Top PDF Dynamics and Heterogeneity of Gene Expression and Epigenetic Regulation at the Single-Cell Level

Dynamics and Heterogeneity of Gene Expression and Epigenetic Regulation at the Single-Cell Level

Dynamics and Heterogeneity of Gene Expression and Epigenetic Regulation at the Single-Cell Level

We set up a two-state HMM to estimate the frequency of state-switching events between the higher and lower Nanog states. We assume each of the two states can produce an independent Gaussian distribution of production rates, with specified mean and variance, including potential overlap between the two states. Over each unit time, a cell can either stay at its current state or switch to the other state with specified probabilities. Thus, given a specific parameter set, there exists for the production rate time-series of each cell a corresponding series of underlying states that has the maximum likelihood. This likelihood is a balance between the probability of observing a production rate at the corresponding state and that of switching to another state, such that a cell that transiently exhibits a production rate far from the mean of its current state is more likely to be fluctuating rapidly within a state than switching away and back. The Baum-Welch algorithm [3] maximizes the sum of this likelihood over all cells by iteratively changing the parameters in small increments, improving the total likelihood each time.
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Heterogeneity of Single Cell Cytokine Gene Expression in Clonal T Cell Populations

Heterogeneity of Single Cell Cytokine Gene Expression in Clonal T Cell Populations

B7 costimulation markedly increased the frequency and the level of IL-2 mRNA expression in individual positive cells in the Thl and Th0 populations, with less effect on the r[r]

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Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data

* Daphne.Ezer@slcu.cam.ac.uk (DE); adryan@sysbiol.cam.ac.uk (BA) Abstract Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expres- sion assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifi- cally an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eigh- teen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expan- sion of single cell genomics.
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Genetic and Epigenetic Regulation of Human lincRNA Gene Expression

Genetic and Epigenetic Regulation of Human lincRNA Gene Expression

Here we analyze the natural variation of lincRNA gene expression by using the GenCord collection 22,25 of three cell-types (primary fibroblast cells, immortalized lympho- blastoid cell lines, and primary T cells) from 195 unrelated European individuals for which transcriptome, genotype, and DNA methylation data are available. By comparing the genetic and epigenetic regulation between lincRNAs and protein-coding genes and by utilizing the advantages offered by this multilayered, multiple-cell-type data set, we have discovered several interesting properties of lincRNAs. Compared to protein-coding genes, we find that lincRNAs have an excess of cis-eQTLs, which are located closer to the TSS and have higher effect sizes, implying that lincRNA expression levels could be less con- strained than those of protein-coding genes. We discover an influence of lincRNA cis-eQTLs on expression level of nearby protein-coding genes and an enrichment of expressed lincRNA promoters in enhancer marks that together suggest an involvement of lincRNAs in the regula- tion of transcription in cis. Finally, comparing epigenetic regulatory patterns between lincRNAs and protein-coding genes reveals mainly similarities, but analogous to eQTLs, DNA methylation sites associated with expression are closer to the TSS of lincRNAs than are protein-coding genes.
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Epigenetic Regulation of Cytokine Gene Expression in T Lymphocytes

Epigenetic Regulation of Cytokine Gene Expression in T Lymphocytes

Epigenetic regulation of IFN-γγ in Th1 differentiation In naïve T cells, most of CpG dinucleotides at regulatory elements are demethylated. Differentiation to Th1 cells showed similar level of DNA methylation to that of naïve T cells, but some of the regulatory elements showed increased demethylation, while Th2 differentiation was associated with substantial overall methylation. 68 Unlike the IL-4 pro- moter region, methylation of IFN- γ promoter in T cells has been controversial. From the detailed quantitative analysis of six CpGs in promoter regions, it has been confirmed that IFN- γ promoter becomes methylated during Th2 cell devel- opment. In contrast, the promoter region as well as trans- cribed region becomes demethylated in naïve and differen- tiated Th1 cells, 69 demonstrating that various changes in DNA methylation at the IFN- γ locus occur during Th1 and Th2 cell development. In addition, there are increased levels of H3 and H4 acetylation, H3K4 dimethylation and DNase I hypersensitive sites at the IFN- γ locus and complete loss of H3K27 methylation, which is the representative mark of repression, in the IFN- γ regulatory elements in Th1 cells. 68,70-72 While differentiated Th2 cells show loss of permissive marks, repressive H3K27 trimethylation appears along with increased level of CpG methylation throughout the IFN- γ locus. 68,73 Th1-specific changes of permissive histone modi- fication are acquired by STAT4 and T-bet. STAT4 binds to the promoter and other elements like CNS-22 and this leads to increased levels of permissive histone modifications.
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Combined single cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

