ log2(1.2)), 0 to no-change (−log2(1.2) < ρ < log2(1.2)), and −1 to down-regulation (ρ ≤  − log2(1.2)). The normalization method reflects the relative changes on signaling proteins after compound perturbation.The expression of each protein (http://www.ingenuity.com). Combining all the top-ranked enriched signaling pathways  from IPA and the MCF7-related pathways widely reported in previous literatures, we manually constructed a generic pathway map  from the preprocessed P100 dataset, and imported them into a public platform of pathway analysis, IPA  or. 1 (activation). After receiving the perturbation with certain compound, the state of each signaling reaction is denoted with logical values 0 or 1, indicating “occur” or “does not occur”. Moreover, the nodes with grey color were observed in the P100 assay, and the remaining nodes were un-observed. Particularly, the nodes with orange color were proved as the targets of certain compounds in literatures , four cell functions  are quantified as integer variables, and are calculated from their upstream TFs (see Methods).Moreover, we defined a linking pattern of protein nodes, so that a complicated signaling network can be represented with this pattern Fig. . In thisIn order to infer the cell-specific network from our constructed generic pathway map that best reproduces the experimental data, we minimized the differences between the model’s predicted values and the corresponding observational values for the subset of protein nodes that are observed, as well as the complexity of network structure. To solve the multi-objective optimization problem, we developed a TILP model of the generic pathway network, and obtained a cell-specific pathway network by optimizing formula  shown in, et al. have identified that there may be a functional link between p53 and BRCA1 in DNA double strand breaks repair process at transcriptional and possible post-transcription level; however, the association between them is still unclear [The inferred MCF7-specific pathway networks are shown in Fig.  unclear . In our assumption is that the topology of cell-specific signaling network are stable if there is no perturbation from its microenvironment on the tumor cells. Once a small compound acts on tumor cells, some intracellular signaling pathways will represent significant changes (up-regulation or down-regulation) to response this external perturbation, which subsequently alters their cell functions or behaviors. Here, we firstly used TILP approach to fit the generic pathway map with the first part of phosphorylation data (10 training compounds) in P100 and obtain a cell-specific pathway network. The second part of data (5 testing compounds) was fitted to optimize the network model of cell-specific pathways, and the states of all the phosphoproteins and signaling reactions can be inferred so that the compound’s effects is easily identified. As shown in Fig. Our Additional file I.Carmustine [rmustine , a potenrmustine .II.Doxorubicin [orubicin , the curorubicin . Liu, etorubicin , and TGForubicin .III.GW-8510 potentially inhibited most of signaling pathways in the MCF7-specific network expect increase the expression of HDAC1.Additional file IV.Daunorubicin [orubicin , a markeorubicin . We alsoorubicin .V.erapamil [As to Verapamil , a calciIn addition, the “cell function” vector consists of four output nodes, which can be potentially used to calculate the similarity between two compounds Fig. . For exaP value < 0.01) from IPA with some important pathways reported in previous literatures, to manually construct a generic pathway map related with MCF7 cells, which includes 50 reactions and 35 proteins (15 proteins were observed in P100 dataset). With the TILP model for pathway modeling and optimization, a MCF7-specific pathway network of 35 nodes and 37 edges was finally inferred. Based on the topological structure of optimized MCF7-specific network, we monitored 5 cases of compound-induced response networks to predict the effects of these compounds using TILP again.In this study, we presented a novel computational modeling approach TILP to reconstruct cell-specific pathway network and predict the system’s response to compound treatment by combining prior knowledge from literature and phosphoproteomic data collected from various types of perturbation experiments. In order to test the power of TILP model, we selected a set of P100 proteomic data of 15 chemical compounds effecting on MCF7 breast cancer cell line. Based on the differential expressed proteins in P100 data, we combined all the top-ranked enriched pathways (An important aspect of our approach is its practicability in inferring cell-specific network. Although continuous modeling approaches (e.g. ODEs ) are comAnother important aspect of our approach is to help identify compound treatment effects in two ways: (1) identify the treatment effects through network topological alterations; (2) calculate the similarity of two compounds with their corresponding outcome vectors from pathway network. As described in the experimental results, most of predictions were consistent with the previous studies in the literature. However, there are still a small part of predictions could not be proved from literature, such as, GW-8510, which means it has never been used for breast cancer treatment. In addition, some evidences on other cell lines provided supports to our findings. For example, carmustine enhances chemotherapeutic efficacy by attenuating AKT activity in gliomas . As a prqualitatively identify the compound treatment effects via pathway alterations, but also quantitatively evaluate the similarity of a pair of compounds on the same cancer cell line.Comparing to our BLP model, the proposed TILP model successfully solved the limitations in BLP: 1) we used three states  to represent the relative changes of a protein when the cancer cells received extracellular stimuli; 2) the training set is related with 10 compounds, which have been validated effectively for breast cancer treatment. And the five compounds included in the testing set are rare studied in the breast cancer therapy. Therefore, the cell-specific pathways optimized by the training set, has potential to find new drugs for cancer therapy. 