Molecular Background of BCP-ALL Cases with an Early Switch to Monocytic Lineage

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3562-3562
Author(s):  
Karel Fišer ◽  
Lucie Slámová ◽  
Alena Dobiášová ◽  
Júlia Starková ◽  
Eva Froňková ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Transcription Factors ◽  
B Cell ◽  
Differentially Expressed ◽  
Lineage Commitment ◽  
Altered Expression ◽  
B Lineage ◽  
And Control ◽  
Monocytic Lineage

Abstract We identified a subset of BCP-ALL with switch towards the monocytic lineage within the first month of treatment (swALL)[Slámová et al Leukemia 2014]. During the switch cells gradually lose CD19 and CD34 expression and acquire CD33 and CD14 positivity. We proved clonal relatedness of switched monocytic blasts with the diagnostic leukemic cells based on identical Ig-TCR rearrangements. SwALL cases are not associated with MLL or BCR/ABL1 aberrancies and lack any known genetic markers of lineage ambiguity (detected by FISH or MLPA). We analyzed transcriptomes of swALL samples at diagnosis (n=4) and at d8 (n=4) where the immunophenotypic switching was already apparent as well as control BCP-ALL (n=4). RNA was isolated form either FACS sorted cells or whole BM when blasts constituted >80% of cells. For RNA-Seq we used Illumina HiSeq 2000 paired-end or single end sequencing. Raw sequencing data were analyzed using adapted protocol from Anders at al [Anders et al Nature Protocols 2013] and custom scripts. For methylome analysis we used Enhanced Reduced Representation Bisulfite Sequencing (ERRBS)[Akalin et al PLoS Genetics 2012]. ERRBS quantitatively measures DNA methylation at ~3M CpGs genome-wide. Samples from swALL at diagnosis (n=7) and at d8 (n=4) and control BCP-ALL (n=4) were processed. Analysis was performed according to [Akalin et al Genome Biology 2012] and followed with custom analysis in R statistical language. Comparison (generalized exact binomial test) of transcriptomes of B-lineage blasts from diagnosis between swALLs and control BCP-ALLs revealed a number of differentially expressed genes. Among 300 most significantly differentially expressed were KLF4, CEBPD, CLEC12A and CLEC12B (upregulated in swALL) and ANXA5, VPREB1, CD9 and IGHG3 (downregulated in swALL). Hierarchical clustering separated not only swALL and control BCP-ALL, but also swALL cells before and during the monocytic switch. Changes in gene expression during lineage switch included downregulation of ITGA6, Id2, EBF1, CD19, CD34, FLT3, MYB, CD79a, BCR, PAX5, GATA3 and TCF3 genes and upregulation of S100A10, AIF1, CD14, CD33, LGALS1, RNF130 and MNDA. When comparing all three cell types (swALL B cell and monocytic blasts and control BCP-ALL blasts) we concentrated on 1) immunophenotype switch markers and 2) lineage related transcription factors (TF): 1) Both markers typical for B cell blasts (CD19, CD34) decreased during the switch. However while CD19 was expressed in swALL at diagnosis at same levels as in control BCP-ALL, CD34 was overexpressed in swALL compared to BCP-ALL at diagnosis. Both monocytic markers (CD33, CD14) increased their expression during the switch. CD14 showed no difference between swALL and control BCP-ALL at diagnosis. However CD33 was interestingly upregulated in swALL already at diagnosis and continued to rise during the switch. SwALL had therefore deregulated expression of lineage commitment markers already at diagnosis favoring stemness marker CD34 and myeloid marker CD33. 2) B lineage commitment related TFs (EBF1, TCF3, PAX5) were expressed in B lineage blasts in both swALL and control BCP-ALL. However they were all downregulated during the switch. On the other hand myeloid lineage related transcription factor CEBPA is overexpressed in diagnostic B lineage blasts in swALL compared to control BCP-ALL cases. Similarly CEBPD is overexpressed in swALL and its expression further rises during the switch. Other hematopoietic TFs upregulated in swALL cases include KLF4, NANOG and GATA3. To confirm some of the epigenetic markers of swALL cases (demethylation of CEBPA promoter) and to widen epigenetic screening we used ERRBS. While some of the upregulated genes had expectedly hypomethylated promoters in swALL (CEBPA, GATA3) other genes (TCF3, PAX5) had demethylated promoters in all cases. While the whole DNA methylation picture is still a challenge to draw both omics method could clearly separate swALL cases from control BCP-ALL using principal component analysis. In summary we show that immunophenotypic shift is associated with gene expression changes of surface markers, lineage specific transcription factors and other genes. Some of the genes have altered expression already at diagnosis. Expression of some key lineage genes is differentially regulated by DNA methylation. Supported by: GAUK 914613, GAČR P301/10/1877, UNCE 204012, IGA NT13462-4 Disclosures No relevant conflicts of interest to declare.