Combined single cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

The intrinsic properties of single-cell data also affect the accuracy of the estimated splicing ratios per cassette exon. We opted for a lenient threshold on read depth to determine splicing ratio, which delivered more cassette exons to train our models, but also rendered splicing ra- tios less accurate in comparison to deep-sequenced bulk data. The low read depth increases the chance of missing an isoform or cassette exon, an effect known as a drop- out. Dropouts in single-cell RNA-seq data can have a strong impact on the fit of the cell or gene model. If one of the isoforms was completely unobserved, this would decrease the fit of the gene model. On the contrary, sequencing multiple cells at once would decrease the fit of the cell model. Given that our results are robust across cassette exons, cell types, and species, the overall findings we report are however not likely to be affected.
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Temporal dynamics and transcriptional control using single cell gene expression analysis

Temporal dynamics and transcriptional control using single cell gene expression analysis

Results: Here we investigate the temporal dynamics of a single-cell transcriptional network of 45 transcription factors in THP-1 human myeloid monocytic leukemia cells undergoing differentiation to macrophages. We systematically measure temporal regulation of expression and variation by profiling 120 single cells at eight distinct time points, and infer highly controlled regulatory modules through which signaling operates with stochastic effects. This reveals dynamic and specific rewiring as a cellular strategy for differentiation. The integration of both positive and negative co-expression networks further identifies the proto-oncogene MYB as a network hinge to modulate both the pro- and anti-differentiation pathways.
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Errors during Gene Expression: Single Cell Heterogeneity, Stress Resistance, and Microbe Host Interactions

Errors during Gene Expression: Single Cell Heterogeneity, Stress Resistance, and Microbe Host Interactions

b MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA ABSTRACT Gene expression has been considered a highly accurate process, and deviation from such fidelity has been shown previously to be detrimental for the cell. More recently, increasing evidence has supported the notion that the accuracy of gene expression is indeed flexibly variable. The levels of errors during gene ex- pression differ from condition to condition and even from cell to cell within geneti- cally identical populations grown under the same conditions. The different levels of errors resulting from inaccurate gene expression are now known to play key roles in regulating microbial stress responses and host interactions. This minireview summa- rizes the recent development in understanding the level, regulation, and physiologi- cal impact of errors during gene expression.
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Beyond comparisons of means: understanding changes in gene expression at the single cell level

Beyond comparisons of means: understanding changes in gene expression at the single cell level

Catalina A. Vallejos 1,2* , Sylvia Richardson 1* and John C. Marioni 2,3* Abstract Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarchical model that builds upon BASiCS to study changes that lie beyond comparisons of means, incorporating built-in normalization and quantifying technical artifacts by borrowing information from spike-in genes. Using a probabilistic approach, we highlight genes undergoing changes in cell-to-cell heterogeneity but whose overall expression remains unchanged. Control experiments validate our method’s performance and a case study suggests that novel biological insights can be revealed. Our method is implemented in R and available at https://github.com/catavallejos/BASiCS.
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Single-cell transcriptomics reveals gene expression dynamics of human fetal kidney development

Single-cell transcriptomics reveals gene expression dynamics of human fetal kidney development

The current understanding of mammalian kidney development is largely based on mouse models. Recent landmark studies revealed pervasive differences in renal embryogenesis between mouse and human. The scarcity of detailed gene expression data in humans there- fore hampers a thorough understanding of human kidney development and the possible developmental origin of kidney diseases. In this paper, we present a single-cell transcrip- tomics study of the human fetal kidney. We identified 22 cell types and a host of marker genes. Comparison of samples from different developmental ages revealed continuous gene expression changes in podocytes. To demonstrate the usefulness of our data set, we explored the heterogeneity of the nephrogenic niche, localized podocyte precursors, and confirmed disease-associated marker genes. With close to 18,000 renal cells from five dif- ferent developmental ages, this study provides a rich resource for the elucidation of human kidney development, easily accessible through an interactive web application.
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Deciphering the Gene Expression Control in Epigenetic, Post-transcriptional and Translational Regulation

Deciphering the Gene Expression Control in Epigenetic, Post-transcriptional and Translational Regulation

