Arabidopsis root.The integration of stress-dependent, tissue- and cell-specific expression profiles and 5'-regulatory sequence motif analysis defines a common stress transcriptome, identifies major motifs for stress response, and places stress response in the context of tissue and cell lineages in the  Arabidopsis thaliana transcript profiles indicate effects of abiotic and biotic stresses and tissue-specific and cell-specific gene expression. Organizing these datasets could reveal the structure and mechanisms of responses and crosstalk between pathways, and in which cells the plants perceive, signal, respond to, and integrate environmental inputs.Arabidopsis transcript profiles for various treatments, including abiotic, biotic, and chemical stresses. Ubiquitous stress responses in Arabidopsis, similar to those of fungi and animals, employ genes in pathways related to mitogen-activated protein kinases, Snf1-related kinases, vesicle transport, mitochondrial functions, and the transcription machinery. Induced responses to stresses are attributed to genes whose promoters are characterized by a small number of regulatory motifs, although secondary motifs were also apparent. Most genes that are downregulated by stresses exhibited distinct tissue-specific expression patterns and appear to be under developmental regulation. The abscisic acid-dependent transcriptome is delineated in the cluster structure, whereas functions that are dependent on reactive oxygen species are widely distributed, indicating that evolutionary pressures confer distinct responses to different stresses in time and space. Cell lineages in roots express stress-responsive genes at different levels. Intersections of stress-responsive and cell-specific profiles identified cell lineages affected by abiotic stress.We clustered Arabidopsis 5'-regulatory DNA sequences, we confirm known stress-related 5' cis-elements on a genome-wide scale, identify secondary motifs, and place the stress response within the context of tissues and cell lineages in the Arabidopsis root.By analyzing the stress-dependent expression profile, we define a common stress transcriptome that apparently represents universal cell-level stress responses. Combining stress-dependent and tissue-specific and cell-specific expression profiles, and  Arabidopsis thaliana to abiotic or biotic stresses has accumulated during the past decade, based on large-scale mutant analyses and genome-wide transcript profiles. In particular, random mutagenesis combined with cell-specific or treatment-specific reporter gene expression has identified many players in the stress response, whereas microarray-based observations have revealed transcriptional responses to stresses on a genome-wide scale [Arabidopsis Information Resource (TAIR), Genevestigator, and the Nottingham Arabidopsis Stock Centre (NASC), have begun to collect data from various sources and merge them with genome sequence-based features [Knowledge about responses of the model plant de scale -4. Howevfeatures -8; howevfeatures ,10. HereArabidopsis, as in all organisms, a variety of stress factors that disturb homeostatic conditions bring about ubiquitous as well as distinct responses at the transcription level. Identification of ubiquitous, cell autonomous responses is based on monitoring the status of macromolecules in cells, gauging DNA damage, protein degradation, or lipid membrane integrity, and eliciting pathways that carry out repair functions [Arabidopsis genes, have addressed specificity and crosstalk of different stress treatments [In unctions . The degeatments ,3,4.cis-elements, within the promoter sequence of regulated genes. In plants, distinct motifs have been correlated with responses to individual treatments, resulting in discovery of a number of motifs related to stress responses and developmental or organ-specific regulation. Among these motifs, those responding to light and osmotic and cold stress treatments have been analyzed most intensely [Control over gene expression is in part determined by motifs, ntensely ,13. Datantensely ,15. How Arabidopsis to a variety of abiotic and biotic stresses that disturb homeostatic conditions [cis-acting Regulatory DNA Elements (PLACE) database [cis-elements within their promoters. In addition, gene expression profiles identifying cell lineages in Arabidopsis roots were used to correlate the cell type-specific response to various stresses in the root [Here, we applied the fuzzy k-means clustering method  to publinditions . The resdatabase , clusterthe root ,19. InteArabidopsis abiotic and biotic transcriptome is presented in four sections  and on the few large clusters  containing stress-repressed genes. Finally, cell-specific and tissue-specific responses to a variety of stresses were determined by integrating the clusters defining stress specificity with the gene expression map established for the Arabidopsis root [An analysis of the s Figure . First, sis root . This anArabidopsis ATH1 chips, which corresponded to about 22,400 genes. In the following analysis, we treated each probe set as a gene. The external conditions selected included treatments with a variety of biotic and abiotic stresses included in AtGenExpress [3, and cycloheximide. The chemical treatments were included because we expected them to add additional power of resolution to the analysis. Considering the large number of genes to be analyzed, fuzzy k-means clustering was conducted initially with a large centroid parameter (k = 320). Subsequently, 10,490 genes with significant membership values emerged from the dataset, which, with the cutoff set at a membership value of 0.035, most parsimoniously assembled into 180 clusters. The composition of 28 clusters (N0 to N27) is shown in Figure The fuzzy k-means clustering method ,20,21 wanExpress , as outlP < 10-15). Among the 10,490 significant genes included in the clustering analysis, 8,520 were differentially expressed and 1,970 were not significantly regulated. This nonregulated category includes 879 (out of 884) and 119 (out of 131) genes from clusters N6 and N53, respectively. Genes in cluster N6 were not regulated under most conditions, whereas genes in cluster N53 exhibited a very small induction in osmotically-stressed roots only . The separation of clusters N6 and N53 reflects the discriminative power of fuzzy k-means clustering and sensitivity in identifying even minute differences in expression patterns. The remaining nonregulated genes were mainly found in downregulated clusters. In the following analysis of common stress responses and promoter motifs, we focus our attention on the 8,520 differentially expressed genes.The 'limma' statistical program was applied to the Affymetrix dataset to identify differentially regulated genes . Of the The majority of these 8,520 genes was concentrated in a few large clusters. The most highly populated 15 clusters, each including more than 100 genes, totaled 5,478 or more than 60% of all significantly clustered transcripts. The largest clusters, namely N0, N2, N5, N18, included 699, 1,206, 705, and 430 genes, respectively. ABA, which acts as an important signaling molecule under a variety of different stress conditions, was implicated in and induced the expression of genes in clusters N3, N9, N10, N12, N13 and N20, whereas genes in clusters N0, N11, N16, N19 and N28 did not respond to ABA  signaling pathway is responsible for osmotic stress sensing [Arabidopsis AtHK1, MEK1, MPK4, and MPK6 can complement yeast deletion mutants of the HOG pathways. Other examples of plant MAPKs are alfalfa stress-induced MAPK (SIMK), tobacco salicylic acid-induced protein kinase (SIPK), wound-induced protein kinase (WIPK), and Nicotiana Fus-3-like kinase6 (Ntf6).Several MAPK pathways, organized into signaling cascades, are conserved in eukaryotic organism ,24. In S sensing ,26. The MPK5, MKK9, and MAPKKK14. The MAPK pathway has been suggested to be involved in ethylene signaling [ACS6, encoding the rate-limiting enzyme of ethylene biosynthesis and a substrate for MPK6 [Among common genes that are upregulated by stress, several MAPK components were identified: ignaling -29. Inclfor MPK6 , togetheArabidopsis Col-0 wild-type [However, the ethylene response transcriptome is not strictly clustered in the stress transcriptome, notwithstanding its importance in developmental processes such as fruit ripening. Incorporating the results from a study that measured transcript changes in ild-type  into theCIPK11  [SKIP2 (a conserved SCF ubiquitin ligase subunit that interacts with SnRKs), and AZF2 and ZAT10 (C2H2 zinc finger proteins) [AZF2 and ZAT10 suppressed the Snf4 deficiency in yeast and function as transcription repressors in Arabidopsis [RD29A [The yeast Snf1 protein kinase and the mammalian AMP-activated protein kinase act as metabolic sensors that monitor cellular AMP and ATP levels. Activation increases the ATP:AMP ratio. Snf4 is part of the Snf1 protein kinase complex. In higher plants, they are involved in response to environmental or nutritional stress. Related common stress-induced genes were sponses) , SKIP2 (roteins) . Both AZbidopsis ,34. ZAT1s [RD29A . SeveralArabidopsis, Bax over-expression causes ROS accumulation and cell death, and BI-1 attenuates the cell death effect without affecting production of ROS [BI-1 (At5g47120), one of three genes in Arabidopsis with this sequence signature, was induced by several other stresses in a specific manner as well, and appears to represent a signature gene and protein of the common stress response cluster.The Bax-inhibitor 1 (BI-1) is an endoplasmic reticulum (ER) protein that suppresses cell death induced by ER stress in both animal and plant cells. It can inhibit the activation of Bax and its translocation to mitochondria, and suppresses the activation of caspase, and functions in reducing calcium release from the ER . In Arabn of ROS ,38. It an of ROS . BI-1 , and lipase class 3 family proteins. PI4P5K leads to the synthesis of PI4,5P2. Mutations in the offsetting phosphatase gene, pathways ,43. The eostasis .SRO5 is a gene that controls ROS in plants, which is upregulated by various stresses. SRO5 transcript expression overlaps partially with that of P5CDH mRNA. The induction of SRO5 leads to production of a 24-nucleotide nat-siRNA that guides cleavage of P5CDH mRNA, resulting in regulated proline levels [ZAT12, and possibly ZAT10 of the Snf1 pathway, also participate in ROS signaling transduction [ROS have been associated with stress sensing and signaling, but have emerged more recently as important, general signals -47. Irree levels . Additiosduction .Multiple calcium-related functions are induced by stresses. Among them is a SOS2-like protein kinase, namely CIPK11. However, little is known about the other genes in this group, including two calmodulins, three calcium-binding proteins, and three calcium-dependent kinases. These calcium-related genes cannot be organized into a pathway-like structure, in part because of the lack of detailed experimental evidence and also based on the multiplicity of functions that are channeled through calcium-binding proteins.CAF1) are critical for mRNA turnover in yeast [CAF1 and Pcf11 have their Arabidopsis homologs upregulated by different stresses, indicating a role for control over mRNA processing and degradation. Another upregulated gene is the eukaryotic translation initiation factor SUI1. Other examples are AZF2 and ZAT10, which encode transcription repressors.CCR4 and CCR4-associated factor 1 , two DIC1-like, one ANT1-like, one MTM1-like, and one other mitochondrial substrate carrier family protein. Furthermore, a ferrochelatase I gene, an NADH dehydrogenase-related gene, and a PP2C are part of this group. Also upregulated here was the Bax-inhibitor 1 gene. To appreciate their precise functions in plants, more studies are required.RPK1 and CYP707A3. RPK1 encodes a leucine-rich repeat receptor-like kinase 1, a membrane-bound regulator of ABA early signaling [rpk1 mutant exhibited decreased sensitivity to ABA, and over-expression resulted in hypersensitivity. CYP707A3 encodes a cytochrome P450 protein catalyzing ABA 8'-hydroxylation and catabolism. Its knockout mutant exhibited exaggerated ABA-inducible gene expression and enhanced drought tolerance, whereas over-expression was associated with growth retardation by ABA and increased transpiration [Among the common stress response genes were two ABA-related genes, ignaling . The rpkpiration .ADC2 led to GA-deficient plants and accumulation of putrescine, a phenotype reversed by GA3 [adc2-1 was sensitive to salt stress, but could be rescued by external putrescine [ADC2 is among the common stress response genes.ADC genes are essential for polyamine (PA) production. Over-expression of d by GA3 . The nultrescine . ADC2 isRelA and SpoT encode bacterial enzymes for ppGpp synthesis. RSH is the higher plant homolog of this RelA/SpoT protein [The stringent response is crucial for stress adaptation in bacteria, mediated by the production of the nucleotide guanosine-3',5'-(bis-)pyrophosphate (ppGpp).  