Chromatin Architecture and Transcription Factor Occupancy of Erythrocyte Membrane Genes Studied by Chromatin Immunoprecipitation on Microarrays (ChIP-chip)

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 2436-2436
Author(s):  
Laurie A Steiner ◽  
Yelena Maksimova ◽  
Clara Wong ◽  
Vincent Schulz ◽  
Patrick G. Gallagher

Abstract Erythrocyte membrane protein genes serve as excellent models of complex gene loci structure and function, as most encode multiple tissue-, cell-, developmental-, and stagespecific isoforms. Dynamic chromatin modifications participate in the regulatory control of many gene loci. We hypothesize that specific DNA sequences, transcription factors, and chromatin architecture (epigenetic modifications) regulate the tissue-specific expression of erythrocyte membrane protein genes. Advances in genomics technology have permitted rapid identification of DNA sequences bound by transcription factors and other DNAassociated proteins on a genome-wide scale. One technique available for mapping protein-DNA interactions in vivo couples chromatin immunoprecipitation to microarrays that contain regions of genomic DNA (ChIP-chip). We are using DNA obtained from chromatin immunoprecipitations performed with histone and erythroid transcription factor antibodies hybridized to genomic DNA microarray chips (ChIP-chip) to study the regulation of membrane protein genes in erythroid and nonerythroid cells. Chromatin immunoprecipitations (ChIP) were done in erythroid (K562) and non-erythroid (HeLa) cell lines using antibodies against H3 tri-methyl lysine 4 (H3K4me3, a marker of active chromatin) and the erythroid transcription factors GATA-1 and NF-E2. The chromatin resulting from these ChIPs was hybridized to a custom made NimbleGen high density human genomic DNA microarray (chip) focused on 15 genes critical to the erythrocyte membrane: ankyrin (ANK1), α-spectrin (SPTA1), β-spectrin (SPTB), band 3 (SLC4A1), β-adducin (ADD2), α-adducin (ADD1), γ-adducin (ADD3), ICAM-4, Erythroid Associated Membrane Protein (ERMAP), Protein 4.1 (EPB41), Protein 4.2 (EPB42), Dematin (ERPB49), β-Actin (ACTB), tropomodulin (TMOD1), and tropomyosin (TPM3). Probes for the chip were ~50bp in length with Tm ≥ 76°C, tiled every 65bp. From 50–100kb of flanking DNA was included on the chip for each locus. The Tamalpais peak calling algorithm using L1–L3 level of stringency (Genom Res16:595, 2006) was used to analyze the resulting data and identify regions of epigenetic modifications and transcription factor binding. Fourteen of 15 genes were enriched for H3K4me3 at promoter and transcriptional start sites (TSS) in K562 cells, with one gene, TMOD1, demonstrating a large peak of enrichment 5′ of the currently identified TSS, but not at the promoter. Two compact genes, β-actin and ICAM4, had H3K4me3 enrichment at the promoter and throughout gene. A total of 19 GATA-1 sites and 18 NF-E2 sites were identified. GATA-1 sites were found in 8 of 15 genes or in their flanking DNA. Three sites were in the 5′ flanking DNA, 1 site was at the promoter (~500bp from transcription start site, TSS), 12 sites were in introns, and 3 sites were in the 3′ flanking DNA. NF-E2 sites were found in 10 of 15 genes or their flanking DNA. 6 sites were in the 5′ flanking DNA, 1 site was at the promoter (~200bp from TSS), 8 sites were in introns, and 3 sites were in the 3′ flanking DNA. 18 of 19 GATA-1 sites (95%) and 13 of 18 NF-E2 sites (72%) were validated using qPCR-based quantitative ChIP. In K562 cells, 15 of 19 (79%) validated GATA-1 sites were associated with regions of chromatin enriched for H3K4me3, suggesting that ~a fifth of GATA-1 sites were in regions of inactive chromatin, consistent with a repressor function for GATA-1 at these sites. Eleven of 13 validated NF-E2 sites (85%) were associated with regions of K562 chromatin enriched for H3K4me3. In HeLa cells, the sites of GATA-1 and NF-E2 occupancy identified in K562 cells were almost never associated with H3K4me3 enrichment. GATA-1 and NF-E2 sites identified by Tamalpais and validated in K562 cells were analyzed in CD71-bright, glycophorin A-bright cultured primary erythroid cells using conventional quantitative ChIP analyses. Of the 13 NF-E2 sites identified in K562 cells, all 13 were also occupied in primary erythroid cells. ChIP-chip is a powerful tool for studying chromatin architecture and identifying transcription factor binding sites in complex genetic loci such as the erythrocyte membrane protein genes. It will be useful in constructing a comprehensive catalogue of chromatin architecture and transcription factor binding of genes expressed in erythroid cells.

