How a living organism maintains its healthy equilibrium in response to endless exposure of potentially harmful chemicals is an important question in current biology. By transcriptomic analysis of zebrafish livers treated by various chemicals, we defined hubs as molecular pathways that are frequently perturbed by chemicals and have high degree of functional connectivity to other pathways. Our network analysis revealed that these hubs were organized into two groups showing inverted functionality with each other. Intriguingly, the inverted activity profiles in these two groups of hubs were observed to associate only with toxicopathological states but not with physiological changes. Furthermore, these inverted profiles were also present in rat, mouse, and human under certain toxicopathological conditions. Thus, toxicopathological-associated anti-correlated profiles in hubs not only indicate their potential use in diagnosis but also development of systems-based therapeutics to modulate gene expression by chemical approach in order to rewire the deregulated activities of hubs back to normal physiology. Living organisms are constantly exposed to potentially harmful chemicals within their living habitats. Detoxification that involves metabolizing and eliminating these chemicals are of great importance to maintain healthy equilibrium. In vertebrates, the liver is an essential organ in detoxifying xenobiotics and monitoring nutritional cues via homeostatic regulation in broad biochemical processes. This offers the liver as an excellent organ to study homeostatic processes in response to chemical imbalances and xenobiotic insults.Several omics studies had revealed that genes involved in xenobiotic metabolism, heat shock signaling, oxidative stress, cell proliferation, apoptotic pathway, nuclear receptor signaling and proteasome are among those mostly reported to show altered expression Thus, it is important to understand the intricate relationship among molecular pathways in the liver under various chemical insults as their deregulated activities often resulting metabolic disorders, cancers, and liver damage. In this study we used the zebrafish model to identify molecular pathways that act as hubs to respond to perturbations triggered by different chemicals. We exposed zebrafish to six different chemicals, Benzo-[a]-pyrene (BAP), 17-β estradiol (E2), 4-Nitrophenol (NP), 4-Chloroaniline (CA), Arsenic (V) acid (As) and Mercury (II) chloride (Hg), at various concentrations and under different exposure durations, and generated hepatic transcriptomic data by DNA microarray. These chemicals were chosen because they serve as representatives of selected environmental toxicants and pollutants that are potential health hazards to various organisms including humans, hence having considerable public health concern. BAP is a polycyclic aromatic hydrocarbon that serves as a representative of persistent organic pollutants while E2 is a steroid that represents estrogenic compounds and these two classes of compounds are known endocrine disruptors found in the environment Using gene set enrichment, functional correlation and network analyses, we successfully defined a group of molecular pathways acting as hubs for their frequent respond to multiple chemicals and high degree of functional correlations (connectivity degrees) to many other molecular pathways. Interestingly, we found that these hubs were organized into two groups showing inverted activity correlation with each other. Our current work revealed that the inverted activity profiles of hubs are obvious only when a fish is confronted with chemically-induced toxicopathological conditions. Since the identified hubs play important roles in liver homeostasis, their malfunctions suggest broad association to liver disorders. Furthermore, analyses using independent external datasets obtained from liver tissues of rat, mouse, and human cells exposed to chemicals We generated a total of 168 arrays  for zebrafish livers exposed to six different chemicals at various concentrations and durations, accounting for a total of 40 specific chemically-induced perturbations. For comparative study, we included 3 additional hepatic transcriptomic data obtained from zebrafish experiments with starvation and specific carbohydrate dietary manipulation To examine whether the expression of genes within a molecular pathway is enriched by a perturbation, enrichment analyses considering the relative ranks of gene expression profile were conducted by Gene Set Enrichment Analysis (GSEA) We next defined a function called normalized pathway coverage (NPC) in order to determine appropriate thresholds to partition pathways into categories composing of high responsive (HR), medium responsive (MR), and low responsive (LR) across multiple perturbations see . The rel plotted . From thAt cellular level, activities among molecular pathways are governed by intricate regulation and expression of their genes. To investigate the correlation and potential functional connectivity of molecular pathways, we performed Pearson correlation to assess linear correlation among 189 molecular pathways across 43 perturbations. Normalized Enrichment Score (NES) generated by GSEA see  were useA pair of pathways is considered functionally connected if the calculated Pearson correlation coefficient (PCC)>0.5 or PCC<−0.5  with p-vResults from Pearson correlation also revealed that pathways with known associated functions also show significant correlations in fish triggered by chemicals . For exaWe are interested in finding molecular pathways acting as hubs that they are not only frequently perturbed by various chemicals but also show high functional connectivity to many other pathways. We plotted the frequency of perturbations and the degree of connectivity in order to visualize their relationships for each of the molecular pathway . InteresWe identified a group of 16 hubs corresponding to 43 perturbations in this study  for rat, mouse, and human treated with various chemicals. Four expression series, GEO:GSE5509 Analysis of transcriptomic data GEO:GSE17184 We found most toxicopathological-associated states studied thus far are biased towards profile of “Scheme B”, with most hubs in Group B are activated accompanied with suppression of hubs in Group A . This isIn this study, we used zebrafish as a vertebrate model to identify hub molecular pathways that respond to multiple chemical perturbations in the liver and assess their functional correlations. We observed a substantial number of hubs involved in lipid metabolism. This is consistent with the fact that the liver plays a central role in lipid metabolism in response to nutritional cues by regulating the synthesis, oxidization, transport and excretion of lipids Other hubs such as ribosome, proteasome, circadian exercise, N-glycan biosynthesis, aminoacyl-tRNA biosynthesis, translation factors, and IL6 pathway form non-lipid metabolic category. Suppression of proteolysis is normally required when a rapid transcriptional response and protein synthesis are needed in a cell such as during cell division. Translation factors, aminoacyl-tRNA and ribosomes are anticipated to coordinate at the subsequent stage after the transcription step. Also, circadian clock had been reported to couple with proteasome It had been reported that amino acids such as isoleucine, leucine, tyrosine, phenylalanine, tryptophan, lysine, and arginine are capable of inhibiting the chymotrypsin-like activity of the proteasome in a dose-dependent manner The concept of “attractor” that is used to describe the behavior of complex systems in physics To maintain healthy equilibrium, a cell undergoes homeostasis processes constituting multiple tiers of negative and positive feedback loops to ensure all processes are tightly regulated such that all cellular processes only take place in the right amount, right place, and right time within a cell. The balanced activities of these negative and positive feedback loops can thus be viewed as balanced of “Yin” and “Yang” components, an ancient Chinese concept refers to opposite but mutually interdependent elements. In biology, this Yin-Yang concept had been utilized to describe the balance of various cellular activities, such as antioxidation/oxidation There are two toxicopathological schemes associated to the inverted profiles. “Scheme A” with activated Group A hubs and suppressed Group B hubs. The vice versa is applicable for “Scheme B”. Our current analyses indicate that most toxicopathological states favor Scheme B for fish and mammalian systems. We envisage lower “pathological barrier”  for Scheme B across an imaginary toxicopathological landscape with normal physiological equilibrium being a global minimum . Also, SThe double-headed arrow at the base of the pyramid in Although we observed the association of toxicopathological states to inverted activity profiles of shared molecular pathways identified from fish to mammals, at this stage, we cannot exclude the possibility that there are other hub pathways showing inverted activities but fail to capture in this study. Furthermore, it is possible that there are totally different sets of molecular pathways showing inverted activities owing to different toxicopathological mechanisms under particular conditions. For instance, as shown in http://toxnet.nlm.nih.gov/) and our preliminary acute toxicity exposure experiments conducted for the compounds Adult zebrafish (6 months–1 year old) were obtained from a local fish farm. The fishes were allowed to acclimatize in aquaria for several days before transferred into smaller containers for chemical exposure. Fishes were exposed to chemicals for up to 96 h at a density of one fish/200 ml at 27°C in a static condition. The treatment regimens used are summarized in Total RNA was extracted using Trizol reagent  according to the manufacturer's instructions. Reference RNA for microarray hybridization was obtained by pooling zebrafish whole adult male total RNA with female total RNA at 9∶1 ratio. The integrity of RNA samples was verified by gel electrophoresis, and their concentrations were determined by UV spectrophotometer.Reference RNA was co-hybridized with RNA samples either from chemically treated or control fish on a glass array spotted with 16.5K and 23K zebrafish oligo probes. Both reference and sample RNAs were reverse-transcribed and labeled differently using fluorescent dyes Cy-3 or Cy-5. After hybridization at 42°C for 16 hours in hybridization chambers, the microarray slides were washed in a series of washing solutions , dried by low-speed centrifugation and scanned for fluorescence detection using the GenePix 4000B microarray scanner (Axon Instruments). More detailed protocols for microarray experiment and data acquisition have been described recently by us All data used in this study is given in http://www.braju.com/R/). The zebrafish