Cancer: Examining the cooperation between extrachromosomal DNA circles
Cells often need to modulate the production of certain proteins to adjust to their ever-changing environment. This is usually achieved by altering the amount of messenger RNAs synthetized from the corresponding genes. Two factors impact the transcription yield of a gene: the number of active copies of this sequence in the genome, and the rate at which each of them is transcribed.
Boosting the production of a protein is usually achieved by increasing transcription rates, but special cases can involve directly creating more copies of the associated gene (Stark and Wahl, 1984). This phenomenon was first identified in amphibian eggs, where it helps cells to produce the elements required for protein synthesis (see Tobler, 1975 for review). In human cells, however, gene amplification is most commonly associated with boosting the expression of cancer-driving genes (Tanaka and Watanabe, 2020). These additional ‘oncogene’ copies can be arranged in tandem in a specific region of a linear chromosome, or they can be contained inside small circles of extrachromosomal DNA (ecDNA) formerly known as double-minute chromosomes (Verhaak et al., 2019; Cox et al., 1965; Figure 1A). A recent study by Hung et al. has reported that oncogenic ecDNAs frequently come together to form hubs of 10 to 100 ecDNA circles; inside these clusters, intermolecular interactions take place that boost oncogene expression (Hung et al., 2021). Now, in eLife, Steven Pollard, Wendy Bickmore and colleagues at the University of Edinburgh — including Karin Purshouse as first author — report new results which contradict these findings (Purshouse et al., 2022).
The team focused on malignant cells from an aggressive type of brain cancer known as glioblastoma; these frequently contain ecDNA circles carrying one or multiple oncogenes. Using super-resolution imaging, Purshouse et al. were able to determine the location of individual ecDNA circles within the nucleus, and how frequently two ecDNAs were found within a given distance. Comparing these numbers with what would be expected if the circles were randomly distributed in the nucleus allowed the team to assess whether ecDNAs form clusters more frequently than anticipated. When analyzing ecDNAs carrying the same oncogene, they found no evidence of clustering of ecDNAs within 200 nm – the distance that corresponds to the estimated diameter of ecDNA hubs.
However, due to the limitation of optical resolution, this approach cannot detect tighter clusters consisting of ecDNAs that are closer than 200nm. To overcome this challenge, Purshouse et al. used another line of glioblastoma cells that carry two distinct types of ecDNAs, which were imaged independently using different probes. This approach makes it possible to spot smaller hubs that bring together different ecDNA ‘species’, yet it also did not provide evidence that ecDNAs cluster in glioblastoma cells.
The team then focused on how ecDNA circles were being transcribed. First, they examined whether oncogenic ecDNAs may be clustering with ‘transcriptional hubs’ that physically bring together various elements of the transcription machinery. However, no spatial correlation was found between ecDNAs and these hubs. Next, they compared how chromosomal and ecDNA copies of the same oncogene were being transcribed. They started by imaging nascent RNA transcripts near individual ecDNA circles, gathering information that allowed them to assess the fraction of ecDNAs that are being actively transcribed at any given time. These analyses captured ‘immature’ RNA transcripts as they were being synthesized, and they showed that ecDNA and chromosomal copies were transcribed at a similar frequency.
Finally, the team switched their focus to mature messenger RNAs. They estimated the fraction of messenger RNAs transcribed from either the ecDNA or the chromosomal copy of an oncogene, using slight differences in the sequences between the two versions. After normalizing for ecDNA and chromosomal copy numbers, they established that an individual sequence, whether chromosomal or extrachromosomal, would produce a similar amount of mature messenger RNAs. Chromosomal DNA and ecDNA therefore appear to be transcribed with a similar efficiency. Taken together, these findings suggest that the transcriptional yield of amplified ecDNAs is primarily determined by the number of ecDNA circles, rather than the cooperative transcription of clustered ecDNAs.
What could explain the discrepancy between these observations and the results reported by Hung et al. (Figure 1B)? Purshouse et al. suggested that both sets of conclusions may in fact be true, but under different circumstances. As these studies relied on a small number of cell lines derived from different types of tumors, diverging results could reflect variations in the size, gene composition or copy number of ecDNAs across cancers. In addition, ecDNAs are highly dynamic; they can recombine, reintegrate within a chromosome or undergo other types of molecular rearrangements which may all alter transcription kinetics (Shoshani et al., 2021; Rosswog et al., 2021). Further studies are now needed to explore the way that ecDNA transcription changes across a wide range of cancers and during disease progression.