2009 ◽  
Vol 29 (20) ◽  
pp. 5399-5412 ◽  
Author(s):  
Laurie A. Steiner ◽  
Yelena Maksimova ◽  
Vincent Schulz ◽  
Clara Wong ◽  
Debasish Raha ◽  
...  

ABSTRACT Erythrocyte membrane protein genes serve as excellent models of complex gene locus structure and function, but their study has been complicated by both their large size and their complexity. To begin to understand the intricate interplay of transcription, dynamic chromatin architecture, transcription factor binding, and genomic organization in regulation of erythrocyte membrane protein genes, we performed chromatin immunoprecipitation (ChIP) coupled with microarray analysis and ChIP coupled with massively parallel DNA sequencing in both erythroid and nonerythroid cells. Unexpectedly, most regions of GATA-1 and NF-E2 binding were remote from gene promoters and transcriptional start sites, located primarily in introns. Cooccupancy with FOG-1, SCL, and MTA-2 was found at all regions of GATA-1 binding, with cooccupancy of SCL and MTA-2 also found at regions of NF-E2 binding. Cooccupancy of GATA-1 and NF-E2 was found frequently. A common signature of histone H3 trimethylation at lysine 4, GATA-1, NF-E2, FOG-1, SCL, and MTA-2 binding and consensus GATA-1-E-box binding motifs located 34 to 90 bp away from NF-E2 binding motifs was found frequently in erythroid cell-expressed genes. These results provide insights into our understanding of membrane protein gene regulation in erythropoiesis and the regulation of complex genetic loci in erythroid and nonerythroid cells and identify numerous candidate regions for mutations associated with membrane-linked hemolytic anemia.


Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 3584-3584
Author(s):  
Laurie A Steiner ◽  
Yelena Maksimova ◽  
Jolinta Lin ◽  
Ashley Nicole Owen ◽  
Vincent Schulz ◽  
...  