protein ,61.Pseudomonas syringae pv. tomato DC3000 carrying avirulence proteins [NHL3 over-expression in Arabidopsis enhances resistance to the virulent Pseudomonas syringae pv. tomato DC3000, without an increase in PR gene expression or H2O2 accumulation [PBS1 and RPS5 are required for avrPphB mediated Pseudomonas syringae resistance in Arabidopsis. AvrPphB can proteolytically cleave PBS1, which is required for RPS5-mediated resistance [These genes function in resistance to the bacterial pathogen proteins ,62,63, amulation . PBS1 ansistance . PUB17 isistance . Anothersistance . The inc3BO3, H2O2, AgNO3, hypoxia, and triiodobenzoic acid . In part, their functions are known by specific activities , whereas most are identified only by domain identifiers , or their functions are not clear or completely unknown. The group included transcripts for 19 zinc-finger family proteins, five protein kinases, four protein phosphatases, a number of glycosyl hydrolases, thioredoxins, cytochromes P450, and hormone-responsive functions, mostly annotated according to similarity criteria, and 40 expressed proteins without any annotation. Among the genes that lack annotation, the majority is most strongly induced by conditions that affect redox homeostasis and ROS responses, in particular treatments with ozone, H dataset ).NHL3, and PUB17. Indeed, the Arabidopsis mutant bos1 exhibited compromised resistance to the pathogen Botrytis cinerea and reduced tolerance to drought, high salinity, and oxidative stress [The high correlation of genes in cluster N12 with experimentally verified or alleged functions in a wide variety of stress conditions in species across all kingdoms suggests that the functions identified by this cluster categorize the basic stress response transcriptome Figure . By theie stress .Other clusters Figure ; ST1 sepcis-elements, which are conserved motifs in the 5'-region of genes with a key role in assembling the transcription machinery. Extracted from the genome sequence were 1,000 base pairs upstream of the translation initiation codon, and genes in each cluster were scanned for motifs listed in the PLACE database [P value was then calculated for every motif and cluster combination, based on the hypergeometric distribution [P values lower than 10-4 to be significantly over-represented. Listed in Table We analyzed database . The occribution . We consThe WB-BOX motif TTTGACT was identified in clusters N0, N11, and N19. Genes in clusters N0 and N19 were generally induced by abiotic stresses, whereas genes in cluster N11 were upregulated markedly in roots by salt treatment. The WB-BOX represents a binding site for WRKY transcription factors , which hGenes in clusters N1 and N8 were responsive to light treatment. The ABRE motif ACGTG(G|T)C was identified in both clusters, together with an unknown motif, namely (A|G)ACCACA(A|G). ACGTG(G|T)C is similar to the G-Box motif that mediates light signaling . Also idClusters N3, N9, N10, N12, N13, and N20 were induced by ABA treatment to variable degrees. The ABRE motif was over-represented in these clusters, but only cluster N3 contained the ABRE-binding proteins AREB1 and AREB2 . Furthercis-acting motifs are suggested by the analysis. More likely, however, is the presence of transcriptional control mechanisms that act on cis-element binding proteins rather than on the promoter elements.Over-representation of the well known ABRE motif in multiple clusters of genes that respond to either light or ABA treatment points toward the existence of additional motifs . These cMotifs of prevalence similar to those in upregulated genes appear to be largely absent from the stress-repressed genes in clusters N4, N7, and N17. For cluster N15 genes, strongly downregulated by osmotic and high salinity stresses in roots, the MYCATERD1 motif (CATGTG) and the SORLRP3AT motif (TGTATATAT) were identified. Clusters N2, N5, N14, and N18 included many genes related to general gene expression functions, protein synthesis, cell organization, and metabolism. Several known motifs were enriched in these clusters. The UP1ATMSD motif GGCCCA(A|T)(A|T)(A|T), which is related to axillary bud growth , was oveIn general, fuzzy k-means clustering applied to 5'-regulatory sequences confirmed known motifs in the major stress-responding clusters, whereas different clusters shared subsets of these motifs. Additional, secondary motifs between and within large clusters are suggested Figure , Table 2Arabidopsis roots was recorded [Very few data are available to date that correlate stress-related transcript changes and cell-specific or tissue-specific gene expression. We focused on the tissue-specific response to stress in detail by including a dataset in which cell type-specific and growth stage-specific gene expression in recorded . Among tArabidopsis root. The nature of the genes at the intersection between cell specificity/development in roots and stresses  and T7 (stages 1 and 2). Cluster N18 includes genes that mainly function in protein synthesis: more than 190 ribosomal proteins, 5 tRNA synthetases, 13 translation initiation/elongation proteins, 10 chaperonin proteins, and a few genes related to lysine or arginine synthesis. Also included in N18 were DNA replication licensing factors, nucleosome assembly proteins, histones H2A and H3, small nuclear ribonucleoproteins, and signaling G proteins. Several GO categories are enriched in cluster N18: GO:0042254 (ribosome biogenesis and assembly), GO:0043037 (translation), GO:0015450 (protein translocase activity), GO:0046112 (nucleobase biosynthesis), GO:0006333 (chromatin assembly), and GO:0006525 (arginine metabolism). In contrast, cluster N14 collected genes mainly related to transcription, such as 12 DEAD/DEAH box helicases and 11 polymerases (or similar to polymerase functions), with a few genes involved in protein synthesis functions. Several GO categories were over-represented in N14: GO:0016072 (rRNA metabolism), GO:0003899 (DNA-directed RNA polymerase activity), GO:0004527 (exonuclease activity), GO:0008026 (ATP-dependent helicase activity), GO:0042254 (ribosome biogenesis and assembly), and GO:0006396 (RNA processing). Cluster N14 was different from N18 in that N14 was slightly induced by cold stress but N18 was not. The expression of genes in these two clusters can most parsimoniously be rationalized by developmental regulation.Second, the stress clusters N4 and N7 contained genes that were placed mainly in the tissue clusters T4, T8, and T14, all of which represent stage 3 specific genes. Interestingly, among the downregulated genes were those related to cell wall modification that are specifically expressed in the stele (T4 and T14), such as expansins, extensins, (putative) cellulases, pectinesterases, and peroxidases. Enriched in these two clusters were GO:0006979 (response to oxidative stress), GO:0007047 , GO:0009653 (morphogenesis), GO:0042545 , GO:0010054 (trichoblast differentiation), and GO:0005516 . Together, these clusters appear to identify the portion of the transcriptome that controls root maturation, which is downregulated under stress treatments of the root system.Finally, genes in the stress clusters N2 and N5 represented a combination of the previously discussed patterns. These clusters included genes regulated developmentally , and genes downregulated by stress signaling . Over-represented in cluster N2 were genes involved in metabolism, which included amino acid, cell wall, carbohydrate, lipid, nucleotide, and secondary metabolism biosynthetic functions. The following GO categories were over-represented: GO:0043038 (amino acid activation), GO:0009658 (chloroplast organization and biogenesis), GO:0006779 (porphyrin biosynthesis), GO:0019321 (pentose metabolism), GO:0004312 (fatty-acid synthase activity), GO:0006769 (nicotinamide metabolism), GO:0005528 (FK506 binding), GO:0016117 (carotenoid biosynthesis), GO:0015994 (chlorophyll metabolism), and GO:0009606 (tropism). Genes related to DNA synthesis, chromatin structure, cell cycle, and cell division were abundant in downregulated cluster N5; the following were over-represented: GO:0006260 (DNA replication), GO:0007049 (cell cycle), GO:0000910 (cytokinesis), GO:0007010 (cytoskeleton organization and biogenesis), and GO:0016071 (mRNA metabolism). Cluster N5 also includes some genes related to metabolic processes, as indicated the over-represented GO categories GO:0009853 (photorespiration), GO:0019758 (glycosinolate biosynthesis), GO:0044272 (sulfur compound biosynthesis), and GO:0009067 (aspartate family amino acid biosynthesis).In essence, the genes downregulated by different stresses are expressed under ideal growth conditions close to the root meristem and in the region of strongest cell expansion. Furthermore, genes related to the functional categories of mRNA and protein synthesis, cell cycle control, and primary metabolism categories were separated into differentially repressed clusters. This indicated active regulatory processes, other than a passive repression brought about merely by a general stressed physiologic state.A significant difference emerged when genes in stress upregulated clusters were viewed in their tissue-specific or cell-specific context. In the majority, these genes exhibited a high expression level at stage 3  or high expression in root cap cells (T1 and T9). Because genes in these tissue clusters appeared with insignificant membership values only in the repressed clusters N14 and N18, we consider them representative of first responders to stress signaling. It appeared significant that these genes were upregulated in cells in the more mature region of the root, coinciding with the region of beginning root hair development.Merging stress and tissue/cell specificity, a framework became recognizable. Genes in tissue clusters T3 and T7 were significantly downregulated during abiotic stress, and genes in tissue clusters T0, T1, T2, and T4 were either upregulated or downregulated under different stress conditions. In