Abstract
Cells interpret identical DNA sequences differently depending on the 3D genome organization, leading to diverse cellular phenotypes and even determining between health and disease. While synthetic biology provides powerful methods to engineer the genome sequence, tools to sculpt the genome spatial architecture are lacking.
We address this profound gap by introducing PICasSO, a toolbox for rationally engineering the 3D genome organization.
Using different CRISPR/Cas orthologs and new, engineered fusion guide RNAs, we created "Cas staples" that bind and connect otherwise non-interacting genomic regions. To streamline the development cycle of new Cas staples, we developed DaVinci, a multi-faceted model integrating both local and long-range DNA-protein interactions into a unified pipeline. Combining PICasSO and DaVinci, we recapitulated mechanistic core principles of enhancer hijacking - a driver of tumor development.
Our comprehensive part collection and extensive education program enable the next generation of iGEMers to efficiently adapt PICasSO and engineer genomes in 3D.
Introduction
All cells in our body carry the same genetic sequence, yet they have highly diverging and specialized functions. Even more, species that look and behave profoundly differently like humans and chimpanzees share almost identical genome sequences (Yang et al., 2019; Mikkelsen et al., 2005).
The answer to this apparent contradiction lies, at least in parts, in another dimension used by living systems for information encoding: The 3D genome conformation. In fact, cells can interpret identical DNA sequences in various ways depending on their spatial genome organization (Rajarajan et al., 2016; Zheng & Xie, 2019).
Over the last decades, synthetic biology has made great advancements in engineering the first dimension of genomes - the DNA sequence. As seen everywhere in iGEM and beyond, we can nowadays easily introduce entirely new genes into organisms, exchange single or multiple nucleotides in existing genes, and control gene activity by implementing or altering regulatory sequences such as promoters or ribosome binding sites.
However, no effective molecular tool exists to date that enables actively shaping the 3D architecture of genomes - despite its profound impact on cell phenotype, health, and even species differences (Rajarajan et al., 2016; Zheng & Xie, 2019).
We, the iGEM Team Heidelberg 2024, share the vision of making a new level of information encoding accessible to synthetic biology by engineering the genome in three dimensions. With our PICasSO toolbox, we provide a foundational advance to both understanding and controlling the code of life.
PICasSO: Engineering the Genome’s Spatial Architecture
Plasmid-Integrated Cas-Stapled Origami (PICasSO) is our pioneering, well-characterized toolbox, designed to rearrange genome 3D architecture with unprecedented spatial precision. At its heart, PICasSO builds on a unique, bivalent CRISPR dead Cas (dCas) complex, composed of a catalytically inactive dCas9 and dCas12a protein, delivered on a single plasmid. The complex is formed by connecting dCas-proteins via our custom-engineered fusion guide RNAs (fgRNA) — chimeras of the dCas9- and dCas12a-specific gRNAs (Kweon et al., 2017). We termed this whole tripartite CRISPR-Cas complex the Cas staple (see Fig. 1). In a Cas staple, each of the dCas-proteins can bind a DNA strand, bringing them into spatial proximity guided by the fgRNA. As the determining factor, the fgRNA defines this DNA-DNA connection's exact distance and flexibility, while being easily programmable and cheap to produce.
But how can such a compact system control the functionality of the 3D genome architecture? In eukaryotic cells, cis- and trans-gene regulatory elements like enhancers and silencers fine-tune gene expression or even render entire gene sets accessible or inaccessible (Halfon, 2020; Riethoven, 2010). Normally, the activity of such regulatory elements is controlled by mediator or insulator proteins within topologically associated domains (TADs) shaping the 3D genome structure (Dixon et al., 2012). PICasSO mimics the function of a mediator but is entirely programmable to any custom DNA locus pair via the underlying fgRNA. By “stapling” an enhancer element into close spatial proximity to the promoter, for instance, PICasSO can be applied to induce and control expression from endogenous genes without the need to add synthetic transcriptional activators. Alternatively, a Cas staple can also block enhancer access to the TATA box of a promoter, creating an insulator effect to prevent gene expression. Beyond mimicking natural systems, PICasSO also enables the engineering of entirely novel regulatory genome architectures by bringing artificial regulatory genetic elements to precise locations within the genome. This synthetic control modality facilitates fine-tuned gene expression control as well as modeling disease mechanisms rooted in disturbed genome 3D organization, e.g. in cancer (see Fig. 2).