genes were mapped to human homologous genes as previously described 10(1/P) where P is the p-value of a gene obtained from Student's t-tests. Positive and negative values of log10(1/P) were assigned for up- and down-regulated genes, respectively. The genes were then ranked in descending order based on values of log10(1/P). The ranked genes were the input array for GSEA and were mapped to 639 pre-defined gene sets of canonical pathways obtained from the GSEA website (http://www.broadinstitute.org/gsea/). Gene sets with less than 10 genes mapped to input were excluded from further analysis.The raw microarray data was normalized using Lowess method in the R package  for partitioning pathway category corresponding to high responsive (HR), medium responsive (MR), and low responsive (LR), we defined a function called normalized pathway coverage (NPC) for one particular chosen threshold asIn this study, we used NES obtained from GSEA to represent the “meta-activity” of a pathway. We used Pearson correlation which assumes linear dependence between variables to infer functional association between pathways. Matrix of z-score-corrected NES values was used as input to calculate Pearson correlation coefficient (PCC) as stated below:To evaluate the significance between correlations, a matrix of p-values for testing the hypothesis of no correlation was performed. Each p-value is the probability of getting a correlation as large as the observed value by random chance, where the true correlation is zero. We considered the correlation between pathways is significant if PCC>0.5 or PCC<−0.5 with p-value<0.0001.We observed there are 20 conditions in our data showing obvious inverted activity profiles among hubs (bolded labels in 10(1/P) was computed for each gene and was used as ranking metric as described in the zebrafish data. Gene symbols provided by platforms in these selected GEO series were used as input for GSEA, with molecular pathways less than 10 genes mapped to the ranked input arrays were discarded. Pathways with nominal p-value<0.05 were considered significantly affected. Molecular pathways showing inverted profiles in zebrafish were selected to assess their activity profile in mammalian systems.In order to assess the present of inverted activity profiles of molecular pathways in mammalian systems, we collected independent external hepatic transcriptome data of rat and mouse treated with chemicals from the Gene Expression Omnibus (GEO). Transcriptome data of human hepatocellular carcinomas was also included. Four GEO expression series, GEO:GSE5509 Figure S1Network of molecular pathways showing anti-correlated behavior. Hubs are indicated with red and green fonts. Pink and green nodes represent pathways associated to Group A and B, respectively.(TIF)Click here for additional data file.Figure S2Demonstration of Normalized Pathway Coverage (NPC) function, which represents the average contribution of significant responses from each pathway with . Lets us assume that the number of significant responses of three pathways Pi, Pj, Pk, are larger or equal to a threshold, say 5, then the NPC for these pathways of threshold 5 will be NPC(5) = (5/43+7/43+6/43)/3≈0.1395. Similar computation is performed for other pathways with different values of thresholds. The computation results are shown in the figure for (JPG)Click here for additional data file.Data S1z-score corrected normalized enrichment scores for each molecular pathway across 43 chemical perturbations.(XLS)Click here for additional data file.Data S2Pairwise Pearson correlation between pathways.(XLS)Click here for additional data file.Data S3Statistical test  for pairwise correlation between pathways.(XLS)Click here for additional data file.Table S1Categories, types, and experimental summary of hepatic transcriptomic data used in this study. As = Arsenic (V); BAP = Benzo-[a]-pyrene; CA = Chloroaniline; E2 = Estradiol; Hg = Mercury (II) chloride; NP = 4-Nitrophenol.(DOC)Click here for additional data file.Table S2Categories of pathways at different levels of responsiveness and degree of connectivities. HR = Highly responsive; MR = Medium responsive; LR = Low responsive; HC = Highly connected; LC = Low connected.(DOC)Click here for additional data file.Table S3Categories of pathways with known associated functions showing significant correlation coefficients.(DOC)Click here for additional data file.Table S4Assigned anti-correlation scores to pathways showing significant responds to 20 selected conditions with obvious inverted activities of hubs as “seed”. Red and green fonts are hub pathways of Group A and B, respectively. Pink and green backgrounds are anti-correlated pathways assigned to Group A and B, respectively. See (DOC)Click here for additional data file.


ModA of Neisseria and Haemophilus influenzae contain a highly variable, DNA recognition domain (DRD) that defines the target sequence that is modified by methylation and is used to define modA alleles. 18 distinct modA alleles have been identified in H. influenzae and the pathogenic Neisseria. To determine the origin of DRD variability, the 18 modA DRDs were used to search the available databases for similar sequences. Significant matches were identified between several modA alleles and mod gene from distinct bacterial species, indicating one source of the DRD variability was via horizontal gene transfer. Comparison of DRD sequences revealed significant mosaicism, indicating exchange between the Neisseria and H. influenzae modA alleles. Regions of high inter- and intra-allele similarity indicate that some modA alleles had undergone recombination more frequently than others, generating further diversity. Furthermore, the DRD from some modA alleles, such as modA12, have been transferred en bloc to replace the DRD from different modA alleles.Phase variable restriction-modification (R-M) systems have been identified in a range of pathogenic bacteria. In some it has been demonstrated that the random switching of the  Restriction-modification (R-M) systems are ubiquitous in bacteria and are involved in protecting the host cell from the invasion of foreign DNA Type I R-M systems are comprised of three subunits, S, M and R, which together form a holoenzyme that performs both methylation and restriction activity. Type I systems cleave DNA at random, often far from their recognition sequences Type III systems are complex although they only consist of two subunits, the methyltransferase and restriction endonuclease. Mod can function independently to methylate one strand of DNA within an asymmetric 5–6 bp recognition sequence res gene of H. influenzae and the pathogenic Neisseria is highly conserved with DEAD box motifs commonly associated with the superfamily II helicases, see 17(D/E)xK. The mod gene however, contains conserved 5′ and 3′ regions that flank a highly variable, central region modA gene encode the S-adenosyl-l-methioine (AdoMet) binding pocket, FxGxG, and a catalytic region, DPPY, which accommodates the target adenine in the recognition sequence after it has been flipped out of the double-helix, see Genetic analysis of type III R-M systems has shown that the mod genes of pathogenic bacteria has identified simple tandem repeats that allow phase variable expression et al.Haemophilus influenzaeNeisseria gonorrhoeae and Neisseria meningitidisHelicobacter pyloriPastuerella haemolyticaMoraxella catarrhaliset al.modA gene of H. influenzaeNeisseria species phase variable regulon, “phasevarion”.Sequence analysis of the type III modA allele as it contains the DNA recognition domain (DRD) that is responsible for DNA recognition sequence specificity. Diversity in this region has been reported H. influenzae strains defined a set of 15 distinct modA alleles (modA1–10 and modA13–17) and three additional modA alleles in the pathogenic Neisseria (modA11–12 and modA18) mod alleles exhibit high conservation of the DRD sequence (>95% nucleotide identity) within groups, but very little similarity between groups. To date, all reported modA DRD sequences have been classified as belonging to one of these 18 mod alleles. The modA alleles of H. influenzae and pathogenic Neisseria are essentially the same gene with evidence of horizontal transfer between the two species H. influenzae and Neisseria, such as modA13 and modA15, while others are only associated with one, for example modA11–12 are Neisseria specific N. meningitidis strains have a second phase variable methyltransferase, modBmodD, has been recently identified in the hypervirulent N. meningitidis clonal complex 41/44 The central region defines the modA alleles in order to better understand the mechanisms giving rise to DRD sequence diversity.In the current study we undertake comprehensive bioinformatics analysis of the 18 available modA alleles were obtained from GenBank. Each of the modA sequences had been previously described in one of three previous studies: A) 112 modA sequences encompassing the DRD domain, and at least 330 nt upstream and 450 nt downstream modA 5′ and DRD up to the entire mod/res region modA DRD sequences, 477 nt to 711 nt in length modA1 Hi R3327 (126508350); modA2 Hi 723 (126508378); modA3 Hi R3366 (126508386); modA4 Hi 3579 (126508396); modA5 Hi 1268 (126508406); modA6 Hi C1626 (126508412); modA7 Hi R3265 (126508420); modA8 Hi C505 (126508426); modA9 Hi 1209 (126508430); modA10 Hi R3157 (126508438); modA11 Nm BZ83 (257124056); modA12 Nm 129E (257124138); modA13 Ng 1291 (26506021); modA14 Hi R1527 (126508442); modA15 Hi R3570 (126508452); modA16 Hi ATCC9007 (126508448); modA17 Hi ATCC9833 (126508446); modA18 Nm NGE28 (257124039). Additionally, 77 N. meningitidis modA12 sequences from dataset B Nucleotide and amino acid sequences for the 18 mod DRD alleles using unfiltered stand-alone BLAST (version 2.2.18). To identify homologous DRD sequences, the amino acid sequence for each representative modA DRD allele was used as the query sequence for tBLASTn searches of the nucleotide databases at NCBI, including the whole-genome-shotgun and environmental databases. Mod and res loci from complete and unfinished genomes were identified by using the H. influenzae KW20 modA1 4.6 kb locus to query the GenBank nucleotide database and Whole Genome Shotgun database, respectively. Matches were further analysed using Artemis Comparison Tool mod and res sequences and determine if either were pseudogenes. The modA alleles for 38 genome-derived mod sequences were determined by unfiltered stand-alone BLASTn against a custom database consisting of representative modA DRD allele nucleotide sequences. Unless otherwise stated, all BLAST searches were carried out with default parameters. Amino acid and nucleotide sequences were aligned using ClustalX 2.0.11 with default parameters. Alignments were viewed and edited using Jalview 2.4.0.b2 Inter-allele comparisons were carried out using all-versus-all BLASTn and BLASTp comparisons