Abstract Insulators are DNA sequences and associated binding proteins that establish and/or maintain the boundaries between euchromatin and heterochromatin. One type of insulator establishes chromatin domains to separate enhancers and promoters and prevent their interaction (enhancer-blocking insulators) whereas another type creates a barrier to protect against heterochromatin-mediated gene silencing (barrier insulators). In the well-characterized chicken beta-globin LCR 5′HS4 insulator, binding of CTCF mediates enhancer-blocking functions and USF proteins mediate barrier activity. Because varying transcripts of erythrocyte membrane protein genes are expressed in both erythroid and nonerythroid cells, we hypothesized that that insulator elements that bind USF and CTCF participate in their regulation. Advances in technology have permitted rapid identification of DNA sequences bound by transcription factors and other DNA-associated proteins on a genome-wide scale. Coupling chromatin immunoprecipitation to microarrays that contain genomic regions (ChIP-chip) is a high resolution technique available for mapping protein-DNA interactions in vivo. We used ChIP-chip to identify CTCF and USF factor binding sites with potential insulator function that regulate membrane protein gene expression in erythroid cells. ChIP was performed with K562 and HeLa cells using antibodies against CTCF, USF1, and USF2. DNA obtained from these IPs was hybridized to a custom NimbleGen high-density human genomic DNA microarray. Chip probes were ~50bp in length, Tm ≥76°C, tiled ~65bp apart. Regions of repetitive DNA excluded. The chip included 15 erythrocyte membrane protein genes, most encoding complex loci with multiple tissue-, cell-, and developmental stage-specific transcripts, including alpha spectrin, beta spectrin, ankyrin, spectrin, band 3, ƒnalpha adducin, beta adducin, gamma adducin, ICAM4, erythroid associated membrane protein, protein 4.1R, protein 4.2, dematin, beta actin, tropomodulin, and tropomyosin. Each gene plus 50 to 100kb of flanking DNA were included on the chip. Binding sites on the custom DNA array were identified using the Tamalpais peak calling algorithm using L1–L3 level of stringency (Genom Res16:595, 2006) or Tilescope using a signal cutoff of 0.9 (Genome Biol8:R81, 2007). In K562 chromatin, 117 sites of CTCF occupancy were identified. There were 49 sites in 5′ flanking DNA, 3 in promoters, 9 in coding sequence, 20 in introns, 1 in 3′ untranslated region, and 35 in 3′ flanking DNA. Sites of USF1 and USF2 occupancy were identified in 7/15 and 9/15 genes, respectively. 15 USF1 sites were identified; 10 in 5′ flanking DNA, 1 in a promoter (<500bp from transcription start site), and 4 in 3′ flanking DNA. 22 sites of USF2 occupancy were identified; 9 in 5′ flanking DNA, 1 in a promoter, 4 in introns, and 8 in 3′ flanking DNA. USF1 and USF2 frequently heterodimerize and 10 sites bound both USF1 and USF2. To validate ChIP-chip results, a site binding USF2 near the ankyrin gene erythroid promoter was further analyzed. This site was chosen because the USF2-associated sequence directs uniform, copy-number dependent expression of a linked reporter gene in transgenic mice, the erythroid ankyrin promoter is positioned between 2 alternate, nonerythroid ankyrin promoters, and DNaseI mapping revealed that this region is contained within an erythroid-specific chromatin domain. Quantitative ChIP confirmed USF2 occupancy, as well as USF1 occupancy, in this region. For functional analysis, we used a gene silencing/position-effect variegation (PEV) assay to determine whether this region possess barrier element function. Using a HS2-beta globin promoter-GFP cassette as negative control and a cHS4-HS2-beta globin promoter-GFP-cHS4 cassette as positive control, the ankyrin promoter region fragment (p<0.001) functioned as barrier elements in this PEV assay. These data demonstrate that there are numerous binding sites for insulator-associated DNA binding proteins in erythrocyte membrane protein genes and indicate that these elements likely play a significant role in the regulation of tissue-specific expression of many genes expressed in erythroid cells.


2021 ◽  
Author(s):  
Janavi S Rambhatla ◽  
Gerry Q Tonkin-Hill ◽  
Eizo Takashima ◽  
Takafumi Tsuboi ◽  
Rintis Noviyanti ◽  
...  

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), a diverse family of multi-domain proteins expressed on the surface of malaria-infected erythrocytes, is an important target of protective immunity against malaria. Our group recently studied transcription of the var genes encoding PfEMP1 in individuals from Papua, Indonesia with severe or uncomplicated malaria. We cloned and expressed domains from 32 PfEMP1s including 22 that were upregulated in severe malaria and 10 that were upregulated in uncomplicated malaria, using a wheat germ cell-free expression system. We used Luminex technology to measure IgG antibodies to these 32 domains and control proteins in 63 individuals (11 children). At presentation to hospital, levels of antibodies to PfEMP1 domains were either higher in uncomplicated malaria or were not significantly different between groups. Using principal components analysis, antibodies to three of 32 domains were highly discriminatory between groups. These included two domains upregulated in severe malaria, a DBLβ13 domain and a CIDRα1.6 domain (which has been previously implicated in severe malaria pathogenesis), and a DBLδ domain that was upregulated in uncomplicated malaria. Antibody to control non-PfEMP1 antigens did not differ with disease severity. Antibodies to PfEMP1 domains differ with malaria severity. Lack of antibodies to locally expressed PfEMP1 types, including both domains previously associated with severe malaria and newly identified targets, may in part explain malaria severity in Papuan adults. Importance Severe Plasmodium falciparum malaria kills many African children, and lack of antibody immunity predisposes to severe disease. A critical antibody target is the P. falciparum erythrocyte membrane 1 (PfEMP1) family of multidomain proteins, which are expressed on the infected erythrocyte surface and mediate parasite sequestration in deep organs. We previously identified var genes encoding PfEMP1 that were differentially expressed between severe and uncomplicated malaria in Papua, Indonesia. Here, we have expressed domains from 32 of these PfEMP1s and measured IgG antibody responses to them in Papuan adults and children. Using Principal Component Analysis, IgG antibodies to three domains distinguished between severe and uncomplicated malaria and were higher in uncomplicated malaria. Domains included CIDRα1.6, implicated in severe malaria; a DBLβ13 domain; and a DBLδ domain of unknown function. Immunity to locally relevant PfEMP1 domains may protect from severe malaria. Targets of immunity show important overlap between Asian adults and African children.


