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Figure 2. Diametrically opposed DNA methylation patterns in MLLr and IDH-mut AMLs.

(A) Unsupervised analysis of DNA methylation by ERRBS using hierarchical clustering (distance = 1-Pearson correlation, Ward's agglomeration method) segregates the samples into their three biological groups using all CpGs. (B) This segregation is maintained when unsupervised analysis is performed on non-promoter CpGs. (C) Chromosome ideogram representing differential methylation in IDH-mut AMLs vs. NBM (left) and MLLr AMLs vs NBM (right). Only CpGs with q-value<0.01 and methylation difference of at least 25% are shown. Magenta points represent hypermethylation and green ones represent hypomethylation relative to NBM. (D) Stacking barplots showing percentage of hyper and hypomethylated DMCs out of all covered CpGs for each chromosome in IDH-mut AMLs (left) and MLLr AMLs (right). Green represents proportion of hypomethylated DMCs and magenta represents hypermethylated ones.

Diametrically opposed aberrant DNA methylation in IDH-mut and MLLr AML subtypes

In order to identify the nature of the differences between IDH-mut and MLLr AMLs , the cytosine methylation profiles of these tumors were compared to normal CD34+ bone marrow cells from healthy donors (NBM), using logistic regression (FDR at alpha = 0.01). In addition to statistical significance, we required a minimum cutoff of 25% methylation difference. This analysis revealed striking differences in the way that these two forms of AML differed from normal hematopoietic stem and progenitor cells. Specifically, we observed that IDH-mut AMLs display profound hypermethylation distributed across all chromosomes. In marked contrast, comparison of the cytosine methylation profiles of MLLr AMLs to NBM samples identified a predominance of aberrantly hypomethylated CpG site ( Figure 2C and 2D ). More specifically, we identified 62,367 differentially methylated cytosines (DMC) between IDH-mut AMLs and NBM, 89.6% of which were aberrantly hypermethylated in the leukemias and only 10.4% hypomethylated. Among the 85,216 DMCs identified in MLLr AMLs we observed a vastly different and opposing distribution (Chi-square test, p-value<0.0001), with only 28.5% of DMCs displaying hypermethylation and 71.5% being hypomethylated. The above results remained valid even when we used a more stringent cutoff of 40% methylation difference or a more relaxed cutoff of 10% ( Figure S4A and S4B ). These results demonstrate that the directionality of DNA methylation changes acquired during malignant transformation of myeloid hematopoietic cells is not uniform across all AML subtypes and that DNA methylation changes are indeed diametrically opposed in these two AML subtypes.

Previous studies in AML were restricted to promoter microarrays [11] , Free Shipping Cheapest Eastbay For Sale Closed crew neck knitted sweater Newest Popular Cheap Price SpDUbw86Cm
or locus specific assays [25] that do not provide wide-spread and unbiased base pair resolution. Thus, it is not yet fully understood how aberrant DNA methylation is distributed in AML beyond these limited regions. Moreover, it is not clear whether results from studies carried out on certain solid tumor specimens [12] , [13] are generally applicable to cancer, nor whether genetic background of tumors, and more specifically AML, can have an influence on what regions are perturbed. The base pair resolution and extended genomic coverage of ERRBS make it well suited to address these questions. To compare methylation status across all samples, we first identified a total of 574,178 CpGs adequately represented by ERRBS (>10× coverage; on average 53× coverage per base) in all specimens. Of these, 94,245 CpGs were differentially methylated (methylation difference >25%) in either one or both subtypes. Notably, 87.3% (n = 82,312) of these DMCs were non-overlapping and thus unique to either IDH-mut or MLLr leukemias ( Figure 3 ). More specifically, 51,586 DMCs were identified in IDH-mut AMLs, of which the majority of CpGs, or 76.8%, were unique and non-overlapping with MLLr. In the case of MLLr AMLs, there were 54,592 DMCs, 78% of which were unique and non-overlapping with IDH-mut cases. Even more strikingly, 93% of the IDH-mut specific DMCs were hypermethylated vs. NBM, whereas 80.8% of MLLr specific DMCs were aberrantly hypomethylated. Comparable results were observed even when either a more stringent 40% or a less stringent 10% cutoff was used for calling DMCs ( Figure S4 ). Pathway enrichment analysis of the DMCs observed in each subtype was performed using GREAT [26] . Only pathways with an FDR q-value<0.05 in both the hypergeometric and binomial tests were included. This analysis revealed that IDH-mut DMCs were enriched in several pathways, including cadherin, Notch and TGFb signaling ( Table S3A ). MLLr DMCs on the other hand featured enrichment of two pathways, one involving integrin signaling while the other included transcriptional activators EP300, CREBBP, FOS, JUN as well as several genes involved in regulation of apoptosis such as BAX, CASP3, CASP6 and TP73 ( Table S3B ). Hence the DNA methylation defect of these two AML subtypes is not only perturbed in opposite directions but is also based on the differential methylation of an almost completely distinct set of CpGs, which affect distinct pathways.

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Distribution of the number of genes per reaction in iBC and iBM by subsystem. The average number of genes per reaction per subsystem was calculated by dividing the total number of genes in the GPRs of the reactions (rxns) in each subsystem by the number of reactions in each subsystem. Solid diamonds and X's show the average number of genes per reaction for iBC and iBM, respectively. For reference, the average number of genes per reaction for iMO1086 ( P. aeruginosa PAO1) is shown as open circles. For each subsystem, the three values in brackets denote the number of reactions for iBC, iBM, and iMO1086.

