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Comprehensive molecular characterization of gastric adenocarcinoma

To identify recurrently mutated genes, we analysed the 215 tumours with mutation rates below 11.4 mutations per megabase (Mb) (none of which were MSI-positive) separately from the 74 ‘hypermutated’ tumours. Within the hypermutated tumours, we excluded from analysis 11 cases with a distinctly higher mutational burden above 67.7 mutations per Mb (including one tumour with an inactivating POLE mutation20, 21) (Supplementary Information S3.2–3.3), because their large numbers of mutations unduly influence analysis. We used the MutSigCV22 tool to define recurrent mutations in the 63 remaining hypermutated tumours by first evaluating only base substitution mutations, identifying 10 significantly mutated genes, including TP53, KRAS, ARID1A, PIK3CA, ERBB3, PTEN and HLA-B (Supplementary Table 3.5). We found ERBB3 mutations in 16 of 63 tumours, with 13 of these tumours having mutations at recurrent sites or sites reported in COSMIC. MutSigCV analysis including insertions/deletions expanded the list of statistically significant mutated genes to 37, including RNF43, B2M and NF1 (Supplementary Fig. 3.9). Similarly, HotNet analysis of genes mutated within MSI tumours revealed common alterations in major histocompatibility complex class I genes, including B2M and HLA-B (Supplementary Fig. 11.5–11.7). B2M mutations in colorectal cancers and melanoma result in loss of expression of HLA class 1 complexes23, suggesting these events benefit hypermutated tumours by reducing antigen presentation to the immune system.


Through MutSigCV analysis of the 215 non-hypermutated tumours, we identified 25 significantly mutated genes (Fig. 3). This gene list again included TP53, ARID1A, KRAS, PIK3CA and RNF43, but also genes in the -catenin pathway (APC and CTNNB1), the TGF- pathway (SMAD4 and SMAD2), and RASA1, a negative regulator of RAS. ERBB2, a therapeutic target, was significantly mutated, with 10 of 15 mutations occurring at known hotspots; four cases had the S310F ERBB2 mutation that is activating and drug-sensitive24.


Figure 3: Significantly mutated genes in non-hypermutated gastric cancer.
Significantly mutated genes in non-hypermutated gastric cancer.

a, Bars represent somatic mutation rate for the 215 samples with synonymous and non-synonymous mutation rates distinguished by colour. b, Significantly mutated genes, identified by MutSigCV, are ranked by the q value (right) with samples grouped by subtype. Mutation colour indicates the class of mutation.





In addition to PIK3CA mutations, EBV-positive tumours had frequent ARID1A (55%) and BCOR (23%) mutations and only rare TP53 mutations. BCOR, encoding an anti-apoptotic protein, is also mutated in leukaemia25 and medulloblastoma26. Among the CIN tumours, we observed TP53 mutations in 71% of tumours. CDH1 somatic mutations were enriched in the genomically stable subtype (37% of cases). CDH1 germline mutations underlie hereditary diffuse gastric cancer (HDGC). However, germline analysis revealed only two CDH1 polymorphisms, neither of which is known to be pathogenic. As in the EBV-subtype, inactivating ARID1A mutations were prevalent in the genomically stable subtype. We identified mutations of RHOA almost exclusively in genomically stable tumours, as discussed below.


We analysed the patterns of base changes within gastric cancer tumours and noted elevated rates of C to T transitions at CpG dinucleotides. We observed an elevated rate of A to C transversions at the 3 adenine of AA dinucleotides, especially at AAG trinucleotides, as reported in oesophageal adenocarcinoma27. The A to C transversions were prominent in CIN, EBV and genomically stable, but as previously observed27, not in MSI tumours (Supplementary Fig. 3.10).