References
-
Gene amplificationAnnual Review of Biochemistry 53:447–491.https://doi.org/10.1146/annurev.bi.53.070184.002311
-
BookOccurrence and developmental significance of gene amplificationIn: Weber Rudolf, editors. Biochemistry of Animal Development. New York: Academic Press. pp. 91–143.https://doi.org/10.1016/B978-0-12-740603-9.50009-7
-
Extrachromosomal oncogene amplification in tumour pathogenesis and evolutionNature Reviews. Cancer 19:283–288.https://doi.org/10.1038/s41568-019-0128-6
Article and author information
Author details
Publication history
Copyright
© 2022, Zhang and Zhang
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 639
- views
-
- 66
- downloads
-
- 0
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Chromosomes and Gene Expression
- Immunology and Inflammation
Genome-wide association studies (GWAS) have identified hundreds of genetic signals associated with autoimmune disease. The majority of these signals are located in non-coding regions and likely impact cis-regulatory elements (cRE). Because cRE function is dynamic across cell types and states, profiling the epigenetic status of cRE across physiological processes is necessary to characterize the molecular mechanisms by which autoimmune variants contribute to disease risk. We localized risk variants from 15 autoimmune GWAS to cRE active during TCR-CD28 co-stimulation of naïve human CD4+ T cells. To characterize how dynamic changes in gene expression correlate with cRE activity, we measured transcript levels, chromatin accessibility, and promoter–cRE contacts across three phases of naive CD4+ T cell activation using RNA-seq, ATAC-seq, and HiC. We identified ~1200 protein-coding genes physically connected to accessible disease-associated variants at 423 GWAS signals, at least one-third of which are dynamically regulated by activation. From these maps, we functionally validated a novel stretch of evolutionarily conserved intergenic enhancers whose activity is required for activation-induced IL2 gene expression in human and mouse, and is influenced by autoimmune-associated genetic variation. The set of genes implicated by this approach are enriched for genes controlling CD4+ T cell function and genes involved in human inborn errors of immunity, and we pharmacologically validated eight implicated genes as novel regulators of T cell activation. These studies directly show how autoimmune variants and the genes they regulate influence processes involved in CD4+ T cell proliferation and activation.
-
- Chromosomes and Gene Expression
- Developmental Biology
Differentiation of female germline stem cells into a mature oocyte includes the expression of RNAs and proteins that drive early embryonic development in Drosophila. We have little insight into what activates the expression of these maternal factors. One candidate is the zinc-finger protein OVO. OVO is required for female germline viability and has been shown to positively regulate its own expression, as well as a downstream target, ovarian tumor, by binding to the transcriptional start site (TSS). To find additional OVO targets in the female germline and further elucidate OVO’s role in oocyte development, we performed ChIP-seq to determine genome-wide OVO occupancy, as well as RNA-seq comparing hypomorphic and wild type rescue ovo alleles. OVO preferentially binds in close proximity to target TSSs genome-wide, is associated with open chromatin, transcriptionally active histone marks, and OVO-dependent expression. Motif enrichment analysis on OVO ChIP peaks identified a 5’-TAACNGT-3’ OVO DNA binding motif spatially enriched near TSSs. However, the OVO DNA binding motif does not exhibit precise motif spacing relative to the TSS characteristic of RNA polymerase II complex binding core promoter elements. Integrated genomics analysis showed that 525 genes that are bound and increase in expression downstream of OVO are known to be essential maternally expressed genes. These include genes involved in anterior/posterior/germ plasm specification (bcd, exu, swa, osk, nos, aub, pgc, gcl), egg activation (png, plu, gnu, wisp, C(3)g, mtrm), translational regulation (cup, orb, bru1, me31B), and vitelline membrane formation (fs(1)N, fs(1)M3, clos). This suggests that OVO is a master transcriptional regulator of oocyte development and is responsible for the expression of structural components of the egg as well as maternally provided RNAs that are required for early embryonic development.