BRAFV600E, but tumors treated with RAF inhibitors, even when they respond dramatically, rapidly adapt and develop resistance. Thus, there is a pressing need to identify the major mechanisms of intrinsic and adaptive resistance and develop drug combinations that target these resistance mechanisms. In a combinatorial drug screen on a panel of 12 treatment-naïve BRAFV600E mutant melanoma cell lines of varying levels of resistance to mitogen-activated protein kinase (MAPK) pathway inhibition, we identified the combination of PLX4720, a targeted inhibitor of mutated BRaf, and lapatinib, an inhibitor of the ErbB family of receptor tyrosine kinases, as synergistically cytotoxic in the subset of cell lines that displayed the most resistance to PLX4720. To identify potential mechanisms of resistance to PLX4720 treatment and synergy with lapatinib treatment, we performed a multi-platform functional genomics analysis to profile the genome as well as the transcriptional and proteomic responses of these cell lines to treatment with PLX4720. We found modest levels of resistance correlated with the zygosity of the BRAF V600E allele and receptor tyrosine kinase (RTK) mutational status. Layered over base-line resistance was substantial upregulation of many ErbB pathway genes in response to BRaf inhibition, thus generating the vulnerability to combination with lapatinib. The transcriptional responses of ErbB pathway genes are associated with a number of transcription factors, including ETS2 and its associated cofactors that represent a convergent regulatory mechanism conferring synergistic drug susceptibility in the context of diverse mutational landscapes.Fifty percent of cutaneous melanomas are driven by activated  BRAFV600E as an oncogenic driver is demonstrated by the rapid response of most BRAFV600E mutant melanomas to the BRaf inhibitors vemurafenib or dabrafenib [BRAFV600E mutant melanomas showed objective response after treatment [The BRaf kinase is activated by mutation from Valine to Glutamate at position 600 (V600E) in approximately 50% of cutaneous melanomas , resultibrafenib ,5: Over reatment ,5.MITF overexpression has been shown to repress the ability of BRaf inhibition to kill melanoma [MITF activity, class III and class IV melanosomes can sequester drugs [MITF activity has been implicated as an indication of resistance to BRaf inhibition [BRAF splice variants facilitate dimerization with RAF1 and result in MEK activation [MEK can be activated by mutation or by activation of COT [PI3K activation [Unfortunately, response to vemurafenib is not durable, with most patients relapsing within 2 to 18 months of initial treatment –8. Severmelanoma ,10 and ier drugs . In morehibition ,13. Finativation ; MEK cann of COT ; the cyttivation ; and thetivation –19. LessBRAFV600E mutant melanoma cell lines, with a wide range of intrinsic sensitivity to the BRaf inhibitor PLX4720, in combination with a library of 60 secondary drugs and tool compounds. Notably, Held et al. [BRAFV600E melanoma cell lines. We also found that the 6 cell lines that were most resistant to PLX4720 displayed synergistic cytotoxicity with lapatinib. In order to determine mechanisms of resistance to PLX4720 and synergy to lapatinib as well as help develop systematic approaches to better predict which combinations might be effective/synergize, we performed a functional genomics and genetics profiling of the12 melanoma cell lines. Novel results from our study include coupling the roles of mutant BRAF zygosity and mutations in RTKs in determining basal drug resistance to broad up-regulation of ErbB pathway genes including ErbB family RTKs in response to PLX4720 treatment. Further analysis revealed enrichment of transcription factors including ETS family members and their associated co-factors as likely regulatory drivers of adaptive PLX4720 resistance, providing a potential convergence point of adaptive resistance within the diversity of response mechanisms.Combination therapies are predicted to overcome intrinsic, adaptive and acquired resistance . For resd et al.  found PLMITF expression level that correlated weakly with drug sensitivity. Dividing the cell lines into groups based on unsupervised clustering of all the single drug and combination cytotoxic responses across a three by three dose response matrix yielded five phenotype groups. Strikingly, these cytotoxicity groupings closely mimicked the groupings observed in the basal transcriptome based on a principal component analysis (PCA). The transcriptional and proteomic responses to PLX4720 treatment were then analyzed to identify molecular responses that were common between the cell lines in each group. The lists of differentially expressed genes and phosphoproteins were subjected to the Mutational Signatures Database (MSigDB) [In order to gain insights into the mechanisms of synergy and sensitivity, and potentially to identify clinically relevant biomarkers, we broadly profiled our panel of lines with multiple functional genomic and genetic assays . Analysi(MSigDB)  enrichme(MSigDB)  to identTo determine whether the transcriptional profile of treatment-naïve cells could predict sensitivity to the drugs, singly or in combination, we classified the 12 cell lines based on unsupervised clustering of the basal (not drug-treated) transcriptome . ClusterTBL1X, IL6, CST7, NRG1, CPA4, SERPINB7, STC2, CALB2, RAB27B) that all contain the JUN motif in their promoter. Among these genes, TBL1X and CALB2 were the only two that were expressed at a relatively high level in both A375 and DM331 while the rest including NRG1 were highly expressed in DM331 only. We note that exposure to NRG1 has been shown to induce resistance to PLX4720 treatment in A375 cells [NRG1 in DM331 contributes substantially to DM331’s intrinsic resistance to PLX4720 treatment.Cluster III (49 genes) contained relatively highly expressed genes in DM331, which is of interest because it is the most resistant line to PLX4720 treatment. A subset of the genes in this cluster was also relatively high-expressed in A375, our second most sensitive line to PLX4720 treatment. To identify the potential functional significance of the genes within cluster III, we performed a MSigDB enrichment analysis using the transcription factor gene set . The top75 cells . DespiteMITF as a hub with 32 MITF target genes  of which 32 are shown in MITF transcriptional program driving this separation. To confirm whether MITF is a regulatory hub of cluster IV genes, we compared the genes in cluster IV with MITF target genes identified by Strub et. al. via a ChIP-Seq experiment [MITF and the expression of its associated targets as potentially driving lineage separation in our cell lines, but not contributing consistently to drug sensitivity.Clustering the expression profile of the 73 genes in Cluster IV separates our cell lines into two distinct groups. Ingenuity Pathway analysis of this list of 73 genes revealed that cluster IV contained periment  and obseMITF expression is associated with response to PLX4720 treatment in our panel, we correlated MITF expression in each of our lines with their respective IC50 values. We observed a modest anticorrelation of -0.40 between the MITF expression level of our lines, and their respective IC50 doses, consistent with Konieczkowski et al. [MITF gene expression and IC50 across the remaining eleven cell lines. Because our synergy studies were performed over a dose range that included concentrations below the IC50 (centered around IC20), we also examined the correlation of MITF expression and cytotoxicity at the low, medium and high doses. We found a relatively strong anticorrelation between MITF gene expression level and PLX4720 cytotoxicity (excluding DM331): -0.79, -0.68 and -0.67 for our low, medium and high doses, respectively. Notably, our PLX4720 doses, which were intended to assess drug synergy, are much lower, 125 nM, in our studies compared to other studies which tend to use ~1μM PLX4720 [MITF across our cell lines are associated with increased sensitivity to PLX4720 treatment while at IC50 doses, we find essentially no correlation.To assess the extent to which the i et al. . In our  PLX4720 –13,19. TMITF expression level in our lines  which reveals dominant sources of variability in the first principal component as well as sub-dominant sources of variability in the second and higher order principal components. As shown in the PCA plot , the sepThe strong correlation of the second principal component with the Bliss synergy score of the cell lines raised the possibility that the basal transcriptional profile could yield insights into mechanisms of synergy and resistance. To explore this possibility, we correlated each gene expression profile across cell lines with PC2  and idenNRG1, a known mechanism of PLX4720 resistance [Given the modest association between basal gene expression and synergy, we wanted to determine if there was a phenotypic classification that better correlated with the basal gene expression analyses. We re-examined the grouping of the cell lines according to cytotoxic drug response over the low dose ranges used to identify synergy. These analyses of cytotoxic responses to PLX4720, lapatinib and the combination of the two drugs revealed that the lines that did not demonstrate synergy had low synergy scores for fundamentally different reasons (detailed below). To organize the cell lines according to drug cytotoxicity profiles, we clustered the cell lines using their cytotoxicity values across drug treatments , again asistance . For thiBRAF locus and response to treatment, with homozygous BRAF mutant lines being more sensitive to BRaf inhibition, as has previously been reported in a panel of cell lines by Sondergaard et al [BRAF locus , which tends to yield repressed chromatin [EZH1 has been shown to maintain stem cell identity and pluripotency [EZH1 and EZH2 have been identified as candidate oncogenes in diffuse large B cell lymphoma [We then searched for genes that contained variants that were unique to individual cytotoxicity groups and could be dominant drivers of drug sensitivity within each group. We found that lines in both the CGE and CGD groups contained unique genetic variants. Variants previously identified by dbSNP were removed, and we further filtered the list by identifying novel nonsynonymous variants; nonsense and missense variants only. The list of 10,000 variants was narrowed to 143 variants across 83 genes that were identified as being known drivers of cancer . We impoer types ,29 and ihromatin . EZH1 haipotency . Importalymphoma .BRAF zygosity potentially explains the response of three of the four CGC and two of the three CGA cell lines to PLX4720 treatment respectively. Consequently, we searched for genes that contained variants specifically in the cell lines that were cytotoxicity group exceptions with regard to BRAF zygosity (SKMEL28 and SKMEL24) that were not shared by other group members. We also identified variants that were specific to SKMEL28 and SKMEL24, which could explain their being placed in CGC and CGA groups, respectively.While we found no cytotoxicity group specific variants in CGA and CGC cell lines, BRAF lines. SKMEL28, uniquely in our panel, contains a mutation in NOTCH1, a transmembrane protein that has been shown to