1995 ◽  
Vol 28 (3) ◽  
pp. 332 ◽  
Author(s):  
Daniel J. Trepanier ◽  
Patrick S. Caines ◽  
Roger J. Thibert ◽  
Michael Goodwin ◽  
Thomas F. Draisey

2013 ◽  
Vol 42 (4) ◽  
pp. 2270-2281 ◽  
Author(s):  
Adam F. Sander ◽  
Thomas Lavstsen ◽  
Thomas S. Rask ◽  
Michael Lisby ◽  
Ali Salanti ◽  
...  

Abstract Many bacterial, viral and parasitic pathogens undergo antigenic variation to counter host immune defense mechanisms. In Plasmodium falciparum, the most lethal of human malaria parasites, switching of var gene expression results in alternating expression of the adhesion proteins of the Plasmodium falciparum-erythrocyte membrane protein 1 class on the infected erythrocyte surface. Recombination clearly generates var diversity, but the nature and control of the genetic exchanges involved remain unclear. By experimental and bioinformatic identification of recombination events and genome-wide recombination hotspots in var genes, we show that during the parasite’s sexual stages, ectopic recombination between isogenous var paralogs occurs near low folding free energy DNA 50-mers and that these sequences are heavily concentrated at the boundaries of regions encoding individual Plasmodium falciparum-erythrocyte membrane protein 1 structural domains. The recombinogenic potential of these 50-mers is not parasite-specific because these sequences also induce recombination when transferred to the yeast Saccharomyces cerevisiae. Genetic cross data suggest that DNA secondary structures (DSS) act as inducers of recombination during DNA replication in P. falciparum sexual stages, and that these DSS-regulated genetic exchanges generate functional and diverse P. falciparum adhesion antigens. DSS-induced recombination may represent a common mechanism for optimizing the evolvability of virulence gene families in pathogens.


2018 ◽  
Author(s):  
Mehran Karimzadeh ◽  
Michael M. Hoffman

AbstractMotivationIdentifying transcription factor binding sites is the first step in pinpointing non-coding mutations that disrupt the regulatory function of transcription factors and promote disease. ChIP-seq is the most common method for identifying binding sites, but performing it on patient samples is hampered by the amount of available biological material and the cost of the experiment. Existing methods for computational prediction of regulatory elements primarily predict binding in genomic regions with sequence similarity to known transcription factor sequence preferences. This has limited efficacy since most binding sites do not resemble known transcription factor sequence motifs, and many transcription factors are not even sequence-specific.ResultsWe developed Virtual ChIP-seq, which predicts binding of individual transcription factors in new cell types using an artificial neural network that integrates ChIP-seq results from other cell types and chromatin accessibility data in the new cell type. Virtual ChIP-seq also uses learned associations between gene expression and transcription factor binding at specific genomic regions. This approach outperforms methods that predict TF binding solely based on sequence preference, pre-dicting binding for 36 transcription factors (Matthews correlation coefficient > 0.3).AvailabilityThe datasets we used for training and validation are available at https://virchip.hoffmanlab.org. We have deposited in Zenodo the current version of our software (http://doi.org/10.5281/zenodo.1066928), datasets (http://doi.org/10.5281/zenodo.823297), predictions for 36 transcription factors on Roadmap Epigenomics cell types (http://doi.org/10.5281/zenodo.1455759), and predictions in Cistrome as well as ENCODE-DREAM in vivo TF Binding Site Prediction Challenge (http://doi.org/10.5281/zenodo.1209308).


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