scholarly journals 808 Tertiary lymphoid structure gene signature detected in immune checkpoint inhibitor-associated renal immune related adverse event

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A845-A845
Author(s):  
Jamie Lin ◽  
Amanda Tchakarov ◽  
Noha Abdel-Wahab ◽  
Houssein Safa ◽  
Salah-Eddine Bentebibel ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Kidney Biopsy ◽  
Marker Gene ◽  
Prognostic Significance ◽  
Acute Interstitial Nephritis ◽  
Differentially Expressed ◽  
Cancer Center ◽  
Induced Response ◽  
And Control

BackgroundTertiary lymphoid structures (TLSs) have been previously associated with ICI induced response in patients with cancer, but a commensurate observation has not been made in ICI associated immune related adverse events (irAEs). Acute interstitial nephritis (AIN) is the predominant lesion reported in patients with renal irAEs, but various etiologies can also trigger the development of AIN including non-ICI drugs (e.g. non-steroidal anti-inflammatory drugs, antibiotics, proton pump inhibitors, etc.), and it is unknown whether these mechanisms are similar. With increasing indications for ICIs in cancer therapy, there is a critical need to define immune pathways driving the emergence of irAEs. To address this critical knowledge gap, we performed gene expression profiling on ICI-AIN, drug-AIN, and control (non-AIN) kidney biopsy specimens.MethodsTotal RNA extracted from ICI-AIN (n = 6), drug-AIN (n = 4), and control (n = 4) fixed formalin paraffin embedded archival kidney biopsy samples was analyzed by Nanostring nCounter PanCancer Immune Profiling Panel using NanoString nCounter FLEX Analysis System.ResultsThree comparisons were conducted: ICI-AIN vs control, drug-AIN vs control, and ICI-AIN vs drug-AIN. A total of 147 genes were differentially expressed in ICI-AIN vs control and the most differentially expressed genes were CXCL 9, 10, and 11. Similarly, cell marker gene expression signatures (GES) revealed significant upregulation of T and B cell markers in ICI-AIN vs control (P < 0.01) and ICI-AIN vs drug-AIN (T cell P < 0.05; B cell P < 0.01). Differences in T and B cell score were not detected in drug-AIN vs control. Since irAEs have been associated with anti-tumor efficacy, we investigated whether a TLS signature could be detected in ICI-AIN using a four GES (CD79A, MS4A1, LAMP3 and POU2AF1). The ICI-AIN group had significantly higher TLS score compared to both control and drug-AIN groups. Since several TLS signatures have been reported, we also calculated a 12-chemokine TLS GES which was also found to be statistically significant (P < 0.05). Th1 and Th17 cells have been associated with the formation of TLS, differential upregulation of Th1 associated genes but not Th17 associated genes were detected. Furthermore, differential expression IFN-y and TNF signature was also observed in ICI-AIN group.ConclusionsThis study is the first to demonstrate the presence of TLS immune signature in irAEs. Further investigations into the prognostic significance and strategies to uncouple ICI-associated anti-tumor benefits from ICI-induced irAEs should be explored.Ethics ApprovalThe study was approved by The University of Texas MD Anderson Cancer Center intuition's Ethics Board, approval number PA16-1016