Mattern et al. 2016; Urrego et al. 2017), the complete characterization of DNA methylation at the single- base level has not been reported. This characterization is critical to understanding the epigenetic reprogramming and regulation that occurs during normal, bovine embryonic development in vivo, and to providing insight into the epigenetic alterations that occur during in vitro maturation of oocytes and culture of embryos after in vitro fertilization. Environmental perturbations experienced during in vitro production are expected to influence the epigenetic reprogramming during this critical period, often leading to nonrandom epigenetic errors (Li et al. 2005; Fernandez-Gonzalez et al. 2010) that are linked to imprinting diseases in humans (Sutcliffe et al. 2006) and large offspring syndrome in ruminants (Young et al. 1998;
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Single-Cell Analysis Reveals Distinct Gene Expression and Heterogeneity in Male and Female Plasmodium falciparum Gametocytes

Single-Cell Analysis Reveals Distinct Gene Expression and Heterogeneity in Male and Female Plasmodium falciparum Gametocytes

Although at least 300 genes are considered to be gametocyte specific in P. falci- parum, their roles in male and female development have not yet been fully defined (13–18). Plasmodium berghei and P. falciparum gender-specific flow sorting studies have revealed late-stage markers for male and female gametocytes, but these studies are based on specific reporter genes and are therefore biased for late stages (19–21). In particular, the recent flow sorting study using P. falciparum represents the first tran- scriptome analysis of male and female gametocytes (20). However, the gender-specific expression of some genes is still debated (22). We hypothesize that some of the controversies about gender-specific expression may result from reliance on population analyses of mixed gametocytes that include multiple differentiation stages and tem- poral changes in gene expression. These issues can best be resolved using single-cell isolation and expression analyses at distinct time points to unequivocally decipher sex-specific transcripts that may ultimately determine male or female fate. Recently, single-cell RNA sequencing revealed that sexually committed schizonts have a distinct program of gene expression (23). Here, we describe our efforts using a single-cell approach to define male and female gametocyte gene expression in an unbiased manner. Our study incorporates the first use of the Fluidigm C1 system for microfluidic capture of single gametocytes, followed by real-time PCR (RT-PCR) quantitation of their sex-specific expression of gametocyte genes on the Biomark HD system. The analysis of stage III through stage V gametocytes separates parasites by gender rather than stage and reveals a number of new candidate genes for male and female development. Additionally, a large female population reveals unexpected cellular heterogeneity among single cells, previously undetected on a population level. Therefore, our study highlights the power of single-cell transcriptome analysis in dissecting the sex-specific gene expression of P. falciparum.
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The Epigenetic Regulation of Blinatumomab Gene Expression: Tumor Cell-dependent T cell Response against Lymphoma Cells and Cytotoxic Activity

The Epigenetic Regulation of Blinatumomab Gene Expression: Tumor Cell-dependent T cell Response against Lymphoma Cells and Cytotoxic Activity

plasmid into pseudo attP site in mammalian genomes (2). PhiC31 integrase system is considered as a specific tool for gene therapy (3, 4) and transgenic research (2, 5). The efficiency of phiC31-integrase has been indicated to be comparable with that of the widely used Cre/loxP system. Furthermore, flippase (FLP) recombinase shows only 10% recombination activity on chromosomal targets in comparison with Cre recombinase (6). Cre and FLP cause deletion of the gene after integration (7) whereas phiC31 integrase can catalyze unidirectional and irreversible recombination between attB and pseudo attP sites (3). Development of phiC31 integrase-based vectors for prolonged therapeutic gene expression, demonstrated that it is a robust and reliable gene delivery system (4, 8). Sodium butyrate (NaBut) treatment increases the specific productivity of recombinant proteins in mammalian cells; but, it declines cell growth and can provoke apoptosis (9). NaBut inhibits the activity of many histone deacetylases, induces hyperacetylation of histones. Histone acetylation could modify chromatin structure, lead to transcription factors and polymerases binding as well as improving gene expression (10). Due to its impact on epigenetic mechanisms, NaBut has attracted many interest for the prevention and treatment of different diseases such as genetic/metabolic conditions and neurological degenerative disorders (11). Valproic acid (VPA), a histone deacetylase inhibitor (HDACi), can cause impaired epigenetic modification and suppress cell growth (12). It can increase the expression of genes that are regulated by transcription factors (13). It has been indicated that the HDACi increases both the specific productivity and mRNA transcription level in stable CHO cell lines. Furthermore, no cellular toxicity was reported with VPA compared with other widely used HDACi such as NaBut (14). Blinatumumab, the most advanced bispecific T-cell engager (BiTE) with dual binding specificities (15),
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Epigenetic regulation of S100 protein expression