of representative modA alleles were compared by multiple sequence alignment. modA alleles at the nucleotide level with respect to the nucleotide sequence and length of the DRD. The closest modA DRD domains share 38% amino acid identity, whereas the most distant share 29%. To probe the origin of DRD diversity and the relative contribution of intra- and inter-species recombination mechanisms we undertook a detailed analysis of all available modA alleles.Representatives of all 18 modA DRDs and DNA methyltransferases from unrelated bacterial species suggesting acquisition of new DRDs en-bloc via horizontal gene transfer. To further explore the source of extant modA alleles, BLAST searches using all available modA DRD regions were carried out. We used the amino acid sequences for the DRD of each modA allele to undertake tBLASTn searches of NCBI nucleotide databases to identify likely source genomes. Ignoring self-matches, all matches with >50% amino acid identity  identified in the WGS division of Genbank are outlined in Legionella pneumophila and Moraxella catarrhalis. As previously stated, the amino acid sequence identity of the DRD between known modA alleles (i.e. ModA1–ModA18) is >29%, therefore the identification of matches >50% between the modA DRD and non-related bacterial species required further investigation. In all cases the match was with a predicted adenine specific type III R-M system methyltransferase. Matches between modA4 and Helicobacter sp.modA5 and Moraxella catarrhalisOur previous work modA DRD. The matches between the modA4 and Helicobacter acinonychis Hac_1417 and modA5 and Moraxella catarrhalis McaRII are shown in The local alignments identified by BLAST were extended using the corresponding complete sequences to determine the actual length of the match and determine if the alignment extended beyond the boundaries of the modA genes within the same species to overall modA allele diversity, we undertook all-versus-all BLASTn searches using the 18 representative modA sequences. Matches between the alleles identified with a length greater than 13 nucleotides were mapped onto the corresponding allele and coloured according to the scheme established in Fox et al.modA alleles have recombined in the past. Most interestingly, new relationships between the modA alleles were identified, and some alleles were found to have undergone recombination much more readily than others, indicating possible selection. modA10 and modA14. The DRDs show evidence of recombination with long stretches of nucleotide identity (>80%) between the two alleles. Sequences within the same modA allele share very high identity over the DRD region (>95%) suggesting that recombination is not presently contributing to the diversity. Indeed, the high conservation suggests that the DRD is moving as a unit. To investigate this further we undertook an analysis of a particular modA allele where a large number of sequences are available.To examine the contribution of recombination between the DRDs of modA alleles, it is apparent from previous studies and our own analysis that the DRD domains are highly conserved amongst members of the same modA allele modA alleles nucleotide alignments were completed with 77 available sequences for the N. meningitidis modA12 DRD alleles and their flanking regions modA12 sequences   . In contequence) . Throughelection . A close alleles . To form alleles , consistmodA allele is >95% at the nucleotide level  and modA20 (H. influenzae R3021), respectively.A multiple nucleotide alignment of all modA6, modA18 and modA19 alleles. In the case of modA19, the high conservation within the DRD, coupled with the relatively non-conserved sequence at either end suggests independent homologous recombination events of the entire DRD region from an unknown source into different modA alleles. This suggests that the same DRD allele should be found within modA genes with less identity in their flanking regions. The identification of Neisserial specific uptake sequences (nUSS), 5′-GCCGTCTGAA-3′, flanking the variable domain in all the aligned genome sequences in this study and H. influenzae specific uptake sequences (hUSS) in a previous study Neisseria and H. influenzae, indicates that the DRD would be preferentially transformed into these species following cell death, lysis, then natural transformation of random genomic fragments. nUSS and hUSS represent a well characterised feature used to enhance transformation efficiency modA DRD is consistent with the hypothesis that the variable domain is frequently transferred between H. influenzae, N. gonorrhoeae and N. meningitidis. The comparison of the relative GC content is also a good indicator that a specific region of DNA originates outside its current organism. The calculated GC content of the DRD of the analysed modA12 sequences is significantly lower than either the 5′ or 3′ regions of the modA gene, see As previously shown , the datmodA phasevarions in Haemophilus influenzae and the pathogenic Neisseria forms the basis of this study. The modA alleles in Haemophilus and pathogenic Neisseria contain conserved 5′ and 3′ coding regions, which flank a highly variable central region. The origins of the diversity of the central, variable region, the DRD, have been previously investigated by Bayliss et al. (25), although this analysis only identified 13 modA alleles as fewer strains were examined. A further 5 distinct modA alleles have been determined through sequencing of modA genes in H. influenzaeN. gonorrhoeae and N. meningitidismodA alleles and took advantage of the increase in publicly available bacterial genome sequences in recent years. Our sequence analysis of the publicly available genomes led to the discovery of an additional 2 modA alleles, which we designate modA19 and modA20.Investigating the origins of the distinct modA alleles has been generated by both small and large scale recombination between species and strains at various evolutionary time-points. The first source of novel DRD sequences that we investigated were other bacterial species. All available bacterial genomic sequences located in the NCBI databases were searched using tBLASTn to identify the initial matches between other bacterial species and the modA DRD. In all cases the most significant matches were with putative adenine-specific type III R-M methyltransferases. There have been over 500 putative type III methyltransferases identified but only a small number of these have been completely characterised modA DRD matches. However, the modA4 matches with H. pylori are homologs of the methyltransferase, JHP1296 or modH1, which has been shown to phase vary H. pylori methyltransferase has been demonstrated with phase-variable lacZ expression modA5 matched a putative phase variable methyltransferase in M. catarrhalisM. catarrhalis mod was predicted to phase vary based on the presence of tetranucleotide repeats in the open reading frame of a type III R-M system The diversity seen between the 20 modA, diversity was generated by the transfer of short segments of the DRD between different H. influenzae and Neisseria modA genes. There are known elements associated with the modA genes that have been established as allowing the transfer to occur, namely H. influenzae and Neisserial specific uptake sequences and remnants of an integrase modA alleles, and it is proposed that these segments have been transferred between the different modA alleles. It is hypothesised that recombination between different modA DRDs affects the sequence specificity recognised by the modA allele and therefore can play a role in the fitness of the organism to an evolving host environment by introducing new regulatory patterns.Early in the evolution of modA allele, strains were found to differ in ≤5% of their nucleotide sequence modA12 allele was chosen for an in depth analysis of intra-allele variability as it was the most highly represented group in available modA sequences and has recently been reported as a functional phasevarion modA12 DRD sequence in these strains. Indeed, the modA12 DRD sequences examined here exhibited only 0.2% diversity within the DRD itself. Bayliss et al.modA alleles indicated that it was a dynamic area that was in a constant flux. Notably, that analysis used only a handful of representative sequences. In contrast, recent recombination between the DRDs of different modA alleles to generate diversity does not hold with our analysis of 77 modA12 sequences. Instead the modA12 sequences show evidence of other modA alleles in their 5′ and 3′ regions, indicating that the entire modA12 DRD had “over written” a previous allele. We propose that the intra-allele sequence shuffling occurred early in the acquisition of the DRD by either H. influenzae or Neisseria and that there is a strong selection to maintain the current sequence of the modA12 DRD.Within the same modA alleles and examined origins of the significant diversity seen in the modA DRD domain. We have shown that several DRD regions of modA alleles found in pathogenic Neisseria and H. influenzae were acquired en-bloc from type III R-M systems from unrelated bacterial species. Subsequent diversity is generated by recombination between modA alleles via frequent horizontal transfer between and within these two species; in some cases this has involved the transfer of the entire DRD region. We find remarkable conservation within any given modA DRD, even when they are found in different genetic contexts. Accordingly, modA alleles should be defined only by the DRD region itself, and exclude the flanking 5′ and 3′ regions that often bare the scars of multiple recombination events. Our previous work on the pathogenic Neisseria has shown that distinct modA alleles recognised distinct DNA targets sites and thus regulate distinct regulons of genes with key roles in model systems of disease modA alleles we observed selection for maintenance the modA12 allele in N. meningitidis. This indicates a strong selective pressure and benefit conferred by presence of the modA12 phasevarion in these N. meningitidis strains. The modA12 allele is the modA allele most frequently observed in N. meningitidis disease isolates  component of the Type III system, indicating selection for particular mod alleles observed in these studies is likely to be driven by the advantage conferred by the gene regulation phenotype, not by a restriction related function.In summary this study has characterised the diversity between the 20 s 78.5%; . The widTable S1Significant BLASTn matches between modA DNA recognition domains of modA alleles in H. influenzae and the pathogenic Neisseria.(DOCX)Click here for additional data file.Table S2Within-allele diversity of DRD sequences.(DOC)Click here for additional data file.Table S3modA alleles identified from available genomes.(DOCX)Click here for additional data file.Data S1(ZIP)Click here for additional data file.


