Katelyn L. Mortenson’s research while affiliated with Salt Lake Community College and other places

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Publications (9)


HAPI analysis identifies cancer-related genes that are highly interactive with enhancers
A Schematic models illustrating the identification of HAPI genes based on enhancer interactions. B Plotted are the number of enhancers (x-axis) and interaction intensity (y-axis) for each gene in LNCaP cells. HAPI cutoff is defined by the inflection point of each scoring metric. C Spearman correlation clustering of cancer cell lines based on interaction scores of their associated HAPI genes. BRCA breast cancer, COAD colorectal adenocarcinoma, LUSC lung squamous cell carcinoma, PRAD prostate adenocarcinoma, SCLC small cell lung cancer, STAD stomach adenocarcinoma. D The percentage of HAPI genes (shared by at least half of the cell lines in each cancer type) that are previously annotated as oncogenes. The numbers outside and within each bar indicate the total number of shared HAPI (red) or other (gray) genes and the ones annotated as oncogenes, respectively. P values are derived from two-sided Fisher’s exact tests.
HAPI analysis identifies known and novel enhancer-hijacking events
A Schematic models illustrating the identification of enhancer hijacking HAPI genes based on the enhancer origin. B The number of cancer cell lines that are identified to contain trans- and cis-enhancer hijacking events in our cohort. C The number of identified trans-enhancer hijacking genes found in each cancer cell line in our cohort. Two immortalized epithelial cell lines are used as negative controls. D Plotted are the calculated trans-enhancer contribution (x-axis) and the copy number estimated by SNP-array data (y-axis) for each HAPI gene in selected cell lines. Besides are the expression levels for the highlighted gene in all CCLE cell lines (violin plot), the trans-enhancer hijacking cell line (red), and other cell lines of the same lineage (blue). E Same as D but with regards to two cis-enhancer hijacking genes in VCaP and CORL88 cells.
Functional validation of identified trans-enhancer hijacking events
A HiChIP and ChIP-seq signal at chromosomal regions containing ETV1 and the hijacked enhancers in MDAPCA2B and LNCaP cells. The H3K27ac ChIP-seq signal in MDAPCA2B cells is derived from HiChIP reads. In LNCaP cells, WGS-identified breakpoints support translocations underlying the enhancer-hijacking events. The DNA position of the left breakpoint on chromosome 7 in LNCaP cells is noted. B RT-qPCR measuring expression changes of ETV1 and FOXA1 after CRISPRi of each enhancer e1–e4 or the promoter of ETV1 in LNCaP cells. Expression levels were normalized to cells treated with a non-targeting sgRNA (NT1). N = 3 biological replicates. Data are presented as mean values ± SEM. P values were derived from two-sided t-tests. Source data are provided as a Source Data file. C HiChIP and ChIP-seq signal at chromosomal regions containing CCND1 and the hijacked enhancers in REC1 and ZR751 cells. In ZR751 cells, WGS-identified breakpoints support translocations underlying the enhancer-hijacking events. D RT-qPCR measuring expression changes of CCND1 and DUSP4 after CRISPRi of each enhancer e1–e5, the promoter of CCND1, or a combination of the five enhancers in ZR751 cells. Expression levels were normalized to cells treated with a non-targeting sgRNA (NT1). N = 3 biological replicates. Data are presented as mean values ± SEM. P values were derived from two-sided t-tests. Source data are provided as a Source Data file.
HAPI analysis identifies enhancer hijacking on known ecDNAs
A Schematic models illustrating the identification of enhancer hijacking events on ecDNAs that involve multiple oncogenes from different chromosomal regions. B Plotted are the calculated trans-enhancer contribution (x-axis) and the copy number estimated by SNP-array data (y-axis) for each HAPI gene in COLO320DM cells. C HiChIP, ChIP-seq, and SNP-array-based copy number signal at the DNA segments of the ecDNA found in COLO320DM cells. The HiChIP data and the ecDNA segments in COLO320DM were published by ref. ⁴⁸. D Enhancer distribution of MYC and the additional oncogenes harbored in the ecDNA found in COLO320DM cells. Enhancers within 2 mb of distance to the promoter of MYC or any other gene in the endogenous chromosomal locus will be considered as the gene’s “own” enhancers, otherwise will be considered as “other” enhancers. Source data are provided as a Source Data file. E Circos plot: layer 1=chromosome, 2 = CNV, 3=enhancers (gray) and super-enhancers (black), 4 = HAPI TSS, 5=ecDNA segments, center=E-P loops (black) and WGS-predicted translocations (red) in COLO320DM cells. F–I Same as (B–E) but with regards to the ecDNAs in SNU16 cells. The HiChIP data and the ecDNA segments in SNU16 were published by ref. ⁴⁸. Note that the APIP and PDHX genes share the same promoter anchor for the HiChIP analysis. Source data for the (H) are provided as a Source Data file.
Enhancer hijacking is prevalent on complex amplicons
A–C Plotted are the calculated trans-enhancer contribution (x-axis) and the copy number estimated by SNP-array data (y-axis) for each HAPI gene in NCIH2170, NCIH446, and MCF7 cells. D HiChIP, H3K27ac ChIP-seq (derived from HiChIP reads), and copy number profiles at DNA segments of the MYC ecDNA in NCIH2170 cells. E Enhancer distribution of MYC and the additional oncogenes harbored in the ecDNA found in NCIH2170 cells. Source data are provided as a Source Data file. F HiChIP, H3K27ac ChIP-seq, and copy number profiles at DNA segments of the MYC amplicon in NCIH446 cells. G. Enhancer distribution of MYC and the additional oncogenes harbored in the amplicon found in NCIH446 cells. Source data are provided as a Source Data file. H Summary of AmpliconArchitect data from PCAWG tumor samples: most of oncogene amplicons contain additional trans or cis DNA segments. 37.6% of these complex amplicons contain multiple oncogenes originating from different DNA segments.