Next to inducing proximity between cis- and trans-acting loci, the PICasSO system can in fact be used for even more complex tasks. Our unique fgRNA design facilitates the use of multiple Cas staples at once, allowing multiplexing. By introducing the dCas9/dCas12a protein encoding genes just once, we can thus mediate spatial connections between multiple, selected loci-pairs simply by supplying several fgRNAs. This is possible because each fgRNA simultaneously encodes two precisely defined target DNA locations to be tethered (see Fig. 3).
Due to this novel fgRNA design, multiple Cas staples do not interfere with one another and can hence work in an orchestrated fashion to establish complex regulatory networks (see our related DaVinci models for multiplexed DNA stapling). PICasSO’s multiplexing capability enables large-scale, complex spatial rearrangements of functional DNA elements within the 3D genome, opening new doors for synthetic biology, gene therapy, and beyond.
Introducing PICasSO’s Digital Twin: DaVinci
3D genome engineering is a very complex task. To optimize our initial Cas staple design and experimental setups before performing costly and
labor-intensive lab experiments, we developed DaVinci — the Digital Architecture for
Visualization and Integration of Novel
Interactions — a digital twin of our PICasSO toolbox/experiments.
Our model was created in several iterations, each highly benefiting from the input of experts and stakeholders in the field, and offering detailed information
on every component of the PICasSO system. To bridge large-scale DNA simulations with atom-level interactions, we established the respective mathematical calculations,
combining AlphaFold3, GROMACS, and oxDNA into a single pipeline (Abramson et al., 2024; Berendsen et al., 1995; Lindahl et al., 2001; Šulc et al., 2012). This way, we can dynamically assess protein-DNA interactions at both macroscopic and atomic levels.
DaVinci allows researchers to input parameters such as target sequences, temperature, and salt concentration to predict the feasibility of stapling at
specific genomic locations and under custom experimental conditions. Validated with our own experimental data as well as additional data from the literature,
DaVinci has significantly accelerated the design and optimization of our PICasSO staples, enabling real-world applications.
Applying PICasSO to Engineering Genome 3D Conformation
We didn’t just stop at creating PICasSO and DaVinci; we were determined to explore their full potential and discover the exciting new research avenues and applications
opened by our toolbox and related models. In close exchange and collaboration with leading experts in the field, we identified various promising applications
to push the boundaries of PICasSO in real-world science. As one of the most exciting challenges, we tackled enhancer hijacking (see above), a widespread
phenomenon in cancer (Wang & Yue, 2024). It describes the increased activation of an (onco-)gene through a distal enhancer brought into spatial proximity due to abnormal
3D structural rearrangements in the genome. This mechanism is a key driver in many malignancies including one of its deadliest forms, Glioblastoma (Yi et al., 2022).
Despite its fundamentality in many diseases (Mortenson et al., 2024), enhancer hijacking has been notoriously difficult to study due to the lack of efficient tools to manipulate
enhancer-promoter spatial proximity in living cells (J. H. Kim et al., 2019; M.-S. Kim & Kini, 2017)—until now.
Using the DaVinci model to design specialized Cas staples, we successfully engineered synthetic enhancer hijacking and validated it in HEK293T cells.
This marks the first time enhancer hijacking has been experimentally modeled with an approach that is highly modular and can easily be multiplexed.
This paves the way for deeper exploration of complex disease mechanisms related to the genome architecture, such as the spatial genome rearrangements
observed in Glioblastomas and other aggressive cancers (Yi et al., 2022), and provides a foundation for research on many other pathophysiological conditions.
Given the success of our initial application, we believe that the future possibilities for PICasSO are limitless. Our technology will shed new light on
information encoding in cells and could provide new perspectives and insights into genome evolution and cell differentiation.
Moreover, PICasSO has the potential to fuel the next generation of precision therapies, targeting diseases through induced genome reorganization. We
believe our standardized PICasSO toolbox, composed of well-characterized parts, guidelines, experimental protocols and mathematical models, will inspire
iGEMers and scientists to explore the untapped potential of engineering the 3D genetic code.
For more on our future experiments and applications, see our Applications page.
Expanding PICasSO’s Functionality
To ensure the broad applicability of our system, we developed a comprehensive part collection, with constructs to create and use custom Cas staples of varying
complexity and corresponding assays for their standardized experimental characterization. Our toolbox also contains well-characterized, simpler non-Cas staples
that serve as a baseline as well as positive control constructs, i.e. benchmarks, providing future researchers with a foundation to establish our assay framework
in various cellular contexts as well as laboratory environments. This standardized testing system facilitates the characterization of new, custom protein staple
constructs regarding their DNA binding properties and resulting spatial DNA (re-)organization. Central to our toolbox is a newly established EMSA workflow applicable
for calculating binding affinities of purified staple constructs as well as a complementary FRET-based assay system for monitoring of DNA-DNA interactions directly in vivo (Hochreiter et al., 2019; McCullock et al., 2020).
