Exemplar Projects

Proteins and the human gut microbiota: who does what?

Supervisory team:

Professor Lesley Hoyles  Antimicrobial Resistance, Omics and Microbiota Research Group
Klaus Winzer Centre for Biomolecular Sciences, University of Nottingham

The human gut microbiota represents a diverse community of bacteria, archaea, viruses and fungi. The collective genome of the gut microbiota (the ‘metagenome’) encodes 150 times more genes than the human genome. Enzymes encoded by the metagenome allow gut bacteria to use dietary substrates that escape digestion by human-encoded enzymes in the gastrointestinal tract. Metabolites produced as a result of these microbial processes act on intestinal cells, or are taken up into the blood via the hepatic portal vein and transported around the body. The interaction of microbial metabolites and host cells – the so-called microbial–mammalian metabolic axis – contributes to homeostasis of the human system. Disruption of homeostasis and the gut microbiota is linked with a range of metabolic diseases (obesity, non-alcoholic fatty liver disease, atherosclerosis, type 2 diabetes), inflammatory bowel disease (Crohn’s, ulcerative colitis) and neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease).

We only know the functions of approximately 30 % of the genes that contribute to the metagenome. While much is known about the bacteria responsible for fermenting carbohydrates in the gastrointestinal tract, little is known about the microbes that use proteins, peptides and amino acids in this environment. A diverse range of products (essential amino acids, phenolic compounds, amines, ammonia, short-chain fatty acids, branched-chain fatty acids, gases, sulfides) is produced as a result of microbial-driven synthesis or proteolysis. All these metabolites have the potential to influence host systems in beneficial or detrimental ways. For example, the microbiota of the small intestine contributes 8–17 % and 5–21 %, respectively, of the essential amino acids lysine and threonine found in the bloodstream. Conversely, circulating phenyl acetate produced via bacterial breakdown of phenylalanine contributes to lipid accumulation in the liver of individuals with non-alcoholic fatty liver disease. The aim of this project is to characterize proteolysis in a range of anaerobic gut bacteria. Understanding which gut bacteria are involved in proteolysis will allow us to develop targeted interventions to promote human health.

Inspiration from fungi: Generating tuneable mycelial networks for directed assembly

Supervisory team:

DOS:  Professor Carole C. Perry
Co-supervisor: Dr Matthias Brock (University of Nottingham)
Co-supervisor: Dr Victor V. Volkov

Fungi are ubiquitous and are important sources of biologically active molecules including antibiotics (e.g. penicillin and cephalosporins), antifungals (e.g. griseofulvin), anti-cholesterol drugs (e.g. statins) and anti-cancer agents. Alternative applications that include recycling of waste materials include the generation of a range of building materials. The importance of the approach is highlighted by the fact that the Defence Advanced Research Projects Agency (DARPA) in 2017 invested $9.1M for the creation of a ‘new class of materials that combines the structural properties of traditional building materials with the attributes of living systems’. There is further research being conducted on the development of building materials in mainland Europe.

Although there is a recognition that fungi could provide an effective way to generate new materials there is scant research into the chemical, biological and concomitant structural changes that occur when the conditions in the local environment are modified.

Here, we propose to use fungi to develop engineered living materials (ELM) where our understanding of the effect of growth environment will lead to physical structures that can be controlled, where growth can be directed and thereafter developed into novel materials. We will study fungi, and in particular their mycelia to (1) understand how living networks form and the role that the local environment has on their biochemistry, structure at both the microscopic and macroscopic scale and physico-chemical/mechanical properties of the resulting biological material and (2) use the materials developed in (1) to fabricate novel bio-inorganic mesoscale composites. To do this we will design novel growing platforms that can be also be used for in situ visualization and chemical mapping of the developing hyphal structures. We will apply existing isolation and coupling technologies to generate additional functionality on the biomolecular components of the hyphal structures to direct the formation of novel mesoscale composites.

The interdisciplinary project includes aspects of microbiology, bio-engineering, biochemical analysis and materials chemistry. The project will lead to increased understanding of how environment affects the growth of fungal mycelia and how this in turn impacts on the ability of the family of biomolecular materials naturally present in the mycelia to act as components of mesoscale composites.

The project hypothesis is that by understanding how the local environment affects biochemical synthesis and growth patterns we will be able to tailor the structure of functional composite materials based on fungal mycelia.