We identified RHOA mutation in 16 cases, and these were enriched in the genomically stable subtype (15% of genomically stable cases, P = 0.0039). RHOA, when in the active GTP-bound form, acts through a variety of effectors, including ROCK1, mDIA and Protein Kinase N, to control actin-myosin-dependent cell contractility and cellular motility28, 29 and to activate STAT3 to promote tumorigenesis30, 31. RHOA mutations were clustered in two adjacent amino-terminal regions that are predicted to be at the interface of RHOA with ROCK1 and other effectors (Fig. 4a, b). RHOA mutations were not at sites analogous to oncogenic mutations in RAS-family GTPases. Although one case harboured a codon 17 mutation, we did not identify the dominant-negative G17V mutations noted in T-cell neoplasms32, 33. Rather, the mutations found in this study may act to modulate signalling downstream of RHOA. Biochemical studies found that the RHOA Y42C mutation attenuated activation of Protein Kinase N, without abrogated activation of mDia or ROCK134. RHOA Y42, mutated in five tumours, corresponds to Y40 on HRAS, a residue which when mutated selectively reduces HRAS activation of RAF, but not other RAS effectors35. Given the role of RHOA in cell motility, modulation of RHOA may contribute to the disparate growth patterns and lack of cellular cohesion that are hallmarks of diffuse tumours.


Figure 4: RHOA and ARHGAP6/26 somatic genomic alterations are recurrent in genomically stable gastric cancer.
RHOA and ARHGAP6/26 somatic genomic alterations are recurrent in genomically stable gastric cancer.

a, Missense mutations in the GTPase RHOA, including residues Y42 and D59, linked via hydrogen bond (red arc). b, Mutated regions (coloured as in panel a) mapped on the structures of RHOA and ROCK1. c, A schematic of CLDN18–ARHGAP26 translocation is shown for the fusion transcript and predicted fusion protein. SH3 denotes SRC homology 3 domain. d, The frequency of RHOA and CDH1 mutations, CLDN18ARHGAP6 or ARHGAP26 fusions are shown across gastric cancer subtypes. e, RHOA mutations and CLDN18ARHGAP6 or ARHGAP26 fusions are mutually exclusive in genomically stable tumours.





Dysregulated RHO signalling was further implicated by the discovery of recurrent structural genomic alterations. Whole genome sequencing of 107 tumours revealed 5,696 structural rearrangements, including 74 predicted to produce in-frame gene fusions (Supplementary Information S3.7–3.8). De novo assembly of mRNA sequencing data confirmed 170 structural rearrangements (Supplementary Information S5.4a), including two cases with an interchromosomal translocation between CLDN18 and ARHGAP26 (GRAF). ARHGAP26 is a GTPase-activating protein (GAP) that facilitates conversion of RHO GTPases to the GDP state and has been implicated in enhancing cellular motility34. CLDN18 is a component of the tight junction adhesion structures36. RNA sequencing data from tumours without whole genome sequencing i

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The DNA methylation landscape of human early embryos