interact with membrane bound ligands [ARID1A and PTEN are mutated in SKMEL28 but not in its cytotoxicity group members. However, ARID1A is also mutated in VMM15, and PTEN is not mutated in any other line. ARID1A is a SWI/SNF family member and potential tumor suppressor in melanoma [APC, a tumor suppressor that antagonizes WNT signaling [BRAF status.In SKMEL28, which was the second most PLX4720 resistant homozygous cell line among the six shown in  ligands  and it c ligands . SKMEL28melanoma . Lastly,ignaling . We hypoTNFRSF14, DNER, ERBB3 and EGFR  and applying a stringent 1% FDR cutoff . The CGAreatment . When weETS2, GATA1, and POU2F1. Additionally, we saw a number of well-established oncogenic transcription factor binding motifs, including JUN and MYB. Lastly, based on ChIP-seq data [MITF targets a number of ErbB pathway genes [Given the striking, simple result that the ErbB pathway is broadly upregulated in response to PLX4720 treatment only in the group of cell lines that require treatment with the PLX4720 and lapatinib combination in order to achieve an optimally cytotoxic response, we asked which transcription factors were likely activated by mutant BRaf inhibition and targeting ErbB pathway genes. Using MSigDB , we idenseq data , we founay genes .LEF1, SP1, AP4, ETS2, and PAX4 that putatively target multiple upregulated genes in the largest central cluster of the heat map. Additionally, ErbB family members contain binding motifs for LEF1, SP1, AP4, NFAT, MAZ, and FOXO4 at their promoters. Notably, many of these factors have been associated with melanoma. LEF1 has been shown to be highly expressed in proliferative cells in melanoma [HMGB1 knockdown in melanoma, a marked decrease in cell proliferation was observed mediated by p21 in a SP1 dependent manner [NFAT has been identified as a potential drug target in melanoma using aminobisphosphonate zoledronic acid [FOXO4 activation can induce senescence in BRAFV600E mutants [We further analyzed transcription factor motifs that appear across multiple cytotoxicity groups in order to identify those that were more strongly enriched in CGC. For motifs that appeared in more than one group, we added the motif’s rank in CGC to the motif’s rank in the other group subtracted from 100. For motifs that appeared in more than the CGC and one other group, we calculated the arithmetic mean of the 100 minus the other groups’ ranks, and added that to the CGC rank. Using these averaged ranks summed with the rank for CGC, we found that CGC is more enriched than any other group for an additional 28 transcription factor binding motifs. We then found which of these transcription factors were associated with the promoters of ErbB signaling pathway genes. To visualize the ErbB pathway gene responses to PLX4720 treatment together with the transcription factors that putatively regulate them in one view, we plotted the log2 fold change of PLX4720 treated versus control of genes in the ErbB signaling pathway for each cytotoxicity group , Left anmelanoma ,41. In at manner . NFAT hanic acid . Lastly, mutants . Putting mutants  are appaTo test our hypothesis that resistant lines upregulate ErbB signaling activity, we performed reverse phase protein array analysis. We assayed approximately 132 protein epitopes across 105 different proteins sampling various oncogenic pathways including MAPK, PI3K, ErbB, etc. eight hours after initial treatment with PLX4720. We log2 transformed the normalized values, performed cytotoxic group-wise paired t-tests as described above and applied a 5% FDR cutoff. We identified 36 differentially expressed phosphoproteins, one cleavage site, and two proteins, whose total amount changed in response to PLX4720 treatment. Using these epitopes, we plotted their fold changes for each group due to treatment with PLX4720 in a heat map . SurprisERBB4 protein levels in response to PLX4720 treatment is the most anticorrelated epitope with PLX4720 cytotoxicity  and cytotoxicity in response to PLX4720 treatment across cell lines (including DM331) for each of the three doses separately. We found the change in total toxicity . Other hBRAFV600E is confounded by a wide variety of mechanisms of resistance, both intrinsic and acquired. Thus, there is considerable interest in understanding the general principles of resistance to BRaf inhibition so that druggable targets could be identified that would guide the development of effective drug combinations. In a high throughput screen, we found that the combination of the mutant BRaf inhibitor PLX4720 with the ErbB family inhibitor lapatinib caused synergistic cytotoxicity in the six lines of our 12 cell-line panel that showed the greatest intrinsic resistance to BRaf inhibition. By performing hierarchical clustering of the cytotoxicity dose-response data, we identified five different cytotoxicity groupings, reflecting extraordinary heterogeneity in biological responses to drug treatment in this panel. Novel results from our systems-level molecular profiling analysis include linking lineage (reflected by MITF expression), BRAF zygosity, cell-line specific mutations in proto-oncogenes and tumor suppressors and widespread transcriptional upregulation of ErbB family members and other receptor tyrosine kinases in the response and resistance of melanoma cells to PLX4720. Examination of the transcriptional responses to PLX4720 treatment revealed the involvement of transcription factors with the potential to be responsive to the MAPK pathway, providing a possible convergent mechanism underlying the diversity of phenotypic responses.Inhibition of BRaf signaling as a therapy for the 50% of cutaneous melanomas that are mutant MITF expression, which has been implicated by several reports in drug resistance and oncogenesis in melanoma [MITF levels and MITF transcriptional target activity as associated with increased sensitivity to PLX4720 treatment [MITF and IC50 for PLX4720, an association that was driven almost entirely by our single most resistant cell line; when this line (DM331) was dropped from the analysis, no association with MITF appeared. However the study by Konieczkowski et al. [MITF expression levels than occurred in our panel; MITF gene expression levels would be classified as MITF high for all our cell lines according to the criteria established by Muller et al. [MITF expression and number of cell lines, was better powered to detect a modest association.Principal Component Analysis of the basal (untreated with drugs) transcriptomes of the five cytotoxicity groups revealed a correlation between drug synergy and the second Principal Component. The strongest transcriptional correlation and anticorrelation to synergy was among genes encoding cell surface receptors including GPCRs and neuroactive ligand signaling GPCRs. Many GPCRs are capable of activating the MAPK pathway, either directly or by transactivating RTKs utilizing neurotropin –48, thusmelanoma ,19,49. Freatment . Howeveri et al.  utilizedr et al. . Thus, tBRAFV600E zygosity status. The high proportion of homozygous BRAF mutants in our study (4 out of 12) helps to highlight this tendency, and was strengthened by comparison with data in the Cell Line Encyclopedia. As shown in BRAFV600E mutations. Regarding cytotoxic response to vemurafenib treatment, the cell lines tend to be more resistant on the left. Additionally, the cell lines on the top of the PCA plot respond synergistically to the combination of vemurafenib and lapatinib while those on the bottom do not. It has been shown that vemurafenib binding to BRAFV600E paradoxically activates downstream MAPK signaling via dimerization with non-mutant RAF family members and allosteric activation of the non-mutant partner [BRAFV600E cell line, opportunities for this paradoxical activation are limited to the heterodimers with RAF1, which could partially explain increased dependency on the output of the mutationally activated BRaf.The groupings of our cell lines according to cytotoxic response to drug treatment and basal expression profile can be rationalized in part by  partner . In a hoBRAFV600E suppresses MITF gene expression: in our cell lines, MITF levels tend to be lower in homozygous versus heterozygous lines. We hypothesize that BRAFV600E levels are inhibiting MITF levels, which in that sense may be acting more as a passenger. Nevertheless, MITF is a potent regulator of melanoma lineage and to a large extent determines the basal transcriptional profile, hence its association with the first Principal Component of our basal transcriptional analysis.Our results support the findings of Konieczkowski et al.  that oveBRAFV600E zygosity and MITF levels in affecting sensitivity to PLX4720, are mutations in additional oncogenes and tumor suppressor genes. Mutations in cell surface proteins and RTKs potentially explain (1) cytotoxicity group-BRAF zygosity exceptions including SKMEL28 (homozygous BRAF yet combination sensitive) and SKMEL24 (heterozygous BRAF yet PLX4720 sensitive), (2) lapatinib resistant cell lines, which are heterozygous BRAF and (3) DM331, which is an outlier from a cytotoxic and transcriptional profile grouping perspective. (1) In SKMEL28, the most striking variant was the EGFR exception mutation, which has previously been identified as a gain of function mutation [BRAFV600E. In SKMEL24 we found a number of potentially damaging mutations in cell surface proteins including TNFRSF14, ERBB3 and EGFR, which could limit SKMEL24’s ability to activate alternative growth pathways in the presence of BRaf inhibition, thereby, potentially explaining its sensitivity to PLX4720 treatment despite its heterozygous BRAF status. The ERBB3 mutation is particularly interesting given that ErbB family members are lapatinib targets; however, this mutation was not found to be damaging by polyphen2 analysis and would require more study to determine if it alters ERBB3 function. (2) Both lapatinib resistant cell lines contained a damaging mutation in ALK, which is a receptor tyrosine kinase that has been previously associated with multiple cancer types [ALK mutation could be stimulating growth, thereby explaining why the cells in the secondary resistant group are resistant to lapatinib. (3) DM331 is an exception in that it is homozygous for BRAFV600E yet it is by far the most resistant cell line to PLX4720 treatment. Additionally, its expression profile at the basal state most closely associates it with A375, one of the most sensitive lines. However, A375 resistant lines have been generated multiple times, indicating that its sensitivity is not immutable [NRG1, an ErbB family ligand is highly upregulated in DM331 only, and not in A375. On the other hand, addition of NRG1 to A375 causes resistance to PLX4720 [EGFR in DM331 this may explain the profound resistance of DM331 to BRaf inhibition.Layered over and interacting with mutation ,52 and cer types  and is ammutable ,18. We fERBB3 [V600E melanoma in response to PLX4720 treatment.We observed broad upregulation of ErbB pathway gene expression 8 hours after PLX4720 treatment, only in the group of cell lines that were sensitive to the combination of PLX4720 and lapatinib. This result is consistent with that of other groups that have found ERBB3 ,21,53 anERBB3 ,13 upregERBB4 total protein and VEGFR phosphorylation levels were anticorrelated