contrast, genes in tissue clusters T5 and T18 were mainly upregulated upon stress. Of particular importance may be the behavior of the genes in clusters T5 and T18. T5 genes were specifically expressed in endodermis cells in stage 3, whereas T18 genes exhibited high expression level in lateral root cap cells.Focusing on abiotic stresses alone , the 12,360 probes present in roots were analyzed by fuzzy k-means clustering. The analysis of this smaller set of treatments separated the genes into 66 clusters . Intersections of stress specificity and spatial or temporal expression characteristics are illustrated by two examples.Figure cis-elements in promoters, and cell-specific and developmental age-specific root transcripts and their reaction to stress in the model crucifer Arabidopsis thaliana. A recent analysis used the AtGenExpress dataset by focusing on responses under nine experimental conditions and identified similarities between conditions [Integration of diverse, large-scale datasets into a framework that then describes and explains the functioning of an organism remains an elusive goal of genomics-type approaches. Combining three types of data, we analyzed in context the genome-wide expression profiles modulated by a number of stress conditions, regulatory nditions , whereasnditions  generatenditions ,4,20,79.Arabidopsis list. Many of the genes in our analysis encode stress-responsive functions in animals and yeasts, such as the Snf1 kinase-related, phoshoinositol-related, and Bax-inhibitor related pathways. They may represent the evolutionarily conserved cellular stress response, originating from damage recognition of, for example, lipid membranes, proteins, or DNA, and mediated by signals related to calcium and ROS [Detailed analysis focused on cluster N12, which included genes responsive to most environmental perturbations. This type of analysis is similar to that in a study that identified cellular stress response genes in yeast from global transcript profiles of stress responses . In term and ROS ,81. In p and ROS  and are Hypothetically, ROS  or ethylcis-acting sequences with particular stress conditions, biotic and abiotic alike, was confirmed [The correlation of a number of previously studied 5'-regulatory, onfirmed ,13,70, aArabidopsis root. In particular, the downregulation of genes during osmotic challenge and, although moderately, by ABA in roots identified genes that, under nonstress conditions, are highly expressed in cells of the vascular tissue and in the mature root  treatments, the CEL files of the Affymetrix ATH1 microarray data were downloaded from TAIR [Transcript profiles that reflect responses of el World , which hel World . For biorom TAIR , and prorom TAIR . For eacArabidopsis roots have previously been described [Arabidopsis Gene Expression Database [The microarray data pertaining to cell type-specific and growth stage-specific gene expression in escribed . The CELDatabase  and procDatabase . The remThe analysis of stress, hormone, chemical, and light treatments was similar to the procedure described previously . The logTo reveal the cell type-specific and stage-specific gene expression patterns, the relative expression value for each probe in each cell type or stage was calculated, by subtracting the probe's average intensity across the cell type samples or the stage samples from its intensity in that cell type or stage samples . The relative expression values were assembled and analyzed by fuzzy k-means, with the parameter k set at 30. Nineteen well defined clusters were recovered, and no cutoff values were applied , and transcription data in different cell lineages and developmental stages of the root.Click here for fileStress datasets used for Fuzzy K-means analysis.Click here for fileClustering results of all stress datasets.Click here for fileComparison between clusters N6 and N53 (legend as in Figure Click here for fileAll genes in the common stresses response cluster N12.Click here for fileOriginal data for gene expression in roots.Click here for fileProcessed root dataset used for fuzzy k-means analysis.Click here for fileClustering results for the root dataset.Click here for fileIntersection between stress clustering and root clustering.Click here for fileClustering results for abiotic stresses in roots.Click here for file


Protease-activated receptors (PAR) are present in the urinary bladder, and their expression is altered in response to inflammation. PARs are a unique class of G protein-coupled that carry their own ligands, which remain cryptic until unmasked by proteolytic cleavage. Although the canonical signal transduction pathway downstream of PAR activation and coupling with various G proteins is known and leads to the rapid transcription of genes involved in inflammation, the effect of PAR activation on the downstream transcriptome is unknown.E. coli lipopolysaccharide (LPS), substance P (SP), and antigen was strongly attenuated by PAR1- and to a lesser extent by PAR2-deficiency.We have shown that intravesical administration of PAR-activating peptides leads to an inflammatory reaction characterized by edema and granulocyte infiltration. Moreover, the inflammatory response to intravesical instillation of known pro-inflammatory stimuli such as -/- mice induced by classical pro-inflammatory stimuli . 75 transcripts were considered to be dependent on PAR-1 activation and further annotated in silico by Ingenuity Pathways Analysis (IPA) and gene ontology (GO). Selected transcripts were target validated by quantitative PCR (Q-PCR). Among PAR1-dependent transcripts, the following have been implicated in the inflammatory process: b2m, ccl7, cd200, cd63, cdbpd, cfl1, dusp1, fkbp1a, fth1, hspb1, marcksl1, mmp2, myo5a, nfkbia, pax1, plaur, ppia, ptpn1, ptprcap, s100a10, sim2, and tnfaip2. However, a balanced response to signals of injury requires a transient cellular activation of a panel of genes together with inhibitory systems that temper the overwhelming inflammation. In this context, the activation of genes such as dusp1 and nfkbia seems to counter-balance the inflammatory response to PAR activation by limiting prolonged activation of p38 MAPK and increased cytokine production. In contrast, transcripts such as arf6 and dcnt1 that are involved in the mechanism of PAR re-sensitization would tend to perpetuate the inflammatory reaction in response to common pro-inflammatory stimuli.Here, cDNA array experiments determined inflammatory genes whose expression is dependent on PAR1 activation. For this purpose, we compared the alteration in gene expression in wild type and PAR1The combination of cDNA array results and genomic networks reveals an overriding participation of PAR1 in bladder inflammation, provides a working model for the involvement of downstream signaling, and evokes testable hypotheses regarding the transcriptome downstream of PAR1 activation.It remains to be determined whether or not mechanisms targeting PAR1 gene silencing or PAR1 blockade will ameliorate the clinical manifestation of cystitis. In general, inflammation plays a role in most bladder pathologies, including bladder cancer -4, and rIn order to better understand the role of PARs in cystitis, we used a well established mouse model -22 to deFour PARs have been cloned to date, and all four PARs are co-expressed in the mouse bladder urothelium , with PAIn addition, PARs are differentially modulated during mouse bladder inflammation. Urothelial PAR2 and, to a lesser extent, PAR1 are down-regulated in acute inflammation, whereas PAR3 and PAR4 are up-regulated . BladderThe signal transduction pathway downstream of PAR activation and coupling with various G proteins is known and leads to the rapid transcription of genes involved in inflammation ,26. Howein silico genomics network analysis reveals an overriding participation of PAR1 receptors in bladder inflammation, provides a working model for the involvement of downstream transcripts, and evokes testable hypotheses regarding the transcriptome downstream of PAR1 activation.The combination of cDNA arrays and -/- mice, and were considered to be PAR1-dependent  were associated with function (magenta) and localized to each compartment according to IPA and gene ontology. Dotted lines further group the genes by activity or function such as signal transduction, cytoskeleton re-organization and cell motility, carbohydrate metabolism, proteins and enzymes, transcription, and RNA editing. Of note, a particular set of genes such as elk1, nfkbia, hspb1, dusp1, and akt2. In addition, this analysis indicates pathways such as VEGF and integrins which share common genes, as is the case of those encoding cytoskeleton proteins actg1 and actb.Overall, PAR1-dependent transcripts belong to several canonical pathways Figure . This tyupk2, jundD, ptprc, and nfkbp1a) encoding proteins associated with renal and urological diseases. Figure Figures adam-3, dctn1, elk1, and mmp2) had their control levels 1.5 times below background (un-transcribed region) and, therefore, their results are not being presented. Results are presented in figure pla2G1b, these results indicate that treatment of wild type mice with PAR1- and PAR2-AP induced up-regulation of the following PAR1-dependent genes: actb, akt2, arf6, ccl7, cd63, dusp1, fkbp1a, nfkbia, phlda1, plaur, s100a10, tnfaip3, ube2h, and upk2.Of the 19 genes tested by Q-PCR analysis of CHIP isolated from wild type mice challenged with control peptide, PAR1- and PAR2-AP, 4 genes  in mediating tryptase-activated PAR in bladder microvascular endothelial cells  phosphatase 1, which dephosphorylates and inactivates MAPKs dATP, according to the manufacturer's protocol . The radioactively labeled complex cDNA probes were hybridized overnight to Atlas™ mouse plastic 5 K arrays  using ExpressHyb™ hybridization solution with continuous agitation at 68 °C. After two high-stringency washes, the hybridized membranes were exposed (at room temperature) to a ST Cyclone phosphor screen overnight. Spots on the arrays were quantified by BD AtlasImage™ 2.7 software .cDNA probes were prepared from DNase-treated RNAs obtained from each of the experimental groups. Five μg of DNase-treated RNA was reverse-transcribed to cDNA and labeled with ,  is a robust and expertly curated database containing up-to-date information on over 20,000 mammalian genes and proteins, 1.4 million biological interactions, and one hundred canonical pathways incorporating over 6,000 discreet gene concepts. This information is integrated with other relevant databases such as EntrezGene and Gene Ontology . IPA comce alone . The expTarget validation was sought for 19 of the genes identified from the microarray data as being PAR1-dependent. Female C57BL/6J mice (n = 20 per group) were anesthetized , then transurethrally catheterized , and the urine was drained by applying slight digital pressure to the lower abdomen. The urinary bladders were instilled with 200 μl of one of the following substances: control inactive peptide (LRGILS ) or PAR-Frozen bladders were shipped to Genpathway  for querQ-PCRs were run in triplicate and the averaged Ct values were transferred into copy numbers of DNA using a standard curve of genomic DNA with known copy numbers. The resulting transcription values for each gene were also normalized for primer pair amplification efficiency using the Q-PCR values obtained with Input DNA (unprecipitated genomic DNA). Results are presented as Transcription Events Detected Per 1000 Cells for each gene tested.Twenty-four hours post-instillation, bladders