Transcriptomic Profiling of Peripheral Blood in 22q11.2 Reciprocal Copy Number Variants: Differential Cell Proportion Highly Impacts Gene Expression and Module Distinctions

2021 ◽  
Vol 89 (9) ◽  
pp. S138
Author(s):  
Gil Hoftman ◽  
Amy Lin ◽  
Jennifer Forsyth ◽  
Daniel Nachun ◽  
Daqiang Sun ◽  
...  
2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Mark A. Corbett ◽  
Clare L. van Eyk ◽  
Dani L. Webber ◽  
Stephen J. Bent ◽  
Morgan Newman ◽  
...  

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 595-595 ◽  
Author(s):  
Li Zhou ◽  
Joanna B. Opalinska ◽  
Davendra P. Sohal ◽  
Yongkai Mo ◽  
Suman Kambhampati ◽  
...  

Abstract Myelodysplasia (MDS) is a clonal hematopoietic disorder that leads to ineffective hematopoiesis and peripheral cytopenias. DNMT inhibitors such as azacytidine have led to clinical responses in patients, though global epigenetic alterations in MDS have not been well described. The transmission of these epigenetic marks during hematopoietic differentiation and their role in disease pathophysiology is also unknown. We first compared global methylation profiles of 8 bone marrow samples with peripheral leucocytes by using a recently described novel method, the HELP assay (HpaII tiny fragment Enrichment by Ligation-mediated PCR; Khulan et al, Genome Res. 2006 Aug;16(8)) that uses differential methylation-specific digestion by HpaII and MspI followed by amplification, two color labeling and hybridization to quantitatively determine individual promoter CpG island methylation. A whole genome human promoter array (Nimblegen) was used to determine the level of methylation of 25626 gene promoters by calculating HpaII/MspI cut fragment intensity ratio. We observed a high correlation (r=0.89–0.96) of epigenetic marks between bone marrow and peripheral blood samples suggesting that a majority of epigenetic marks can be also be seen in differentiated cells. We subsequently compared peripheral blood leucocytes from 20 patients with MDS with 10 age-matched normal and anemic controls. Parallel gene expression analysis was performed using 37K oligo maskless arrays on cDNA from the same samples. Analysis showed that whole genome methylation profiling has greater discriminatory power in separating clusters of MDS samples from normal and anemic controls when compared to gene expression analysis. Epigenetic profiling demonstrated two clusters of MDS based on similarity of aberrant epigenetic changes. Overall, there was a trend towards hypermethylation in MDS, albeit not statistically significant given the large number of relatively unchanged genes. Detailed analysis revealed several novel differentially methylated genes that had corresponding changes in gene expression, when MDS samples were compared to the controls with a low false discovery rate of analysis. Interesting genes getting hypomethylated and overexpressed included TNF superfamily member 9, granulocyte pep A, microsomal glutathione S-transferase, homeo box B4, mitochondrial RPL11, and others. Similarly, the set of genes that were getting hypermethylated with associated decrease in gene expression included Evi-1, DAPK, HOXB3, Protein Phosphatase 1, CEBPB, mutated in colorectal cancer (MCC), myeloid-lymphoid or mixed-lineage leukemia 5 (MLL5), plasminogen-related protein B, ovarian cancer related protein 1 (ORP1), and others. In addition, we did array-based comparative genome hybridization (aCGH) to look at exact genome copy number changes in these samples. We found changes that were not detectable by conventional karyotyping in all samples. Commonly seen alterations were del(14q11), del(20q11), del(5q13), del(8p23), amp(1q42), amp(5q11), amp(17q12), amp(19q13) and amp(7q22). Integrative analysis revealed sets of genes that were either silenced by methylation or deletion in different patients. Thus, our data demonstrates that promoter DNA methylation changes are an important phenomenon in MDS evolution, and are associated with changes in expression of genes playing important roles in cancer development and/or progression. We also show that previously unrecognizable changes in copy number exist in most patients with MDS. In addition, our work shows that whole genome methylation assays, even when done on peripheral blood leukocytes, can be used for potential biomarker studies in the diagnosis of MDS.


2015 ◽  
Vol 22 (12) ◽  
pp. 1907-1910 ◽  
Author(s):  
Dong Wang ◽  
Xia Li ◽  
Shanshan Jia ◽  
Yan Wang ◽  
Zhijing Wang ◽  
...  

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1742-1742
Author(s):  
Thorsten Zenz ◽  
Almut Luetge ◽  
Junyan Lu ◽  
Huellein Jennifer ◽  
Sascha Dietrich ◽  
...  