After adjusting for cancer site, stage, age, gender, alcohol consumption, and smoking status, HPV status was found to be a significant predictor for DNA methylation at an additional 11 genes, including CASP8 and SYBL1. These findings provide insight into the epigenetic regulation of viral vs. chemical carcinogenesis and could provide novel targets for development of individualized therapeutic and prevention regimens based on environmental exposures.Head and neck squamous cell carcinoma (HNSCC) is the eighth most commonly diagnosed cancer in the United States. The risk of developing HNSCC increases with exposure to tobacco, alcohol and infection with human papilloma virus (HPV). HPV-associated HNSCCs have a distinct risk profile and improved prognosis compared to cancers associated with tobacco and alcohol exposure. Epigenetic changes are an important mechanism in carcinogenic progression, but how these changes differ between viral- and chemical-induced cancers remains unknown. CpG methylation at 1505 CpG sites across 807 genes in 68 well-annotated HNSCC tumor samples from the University of Michigan Head and Neck SPORE patient population were quantified using the Illumina Goldengate Methylation Cancer Panel. Unsupervised hierarchical clustering based on methylation identified 6 distinct tumor clusters, which significantly differed by age, HPV status, and three year survival. Weighted linear modeling was used to identify differentially methylated genes based on epidemiological characteristics. Consistent with previous  Head and neck squamous cell carcinomas (HNSCCs), the eighth most commonly diagnosed cancer in the U.S. population, have a complex etiology that includes life style behaviors, classical chemical carcinogenesis, and infection with high risk types of human papillomavirus (HPV). Traditionally, head and neck cancer is associated with a profound history of tobacco and alcohol use, and poor survival compared to other cancers p53, are frequently detected in tobacco and alcohol-related head and neck cancers p53 and Rb, which is mediated by E6 and E7 viral oncoproteins, resulting in overexpression of p16Both tobacco- and alcohol-related, as well as HPV-associated, head and neck cancers have a well-described multistep model of carcinogenesis Epigenetic modifications are an important mechanism in carcinogenic progression −) squamous cell carcinoma cell lines, noting that HPV(+) cell lines have higher amounts of genic methylation as well as increased expression of DNMT3ARecently, we reported an epigenome-wide analysis of concurrently measured DNA methylation and gene expression in HPV(+) and HPV to confirm the original diagnosis and to circle areas for DNA extraction. Percent cellularity was estimated for each tumor and areas with >70% cellularity of cancer were designated for use in the analyses.Individuals eligible for participation included patients diagnosed with first primary head and neck cancer between January 1, 2003 and December 31, 2005 completed an epidemiologic questionnaire, and had paraffin-embedded tumors available for analysis with adequate residual tissue for microdissection (N = 82). The epidemiologic questionnaire included questions about lifestyle behaviors, including smoking and drinking. Clinical characteristics included tumor site and stage, comorbidities, depression, quality of life, and recurrence status, as well as treatment modalities. Tumor blocks were reRegions identified for DNA extraction were cored from the formalin fixed paraffin embedded (FFPE) tissue blocks using an 18 gauge needle. Isolation of DNA from cored tissue samples was performed using the QIAamp DNA FFPE Tissue Kit  modified to include overnight incubation at 56°C in lysis buffer. DNA concentration and purity were confirmed via NanoDrop spectrophotometer . Sodium bisulfite modification was performed on 500 ng to 1 µg of extracted DNA using the EZ DNA Methylation kit  following the manufacturer’s recommended protocol.HPV status was determined by an ultra-sensitive method using real-time competitive polymerase chain reaction and matrix-assisted laser desorption/ionization-time of flight mass spectroscopy with separation of products on a matrix-loaded silicon chip array, as described in Tang et al. The commercially available Illumina Goldengate® Methylation Cancer Panel was used to detect DNA methylation patterns in tumor samples. The Cancer Panel measure DNA methylation at 1505 CpG sites located in known CpG islands across 807 genes related to cancer, including oncogenes, tumor suppressor genes, imprinted genes, and genes involved in cell cycle regulation, DNA repair, apoptosis and metastasis. Bead arrays were processed at the University of Michigan DNA Sequencing Core Facility according to the manufacturer’s protocol. Briefly, bisulfite converted tumor DNA was hybridized to the bead array as described previously Methylation at specific CpG probes on the Goldengate BeadArray has been shown to be biased by probe thermodynamic properties Detection p-values on the Goldengate BeadArray are calculated based on fluorescence signal at a probe compared to background fluorescence and represent the (1-probability) that a signal is stronger than background fluorescence. The weighted methodology proposed by Kuan et al. was used to develop sample and site weights based on p-values of detection. Both samples and sites with larger detection p-values are generally considered less reliable and were down-weighted in further gene specific analyses.CCNA1 was quantified in an additional sample of 128 pretreatment head and neck tumors using the Sequenom EpiTyper, a MALDI-TOF mass spectrometry based platform. DNA was extracted from FFPE tumors, HPV status was determined, and the DNA was bisulfite converted as described above. Bisulfite PCR amplification was performed using FastStart Taq Polymerase  with a forward and reverse primer concentration of 0.2 µM and an annealing temperature of 48C and 45 PCR cycles. The primer sequences, including the forward and T7 promoter tags required for Sequenom analysis were: 5′-AGGAAGAGAGATGTATTTTGGATTTTTTATTGGGG (forward primer) and 5′-CAGTAATACGACTCACTATAGGGAGAAGGCTAAAAAAACATTCTAACAAACCTCCA (reverse primer). Methylation analyses were performed at the University of Michigan Sequencing Core Facility following the manufacturer’s recommended protocol.DNA methylation of four CpG sites in the promoter region of hclust package in R version 2.10.1. Unsupervised hierarchical clustering was performed using the Euclidean distance metric and the Ward clustering method in the survival package. Differences in age were compared using analysis of variance (ANOVA). Fisher’s Exact test was performed to test differences in cancer site, stage, and HPV status. Cox proportional hazards models were constructed to test the association between methylation at each CpG site on the Goldengate BeadArray and survival, adjusting for HPV status, gender, age, disease stage, cancer site, smoking status, and problem drinking using the coxph function in the R survival package. Individuals with a tumor testing positive for any strain of HPV were considered HPV positive. Age was treated as a continuous variable, while disease stage and cancer site were treated as categorical variables. Smoking was categorized into never smoker, past former smoker (quit more than 12 months ago), recent former smoker (quit in previous 12 months), and current smoker. Problem drinking was defined as a score of greater than 8 on the validated Alcohol Use Disorders Identification Test, as previously described qvalue R package.Clinical characteristics were evaluated across clusters based on cluster membership using non-parametric rank-based and exact statistics. For survival analyses, death was considered an “event”; survival time was censored at 3 years (1095 days). The Kaplan-Meier method was used to estimate overall survival and the log-rank test was used to test differences in survival distributions using the R Limma in R 2.10.1 LumiWCluster based on detection p-values across samples were used in the lmFit function from the Limma package to downweight samples with higher detection p-values. CpG sites were identified as differentially methylated between HPV(+) and HPV(−)tumors, adjusting for cancer site , cancer stage, sex and age. An empirical Bayes method (using the eBayes function in Limma) was used to rank CpG sites in order of significance of differential methylation. Additionally, Limma was used to examine methylation differences between the case cluster with significantly worse survival compared to the remaining cases. For CCNA1 validation, mean methylation was calculated across the 4 sites measured by the EpiTyper and compared across HPV(+) and HPV(−) tumors using the Wilcoxon rank-sum test. Additionally, a multiple linear regression model was constructed with mean CCNA1 methylation as the independent variable and HPV status as the main predictor, adjusting for age, sex, tumor site, and tumor stage. Gene Set Enrichment Analysis (GSEA) was used to identify common pathways and chromosomal locations for genes identified as significant (p<0.05) in the Limma analysis Overall site specific methylation differences between HPV(+) and HPV(−) tumors were compared by calculating the difference in the mean methylation per CpG site in HPV(+) and HPV(−) tumors. The effects of clinical characteristics on DNA CpG methylation measured on the Goldengate array were examined using ; 75% of the subjects were male. The majority of the HNSCCs were from the oropharynx (47%), oral cavity (25%), and larynx (19%), with a large proportion of cancers diagnosed as late stage ). Clusters by epidemiological and clinical characteristics were assessed first. Individuals who grouped in Cluster 3 tended to be older , and limiting the cluster analysis to the 50% of CpG sites with the most variance (n = 711), six distinct clusters were identified . In order to better understand how HPV infection affects the DNA methylation profile in head and neck cancer, associations between methylation at each of the 1505 CpG sites on the Goldengate array and HPV status were calculated. Thirteen individual CpG sites on the array were found to be significantly associated with the HPV status of the tumor with a q-value <0.05  tumors (p = 1.8×10−6). This finding corroborates our recent analysis of epigenome-wide DNA methylation differences in HPV(+) and HPV(−) cell lines where CCNA1 was found to be a major interaction hub following bioinformatic analyses GRB7, CDH11, RUNX1T1, SYBL1, and TUSC3 were also found to be significantly more methylated in HPV(+) tumors. CpG sites in SPDEF, RASSF1, STAT5A, MGMT, ESR2, JAK3, and HSD17B12 were found to be significantly hypomethylated in HPV(+) tumors (.Plotting average differences in methylation at each site showed that HPV(+) tumors tended to be hypermethylated at more sites than HPV(−) tumors (ue <0.05 Table 3.CCNA1 methylation HPV(+) tumors, we quantified methylation at 4 CpG sites in the promoter region of CCNA1 in an additional 128 pretreatment head and neck tumors. Mean CCNA1 methylation was significantly higher in HPV(+) tumors (p = 0.0005). After adjusting for age, sex, tumor site, and tumor stage, HPV(+) tumors were found to be, on average, 9.6% more methylated at the CCNA1 promoter compared to HPV(−) tumors (p = 0.029).To validate our findings of increased RASSF1, CDK10, CHFR, RUNX3, APC, and CDKN2A (p16). An analysis of enriched gene sets