Understanding the complexities of the human genome goes beyond simply analyzing its linear sequence. The three-dimensional (3D) folding of DNA within the nucleus plays a crucial role in gene regulation, influencing how genes are expressed across different cell types. This spatial organization allows regulatory elements to interact with genes over long distances, shaping cellular identity and function.

Recent advances in 3D genomics have provided new insights into how genome architecture affects gene activity, offering a more comprehensive approach to studying diseases and identifying potential drug targets. By integrating 3D genomic data with traditional multiomics techniques, researchers can gain a deeper understanding of disease mechanisms, paving the way for more precise and efficient drug discovery.

Daniel Turner, chief scientific officer at Enhanced Genomics, has over 20 years of senior leadership experience within the fields of genetics, molecular biology, and sequencing research and development. Drug Discovery News recently interviewed Turner to explore the role of 3D genomics in modern biomedical research, its challenges, and its transformative potential for therapeutic development.

Can you provide a brief description of what 3D genomics is? How does it differ from traditional linear genomics, and why is it crucial for understanding gene regulation?

The human body has around 200 distinct types of cells, each specialized in performing specific functions. Most of these cell types have a nucleus, which contains the genome, and the genomic DNA must be folded up to make it fit. If you were to sequence the genome of different cell types from an individual, they would all have essentially the same sequence. But if you look at the 3D structure of the genome, you will see that it is folded differently, and non-randomly, in the different cell types.

Folding brings regions of the genome into close proximity that might be far apart in the linear sequence, and this allows genes to be controlled by regulatory regions of the genome that can be hundreds of kilobases away. Because of this, a gene might be turned off in one cell type but might be highly expressed in another. Enhanced Genomics has developed a genome-wide assay that looks at the way entire genomes are folded and how genes are controlled in different cell types and helps us to understand disease biology more deeply.

What are the current challenges in translating multi-omics research into clinical applications?

A common way to look at the genetics of complex diseases is with a genome-wide association study to compare genome sequences from a large group of patients with the disease with a control group. The idea is to see if patients tend to share variants in their genomes that are not present in the control group. You then need to identify which genes are being affected by the disease variants that you uncover, since these genes might represent good drug targets. If a disease variant is within a gene, you immediately know that it is likely to affect the protein produced by that gene, but 95% of disease-associated variants are in non-coding regions of the genome and are often far away from the genes they influence. A range of other omics analyses is then used to try to identify the affected genes. It is common to assume that the relevant genes are the closest ones to a disease variant along the primary sequence, but this is often incorrect because genome folding enables regulation over long distances.

An additional problem is that different multi-omics analyses do not tend to give consistent answers and are difficult to integrate.

How does 3D genomics contribute to what you describe as 3D multiomics and how are you harnessing this for drug discovery and development?

3D genomics provides a lens through which other multiomics datasets can be viewed, which allows them to be integrated successfully. It is this integrated combination of 3D genomics and traditional multi-omics (e.g., RNA-seq, ATAC-seq, histone modifications) that we call “3D multiomics”.

This approach allows us to get stronger genomic support for potential drug targets and provides a more complete picture than conventional multiomics, meaning that we find better gene targets, more quickly.

Earlier this year, Enhanced Genomics announced the launch of a suite of integrated multi-omics solutions. Can you tell us more about this platform and how it aims to accelerate and improve the precision of drug discovery?

If you try to identify druggable gene targets using classic multi-omics approaches, you will tend to miss around half of them and you will also generate many false positives. False positives mean that your longlist of potential genes contains a substantial proportion of unsuitable targets. Many of these will be discarded after literature review, but this is a very lengthy process, so the fewer false positives you have, the more time you save. 3D multiomics generates fewer false positives, meaning that we can get to a gene shortlist around 10x faster.

In addition to the time saving, 3D multiomics reveals the complete picture, whereas conventional multi-omics approaches miss a lot of potential gene targets. So, when you are applying regular multi-omics, you might conclude that you have identified a good drug target. But you are not seeing the complete picture. When you apply a 3D multiomics approach, you can see why that gene was not such a good target after all, which saves time and money spent pursuing poor targets.  

How does the Promoter Capture Hi-C (PCHi-C) technology function to create high-resolution, genome-wide regulatory maps, and what advantages does it offer over traditional assays?

Hi-C reveals 3D interactions from across the entire genome, but the majority of these are not relevant to drug target discovery. PCHi-C enriches all the annotated gene promoters in the human genome, along with whatever other regions of the genome those promoters were in close 3D proximity to. This reveals functional contacts between regulatory regions and all gene promoters in a more efficient way than regular Hi-C, making it a more scalable and cost-effective approach for studying 3D regulatory contacts across the genome.    

What is the 3D multiomic atlas, and how does it contribute to providing insights into disease mechanisms and therapeutic opportunities?

Our 3D multiomics atlas is a database that we have built consisting of regular multiomic and 3D genomic datasets for a wide collection of cell types. It means that if we want to connect disease variants to the affected genes, we already have the data we need without needing to generate it each time. As a result, our approach to target gene discovery is much faster than conventional approaches not only because we have a far lower false-positive rate, but we have also already generated the experimental data that we need to identify these target genes.  

How will future advances in 3D multiomics reshape the drug discovery pipeline in the next decade?

The recognition that the 3D structure of the genome plays a crucial functional role has significantly deepened our understanding of disease biology, paving the way for more effective treatments. However, a 3D multiomics approach offers value far beyond this—it has the potential to transform multiple stages of the drug discovery process. By providing insights into drug mechanisms of action, enabling precise patient stratification for treatment response, and further enhancing our understanding of disease biology, this approach is poised to reshape the drug discovery pipeline over the next decade.