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3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression
  • Article
  • Full-text available

July 2024

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46 Reads

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6 Citations

Katelyn L. Mortenson

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Courtney Dawes

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Emily R. Wilson

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[...]

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Xiaoyang Zhang

Cancer genomes are composed of many complex structural alterations on chromosomes and extrachromosomal DNA (ecDNA), making it difficult to identify non-coding enhancer regions that are hijacked to activate oncogene expression. Here, we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking. HAPI analysis of HiChIP data from 34 cancer cell lines identified enhancer hijacking events that activate both known and potentially novel oncogenes such as MYC, CCND1, ETV1, CRKL, and ID4. Furthermore, we found enhancer hijacking among multiple oncogenes from different chromosomes, often including MYC, on the same complex amplicons such as ecDNA. We characterized a MYC-ERBB2 chimeric ecDNA, in which ERBB2 heavily hijacks MYC’s enhancers. Notably, CRISPRi of the MYC promoter led to increased interaction of ERBB2 with MYC enhancers and elevated ERBB2 expression. Our HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation.

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HiChIP-Based Epigenomic Footprinting Identifies a Promoter Variant of UXS1 That Confers Genetic Susceptibility to Gastroesophageal Cancer

May 2024

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32 Reads

Cancer Research

Genome-wide association studies (GWAS) have identified more than a hundred single nucleotide variants (SNV) associated with the risk of gastroesophageal cancer (GEC). The majority of the identified SNVs map to noncoding regions of the genome. Uncovering the causal SNVs and genes they modulate could help improve GEC prevention and treatment. Herein, we used HiChIP against histone 3 lysine 27 acetylation (H3K27ac) to simultaneously annotate active promoters and enhancers, identify the interactions between them, and detect nucleosome-free regions (NFR) harboring potential causal SNVs in a single assay. The application of H3K27ac HiChIP in GEC relevant models identified 61 potential functional SNVs that reside in NFRs and interact with 49 genes at 17 loci. The approach led to a 67% reduction in the number of SNVs in linkage disequilibrium at these 17 loci, and at 7 loci, a single putative causal SNV was identified. One SNV, rs147518036, located within the promoter of the UDP-glucuronate decarboxylase 1 (UXS1) gene, seemed to underlie the GEC risk association captured by the rs75460256 index SNV. The rs147518036 SNV creates a GABPA DNA recognition motif, resulting in increased promoter activity, and CRISPR-mediated inhibition of the UXS1 promoter reduced the viability of the GEC cells. These findings provide a framework that simplifies the identification of potentially functional regulatory SNVs and target genes underlying risk-associated loci. In addition, the study implicates increased expression of the enzyme UXS1 and activation of its metabolic pathway as a predisposition to gastric cancer, which highlights potential therapeutic avenues to treat this disease. Significance: Epigenomic footprinting using a histone posttranslational modification targeted 3D genomics methodology elucidates functional noncoding sequence variants and their target genes at cancer risk loci.


Cis-regulatory control of transcriptional timing and noise in response to estrogen

April 2024

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16 Reads

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4 Citations

Cell Genomics

Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single-cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We found that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene’s ability to quickly respond to incoming signals and maintain low variation across cells.