were rapidly removed and placed in ice cold PBS with protease inhibitors  on ice where the mucosa was dissected away from the detrusor smooth muscle, as described . Tissues2  at 1:500 overnight at 4°C. An HRP-conjugated anti-rabbit secondary antibody was used for detection at 1:10,000  and an enhanced chemiluminescent detection kit  was used to visualize. Images were taken using the FluorChem HD digital darkroom  and quantified using ImageJ [Immunoblot analyses were performed using 15 μg cytosolic extract loaded onto a 10% tris-glycine SDS gel and run at 125 V for 1.5 hours in tris-glycine SDS running buffer . The proteins were then transferred to a 0.45 μm nitrocellulose membrane in 1/2 × tris-glycine SDS running buffer with 20% methanol using a BioRad Mini-TransBlot Cell. After blocking in 2% BSA, blots were incubated with rabbit anti-iPLAg ImageJ .PAR-AP – PAR1-, PAR2-, and PAR4-AP were synthesized at Molecular Biology Resource Facility, William K. Warren Medical Research Institute, OUHSC, as carboxyl-terminal amides, purified by high-pressure liquid chromatography, and characterized by mass spectroscopy. Peptide solutions were made fresh from powder for most experiments. PAR3-AP was not used in this research because of lack of specificity.The author(s) declare that they have no competing interests.RS conceived of the study and drafted the manuscript, MRD participated in the design and reviewed the morphological results, PAG participated in the experimental design and provided PAR1-/- and PAR2-/- mice, CKD participated in design and proper use of PAR-APS, ID performed the statistical analysis of microarray results, MI participated in its design and helped to draft the manuscript, REH participated in its design and helped to draft the manuscript, CS performed the western blotting analysis and helped MRS with animal experiments, and MRS participated in its design, carried out the animal experiments, removed the tissues, and performed gene array experiments.All authors read and approved the final manuscript. PAR1-Dependent Transcripts. Table 1Click here for filePrimers for Q-PCR. Table 2Click here for file


The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties.To determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features.The SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function. The high-throughput genome projects have resulted in a rapid accumulation of predicted protein sequences for a large number of organisms. Researchers have begun to systematically tackle protein functions and complex regulatory processes by studying organisms on a global scale, from genomes and proteomes to metabolomes and interactomes. Meanwhile, structural genomics projects have generated a growing number of protein structures of unknown function. To fully realize the value of these high-throughput data requires better understanding of protein function. With experimentally-verified information on protein function lagging behind, computational methods are needed for functional prediction of proteins. In particular, knowledge of the location of catalytic residues provides valuable insight into the mechanisms of enzyme-catalyzed reactions.Many computational methods have been developed for predicting protein functions and functional residues involved in catalytic reactions, binding activities, and protein-protein interactions. Automated propagation of functional annotation from a protein with known function to homologous proteins is a well-established method for the assignment of protein function. However, reliable functional propagation generally requires a high degree of sequence similarity. For example, to transfer all four digits of an EC number at an error rate of below 10% needs at least 60% sequence identity .1L]; in another study  were compared using their default parameters and a benchmarking dataset of 79 enzymes with 254 catalytic residues. The performance of the algorithms was measured by the Matthews correlation coefficients (MCC) in a 10-fold cross-validation analysis using three balanced datasets generated from the benchmarking data, each with an equal number of non-catalytic residues randomly chosen from all non-catalytic residues of the benchmarking dataset. Each residue was represented by a set of 24 sequence and structural attributes and a label of {+1/-1} to indicate whether the residue is catalytic (+1) or not (-1).To determine the best machine learning algorithm for the predictive task, 26 classifiers currently available in the WEKA software package  werSequential Minimal Optimization (SMO) algorithm , which is a Support Vector Machine (SVM) , which consisted of 615 manually-curated catalytic residues from 178 enzymes [4L] was used as the benchmarking data, which included 79 enzymes and 254 catalytic residues. Protein members in PIRSF families are homologous (sharing common ancestry) and homeomorphic (sharing full-length sequence similarity with common domain architecture).The benchmarking dataset was compiled from the CATRES  database  was5L], enzyme classification (EC number) [6L], and BLAST sequence similarity , enmilarity . The folThe 79 proteins (identified by the PDB code) were: 1a26, 1a4i, 1a4s, 1ab8, 1ae7, 1afw, 1ah7, 1akm, 1aop, 1apx, 1apy, 1aq2, 1aw8, lb3r, lb57, lb93, 1bo1, 1brm, 1bs4, 1btl, 1bzy, 1cd5, 1chd, 1ctt, 1d4a, 1daa, 1dae, 1db3, 1dbt, 1dco, 1diz, 1dj0, 1dnk, 1dnp, 1dqs, 1dzr, 1e2a, 1ef8, 1eyi, 1fua, 1gim, 1gpm, 1gpr, 1grc, 1hxq, 1iph, 1jdw, 1kas, 1kra, 1lba, 11xa, 1mbb, 1mek, 1mla, 1moq, 1mpy, 1nba, 1nsp, 1pfk, 1pjb, 1pnl, 1pud, 1qfe, 1smn, 1uae, 1ula, 1uok, 1uox, 1wgi, 1xva, 2acy, 2alr, 2bbk, 2cpo, 2hgs, 2jcw, 2pfl, 2plc, 3eca.For the initial analysis, each residue of the benchmarking dataset was represented as a vector with 24 residue property values and a label {+1/-1} to indicate the catalytic (+1) and non-catalytic (-1) residue. The list of properties was chosen based mostly on the work of Bartlett et al.  and otheAmino acids have different propensities to be catalytic . These pAA_identity: A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y- AA_type: The amino acids are grouped based on their chemico-physical properties into three types: charged , polar , and hydrophobic   for biologically active chain from PDB. Since atoms of the same residue can be in different clefts, the cleft number is the largest cleft for a given residue. CASTp numeration of the clefts starts with the smallest one first, so we reversed the numbering so that the largest cleft of the protein would be the first cleft of the protein. If a residue was not part of any cleft, the cleft number was assigned zero. The attributes include:Enzyme active sites are usually located in large and deep protein clefts . It was nearest_cleft_rank, nearest_cleft_SA_volume, nearest_cleft_SA_area: the three attributes for rank, solvent accessible volume and solvent accessible area are obtained directly from the output of the CASTp server.- nearest_cleft_distance: The distance to the nearest cleft was calculated as a minimal distance between any atom (except hydrogen) of the residue and any atom (except hydrogen) of the residues of the closest cleft. If a residue was in a cleft, the distance was assigned zero.- distance_to_3_largest_clefts was calculated as a minimal distance between any atom (except hydrogens) of the residue and any atom (except hydrogens) of the residues of the 3 largest clefts. If a residue was a part of the 3 largest clefts, the distance was assigned zero.- The majority of catalytic residues participate in hydrogen bonding through their main chain . Two attHB_main_chain_protein, HB_side_chain_protein: number of hydrogen bonds of the residue atoms from the main chain or side chain, respectively, with any other atom in the protein.- It was observed that about half of catalytic residues are localized in the coiled regions of the protein . The att2D_structure: the 2D structure of individual residues was based on the DSSP program, which assigns a single letter code  to represent different 2D structure types  with an open source issued under the GNU General Public License. The package provides a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from the user's own Java code. WEKA contains tools for data pre-processing, classification, regression, clustering, association rules and visualization, and is well suited for developing new machine learning schemes. In this study, all algorithms were trained using WEKA's default settings, except in the IBK algorithm where the parameter K was chosen to be 13 to maximize the algorithm's performance.The selections of the best-performing algorithm and an optimal set of properties for the selected algorithm were performed using WEKA . WEKA is a JAVA software package from the University of Waikato, New Zealand  witThe WEKA's implementation of SMO converts all nominal attributes into binary ones and normalizes all attributes by default. We used the default polynomial kernel function for the analysis with default parameters, such as the complexity parameter C = 1.0, exponent = 1.0.The performance of each algorithm was measured as an average value in a 10-fold cross-validation analysis, where each dataset was divided into 10 parts – 9 parts for model learning (training) and the remaining part for validation (testing). Four performance measures were used: Matthews Correlation Coefficient (MCC) , true poWhere Tp, Fp, Tn, Fn, P, N, The FP rate and TP rate can be used for comparison of the results with different positive-to-negative ratios, whereas accuracy and MCC are sensitive to dataset imbalance.2D, secondary; Å, angstrom; AA, amino acid; ABS, ABSolute; ADTree, Alternating Decision Tree; CASTp, Computed Atlas of Surface Topography of proteins; CDD, Conserved Domain Database; DSSP, Database of Secondary Structure in Proteins; EC, Enzyme Commission; ET, Evolutionary Trace; Fn (FN), False negatives; Fp (FP), False positives; HB, Hydrogen Bond; IB1, nearest-neighbor classifier; IBK, K-nearest neighbors classifier; J48, a pruned C4.5 decision tree; Jrip, a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER); LMT, Logistic Model Trees; LWL, Locally Weighted Learning algorithm; MCC, Matthews Correlation Coefficient; MolMol, MOLecule analysis and MOLecule display; NBTree, decision Tree with Naive Bayes classifiers at the leaves; NN, neural network; NNge, Nearest-Neighbor-like algorithm using non-nested generalized exemplars; OneR, One-Rule classifier; PART, a partial C4.5 decision tree algorithm; PDB, Protein Data Bank; PIR, Protein Information Resources Database; RBFNetwork, a normalized Gaussian radial basis function network; REL, relevant; REPTree, a decision tree learner; Ridor, RIpple-DOwn Rule learner; SA, solvent accessible; SAS, Solvent Accessible Surface; Scorecons, Score Conservation; SMO, Sequential Minimal Optimization; SVM, Support Vector Machine; Tn (TN), True negative; Tp (TP), True positive; WEKA, Waikato Environment for Knowledge Analysis.NP designed the analysis, developed the source code, conducted the study, and wrote the manuscript. CW coordinated the study, helped drafting the manuscript, and critically revised its content. All authors read and approve of the final manuscript.The Protein Data Bank (PDB)1L. Catalytic Residue Dataset database (CATRES)2L. Waikato Environment for Knowledge Analysis (WEKA)3L. PIRSF protein family database4L. Structural Classification of Proteins (SCOP)5L. Enzyme Nomenclature (EC)6L. The Scorecons server7L. The CASTp server, Computed Atlas of Surface Topography of proteins8L.


