功能基因组学动力学Dynamics-of-Functional-Genomics

1、Dynamics of Functional GenomicsInformation Encoded in Genomics Heritable Information Embedded in EpigenomicsDeliver Information by Transcriptional ProgramDynamics Nature of the human BodyDynamic Changes in the Copy Number of Pluripotency and Cell Proliferation Genes in Human ESCs and iPSCs during Re

2、programming and Time in CultureThe tremendous self-renewal and differentiation capabilities of human pluripotent stem cells (hPSCs) make them potential sources of differentiated cells for cell therapy. Cell therapies are subject to rigorous safety trials, and high priority is placed on demonstrating

3、 that the cells are nontumorigenic.Because genetic aberrations have been strongly associated with cancers, it is important that preparations destined for clinical use are free from cancer-associated genomic alterations. Human embryonic stem cell (hESC) lines have been shown to become aneuploid in cu

4、lture, and the most frequent changes, trisomies of chromosomes 12 and 17, are also characteristic of malignant germ cell tumors.Aneuploidies can be detected by karyotyping, but less easily detectable subchromosomal genetic changes may also have adverse effects. Small abnormalities have been detected

5、 in hESCs by using comparative genomic hybridization (CGH) and single-nucleotide polymorphism (SNP) genotyping. These studies lacked sufficient resolution and power to identify cell type-associated duplications and deletions.In this study, we performed high-resolution SNP genotyping on a large numbe

6、r of hESC lines, induced human pluripotent stem cell lines (hiPSCs), somatic stem cells, primary cells, and tissues. Data resources:324 samples, including 69 hESC lines (130 samples), 37 hiPSC lines (56 samples), 11 somatic stem cell lines (11 samples), 41 primary cell lines (41 samples), and 20 tis

7、suetypes (67 samples), as well as samples of differentiated hESC lines and mixtures of known ratios of a sample with a known duplication with a sample without that duplicationDynamic Changes in the Copy Numberde novo methylationHow many genes are mutated in ahuman tumor? Answering thisquestion would

8、 have seemed likescience fiction just a decade ago. However, as a result of advances in technology, wehave been able to answer this question in breast and colorectal cancers: There are 80 DNA mutations that alter amino acids in a typical cancer. Examining the overalldistribution of these mutations i

9、n different cancers of the same type leads to a newview of cancer genome landscapes: They arecomposed of a handful of commonly mutated gene “mountains” but are dominatedby a much larger number of infrequently mutated gene “hills.”The DNA methylation landscape of human early embryosDNA methylation is

10、 an important form of epigenetic modification and has a crucial role in many biological processes, including repression of gene transcription, maintenance of gene imprinting and X-chromosome inactivation, and repression of transposable elements. The most dramatic genome-wide changes of the methylome

11、 in mammals occur in primordial germ cells and during pre-implantation development.DNA methylation is a crucial element in the epigenetic regulationof mammalian embryonic development15. However, its dynamic patterns have not been analysed at the genome scale in human preimplantation embryos due to t

12、echnical difficulties and the scarcity of required materials. Here we systematically profile the methylome of human early embryos from the zygotic stage through to postimplantation by reduced representation bisulphite sequencing and whole-genome bisulphite sequencing. Mapping and analysis of chromat

13、in statedynamics in nine human cell typesA major challenge in biology is understanding how a single genomecan give rise to an organism comprising hundreds of distinct cell types. Much emphasis has been placed on the application of high-throughput tools to study interacting cellular components1. The

14、field of systems biology has exploited dynamic gene expression patterns to reveal functional modules, pathways and networks2. Yet cis-regulatory elements, which may be equally dynamic, remain largely uncharted across cellular conditions.Chromatin profiling provides a systematic means of detecting ci

15、s regulatory elements, given the central role of chromatin in mediatingregulatory signals and controlling DNA access, and the paucity ofrecognizable sequence signals. Specific histone modifications correlate with regulator binding, transcriptional initiation and elongation, enhancer activity and rep

16、ression1,36. Combinations of modifications can provide even more precise insight into chromatin state7,8.Cell-type-specific promoter and enhancer states and associated functional enrichmentsCorrelations in activity patterns link enhancers to gene targetsand upstream regulators Studying and modelling

17、 dynamic biological processes using time-series gene expression data Dynamic regulation of miRNAexpression in ordered stagesof cellular developmentCharting a dynamic DNA methylation landscape of the human genomeHuman embryonic stem (ES) cells and human ES-cell-derived cell populationsHUES64, dMEEN,

18、dMEEN, dEC, dME, dEN, dNPC, dMesenchyprimary cellsFbrain, hippocampus, snigra, CD34, bcell, HSCP, CD4, CD8, sperm, liver, fheart, fthymus, fmuscle, adipocyte, cmucosa, colondisease conditionscolonTumor, cortexNormal, cortexADlong-term cultured cell lines fFF, IMR90, HepG2Data sourcesdevelopmental pr

19、ocess (24)30 diverse human cell and tissue types30 diverse human cell and tissue typesWGBSIn house generated WGBS libraries were aligned using MAQPublished data sets were aligned using BSMapThe methylation level of CpG clusters is determined by computing the coverage weighted mean across all CpGswit