a, The line chart of the relative expression levels (sequencing read counts, normalized by total mappable RefSeq read counts) of SVAs. Notably, the expression levels of SVAs increased dramatically from 4-cell stage to morula stage. Biological replicates in panel a: MII oocyte (n = 3), zygote (n = 3), 2-cell (n = 6), 4-cell (n = 12), 8-cell (n = 20), morula (n = 14), ICM (n = 10), post-implantation (n = 3). b, The average DNA methylation levels of SVAs. The green dot in gamete stage indicates the average DNA methylation level of the corresponding regions in sperm, while the purple dot in gamete stage indicates that in MII oocytes, respectively. Details of biological replicates of each stage are listed in Supplementary Table 1. c, The line chart of the relative expression levels (sequencing read counts, normalized by total mappable RefSeq read counts) of four major subfamilies (ERV1, ERVK, ERVL and ERVL-MaLR) of LTRs during early embryonic development. Biological replicates in panel c: MII oocyte (n = 3), zygote (n = 3), 2-cell (n = 6), 4-cell (n = 12), 8-cell (n = 20), morula (n = 14), ICM (n = 10), post-implantation (n = 3). d, The average DNA methylation levels of four major subfamilies of LTRs during early embryonic development. The green dot in gamete stage indicates the average DNA methylation level of the corresponding regions in sperm, while the purple dot in gamete stage indicates that in MII oocytes. Details of biological replicates of each stage are listed in Supplementary Table 1. e, DNA methylation levels of the subfamilies of Alu, including AluY (the evolutionarily youngest one, the left panel), AluS (the middle panel) and AluJ (the evolutionarily oldest one, the right panel). Details of biological replicates of each stage are listed in Supplementary Table 1. f, DNA methylation levels of the subfamilies of L1, including L1PA (the evolutionarily youngest one in L1 family), L1PB, L1MA, L1MB, L1MC, L1MD and L1ME (the evolutionarily oldest one in L1 family). The green and red dots represented sperm and MII oocytes, respectively. Details of biological replicates of each stage are listed in Supplementary Table 1. g, Histograms of expression levels (RPKM) of DNA methylation-related genes across different human early embryonic stages, including DNA-demethylation-related genes TET1,TET2,TET3 and TDG, as well as DNA-methylation-related genes DNMT1, UHRF1, DNMT3A, DNMT3B and DNMT3L. Biological replicates in panel g: MII oocyte (n = 3), zygote (n = 3), 2-cell (n = 6), 4-cell (n = 12), 8-cell (n = 20), morula (n = 16), blastocyst (n = 30). All data in panel ag are mean ± 95% confidence interval ( ± 1.96 s.e.m.).

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Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche


Contributions


Overall project management: J.R.B.P., F.D., C.E.E., P.S., D.J.T., D.F.E., K.S., J.M.M. and K.K.O. Core analyses: J.R.B.P., F.D., C.E.E., P.S., T.F., D.J.T., D.I.C. and T.E. Individual study analysts: A.A.R., A.D., A.G., A.J., A.T., A.V.S., B.Z.A., B.F., C.E.E., D.F.G., D.I.C., D.J.T., D.L.C., D.L.K., E.A., E.K.W., E.M., E.M.B., E.T., F.D., G.M., G.McMahon, I.M.N., J.A.V., J.D., J.H., J.R.B.P., J.T., J.Z., K.L.L., K.M., L.L.P., L.M.R., L.M.Y., L.S., M.M., N.F., N.Ts., P.K., P.S., R.M., S.K., S.S., S.S.U., T.C., T.E., T.F., T.Fo., T.H.P., W.Q.A. and Z.K. Individual study data management and generation: A.A.R., A.C.H., A.D., A.D.C., A.G.U., A.J.O., A.M.S., A.Mu., A.P., A.Po., B.A.O., C.A.H., D.C., D.I.C., D.J.H., D.K., D.Lw., D.P.K., D.P.S., D.S., E.A.N., E.P., E.W., F.A., F.B.H., F.G., F.R., G.D., G.E., G.G.W., H.S., H.W., I.D., J.C., J.H., J.P.R., L.F., L.Fr., L.M., L.M.R., M.E.G., M.J.S., M.J.W., M.K.B., M.Melbye, M.P., M.W., N.A., N.J.T., N.L.P., P.K.M., Q.W., R.H., S.B., S.C., S.G., S.L., S.R., S.S.U., T.E., U.S., U.T., V.S. and W.L.M. Individual study principal investigators: A.C., A.G.U., A.H., A.J.O., A.K.D., A.L., A.M., A.M.D., A.Mannermaa, A.Mu., A.R., B.B., B.Z.A., B.H.R.W., C.B., C.E.P., C.G., C.H., C.van Duijn, D.I.B., D.F., D.F.E., D.J.H., D.L., D.Lw., D.S.P., D.P.S., D.Schlessinger, E.A.S., E.B., E.E.J.d.G., E.I., E.W., E.W.D., F.B.H., F.J.C., G.C., G.D., G.G.G., G.Wa., G.Wi., G.W.M., H.A., H.A.B., H.B., H.Be., H.F., H.N., H.S., H.V., I.D., I.L.A., J.A.K., J.B., J.C.C., J.G.E., J.E.B., J.L.H., J.M.C., J.M.M., J.P., K.C., K.K., K.K.O., K.P., K.S., L.C., L.F., L.J.B., M.C.S., M.G., M.I.M., M.J., M.J.E., M.J.H., M.J.S., M.K.S., M.W.B., M.Z., N.G.M., N.J.W., P.A.F., P.D., P.D.P.P., P.F.M., P.G., P.H., P.K., P.M.R., P.N., P.P., P.P.G., P.R., P.V., R.J.F.L., R.L.M., R.W., S.B., S.Bergmann, S.C., S.E.B., T.B.H., T.D.S., T.I.A.S., U.H., V.G., V.K. and V.S.