Volatile organic compounds (VOCs) are present in much higher concentrationsindoors, where people spend most of their time, than outdoors andmay have adverse health effects. VOCs have been associated with respiratorysymptoms, but few studies address objective respiratory end pointssuch as pulmonary function. Blood levels of VOCs may be more indicativeof personal exposures than are air concentrations; no studies haveaddressed their relationship with respiratory outcomes.We examined whether concentrations of 11 VOCs that were commonly identifiedin blood from a sample of the U.S. population were associated withpulmonary function.We used data from 953 adult participants (20–59 years of age) inthe Third National Health and Nutrition Examination Survey (1988–1994) whohad VOC blood measures as well as pulmonary function measures. Linearregression models were used to evaluate the relationshipbetween 11 VOCs and measures of pulmonary function.After adjustment for smoking, only 1,4-dichlorobenzene  was associatedwith reduced pulmonary function. Participants in the highestdecile of 1,4-DCB concentration had decrements of −153 mL  inforced expiratory volume in 1 sec and −346 mL/sec  in maximum mid-expiratoryflow rate, compared with participants in the lowest decile.Exposure to 1,4-DCB, a VOC related to the use of air fresheners, toiletbowl deodorants, and mothballs, at levels found in the U.S. general population, mayresult in reduced pulmonary function. This common exposuremay have long-term adverse effects on respiratory health. Volatile organic compounds (VOCs) are a diverse group of chemicals emittedas gases from a variety of commonly used products. The general populationis exposed to VOCs from cleaning and degreasing agents, pesticides, airfresheners, toilet bowl deodorants, furniture, tobacco smoke, andbuilding materials such as pressed wood products, adhesives, carpeting, paints, andvarnishes. Although VOCs are also released into theoutdoor air through automotive exhaust and industrial emissions, indoorVOC concentrations are much higher , 1991.Because people spend most of their time indoors, health effects relatedto VOCs in the residential setting are a concern, particularly with respectto respiratory illness . SeveralA variety of VOCs were measured in a subset of participants in the ThirdNational Health and Nutrition Examination Survey (1988–1994) (NHANESIII) to determine background exposure levels for adults in thegeneral U.S. population . BecauseWe used data from NHANES III and its component Priority Toxicant ReferenceRange Study, designed to assess the levels of common pesticides andVOCs in a representative sample of the U.S. adult population. The studieswere conducted from 1988 through 1994. Detailed information aboutNHANES III and the Priority Toxicant Reference Range Study may be foundelsewhere , 2000. BIn NHANES III, spirometry was conducted according to the 1987 AmericanThoracic Society recommendations . The Nat1; milliliters), forced vital capacity , peak expiratoryflow rate , and maximum mid-expiratoryflow rate . We adjusted all models forrace/ethnicity group , age (continuous), age squared (continuous), standingheight (continuous), body mass index (continuous), and sex, toaccount for differences in pulmonary function based on these characteristics.Our analyses included forced expiratory volume at 1 sec , becauseresults for some VOCs were not available for allparticipants , numberof cigarettes smoked per day (continuous), years smoked (continuous), andserum cotinine level (continuous). Smoking was a confounderfor most VOCs.We then used a change-in-estimate method to evaluate additional variablesas confounders for the VOCs still related to pulmonary function afteradjustment for smoking . Our cutCharacteristics of the study population are shown in p < 0.05), except for 1,1,1-TCE, 1,4-DCB, and tetrachloroethene (TCE). Ashas been reported previously for this population  to −1 mL (p = 0.95) for benzene, from −51 mL (p = 0.03) to 15 mL (p = 0.57) for ethylbenzene, from −61 mL (p = 0.01) to 42 mL (p = 0.19) for styrene, and from −69 mL (p < 0.01) to 16 mL (p = 0.60) for toluene, whereas the β-coefficient for 1,4-DCBremained unchanged .In the models unadjusted for smoking, reductions in at least one pulmonaryfunction outcome were statistically significant for 1,4-DCB, benzene, ethylbenzene, styrene, and toluene (data not shown). However, whenthese models were adjusted for smoking variables, only 1,4-DCB remainedstatistically significantly associated with reduced pulmonary function. Forexample, after adjustment for smoking, VOC β-coefficientsfor FEVp < 0.0001), with African Americans having the highest exposures  and African-American males , although we did not find significant evidence of effectmodification by race/sex combinations .Among all participants, 1,4-DCB was inversely related to all four pulmonaryfunction measures but was statistically significant only for FEVnd MMEFR . Power in = 299; data not shown).Higher levels of 1,4-DCB were related to reduced pulmonary function innever-smokers as well as smokers . Results1 was more statistically precise. For example, the expected change in FEV1 with each increase in exposure from the 10th to 90th percentile  was −96 mL (p = 0.03) for 1,4-DCB and −134 mL (p = 0.02) for 2,5-DCP.To further examine the relationship between 1,4-DCB and pulmonary function, weconducted additional analyses using urinary concentrations of 2,5-dichlorophenol , the major metabolite of 1,4-DCB . 2,5-DCP1 (milliliters) and MMEFR (milliliters per second) for each decile of 1,4-DCBexposure, compared with participants in the lowest decile. Testsfor linear trend across deciles were statistically significant . Subjects in the highest decile of exposure had FEV1 decrements of −153 mL  and MMEFR decrements of −346 mL/sec , comparedwith participants in the lowest decile.To facilitate interpretation of the association between 1,4-DCB and pulmonaryfunction that we observed in these data using logarithmic transformation, wecategorized nontransformed values of 1,4-DCB into deciles. 1 and MMEFR decrements of −153 mL  and −346 mL/sec , respectively, compared with participants in the lowestdecile. This compares with a 100-mL deficit in FEV1 for the highest tertile of serum cotinine in nonsmoking females in theNHANES III population . Except for acetone and 2-butanone, that study measuredthe same VOCs we included in our analyses.It is possible that 1,4-DCB blood concentrations may reflect exposure betterthan do blood concentrations of other VOCs, because air and bloodconcentrations are better correlated for 1,4-DCB . In the Although VOCs generally do not persist in the blood after termination ofacute exposure , after f3 or greater, which is close to the proposed chronic duration minimal risklimit (120 μg/m3) [Apart from the findings in the SHIELD study , little 0 μg/m3) . Blood lPeople who use air fresheners, toilet bowl deodorants, and mothballs havepotential for high exposure to 1,4-DCB because it is an important componentof these products . However3) for acute duration (≤ 24 hr), 0.1 ppm (0.6 mg/m3) for intermediate duration (> 14 days but < 1 year), and 0.02 ppm (0.12 mg/m3) for chronic duration (up to a lifetime)  .n = 88) had higher odds of asthma with increasing indoor air concentrationsof 1,4-DCB (3).Among the studies that have measured 1,4-DCB air or blood concentrations , 1991, o 1,4-DCB . In a pa 1,4-DCB . Childre3 and indoor concentrations of 30 μg/m3, compared with outdoor concentrations of 2.0 μg/m3 (3 (Indoor air concentrations of 1,4-DCB are significantly greater than outdoorair concentrations . For exa.0 μg/m3 . Accordiμg/m3 (3 , which i1 is a sentinel event for adverse health effects from inhaled exposures, suchas air pollution (1 has been identified as a risk factor in cardiovascular disease, stroke, andlung cancer, as well as an important predictor of all-cause mortality (The chronic duration minimal risk limit is based on observed eosinophilicchanges in the olfactory epithelium of rats; no information was availableon effects of exposure on pulmonary function . Althougollution . In partortality .It is probable that most exposures to 1,4-DCB are chronic, rather thanacute and sporadic, because 1,4-DCB is a component of household productsused for prolonged periods. For example, air fresheners, toilet bowldeodorants, and mothballs are used until their emissions cease, andthen they are replaced. Staff interviewers in the SHIELD study, whereblood concentrations of 1,4-DCB were high, noted that many children’shomes had pervasive scents of air fresheners . In NHANBecause NHANES III is a cross-sectional study, measurements of exposureand outcome were made at the same time, and it is not possible to determineif 1,4-DCB exposure preceded pulmonary function decline. A longitudinalstudy measuring pulmonary function and exposure to 1,4-DCB atvarious time points would be necessary to evaluate the temporality ofthis relationship. Although it is possible that people who are exposedto toilet bowl or air fresheners and other room deodorizers might alsobe exposed to cleaning products that impair pulmonary function, wehad no data to address this.The inverse association between 1,4-DCB concentration and pulmonary functionmay have been affected by unmeasured confounders. We assessed theinfluence of other factors that may be related to pulmonary functionand to 1,4-DCB exposure, such as type of heating, use of wood fires, ageof house, presence of furred pets, occupation, socioeconomic status, presenceof environmental tobacco smoke, smoking history, and diagnosisof asthma or emphysema. Only emphysema confounded the relationshipbetween 1,4-DCB and pulmonary function deficits. The ability to carefullyadjust for smoking with several variables, including the objectivemeasure of environmental tobacco smoke exposure, serum cotinine, wasa considerable strength of our analyses.The size and diversity of this NHANES III sample make it possible to examinethe relationships between VOCs and pulmonary function in more detailthan has been possible in smaller studies. Our findings suggest that 1,4-DCBexposure at levels found in the U.S. general population mayresult in decreases in pulmonary function. Larger and longitudinal studieswould be necessary to properly evaluate the effects on respiratorysymptoms and disease.