with PLX4720 cytotoxic response across cell lines. This indicates that treatment with PLX4720 induces receptor tyrosine kinase expression, most likely as a compensatory survival mechanism in lines resistant to treatment as demonstrated in other studies [NRAS signaling and MAPK pathway reactivation.Surprisingly, we observed a relatively strong downregulation of oncogenic phosphoprotein levels after 8 hours of PLX4720 treatment in CGD and CGE cell lines with a similar but much weaker downregulation of the same proteins in CGA and CGC. We hypothesize that the downregulation observed at the protein level is the direct result of PLX4720 treatment followed by delayed, compensatory signaling in the combination and PLX4720 sensitive cell lines. As partial confirmation of this, we found that  studies ,53. ThisERBB3 activation due to vemurafenib treatment; however they saw activation via FOXD3. Chandarlapaty et al. [ERBB3 activation in response to AKT inhibition via phosphorylated FOXO3 in cancer cell lines from multiple tumor types, which were resistant to AKT inhibition. However, the correlation between changes in phosphorylated FOXO3 induced by PLX4720 and PLX4720 cytotoxicity in our melanoma lines indicate that FOXO3 phosphorylation is increased in the most sensitive lines suggesting alternative regulatory programs in the BRAFV600E melanoma cell lines that we studied. If FOXO3 phosphorylation activates ERBB3 as a compensatory mechanism to BRaf inhibition, we would expect its log2 fold change to be anticorrelated with cytotoxic response to PLX4720, similar to the response of ERBB4. We do see FOXO4 among the transcription factors whose motifs are enriched at the promoters of upregulated ERBB pathway genes. Importantly, while previous studies have observed ERBB3 upregulation in response to various inhibitors [Lastly, it appears that PLX4720 treatment potentially activates ETS family transcription factors and co-factors to upregulate ErbB pathway genes as well as those of other receptor tyrosine kinase signaling pathways. Abel et al. observed  ERBB3 acy et al.  identifihibitors ,24,54, wBRAFV600E melanoma cell lines. Additionally, we have observed that inhibition of other RTKs, also elicits a synergistic response when combined with BRaf inhibition. Taken together, our results suggest RTK mediated signaling is an adaptive response to mutant BRaf inhibition in melanomas and suggests combination therapy with specific RTK inhibitors to have potential clinical benefit.It has been noted that multiple receptor tyrosine kinases confer resistance to many forms of treatment . We findMelanoma cell lines used in this study were derived from tumors taken from tumor-involved lymph nodes from patients at the University of Virginia  or from patients at Duke University  as described previously –60. A375Four hours after being plated in 96-well plates, cells were treated with inhibitors or vehicle control in phenol red–free RPMI-1640 (Invitrogen) without FBS and incubated for 3 days at 37°C. alamarBlue (Invitrogen) was added to wells and incubated for 4 hours at 37°C. Fluorescence was measured at 560 nm excitation/590 nm emission on a Synergy 2 plate reader (BioTek Instruments). Mean results and SE were calculated for triplicate samples.Cell Culture and Generation of Transcriptome, Proteome and Exome Data.Cells were plated and incubated overnight before being treated, in duplicate, with inhibitors or vehicle control in phenol red–free RPMI-1640 without FBS for 8 hours at 37°C. Cells were placed on ice and rinsed with cold 1× PBS. Cells were collected and RNA was isolated using the Qiashredder (Qiagen) and RNeasy Mini Kit (Qiagen). RNA was quantified on the NanoDrop 2000 spectrophotometer (Thermo Scientific) and RNA quality was inspected on a 1% agarose gel. Biotin labeled RNA was hybridized to Illumina 3′IVT human HT-12 BeadChip arrays.Cells were plated in p60 dishes and incubated overnight to allow the cells to adhere before being treated with inhibitors or vehicle control in phenol red-free RPMI Medium 1640 without fetal bovine serum for 8 hours. Cells were washed and lysed in 1:1 2x Sample Buffer:Tissue Extraction Reagent (T-PER) (Life Technologies). Following lysis the samples were sonicated and centrifuged to clear. Samples were diluted to requested concentration and sent to the lab of Emanuel (Chip) Petricoin at George Mason University for Reverse Phase Array Analysis. Raw sequencing reads were generated using a combination of Nimblegen v2 and Agilent v1 capture probes.Cells were grown to 80% confluent, trypsinized, and collected in normal growth media. Cells were centrifuged to pellet, wash with PBS, and centrifuged a second time. DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen) and quantified on the Nanodrop 2000 spectrophotometer (Thermo Scientific). Exome sequencing was performed by Hudson Alpha Institute for Biotechnology .lumi Bioconductor package in R [limma to perform one way ANOVA and derive Benjamini-Hochberg False Discovery Rate (FDR) adjusted p-values [pvclust [pvclust with a correlation distance measure and average linkage  data was generated using methods described in [Illumina microarray data was variance stabilized transformed ,62 usingage in R . Genes tp-values ,65 and a[pvclust  with the linkage . We idenSeattleSeq. Genes with novel variants were identified by removing variants previously identified in dbSNP, or 1000Genomes. Novel variants were further filtered by selecting only nonsense and missense mutations as identified by SeattleSeq. The list of genes containing novel nonsynonymous variants were selected by retaining only those that were identified by [PolyPhen2 to determine the potential functional severity of these nonsynonymous variants. Genes were identified that uniquely contained variants in each cell line, and within each cytotoxicity group.Exome probes were genotyped using the Genome Analysis Toolkit and the HG19 build as a reference genome. Single nucleotide variants were annotated by ified by  as beingGene sets found to be significant underwent pathway enrichment analysis via Pathway Express and Gene Set Enrichment Analysis (GSEA) using MSigDB datasets. Significant gene sets and pathways were identified as having a FDR enrichment value of 0.05 for both tools. To ascertain significance of transcription factor binding motifs, we identified motifs unique to the CGC. For motifs not unique to the CGC, we summed the rank of CGC with 100 minus the rank of the other group. When multiple groups contained the motifs, we averaged the ranks, then summed the rank of CGC with 100 minus the averaged rank.S1 FigCombination − (1 – (1 – CSecondary Drug)*(1 – CPrimary Drug)) where CCombination is the observed cytotoxicity of the combination treatment, CSecondary Drug is the observed cytotoxicity of the secondary drug, and CPrimary Drug is the observed cytotoxicity of the primary drug.Synergy score (left y-axis) across all 12 cell lines (bottom x—axis) for escalating doses of lapatinib (top x-axis) and PLX4720 (right y-axis). Synergy Score was calculated using the Bliss model of independence. Synergy Score = C(EPS)Click here for additional data file.S2 FigMITF ChIP-Seq binding sites (blue) and Cluster IV gene list (pink).Number of overlapping genes (purple) between (EPS)Click here for additional data file.S3 FigBRAF mutants denoted with a “*” (A). Relative expression in Log2 of AXL (Red) and MITF (Blue) (B).Basal PCA plot with cytotoxicity groups encircled, and homozygous (EPS)Click here for additional data file.S4 FigBox plots of cytotoxic response to single drug treatment (top 2 rows) and escalating PLX4720 doses of the combination drug treatment (bottom 3 rows). Doses along the bottom refer to either the single drug treatment (top 2 rows), or the secondary drug treatment dose for the combination drug treatment (bottom 3 rows). Red dots denote negative cytotoxic responses, and blue dots denote positive cytotoxic responses.(EPS)Click here for additional data file.S5 FigThe responses for each group are CGD (top left), CGE (top right), CGA (bottom left), and CGC (bottom right). Nodes in red denote an upregulation, nodes in blue denote a downregulation. Refer to (EPS)Click here for additional data file.S6 FigThe responses for each group are CGD (top left), CGE (top right), CGA (bottom left), and CGC (bottom right). Nodes in red denote an upregulation, nodes in blue denote a downregulation. Refer to (EPS)Click here for additional data file.S7 FigThe responses for each group are CGD (top left), CGE (top right), CGA (bottom left), and CGC (bottom right). Nodes in red denote an upregulation, nodes in blue denote a downregulation. Refer to (EPS)Click here for additional data file.S8 FigThe responses for each group are CGD (top left), CGE (top right), CGA (bottom left), and CGC (bottom right). Nodes in red denote an upregulation, nodes in blue denote a downregulation. Refer to (EPS)Click here for additional data file.S9 FigUnsupervised clustering of gene expression response to PLX4720 treatment (y-axis) of the ErbB signaling pathway of each cell line (Left). Genes were selected for plotting based on passing the FDR corrected P Value threshold of 1% or better in at least one cytotoxicity group. Genes that demonstrated a FDR corrected P Value of 1% or better for the t-test for an individual cell line are denoted with an “*”. Transcription factors were identified that have binding motifs in the promoters of these genes (Right).(EPS)Click here for additional data file.S1 TableClusters III and IV were identified from the one way ANOVA analysis . Each cl(XLSX)Click here for additional data file.S2 TableList of probes from gene expression microarray that demonstrated a correlation coefficient greater than 0.8 or less than -0.8 with the loadings of the first principal component.(XLSX)Click here for additional data file.S3 TableList of probes from gene expression microarray that demonstrated a correlation coefficient greater than 0.8 or less than -0.8 with the loadings of the second principal component.(XLSX)Click here for additional data file.S4 TableProbes that demonstrated significant correlation  Tables u(XLSX)Click here for additional data file.S5 TableVariants that were present in individual cell lines of interest or present in all cell lines of a cytotoxicity group were submitted to Polyphen2 to ascertain potential functional significance.(XLSX)Click here for additional data file.S6 TableLog2 Fold changes, p values, and Bonferroni corrected p values (FDR) for differential expression analysis of each cytotoxicity group.(XLSX)Click here for additional data file.S7 TableDifferentially expressed genes for each cytotoxicity group that passed a FDR threshold of 1% or better were submitted to Pathway Express to identify pathways significantly affected by treatment.(XLSX)Click here for additional data file.S8 TableDifferentially expressed genes for each cytotoxicity group that passed a FDR threshold of 1% or better were submitted to MSigDB to identify pathways and transcription factors significantly affected by treatment.(XLSX)Click here for additional data file.S9 TableTable of correlations between log2 fold changes of cell lines’ protein responses to treatment of PLX4720 with cell lines’ cytotoxic responses to PLX4720.(XLSX)Click here for additional data file.