Plots of all 106 menarche loci and genome-wide summary level statistics are available at the ReproGen Consortium website: http://www.reprogen.org.

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Alterations of the human gut microbiome in liver cirrhosis




  1. These authors contributed equally to this work.


    • Nan Qin,

    • Fengling Yang,

    • Ang Li,

    • Edi Prifti,

    • Yanfei Chen &

    • Li Shao


Affiliations



  1. State Key Laboratory for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, College of Medicine, Zhejiang University, 310003 Hangzhou, China


    • Nan Qin,

    • Fengling Yang,

    • Ang Li,

    • Yanfei Chen,

    • Li Shao,

    • Jing Guo,

    • Jian Yao,

    • Lingjiao Wu,

    • Jiawei Zhou,

    • Shujun Ni,

    • Lin Liu,

    • Chunhui Yuan,

    • Wenchao Ding,

    • Yuanting Chen,

    • Xinjun Hu,

    • Beiwen Zheng,

    • Guirong Qian,

    • Wei Xu &

    • Lanjuan Li



  2. Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University, 310003 Hangzhou, China


    • Nan Qin,

    • Li Shao,

    • Jian Yao,

    • Beiwen Zheng,

    • Shusen Zheng &

    • Lanjuan Li



  3. Metagenopolis, Institut National de la Recherche Agronomique, 78350 Jouy en Josas, France


    • Edi Prifti,

    • Emmanuelle Le Chatelier,

    • Nicolas Pons,

    • Jean Michel Batto,

    • Sean P. Kennedy,

    • Pierre Leonard &

    • S. Dusko Ehrlich



  4. King’s College London, Centre for Host-Microbiome Interactions, Dental Institute Central Office, Guy’s Hospital, London Bridge, London SE1 9RT, UK


    • S. Dusko Ehrlich



  5. Key Laboratory of Combined Multi-organ Transplantation, Ministry of Public Health, the First Affiliated Hospital, Zhejiang University, 310003 Hangzhou, China


    • Shusen Zheng




Contributions


L.J.L., S.D.E., S.S.Z. and N.Q. designed the project. L.J.L., S.P.K. and N.Q. managed the project. F.L.Y., N.Q., Y.F.C., J.G., G.R.Q., X.J.H. and B.W.Z. collected samples and performed clinical study. J.G., Y.T.C. and W.X. performed DNA extraction experiments. Y.J., L.J.W., J.W.Z. and S.J.N. performed library construction and sequencing. L.J.L. and S.D.E. designed the analysis. N.Q., A.L., E.P., E.L.C., L.L., N.P., P.L., J.M.B., C.H.Y. and W.C.D. analysed the data. A.L. and N.Q. did the functional annotation analyses. L.S., E.P., E.L.C. and A.L. analysed the statistics. N.Q., F.L.Y., L.S. and E.P. wrote the paper. L.J.L. and S.D.E. revised the paper.




Competing financial interests


The authors declare no competing financial interests.




Corresponding authors


Correspondence to:



The raw Illumina read data for all samples have been deposited in the European Bioinformatics Institute European Nucleotide Archive under accession number ERP005860.