Epigenetic regulation of S100 protein expression

Although the genes of S100 proteins are located in a cluster, there is no evidence that their expression is by any means synchronized either in a cell-specific or develop- mental manner. Quite the opposite—there are many reports showing that in a given cell type, a certain S100 protein may be abundant while the one encoded by a neighboring gene is expressed at a low level or absent. Therefore, studies which compared the expression of a panel of S100 proteins in a given cell type or tissue, or in a set of normal versus cancerous tissues, led to the conclusion that, despite structural similarities and clustered genes, each S100 protein has a very specific expression pattern (Pedrocchi et al. 1994; Elder and Zhao 2002; Cross et al. 2005). Another interesting feature of S100 proteins is that expression of an individual protein may be completely different between cell lines, even those derived from related sources. Attempts aimed at identifying cell-specific tran- scription factors that would underlie this phenomenon have failed because exogenously introduced promoter constructs appeared to be equally active in cells differing in endogenous expression of a given S100 protein (Tulchinsky et al. 1992; Wicki et al. 1997; Lesniak et al. 2000). These observations turned the attention to epigenetic factors that could be involved in the control of S100 protein expression.
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G-Quadruplex Dynamics Contribute To Regulation Of Mitochondrial Gene Expression

G-Quadruplex Dynamics Contribute To Regulation Of Mitochondrial Gene Expression

This notion was further supported by mtDNA replication inhibition assays in a cell-free system with isolated mitochondria. All together, these findings suggest that low dose RHPS4 is a useful condition to study the effect of G4 stabilization on mitochondrial nucleic acids. Although the specific sequences bound by RHPS4 in living cells are not known, clues arise from the data presented here. Our results demonstrate that RHPS4 preferentially interferes with polymerase amplification of specific regions of the mitochondrial genome, notably ND3. This region contains a predicted G4 that we demon- strated can fold in vitro into a G4 structure. A pathological variant in that ND3 G4 structure, m.10191T > C, increased G4 thermal stability and antiparallel character (Fig. 8a,b). We expect that a sequence with higher G4-forming potential, such as m.10191T > C, would form G4 more frequently in vivo thus would be more subject to RHPS4 binding. The antiparallel fold may also serve as a preferred substrate for RHPS4. Indeed, the m.10191T > C variant also enhances the cell sensitivity to RHPS4, exacerbating the mitochondrial respiratory defects, but the molecular alterations caused by RHPS4 remains the subject of speculation. It may be that RHPS4 stabilized the G4 structures that it binds to, but it is equally possible that the higher level of G4 formed by the variant sequence provides more opportunity for RHPS4 to have effects that extend beyond G4 stabilization, per- turbing the G4 biology. Our combined results provide strong evidence that low concentrations of RHPS4 can be used to alter mitochondrial G4 structures.
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Epigenetic regulation of gene expression in osteoarthritis

Epigenetic regulation of gene expression in osteoarthritis

SOX9 is a master transcription factor for chondrogenesis during the development of the skeletal system, in cooper- ation with SOX5 and SOX6. 36,37 Although mice with condi- tional postnatal deletion of Sox9 in articular cartilage did not develop OA even by the age of 18 months, 38 later OA usually is associated with decreased SOX9 expression in humans. 39 Kim et al recently reported that down-regulated SOX9 expression in advanced hip OA chondrocytes is mediated by DNA methylation and histone modification, including histone methylation and acetylation. 18 Moreover, miRNA-145 has been identified as an inhibitor of SOX9 expression in human chondrocyte; increased miRNA-145 directly represses SOX9 expression, causing reduced expression of COL2A1 and aggrecan and an increased level of matrix metal- loproteinases 13 (MMP13). 19 In addition, miRNA-199a-3p and miRNA-193b has been found to down-regulate SOX9 expression. 20 While SOX9 is considered a typical anabolic factor in articular cartilage, the response of cultured chon- drocytes to forced expression of SOX9 has been controver- sial. Kypriotou et al found that overexpression of SOX9 itself was unable to restore the chondrocyte phenotype in dedif- ferentiated osteoarthritic chondrocytes, 40 whereas Cuc- chiarini et al reported that r-AAV mediated SOX9 gene transfer up-regulated the expression levels of proteoglycans and type II collagen in normal and OA ACs. 41 Therefore, more studies are needed to determine whether the epigenetically regulated change in SOX9 expression in articular cartilage is the cause or the result of OA.
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Epigenetic Regulation of Gene Expression in Keratinocytes