Abstract 6599: 3D genomic analysis reveals novel enhancer hijacking mechanisms caused by complex structural alterations that drive oncogene overexpression

March 2024

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6 Reads

Cancer Research

Enhancer hijacking, caused by structural alterations on chromosomes as well as extrachromosomal DNA (ecDNA), is a common cancer driver event. The complexity and ubiquity of structural alterations in cancer genomes make it difficult to identify enhancer hijacking with genome sequencing alone. Here we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking caused by various types of structural alterations. Applying HAPI analysis to HiChIP data from 34 cancer cell lines, we identified novel enhancers hijacked through chromosomal rearrangements to activate both known and potentially novel oncogenes such as MYC, CCND1, ETV1, CRKL, and ID4, which we validated using CRISPRi assays and RNA-seq analysis. Furthermore, we found that ecDNAs often contain multiple oncogenes from different chromosomes, which cause nested enhancer hijacking among them. For instance, we found that MYC ecDNAs relocate additional oncogenes from other chromosomes such as CDX2, ERBB2, or CD44 near the MYC locus, co-opting MYC’s enhancers for their overexpression, which we validated using dual-color DNA FISH and CRISPRi assays. This multiple oncogenes-involved enhancer hijacking mechanism may suggest novel therapeutic strategies such as targeting either the co-opting oncogenes or the hijacked enhancers for ecDNAs. Our study provides a robust strategy to detect enhancer hijacking events using our publicly available HAPI analysis tool and reveals novel mechanisms underlying oncogene activation caused by chromosomal and extrachromosomal structural alterations. Citation Format: Katelyn L. Mortenson, Courtney Dawes, Emily R. Wilson, Nathan E. Patchen, Hailey Johnson, Jason Gertz, Swneke D. Bailey, Yan Liu, Katherine E. Varley, Xiaoyang Zhang. 3D genomic analysis reveals novel enhancer hijacking mechanisms caused by complex structural alterations that drive oncogene overexpression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6599.


ASPSCR1-TFE3 reprograms transcription by organizing enhancer loops around hexameric VCP/p97

February 2024

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163 Reads

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4 Citations

The t(X,17) chromosomal translocation, generating the ASPSCR1::TFE3 fusion oncoprotein, is the singular genetic driver of alveolar soft part sarcoma (ASPS) and some Xp11-rearranged renal cell carcinomas (RCCs), frustrating efforts to identify therapeutic targets for these rare cancers. Here, proteomic analysis identifies VCP/p97, an AAA+ ATPase with known segregase function, as strongly enriched in co-immunoprecipitated nuclear complexes with ASPSCR1::TFE3. We demonstrate that VCP is a likely obligate co-factor of ASPSCR1::TFE3, one of the only such fusion oncoprotein co-factors identified in cancer biology. Specifically, VCP co-distributes with ASPSCR1::TFE3 across chromatin in association with enhancers genome-wide. VCP presence, its hexameric assembly, and its enzymatic function orchestrate the oncogenic transcriptional signature of ASPSCR1::TFE3, by facilitating assembly of higher-order chromatin conformation structures demonstrated by HiChIP. Finally, ASPSCR1::TFE3 and VCP demonstrate co-dependence for cancer cell proliferation and tumorigenesis in vitro and in ASPS and RCC mouse models, underscoring VCP’s potential as a novel therapeutic target.


Figure 6
3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression

January 2024

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32 Reads

Enhancer hijacking, caused by structural alterations on chromosomes as well as extrachromosomal DNA (ecDNA), is a common cancer driver event. The complexity and ubiquity of structural alterations in cancer genomes make it difficult to identify enhancer hijacking using genome sequencing alone. Here we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking caused by structural alterations. HAPI analysis of HiChIP data from 34 cancer cell lines identified novel enhancer hijacking events that involve chromosomal rearrangements and activate both known and potentially novel oncogenes such as MYC, CCND1, ETV1, CRKL, and ID4, which we validated using CRISPRi assays and RNA-seq analysis. Furthermore, we found that ecDNAs often contain multiple oncogenes from different chromosomes, which causes nested enhancer hijacking among them. We found that ecDNAs containing MYC often harbor additional oncogenes from other chromosomes such as CDX2, ERBB2, or CD44 that co-opt MYC enhancers for their overexpression, which we validated using dual-color DNA FISH and CRISPRi assays. These enhancer hijacking events involving multiple oncogenes on ecDNAs have important implications for therapeutic strategies that either target the co-opting oncogenes or the hijacked enhancers. Our publicly available HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation caused by chromosomal and extrachromosomal structural alterations.