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DNA methylation dynamics of the human preimplantation embryo

a, Violin plots for LTRs over human and mouse development. In human, LTRs demonstrate a bimodal distribution in sperm. Hypermethylated LTRs display a range of demethylation in the early embryo that reflects the dynamics of subfamilies. Upon ES cell derivation, and within fetal tissues, LTRs become stably hypermethylated. Alternatively, during mouse preimplantation, LTRs are consistently hypermethylated in sperm and generally retain methylation over preimplantation. E6.5 Epi and E6.5 ExE refer to dissected epiblast and extraembryonic tissue from E6.5 embryos. b, Violin plots for LINEs over human and mouse development. In human sperm, LINEs are unstably hypermethylated, with discrete populations methylated with a mean of ~0.75, 0.9, and a small subpopulation showing gametic escape from high methylation. Alternatively, LINEs are indiscriminately hypermethylated in mouse sperm. In both species, several populations of elements demonstrate different extents of demethylation during preimplantation, including many that retain higher levels in cleavage and only minor, passive depletion into blastocyst. Upon human ES cell derivation or during mouse implantation, elements are generally remethylated, though only partially for those elements that are demethylated after fertilization. Hypermethylation is complete in fetal tissue. In human, these discrete dynamics can be attributed to the unstable methylation for L1HS-L1PA3a subfamilies while, in mouse, subsets of L1Md_Tf and L1Md_Gf subfamilies are similarly demethylated and elements of the independently emerging L1Md_A lineage remain largely methylated. c, Violin plots for SINEs highlight intermediate methylation in sperm in both species, though more so for humans. After fertilization, SINE methylation rapidly diminishes to near complete hypomethylation over preimplantation, similar to what is observed for intergenic sequence, before complete hypermethylation during ES cell derivation in human or in postimplantation mouse E6.5 embryos. Taken globally, SINEs appear to be uniformly regulated regardless of subfamily, though differences in regulatory status for specific SINE elements may be reflected by their surrounding genomic context. Unfortunately, such inferences require higher genomic resolution than is currently available to distinguish the dynamics of specific integrations. dg, Violin plots of the four major LTR families present in mouse over the complete preimplantation timeline. ERV1 elements (d) are hypermethylated in sperm and display a range of demethylation following fertilization and prompt remethylation upon implantation. In mouse, ERVK elements (e) are emergent and largely consist of the dominating, constitutively hypermethylated IAP subfamilies. ERVL and MalR (ERVL-MalR) elements (f and g), the evolutionarily oldest mammalian LTRs, are hypermethylated in sperm and rapidly demethylated after fertilization, frequently in association with their rapid zygotic induction. h, Distribution (as boxplots) of per element expression and CpG density at different methylation levels for LTR12c demonstrates negative correlation between methylation and expression. On average, LTR12C is hypomethylated in sperm and the early embryo, but demonstrates a consistent range of values at the level of single elements, with least methylated elements contributing the most to LTR12c expression. The CpG density of these elements corresponds to their degree of hypomethylation, suggesting that escape from de novo methylation during spermatogenesis and preimplantation is maintained for specific elements over generations. Once targeted, element expression is apparently restricted and its CpG density decays correspondingly. During ES cell derivation, the kinetics of LTR12c methylation is more rapid for those of lower CpG density, as evident from p0 to p5 in the ES cell lines. DNA methylation in the early embryo is therefore not exclusive to the regulation of different ERV1 subfamilies, but also affects the contribution of single elements to the broader transcriptional pattern. Bold line signifies the median, boxes and whiskers the 25th and 75th, and 2.5th and 97.5th percentiles, respectively. Expression is calculated as the number of fragments per million that align to a given element divided by its length in kb (FPKM).