As already mentioned in the name of our toolbox, Plasmid-Integrated Cas Stapled Origami, we aim to deliver all parts of a Cas staple on a single plasmid
allowing us to easily deliver PICasSO to target cells. As our system is quite complex, this causes a large plasmid size (up to 15 kb) rendering delivery to
mammalian cells using conventional transfection or transduction systems a challenge. To overcome this limitation, we conceived and are in the development of
a cheap and scalable delivery system for large DNA constructs based on bacterial-to-mammalian interkingdom conjugation. Notably, cell targeting in this new
system is enhanced with a target-seeking nanobody, allowing for selective delivery to specific cell types.
The parts in our toolbox are extensively tested, well-documented in the parts registry and accompanying protocols and guides support researchers at every stage of
the engineering cycle while creating and using their own Cas staples for engineering cellular genomes in 3D (see Fig. 4).
Enabling Future Scientists: Education in Synthetic Biology
History has shown that even the most groundbreaking ideas can only succeed when society is open-minded and well-informed.
Through conducting a nationwide survey with over 800 participants, we observed that a person’s level of education significantly
influences their attitude toward emerging technologies. We recognized education as a crucial element, forming the foundation upon
which all scientific advances rely. With our comprehensive educational program, we equip the next generation of scientists with
skills to shape the future of synthetic biology.
Our program is built on four key principles:
Dialogue:
We nurture communication inside the scientific community and points of contact with the general public.
Lasting impact:
Our projects persist past iGEM. Through collaborations, we will continue our actions beyond the end of our project and provide an extensive record of
all our materials online, freely available to teachers, tutors, professors, and anyone curious.
Holistic approach:
In our educational programs, we offer a broad range of access points to science and synthetic biology.
We made sure to address multiple age groups, including theoretical and practical activities, expanding unrestricted educational opportunities.
Scientific foundation:
To ensure our actions are grounded in a robust scientific framework, we collaborated with renowned institutions including the
German Ministry of Education, and the German Cancer Research Center as well as teachers, professors, and education experts, creating a high-quality
program that meets the demands of modern science education.
To bring these principles to life, our program consists of six complementary projects
that come together like pieces of a puzzle. These include a fully funded summer school as well as workshops for school students, complemented by
two lecture series covering topics from cutting-edge research to inclusivity.
By fostering a culture of curiosity, dialogue, and responsible innovation, our education initiatives provide the next generation with the tools
they need to transform synthetic biology and drive meaningful societal change in the years to come.
Summary
We successfully developed and characterized the PICasSO toolbox and its digital twin DaVinci, offering a transformative solution for achieving precise control over the
3D spatial organization of the genome. This innovation fills a critical gap in genome regulation, allowing programmable manipulation of DNA architecture - a level of
information encoding distinct from the DNA sequence - to control gene expression and function. Complemented by an advanced assay system for engineering and testing
new staples, PICasSO uses a novel bivalent CRISPR complex and fusion guide RNA to mimic natural mechanisms of DNA-DNA interaction or even create entirely new regulatory
networks via synthetic interactions that do not exist in nature. To streamline the engineering process, our sophisticated and validated computer model DaVinci facilitates
the creation of new PICasSO designs. Together with our part collection, these tools provide fellow iGEMers and scientists with a tested and well-documented system to explore
new science with this foundational technology. Calling on expert interviews we identified key research questions that PICasSO can address and applied our novel Cas staples
to model enhancer hijacking, a barely studied yet important driver of cancer progression. In doing so, we demonstrated that PICasSO is working successfully.
With our comprehensive part collection, advanced modeling tools, and public engagement efforts, PICasSO provides a foundational advance to genome engineering in synthetic biology.