We additionally discuss the effects of DNA methylation and RNA N6-methyladenosine (m6A) of specific genes in cancer progression, recurrence, and radioresistance. As epigenetic alterations could be reversed by drug treatment or inhibition of specific genes, they are also considered potential targets for anticancer therapy and/or radiotherapy sensitizers. The mechanisms of irradiation-induced alterations in DNA and RNA m6A methylation, and ways in which this understanding can be applied clinically, including utilization of methylation patterns as prognostic markers for cancer radiotherapy and their manipulation for anticancer therapy or use as radiotherapy sensitizers, have been further discussed.Radiotherapy is a well-established regimen for nearly half the cancer patients worldwide. However, not all cancer patients respond to irradiation treatment, and radioresistance is highly associated with poor prognosis and risk of recurrence. Elucidation of the biological characteristics of radioresistance and development of effective prognostic markers to guide clinical decision making clearly remain an urgent medical requirement. In tumorigenic and radioresistant cancer cell populations, phenotypic switch is observed during the course of irradiation treatment, which is associated with both stable genetic and epigenetic changes. While the importance of epigenetic changes is widely accepted, the irradiation-triggered specific epigenetic alterations at the molecular level are incompletely defined. The present review provides a summary of current studies on the molecular functions of DNA and RNA m Radiotherapy has been established as one of the major treatment options for patients with cancer in the clinic for over 100 years, based on the theory that cancerous regions can be destroyed with targeted ionizing radiation exposure, while normal tissue parts surrounding tumor lesions can withstand and recover after radiotherapy ,3,4. MorEfforts to improve radiotherapeutic strategies, such as optimization of treatment plans and precision of dose delivery, are not beneficial in some cases, and inter-individual differences in therapeutic activity are commonly observed . Cellula6A methylation, have been implicated in several critical biological roles, including cellular proliferation, differentiation, and development of multiple organisms [Alterations in the epigenetic patterns in the structure and function of chromosomes are heritable events that occur without changes in the DNA sequence. In mammalian cells, epigenetic changes, including DNA and RNA mrganisms ,12. Dysrrganisms . Thus, r6A methylation in both irradiation-triggered DNA damage response and radioresistance [6A methylation in radiobiology and cancer progression. Application of epigenetic regulators and biomarkers in radiotherapy is further discussed. Epigenetic regulators are speculated to contribute to radioresistance and metastasis of tumors. Elucidation of the molecular cues underlying the effects of epigenetic changes following irradiation should facilitate the design and development of effective strategies to improve the therapeutic effects of radiotherapy and prevent cancer recurrence. Cell proliferation and survival require tight regulation and propagation of genetic material, which are attacked by both intracellular and extracellular environmental sources of DNA damage. Irradiation is considered a potent DNA damage inducer and epigenotoxic agent . Within sistance ,17. In tDNA methylation is considered the most common mechanism regulating epigenetic events and is closely associated with the progression of several cancer types, including breast, colon, lung, and prostate cancer ,19. The Escherichia coli 15T following irradiation [The effects of radiotherapy on genetic alterations have been extensively documented ,25,26. Hadiation . Subsequadiation . Moreoveadiation . Follow-adiation ,32,33,34adiation ,36,37. Tadiation , and appadiation ,40. As global hypomethylation is linked to malignant transformation and carcinogenesis, DNA hypomethylation triggered by radiation therapy may be utilized as a marker of oncogenic transformation ,34. In aMGMT (O6-methylguanine-DNA methyltransferase), a radiation-induced gene that encodes a DNA repair enzyme responsible for removing alkyl groups from guanine, was identified as a predictive epigenetic biomarker in glioblastoma [MGMT promoter have been shown to display better survival following adjuvant chemotherapy or radiotherapy [MGMT expression, and consequent blockage of the inhibitory effects on the chemotherapeutic activity of drugs or irradiation in tumor killing. In contrast to data obtained from patients with glioblastoma, the methylation level of the MGMT promoter was associated with poorer prognosis or higher chance of relapse after chemo- or radiotherapy in other solid tumors, such as cervical cancer and non-small-cell lung cancer patients with brain metastasis [The methylation level of the promoter of blastoma ,44,56. Potherapy ,57,58. Ttastasis ,59. MGMT and CDKN2A (cyclin-dependent kinase 2A) genes in sputum of uranium miners was previously reported [CDKN2A in lung adenocarcinomas from plutonium-exposed workers, compared to non-exposed workers at MAYAK, a Russian nuclear enterprise [CDKN2A expression has also been observed in a murine model of radiation-induced thymic lymphoma [CDKN2A is therefore proposed as a potentially useful marker to predict tumor cell response to chemo- and radiotherapy. In non-small cell lung cancer (NSCLC), global analysis of CpG methylation has been used to determine the factors associated with epigenetic control of radiosensitivity. In a study by Kim et al. [SERPINB5 (serpin family B member 5) and S100A6  hypermethylation, and CAT  and BNC1 (basonuclin 1) hypomethylation were implicated in radioresistance of NSCLC. Data from this study suggest that response to irradiation is highly dependent on the overall methylation profile of tumors [Significant DNA hypermethylation of reported . These greported ,61. Furtterprise . Hypermelymphoma . Aberranm et al. , a highef tumors . While these studies clearly suggest a role for epigenetic biomarkers in determining response to radiotherapy, the prognostic value of the potential markers identified requires validation in large patient populations to ensure their clinical utility.CDKN2A, RPRM (reprimo), CDKN1C (cyclin dependent kinase inhibitor 1C), TP73 (tumor protein p73), RUNX3 (runt related transcription factor 3), CHFR (checkpoint with forkhead and ring finger domains), MGMT, TIMP3 , and HPP1  comprise a marker panel showing decreased methylation in radiation-responsive patients. Conversely, increased methylation of these genes is significantly correlated with poor responsiveness to chemoradiation [RUNX3), a tumor suppressor that mediates transforming growth factor TGF-β (Transforming growth factor beta) dependent apoptosis [RUNX3 expression and methylation levels in pretreatment specimens may be applied to predict radiosensitivity of esophageal squamous cell carcinomas [In esophageal cancer, adiation . Runt-repoptosis ,51, is rrcinomas . TP73, a member of the p53 family of transcription factors involved in cellular responses to stress and development, is hypermethylated in radioresistant cervical cancers, and significantly associated with silencing of p73 expression [TIMP3, an inhibitor of matrix metalloproteinases, and CDH1 (cadherin 1), a calcium-dependent cell–cell adhesion protein, have been identified as markers to predict better therapeutic outcome [IRX1 (iroquois homeobox 1), EBF3 (early B cell factor 3), SLC5A8 (solute carrier family 5 member 8), SEPT9 (septin 9), and SKOR2 , in HNSCC following radiotherapy. Alterations in methylation of promoters of this subset of genes were enriched in pathways implicated in radiation responses, including cell cycle regulation, DNA repair, and apoptosis [CCND2), a potent cell cycle regulator, is hypermethylated at the promoter region, and downregulated in radioresistant head and neck squamous cells [In patients with advanced head-and-neck squamous cell carcinoma (HNSCC) treated solely with radiotherapy, promoter hypermethylation-driven silencing of  outcome . Anotherpoptosis . A recenus cells . Radiotherapy is the standard therapy of choice for nasopharyngeal carcinoma (NPC), and aberrant DNA methylation is known to be involved in NPC response to radiotherapy. A recent study reported that inactivation of miR-24 through hypermethylation of its precursor promoter is associated with NPC radioresistance. Furthermore, treatment with 5-aza-2′-deoxycytidine compensated for reduced miR-24 expression and sensitized radioresistant NPC cells to therapy . FHIT (Fragile histidine triad) gene, a triphosphate hydrolase involved in purine metabolism, is hypermethylated and consequently silenced in established radioresistant oral cancer cells [FHIT leads to significant resensitization of the radioresistant oral tumors. Furthermore, hypermethylation of the FHIT promoter was inversely correlated with its expression, and served as an independent predictor of both overall survival and locoregional control in oral cancer patient samples. These data suggest that expression and hypermethylation-driven silencing of FHIT are the determining factors for radiosensitivity in oral cancer. er cells . FurtherTOP2A , PLXDC2 (plexin domain containing 2), ETNK2 (ethanolamine kinase 2), GFI1 , and IL12B (interleukin 12B) were detected in radioresistant laryngeal cancer cells. Elimination of methylation of TOP2A by treatment with the DNA methyltransferase inhibitor, 5-aza-2′-deoxycytidine, not only sensitized resistant laryngeal cancer cells to radiotherapy, but re-activated expression of these genes, clearly supporting the theory that changes in DNA methylation levels contribute to radioresistance of laryngeal squamous cell carcinoma [Another study used radioresistant laryngeal cancer cells established via long-term fractionated irradiation to identify the crucial genes with DNA hypermethylation involved in radioresistance of cancer. Increased methylation levels of promoters of arcinoma .ADAMTS9 , FOXC1 (forkhead box C1), and TRAPPC9 (trafficking protein particle complex 9), while the methylation level of AMIGO3 (adhesion molecule with Ig like domain 3) was decreased in response to radiation. Further in vitro experiments showed significant methylation loss in FOXC1 and TRAPPC9 after a recovery period in which irradiated cells displayed regrowth, compared to control cells. As both genes are implicated in cell death control, alterations in their DNA methylation patterns may lead to reduced apoptotic signaling, resulting in regrowth of breast cancer cells after radiation [AGAP1 , ARHGEF10 (Rho guanine nucleotide exchange factor 10), ATP11A (ATPase phospholipid transporting 11A), HDAC4 (histone deacetylase 4), PRR4 (proline rich 4), PTPRD , and TBCD (tubulin folding cofactor D)) in MDA-MB-231 cells induced by irradiation exposure are enriched in pathways related to control of the cell cycle, DNA repair, and apoptosis. The effects of fractioned ionizing radiation at a dose of 2 Gy with cumulative doses of 10 and 20 Gy on DNA methylation in the human breast cancer cell line, MCF7, were examined . Cells wadiation . Antwih adiation  globallyAs intrinsic cancer stem cells (CSCs) are responsible for radioresistance and metastasis in various cancer types ,66,67,68Accumulating evidence suggests that the DNA methylation landscape influences cellular responses to irradiation. This knowledge may be further utilized in modulation of the response of normal and cancerous cells to irradiation, as well as application of DNA methylation-targeted drugs as radiosensitizers.−/−, displayed a trend of increased radiosensitivity, which was not observed in DNMT1-deficient cells [Based on the finding that depletion of DNMTs results in global demethylation , severalnt cells . As aza and decitabine are relatively toxic to normal cells and cannot be administered orally, other DNMT inhibitors, such as zebularine and 5-fluoro-2′-deoxycytidine, have been developed  that areDespite the promising results, the majority of data have been generated from in vitro studies so far, and several concerns regarding the safety of radiation treatment in combination with DNMT inhibitors to healthy tissue need to be addressed. Demethylating agents also cause hypomethylation of normal tissue, which may influence radiation-induced abscopal effects, risk of secondary tumor development, and virus reactivation  ,82. MoreN6 position of adenosine (m6A) of transcripts, leading to context-dependent perturbations in the duplex structure. m6A, initially identified in 1974 [6A)C (70%) or A(m6A)C (30%) consensus sequence per transcript [6A on mRNA is installed, recognized, and erased post-transcriptionally through m6A methyltransferases (writer) [6A-specific binding proteins (readers) [6A in mature transcripts is not random, but mainly occurs in the 5′, 3′ untranslated regions  and within internal long exons [6A patterns play important roles in regulating biological functions of mammalian and cancer cells [6A in RNA was shown to be involved in the DNA damage response following irradiation [6A in RNAs involved in the damage response and repair processes of tumor cells potentially have significant effects on the outcomes of radiotherapy. Analogous to DNA methylation, RNA methylation occurs at the  in 1974 , is the anscript ,85,86. m(writer) ,89,90,91(writer) ,93, and readers) ,95. Highng exons ,97,98, cng exons ,100, mRNng exons ,102,103,ng exons ,105 and ng exons . Due to er cells ,107,108.adiation . Since t6A modification is involved in CSC generation and radioresistance of tumor cells. Thus, manipulation of inhibitors or inducers of m6A modifications could be advantageous in the treatment of radioresistant tumor cells [CSCs are a crucial contributor to radioresistance and disease recurrence after radiotherapy in the majority of cancers ,3,4. Notor cells . 