Clostridium and an increase in Enterobacter, Escherichia/Shigella, and Pseudomonas in stool after ABX + DS for 10 days. Goblet cell density was significantly lower in ABX treated groups compared to controls. Stool from SS subjects had greater relative abundances of Pseudobutyrivibrio, Escherichia/Shigella, Blautia, and Streptococcus, while relative abundance of Bacteroides, Parabacteroides, Faecalibacterium, and Prevotella was reduced compared to controls. The severity of SS ocular and systemic disease was inversely correlated with microbial diversity. These findings suggest that SS is marked by a dysbiotic intestinal microbiome driven by low relative abundance of commensal bacteria and high relative abundance of potentially pathogenic genera that is associated with worse ocular mucosal disease in a mouse model of SS and in SS patients.There is mounting evidence that the microbiome has potent immunoregulatory functions. We assessed the effects of intestinal dysbiosis in a model of Sjögren syndrome (SS) by subjecting mice to desiccating stress (DS) and antibiotics (ABX). We characterized the conjunctival, tongue and fecal microbiome profiles of patients with SS. Severity of ocular surface and systemic disease was graded. 16S ribosomal RNA gene sequencing characterized the microbiota. ABX + DS mice had a significantly worse dry eye phenotype compared to controls, a decrease in  The conjunctiva is infiltrated with dendritic cells (DC) and CD4+ T cells. These T cells produce increased levels of IFN-γ and IL-17 that cause ocular surface epithelial disease. IFN-γ causes epithelial apoptosis, goblet cell loss, and increased production of cornified epithelial precursor proteins by the surface epithelium that leads to a poorly wettable skin-like surface121315DQA1*0501 allele18The glandular and mucosal immunopathological changes in SS have been thoroughly investigated3Intestinal commensal microbiota limit colonization by enteropathogens and maintain mucosal immune homeostasis throughout the body. Commensal symbionts produce factors that support intestinal barrier function, promote generation of tolerogenic DCs and regulatory T cells (Tregs), and modulate cytokine production by NKT cells. Short chain fatty acids (SCFA), primarily butyryric acid (and to a lesser degree propionic and acetic acid) produced by fermentation of dietary starches by commensal taxa has been found to have potent immunomodulatory activity22Escherichia coli or decreased Faecalibacterium prausnitzii, a major intestinal butyrate produce, have been associated with disease activityEvidence suggests that intestinal dysbiosis  contributes to the pathogenesis of inflammatory bowel disease (IBD)+ T cell infiltration in the conjunctiva, and increased expression of inflammatory cytokines and T- cell related IFN-γ and IL-17 mRNA transcripts in conjunctiva282930303233343536We have developed and refined an environmental mouse model of dry eye that recapitulates findings in SS. The observed changes correlate well with human disease and include: corneal barrier disruption, decreased conjunctival goblet cell density, increased CD4SS affects multiple mucosal tissues and their secretory glands leading to reduced levels of innate antimicrobial factors 438We found that depletion of the intestinal microbiome significantly worsens the ocular surface response to desiccation, and there is significantly altered diversity in the oral and intestinal microbiome in SS that is associated with disease severity.SS affects multiple mucosal tissues and their supporting glands. Because secretory dysfunction of these glands results in reduced levels of growth factors and innate antimicrobial factors, we hypothesized this could result in mucosal microbial dysbiosis. We evaluated the effects of antibiotic (ABX) treatment on severity of ocular disease in a validated mouse dry eye model with features of SS. This model is induced by pharmacological suppression of glandular tear secretion by cholinergic blockade and exposure to a desiccating stress (DS) environment (drafty and <30% relative humidity). Mice develop severe keratoconjunctivitis sicca with features resembling SS, including corneal barrier disruption, loss of conjunctival goblet cells, and increased expression of inflammatory and T-cell related cytokines in the conjunctiva in response to DS282930Our experimental design is presented in Blautia, Alistipes, Lactobacillus, Allobaculum, Bacteroides, Desulfovibrio, Intestinimonas, and Clostridium, while there were significant increases in Enterobacter, Parasutterella, Escherichia/Shigella, Pseudomonas, Staphylococcus +T cells infiltrating the conjunctival epithelium , and greater corneal barrier disruption  loss , increased number of CD4Forkhead box A2 (Foxa2), a transcriptional repressor of GC differentiation and mucus biosynthesis, as IL-13-stimulated cultured murine GC had upregulated Muc5ac, Muc2 and resistin-like molecule β (RELMβ) and decreased Foxa2 gene expression44We have previously shown that IL-13 is a homeostatic factor maintaining conjunctival GC, while IFN-γ induces conjunctival GC apoptosis and cornification1241Taken together, these results demonstrate that desiccating stress alone (regular water) causes a significant increase in Proteobacteria, while oral antibiotic treatment and DS results in extreme changes in the gut microbiota, caused by in a reduction of commensal bacteria and increase in Proteobacteria that was associated with a more severe ocular phenotype. Furthermore, these data provide insight into the molecular mechanisms involved in dysbiosis-induced goblet cell loss.After identifying that dysbiosis induced a more severe phenotype in an animal model of SS, we performed a pilot study comparing the microbiome of the conjunctiva, tongue, and stool in subjects with primary SS and healthy control subjects. The conjunctival microbiome in subjects with rosacea that results in a lipid tear deficiency were also evaluated as a comparator group. Demographic features of the study groups are presented in We collected microbiome samples from the inferior conjunctiva of eight normal control subjects, six rosacea patients, and 15 SS patients. Due to the low bacterial biomass on the ocular surface, sequencing yield was lower than anticipated, with an average of 212 mapped 16S sequences per sample. A core microbiome consisting predominantly of Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes phyla was found in conjunctival samples from all three groups . We did not observe any significant differences in overall composition, richness, or structure of the ocular surface microbiome between the three study groups  dataThe association between diversity of the stool microbiome and categorical severity of ocular surface, systemic, or combined ocular and systemic disease severity was evaluated. There was a significant inverse correlation between fecal microbial diversity and combined ocular and systemic disease index  and produces SCFA, including butyrate. Decreased abundance of this species has been reported in recurrent IBDThe intestinal microbiome is one of the more diverse niches in the human body. Members of the Bacteroidetes and Firmicutes phyla dominate the intestinal microbiome, and the microbiome of an individual is more similar to self over time than to othersFaecalibacterium. Desiccating stress in ABX-mice decreased IL-13, and increased IFN-γ mRNA in the conjunctiva compared to mice that drank ABX but were non-stressed. Consistent with our findings in the SS model, we observed that stool from SS patients had approximately a 50% reduction in the genus Faecalibacterium, which includes Faecalibacterium prausnitzii one of the predominant butyrate producers in the intestine. There was also a significant increase in potential enteric pathogens, such as Escherichia/Shigella and Enterobacter in the ABX-treated mice and SS stools. Previous studies have found that antibiotic-treated mice have marked reduction of stool SCFAs+Tregs has been noted in colonic lamina propria of GF and ABX-treated B6 mice and conventionalization of GF with feces from normal mice restored the normal frequency of Foxp3+Tregs, indicating that signals from microbiota modulate Treg numbers in the gut+Tregs generation.The possible role of the microbiome in promoting autoimmunity is just now beginning to be understood, and studies have demonstrated that they can be tissue-specific. For example, in the NOD model of type 1 diabetes, the microbiome was found to inhibit the onset of diabetes in male mice; however, the effects of the microbiome in the SS-like disease that develops in this strain has not been evaluated5153IL-13, decreased Foxa2 expression, and prevented GC lossintegrin alpha 2 transcript, a NK and NKT cell marker. This has potential significance because we have demonstrated that conjunctival resident NKT cells (CD49b + cells) are the major IL-13 producers in the conjunctivaConjunctival goblet cells express IFN-γ and IL-3 receptors41411214In this pilot study, we observed distinct differences in the oral and stool microbiome in the SS group compared to controls, characterized by a decreased microbial richness in the mouth and significant differences in certain genera in both mucosal sites. Subjects with the most severe keratoconjunctivitis sicca and combined systemic and ocular disease were found to have the lowest diversity of stool microbiota. It remains to be determined if the observed changes in the mucosal microbiome result from the secretory glandular dysfunction, defective cholinergic signaling with altered gastrointestinal motility, or mucosal disease in SS and/or if the mucosal dysbiosis is a stimulus for development or exacerbation of the autoimmune mucosal and glandular disease immunopathology at these sites.Doan et al, ARVO meeting abstract, IOVS June 2015, Vol.56, 4067). It is possible that differences exist in number and diversity of taxonomic units on the ocular surface between normal and SS eyes, but it will require improved methods to extract and identify the low abundance microbial specific DNA sequences to confirm this.Although we found differences in the stool and tongue microbiome between control and SS groups, we did not find differences in the conjunctival microbiome. Despite being an exposed mucosa, studies have found very few cultivable bacteria from a swab sample of the conjunctivaIn summary, we found evidence of oral and intestinal dysbiosis in SS and associations between reduced diversity of intestinal taxa and severity of ocular and systemic disease. This study adds SS to the growing list of autoimmune conditions that are associated with intestinal dysbiosis.This research protocol was approved by the Baylor College of Medicine Center for Comparative Medicine, and it conformed to the standards of the Association for Research in Vision and Ophthalmology Statement for the Use of Animals in Ophthalmic and Vision Research. Female C57BL/6 (B6) mice were purchased from Jackson Laboratories  and were used at 6-8 weeks of age.Desiccating stress (DS) was induced in female C57BL/6 mice aged 6–8 weeks by sterile subcutaneous injection of 0.5 mg/mL scopolamine hydrobromide  QID into alternating flanks and exposure to a drafty low humidity (<30% relative humidity) environment for 5 or 10 days (DS5 and DS10 respectively) as previously describedSix-to-eight week old female C57BL/6 mice  were treated with a cocktail of broad-spectrum antibiotics  dissolved in drinking water with 5 mg/ml artificial sweetener  as previously describedRight eyes and ocular adnexa were surgically excised (n = 5/group), fixed in 10% formalin, paraffin embedded and were cut into 8-μm sections. Goblet cells in sections were stained with periodic acid-Schiff (PAS) reagent and were examined, photographed and counted with a microscope equipped with a digital camera  as previously described+ T cells in the conjunctival epithelia was counted in