ASPSCR1-TFE3 reprograms transcription by organizing enhancer loops around hexameric VCP/p97

October 2023

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75 Reads

The t(X,17) chromosomal translocation, generating the ASPSCR1-TFE3 fusion oncoprotein, is the singular genetic driver of alveolar soft part sarcoma (ASPS) and some Xp11-rearranged renal cell carcinomas (RCC), frustrating efforts to identify therapeutic targets for these rare cancers. Proteomic analysis showed that VCP/p97, an AAA+ ATPase with known segregase function, was strongly enriched in co-immunoprecipitated nuclear complexes with ASPSCR1-TFE3. We demonstrate that VCP is a likely obligate co-factor of ASPSCR1-TFE3, one of the only such fusion oncoprotein co-factors identified in cancer biology. Specifically, VCP co-distributed with ASPSCR1-TFE3 across chromatin in association with enhancers genome-wide. VCP presence, its hexameric assembly, and its enzymatic function orchestrated the oncogenic transcriptional signature of ASPSCR1-TFE3, by facilitating assembly of higher-order chromatin conformation structures as demonstrated by HiChIP. Finally, ASPSCR1-TFE3 and VCP demonstrated co-dependence for cancer cell proliferation and tumorigenesis in vitro and in ASPS and RCC mouse models, underscoring VCP's potential as a novel therapeutic target.


Figure 1. General transcription factors are the strongest predictors of gene expression levels. (A, left) Boruta feature ranking of genomic features shows importance of a feature for predicting mean levels. (A, right) Average signal intensity for each genomic dataset, grouped by mean expression levels is shown. Datasets shown in bold were performed in the absence of ER activation. (B-E) Distributions of the top 4 most important ranked features in the DMSO condition, separated by mean expression levels, show higher signal for "High" expression groups. X-axis represents Z-scores and error bars show the mean ± 95% confidence intervals. (F) Mean enhancer score signal for all Boruta confirmed features vs. mean promoter signal across all confirmed features is shown where error bars show the mean ± 95% confidence intervals.
Figure 4. Determinants of noise levels show a balance between active promoters driving low noise levels and active enhancers driving high noise levels. (A, left) Boruta feature rankings shows features predictive of noise levels. (A, right) Average signal intensity is shown by noise group for top ranked features. Datasets shown in bold were performed in the absence of ER activation. (B-D) Distribution of signal for top ranked noisepredicting DMSO-treated features is shown with Z-scores on the x-axis. (E) Mean enhancer score signal for all Boruta confirmed features vs. mean promoter signal across all confirmed features for each noise level exhibits an inverse relationship. Error bars show 95% confidence intervals. (F-G) Distribution of enhancer counts per gene, separated by noise level, are shown for (F) Ishikawa and (G) T-47D cells.
Figure 6. Co-expression changes are observed based on looping, trajectory, and levels of noise. (A-B) Pairs of genes with promoters that loop to one another are significantly more correlated across cells at the 0-hour timepoint than randomly selected gene pairs for Ishikawa (A) and T-47D (B). Bonferroni adjusted Wilcoxon p-values are shown with respect to control. (C-D) Pairs of genes with a shared enhancer are more correlated than randomly paired genes for Ishikawa (C) and T-47D (D). Wilcoxon p-values are shown with respect to control. (E-F) Pairwise spearman correlation for genes within different trajectories is shown. Significance values show Wilcoxon p-values of the 8-hour (E) or the 2-hour (F) timepoint with respect to the 0-hour timepoint. (G-H) Range of pairwise correlations for high noise levels is greater than the range for pairs of low noise genes in Ishikawa (G) and T-47D (H). (left panel) Distribution of Spearman pairwise correlations for genes with high and low noise. (right panel) Spearman correlations were grouped into quantiles and bars show proportion at each quantile that are pairs of low or high noise genes. Significance values for all subpanels are as follows: (* p < 0.05; ** p < 1x10-5; *** p < 1x10-10; **** p < 1x10-15).
Cis-regulatory control of transcriptional timing and noise in response to estrogen

March 2023

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52 Reads

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1 Citation

Cis-Regulatory Elements (CREs) control transcription levels, temporal dynamics, and cell-cell variation — often referred to as transcriptional noise. However, the combination of regulatory proteins and epigenetic features necessary to control different transcription attributes is not fully understood. Here, single-cell RNA-seq (scRNA-seq) is conducted during a time course of estrogen treatment to identify genomic predictors of expression timing and noise. We find that genes associated with multiple active enhancers exhibit faster temporal responses. Synthetic modulation of enhancer activity verifies that activating enhancers accelerates expression responses, while inhibiting enhancers results in a more gradual response. Noise is controlled by a balance of promoter and enhancer activity. Active promoters are found at genes with low noise levels, whereas active enhancers are associated with high noise. Finally, we observe that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise levels. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.