While iMO1086 shows a higher number of average genes per reaction in a subset of amino acid metabolism pathways and a few other subsystems, overall, iBC has the most genes per reaction across all subsystems, followed by iBM and iMO1086. The most notable increases in the average number of genes associated with reactions in iBC over the other reconstructions are incorporated in lipid metabolism. However, iBM may have higher metabolic capacity in certain energy and carbohydrate metabolic pathways as it has a higher gene-per-reaction average in these pathways. Pathways where iBC and iBM have similar numbers of genes associated with a reaction include metabolism of amino acids as well as terpenoid and polyketide metabolism. The reconstructions offer a method of probing specific subsystems to identify gene duplications or isozymes that may indicate concentrated genetic redundancy.

Gene essentiality. An important consequence of the presence of increased genes per reaction in iBC and iBM compared to reconstructions of other bacteria was the reduction in the number of genes predicted to be essential for growth. During in silico growth in LB medium, our models predicted 66 essential genes in iBC and 73 essential genes in iBM ( Table 2 ). Sixty of these genes were orthologs between iBC and iBM, 6 genes were uniquely essential in iBC, and 13 genes were uniquely essential in iBM. In comparison, iMO1086 required 150 genes to grow in silico on LB (with an accuracy of 83.9%) ( 18 ). Because iBM and iBC are not currently reconciled with iMO1086, we compared our predicted essential genes with a list of potentially essential P. aeruginosa PAO1 genes identified experimentally; these PAO1 genes had no recorded transposon mutants as identified in the Pseudomonas Genome Database based on genome-scale transposon mutagenesis libraries ( 59 ). As shown in Table S4 in the supplemental material, 35 out of 78 predicted essential Burkholderia genes matched P. aeruginosa PAO1 probable essential genes using a BLAST comparison. Another 12 predicted essential Burkholderia genes matched PAO1 genes with likely isozymes or duplications. Two predicted essential Burkholderia genes had no match to any PAO1 gene locus. The low number of potential essential Burkholderia genes compared to other species corresponds with a recent study that created promoter-based conditional mutants to identify essential Burkholderia cenocepacia K56-2 genes ( 60 ). However, the authors saw unexpectedly low essential operon hit rates during their mutant library screening.

Froehlich, a two-time bracelet winner, figures to command considerable attention. The longtime professional is the lone local left in the field, and has an above-average stack of 15.28 million to put him in 10th place.

Aram Zobian, who’s from Cranston, Rhode Island, currently sits atop the leaderboard with 41.56 million chips. Zobian’s previous highest cash at the World Series of Poker was for $4,324 two years ago.

All 26 remaining players in the Main Event will earn at least $282,630 with pay jumps increasing significantly every few spots. Eleven nations are still represented, though 14 players are American. France and China each have two players remaining.

ESPN will show the entirety of the final table beginning at 6 p.m. each night from Thursday to Saturday. It’s every poker players’ dream to reach that point of the Main Event.

Cada, and Loosli, are close to realizing it for a second time.

Check below for full chip counts heading into the final day of play.

1. Aram Zobian, Cranston, R.I. — 41,585,000

2. Artem Metalidi, Kiev, Ukraine — 30,845,000

3. Antoine Labat, Vincenna, France — 28,445,000

4. Michael Dyer, Houston — 26,515,000

5. Alex Lynskey, Brisbane, Australia — 22,045,000

6. Yueqi Zhu, Benxi, China — 19,245,000

7. Kao Saechao, Portland, Ore. — 18,985,000

8. Matijn Gerrits, Netherlands — 17,790,000

9. Nicolas Manion, Muskegon, Mich. — 17,630,000

10. Eric Froehlich, Las Vegas — 15,285,000

11. Paulo Goncalves, Brazil — 15,230,000

12. Tony Miles, Jacksonville, Fla. — 14,945,000

13. John Cynn, Indianapolis — 14,750,000

14. Alexander Haro, Claremont, Calif. — 12,940,000

15. Hari Bercovici, Beer Sheva, Israel — 12,775,000

16. Frederik Jensen, Copehagen, Denmark — 12,100,000

17. Sylvain Loosli, Toulon, France — 11,635,000

18. Ryan Phan — Omaha, Neb. — 9,545,000

19. Joe Cada — Shelby Township, Mich. — 8,850,000

20. Ivan Luca — Purta Alta, Argentina — 8,820,000

21. Konstantin Beylin — St. Louis —8,305,000

22. Ming Xi, China — 7,550,000

23. Jeff Trudeau, Orlando, Fla. — 5,090,000

24. Nirath Rean, Jacksonville, Fla. — 4,950,000

25. Bart Lybaert, Miehlen, Germany — 3,825,000

26. Barry Hutter, Hollywood, Fla. — 2,250,000

Case Keefer can be reached at 702-948-2790 or . Follow Case on Twitter at .

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The CMCR project builds on work originally conducted with the International Media Concentration Research () project, 2009-2013, a study led by Eli Noam, Columbia University that included thirty-plus researchers studying media concentration in as many countries around the world. The CMCR project is supported by the Social Sciences and Humanities Research

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