from the Kyoto Encyclopedia of Genes and Genomes (KEGG) found that genes associated with Neuroactive Ligand Receptor Interactions were hypermethylated in HPV(+) tumors  tumors, specifically STAT5A, JAK3, OSM, MPL, and EPO.To analyze if specific gene sets or pathways display differential epigenetic regulation in HPV(+) versus HPV(−) tumors, a GSEA of the genes associated with HPV status was conducted. An analysis of Gene Ontology (GO) Biological Processes significantly enriched for differentially methylated genes implies that three gene sets associated with cell cycle regulation were hypomethylated in HPV(+) tumors Table 3.Table S2. While no individual CpG site was found to have a false discovery rate less than 0.15, methylation at a number of genes was found to be potentially associated with survival, including NOTCH1, UGT1A1, and IL-6.Cox Proportional Hazards Regression was used to determine whether methylation at individual CpG sites is associated with three year survival rates. Significantly associated genes  are listed in Table S3). Genes located in chromosome 7q21 were found to be significantly hypomethylated in Cluster 5 cases. Biological processes associated with negative regulation of cellular metabolism as well as homeostatic processes were found to be enriched with genes hypomethylated in this cluster. An analysis of molecular functions identified dysregulation of nucleotide binding, particularly purine and adenyl nucleotide binding as well as kinase and phosphorus transferase activity.After comparing survival by cluster, where we noted that cases in Cluster 5 had significantly worse survival as well as apparent widespread differences in methylation, we conducted a post hoc analysis to identify specific genes differentially methylated in that cluster. After adjusting for other clinical covariates, including age, sex, cancer site, stage, smoking, problem drinking, and HPV status, a substantial number of genes were found to be differentially methylated in Cluster 5 compared to all other clusters. Gene set enrichment analysis identified pathways, molecular functions, and a chromosomal region significantly differentially methylated in Cluster 5 cases  cases, we confirmed a distinct epigenetic profile in HPV(+) head and neck cancers when compared to HPV(−) cancers. This has been previously noted by others for global methylation CCNA1 with an average percent methylation level of 10% in HPV(−) tumors and 31% in HPV(+) tumors. This was also one of our top ranked genes in HNSCC cell lines CCNA1 is an important regulator of the cell cycle and is required for S phase and passage through G2 RASSF1CHFRRUNX3APCCDKN2A (p16) Our prior work has shown how epigenetic profiles and expression patterns correspond to these divergent mechanisms of carcinogenesis in HPV(+) and HPV(−) cell lines These analyses represent essentially a sizeable candidate-gene study, and the large number of loci allowed for initial pathway and positional analyses of the methylated CpGs. This was particularly useful when evaluating the contribution of epigenetic modifications to the prediction of survival, where methylation at single genes or sites did not predict survival time in this cohort. Hierarchical cluster analysis identified one set of patients with particularly worse survival solely based on methylation. Notably, this cluster did not include any HPV(+) cases, and contained the lowest proportion of males of all clusters (63%). This cluster had significant hypomethylation of 7q21, a region amplified in multiple cancers NOTCH1 was the strongest predictor of survival in this study (p = 0.0002), and was also identified as frequently mutated in head and neck tumors in Stransky et al. and Agrawal et al. Interestingly, truncating mutations in NOTCH1 indicate a tumor suppressor function as opposed to activating mutations seen in other cancers, and methylation of this gene also indicates a tumor suppressor function. Loss of heterozygosity (LOH) at the NOTCH1 locus has also been reported for a small number of tumors These and other epigenetic studies have strong implications for head and neck cancer research, particularly in light of recent reports on the complex landscape of head and neck cancer research This study has a number of limitations. The population size was relatively small, however, the technology used was able to detect differences in promoter methylation in a large number of genes associated with cancer. The Goldengate cancer panel, however, does not provide a measurement of promoter methylation in other genes with less well characterized functions, nor does it measure methylation at other genomic features, such as intergenic regions, which could provide information about genomic structure and stability. While the sample was representative of the patients seem in the institutions from which participants were recruited, women and particularly minorities were under-represented. Future planned studies will include a more diverse patient population and a more comprehensive view of the cancer epigenome, integrating epigenetic and transcriptional measures.Clinically and pathologically relevant subsets of tumors defined by methylation status have been identified in many cancer types, most notably the CpG Island Methylator Phenotype (CIMP) in colorectal cancer Figure S1Average differences in methylation per CpG site comparing HPV(+) and HPV(−) tumors.(TIF)Click here for additional data file.Table S1All CpG sites with DNA methylation values significantly associated (p<0.05) with HPV status of the tumor. Positive T-values correspond with sites more highly methylated in HPV(+) while negative T-values correspond with sites more highly methylated in HPV(−) tumors. Adjusted p-values were calculated via the Benjamini-Hochberg Method.(DOCX)Click here for additional data file.Table S2CpG sites identified as significantly associated (p<0.05) with three year survival by Cox Proportional Hazards Modeling.(DOCX)Click here for additional data file.Table S3Significantly enriched gene sets for genes identified as differentially methylated in cases from the cluster identified with worst survival (Cluster 5) compared to all other cases.(DOCX)Click here for additional data file.


However, the genetic determinants of elevated tHcy (hyperhomocysteinemia), dysregulation of methionine metabolism and the underlying biological processes remain unclear. We conducted independent genome-wide association studies and a meta-analysis of methionine metabolism, characterized by post-methionine load test tHcy, in 2,710 participants from the Framingham Heart Study (FHS) and 2,100 participants from the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, and then examined the association of the identified loci with incident stroke in FHS. Five genes in the FOCM pathway  were strongly associated with the difference between pre- and post-methionine load test tHcy levels (ΔPOST). Of these, one variant in the ALDH1L1 locus, rs2364368, was associated with incident ischemic stroke. Promoter analyses reveal genetic and epigenetic differences that may explain a direct effect on GNMT transcription and a downstream affect on methionine metabolism. Additionally, a genetic-score consisting of the five significant loci explains 13% of the variance of ΔPOST in FHS and 6% of the variance in VISP. Association between variants in FOCM genes with ΔPOST suggest novel mechanisms that lead to differences in methionine metabolism, and possibly the epigenome, impacting disease risk. These data emphasize the importance of a concerted effort to understand regulators of one carbon metabolism as potential therapeutic targets.Circulating homocysteine levels (tHcy), a product of the folate one carbon metabolism pathway (FOCM) through the demethylation of methionine, are heritable and are associated with an increased risk of common diseases such as stroke, cardiovascular disease (CVD), cancer and dementia. The FOCM is the sole source of  GNMT harbors genetic and epigenetic differences that influence gene function, which may have downstream effects on the epigenome of the cell, affecting disease risk. We developed a genetic risk score that predicts post-methionine load homocysteine levels that may be useful in clinic. Finally, we identified a novel association between ischemic stroke and ALDH1L1, which emphasizes the clinical importance of this work. Our results highlight the importance of a concerted effort to target the FOCM (beyond tHcy) and parallel pathways in future pharmacogenetic work using the genetic variation we describe here.Elevated homocysteine (tHcy) is strongly associated with risk for common disorders such as stroke, cardiovascular disease and Alzheimer disease. Lowering tHcy levels has proven to have variable success in reducing clinical risk, so the question remains, “Are we correctly targeting these disorders by lowering tHcy?” Understanding folate one-carbon metabolism pathway (FOCM) genetic variation will aid us in developing new targets for therapy. The FOCM is essential in regulation of the epigenome, which controls genes in ways beyond nucleotide sequence. We present data generated from stroke-only and general populations where we identify strong association of genetic risk factors for variation in one-carbon metabolism function, characterized by the post-methionine load test. We show that  Dysregulation of this step in the FOCM could have broad implications on many cellular processes, including risk for stroke and cardiovascular disease.As the fourth leading cause of death and the leading cause of disability in American adults, stroke constitutes a major public health burden. Epidemiological data consistently demonstrate an association between elevated plasma homocysteine (tHcy) and increased risk for stroke The post-methionine load test is a more sensitive tool for diagnosing hyperhomocysteinemia than circulating plasma homocysteine levels Here we present a genomic, genetic and epigenetic investigation into the regulation of methionine metabolism in the Vitamin Intervention for Stroke Prevention Trial (VISP) and the Framingham Heart Study (FHS). We first present genome-wide association (GWAS) data linking five loci to differences in ability to convert methionine to homocysteine. Strikingly, all of the most significant genes identified within these loci are members of the same pathway (FOCM), a feature rarely observed in GWAS studies.GNMT [MIM 606628] locus, our most significant GWAS finding, and devised a scheme to test methionine loading in vitro based on GNMT genotype. Additionally, we have shown epigenetic regulation of the GNMT promoter, based on a CpG-SNP rs11752813, which likely contributes to GNMT transcription and methionine metabolism. These data may one day contribute to identification of new targets for stroke and cardiovascular disease prevention as well as other complex diseases where epigenetics play a role.We observed haplotype differences at the The FHS cohort is a community based longitudinal study to determine the risk for cardiovascular disease and is comprised of randomly recruited participants and their family members in the town of Framingham, Massachusetts . VISP waThe VISP study consisted of 1725 (82.1%) individuals of European descent, 258 (12.2%) individuals from African descent and 117 (5.6%) individuals of other ancestral populations. All VISP participants are unrelated. FHS samples are primarily Caucasian. In FHS the 3110 individuals