Gene regulation in health and disease

A simple organism like C.Elgans has similar numbers of genes as a human even though it is made of only a few
thousand cells compared to the many trillions in a human. This massive increase in complexity is therefore not
linked to the genes themselves, but rather to the complexity of the combinations in which they are used.
Therefore, complex mammalian life is founded on exquisite control of gene usage (expression); when and where
they are switched on and off and their level of expression. The “switches” that control gene expression are
located in the “non-coding” region of the genome rather than the regions that form the blueprint for production of
the proteins of which we are made. However, this represents ~98% of the total genome and the DNA code that
activates these switches is different in each cell type so understanding the systems that control gene expression
is extremely complex. However, understanding these mechanisms is extremely important for human heath as
nearly 90% of the genetics linked to all common human diseases (Heart disease, cancer, dementia, multiple
sclerosis etc) lie within this region and change the activity of these switches. We use a combination of
experimental, computational and machine learning approaches to understand the basic principles of gene
regulation to decode common human disease genetics. We use these approaches to understand the genes and
cellular functions affected by these genetics to ultimately guide therapeutic development.

The Hughes group researches the basic mechanisms of gene regulation. We have a track
record in method development to enable the mapping of gene regulatory landscapes and
mechanisms in the genome. The lab combines molecular technologies with computational
biology and machine learning. Previously, we developed a suite of Chromosome Conformation
Capture, transcriptomics and machine leaning technologies to mechanistically understand gene
regulation and the function of the non-coding genome. We leveraged these technologies to
develop a platform to decode non-coding genetics such as those associated with COVID-19
severity. We aim to build on this work to develop novel approaches to measure the dynamics of
chromatin proteins which drive the formation of regulatory domains. We will combine large-scale
synthetic biology and machine learning approaches to build artificial regulatory domains in
mammalian cells to test the rules of gene regulation and to develop a toolkit for the design and
creation of bespoke regulatory circuits in the genome. We will continue to refine and employ our
platform to decode non-coding human genetics, using machine learning, single cell epigenomics
and super-resolution 3C technologies and deploy this via collaboration with clinical and research
colleagues in the MHU, WIMM and beyond.