The bacteria in our guts play a crucial role in shaping our metabolism and health. Our gut bacteria help us break down otherwise indigestible foods, such as fibres, into smaller molecules we can absorb. The microbiome, all the genetic material carried by our gut microbes made of 20 million genes, is a tiny pharmaceutical factory in our guts making compounds, some of which act like drugs. These compounds, called metabolites, not only are the building blocks of life but are also essential chemical messengers. However, the critical microbial signals influencing human health remain elusive. Bringing together leading experts from across the UK in London, Cambridge and Dundee and an international collaborator from Montréal in Canada, this new Research Project focusses on understanding how our gut bacteria talk to our organs through these microbial chemicals and how they bind a certain type of effector in the cell acting like molecular switches, called kinases, which regulate how our cells react to a changing environment. This Research Project focuses on how a chemical produced by our gut bacteria called hippurate regulates a kinase called Mnk1. This kinase controls the translation of messenger RNAs, the copy of the DNA blueprint, into proteins, which carry out various jobs in the body. These jobs include metabolism of sugar and lipids, hormone production or inflammation, as this is the case for patients living with metabolic diseases. Our pilot data show that hippurate, by blocking Mnk1, stops mRNA translation and synthesis of particular proteins, which has already been shown to be beneficial in metabolic diseases. If we can demonstrate this mechanism, this means we could harness the microbiome to improve the health of patients with metabolic conditions such as type 2 diabetes and obesity. In this Research Grant, we have three major aims: First, we will study in Cambridge and Montréal the effect of hippurate on gut, liver and fat cells and in mice fed a high-fat diet to trigger metabolic diseases. Partnering with UK biotech start-up CN Bio Innovations specialised in Organs-on-Chip, we will model the effect of hippurate on gut barrier and liver function which are both important in metabolic diseases, and how it can make our gut and liver healthier. Second, we will identify the mRNAs and proteins responding to hippurate to understand how hippurate improves health. This will be achieved by using technologies such as RNA-Seq and proteomics, which are mastered by our co-applicants at Imperial, Cambridge and Dundee. This will allow us to precisely map the hippurate mechanism in human cells. Finally, we will analyse data from several studies of human populations with metabolic diseases to find more evidence about hippurate's beneficial roles for people living with metabolic diseases. We will identify the clinical conditions and risk factors affected by hippurate, to define hippurate's direct role in humans. In conclusion, this research will help us discover how gut bacteria turn nutrients into chemical messengers regulating human metabolism in obesity and metabolic diseases. We will zoom in on hippurate in particular to better understand an important mechanism by which the microbiome controls human physiology. This will allow us to understand better how the microbiome beneficially hacks the host cellular machinery to shape metabolic health and disease.

MRC : Jessica Myatt : MC_ST_BNDU_2019 Across the world, rules regarding the legalisation of the recreational use of cannabis are changing. This is leading to a perception that cannabis is safe to use: people, therefore, remain unaware of possible consequential health risks. The concentration of delta-9-tetrahydrocannabinol (THC), often found in cannabis products, is rising - a concern as THC is known to have psychoactive effects. This is concerning as it could mean a person may display significant disturbances in motor function, behaviour, perception or thinking, or hallucinate for example. Previous studies are now outdated as they were undertaken when the concentration of THC remained low - in the last 20 years THC concentration has risen from around 1.5-4% up to 29%. Furthermore, it has been shown that maternal and paternal cannabis use during pregnancy increases the risk of psychotic-like experiences in offspring. Additionally, cannabis/cannabis products are being used to counteract nausea in pregnancy, therefore it is imperative that studies addressing the effect THC exposure may have on offspring are carried out sooner rather than later. There is general acceptance for disorders such as schizophrenia, to have a substantial neurodevelopmental basis. This means evidence for both environmental and inherited risk factors that could result in a wide range of outcomes following disrupted or altered development of the brain. Taken together, the increase in the concentration of THC, alongside the substantial neurodevelopmental basis of such disorders, could, therefore, result in an emergent risk of psychotic disorders within the population. To begin to address this, this project will investigate the dose-dependent effects of exposure to THC before birth on the neurodevelopment of adolescent offspring in rodents. Offspring will be exposed to one of two different concentrations of THC or a control substance before birth. After pups have been born and reached adolescence, they will be scanned to acquire images of the brain a week apart which will allow us to look for any structural changes across time. The offspring will also be assessed using behavioural tasks to determine if there are any changes in their associated behaviour. Scientific data collected may provide knowledge to guide policies aimed to prevent future public health challenges that could be associated with exposure to THC before birth.