While recurrent mutations in CLL have been extensively catalogued, how driver mutations affect disease phenotypes remains incompletely understood. To address this, we performed RNA sequencing on 184 CLL patient samples and linked gene expression changes to molecular subgroups, gene mutations and copy number variants. Library preparation was performed according to the Illumina TruSeq RNA sample preparation v2 protocol. Samples were paired-end sequenced and two to three samples were multiplexed per lane on Illumina HiSeq 2000, Illumina HiSeq3000/4000 or Illumina HiSeqX machines. Raw RNA-seq reads were demultiplexed and quality control was performed using FastQC version 0.11.5. Internal trimming with STAR version 2.5.2a was used to remove adapters before mapping. Mapping was performed using STAR version 2.5.2a against the Ensembl human reference genome release 75 (Homo sapiens GRCh37.75). STAR was run in default mode with internal adapter trimming using the clip3pAdapterSeq option. Mapped reads were summarized into counts using htseq-count version 0.9.0 with default parameters and union mode. Thus, only fragments unambiguously overlapping with one gene were counted. The count data were then imported into R (version 3.4) for subsequent analysis. We identified robust and previously unknown gene expression signatures associated with recurrent copy number variants (including trisomy 12, del11q22.3, del17p13, del18p12 and gain8q24), gene mutations (TP53, BRAF and SF3B1) and the mutation status of the immunoglobulin heavy-chain variable region (IGHV). The most profound gene expression changes were associated with IGHV, methylation groups and trisomy 12. We found evidence for a significant influence of CNVs beyond the gene dosage effect. In line with these observations, unsupervised clustering showed that these major biological subgroups form distinct clusters and are discernible by unsupervised clustering (IGHV, methylation groups and trisomy 12). We found 3275 genes significantly differentially expressed between M-CLL and U-CLL after adjustment for multiple testing using the method of Benjamini and Hochberg for FDR = 1% . In total 9.5 % of variance within gene expression was associated with the IGHV status. These data suggest a much larger impact on transcriptional changes than previously detected (Ferreira et al. 2014), a finding much more in line with the key impact of IGHV on clinical course and biology of disease. We found distinct expression pattern of up- and downregulated genes for trisomy 12 samples. Even though many upregulated genes are located on chromosome 12, the majority of differentially expressed genes are indeed distributed among the other chromosomes and cannot be therefore not be ascribed to a simple gene dosage effect. To investigate the role of genetic interactions, we tested the collaborative effect on gene expression phenotypes. We investigated epistatic gene expression changes for IGHV status and trisomy 12. Epistasis was defined as a non-linear effect on gene expression between sample with both variants co-occuring and the single variants alone. In total 893 genes showed specific expression pattern in a combined genotype (padj<0.1). These expression changes differed from the expected change by simple combination of the single variant's effects. We observed different ways of epistatic interaction and clustered genes by them. In total, we identified five cluster of genes representing different ways of mixed epistasis as inversion down, suppression, different degrees of buffering and inversion up. To further investigate this interaction we used enrichment tests for genes in the different mixed epistasis cluster. We found genes upregulated in trisomy12 U-CLL sample, but suppressed in M-CLL trisomy12 samples were enriched in Wnt beta catenin and Notch signaling. In summary, our study provides a comprehensive reference data set for gene expression in CLL. We show that IGHV mutation status, recurrent gene mutations and CNVs drive gene expression in a previously underappreciated fashion. This includes epistatic interaction between trisomy 12 and IGHV. Using a novel way to describe coordinated changes we can group genes into sets related to buffering, inversion and suppression. Disclosures Sellner: Takeda: Employment.


2014 ◽  
Vol 8 (1) ◽  
pp. 148-175 ◽  
Author(s):  
Alberto Cassese ◽  
Michele Guindani ◽  
Mahlet G. Tadesse ◽  
Francesco Falciani ◽  
Marina Vannucci

2018 ◽  
Author(s):  
Matthew Jensen ◽  
Santhosh Girirajan

ABSTRACTVariably expressive copy-number variants (CNVs) are characterized by extensive phenotypic heterogeneity of neuropsychiatric phenotypes. Approaches to identify single causative genes for these phenotypes within each CNV have not been successful. Here, we posit using multiple lines of evidence, including pathogenicity metrics, functional assays of model organisms, and gene expression data, that multiple genes within each CNV region are likely responsible for the observed phenotypes. We propose that candidate genes within each region likely interact with each other through shared pathways to modulate the individual gene phenotypes, emphasizing the genetic complexity of CNV-associated neuropsychiatric features.


2020 ◽  
Vol 16 (S4) ◽  
Author(s):  
Apoorva Bharthur Sanjay ◽  
Diana Otero Svaldi ◽  
Liana G. Apostolova

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243251
Author(s):  
Gustavo de los Campos ◽  
Torsten Pook ◽  
Agustin Gonzalez-Reymundez ◽  
Henner Simianer ◽  
George Mias ◽  
...  

Modern genomic data sets often involve multiple data-layers (e.g., DNA-sequence, gene expression), each of which itself can be high-dimensional. The biological processes underlying these data-layers can lead to intricate multivariate association patterns. We propose and evaluate two methods to determine the proportion of variance of an output data set that can be explained by an input data set when both data panels are high dimensional. Our approach uses random-effects models to estimate the proportion of variance of vectors in the linear span of the output set that can be explained by regression on the input set. We consider a method based on an orthogonal basis (Eigen-ANOVA) and one that uses random vectors (Monte Carlo ANOVA, MC-ANOVA) in the linear span of the output set. Using simulations, we show that the MC-ANOVA method gave nearly unbiased estimates. Estimates produced by Eigen-ANOVA were also nearly unbiased, except when the shared variance was very high (e.g., >0.9). We demonstrate the potential insight that can be obtained from the use of MC-ANOVA and Eigen-ANOVA by applying these two methods to the study of multi-locus linkage disequilibrium in chicken (Gallus gallus) genomes and to the assessment of inter-dependencies between gene expression, methylation, and copy-number-variants in data from breast cancer tumors from humans (Homo sapiens). Our analyses reveal that in chicken breeding populations ~50,000 evenly-spaced SNPs are enough to fully capture the span of whole-genome-sequencing genomes. In the study of multi-omic breast cancer data, we found that the span of copy-number-variants can be fully explained using either methylation or gene expression data and that roughly 74% of the variance in gene expression can be predicted from methylation data.


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