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Putative cis-regulatory drivers in colorectal cancer


Affiliations



  1. Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland


    • Halit Ongen,

    • Pedro G. Ferreira,

    • Alexandra Planchon,

    • Ismael Padioleau,

    • Deborah Bielser,

    • Luciana Romano &

    • Emmanouil T. Dermitzakis



  2. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland


    • Halit Ongen,

    • Pedro G. Ferreira,

    • Alexandra Planchon,

    • Ismael Padioleau,

    • Deborah Bielser,

    • Luciana Romano &

    • Emmanouil T. Dermitzakis



  3. Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland


    • Halit Ongen,

    • Pedro G. Ferreira,

    • Alexandra Planchon,

    • Ismael Padioleau,

    • Deborah Bielser,

    • Luciana Romano &

    • Emmanouil T. Dermitzakis



  4. Department of Molecular Medicine, Aarhus University Hospital, 8000 Aarhus, Denmark


    • Claus L. Andersen,

    • Jesper B. Bramsen,

    • Bodil Oster,

    • Mads H. Rasmussen &

    • Torben F. Orntoft



  5. Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), 08908 Barcelona, Catalonia, Spain


    • Juan Sandoval,

    • Enrique Vidal &

    • Manel Esteller



  6. Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey SM2 5NG, UK


    • Nicola Whiffin &

    • Richard S. Houlston



  7. Nuffield Department of Clinical Medicine and Oxford NIHR Comprehensive Biomedical Research Centre, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK


    • Ian Tomlinson



  8. Department of Physiological Sciences II, School of Medicine, University of Barcelona, 08007 Barcelona, Barcelona, Spain


    • Manel Esteller



  9. Instituci Catalana de Recerca i Estudis Avanats (ICREA), 08010 Barcelona, Spain


    • Manel Esteller




Contributions


H.O., C.L.A., J.B.B., T.F.O. and E.T.D. designed the study. H.O. and E.T.D. coordinated the project. H.O., J.B.B., A.P., I.P., D.B., L.R. and M.H.R. participated in RNA-sequencing data production. J.S., E.V. and M.E. designed and conducted the methylation experiment. N.W., I.T. and R.S.H. conducted the CRC GWAS. H.O. and P.G.F. analysed the data. H.O., C.L.A. and E.T.D. drafted the paper.




Competing financial interests


The authors declare no competing financial interests.




Corresponding authors


Correspondence to:



The RNA-sequencing and genotype data are deposited in the European Genome-phenome Archive (EGA, https://www.ebi.ac.uk/ega/) for controlled accesses; the study accession number is EGAS00001000854.

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Epigenetics: Cellular memory erased in human embryos


Two analyses of human eggs, sperm and early-stage embryos reveal a pronounced loss of DNA methylation — a molecular modification that affects gene transcription — after fertilization.



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Cancer: Directions for the drivers


A comparison of colorectal cancer and normal cells from 103 patients identifies dozens of genes that are differently expressed in tumour cells as a result of altered regulation of transcription.



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Universality of core promoter elements?


Arising from B. J. Venters & B. F. Pugh Nature 502, 5358 (2013); doi:10.1038/nature12535


How cells locate the regions to initiate transcription is an open question, because core promoter elements (CPEs) are found in only a small fraction of core promoters1, 2, 3, 4. A recent study5 measured 159,117 DNA binding regions of transcription factor IIB (TFIIB) by ChIP-exo (chromatin immunoprecipitation with lambda exonuclease digestion followed by high-throughput sequencing) in human cells, found four degenerate CPEs—upstream and downstream TFIIB recognition elements (BREu and BREd), TATA and initiator element (INR)in nearly all of them, and concluded that these regions represent sites of transcription initiation marked by universal CPEs. We show that the claimed universality of CPEs is explained by the low specificities of the patterns used and that the same match frequencies are obtained with two negative controls (randomized sequences and scrambled patterns). Our analyses also cast doubt on the biological significance of most of the 150,753 non-messenger-RNA-associated ChIP-exo peaks, 72% of which lie within repetitive regions. There is a Retraction accompanying this Brief Communication Arising by Venters, B. J. & Pugh, B. F. Nature 511, http://dx.doi.org/10.1038/nature13588 (2014).




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Population biology: Fur seals signal their own decline


Data on three generations of Antarctic fur seals suggest that climate change is reducing the survival of less-fit individuals with low genetic variation, but that overall seal numbers are falling. See Letter p.462




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