Mechanisms of MEF2C-Dependent Transcription in Lymphoid Differentiation

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 4341-4341
Author(s):  
Nikki R. Kong ◽  
Li Chai ◽  
Astar Winoto ◽  
Robert Tjian
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Cell Differentiation ◽  
B Cell ◽  
Progenitor Cells ◽  
Luciferase Reporter ◽  
B Cell Differentiation ◽  
Reporter Assays ◽  
B Lineage ◽  
Transcription Program

Abstract Hematopoiesis is a multi-step developmental process that requires an intricate coordination of signal relays and transcriptional regulation to give rise to all blood lineages in the organism. Hematopoietic stem/progenitor cells (HSPCs) can be driven to differentiate along three main lineages: myeloid, erythroid, and lymphoid. One of the earliest lineage decisions for HSPCs is whether to adopt the lymphoid or myeloid fate. Despite the discovery of several transcription factors required for different lineages of hematopoietic differentiation, the understanding of how gene expression allows HSPCs to adopt the lymphoid fate still remains incomplete. A study using an inducible hematopoietic-specific (Mx1-Cre) KO mouse line found that Myocyte Enhancer Factor 2C (MEF2C) is required for multi-potent HSPCs to differentiate into the lymphoid lineage (Stehling-Sun et al, 2009). However, the mechanisms of how MEF2C is activated and in turn, drives lymphoid fate specification are not known. Through a candidates approach with co-expression and co-immunoprecipitation, we have identified Early B Cell Factor 1 (EBF1) to be a specific interacting partner of MEF2C, and not other B cell specific transcription factors such as E12, E47, or PAX5. Genome-wide survey of MEF2C and EBF1 binding sites via chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) in a proB cell line revealed that these two sequence-specific transcription factors co-occupy the promoters and intragenic regions of many B cell specific genes such as Il7ra, Myb, Foxo1, Ets1, Ebf1 itself, and Pou2af1. Regulatory regions of Il7ra and Foxo1 derived from the ChIP-seq data, as well as an artificial enhancer containing trimerized MEF2C and EBF1 binding sites, were examined in luciferase reporter assays and found to be sufficient to drive transcription from a minimal reporter in 293T cells. Further, this activation was co-dependent on MEF2C and EBF1 expression. The functional relevance of MEF2C binding was further supported by gene expression analyses of MEF2C-regulated B lineage genes in Mx1-Cre Mef2c KO mice compared to WT mice. Consistent with ChIP-seq and luciferase reporter assays, Myb, Ebf1, Il7ra, and Foxo1 all had significantly decreased expression levels in MEF2C-null HSPCs as well as B lineage progenitor cells, compared to sex-matched littermate control mice. Interestingly, myeloid gene expression in Mef2c-KO mice was increased compared to WT control. MEF2C ChIP-seq in a murine HSPC line revealed that it binds myeloid lineage gene targets that are not regulated by MEF2C in proB cells. These results suggest that MEF2C can repress myeloid gene expression in HSPCs. To elucidate the mechanism of this functional switch, we tested the requirement for MAPK pathways to phosphorylate and activate MEF2C at three previously identified residues in order to drive B cell differentiation. Inhibition of p38 MAPK (p38i), but not ERK1/2/5, decreased the potential of HSPCs to differentiate into B220+CD19+ B cells cultured with cytokines that drive this particular lineage fate. Instead, p38i-treated progenitor cells gave rise to more myeloid cells. 65% of this phenotype was rescued by over-expressing a phosphomimetic mutant of MEF2C that can bypass p38 inhibition. Furthermore, MEF2C is known to bind class II HDAC proteins to repress gene expression, providing a possible mechanism for its repression of myeloid transcription program. Supporting this mechanism, phosphomimetic and HDAC-binding double mutant of MEF2C can rescue p38 inhibition phenotype almost 100%. Taken together, this study elucidated the molecular mechanisms of a key lymphoid-specific lineage fate determinant, MEF2C. We discovered that p38 MAPK converts MEF2C from a transcriptional repressor to an activator by phosphorylation in B cell specification, which can be applied to understanding other cell differentiation processes regulated by this important stress-induced signaling pathway. Furthermore, we identified MEF2C’s binding and co-activating partner EBF1, several novel B cell specific targets that it activates in proB cells, and a novel myeloid transcription program that it represses in hematopoietic progenitors. Therefore, these results expanded our understanding of the intricate transcription network that regulates B cell differentiation. Disclosures No relevant conflicts of interest to declare.