contributing to GWAS belong to 1055 families with extended family size ranging from 1 to 140. In FHS, 1772 individuals have at least one blood relative in the family, 279 individuals have at least one first degree relative, 278 have at least one second degree relative, and 586 have at least one third degree relative.GNMT, CBS [MIM 613381], CPS1 [MIM 608307], ALDH1L1 [MIM 600249] and PSPH [MIM 172480]) reached our pre-determined genome-wide significance threshold of 5×10−8 for the ΔPOST phenotype. These findings were confirmed in the VISP study sample of 2,100 persons for whom two of these loci (GNMT and CBS) independently reached genome wide significance  located on 6p21.1, a region associated with large artery atherosclerotic stroke In a GWAS of 2,710 persons from the FHS study, five loci . These data suggest functional differences in the GNMT gene impact an individual's ability to metabolize dietary methionine. The lack of a disruptive coding mutation identified by GWAS, or in sequencing of GNMT in 24 high and 24 low methionine metabolizers (data not shown), and the expectation that a higher rate of transcription of the GNMT gene should lead to higher tHcy levels, suggest a regulatory mechanism for the differences in ΔPOST rather than protein dysfunction. This metabolic difference mediated by genetic variation is of functional significance in both the general population (FHS) and a population with tHcy above the 25th percentile as required by the inclusion criteria for the clinical trial (VISP).Using ten genotyped single nucleotide polymorphisms (SNPs) on chromosome 6 in the ulation, , we condulation,  and corrulation, . One SNPGNMT promoter ΔHighLuc and GNMTΔLowLuc constructs  a feedback mechanism appears to be induced, which reduces GNMT expression , and either creates or eliminates a CpG site that can be methylated depending on genotype (rs11752813 and flanking sequence: C(C/G)A). The high methionine metabolizing haplotype (ΔHighLuc in ΔLowLuc in GNMT gene (GNMT transcription and methionine metabolism.Given the differences in promoter activity seen in aplotype  harbors NMT gene . BisulfiNMT gene . These rNMT gene . As seenWe next investigated cumulative effects of ΔPOST risk variants by generating a combined genetic risk score using the most significant SNPs from ALDH1L1) and incident ischemic stroke in the FHS cohort . The protein encoded by ALDH1L1 converts 10-formyltetrahydrofolate to tetrahydrofolate and is an essential component of the FOCM pathway. These results provide a new and significant link between the FOCM pathway and risk of initial ischemic stroke. It is important to note that the VISP population consists of exclusively ischemic stroke patients examined for recurrent stroke over a 2 year period; rs10934753 was not associated with recurrent stroke. Additionally, we did not observe an association of genetic variation in GNMT or the other 3 loci with incident ischemic stroke in FHS but our sample has limited power to detect moderate effect size .Importantly, when interrogating the most significant five SNPs associated with ΔPOST, we also identified an association between the aldehyde dehydrogenase 1 family member L1 gene (MTHFR [MIM 607093]) and CBS genes MTHFR, which also participates in the FOCM pathway, is tightly related to all the genes products identified in our study and has been implicated in susceptibility to vascular disease, neural tube defects, colon cancer and acute leukemia Elevated tHcy has long been associated with increased risk for stroke and cardiovascular disease but to date functional evidence for the driving genetic forces behind elevated tHcy levels have only been attributable, in part, to dysfunction in the methylenetetrahydrofolate reductase gene , and two, VISP is a more homogenous population than FHS lacking a normal distribution of tHcy. Our functional studies start with the GNMT gene as it contributes to the majority of the variance in both VISP and FHS. The GNMT association makes biological sense given that GNMT catalyses the conversion of S-adenosyl-methionine (SAM) to S-adenosyl-homocysteine (SAH), using SAM as the methyl donor GNMT could affect risk status. Further, hyperhomocysteinemia is a risk factor for stroke and cardiovascular disease, and these data indicate GNMT may represent a new pharmacogenetic target for reducing stroke risk. It is our assertion that targeting the FOCM pathway before methionine is converted to homocysteine may allow us to modulate parallel pathways, such as DNA and histone methylation, which directly impact stroke risk. A recent review by Krishna et al. In the current study we followed up with a functional evaluation of our GWAS findings. These independent GWAS analyses and a meta-analysis of FHS and VISP find the GNMT mutations associated with Glycine N-Methyltransferase Deficiency [MIM 606664]  in sequencing of GNMT in 24 high and 24 low methionine metabolizers. Additionally, a lookup of large GWAS studies of cardiovascular and cerebrovascular disease, identified many of our most significant chromosome 6 findings in a meta-analysis for blood lipids −4), suggesting that GNMT may play a broader role in risk traits for CVD beyond tHcy measures.We did not observe any of the deleterious GNMT in variation of methionine metabolism. The consistent and biologically plausible results from the individual GWAS and meta-analysis emphasize that the other significant associations observed in the FOCM pathway cannot be ignored. The cystathionine-beta-synthase (CBS) gene has been associated with stroke CBS mutations are associated with homocystinuria, iridodonesis and agitated motion of the iris [MIM 236200] CPS1) gene, ΔPOST rs1047891 was found to be associated with a missense Ser(ACC)/Phe(AAC) mutation (p = 9.10×10−13). These findings are related to a sex-specific association of CPS1 with tHcy and women, performed in the Woman's Health Genome Study PSPH) mutations have been associated with phosphoserine phosphatase deficiency [MIM 614023], which results in pre- and postnatal growth retardation, moderate psychomotor retardation, and facial features suggestive of Williams syndrome Our functional studies support the role of Taken together, these data present a new link to the genetics of the FOCM pathway with methionine metabolism both in stroke and non-stroke populations. Because of the impact that the FOCM pathway has on the biology of the cell, including overall epigenetic state and DNA methylation, gluconeogenesis, and DNA repair, understanding how individual genetic composition impacts this pathway is essential. The FOCM has also been implicated in many aspects of human health and the work presented may be relevant to several key biological mechanisms, affecting tumorogenesis While a direct link between tHcy levels and stroke and cardiovascular disease remains debated, we have shown that understanding sequence variation in the FOCM pathway may provide a link to functional differences in the population, that in turn tie one carbon metabolism to a broad range of disease risk factors. Additionally, because tHcy-lowering therapies have had variable success in reducing stroke risk in subtype populations All human research was approved by the relevant institutional review boards, and conducted according to the Declaration of Helsinki. The Framingham Heart Study protocol was approved by the institutional review board (IRB) of the Boston University School of Medicine and all participants provided written, informed consent. The VISP study protocol was approved by the IRBs of the Wake Forest University School of Medicine (coordinating center) and University of North Carolina Chapel Hill School of Medicine . The local IRB for each of the individual recruiting sites approved the VISP protocol and all participants provided written, informed consent. The Genomics and Randomized Trial Network (GARNET) analysis of the VISP data was approved by University of Virginia School of Medicine IRB.FHS started in 1948 for evaluation of cardiovascular diseases and risk factors Plasma tHcy levels were measured using high-performance liquid chromatography with fluorescence detection Clinical stroke was defined as rapidly developing signs of focal neurologic disturbance of presumed vascular etiology lasting more than 24 hours. Additional details of stroke classification and diagnosis can be found in prior publications http://www.sph.umich.edu/csg/abecasis/MACH/) based on 412,053 good quality SNPs .Imputation of about 11 million 1000 Genomes SNPs  was performed using MACH version 1.0.16  Project 2) was used to summarize the imputation quality. The median dosage r2 was 0.933 for chromosome 22 masked SNPs and 0.930 for X chromosome masked SNPs. The imputed dataset, along with a detailed report on imputation methodology, is available through the authorized access portion of the VISP dbGaP posting.Imputation was carried out using BEAGLE imputation software Genotyping was performed on the Illumina HumanOmni1-Quad-v1 array  at the Center for Inherited Disease Research, Johns Hopkins University. The genome-wide association analysis was conducted using PLINK v1.0.7. Multivariate linear regression model was used to test correlation of quantitative traits and SNP markers. Using the KING software, the top 10 principle components were derived from genotype data Meta-analysis of the 2100 VISP and 3110 FHS cohorts was conducted using the METAL software https://statgen.sph.umich.edu/locuszoom/genform.php?type=yourdata). Plots were created utilizing the genome build/LD population hg19/1000 Genomes Mar 2012 EUR.2 incubator.Unless otherwise noted, HepG2 cell lines were cultured in complete media containing high glucose Dulbecco's Modified Eagle's Medium (DMEM) (Invitrogen) with 10% (v/v) FBS, 2 mM L-glutamine supplemented with 1× nonessential amino acids, 1 mM sodium pyruvate (Invitrogen) and 1X antibiotic-antimycotic (Invitrogen). Cells were maintained at 37°C in a 5% CO2 tissue culture flasks or 6 well culture plates. For methionine starvation, after 24 hrs media was removed and replaced with either complete media or complete media lacking L-methionine. After 24 hrs, cells starved of methionine were supplemented with L-methionine (Ameresco) at concentrations of .HepG2 cells were culture in complete media as described above for 24 hrs in 25 cmGNMT quantitative real-time PCR, Taqman MGB probes and primers were used (Hs002219089) (Applied Biosystems). All samples were run in triplicate in 10 µl reaction volumes. PCR conditions were the default settings of the ABI Prism 7900 HT Sequence Detection System (Applied Biosystems) using the standard curve setting to achieve raw data, which was analyzed in Microsoft Excel. The cycle threshold (Ct) was determined during the geometric phase of the PCR amplification plots as automatically set by the 7900 software. Relative differences in transcript levels were quantified using the ΔΔCt method with GAPDH [MIM 138400] (probe 4333764F) mRNA as an endogenous control.RNA isolation was conducted using the Qiagen RNeasy kit according to standard manufacture's protocols. cDNA synthesis was conducted using 1 ug of total RNA and the Verso cDNA Synthesis Kit (Fisher Scientific). A 3∶1 mix (v/v) of random hexamers and anchored oligo-dT was used following standard theromocycling conditions. For GNMT promoters were PCR amplified using primers  as previously described ΔHighLuc and GNMTΔLowLuc.