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., Bodenstein, S. W., Evans, D. A., Hung, C., O’Neill, M., Reiman, D., Tunyasuvunakool, K., Wu, Z., Žemgulytė, A., Arvaniti, E., . . . Jumper, J. M. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630(8016), 493–500. https://doi.org/10.1038/s41586-024-07487-w
Berendsen, H., Van der Spoel, D. & Van Drunen, R. (1995). GROMACS: A message-passing parallel molecular dynamics implementation. Computer Physics Communications, 91(1–3), 43–56. https://doi.org/10.1016/0010-4655(95)00042-e
Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012). Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 485(7398), 376–380. https://doi.org/10.1038/nature11082
Halfon, M. S. (2020). Silencers, Enhancers, and the Multifunctional Regulatory Genome. Trends in Genetics, 36(3), 149–151. https://doi.org/10.1016/j.tig.2019.12.005
Hochreiter, B., Kunze, M., Moser, B., & Schmid, J. A. (2019). Advanced FRET normalization allows quantitative analysis of protein interactions including stoichiometries and relative affinities in living cells. Scientific Reports, 9(1), 8233. https://doi.org/10.1038/s41598-019-44650-0
Kim, J. H., Rege, M., Valeri, J., Dunagin, M. C., Metzger, A., Titus, K. R., Gilgenast, T. G., Gong, W., Beagan, J. A., Raj, A., & Phillips-Cremins, J. E. (2019). LADL: Light-activated dynamic looping for endogenous gene expression control. Nature Methods, 16(7), 633–639. https://doi.org/10.1038/s41592-019-0436-5
Kim, M.-S., & Kini, A. G. (2017). Engineering and Application of Zinc Finger Proteins and TALEs for Biomedical Research. Molecules and Cells, 40(8), 533–541. https://doi.org/10.14348/molcells.2017.0139
Kweon, J., Jang, A.-H., Kim, D., Yang, J. W., Yoon, M., Rim Shin, H., Kim, J.-S., & Kim, Y. (2017). Fusion guide RNAs for orthogonal gene manipulation with Cas9 and Cpf1. Nature Communications, 8(1), 1723. https://doi.org/10.1038/s41467-017-01650-w
Lindahl, E., Hess, B. & Van der Spoel, D. (2001). GROMACS 3.0: a package for molecular simulation and trajectory analysis. Journal Of Molecular Modeling, 7(8), 306–317. https://doi.org/10.1007/s008940100045
McCullock, T. W., MacLean, D. M., & Kammermeier, P. J. (2020). Comparing the performance of mScarlet-I, mRuby3, and mCherry as FRET acceptors for mNeonGreen. PLOS ONE, 15(2), e0219886. https://doi.org/10.1371/journal.pone.0219886
Mikkelsen, T. S., Hillier, L. W., Eichler, E. E., Zody, M. C., Jaffe, D. B., Yang, S., Enard, W., Hellmann, I., Lindblad-Toh, K., Altheide, T. K., Archidiacono, N., Bork, P., Butler, J., Chang, J. L., Cheng, Z., Chinwalla, A. T., De Jong, P., Delehaunty, K. D., Fronick, C. C., . . . Waterston, R. H. (2005). Initial sequence of the chimpanzee genome and comparison with the human genome. Nature, 437(7055), 69–87. https://doi.org/10.1038/nature04072
Mortenson, K. L., Dawes, C., Wilson, E. R., Patchen, N. E., Johnson, H. E., Gertz, J., Bailey, S. D., Liu, Y., Varley, K. E., & Zhang, X. (2024). 3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression. Nature Communications, 15(1), 6130. https://doi.org/10.1038/s41467-024-50387-w
Rajarajan, P., Gil, S. E., Brennand, K. J., & Akbarian, S. (2016). Spatial genome organization and cognition. Nature Reviews Neuroscience, 17(11), 681–691. https://doi.org/10.1038/nrn.2016.124
Riethoven, J. M. (2010). Regulatory Regions in DNA: Promoters, Enhancers, Silencers, and Insulators. Methods in Molecular Biology, 33–42. https://doi.org/10.1007/978-1-60761-854-6_3
Šulc, P., Romano, F., Ouldridge, T. E., Rovigatti, L., Doye, J. P. K. & Louis, A. A. (2012). Sequence-dependent thermodynamics of a coarse-grained DNA model. The Journal Of Chemical Physics, 137(13). https://doi.org/10.1063/1.4754132
Wang, X. & Yue, F. (2024). Hijacked enhancer–promoter and silencer–promoter loops in cancer. Current Opinion in Genetics & Development, 86, 102199. https://doi.org/10.1016/j.gde.2024.102199
Yang, Y., Zhang, Y., Ren, B., Dixon, J. R., & Ma, J. (2019). Comparing 3D Genome Organization in Multiple Species Using Phylo-HMRF. Cell Systems, 8(6), 494-505.e14. https://doi.org/10.1016/j.cels.2019.05.011
Yi, E., Chamorro González, R., Henssen, A. G., & Verhaak, R. G. W. (2022). Extrachromosomal DNA amplifications in cancer. Nature Reviews Genetics, 23(12), 760–771. https://doi.org/10.1038/s41576-022-00521-5
Zheng, H., & Xie, W. (2019). The role of 3D genome organization in development and cell differentiation. Nature Reviews Molecular Cell Biology, 20(9), 535–550. https://doi.org/10.1038/s41580-019-0132-4