Epigenetic Regulation of Gene Expression in Keratinocytes

Developmentally regulated gene repositioning might be explained by the necessity for selected genes to reach a permissive nuclear environment required for maintenance or co-regulation of their expression (Lanctot et al., 2007). Nuclear speckles localized in the interchromatin compart- ments and enriched by the components of the RNA splicing machinery might provide such a permissive environment, and, indeed, many highly transcribed genes including the EDC locus in keratinocytes show a tendency to cluster around speckles (Brown et al., 2008; Hu et al., 2008; Szczerbal and Bridger, 2010; Spector and Lamond, 2011) (Figure 1a). Recent data demonstrate that relocation of the proliferation- associated genes from the repressive polycomb bodies to nuclear speckles depends on methylation/demethylation of the PRC1 protein Cbx4: methylated form of Cbx4 binds to the non-coding RNA TUG1, while demethylated Cbx4 binds to non-coding RNA MALAT1/NEAT2, located in the polycomb bodies or speckles, respectively (Yang et al., 2011). Reposi- tioning of genes to speckles results in their activation through MALAT1/NEAT2-mediated interactions with multiple co- activators of transcription and/or splicing factors (Yang et al., 2011). Frequent associations between EDC and nuclear speckles are also seen in the epidermal keratinocytes (Figure 1b). However, functional significance of keratinocyte-speci- fic gene loci colocalization with nuclear speckles remains to be determined.
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Characterizing heterogeneity in leukemic cells using single cell gene expression analysis

Characterizing heterogeneity in leukemic cells using single cell gene expression analysis

Importantly, Meis1, a rate-limiting factor for the de- velopment of AF9-MLL induced AML [43], is highly expressed only in the leukemic cells. We also found important differences between the two subtypes of leu- kemic cells, with Leukemia 1 cells overexpressing a num- ber of important leukemia regulators, including Etv6 and Runx1, providing support that these cells are more im- portant for tumor initiation. Notably, Leukemia 1 cells also over-express a number of chromatin regulators, in- cluding Brd3 and Polycomb complex members Ezh2 and Suz12, all of which have been linked with leukemia and other cancers [8,47,48]. By using in vitro colony-forming assays we found that the Leukemia 1 population, which is enriched with a Kit+CD24- immunophenotype, has a higher proliferation rate and differentiation capability than the Leukemia 2 population. However, we note that Kit and CD24 markers alone are insufficient to completely distinguish the two leukemic cell subtypes.
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Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

Results: We subjected 48 of our samples from transcriptional clusters 1, 2, 3, and 5 to Infinium HumanMethylation450 arrays. Samples belonging to transcriptional clusters 1 – 3 still showed visible relationships to each other by methylation, but cluster 5, with known chromosomal abnormalities, no longer formed a cohesive group. Within transcriptional clusters 2 and 3, controlling for fetal sex and gestational age in the identification of differentially methylated sites, compared to the healthier cluster 1, dramatically reduced the number of significant sites, but increased the percentage that demonstrated a strong linear correlation with gene expression (from 5% and 2% to 9% and 8%, respectively). Locations exhibiting a positive relationship between methylation and gene expression were most frequently found in CpG open sea enhancer regions within the gene body, while those with a significant negative correlation were often annotated to the promoter in a CpG shore region. Integrated transcriptome and epigenome analysis revealed modifications in TGF-beta signaling, cell adhesion, oxidative phosphorylation, and metabolism pathways in cluster 2 placentas, and aberrations in antigen presentation, allograft rejection, and cytokine-cytokine receptor interaction in cluster 3 samples. Conclusions: Overall, we have established DNA methylation alterations underlying a portion of the transcriptional development of “ canonical ” PE in cluster 2 and “ immunological ” PE in cluster 3. However, a significant number of the observed methylation changes were not associated with corresponding changes in gene expression, and vice versa, indicating that alternate methods of gene regulation will need to be explored to fully comprehend these PE subtypes.
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Epigenetic and Expression Regulation -.ppt

Epigenetic and Expression Regulation -.ppt

Epigenetic mechanisms can regulate genes involved in Epigenetic mechanisms can regulate genes involved in differentiation, cell cycle, and cell survival differentiation, cell cycle, and cell survival Deregulation of epigenetic mechanisms results in aberrant gene Deregulation of epigenetic mechanisms results in aberrant gene expression, which can lead to cancer

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