−8 were assessed using version 3, release 27, analysis panel CEU+TSI.Haploview version 4.2 Average ΔPOST values were taken from the VISP population, and any individuals with missing data from 1 or more SNPs were excluded from the analysis. The top two haplotypes, encompassing 80% of the total VISP population were assessed.4 HepG2 cells were transfected in quadruplicate with GNMTΔHighLuc or GNMTΔLowLuc and pGL4.74[hRluc/TK] (Promega) at a 10∶1 ratio using TransIt-LT1 transfection reagent (Mirus biosciences) in 96 well plates. Luciferase assays were conducted following the Dual-glo kit standard protocol (Promega). Luciferase readings were taken using the Beckman Coulter DTX880 luminometer at a 1 second integration time. Firefly luciferase measures from GNMTΔHighLuc or GNMTΔLowLuc were taken for each well, followed by treatment for renilla luciferase activity and renilla measurement. Relative luciferase activity of each promoter was calculated by dividing the average firefly luciferase counts from GNMTΔHighLuc or GNMTΔLowLuc by pGL4.74[hRluc/TK] for each independent condition. L-Methionine treatment used 0.2 mM reagent. Total relative luciferase activity for each plasmid encompasses the average of 3 biological replicates.2×10AGTAGAGAAGTGTTAGTTAGGTTTTAT, Reverse (Biotin labeled): ACCCATACAAAAAAAACAAAAAAAATCTC, Sequencing primer: TTTGGATTAGGTGGATAG.Pyrosequencing was performed as previously described Scores were determined by using imputation dosage measures from VISP and FHS. Alleles were assessed for the average ΔPOST values in FHS and VISP. If a dosage for a homozygous SNP was associated with high homocysteine on average  the number was not changed. However, if the homozygous allele was not associated with the risk variant (i.e. close to 0) but was represented as a number above 1.5, 2 was subtracted from that number and made positive. After correction, homozygous risk variants would have a dosage value near 2, heterozygous variants would have a value near 1, and non risk variants would be assigned a number near 0. All imputation dosage values were summed. The sum of each risk value was then taken for each individual to give a score from 0 to a possible 10, and that score was rounded to the nearest integer.For calculation of the variance explained by the risk score, linear regression was used for VISP, and a linear mixed effects model was used for FHS, as these methods were used in the initial GWAS for each study.All statistics for Figure S1−8. (A) GWAS of the VISP cohort for ΔPOST imputed with 1000 Genomes. (B) GWAS of the FHS cohort for ΔPOST using imputation with 1000 Genomes.Association of single nucleotide polymorphisms (SNPs) with ΔPOST. In Panels A and B, each SNP is represented by a point. The higher the point, the lower the negative -log10 p-value seen on the y-axis and the more significant the association with ΔPOST. Points above the dashed line indicate SNPs with a p-value of less than 5×10(PDF)Click here for additional data file.Figure S2QQ plots of meta-analyzed data of ΔPOST. Minus logarithm to base 10 of the p-values are plotted against the minus logarithm to base 10 of the quantiles of uniform  distribution to compare the p-value distribution with expected uniform distribution with all SNPs with 1 Mb of the top SNP of each associated loci removed. A diagonal line was drawn to show any departure of the p-value distribution from expected uniform distribution. The plotted genomic control parameter (lambda) is the ratio of median chi-squared test statistics to the median of an expected 1 degree-of-freedom chi-squared distribution. (A) All data from meta-analysis. (B) All data excluding meta-analysis significant SNPs and all SNPs within 1MB to rule out LD.(PDF)Click here for additional data file.Figure S3Folate one-carbon metabolism pathway. Diagram shows all genome wide significant genes and their role in FOCM.(PDF)Click here for additional data file.Figure S4http://www.mbio.ncsu.edu/bioedit/bioedit.html) and the pairwise alignment function.Alignment of GNMT promoters from HighDeltaPOST and LowDeltaPOST constructs. Sequences were aligned using Bioedit software ((PDF)Click here for additional data file.Figure S5Genetic risk score in VISP using top genotyped SNPs. (A) Distribution of genetic risk scores in VISP. (B) Risk score vs. ΔPOST in VISP. Error bars represent standard error.(PDF)Click here for additional data file.Table S1Independent GWAS results for ΔPOST phenotype in VISP and FHS.(PDF)Click here for additional data file.Table S2Correlation coefficients of principal components and ΔPOST. As an exploratory analysis, we calculated the Pearson correlation coefficients of ΔPost with all the covariates  or KING (VISP)) and corresponding p-values in FHS and VISP.(PDF)Click here for additional data file.Table S3Test of heterogeneity for VISP-FHS meta-analysis of ΔPOST. We use Cochrane's Q statistic to test heterogeneity between FHS and VISP results. Cochrane's Q statistic is the sum of squared deviations of each study's effect estimate from the overall meta-analyzed effect estimate, weighted by inverse variance of the corresponding estimates.(XLSX)Click here for additional data file.


Aging is a major co-risk factor in many neurodegenerative diseases. Cognitive enrichment positively affects the structural plasticity of the aging brain. In this study, we evaluated effects of a set of structured multimodal activities  on cognitive performances, functional connectivity, and cortical thickness of a group of healthy elderly individuals. CT lasted six months.Neuropsychological and occupational performances were evaluated before and at the end of the training period. fMRI was used to assess effects of training on resting state network (RSN) functional connectivity using Independent Component Analysis (ICA). Effects on cortical thickness were also studied. Finally, we evaluated whether specific dopamine-related genes can affect the response to training.DRD3 ser9gly and COMT Val158Met polymorphisms had the greatest benefits from exposure to CT.Results of the study indicate that CT improves cognitive/occupational performances and reorganizes functional connectivity. Intriguingly, individuals responding to CT showed specific dopamine-related genotypes. Indeed, analysis of dopamine-related genes revealed that carriers of Overall, our findings support the idea that exposure to a set of structured multimodal activities can be an effective strategy to counteract aging-related cognitive decline and also indicate that significant capability of functional and structural changes are maintained in the elderly. End points of the study were: 1) cumulative effects on the activity of different RSNs The study protocol was approved by our institutional Ethics Committee. All procedures were conducted in accordance with the principles expressed in Helsinki Declaration. Thirty elderly adults  were enrolled. All participants underwent medical, psychiatric, and neurological examinations and had neither history nor signs of drug abuse, stroke, hypertension, diabetes, psychiatric or neurodegenerative conditions. All subjects signed an informed consent and were randomized in two groups: training group (7 men and 8 women) and control group (8 men and 7 women). Groups were matched for age and years of education (8–18). All subjects showed non-pathological scores in the Mini Mental State Examination . Subjects were also tested with clock drawing, phonemic verbal fluency (FAS), Trail Making Test (TMT), Frontal Assessment Battery (FAB), and prose memory (Babcock story) tests. Failure in one or more of these tests was a criterion for exclusion from the study. For occupational evaluations we administered the interest checklist OPHI-II and the Occupational Therapy Evaluation (OT-E). OT-E is a simplified version of the Assessment Motor and Process Skills (AMPS) http://faculty.washington.edu/aimgroup/proj/art/artweb/. Group and time were the investigated factors. Statistical significance was set at p<0.05. Statistical analysis was performed using Statistica 6.0  software.Two factor ANOVA followed by Tukey HSD post hoc test was performed using the general linear model (GLM) approach which takes account of unequal sample size of the study groups. First aligned rank transformation (ART) of data was performed using the ARTweb program available at CT was structured with a set of multimodal activities that included: cognitive, aerobic, and sensorial stimuli as well as fun-recreational activities  equipped with an eight channel dedicated head coil, using T2*-weighted echo planar imaging (EPI) free induction decay (FID) sequences applying the following parameters: TE 35 ms, matrix size 64×64, FOV 256 mm, in-plane voxel size 4×4 mm, flip angle 75°, slice thickness 4 mm and no gap. Functional volumes consisted of 30 trans-axial slices, acquired with a volume TR of 1671 ms. High resolution structural images were acquired at the end of four fMRI runs via a 3D MPRAGE sequence with the following parameters: sagittal, matrix 256×256, FOV 256 mm, slice thickness 1 mm, no gap, in-plane voxel size 1 mm×1 mm, flip angle 12°, TR = 9.7 ms and TE = 4 ms. Image data processing was carried out using Brain Voyager QX 1.9 . Functional image time-series were first corrected for differences in slice acquisition time, de-trended, realigned with structural images and warped into standard Talairach anatomical space as previously described For statistical analysis, individually clustered fMRI datasets were divided into four different groups: trained/not trained and T0/T6. For each group, a random effect analysis was performed on RSN single-subject maps to define brain areas consistently found at the group level . A two-factor variance analysis (ANOVA) with repeated measures (scores of the different subjects) was then performed (p<0.05 uncorrected). For ANOVA analysis we considered two parameters: group (control and trained subjects) and time (T0–T6). We therefore assessed: 1) the effect of group regardless of time; 2) the effect of time regardless of group; and 3) the combined effect of time and group (interaction). Furthermore, we carried out an ANOVA post-hoc analysis, by calculating a paired t-test between single-subjects maps for the trained group at T0 and T6 (p<0.04 uncorrected).Additional statistical analyses were performed only for brain regions showing a significant ANOVA interaction ] polymorphisms. Additionally, we performed a statistical descriptive analysis on the same areas, highlighting the average and standard errors of z-score values for each group.Network nodes showing significant modifications of connectivity between trained/untrained and T0/T6 were also evaluated to investigate possible correlations between functional connectivity data and expression of DRD3  polymorphisms.As with fMRI data, brain areas showing significant changes of cortical thickness between trained/not trained and T0/T6 pairs were analyzed to investigate possible correlations between these changes and the expression of the DRD3 [DRD1, DRD2, DRD3, DRD4, and COMT gene polymorphisms were genotyped according to polymerase chain reaction-restriction fragment length polymorphism technique using forward and reverse primers and digestion enzymes as previously described DRD5 promoter dinucleotide was amplified using a hot-start PCR protocol as described by Mill and colleagues All study subjects were informed about the procedure and gave informed consent for the collection of genomic DNA for genetic analysis. DNA was isolated using