The MDS-Associated Gene DLK Modulates Gene Expression in Human Myeloid Cells by Altering the Activity of Multiple Transcription Factors.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3428-3428
Author(s):  
Liang Li ◽  
Rushabh Modi ◽  
Xiwei Wu ◽  
Stephen J. Forman ◽  
Ravi Bhatia
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Cell Differentiation ◽  
Linear Model ◽  
Expression Profiles ◽  
Myeloid Cells ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Differentiation And Proliferation ◽  
And Control

Abstract Delta-Like 1 (DLK) is an EGF-like transmembrane protein, which is overexpressed in myelodysplastic syndrome (MDS) CD34+ cells. We have previously shown that ectopic DLK expression inhibits HL-60 cell differentiation and proliferation through intracellular domain interactions. To further investigate mechanisms underlying DLK effects on myeloid cell differentiation and proliferation, we compared gene expression profiles of DLK expressing and control HL-60 cells, with or without differentiating induction with ATRA, using Affymetrix HG-U133A arrays. Gene expression data was analyzed using affy and limma (linear model of microarray analysis) packages in the open-source BioConductor project (v 1.6). Raw data were processed using robust multi-chip average (RMA) algorithm, a linear model fit to each gene, and the following comparisons were made: (a) effects of DLK expression in unstimulated cells, (b) effects of DLK expression in ATRA exposed cells, (c) effects of ATRA induction on R1 cells, (d) effects of ATRA induction on DLK+ cells, and (e) differences in the response of DLK+ vs. control cells to ATRA. Adjusted P values and log odds of differential expression (B statistic, 50% probability when B=0) were calculated. B values &gt; 0 were considered statistically significant. 523 genes were differentially expressed between unstimulated control and DLK+ cells, 343 genes were differentially expressed between control and DLK+ cells after ATRA stimulation, and 204 genes were common to the two sets. 802 genes were differentially expressed after ATRA stimulation in control cells, 742 genes in DLK+ cells, with 550 genes common to the two sets. 13 genes were differentially expressed when ATRA responses of control and DLK+ cells were compared. Gene ontology (GO) analyses indicated that "Biological processes" significantly affected by DLK overexpression included signal transduction, cell cycle, proliferation, cell death, protein metabolism and enzyme cascades, and "Molecular functions" most affected included nucleotide/DNA binding and protein kinase activity. These observations are consistent with observed cellular effects of DLK. Using MotifRegressor software, we performed promoter analysis correlating common transcription factor-binding motifs with expression profiles of genes differentially expressed between DLK+ and control cells. We identified the transcription factors (TF) PBX, GATA-1, c-Myc: Max, HIF-1, DEC1, Hand1, Lmo2, NKX25, GKLF and AP-1 as being potentially involved in DLK-mediated changes in gene expression. The observed patterns of differential gene expression were consistent with altered activities of these TF. Electrophoresis mobility shift assays (EMSA) indicated increased PBX and reduced HIF-1 and GATA-1 activities in DLK+ cells. Interestingly, Hand1, c-Myc: Max and Dec1 are basic Helix-loop-Helix (b-HLH) factors with E box binding sites, which are known to associate and form regulatory complexes with other TF. TF such as GATA-1, GLKF and Lmo2, also identified in our analysis, are known to be associated with such complexes. In conclusion, gene expression profiles of DLK expressing human myeloid cells are consistent with observed alterations in cell proliferation and differentiation. We have identified TF that may act individually and/or in concert to induce the observed changes in gene expression in DLK+ cells. Further evaluation of their role of these TF in mediating DLK effects and in abnormal hematopoietic cell growth in MDS is warranted.