20、hin a cluster：On a global scale, human ES cells and their derivatives exhibit the highest DNA methylation levels, followed by primary cells (,5% less), which is in sharp contrast to the global hypomethylation observed in colon cancer (,1015% less) and long term cultured cell lines (1030% less). Prin

21、cipal component (PC) analysis based on CpG methylation levels for 1-kb tiles across 30 diverse human cell and tissue samples.Focusing initially on our developmental sample set (n=24 total, ES cells, in-vitro-derived cell types and primary cells) we identified, 5.6 million dynamic CpGs (21.8% of capt

22、ured autosomal CpGs) distributed across 716,087 discrete DMRs. the average variation in DNA methylation levels across all RefSeq promoters (n530,090) does still exhibit a clear increase specifically at the transcription start sites, with most of this variation occurring at intermediate and low CpG d

23、ensity promoters.25%75%To gain insights into the role of the remaining set, we first investigated their co-localization with DNase I hypersensitive sites across 92 distinct cell types as well as a catalogue of putative enhancer elements for 31 cell and tissue types.Notably, we found that 42.3% of ou

24、r DMRs overlap with at least one DNase I hypersensitive site, and 26.1% co-localize with enhancer like regions represents one of the most differentially methylated features.165 TFBS from ENCODEIn summary, 60% DMR on regulatory elementsIn fact, more than 60% of all ENCODE TFBSs are hyper-methylated i

25、n most samples, but become hypo-methylatedvery specifically in only one or two cell types , whereas 25% are constitutively un-methylated and never change.Specifically hypomethylated DMRs Our study highlights and defines a relatively small subset of all genomic CpGs that change their DNA methylation

26、state across a large number of representative cell types. Although we expect that number to increase with more diverse cell types as more WGBS data sets becoming available, our analysis suggests that the rate of newly discovered regulatory CpGs will drop rapidly once all major cell and tissue types

27、have been mapped. Extreme conditions in vitro or in vivo such as loss or misregulation of the maintenance methylation machinery will affect a larger subset including many intergenic CpGs that are generally static, but most of these additional CpGs are unlikely to overlap with functional elements suc

28、h as TFBSs or enhancers. In combination with the fact that sequencing of WGBS libraries is very inefficient, asabout 65% of all 101-bp reads in our set did not even contain any CpGs to begin with, this amounts to an approximate, combined loss of around 80% of sequencing depth on non-informative read

29、s and static regions. Furthermore, once defined, it will probably be sufficient in most cases to profile only a representative subset of CpGs across a comprehensive set of DMRs using an array-based or hybrid-capture-based technologyto recover representative dynamics and measure regulatory events. Us

30、ing these results as a guiding principle, we expect further improved efficiencies in mapping DNA methylation and enhance its applicability as a marker for various regulatory dynamics in normal and disease phenotypes.Clinical Applications: Classification & SurvivalMolecular classification of cuta

31、neousmalignant melanoma by geneexpression profilingGene expression profiling predicts clinical outcome of breast cancerMicroRNA expression profiles classify human cancersRecent work has revealed the existence of a class of small noncoding RNA species, known as microRNAs (miRNAs), whichhave critical

32、functions across various biological processes1,2.Here we use a new, bead-based flow cytometric miRNA expressionprofiling method to present a systematic expression analysis of 217mammalian miRNAs from 334 samples, including multiple human cancers. The miRNA profiles are surprisingly informative, refl

33、ecting the developmental lineage and differentiation state of the tumours.Samples of epithelial (EP) origin or derived from the gastrointestinal tract (GI) are indicatedClustering of 73 bone marrow samples from patients with acute lymphoblastic leukaemia(ALL)89 epithelial sampleswe next asked whethe

34、r miRNAs could be used to distinguish tumours from normal tissues. We have previously reported that there are no robust mRNA markers that show consistent differential expression between tumours and normal tissues of different lineages.MouseHumanOur observation that miRNA expression seems globally hi

35、gher innormal tissues compared with tumours led us to the hypothesis thatglobal miRNA expression reflects the state of cellular differentiation.To test this hypothesis, we explored an experimental model in whichwe treated the myeloid leukaemia cell line HL-60 with all-transretinoic acid, a potent in

36、ducer of neutrophilic differentiationMicroRNA Signature Predicts Survivaland Relapse in Lung CancerOverexpression of long noncoding RNA PCAT-1 is a novel biomarker of poor prognosis in patients with colorectal cancerLncRNA profile study reveals a three-lncRNA signature associated with the survival o

37、f patients with oesophageal squamous cell carcinomaCoexpression Network Analysis Identifies Transcriptional Modules Related to Proastrocytic Differentiation andSprouty Signaling in GliomaGliomas are primary brain tumors with high mortality and heterogeneous biology that is insufficiently understood.

38、 In this study, we performed a systematic analysis of the intrinsic organization of complex glioma transcriptome to gain deeper knowledge of the tumor biology. Gene coexpression relationships were explored in 790 glioma samples from 5 published patient cohorts treated at different institutions.The p

39、rimary analysis was focused on the Gravendeel data set as it contained the largest number of samples. Normalization of the raw datasets was performed using MAS5 algorithm followed by quantile normalization. TOThe mesenchymal proliferation and neurogenesis modules are known to define prognostic subtypes of glioma. In contrast, the proastrocytic module represents a novel signature,