A predominant enhancer co-amplified with the SOX2 oncogene is necessary and sufficient for its expression in squamous cancer

December 2021

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173 Reads

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24 Citations

Amplification and overexpression of the SOX2 oncogene represent a hallmark of squamous cancers originating from diverse tissue types. Here, we find that squamous cancers selectively amplify a 3’ noncoding region together with SOX2, which harbors squamous cancer-specific chromatin accessible regions. We identify a single enhancer e1 that predominantly drives SOX2 expression. Repression of e1 in SOX2-high cells causes collapse of the surrounding enhancers, remarkable reduction in SOX2 expression, and a global transcriptional change reminiscent of SOX2 knockout. The e1 enhancer is driven by a combination of transcription factors including SOX2 itself and the AP-1 complex, which facilitates recruitment of the co-activator BRD4. CRISPR-mediated activation of e1 in SOX2-low cells is sufficient to rebuild the e1-SOX2 loop and activate SOX2 expression. Our study shows that squamous cancers selectively amplify a predominant enhancer to drive SOX2 overexpression, uncovering functional links among enhancer activation, chromatin looping, and lineage-specific copy number amplifications of oncogenes. SOX2 amplification and overexpression represents a hallmark of squamous cancers with distinct distribution of chromatin accessible regions depending on cancer type. Here, the authors identify a single enhancer e1 that predominantly drives SOX2 expression in squamous cancer.

Citations (5)


... 22,36 Through this process, an oncogene on ecDNA can not only interact with distal enhancers that are several mega-bases away from its original chromosomal location, 6,37 but also hijack enhancers from different chromosomes (Fig. 2c, right panel). 38 Although it is unclear whether hijacking distal enhancers is more advantageous for maximizing transcriptional output compared to local ones, it likely offers flexibility for the oncogene on ecDNA to form versatile transcription circuits that can respond to alternative upstream signaling cues. In addition, co-amplification and even co-selection of local and distal insulators may be involved in building the transcriptional circuits in an ecDNA. ...

Reference:

Modern biology of extrachromosomal DNA: A decade-long voyage of discovery
3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression

... cis-eQTLs typically exhibit larger effect sizes, implying even minor genomic variations can lead to significant changes in gene expression 73 . The close physical proximity of cis-regulated genes to their regulatory elements minimizes c the impact of other genes or environmental confounders, offering a distinct advantage for therapeutic drug development 74 . Moreover, clinical blood tests may also provide serve an effective approach for assessing an individual's genetic susceptibility to PD. ...

Cis-regulatory control of transcriptional timing and noise in response to estrogen
  • Citing Article
  • April 2024

Cell Genomics

... The mammalian target of rapamycin (mTOR) as TFE3 upstream regulator is targetable in some TFE3 rearranged tumor, like Xp11-RCC and ASPS. mTOR inhibitors (sirolimus) maybe an efficacious systemic treatment for EHE (33,34). Correct diagnosis is essential for treatment. ...

ASPSCR1-TFE3 reprograms transcription by organizing enhancer loops around hexameric VCP/p97

... The precise timing of gene expression reaching a necessary threshold is critical in nearly all biological processes, including cell fate determination and response to various stimuli. To gain insights into utilization of CREs in regulating temporal transcription responses, Ginley-Hidinger et al. (2023) conducted a study investigating chromatin features in response to estrogen across multiple time points. By employing single-cell RNA sequencing (scRNA-seq), they observed distinct early and late gene expression patterns. ...

Cis-regulatory control of transcriptional timing and noise in response to estrogen

... The pluripotency gene Sox2 has multiple enhancer sites, and its expression has been shown to be dependent on different ones in different cancer types. 129,130 In the context of breast and lung cancers, it has been demonstrated that an enhancer site, which is essential for the development of the digestive and respiratory systems, is also activated in a FOXA1-overexpression-dependent manner, resulting in aberrant Sox2 expression. 131 supervision; resources; funding acquisition. ...

A predominant enhancer co-amplified with the SOX2 oncogene is necessary and sufficient for its expression in squamous cancer