cryosections stained with rat-anti mouse CD4  as previously describedFor immunohistochemistry, left eyes and adnexa of mice at each time point (n = 5) were excised, embedded in optimal cutting temperature , and flash frozen in liquid nitrogen. Sagittal 8-μm sections were cut with a cryostat , placed on glass slides and stored at −80 °C. The number of CD4Corneal epithelial permeability to Oregon Green Dextran  was assessed by instilling 0.5 μL of OGD onto the ocular surface one minute before euthanasia, as previously describedIl17a, Mm0043918_m1), IFN-γ , IL-13 , Foxa2 , Integrin α2   in a commercial thermocycling system , according to the manufacturer’s recommendations. The beta-2 microglobulin (β2 m) (B2m) (Mm00437762_m1) gene was used as an endogenous reference for each reaction. The results of quantitative PCR were analyzed by the comparative Ct method in which the target of change = 2−ΔΔCt and were normalized by the Ct value of β2 m and the mean Ct of relative mRNA level in the untreated naïve group. Gene expression of 3–4 mice per group per time point in two independent experiments with a total of 6–8 mice was determined.Total RNA from conjunctiva or the corneal epithelium was extracted using a QIAGEN RNeasy Plus Micro RNA isolation kit (Qiagen) following the manufacturer’s protocol. Conjunctiva was surgically excised. One sample equaled the tissue pooled from both eyes of each animal. After isolation, the concentration of RNA was measured and cDNA was synthesized using the Ready-To-Go™ You-Prime First-Strand kit  as previously describedAll experiments were repeated at least two times. After completion of all experiments, data were averaged, and graphs were generated. Sample size calculation was performed with StateMate Software version 2.0 , based on preliminary data.The data consisting of two groups or more groups were analyzed by non-parametric Kruskall Wallis followed by Sidak’s multiple comparison test. P ≤ 0.05 was considered statistically significant. These tests were performed using GraphPad Prism 6.0 software .The study was conducted in accordance with the Declaration of Helsinki, and the Baylor College of Medicine Institutional Review Board approved the protocol and informed consent form prior to study initiation. Informed consent was obtained from all patients prior the study. Patients with SS were recruited from the multispecialty SS clinic at Baylor College of Medicine (BCM). All SS patients had a complete ocular, oral, and rheumatologic evaluation, including panel of serum autoantibodies and met proposed American College of Rheumatology diagnostic criteria for SS602 area on the dorsal tongue for 5 seconds. Ocular and oral samples were placed into MO-BIO 0.1 mm glass bead tubes,  containing 500 μL solution SW1 and immediately frozen at −80 °C. Stool was collected in a commode collection container  and immediately placed in a Styrofoam container containing cool packs, sealed and delivered to the clinic within 60 minutes where it was placed in a −80 °C freezer until the DNA was extracted.Ocular microbiome was sampled using an EyePrim device  that applies a sterile Supor 450 membrane against the inferior conjunctival fornix. Tongue microbiome was sampled using a Catch-All™ Specimen Collection swab  rubbed over a 1 cmVariable regions 1–3 (v1-3) of the 16S rRNA gene were amplified using previously described primers for conjunctival samplesBacterial genomic DNA from tongue and stool samples was extracted using the PowerSoil DNA Isolation Kit (MO BIO Laboratories). The 16S rDNA V4 region was amplified by PCR and sequenced in the MiSeq platform (Illumina) using the 2 × 250 bp paired-end protocol yielding pair-end reads that overlap almost completely. The primers used for amplification contain adapters for MiSeq sequencing and dual-index barcodes so that the PCR products may be pooled and sequenced directly16S rRNA gene sequences were assigned into OTUs at a similarity cutoff value of 97% using UPARSE and the SILVA Database63How to cite this article: de Paiva, C. S. et al. Altered Mucosal Microbiome Diversity and Disease Severity in Sjögren Syndrome. Sci. Rep. 6, 23561; doi: 10.1038/srep23561 (2016).


Aberrant DNA methylation is frequently observed in breast cancer. However, the relationship between methylation patterns and the heterogeneity of breast cancer has not been comprehensively characterized.Whole-genome DNA methylation analysis using Illumina Infinium HumanMethylation450 BeadChip arrays was performed on 188 human breast tumors. Unsupervised bootstrap consensus clustering was performed to identify DNA methylation epigenetic subgroups (epitypes). The Cancer Genome Atlas data, including methylation profiles of 669 human breast tumors, was used for validation. The identified epitypes were characterized by integration with publicly available genome-wide data, including gene expression levels, DNA copy numbers, whole-exome sequencing data, and chromatin states.BRCA1 mutations, one epitype contained a subset of ERBB2-amplified tumors characterized by multiple additional amplifications and the most complex genomes, and one epitype displayed a methylation profile similar to normal epithelial cells. Luminal tumors were stratified into the remaining four epitypes, with differences in promoter hypermethylation, global hypomethylation, proliferative rates, and genomic instability. Specific hyper- and hypomethylation across the basal-like epitype was rare. However, we observed that the candidate genomic instability drivers BRCA1 and HORMAD1 displayed aberrant methylation linked to gene expression levels in some basal-like tumors. Hypomethylation in luminal tumors was associated with DNA repeats and subtelomeric regions. We observed two dominant patterns of aberrant methylation in breast cancer. One pattern, constitutively methylated in both basal-like and luminal breast cancer, was linked to genes with promoters in a Polycomb-repressed state in normal epithelial cells and displayed no correlation with gene expression levels. The second pattern correlated with gene expression levels and was associated with methylation in luminal tumors and genes with active promoters in normal epithelial cells.We identified seven breast cancer epitypes. One epitype was distinctly associated with basal-like tumors and with Our results suggest that hypermethylation patterns across basal-like breast cancer may have limited influence on tumor progression and instead reflect the repressed chromatin state of the tissue of origin. On the contrary, hypermethylation patterns specific to luminal breast cancer influence gene expression, may contribute to tumor progression, and may present an actionable epigenetic alteration in a subset of luminal breast cancers.The online version of this article (doi:10.1186/s13058-016-0685-5) contains supplementary material, which is available to authorized users. ERBB2-amplified tumors [human epidermal growth factor receptor 2 (HER2)-enriched], a subtype associated with triple-negative  tumors , and a subtype with an expression profile similar to that of normal breast tissue   representing the methylation levels. CpG sites with detection Adjustment for bias between Infinium I and II assay CpG probes was performed by using a peak normalization algorithm. Briefly, for each sample, we performed a peak-based correction of Illumina I and II chemical assays inspired by Dedeurwaerder et al.  as previA DNA hypermethylation score was calculated as described elsewhere . The hypUnsupervised bootstrap consensus clustering was performed to identify DNA methylation subgroups of tumors using 2000 bootstrap iterations as described elsewhere .2-transformed, and mean-centered across the 994 tumor samples to generate relative gene expression levels for 20,531 genes in the validation cohort. For some analyses, we were interested in comparing estimates of the expression levels of different genes and therefore could not use relative expression levels across tumors. In these analyses, we took the effective transcript length into account by using the gene RSEM scaled estimates (tau) from the TCGA data transformed into transcripts per million (TPM), and used log2(TPM + 1) as a measure of gene expression [Gene expression data from oligonucleotide microarrays were available for 158 of the tumors in the discovery cohort as part of accession number GEO:GSE25307], which encompasses 577 breast tumors . The norpression . The R ppression . Express307, whicpression . Genes ip values much less than 10−6. Hence, correcting for multiple hypothesis testing, less than 1 CpG site having a Pearson correlation greater than 0.2 or less than −0.2 is expected by chance across the 661 tumors.We calculated correlations between methylation and relative gene expression levels using the validation cohort because the number of genes was limited on the expression platform used in the discovery cohort. Matching on gene symbol, 324,991 CpG sites were associated with a unique gene in the TCGA gene expression data and displayed variation in methylation levels across the validation cohort. Pearson correlations of 0.2 and −0.2 between gene expression and methylation levels were associated with Enrichment of functional classification of genes in identified gene sets was analyzed using the DAVID Functional Annotation Tool  with theChromatin states in human embryonic stem cells (H1hESCs) and HMECs, as well as peak calls for DNase I hypersensitive sites and EZH2 binding sites in HMECs from the uniform pipeline, all generated by the ENCODE consortium, were obtained using the UCSC Genome Browser , 29. CpGBRCA1 promoter methylation analysis was performed using the 450K methylation data. To identify informative CpG sites, we screened all 44 CpG sites on the platform located within BRCA1 transcripts or 1 kb upstream for negative correlation (Pearson correlation less than −0.2) with BRCA1 gene expression levels using the validation cohort. We identified 21 informative CpG sites. All informative CpG sites were located within 1 kb centered on the BRCA1 transcription start site. Tumors were classified as BRCA1 promoter methylated if the average β value for the informative CpG sites was greater than 0.2. The average β value for the informative CpG sites ranged from 0.004 to 0.03 across the 96 normal tissue samples in the normal cohort. To validate BRCA1 promoter status, we used data available for 71 tumors from a previous study in the discovery cohort and obtained with a PSQ HS 96 pyrosequencing system  as described . For some tumors, the MAF file contained mutations called from multiple exome sequencing experiments with different reference samples or different tumor aliquots.  