scholarly journals Mimulus sRNAs Are Wound Responsive and Associated with Transgenerationally Plastic Genes but Rarely Both

2020 ◽  
Vol 21 (20) ◽  
pp. 7552
Author(s):  
Jack Colicchio ◽  
John Kelly ◽  
Lena Hileman
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
A Priori ◽  
Differentially Expressed ◽  
Environmental Cues ◽  
Next Generation ◽  
Small Interfering Rnas ◽  
Transgenerational Plasticity ◽  
Or Gene ◽  
And Control

Organisms alter development in response to environmental cues. Recent studies demonstrate that they can transmit this plasticity to progeny. While the phenotypic and transcriptomic evidence for this “transgenerational plasticity” has accumulated, genetic and developmental mechanisms remain unclear. Plant defenses, gene expression and DNA methylation are modified as an outcome of parental wounding in Mimulus guttatus. Here, we sequenced M. guttatus small RNAs (sRNA) to test their possible role in mediating transgenerational plasticity. We sequenced sRNA populations of leaf-wounded and control plants at 1 h and 72 h after damage and from progeny of wounded and control parents. This allowed us to test three components of an a priori model of sRNA mediated transgenerational plasticity—(1) A subset of sRNAs will be differentially expressed in response to wounding, (2) these will be associated with previously identified differentially expressed genes and differentially methylated regions and (3) changes in sRNA abundance in wounded plants will be predictive of sRNA abundance, DNA methylation, and/or gene expression shifts in the following generation. Supporting (1) and (2), we found significantly different sRNA abundances in wounded leaves; the majority were associated with tRNA fragments (tRFs) rather than small-interfering RNAs (siRNA). However, siRNAs responding to leaf wounding point to Jasmonic Acid mediated responses in this system. We found that different sRNA classes were associated with regions of the genome previously found to be differentially expressed or methylated in progeny of wounded plants. Evidence for (3) was mixed. We found that non-dicer sRNAs with increased abundance in response to wounding tended to be nearby genes with decreased expression in the next generation. Counter to expectations, we did not find that siRNA responses to wounding were associated with gene expression or methylation changes in the next generation and within plant and transgenerational sRNA plasticity were negatively correlated.

scholarly journals Epigenetic Changes During Mechanically Induced Osteogenic Lineage Commitment

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Julia C. Chen ◽  
Mardonn Chua ◽  
Raymond B. Bellon ◽  
Christopher R. Jacobs
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Shear Stress ◽  
Fluid Shear Stress ◽  
Methylation Status ◽  
Lineage Commitment ◽  
Fluid Shear ◽  
Osteogenic Lineage ◽  
Osteogenic Markers ◽  
Heterochromatin Structure

Osteogenic lineage commitment is often evaluated by analyzing gene expression. However, many genes are transiently expressed during differentiation. The availability of genes for expression is influenced by epigenetic state, which affects the heterochromatin structure. DNA methylation, a form of epigenetic regulation, is stable and heritable. Therefore, analyzing methylation status may be less temporally dependent and more informative for evaluating lineage commitment. Here we analyzed the effect of mechanical stimulation on osteogenic differentiation by applying fluid shear stress for 24 hr to osteocytes and then applying the osteocyte-conditioned medium (CM) to progenitor cells. We analyzed gene expression and changes in DNA methylation after 24 hr of exposure to the CM using quantitative real-time polymerase chain reaction and bisulfite sequencing. With fluid shear stress stimulation, methylation decreased for both adipogenic and osteogenic markers, which typically increases availability of genes for expression. After only 24 hr of exposure to CM, we also observed increases in expression of later osteogenic markers that are typically observed to increase after seven days or more with biochemical induction. However, we observed a decrease or no change in early osteogenic markers and decreases in adipogenic gene expression. Treatment of a demethylating agent produced an increase in all genes. The results indicate that fluid shear stress stimulation rapidly promotes the availability of genes for expression, but also specifically increases gene expression of later osteogenic markers.