Ecological responses to drought in dynamic river ecosystems

Supervisory team:

Associate Professor Rachel Stubbington (BIO)
Carl Smith - School of Animal, Rural and Environmental Sciences, Nottingham Trent University
Judy England - Environment Agency, Wallingford, UK
Mark Warren - Environment Agency, Tewkesbury, UK

Temporary rivers, including England’s iconic chalk streams, experience natural shifts between flowing, ponded, and dry habitat conditions, which contributes substantially to their high aquatic–terrestrial biodiversity. However, their ecological communities are at risk from global change, with water abstraction, physical habitat modification and land use interacting to impact on instream communities. In our changing climate, drought disturbance events are occurring at increasingly
frequent intervals, exacerbating these impacts. This project represents an exciting opportunity to collaborate with leading researchers from academia and industry to address the risks posed by drought to river ecosystems. Specifically, the research will focus on catchment management, using a data science approach to characterize structural and functional responses of ecological communities (invertebrates, fish and/or plants) to drought through analysis of extensive, long-term Environment Agency data. Project results will inform the development of ecohydrological tools to manage water resources in perennial and temporary rivers. The research will contribute to regional and global-scale understanding of temporary river biodiversity and ecosystem services, thus informing future initiatives to maintain and enhance the ecological quality of these undervalued ecosystems.

Development and validation of machine learning based approaches for pathway inference in breast cancer.

Supervisory team:

Professor Graham Ball of Bioinformatics
Colleagues in University of Nottingham

Since the sequencing of the human genome new approaches for studying disease systems at the genomic, epigenetic, proteomic and metabolomics levels are being continually developed.  One of the challenges with the analysis of such data is the volume, resolution and complexity of the data generated; plus, the quality of the data. These issues from a bioinformatics perspective are often typified by the criticism that different data sets do not yield consistent results.  This would indicate that often such individual data sets have high levels of noise and thus do not have sufficient cases to achieve a sufficient statistical power.  Thus, analysis of such data requires careful consideration, paying attention to the non-linearity of biological systems, the interaction of molecular entities in pathways, the fluidity of biological systems and the need for determination of consistent entities across multiple data sets.

We have developed cutting edge systems biology and bioinformatics approaches, based on computational intelligence, which identify robust nonlinear biomarkers associated with clinical features which are concordant across multiple data sets.  Furthermore, we have developed approaches which study the interactions between key features in the context of a given problem.  These approaches in effect determine the level of influence of a set of driver markers in a given biological system.  This approach allows us to determine the molecular drivers of a system which result in a given phenotype.

Furthermore, we have developed a systems biology and pathways analysis approach based on machine learning.  Here a set of markers defining a given pathway or phenotype are used in network inference algorithms to identify a network of interaction weights.  This network is then analysed analysed to identify the key molecular drivers and the most influential molecules in a given system.  The approach has been used in a commercial contract with Syngenta and UoN to identify transcriptomic regulators of ripening in tomato.  (Pan Y. et al, Plant Physiology, 2013).  The approach has also used to analyse breast cancer molecular aetiology defining driver features of proliferation (Abdel fatal (2016), Lancet Oncology). This use of a systems approach goes further than a simple list of markers because biology is defined by the interaction of molecular markers.  The approach refines the marker set identified and can be used to identify molecular based disease processes that differentiate between a healthy population a diseased population (Therapeutic target identification In Silico), processes associated with therapeutic response or pharmacodynamics.

Quantification of collagen turnover in musculoskeletal tissues in response to lifestyle interventions and disease.

Supervisory team:

Professor Craig Sale (SPO)
Phillip Atherton -Medical School, University of Nottingham, Royal Derby Hospital
Associate Professor Kirsty Elliott-Sale (SPO)
Jessica Piasecki- (SPO)
Beth Phillips - Medical School, University of Nottingham, Royal Derby Hospital
Matthew Brook - Queens Medical Centre, University of Nottingham