3) The TILP model not only In order to prove the reliability of our approach TILP, we tested the effects of leave-one-out cross-validation on the p100 data of the first 10 compounds which were used in model training. For each compound, we extracted phospho-proteomic data of 9 compounds to infer a cell-specific pathway network and then identify the treatment effects of the remaining one by predicting the states of all the proteins via TILP. The mean value of fitting precision for above 10 cases was 84%, suggesting that the inferred cell-specific pathway network is reliable for predicting a compound’s effects. Based on the results of the leave-one-out cross-validation we did on MCF7 cell line, we calculated the similarity of network diagrams among 10 cases (each corresponds to one compound) where each case represents a cell-specific pathway network trained from the remaining 9 compounds after leaving one compound out. In overall, the connected edges in the final inferred cell-specific pathway network , H9 (Human embryonic stem cell), et al.In our work, we selected the validation dataset of MCF7 cell line in P100, which contains measurements of phosphor-signal treated by 26 different small compounds. The treatments were each monitored for 6 h with two replicates. All raw expression values in P100 experiment represent log2 ratios of the treatment VS. Control.In this study, the TILP computational framework was applied to systemically model a signaling pathway network with phospho-proteomic data. The developed model was used in two ways: firstly, TILP infers the cell-specific pathway network from the discrete representation of a generic pathway map; secondly, TILP predicts the treatment effects of a small compound based on the inferred cell-specific signaling network. The detail steps of the TILP framework is described as follows:At first, we selected the differentially expressed proteins (DEPs) from the observational data (LINCS P100). Second, we chose the top-ranked enriched signaling pathways from the database IPA  based on the DEPs. Third, the generic pathway map was manually constructed by merging all the enriched signaling pathways with some important pathways studied in literature. and activation  which were encoded by integer variables (−1 or +1). In our study, we defined a linking pattern of signaling proteins which was commonly seen in most of pathway network structures , indexed by the set C = {c1, c2, …, cL}. An integer variable xj, kϵ{−1, 0, 1} indicates if the protein j (j ∈ P) is down-regulated , up-regulated , or un-changed  after the tumor cells were treated by compound k, where k ∈ C. An output node in the network denotes a type of cell function, which is calculated from its upstream TFs. We used OR date  is represented as  (activation) or  (inhibition), where u and d are the parent (start) and child (end) nodes, respectively . Moreover, the impact (“positive regulatory” or “negative regulatory”) of the parent node u on child node d is described as the regulation from u to d when the expression of u is increased or decreased. Considering that different compounds may act on different signaling pathways, a reaction may take place under some treatment conditions but not others [zi, k, which is 0 if reaction i (i ∈ E) takes place after compound k treatment, and 1 otherwise. The goal of this step is to detect and remove the inconsistent reactions from the topological structure of generic pathway map which do not occur in any conditions. Therefore, the binary variable yi is equal to 1 if reaction i is removed from the generic pathway map, and 0 elsewise. Eq. (6–7) are defined to constrain the presence of reaction i in the optimized cell-specific network through the state of reaction i in the condition k.A signaling network is defined as a set of phosphoproteins ate Fig.  to modelt others . Thus, wX, Y, and Z denote all variables xj, k ∈ X, yi ∈ Y, and zi, k ∈ Z, respectively. Eq.  approach to minimize the differences between the predicted values and observational values for the subset of protein nodes that are observed, as well as the complexity of network structure. The global error function is defined as below:j–th protein in condition k were defined as mj, k, xj, k ∈ {1, 0, −1}, respectively. When mj, k and xj, k are equal, the square error 2 is equal to 0; otherwise it is either 4 or 1. Therefore, optimization of the above objective function might induce local optimal solution because of the non-uniform distribution of the term 2. To address the bias mentioned above, a binary variable aj, k (0 or 1) was designed as the difference between mj, k and xj, k as following: aj, k will be 1, if mj, k is not equal to xj, k, and 0 elsewise. The square error 2 in function (8) was thus replaced by aj, k. The value of aj, k was restricted by constraints (24–25) as shown in Additional file X, Y, and Z denote all variables xj, k ∈ X, yi ∈ Y, and zi, k ∈ Z, respectively. By optimizing the above objective function, the predicted values for variable sets X, Y, and Z can be obtained. The negative constant γ in Eq. . ComparThe formulations in TILP model described above were implemented in GUROBI 6.5.1 , which iqualitatively identify the treatment effects via compound-induced pathway alterations. The states of all the phosphor-proteins and signaling reactions in the network could be inferred, so that we can easily find which pathway is activated (nodes are up-regulated and edges take place in this pathway), inhibited (nodes are down-regulated and edges take place in this pathway), or un-changed (the expression of nodes have no significant changes). Additional file quantitatively evaluate the similarity of treatment effects between two compounds according to the outcome vectors of signaling network. The similarity score (SS) is defined as correlation coefficient, to represent the similarity of treatment effects between compound A and C with their corresponding outcome vectors of response networks.To predict a compound’s treatment effects, TILP model was used on the optimaml cell-specific pathway network to fit the testing data. We can identify the treatment effects of a compound from two aspects:TA and TC are the output vectors from compound