scholarly journals Proteomic Identification of Altered Cerebral Proteins in the Complex Regional Pain Syndrome Animal Model

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Francis Sahngun Nahm ◽  
Zee-Yong Park ◽  
Sang-Soep Nahm ◽  
Yong Chul Kim ◽  
Pyung Bok Lee
Keyword(s):  
Animal Model ◽  
Complex Regional Pain Syndrome ◽  
Proteomic Analysis ◽  
Protein Identification ◽  
Pain Syndrome ◽  
Differentially Expressed ◽  
Control Groups ◽  
Altered Expression ◽  
Rat Cerebrum ◽  
And Control

Background. Complex regional pain syndrome (CRPS) is a rare but debilitating pain disorder. Although the exact pathophysiology of CRPS is not fully understood, central and peripheral mechanisms might be involved in the development of this disorder. To reveal the central mechanism of CRPS, we conducted a proteomic analysis of rat cerebrum using the chronic postischemia pain (CPIP) model, a novel experimental model of CRPS.Materials and Methods. After generating the CPIP animal model, we performed a proteomic analysis of the rat cerebrum using a multidimensional protein identification technology, and screened the proteins differentially expressed between the CPIP and control groups.Results. A total of 155 proteins were differentially expressed between the CPIP and control groups: 125 increased and 30 decreased; expressions of proteins related to cell signaling, synaptic plasticity, regulation of cell proliferation, and cytoskeletal formation were increased in the CPIP group. However, proenkephalin A, cereblon, and neuroserpin were decreased in CPIP group.Conclusion. Altered expression of cerebral proteins in the CPIP model indicates cerebral involvement in the pathogenesis of CRPS. Further study is required to elucidate the roles of these proteins in the development and maintenance of CRPS.

scholarly journals Transcriptome analyses and virus induced gene silencing identify genes in the Rpp4-mediated Asian soybean rust resistance pathway

2013 ◽  
Vol 40 (10) ◽  
pp. 1029 ◽  
Author(s):  
Aguida M. A. P. Morales ◽  
Jamie A. O'Rourke ◽  
Martijn van de Mortel ◽  
Katherine T. Scheider ◽  
Timothy J. Bancroft ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Silencing ◽  
Differentially Expressed ◽  
Soybean Rust ◽  
Phakopsora Pachyrhizi ◽  
R Gene ◽  
Virus Induced Gene Silencing ◽  
Asian Soybean Rust ◽  
Defence Responses

Rpp4 (Resistance to Phakopsora pachyrhizi 4) confers resistance to Phakopsora pachyrhizi Sydow, the causal agent of Asian soybean rust (ASR). By combining expression profiling and virus induced gene silencing (VIGS), we are developing a genetic framework for Rpp4-mediated resistance. We measured gene expression in mock-inoculated and P. pachyrhizi-infected leaves of resistant soybean accession PI459025B (Rpp4) and the susceptible cultivar (Williams 82) across a 12-day time course. Unexpectedly, two biphasic responses were identified. In the incompatible reaction, genes induced at 12 h after infection (hai) were not differentially expressed at 24 hai, but were induced at 72 hai. In contrast, genes repressed at 12 hai were not differentially expressed from 24 to 144 hai, but were repressed 216 hai and later. To differentiate between basal and resistance-gene (R-gene) mediated defence responses, we compared gene expression in Rpp4-silenced and empty vector-treated PI459025B plants 14 days after infection (dai) with P. pachyrhizi. This identified genes, including transcription factors, whose differential expression is dependent upon Rpp4. To identify differentially expressed genes conserved across multiple P. pachyrhizi resistance pathways, Rpp4 expression datasets were compared with microarray data previously generated for Rpp2 and Rpp3-mediated defence responses. Fourteen transcription factors common to all resistant and susceptible responses were identified, as well as fourteen transcription factors unique to R-gene-mediated resistance responses. These genes are targets for future P. pachyrhizi resistance research.