the Master-AMP Buccal Swab and DNA Extraction Kit (Epicentre Technologies) in accordance with the manufacturer’s protocol. No subjects were excluded because they failed one of the screening tests or did not reach the minimum requirement for compliance. A detailed description of statistical analysis results can be seen in Supporting Information  and S3. We also studied both groups for their performances in prose memory using the prose memory test (Babcock story) We then studied the performance of both groups in their attention skills using TMT Analysis of FAB Finally, we investigated motor and process skills using OT-E FMRI analysis indicated that both groups showed typical DMN areas  as previously reported ANOVA analysis of possible correlations between the investigated genotypes and connectivity findings did not reveal a significant facilitation exerted by specific polymorphisms on effects elicited by CT.Finally, we performed a correlation analysis between neuropsychological/occupational results and fMRI data. For the trained group, we found a significant negative correlation between the strength of functional connectivity of PrC, delayed recall , and OT-E process skills  scores. Negative correlations between rAg and delayed recall  or OT-E process skills  scores as well as between lFEF and TMT/B-A scores  were also found in the study group. Furthermore, significant positive correlations were found between strength of functional connectivity in PCC and global prose memory , immediate recall , delayed recall  scores, and between lFEF and immediate recall scores . In contrast, we did not find significant correlations between neuropsychological/occupational and fMRI data in the control group, thereby lending support to the idea that all the observed changes in the treated group were driven by CT exposure and not by the six month time interval. A detailed description of correlation analysis is shown in Supporting Information, Two subjects from the training group were excluded from analysis due to withdraw prior to the second scanning session. Two time-point longitudinal analysis modelling vertex-wise difference maps with group as two separate planes (two levels of a single discrete factor) with one continuous factors as covariate (age) yielded several areas with statistically significant differences in cortical thickness between the two groups . No effeDRD1–5, have been cloned and characterized so far. Dopaminergic neurotransmission is also regulated by the catechol-O-methyltransferase gene  and by the dopamine transporter gene . As dopaminergic neurotransmission affects cognition and behavior, in this part of the study we tested the hypothesis that specific dopamine-related gene polymorphisms may differently modulate the individual response to CT.Five different dopamine receptors subtypes, To that aim, we investigated DRD1, DRD2, DRD3, DRD4, DRD5, COMT and DAT1. Each participant was screened for the following polymorphisms: DRD1–48 A/G, DRD2 TAQ-IA, DRD3 Ser9Gly, DRD4 521C/T, DRD5 (TC)n, COMT Val158Met and DAT1 40 VNTR in the 3‘ untranslated region .DRD3 and COMT genotypes.When, both in the control and trained group, we matched the results of neuropsychological/occupational tests with the investigated dopamine-related polymorphisms, no differences were found with the exception of performance in OT-E process skills in trained individuals carrying specific DRD3 ser9gly  and COMT Val158Met  variants, we found that, after CT, individuals jointly carrying the DRD3 Ser/Gly and COMT Val/Val-Val/Met genotypes performed better in OT-E process skills  when compared to carriers of the Ser/Ser and Met/Met genotypes  and on the modulation of functional connectivity (DMN and DAN) and that these results translate in an overall better performance in ADL activities Our fMRI results indicate a rearrangement of RSNs that, overall, results in concomitant increase or decrease strength of functional connectivity within different network regions. These modifications can be interpreted as CT-driven mechanisms set in place to modify cognitive and occupational abilities DMN activity is associated with internal mental processes. Several fMRI studies have shown that aging induces a reduction in DMN connectivity Our fMRI results indicate a CT-driven functional reorganization of DMN connectivity at T6, which involves decreased PrC activity and increased PCC activity in the trained group Our fMRI findings are in agreement with the neuropsychological/occupational results which show a better performance in memory tests and OT-E in the trained group, thereby further substantiating the idea of a functional relationship between connectivity changes and increased cognitive functioning. In fact, correlation analysis between BOLD data and neuropsychological/occupational test scores show a significant correlation in which decreased strength of functional connectivity in PrC is matched with better performances in delayed recall and process skills. This correlation suggests that CT rearranges functional connectivity within the DMN and promotes higher levels of cognitive performance. The fact that a positive neuropsychological effect was also linked to lower PrC activity is not completely surprising as increased activation of a specific brain area is not always beneficial [see for instance the unbalanced hyper-activation found in different hippocampal areas in patients affected by schizophrenia, depression or post-traumatic stress disorder (PTSD) DAN connectivity has been associated with voluntary re-orienting of attention and is typically triggered by cognitive tasks Our exploratory results show that CT can affect a positive rearrangement of functional connectivity and higher cognitive functions, but as described in the literature Analysis of cortical thickness changes between the two groups showed modification in regions like the orbitofrontal cortex (OFC), PrC and selected regions of the temporal lobe especially in the Hp which are located within DMN and DAN. With correlation analysis we tested the hypothesis that changes in functional connectivity can be linked to underlying modifications of cortical thickness. However, no significant correlations were found, thereby suggesting that CT promotes functional and structural changes that are not directly related.Recent studies indicate that mental and aerobic exercises can counteract age-related cognitive decline and reduce the risk of dementia DRD3 (Ser9Gly) and COMT  polymorphisms is likely associated with changes in functional connectivity and the capability to maximize positive effects of the training. These results are in line with the fact that the dopaminergic system is a key modulator of cognition and its impairment plays an important role in the development of age-related cognitive decline DRD3 Ser/Gly and the COMT Val/Val, Val/Met genotypes.Evaluation of the influence of dopamine-related genes on the response to CT, with the limitations related to the small size of our sample, show that the joint presence of DRD3 is an inhibitory receptor and considered to be involved in the modulation of dopamine-dependent prefrontal functions Ser9Gly that is associated with lower receptor affinity for dopamine, thereby producing a decreased inhibitory dopaminergic tone. Contrary to what we found in response to CT, where the presence of DRD3 Ser9Gly seems to be advantageous, a large number of studies  have linked the presence of the Gly allele with a high risk for schizophrenia, ADHD, substance abuse as well as the development of attention and working memory deficits Gly allele and improved social interactions has also been suggested COMT is the main mammalian enzyme involved in the extracellular degradation of synaptically released dopamine. The COMT gene has a functional variant in which substitution of valine  for methionine (Met) at codon 158  occurs. The Met allele has been associated with low enzymatic activity, thereby producing higher levels of dopamine in the prefrontal cortex. Conversely, the Val allele is associated with higher enzymatic activity and lower levels of prefrontal dopamine COMT polymorphism Val/Val, analyzed in a cohort of healthy subjects, was found to be more sensitive to stress-related epigenetic changes and associated with more efficient working memory performance COMT Val/Val and Val/Met polymorphisms may be associated with a higher sensitivity to changes that reduce the enzymatic activity, thereby favoring increased dopamine levels in the PCC. Supporting this hypothesis, a recent study COMT Val/Val genotype favourably modulates PCC activity, thereby providing a rationale for our DMN results.Translation of these findings to our results leads to an intriguing hypothesis: the increased cognitive functioning found in individuals carrying the DRD3 and COMT polymorphisms may increase the positive CT effects. A major limitation of this study is the relatively small number of investigated individuals making the genotyping results largely exploratory at this point. These findings need to be validated in a larger study cohort.In conclusion, our findings lend support to the idea that multimodal enrichment can promote morpho-functional changes, enhances cognitive and occupational performances in the elderly. We also show that specific Surprisingly, CT-driven beneficial effects can be seen on a short time scale of six months.The molecular determinants underlying the process are likely to be multiple, but a major role can be speculated for the Brain Derived Neurotrophic Factor (BDNF). In that respect, aerobic exercise has been shown to increase BDNF levels and promote changes in the hippocampus size in elderly individuals Further studies will identify the specific roadmap of all the mechanisms set in motion by multimodal stimulations; however, our findings suggest that engagement in these activities can be a rapidly effective and largely inexpensive strategy to promote neural and cognitive modifications.Table S1fMRI statistical analysis results.(DOC)Click here for additional data file.Table S2Psychometric and Occupational Therapy test results. Psychometric and Occupational Therapy test* results. Data are shown as mean and median for the trained and control groups before (t0) and after (t6) completion of the training.(DOC)Click here for additional data file.Table S3Results of two factor ANOVA followed by Tukey HSD post-hoc test for neuropsychological evaluations. Results of two factor ANOVA followed by Tukey HSD post-hoc test for neuropsychological evaluations. Data are p values. Significant p values (95% confidence level) are indicated in bold. Data showed underlined are p values significant at p<0.1 (90% confidence level).(DOC)Click here for additional data file.Table S4Correlation analysis between neuropsychological and fMRI variables. Correlation analysis between neuropsychological and fMRI variables. Data are correlation coefficients (R) and p values from Spearman correlation analysis. p values lower than 0.05 and the corresponding correlation coefficients are indicated in bold.(DOC)Click here for additional data file.Table S5Correlation analysis between fMRI data and cortical thickness scores. Correlation analysis between fMRI data and cortical thickness scores. Data are shown as correlation coefficients (R) and p values from Spearman correlation analysis.(DOC)Click here for additional data file.Text S1Combination Training: tasks.(DOC)Click here for additional data file.