6A formation within mRNA is catalyzed by the m6A writers containing catalytically active METTL3 (methyltransferase-like 3)–METTL14 (methyltransferase-like 14) complex and other associated proteins [6A methyltransferase associated), RBM15 (RNA binding motif protein 15), and RBM15B/OTT3 (RNA-binding motif protein 15B) are established partners of METTL3. METTL3 contains a catalytically active methyltransferase domain that methylates mRNA, but not rRNA [6A methyltransferase complex that forms a heterocomplex with METTL3 [6A writer complex.mproteins . WTAP  for consequent repression [RBM15 and its paralog RBM15B associate with METTL3 in a WTAP-dependent manner ,117. Botpression . 6A writer complex containing METTL3 [S-adenosylmethionine (SAM) by modulating alternative splicing of MAT2A (methionine adenosyltransferase 2A) through differential methylation of mRNA hairpin loops [Recently, METTL16 was identified as an RNA methyltransferase that exerts its functions independently of the mg METTL3 ,120. Thein loops . 6A modifications in RNA. FTO was initially reported to demethylate single-stranded DNA (ssDNA) and single-stranded RNA (ssRNA) via removal of N3-methylthymidine [6A in polyadenylated RNA, while its overexpression causes a decrease in m6A levels [N6, 2′-O-dimethyladenosine (m6Am) modification co-detected with m6A [6A and m6Am to facilitate detailed examination of the substrate spectrum of FTO. Biochemical analyses revealed that m6Am is the preferred cellular target of FTO in vivo [Fat mass and obesity-associated protein (FTO) is a member of the AlkB (Alkylation repair homologs) subfamily of Fe(II)/alpha-ketoglutarate-dependent dioxygenases, and was originally described as an eraser of mhymidine ,122. FurA levels ,117. Recwith m6A . Advance in vivo . FTO con in vivo ,124, whi in vivo ,126. 6A in mRNA. ALKBH5 contains an alanine-rich sequence and potential coiled–coil structure within the N-terminal region that is responsible for its nuclear localization [ALKBH5 (AlkB homolog 5) is another mammalian demethylase belonging to the AlkB subfamily with efficient demethylation activity on mlization . Structulization ,127,128.lization ,93,129. 6A methylations of RNAs are mainly read by eukaryotic initiation factor 3 (eIF3) and proteins containing a YT521-B homology (YTH) domain belonging to the YTH protein family. However, several RNA binding proteins (RBP) that associate with m6A do not belong to the classical m6A YTH domain family. YTHDF1 (YTH N6-methyladenosine RNA binding protein 1), YTHDF2 (YTH N6-methyladenosine RNA binding protein 2), YTHDF3 (YTH N6-methyladenosine RNA binding protein 3), YTHDC1 (YTH domain containing 1), and YTHDC2 (YTH domain containing 2), members of the YTH domain family, represent the predominant cytoplasmic m6A reader proteins [6A at the (G>A)m6ACU consensus sequence, compared to unmethylated RNA of the same transcript [6A reader protein shown to mediate RNA decay by targeting RNA substrates to P-bodies in an m6A-dependent manner [mproteins . Proteinanscript . YTHDF2 t manner ,131. YTHt manner ,133,134.6A reader that binds m6A through tryptophan residues at positions 377 and 428 by forming an aromatic cage [Nuclear YTH domain-containing 1 (YTHDC1), also designated YT521-B, has been identified as a nuclear mtic cage . A glutatic cage . Due to tic cage . 6A reader essential for male and female fertility in mice via regulation of the m6A transcriptome. Additionally, this factor is highly expressed in germ cells, and interacts with an essential meiosis-specific protein, MEIOC (meiosis specific with coiled-coil domain) [Nuclear YTH domain-containing 2 (YTHDC2) was initially reported as a factor required for HCV genome replication . This fa domain) .6A in the nucleus [6A site of RNA transcripts, and regulates splicing and maturation of microRNA (miRNA) [Other than the YTH domain family, pulldown assays revealed that heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1) binds m nucleus . Another (miRNA) . 6A binding protein. Following binding to m6A in the 5′-UTR of RNA transcripts, initiation of translation can be triggered by eIF3 in a 5’cap- and eukaryotic initiation factor 4E (eIF4E)-independent mechanism [6A modifications in 5′-UTRs of mRNA when eIF4-dependent initiation is hindered by specific cell states. Furthermore, m6A has been shown to influence the secondary structure of RNAs through binding of regulatory proteins, consequently modulating the expression or maturation of RNA transcripts [Eukaryotic initiation factor 3 (eIF3), a protein complex that functions in the initiation of eukaryotic translation, has additionally been identified as an important mechanism . These fnscripts ,144,145.6A modification facilitate binding of HNRNPC (heterogeneous nuclear ribonucleoprotein C) and RBMX  for targeting and regulation of mRNA expression and splicing [For instance, secondary structure changes induced via msplicing . 6A in RNA is associated with tumorigenesis and progression of glioblastoma [ADAM19 , EPHA3 (EPH receptor A3), and KLF4 (Kruppel like factor 4), are upregulated in METTL3 or METTL14-depleted GSCs, and conversely downregulated in GSCs with METTL3 or METTL14 overexpression [Glioblastoma multiforme (GBM) is the most aggressive glioma type, affecting ~17,000 patients per year. Treatment failure of glioblastoma may be due to tumor heterogeneity and treatment resistance of cancer stem cells, triggering disease recurrence ,147. Sevblastoma ,148. Ovepression . 6A modification is proposed to play a crucial role in glioma stem-like cell (GSC) maintenance and dedifferentiation of glioma cells [SOX2 is the m6A target of METTL3. Recruitment of human antigen R (HuR) to m6A sites was essential for SOX2 mRNA stabilization by METTL3. Notably, silencing of METTL3 enhanced sensitization of GSC to radiotherapy, and expression levels of METTL3 predicted poor survival in GBMs enriched for GSC-specific signatures [FOXM1 (forkhead box M1) transcripts, consequently enhancing FOXM1 expression. FOXM1-AS, a lncRNA that localizes in the nucleus, has been shown to promote interactions between ALKBH5 and nascent FOXM1 transcripts. Silencing of ALKBH5 and FOXM1-AS inhibited the tumorigenic potential of GSCs via suppression of FOXM1. Thus, m6A modifications could influence the expression or function of genes associated with the malignant phenotype of cancer, presenting a promising therapeutic target in the tumor. METTL3-mediated mma cells , based ognatures . Zhang egnatures  recently6A eraser, promotes leukemic oncogene-mediated cell transformation and leukemogenesis, and suppresses all-trans-retinoic acid (ATRA)-triggered AML cell differentiation by regulating the expression of specific target genes, including ankyrin repeat and SOCS box-containing 2 (ASB2) and retinoic acid receptor a (RARA), through repressing m6A levels in these transcripts [ASB2 (ankyrin repeat and SOCS box containing 2) and RARA  are induced during hematopoiesis and function as crucial regulators of ATRA-induced differentiation of leukemia cells. Furthermore, inhibition of FTO-mediated ASB2 and RARA suppression contributes to the response of AML cells to ATRA treatment [6A in RNA transcripts than isogenic IDH2 wild type expressing cells. Therefore, the precise roles of FTO in the pathogenesis of AML require elucidation in the context of the IDH1/2 mutation in this setting [Leukemias are a clinically and genetically heterogeneous group of hematopoietic disorders that begin in early blood-forming cells found in the bone marrow. Acute myeloblastic leukemia (AML) is more frequent in older patients, with diagnosis at about 70 years of age on average . Geneticnscripts . ASB2 , BCL-2 (B cell leukemia/lymphoma 2), and PTEN (phosphatase and tensin homolog) in human AML cells [6A modification within the coding region of the associated mRNA transcripts, and consequently, enhancing its translation [6A regulatory genes are strongly associated with the presence of TP53 mutations in AML patients [6A regulatory genes in cooperation with TP53 contribute to the pathogenesis and maintenance of AML, and provide a rationale for therapeutic targeting of METTL3 in myeloid leukemia. Recently, a role for METTL3 in regulating myeloid differentiation and maintaining myeloid leukemia has been reported ,157. BotML cells . Anotherpatients . These rEGFR  and Hippo pathway effector, TAZ (tafazzin), through recruitment of eIF3 to the translation initiation complex. Although m6A is implicated in regulation of translation through binding YTHDF1 [6A methylated sites, and enhance the translation of a distinct subset of genes, including those encoding EGFR and TAZ. Lung cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality worldwide, with a <15% 5-year survival rate, despite the progressive improvements in standard therapies . METTL3 g YTHDF1 , another6A-related mechanisms in HCC have provided new ideas for treatment. Silencing of METTL14 decreases the m6A level in RNA transcripts and enhances cancer metastasis, both in vitro and in vivo [6A-dependent manner. These findings suggest that METTL14 participates in the regulation of microRNAs in tumor biology, and highlight its potential as a therapeutic target in HCC [HCC is the fifth most prevalent malignant tumor, and third leading cause of cancer-related deaths worldwide . Recent  in vivo , and dowt in HCC . 6A modification of the SOCS2 transcript, and consequently decreases its stability in a YTHDF2-dependent manner [In addition, METTL3 is overexpressed in HCC, and associated with poor prognosis. Silencing of METTL3 is reported to suppress tumorigenesis and progression of HCC, and conversely, its overexpression significantly promotes HCC growth, both in vitro and in vivo. Further experiments revealed that METTL3 induces mt manner . YTHDF2 mRNA, and inhibits its expression in HCC. Moreover, overexpression of miR-145 induced a strong increase in m6A levels which could be blocked upon YTHDF2 overexpression [6A, control the global m6A level in cells to influence cancer progression. Another recent study demonstrated that YTHDF2 expression is closely associated with malignancy of HCC, and negatively correlated with miR-145 . Bioinfopression . These s6A in NANOG transcripts, consequently enhancing NANOG expression. NANOG is a potent pluripotency factor that stimulates CSC maintenance. Similar to hypoxia exposure, overexpression of ALKBH5 not only decreases NANOG mRNA methylation, but also increases NANOG (Nanog homeobox) levels, resulting in elevation of the CSC population in breast cancer [6A methylation of NANOG and KLF4 transcripts, both important pluripotency factors that mediate CSC maintenance, in turn, influencing cancer radiosensitivity and recurrence [Breast cancer is the most common cancer type, and the fourth leading cause of cancer-related deaths among women worldwide . Tumor ht cancer . Exposurcurrence . TherefoRenal cell carcinoma (RCC) is currently the ninth most common cancer type in men and 14th most common in women globally . The mor6A mRNA methylation levels in 286 pairs of cervical cancer samples disclosed significant downregulation in cervical cancer and negative correlation with characteristics of malignancy, including pathologic stage, tumor size, differentiation, lymph invasion, and cancer recurrence. Suppression of the m6A level, via FTO or ALKBH5 overexpression, promoted proliferation of cervical cancer cells, whereas increasing the m6A level led to significant suppression of tumor development, both in vitro and in vivo. Thus, inhibition of the m6A level is tightly associated with cervical cancer progression, and regulators of m6A mRNA methylation present potential therapeutic targets in cervical cancer [Cervical cancer is one of the most prevalent gynecological malignancies worldwide ,172. A rl cancer . 6A in RNAs play important roles in cancer stem cell maintenance and progression, targeting of m6A editing may present an effective treatment strategy. For instance, inhibition of the RNA demethylase, FTO, suppresses GSC growth and self-renewal, consequently suppressing tumor progression of GBM. Additionally, FTO exerts a critical oncogenic effect on ATRA-induced leukemic cell differentiation by decreasing m6A levels in critical mRNA transcripts, including ASB2 and RARA [6A demethylation within cells [6A-containing regions in RNAs [6A demethylase that maintains tumorigenicity of glioblastoma stem cells [6A regulators should aid in overcoming disease recurrence and resistance to cancer therapy. In addition to FTO and ALKBH5, S-adenosylhomocysteine (SAH) acts as a kind of competitive inhibitor of adenosylmethionine-dependent methyltransferases [6A methylation of mRNA substrates [6A-containing mRNA [Cancer stem cells (CSC) represent a subpopulation of cancer cells with the capacities of radioresistance, metastasis, and tumor initiation in various cancer types ,66,67,68and RARA . Rhein, in cells . Meclofe in RNAs . Moreove in RNAs . Thus, Rem cells ,127. Howsferases . SAH is bstrates . Moreoveing mRNA ,175. TheRadiotherapy is one of the major forms of cancer treatment for various malignant tumor types. Radiation treatment triggers DNA damage through ionization or production of reactive oxygen species (ROS), leading to death of tumor cells, but can concomitantly promote radioresistant cancer cell metastasis and progression . Accumul