scholarly journals Gene expression and DNA methylation analyses suggest that two immune related genes are prognostic factors of colorectal cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiao-Liang Xing ◽  
Zhi-Yong Yao ◽  
Chaoqun Xing ◽  
Zhi Huang ◽  
Jing Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Risk Assessment ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Risk Score ◽  
Immune Cells ◽  
Assessment Model ◽  
Differentially Expressed ◽  
Risk Assessment Model

Abstract Background Colorectal cancer (CRC) is the second most prevalent cancer, as it accounts for approximately 10% of all annually diagnosed cancers. Studies have indicated that DNA methylation is involved in cancer genesis. The purpose of this study was to investigate the relationships among DNA methylation, gene expression and the tumor-immune microenvironment of CRC, and finally, to identify potential key genes related to immune cell infiltration in CRC. Methods In the present study, we used the ChAMP and DESeq2 packages, correlation analyses, and Cox regression analyses to identify immune-related differentially expressed genes (IR-DEGs) that were correlated with aberrant methylation and to construct a risk assessment model. Results Finally, we found that HSPA1A expression and CCRL2 expression were positively and negatively associated with the risk score of CRC, respectively. Patients in the high-risk group were more positively correlated with some types of tumor-infiltrating immune cells, whereas they were negatively correlated with other tumor-infiltrating immune cells. After the patients were regrouped according to the median risk score, we could more effectively distinguish them based on survival outcome, clinicopathological characteristics, specific tumor-immune infiltration status and highly expressed immune-related biomarkers. Conclusion This study suggested that the risk assessment model constructed by pairing immune-related differentially expressed genes correlated with aberrant DNA methylation could predict the outcome of CRC patients and might help to identify those patients who could benefit from antitumor immunotherapy.

scholarly journals Global impacts of chromosomal imbalance on gene expression in Arabidopsis and other taxa

2018 ◽  
Vol 115 (48) ◽  
pp. E11321-E11330 ◽  
Author(s):  
Jie Hou ◽  
Xiaowen Shi ◽  
Chen Chen ◽  
Md. Soliman Islam ◽  
Adam F. Johnson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Transcription Factors ◽  
Leaf Tissue ◽  
Global Gene Expression ◽  
Published Data ◽  
Chromosomal Imbalance ◽  
Regulatory Processes ◽  
Expression Of Genes ◽  
Global Impacts

Changes in dosage of part of the genome (aneuploidy) have long been known to produce much more severe phenotypic consequences than changes in the number of whole genomes (ploidy). To examine the basis of these differences, global gene expression in mature leaf tissue for all five trisomies and in diploids, triploids, and tetraploids of Arabidopsis thaliana was studied. The trisomies displayed a greater spread of expression modulation than the ploidy series. In general, expression of genes on the varied chromosome ranged from compensation to dosage effect, whereas genes from the remainder of the genome ranged from no effect to reduced expression approaching the inverse level of chromosomal imbalance (2/3). Genome-wide DNA methylation was examined in each genotype and found to shift most prominently with trisomy 4 but otherwise exhibited little change, indicating that genetic imbalance is generally mechanistically unrelated to DNA methylation. Independent analysis of gene functional classes demonstrated that ribosomal, proteasomal, and gene body methylated genes were less modulated compared with all classes of genes, whereas transcription factors, signal transduction components, and organelle-targeted protein genes were more tightly inversely affected. Comparing transcription factors and their targets in the trisomies and in expression networks revealed considerable discordance, illustrating that altered regulatory stoichiometry is a major contributor to genetic imbalance. Reanalysis of published data on gene expression in disomic yeast and trisomic mouse cells detected similar stoichiometric effects across broad phylogenetic taxa, and indicated that these effects reflect normal gene regulatory processes.

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