We called gene mutations when genes were mutated in at least one experiment for the tumor, and the total number of single-base substitutions for each tumor was calculated as the average for the multiple experiments.Copy number estimates and CNAs obtained from bacterial artificial chromosome (BAC) arrays were available for 180 of 188 tumors in the discovery cohort from previous studies , 31, 32.2 tests, t tests, and Fisher’s exact tests were performed in R. Adjustment of p values for multiple-testing correction of these statistical tests was performed using p.adjust in R with the Benjamini-Hochberg method to control the FDR . Similar results were obtained using the gene expression–based subtype HER2-enriched (in the discovery cohort 7 (22 %) of 32 of HER2-enriched tumors in ET6 and 17 (23 %) of 73 in the validation cohort). A scheme to classify breast cancers into ten clusters based on CNAs that influence gene expression patterns (IntClust) has been proposed   of 15; p = 8e-5, Fisher’s exact test (df = 1)]. In the discovery cohort, 22 (85 %) of 26 tumors from patients harboring BRCA1 germline mutations were classified as ET7 . In the validation cohort, 7 (58 %) of 12 tumors with somatic nonsilent BRCA1 mutations were classified as ET7 .We screened for associations between somatic mutations and epitypes using TCGA exome sequencing data for the validation set. There were 2205 genes with nonsilent mutations in at least 5 tumors in the validation set. Of these, only three genes,  %] Fig. . Overall %] Fig. . GermlinBRCA1 and HORMAD1 to explore if aberrant methylation of these candidate drivers of genomic instability in basal-like breast cancer . The median correlation of methylation of the 21 promoter CpG sites used to assess BRCA1 methylation status and BRCA1 expression was −0.34 across the validation cohort. These CpG sites were all in the chromatin state active promoter in both H1hESCs and HMECs. Using the 450K methylation data, we identified 24 (13 %) and 47 (7 %) HORMAD1 promoter unmethylated tumors in the discovery and validation cohorts, respectively. HORMAD1 promoter unmethylated tumors were primarily of the ET7 epitype (23 (96 %) of 24 in discovery cohort, 40 (85 %) of 47 in validation cohort). The median correlation of methylation of the seven promoter CpG sites used to assess HORMAD1 methylation status and HORMAD1 expression was −0.63 across the validation cohort. These CpG sites were all in the heterochromatin/low signal chromatin state in both H1hESCs and HMECs. BRCA1 methylation and HORMAD1 demethylation were not mutually exclusive . These results demonstrate a strong association of BRCA1 methylation and HORMAD1 demethylation with the basal-like epitype ET7. Moreover, these candidate drivers of genomic instability in basal-like breast cancer may be regulated by aberrant methylation in subsets of these tumors.Tumors of the basal-like epitype ET7 displayed highly altered genomes, but limited epitype-specific hyper- and hypomethylation, compared with normal samples. We investigated the promoter methylation status of t cancer , 53 was BRCA1 deficiency is associated with a different epigenetic entity within basal-like tumors. Therefore, we screened for global methylation differences within the ET7 epitype between BRCA1 promoter methylated tumors, BRCA1 germline mutated tumors, and tumors with no known BRCA1 aberration [BRCA1 wild type (WT)]. We identified only 18 CpG sites with different methylation levels between the three tumor groups in the discovery cohort, even at relatively nonstringent statistical significance , and all of these CpG sites were located around the BRCA1 transcription start site. Similar results were obtained comparing BRCA1 methylated tumors with only BRCA1 WT tumors. In the validation cohort, we identified 90 CpG sites with different methylation levels between BRCA1 WT, BRCA1 methylated, and BRCA1 somatically mutated tumors . Of these, 29 CpG sites were located around the BRCA1 transcription start site, while the other CpG sites were scattered across the genome. One sample with both a somatic missense mutation and promoter methylation were assigned to the methylation group in these analyses. Comparing BRCA1 methylated tumors with only BRCA1 WT tumors in the validation cohort, we identified 31 significant CpG sites ; all but one located around the BRCA1 transcription start site. Taken together, separating ET7 tumors into BRCA1 promoter methylated, BRCA1 mutated, or BRCA1 WT revealed that these three groups have strikingly small differences in their genome-wide methylation patterns.It is unclear if p = 1e-17 and p = 7e-15, respectively; χ2 test) and the validation cohort , as expected from the association between epitypes and gene expression phenotypes. Node status was more weakly associated with the epitypes with higher fractions of node positive tumors in ET4 and ET6 and lower fractions in ET7 . Tumor size (in millimeters) was significantly associated with epitypes in the discovery cohort , with the largest median tumor size in ET4–ET7 and the median tumor size increased across luminal epitypes from ET2 to ET4. Tumor size in millimeters was not available for the validation cohort. We therefore compared T1 tumors (≤20 mm) with larger tumors (T2 and T3) in this cohort and found that this dichotomized tumor size was associated with epitypes . Our observations for the validation cohort were similar to the results in the discovery cohort: The largest fractions of large tumors (>20 mm) were in ET4–ET7, and the fraction of large tumors increased across the luminal epitypes from ET2 to ET5. In general, there were no associations between histological subtypes and epitypes. Lobular tumors were associated with the luminal epitypes , but they were not associated with a particular luminal epitype. There was a significant association between age at diagnosis and epitype . Convincingly, the oldest patients were in ET5 and the youngest patients in ET7 for both the discovery cohort  and the validation cohort .We investigated associations between epitypes and clinicopathological features for both the discovery and validation cohorts  and the full follow-up . Patient outcome analysis in the validation cohort is hampered by limited follow-up information. Reassuringly, the epitypes had similar overall survival patterns in both cohorts  restricted to CpG sites in promoters [DNA methylation of CpG sites in the genome is a normal developmental process that is of interest in cancer because many sites become aberrantly methylated or demethylated in the disease state. Moreover, it is often claimed that DNA methylation processes are of importance for tumor initiation and progression. We conducted a comprehensive analysis of genome-wide DNA methylation profiles of 188 breast tumor samples. Our overarching goal was to gain insights into how DNA methylation patterns on a genome-wide scale are associated with breast cancer heterogeneity. The findings were extensively validated in an independent cohort from TCGA encompassing 669 breast tumor samples. Previously, TCGA identified five epitypes—essentially corresponding to two luminal A epitypes, two luminal B epitypes, and a basal-like epitype—in an analysis of a large tumor set n = 66 restriTP53 mutation frequency, number and complexity of CNAs, number of amplifications, and tumor size all increased from ET2 to ET5. These findings are consistent with observations based on gene expression–based analyses suggesting that the separation of tumors into luminal A and luminal B is not well-defined, but rather reflects an arbitrary cutpoint in a continuous distribution of expression levels of proliferation-related genes [BRCA2 germline mutations. However, the association between BRCA2 germline mutations and ET4 remains to be validated in an independent dataset. ET5 displayed a global hypermethylation phenotype and was associated with older patients. The different global methylation patterns of ET4 and ET5 provide an example of the opportunities of going beyond analyses restricted to promoter CpG islands. On the contrary, the more luminal A-like epitypes ET2 and ET3 seemed to reflect more of a continuum, and the separation of these tumors into groups is likely cohort size–dependent. Indeed, in an unsupervised analysis of the large validation cohort (n = 669), there was support to separate ET3 into two groups  were characterized by a gradual increase of many variables from ET2 to ET5. For example, proliferative rate, fraction of luminal B tumors, promoter CpG island methylation levels, overall mutation rate, ed genes –39. Impo–ET5 wereERBB2-amplified tumors (around 20 %), and it is likely that our use of tumor sets containing many HER2-enriched tumors . However, we did not identify any methylation differences within the basal-like epitype when stratified according to BRCA1 status (either germline or somatic), with the exception that BRCA1 alone displays promoter methylation in a subset of tumors with the basal-like epitype. These observations are consistent with findings reported by Prat et al., who observed very minor molecular differences at multiple levels  according to BRCA1 status in basal-like breast cancer [We identified only a few associations between somatic mutations and epitypes in a screen taking multiple testing into account. As expected, t cancer .BRCA1 germline mutations. On the other hand, the validation cohort contains a larger fraction of ER-positive luminal tumors and more tumors from older patients (Table Analyses of whole tumor tissues have revealed that DNA methylation patterns are heavily influenced by surrounding or infiltrating stromal cells , 54. We Traditionally, epigenetic reprogramming has been thought to contribute to tumor progression by silencing tumor suppressor genes. This model has been challenged by the finding that most cancer-associated methylation occurs in genes that are already repressed in the normal tissue from which the cancer derives , 58. We Constitutive methylation in breast cancer and methylation specific to luminal cancer occurred in regions in different chromatin contexts in normal mammary epithelial cells. Constitutive methylation occurred primarily in regions in a Polycomb-repressed state, consistent with this methylation not being the original cause of repression of gene expression. Luminal-specific methylation was enriched in regions in active promoter states in normal cells, adding support to the picture in which aberrant methylation contributes to a block to keep some luminal cancers more undifferentiated. Because breast cancer–specific methylation to a large extent is associated with chromatin states and thus with aberrant methylation of very many genes, often already repressed in precancerous tissue, it is not straightforward to identify potential epigenetic driver genes. It may be that epigenetically deregulated driver genes are rare and that most methylation in cancer is a passenger event of general epigenetic deregulation in cancer. Perhaps the methylated genes are prone to methylation merely because they are repressed in a tissue-specific fashion . MoreoveBRCA1 and HORMAD1 are clear candidates for driver genes directly regulated by aberrant methylation in some basal-like breast cancers. Taken together, our results suggest that the dominant patterns of breast cancer–specific hyper- and hypomethylation are associated with their genomic contexts, but also that there may be epigenetically deregulated driver genes for subsets of samples.We focused our analyses on genome-wide screens for CpG sites that display changes in methylation state between macrodissected tumor tissue and normal breast tissue. There are limitations with use of this approach, although its utility in identifying and characterizing robust epitypes is clear. Directions for future improved characterization of breast cancer epigenetic heterogeneity include using different normal cell subpopulations separately instead of normal breast tissue, and investigating CpG sites that display varying or intermediate methylation in normal cell populations. Another limitation of the present study is that we restricted our analyses to epitype-specific methylation patterns. These analyses revealed very low numbers of CpG sites with specific hyper- and hypomethylation across the basal-like epitype. However, directed analyses showed that The gene expression–based molecular subtypes of breast cancer have been included in international guidelines for breast cancer treatment . The epiBRCA1 and HORMAD1 display aberrant methylation in subsets of basal-like tumors, the results of our study imply that the dominant methylation patterns across basal-like breast cancer are passenger events reflecting the tissue of origin and infiltrating cells. However, methylation patterns specific to luminal breast cancer drive gene expression and may contribute to tumor progression in this subtype.We performed a comprehensive analysis of DNA methylation patterns in human breast tumors. The methylation epitypes we describe exemplify how integrating different types of molecular genome-wide data can improve the characterization of breast cancer heterogeneity. We identified differences in the methylation patterns across breast cancer subtypes. Although we showed that candidate driver genes such as