The number of older people in the world is rapidly increasing, which also increases the number of conditions associated with ageing, including low bone mass (e.g., osteoporosis [OP]). OP is a major worldwide problem with >200 million women alone living with the disease. A significant clinical problem associated with OP relates to fragility fractures of the bone and, in England, >300,000 individuals suffer these each year, with >1100 deaths occurring in the UK each month due to hip fractures alone. Not only our bones, but also our muscles, ligaments and tendons, change as we get older and these changes can lead to other conditions, such as osteoarthritis (OA), which causes joint pain and stiffness. OA is also a major health issue for the ageing population, affecting ~8.5 million people in the UK. Bone mineral density (the amount of mineral [calcium] in bone) is considered a key predictor of OP fracture, although it only relates to about two-thirds of the bones strength. Other factors in the non-mineral compartments of bone are equally important. The extracellular matrix (molecules secreted by nearby cells to provide structure and support) of bone is largely made up of a protein called collagen, which is vital in providing underlying strength to the bone. Unfortunately, the normal and abnormal changes in bone collagen in health and disease are not well known because of a lack of good methods to measure it. We have developed an accurate method to determine musculoskeletal tissue collagen turnover in humans using “heavy water”; a form of water that has a heavier than normal hydrogen isotope (part of its chemical structure), meaning that we can measure where in the body it goes after it has been ingested. Our aim is to better understand the responses of collagen in the musculoskeletal tissues of healthy and diseased humans, leading us to examine potential intervention strategies to improve musculoskeletal health across the lifespan.

Osteoprotegerin (OPG) as a therapeutic target to combat inflammation in age-associated muscle loss

Supervisory team:

Dr Jessica Piasecki (SPO)
Professor Craig Sale (SPO)
Dr Craig Doig (BIO)
Dr Mathew Piasecki (UoN)

The proportion of people over the age of 60 years within the UK is rapidly increasing. Worldwide, it is estimated by 2050 there will be two billion people above the age of 60 years, a 10% increase for that age group. This increasingly aged population necessitates new interventions to minimise levels of disability in later life.
As we get older our muscles become progressively smaller due to a decrease in the size and number of individual muscle fibres, referred to as sarcopenia. This results in weaker muscles which are more difficult to control, and everyday tasks such has getting out of a chair or climbing the stairs become more difficult. People with sarcopenia have an increased risk of falling and fracture, loss of independent living, more hospital admissions and higher mortality rates (Bian et al., 2017; Walrand et al., 2011).
Sarcopenia in very old age is also associated with a pro inflammatory response; cells within the body become damaged and loose functionality, triggering the body’s inflammatory cells to travel to the damaged cells in an attempt to repair. This cell damage can accumulate resulting in a prolonged inflammatory response, which may contribute to muscle fibre denervation and poor communication between nerve and muscle.
Osteoprotegrin (OPG) is circulatory factor originally identified as a key component of bone formation. More recently it has been suggested it also plays a key role in limiting total muscle atrophy and dysfunction (Bonnet et al., 2019), and our recent preliminary data from human cell lines supports this notion (Figure 1). The overarching aim of this project is to employ cell culture models and recently developed techniques applied to human neuromuscular function in vivo, in order to expand upon these recent findings to further elucidate the role of OPG in neuromuscular function in older age

In silico stratification of patients with acute myeloid leukaemia – a network approach

Supervisory team:

Professor Nadia Chuzhanova
Dr Jonathan J. Crofts
Professor Sergio Rutella
Colleagues in University of Nottingham

Acute myeloid leukaemia (AML) is the most common type of acute leukaemias. In 2015, 3,126 new cases of AML were diagnosed in the UK and 2,601 patients died. In children, AML accounts for 20% of leukaemias with an incidence of seven cases per million children under the age of 15. The general therapeutic strategy has not changed substantially in more than 30 years. Refractory disease is common and relapse represents a major cause of treatment failure. Although intensive multi-agent chemotherapy in conjunction with improved supportive care has increased survival rates to 70%, 30-40% of children with AML relapse and only one-third of them survive to adulthood. Investigation of new therapeutic targets for high-risk AML, including immunotherapy, remains a high priority, both in children and in adults, and is expected to support the identification of patients who may benefit from more aggressive treatments earlier in their disease course. Although several AML classification procedures based on either somatic mutations, gene-expression profiles or a combination of molecular and clinical data have been proposed, many patients do not fall into any of categories or groups defined by the World Health Organisation, prompting the need for the development of better stratification procedures for AML patients and advanced techniques for biomarkers/signatures discovery.
The aim of this project is to develop computational network-based models capable of revealing the interplay between diverse processes such as DNA methylation, mRNA expression, miRNA expressions, 3D structure of chromatin, etc. for finding subgroups of patients that share similar disease properties and for identifying specific multi-omics biomarkers/signatures for each subgroup. Several multi-omics datasets including the Cancer Genome Atlas (TCGA) data together with chromosome conformation data available for AML-related cell lines will be used.