A and compound C induced response networks, respectively. Based on Eq.  model to identify cell-specfic signaling pathways and predict compoun treatment effects by combining phosphorproteomics data and prior knowledge. To validate the effectiveness, we tested our TILP model on MCF7 cell line with LINCS P100 proteomics. The simulation results indicate that our model is capable of inferring the cell-specific pathway network, and further qualitatively and quantitatively predicting the compound treatment effects. For large-scale biological networks, it is difficult to estimate a large number of parameters from limited observations using ODE-based modeling approaches. As a parameter-free model, the TILP model is more efficient than continuous models (such as ODEs) because all of the elements in the network were defined as discrete variables so that the optimal solusion can be qucikly searched from an allowable subspace with constraints. Although MCF7 is used as a case of disease, the proposed model will be applicable to other disease studies with proteomic data.Additional file 1: Text S1. Computational procedure: Ternary status based Integer Linear Programming (TILP). Text S2 Computational procedure: fitting precision of data (goodness of fit). (PDF 666 kb)Additional file 2: Figure S1. The response network induced by carmustine. (PDF 472 kb)Additional file 3: Figure S2. The response network induced by doxorubicin. (PDF 474 kb)Additional file 4: Figure S3. The response network induced by GW-8510. (PDF 481 kb)Additional file 5: Figure S4. The response network induced by daunorubicin. (PDF 478 kb)Additional file 6: Figure S5. The response network induced by verapamil. (PDF 476 kb)


In Vitro Sealing Properties of Calcium-Enriched Mixture and Mineral Trioxide Aggregate Orifice Barriers during Intra-Coronal Bleaching. Iran Endod J. 2017;12(2):231-5. Doi: 10.22037/iej.2017.45Moghadam N, Abdollahi AA, Aghabalayi Fakhim H. et al. [We have read with great interest the article by Moghadam et al.  and we fet al. , therefoCurrently there is controversy regarding the measurement of filtration. Although no consensus has been reached on the most appropriate technique for the evaluation of this filtering process, it is known that some methods such as bacterial penetration, fluid filtration and glucose penetration model presents more real results and have less disadvantages for measurement.Various investigations indicate that the penetration of dyes presents inconveniences by a possible effect of entrained air at the entrance of the dye solution. Previous studies have shown that at normal atmospheric pressure, the effect of air trapped along the margin of the root canal can impede the flow of penetration , 3. ThusFurthermore, to perform measurements of dye penetration in millimeters, the teeth were cut longitudinally using diamond discs 0.1 mm in diameter. This procedures present also disadvantages, because it is possible that part of the sample is eliminated and consequently, part of the measurement can be lost. A procedure that could prevent these drawbacks is the diaphanization. This technique allows to obtain a three-dimensional view of the root canals. In addition, in comparison to the conventional process of dye penetration measurement, the diaphanization has the advantage of not requiring cutting and practice to perform an evaluation in the three dimensions of the tooth .We hope these recommendations will contribute to enrich future related studies and to better understand the methods used to assess the microleakage of different materials.  Thanks for reading and commenting on our manuscript. Here are the comments and some responses to them:1. Currently there is controversy regarding the measurement of filtration. Although no consensus has been reached on the most appropriate technique for the evaluation of this filtering process, it is known that some methods such as bacterial penetration, fluid filtration and glucose penetration model presents more real results and have less disadvantages for measurement.Response:We agree with your comment; however we chose dye penetration in this study since it is a simple and useful method based on chemical color reaction and it has been used in several studies , 2. Also2. Various investigations indicate that the penetration of dyes presents inconveniences by a possible effect of entrained air at the entrance of the dye solution. Previous studies have shown that, at normal atmospheric pressure, the effect of air trapped along the margin of the root canal can impede the flow of penetration. Thus, a high degree of variation has been found in the results of penetration, when the same materials and methods were used with different atmospheric pressures. In order to solve this effect, it is recommended to follow the vacuum methodology, in which a partial pressure is applied which facilitates the penetration of the coloring agent.Response:It is true that the air trapped along the margin of root canal can impede the flow of penetration, but there is no consensus about the atmospheric pressure in which the dye penetration test should be done. Also no studies have been recommended the standard conditions such as atmospheric pressure to do the dye penetration test.3. Furthermore, to perform measurements of dye penetration in millimeters, the teeth were cut longitudinally using diamond discs of 0.1 mm in diameter. This procedures present also disadvantages, because it is possible that part of the sample is eliminated and consequently, part of the measurement can be lost. A procedure that could prevent these drawbacks is the diaphanization. This technique allows to obtain a three-dimensional view of the root canals. In addition, in comparison to the conventional process of dye penetration measurement, the diaphanization has as main advantage that it does not require cutting and practice to perform an evaluation in the three dimensions of the tooth.Response:We admire your recommendation; however there were no facilities of diaphanization in our faculty or other Iranian Dental Faculties. Also diaphanization has not been a common method and no previous studies have been used this procedure and cutting the teeth with discs has been used.