Phaseolus vulgaris L.) grown in high pH, calcareous soils with low organic matter. Field studies were conducted at the University of Wyoming Sustainable Agriculture Research and Extension Center in 2009 and 2010. Black- and navy beans were grown alone or intercropped with annual ryegrass (Lolium multiflorum Lam.), oat (Avena sativa L.), corn (Zea mays L.), or spring wheat (Triticum aestivum L.) in a two-factor factorial strip-plot randomized complete block design. All four grass species increased chlorophyll intensity in dry beans. However, grass species did not increase iron (Fe) concentration in dry bean tissues suggesting inefficient utilization of Fe present in the dry bean tissues. In 2009, nitrate-nitrogen (NO3-N) and manganese (Mn) concentration in bean tissue were greater in bean monoculture than in grass intercropped beans. Bean monoculture also had greater soil NO3-N concentrations than grass intercropped treatments. In 2009, grass intercrops reduced dry bean yield >25% compared to bean monoculture. Annual ryegrass was the least competitive of the four annual grass species. This suggests that competition from grasses for nutrients, water, or light may have outweighed benefits accruing from grass intercropping. Additional studies are required to determine the appropriate grass and dry bean densities, as well as the optimum time of grass removal.This study evaluated whether grass intercropping can be used to alleviate Fe deficiency chlorosis in dry beans ( The northern Great Plains is a key contributor to dry edible bean production in the United States accounting for 52% of the total dry bean production in 2008 −1) Conventional management of Fe deficiency involves foliar application of 1 to 2% of Fe sulfate solution (FeSO4.7H2O) at 200 to 400 l ha+) extrusion, increasing Fe3+-chelates' solubility and the concomitant reduction by a ferric reductase to Fe2+, which can then be taken up by the plants Triticum aestivum L.), barley (Hordeum vulgare L.), and corn (Zea mays L.), phytosiderophores are synthesized by the plant roots and secreted in the rhizosphere where they chelate Fe3+ and make it more bioavailable Helianthus annuus L.) in nutrient solution.Higher plants have developed two different Fe-efficient strategies to increase Fe availability in soils; referred to as Strategy I and II 3-N) concentration in the soil can also induce Fe chlorosis. Bloom et al. Glycine max (L.)] plants growing on wheel tracks to lower soil and leaf tissue NO3-N on those tracks compared to adjacent areas that had more chlorotic soybeans. They also found significantly greater soil and leaf tissue NO3-N of the more chlorotic soybean monoculture than the less chlorotic soybean plants intercropped with oat. Bloom et al. 3-N through fertilization resulted in increased Fe deficiency chlorosis and decreased yields of the Fe-deficiency tolerant soybean variety used in the study. In addition to the proton extrusion strategy developed by Strategy I plants in response to Fe stress, N nutritional status considerably influences proton or hydroxyl (OH-) ion excretion from plant roots 3-N exude OH- or HCO- into the nutrient medium as long as there is sufficient NO3 in the medium + ions into the rhizosphere when Fe stress develops, regardless of the NO3 status of the soil, and continue to excrete protons even after soil NO3 supply is depleted − and HCO− ions into the rhizosphere when NO3 is sufficiently available, even when Fe stress develops, with excretion of protons beginning only after NO3 supply in the soil has been depleted. Elevated HCO3− concentration can make Fe insoluble thereby inhibiting its uptake by roots and subsequent translocation into shoots and leaves 3− solution culture and simulated calcareous soil solution (NO3− together with bicarbonate) severely affected the physiological Fe efficiency in roots and leaves of sunflower resulting in Fe deficiency chlorosis symptoms.As with most micronutrients, the concentration of Fe in the soil can be altered by the availability of other nutrients creating antagonistic or synergistic effects 3+ evidenced by re-greening of the sunflower plants. Contrary to this observation, corn plants were not able to lower the pH of the nutrient solution as a result of which they were unable to utilize Fe3+ as a source of Fe. When the two plant species were intercropped in the nutrient solution under Fe-stress, the Fe-efficient sunflower lowered the pH of the nutrient solution enabling corn plants to also re-green with Fe3+ as the source of Fe.Practiced worldwide for many generations, mixed cropping, especially of legumes and grasses, can enhance on-farm biodiversity, promote biological N fixation, increase dry matter production and grain yield, and enhance resource use efficiency In 2002, a Wyoming farmer observed that pinto beans accidentally growing with annual ryegrass were less chlorotic and produced better yields than a nearby monoculture of pinto beans growing in the absence of ryegrass. A subsequent bean-ryegrass intercropping on-farm study by Omondi et al. −1 and corn was planted at a density of 79,000 seeds ha−1. Grass species other than corn were seeded using a Tye double disk drill at 20 cm spacing. Annual ryegrass was seeded at the rate of 22 kg ha−1−1A field experiment was established under sprinkler irrigation at the University of Wyoming James C. Hageman Sustainable Agriculture Research and Extension Center near Lingle, Wyoming in 2009 and repeated in 2010 close to the same field . Soil at the site was a Haverson and McCook loam . The study was a two-factor factorial strip plot randomized complete block design with four replicates. The first factor consisted of dry bean market class and included 3 levels: ‘Schooner’ navy bean, ‘T-39’ black bean, and a control with no beans planted. The second factor consisted of grass species and included 5 levels: ‘Gulf’ annual ryegrass, ‘Oslo’ spring wheat, ‘Russell’ oat, ‘Pioneer 38N85’ corn, and a control with no grass planted. Grass species were planted perpendicular to dry bean rows, and levels of each factor were randomly assigned to plots within each replicate . Plots wHalosulfuron-methyl (‘Permit’ – Gowan Company) herbicide was applied pre-emergence over the entire trial area for weed control. Weeds surviving herbicide treatment were removed by hand as needed. Clethodim  herbicide was applied to all intercropped plots 4 weeks after planting to selectively remove grass species.Chlorophyll intensity in dry bean leaves was measured nondestructively using a SPAD-503Plus meter when dry bean had 2 to 4 trifoliolate leaves, and again at the 8 to 16 trifoliolate leaf stage. Two to three of the youngest fully expanded leaves from an average of 3 consecutive plants from the two middle rows of each plot were measured. Conversion of SPAD units to actual chlorophyll content requires calibration of SPAD chlorophyll meter readings using calibration curves developed from actual spectrophotometric measurements of extracted chlorophyll 3-N was determined using the phenoldisulfonic acid (PDA) procedure described by Johnson and Ulrich Dry bean leaf tissue samples were collected when dry bean had 2 to 4 trifoliolate leaves and again at the 8 to 16 trifoliolate leaf stage in both years. In 2010, additional bean leaf samples were collected at 4 to 8 trifoliolate leaf stages, and again at flowering. Two to three of the youngest fully expanded leaves were collected from an average of 30 consecutive plants from the two middle rows of each plot 3-N, soil organic matter (SOM), pH, and Mn. Soil Fe, Zn, and Mn were extracted from soil samples using the DTPA micronutrient extraction method developed by Lindsay and Norvell Soil samples were collected from each plot at planting and again when dry bean had 2 to 4 trifoliolate leaves. In 2010, additional soil samples were collected at 4 to 8 and 8 to 16 trifoliolate stage, and at bean flowering. Each soil sample consisted of five soil cores randomly located within each plot to 15-cm depth using a 2.5-cm diameter soil probe. Cores from each plot were thoroughly mixed together into a composite sample, dried at 60°C for 72 hours, and then shipped to Ward Laboratories, Inc. Kearney, Nebraska for analysis. Soil samples were analyzed for Fe, Zn, NO3-N was extracted with 2 M potassium chloride in accordance with Keeney and Nelson The pH of the samples was determined using the saturation paste method At dry bean maturity, yields were measured by harvesting three meters of row from the middle two rows with a plot combine in September. A three meter pole was used to determine the length of rows from which to harvest; beans from those rows were pulled out and air dried in the field for five days before threshing and weighing using a plot combine. Yields were adjusted to 13% moisture content.3-N data 3-N), soil pH, SOM, dry bean leaf chlorophyll intensity, and dry bean grain yield were subjected to analysis of variance (ANOVA) using the MIXED procedure of SAS We reviewed the data and removed a single outlier in the 2009 pre-treatment soil NOThere was a significant (P<0.001) year by date by market class interaction for bean leaf chlorophyll intensity. The three-way interaction was due to black bean having a lower chlorophyll intensity compared to Navy bean at the second sampling date in 2010, but no differences among bean market class were observed at other sampling dates (data not shown). There was a significant date by grass interaction for chlorophyll intensity (P = 0.002), thus data was analyzed separately by sampling date. Chlorophyll intensity was greater in all grass-intercropped beans at 2 to 4 trifoliate leaf stage compared to bean monoculture ; Table 1Tissue nutrient data were analyzed separately for each year because tissue sampling was conducted at different times in 2009 and 2010 (twice and five times respectively). In 2009, there was a significant (P<0.05) sampling date by grass species interaction with respect to bean tissue Fe and Mn concentration, and a marginally significant (P = 0.10) date by grass species interaction with respect to tissue Zn concentration, thus data for these nutrients were analysed separately at each sampling stage.In 2009, dry bean monoculture had greater tissue Fe concentration compared to corn intercropped beans at the first sampling date, and greater tissue Fe concentration than all grass intercropped beans at the second sampling date . Dry bea3-N  than beans intercropped with any grass species  (P<0.0001). However, there were no differences among intercrop treatments for bean tissue NO3-N in 2010. In fact, contrary to 2009, there were no differences in tissue Fe, Zn, or Mn due to intercropping treatment.In 2009, dry bean monoculture had nearly twice as much tissue NOGiven that soil was sampled at different times in 2009 and 2010, (twice and five times respectively), soil nutrient data were analyzed separately for each year. No differences between treatments were present for any soil variable at the time of bean planting (data not shown). There were no differences between treatments for soil pH, soil Fe, or SOM in either year.3-N compared to either dry bean monoculture or bare soil, while oat and corn intercropped treatments had lower soil NO3-N compared with the dry bean monoculture. The ryegrass intercropping treatment was not statistically different from any other treatment with respect to soil NO3-N. The wheat intercropped treatment was the only treatment to cause a statistically significant (P<0.05) decline in soil NO3-N concentrations between planting and the bean 8 trifoliolate stage. The bean monoculture treatment caused a statistically significant increase in soil NO3-N concentrations between planting and the 8 trifoliolate stage (data not shown).In 2009, none of the grass intercropped treatments