Introduction. Lung adenocarcinoma (LAC) is the most frequent type of lung cancer and has a high metastatic rate at an early stage. This study is aimed at identifying LAC-associated genes. Materials and Methods. GSE62950 downloaded from Gene Expression Omnibus included a DNA methylation dataset and an mRNA expression profiles dataset, both of which included 28 LAC tissue samples and 28 adjacent normal tissue samples. The differentially expressed genes (DEGs) were screened by Limma package in R, and their functions were predicted by enrichment analysis using TargetMine online tool. Then, protein-protein interaction (PPI) network was constructed using STRING and Cytoscape. Finally, LAC-associated methylation sites were identified by CpGassoc package in R and mapped to the DEGs to obtain LAC-associated DEGs. Results. Total 913 DEGs were identified in LAC tissues. In the PPI networks, MAD2L1, AURKB, CCNB2, CDC20, and WNT3A had higher degrees, and the first four genes might be involved in LAC through interaction. Total 8856 LAC-associated methylation sites were identified and mapped to the DEGs. And there were 29 LAC-associated methylation sites located in 27 DEGs . Conclusions. These key genes might play a role in pathogenesis of LAC.  As the most common type of lung cancer and a non-small cell lung carcinoma (NSCLC) , lung ad HOXB9 and LEF1 which mediates chemotactic invasion and colony outgrowth [ Oct4 and Nanog, which are homeobox transcription factors important for self-renewal of stem cells, commands epithelial-mesenchymal transdifferentiation, mediates tumor-initiating ability, and contributes to metastasis of LAC [ MALAT-1 enhances motility of LAC cells via regulating motility-associated gene expression in transcriptional and posttranscriptional levels [ BRAF mutation is frequently detected in human LAC, indicating that BRAF may serve as a therapeutic target for a subset of patients with the disease [ caveolin-1 is essential for mediating filopodia formation, which may promote invasion of LAC cells [The WNT/TCF signaling can promote osseous and brain metastasis of LAC cells through targetingutgrowth . Coexpres of LAC . As a nol levels , 9. BRAF disease , 11. OveAC cells . In spitRecently, along with the development of chip technology, microarray data have been obtained and uploaded to Gene Expression Omnibus (GEO) for us to study . Using mhttp://www.ncbi.nlm.nih.gov/geo/), which included a DNA methylation dataset and an mRNA expression profiles dataset. The DNA methylation dataset and the mRNA expression profiles dataset separately were based on the platform of GPL8432 Illumina HumanRef-8 WG-DASL v3.0 and GPL8490 Illumina HumanMethylation27 BeadChip (HumanMethylation27_270596_v.1.2), and both of them included 28 LAC tissue samples and 28 adjacent normal tissue samples.Microarray data GSE62950 was downloaded from the database of GEO  method [p value < 0.05 and |log2fold-change(FC) | ≥1 were taken as the thresholds.Using Limma package  in R, th) method . The adjp values of enriched terms were corrected by Holm-Bonferroni [p value < 0.05 was used as the cut-off criterion.GO is utilized for predicting potential functions of gene products in three categories  . The KEGnferroni . The adjhttp://www.cytoscape.org/) [The STRING  databasepe.org/) . Subsequpe.org/)  in R wasFurthermore, MCODE plugin  in Cytosp value < 0.05 was used as the cut-off criterion. Then, the methylation sites located in sex chromosomes were removed. Finally, the associations between the screened methylation sites and LAC were analyzed by CpGassoc package [p value < 0.05 was taken as the threshold.Using genefilter package  in R, th package  in R. ThAccording to the annotation information of the DNA methylation profiles, the nearest genes to the LAC-associated methylation sites were obtained and then mapped to the DEGs. At last, LAC-associated methylation sites of the DEGs were gained.http://cancergenome.nih.gov/) database, which included 515 LAC tissue samples and 59 adjacent normal tissue samples. Using Limma package [p value < 0.05 and |log2FC| ≥ 1 were also screened. Meanwhile, the methylation data of LAC were also downloaded from TCGA database, which included 459 LAC tissue samples and 32 adjacent normal tissue samples. Moreover, LAC-associated methylation sites were also identified and mapped to the DEGs using the same methods as the above.The RNASeqV2 data of LAC were downloaded from The Cancer Genome Atlas  in LAC tissue samples. In the heat map of the DEGs, LAC tissues could be definitely separated from adjacent normal tissues by the DEGs .p value = 8.59E − 07) in BP category, as well as extracellular region  and extracellular space  in CC category  and cell cycle  , germ cell cancer , embryoma , and embryonal cancer  ( MAD2L1 and aurora B kinase (AURKB). The enriched GO functions for downregulated genes were listed in p value = 5.73E − 05) and single-multicellular organism process  in BP category, as well as cell periphery in CC category . In addition, there were nonsignificant KEGG pathways and DO terms enriched for the downregulated genes.Using TargetMine online tool, functions of the DEGs were predicted by GO, KEGG, and DO enrichment analyses. For the upregulated genes, the enriched GO functions included multicellular organismal catabolic process (category . The enr MAD2L1) . The enr.009481) . And all MAD2L1 (degree = 36), AURKB (degree = 38), CCNB2 (degree = 40), and CDC20 (degree = 42) had higher degrees, and they can interact with each other in the PPI network. Several modules were screened from the PPI network for upregulated genes, and the largest module (module 1) is also showed in PPI networks for upregulated and downregulated genes were constructed, respectively. The PPI network for upregulated genes had 229 nodes and 725 interactions . ParticuPRKG2, degree = 11), VE-cadherin , WNT3A (degree = 15), adenylyl cyclase 8 , and adenylyl cyclase 4  were the top 5 nodes which had higher degrees. What is more, nonsignificant modules were screened from the PPI network for downregulated genes.The PPI network for downregulated genes had 233 nodes and 368 interactions . Connectp value < 0.05. As a result, 230 LAC-associated methylation sites were identified.After screening, total 8856 methylation sites were obtained. Then, the associations between the screened methylation sites and LAC were analyzed under the threshold of  SH3GL2; brain-specific angiogenesis inhibitor 3, BAI3; H-cadherin, CDH13; junctional adhesion molecule 2, JAM2; metallothionein 1A, MT1A; LIM-homeobox containing 6, LHX6; and insulin-like growth factor binding protein-3, IGFBP3)  . And all WNT3A, MAD2L1, AURKB, CCNB2, CDC20, SH3GL2, BAI3, CDH13, JAM2, MT1A, and IGFBP3. In addition, the 29 LAC-associated methylation sites identified in the microarray data of GSE62950 were also detected in the methylation data downloaded from TCGA database.Total 4893 DEGs (including 2191 upregulated genes and 2702 downregulated genes) were identified in the LAC sample data downloaded from TCGA database. There were 691 common DEGs (including 310 upregulated genes and 381 downregulated genes) between the 4893 DEGs identified in the LAC sample data downloaded from TCGA database and the 913 DEGs identified in the microarray data of GSE62950. The common DEGs included genes such as WNT3A, MAD2L1, AURKB, CCNB2, CDC20, SH3GL2, BAI3, CDH13, JAM2, MT1A, and IGFBP3) between the 913 DEGs identified in the microarray data of GSE62950 and the 4893 DEGs identified in the LAC sample data downloaded from TCGA database. In addition, the 29 LAC-associated methylation sites identified in the microarray data of GSE62950 were also detected in the methylation data downloaded from TCGA database. Functional enrichment indicated that WNT3A was involved in single-multicellular organism process and cell periphery. Overexpression of Wnt5a, which belongs to Wnt family that encodes signaling glycoproteins, promotes invasion of NSCLC during tumor progression [ Wnt-7a and Fzd-9 signaling plays role in inducing the receptor tyrosine kinase inhibitor Sprouty-4 and cadherin proteins and is essential for maintaining epithelial differentiation and inhibiting transformed cell growth in some NSCLC patients [In this study, 913 DEGs including 409 upregulated and 504 downregulated genes were identified in LAC tissue samples compared with adjacent normal tissue samples. Total 230 LAC-associated methylation sites were identified, among which 29 LAC-associated methylation sites were located in 27 DEGs. Afterwards, the RNASeqV2 data and methylation data of LAC were downloaded from TCGA database to validate the obtained methylation sites and DEGs. There were 691 common DEGs  and AURKB can result in bad outcome of lung cancer patients; thus, suppression of the CDCA8-AURKB pathway is a potential therapeutic strategy for lung cancer [MAD2) is overexpressed in a high percentage of lung cancers, and multivariate analysis suggests that high-level MAD2 can be a prognostic marker independently [ MAD2L1, AURKB, CCNB2, and CDC20 had higher degrees, and they can interact with each other. Therefore, MAD2L1, AURKB, CCNB2, and CDC20 might be implicated in LAC by interacting with each other.Besides,arcinoma . Overexparcinoma . The 5-ywith LAC , 35. Immrcinomas , 37. Immg cancer . Semiquaendently . In the  SH3GL2, BAI3, CDH13, JAM2, MT1A, LHX6, and IGFBP3). Loss of SH3GL2 is frequently detected in NSCLC and SH3GL2 can mediate cellular growth and invasion through interacting with EGFR [ CDX2, VIL1, and BAI3 levels have significant differences in SCLC and large-cell neuroendocrine lung carcinoma (LCNEC); therefore, they can be diagnostic markers of these tumor types [ CDH13, located on chromosome 16q24.2–3, is downregulated in lung cancer and its aberrant methylation may be a potential marker for cancer detection [β1 integrin subunit and ERK activation in human dermal lymphatic endothelial cells (HDLEC), junctional adhesion molecule-C (JAM-C) contributes to lymphangiogenesis and nodal metastasis, suggesting that JAM-C may be a target for treating lymphatic metastases in NSCLC [MT) can be used as an independent predictor of short-term survival in SCLC patients enduring chemotherapy [ LHX6 is a candidate tumor suppressor gene that has epigenetic silencing in patients with lung cancer [ IGFBP-3 before cisplatin therapy seems to be a biomarker of prognosis, helping to select appropriate therapeutic method for patients [ SH3GL2, BAI3, CDH13, JAM2, MT1A, LHX6, and IGFBP3 might relate to LAC.Additionally, LAC-associated methylation sites were identified and mapped to the DEGs. And there were 29 LAC-associated methylation sites located in 27 DEGs . However, these bioinformatic findings need to be validated by further researches.In conclusion, we carried out a comprehensive bioinformatics analysis to screen LAC-associated genes. We identified 913 DEGs and 8856 methylation sites in LAC tissue samples. Besides, LAC might correlate with several key genes (e.g.,