had soil Zn concentrations different from either the bare soil or dry bean monoculture treatment . The whe3-N was observed; all grass intercrops had lower (P = 0.0004) soil NO3-N compared to the bean monoculture, while bare soil was intermediary  was far above the threshold found by Fageria and Stone −1−1 in 2009 which was within the range found by Jacobsen et al. −1, which was above the 6 mg kg−1 recommended by Fageria and Stone Symptoms of Zn, Mn, and Fe deficiency can be similar in dry bean. Symptoms of Zn deficiency include interveinal chlorosis of leaves, shortening internodes and rosetting of terminal leaves. Minimum soil Zn concentration (DTPA) before Zn chlorosis symptoms can occur is 0.3 mg kg4+ non-enzymatically to ferric Fe 2+−1 in 2009) was below the 400 mg kg−1 determined by Fageria and Stone −1) was also only slightly above the 6 mg kg−1 determined by Fageria High concentration of Zn, Mn, and several other heavy metals might compete with normal Fe uptake resulting in Fe deficiency chlorosis 3 and –HCO3 in the same way as in the roots. Transportation and reduction of Fe (III) to Fe (II) is mediated by a system of ferric-chelate reductase ensymes –HCO3 in the root or leaf apoplast can neutralize H+ pumped out of the cytosol, which, together with NO3 taken up by H+/NO3 cotransport across the plasma membrane, can result in high pH levels at the root and leaf apoplast, thereby hampering or blocking Fe (III) reduction. The authors found a highly significant negative correlation between the leaf apoplast pH and the degree of Fe chlorosis in susceptible plants. These studies are consistent with our study findings that grass intercropped treatments tended to have less soil and tissue NO3-N, suggesting that high NO3-N in the monoculture treatments interfered with Fe metabolism in bean leaves, depressed chlorophyll synthesis, and induced Fe deficiency chlorosis.Even though the dry bean monoculture exhibited greater Fe deficiency symptoms than intercropped beans, the monoculture had greater concentrations of Fe (DTPA) in leaf tissue. Several studies have shown that plants growing in high pH calcareous soils exhibiting Fe deficiency chlorosis frequently have comparable or higher Fe concentrations than green ones 3-N thereby reducing the amount available to the soybean crop and alleviated Fe deficiency chlorosis in soybean compared to soybean grown without the oat treatment. Our data suggest a similar finding. Lower soil NO3-N concentration in plots with grasses suggests that grasses reduced soil NO3-N concentration in those plots. Nitrogen fixation by Rhizobia associated with the legume may also have contributed to the greater NO3-N concentration in bean monoculture plots. Intercropping dry beans with annual ryegrass, spring wheat, oat, or corn as a means to reduce NO3-N in the soil and bean tissues may therefore provide a potential management tool for Fe deficiency chlorosis induced by high NO3-N in dry beans.Our results are also consistent with findings by Bloom et al. Although soil moisture was not measured in our study, grasses may also have reduced excess moisture from the soil that might induce Fe deficiency chlorosis. Studies have shown that increase in soil moisture can increase soil solution bicarbonate, which in turn can induce Fe deficiency chlorosis −1 and 70 mg kg−1 respectively in non-chlorotic and chlorotic soybeans leaves grown in Calciaquoll soils in pots. This contrasted with their findings in fields with similar soils that revealed soybean leaf tissue concentration ranging from 108 to 236 mg kg−1. They speculated that greater leaf Fe in chlorotic leaves is caused by fine soil particles containing aluminosilicates on both leaf surfaces that may not be completely removed despite careful washing of leaves in preparation for analysis Soil splash may also have contributed to high dry bean tissue Fe concentrations measured across-the-board in our experiment While results from our study showed that grasses used in this study can mitigate early season chlorosis symptoms in dry beans, grass intercropping did not result in greater dry bean yield. In fact, competition from the intercropped grasses (probably for water) decreased dry bean yield in 2009, although no negative effect on dry bean yield from grass intercrops was observed in 2010. Greater precipitation in 2010 (475 mm) compared to 2009 (404 mm) may have mitigated grass-bean competition for water. Greater yield in bean monoculture, despite observations and analytical results showing that they were more chlorotic than grass intercropped beans, suggest that benefits accruing from grass intercropping may have been outweighed by competition from grasses for nutrients, water, or light. Additional studies are required to determine the appropriate grass and dry bean densities, as well as the optimum time of removal of grasses from the intercropped treatment. Further research should focus on determining whether it is possible to find a grass removal timing that will both alleviate Fe deficiency chlorosis symptoms, but reduce the duration of competition so that a corresponding increase in dry bean yield is observed. This research will only be useful in a location where early season chlorosis symptoms are severe enough to significantly reduce dry bean yield.Our results showing that intercropping dry bean with annual grasses and other grass species can reduce Fe chlorosis symptoms early in the season are in agreement with Bloom et al.


Celiac disease (CD) is now considered, more than a just gluten sensitivity enteropathy, a multiple and systemic immune-mediate disorder triggered by the ingestion of wheat gluten and related proteins. Following the discovery of a link between gluten and CD, it was demonstrated that gliadin, one of the two principal protein groups comprising gluten, plays a key role in CD pathogenesis. It has since become clear that the different and crucial roles of gliadin in CD result from its ability to activate multiple signalling pathways that modulate CD pathology and CD progression ,2. It is well known that CD is strongly associated with specific human leukocyte antigen (HLA) class II genes, known as HLA-DQ2 and HLA-DQ8, located on chromosome 6p21 . Gliadin-specific T-cell responses have been found to be enhanced by the action of intestinal transglutaminases. The infiltration of T cells in the lamina propria of the active celiac lesion is dominated by CD4+ memory T cells (CD45RO+) bearing the α/β T-cell receptor (TCR)  but also macrophages and B cell subsets, which present to T-cell antigenic fragments in complex with cell surface MHC class II molecules .However, it is now known that to induce sufficient adaptive immune responses, antigens have to be recognized in the context of an activated innate immune system .Activation of innate immunity has been associated with the presence of toxic p31–49 gliadin peptides. In epithelial cells (ECs) toxic glaidin peptides are internalized into early endosomes and lysosomial enzymes or cytosolic enzymes can degrade late endosomes and the peptides. In celiac disease, toxic p31–49 peptide accumulates in the early endosome . This evBreaking immunological tolerance, gliadin peptides stimulate different type of cell, particularly dendritic cells (DCs), which results in leukocyte infiltration and inflammation of gut mucosa . Given tIn addition to their role as sentinels DCs also act as immune system sensors given their high expression of pattern recognition receptors, including Toll-like receptors (TLRs) . Drosophila in the mid-1990s as a Toll protein (TLRs comprise a large family of the pathogen-pattern recognition receptors (PPRR) originally identified in Some studies have provided important clues about the mechanisms of TLR-mediated control of adaptive immunity orchestrated by dendritic cell populations in distinct anatomical locations .However, an increasing number of reports show a more diverse expression of TLRs, including epithelial cells . TLR4 is one of the best characterized and the first member of the TLR family to be discovered as a PPRR. TLR4 signalling is implicated in the innate immune responses against a wide-range of microbes, including gram-negative and -positive bacteria, mycobacteria, spirochetes, yeasts, and some viruses and mammary tumour viruses . TLR4 is implicated in a diverse range of pathological processes associated with autoimmune diseases such as psoriasis, diabetic retinopathy, thrombosis, and inflammatory disorders including arthritis and atherosclerosis .Recently some finding suggested that the TLR4 are involved in pathogenesis of CD and Non-Celiac Gluten Sensitivity (NCGS) . HoweverIn contrast to CD, small-intestine expression TLR4 and generally of innate immunity is greater in patients with NCGS . In a very recent work Jelinkova et al. defined a hypothetical signalling pathways related to innate immunity that involved TLR4, NF-kB and inflammasome in the pathogenesis of gluten sensitivity . Furthermore, Junker et al. demonstrated that mice deficient in TRL4 signalling were protected from intestinal and systemic immune response to gluten or to strong activation of innate immunity system . In the recent years the long pentraxin PTX3 are characterized as a key component of humoral pattern and innate immune system. Pentraxins are a family of multimeric proteins that were divided into short and long pentraxin, based on the primary structure: C-reactive protein (CRP) is the prototype of the short pentraxin subfamily while pentraxin 3 (PTX3) is the prototypic long pentraxin. . Cells iThe corresponding PTX3 human gene is located on chromosome 3 band q 25. The proximal promoter shares numerous transcription factor binding sequences  . It has Under the influence of TNF-alpha and IL-1, ECs are able to produce considerable amounts of PTX3 ,20. It wLactobacillus acidophilus could transiently regulate the immunity and inflammatory mediator factor PTX3 expression through rapidly activating NF-kB signalling pathway in intestinal epithelial cells (CaCo-2 cells)  .In a recent study Chen et al. clearly demonstrated that endothelial PTX3 plays a pivotal role in the pathogenesis of ischemic acute kidney injury, via TLR4 and reactive oxygen species .In a very recent study we demonstrated that PTX3 serum level was high in patients with active disease patients in different intestinal mucosa condition . Furthermore, PTX3 serum concentration was significantly low in GFD patients than in active CD patients with mucosal damage. These evidences suggested that PTX3 concentration could reflect disease activity. The GFD and consequently the absence of toxin gliadin peptides could represent the “silencer” element of PTX3 expression. Yet the levels of PTX3 correlated with the DGP-IgA levels but not with the tTG IgA levels or with serum CRP. In addition, PTX3 serum levels correlated with the major extraintestinal manifestation such as anaemia and lower iron concentration . We also provided new evidences showing that after a strict compliance of GFD, PTX3 serum levels could follow the improvement of both gastrointestinal and extraintestinal symptoms. In the same work we demonstrated a linear correlation between DGP-IgA levels and PTX3 serum levels . This evWe suggested an interesting hypothesis in which PTX3 could be ever overexpress during gliadin and gluten exposure and modulate intestinal immune response to gluten and relate proteins by the pathway that involve NF-kB and TLR4. In this interesting scenario we certainly advanced the hypothesis that PTX3 could perform a critical role as mediator of inflammation in several steps that link toxic glaidin ingestion and tissue damage.At the moment we don’t have adequate elements to suggest the use of PTX3 in diagnosis of NCGS, but we are obliged to speculate about the possible role of PTX3 molecules in NCGS pathogenesis. PTX3 is an exploring molecule in the “No man’s land” of NCGS biomarkers.