ESRC : Emily MacLeod : ES/P000592/1. This exchange provides me with the opportunity to develop my existing expertise within science identities research, and make links within the field of teacher education and teaching identities research. There is a critical shortage of teachers globally; an ongoing issue which has far-reaching and negative consequences for schools and their students. The teacher shortage in the UK, where I am conducting my PhD and where I myself was a teacher, is particularly acute. Government teacher recruitment targets in England have been missed for the last seven years. However, this shortage is not evenly spread, and raises significant social justice concerns. For example, it has been estimated that schools in England would need an additional 68,000 Black and minority ethnic teachers for the workforce to reflect the population it teaches. Science especially faces some of the worst teacher shortages. But incentives to attract more people into science teaching have so far failed to make a significant impact on this shortage, and have tended to be financial; based upon the assumption that science graduates can earn considerably more outside of the relatively low-paid role of teaching. Unlike the well-documented shortage of teachers in England, there is currently very little research into the scale of the teacher shortage in Canada, partly due to differences in governance and contexts across the different provinces. However, in contrast to the surplus of teachers seen in recent years, there are now signs of an increasing shortage of teachers. This summer in Québec, where I intend to complete this exchange, the government reported that there were over 250 empty teacher vacancies in the province, and there are concerns that Covid-19 is likely to make things worse. As in England, there is also a severe and growing underrepresentation of people of colour in Canada's teaching workforce. This is particularly worrying within the context of an increasingly diverse Canadian population. Also as in England, this shortage is not spread evenly. Science teachers are some of the most needed. However, unlike in England, teacher salaries across Canada are amongst the highest of the OECD community, and subject-specific incentives have yet to be used. The shortage of science teachers especially, seen in both England and Canada, is of particular concern given that there is a globally-recognised STEM (Science, Technology, Engineering and Mathematics) skills shortage, likely to increase due to Covid-19. This growing demand for more young people studying and working in STEM will not be met without enough qualified science teachers. Yet in order to improve this situation, we need to better understand science teacher supply patterns. To date, research into teacher supply in science (and other disciplines) has been conducted by specialists in teacher education. From this we know that science teachers report becoming teachers not because they always wanted to, but after having had positive teaching-like experiences. We also know from existing science identities research from both the host and home supervisors that social and cultural influences work to influence whether and how people see different sciences roles as 'for me' or not. This exchange will help me to develop my research and communication skills whilst conducting comparative research to develop understandings of who does, and importantly who does not, want to become a science teacher in the UK and Canada, and why. I will build upon my existing expertise in science identity development amongst young people, and learn from the expertise of Dr Gonsalves and her colleagues in science teacher identities, and how teaching-like experiences can affect these identities. Combining these fields will help me to contribute to understandings of how people's identities shape how they feel about becoming science teachers.

Recent advances in biological technology enable the measurement of multiple measures of complex systems from the cell to the whole organism. However, these technologies generate massive amount of data and it is a major task to process these robustly and efficiently. The aim of our multidisciplinary project is to devise methods to combine and analyze different data measurements arising from experiments in modern biology that will ultimately aid in the understanding of the causes of common diseases, and lead to the development of new treatments. It is now possible to investigate how complex organisms function by measuring in great detail the chemical composition of, for example, a sample of blood or urine, and also to measure how that composition changes over time, or in reaction to different treatments or experimental conditions. Perhaps most importantly, it is also possible to compare the composition across different groups that may have or not have a particular disease, and to use this comparison to understand how treatments might be developed. This exciting prospect can only be achieved, however, if the experimental data are collected and analyzed as accurately possible. This is the principal goal of our research. We will focus on so-called 'metabolic' analysis using two specific types of technology (known by the initials NMR and MS) that allow us to measure the amount of a large number of different chemicals (or metabolites) that are present in the samples of blood or other body fluids being analyzed. Metabolites are small molecules present in all organisms which are essential to the functioning of their living cells. NMR and MS are both extremely sophisticated measurement procedures that each produce a large amount of data (spectra), but although the measurements from the two technologies contain some information on the same metabolites, most of the information from the two sources is not identical, and an important statistical modelling task involves combining data from them in the most sensible fashion. We will separate this task into two components; first, the mathematical modelling of the NMR and MS metabolite spectra, and secondly the combination of the data across the two measurement systems. Both components require major input from both biologists and statisticians involved in our research programme. The statistical analysis of the large amounts of data generated by NMR and MS technologies is an extremely challenging task. Some methods for data analysis do already exist, but they do not use all the information at hand. An important advantage of our approach is that we will use physico-chemical information already available about typical metabolites to direct how we build our models and carry out our analysis. Such physico-chemical 'prior' information has been only rarely used in the analysis of metabolite data, but we feel that it provides an important guide as to how analysis should proceed. Thus we will adopt a Bayesian statistical approach that combines data and prior information in a principled fashion. However, despite being scientifically attractive, this modelling approach needs advanced computing methods so that the analysis can be implemented, and a major component of the research we will carry out will be to implement the most efficient computational strategies. Understanding and modelling the content of NMR and MS metabolite spectra is a complicated task that requires both highly specialized chemical knowledge and state of the art statistical techniques. The novelty of our project is that by using a Bayesian analysis framework we are able to harness and incorporate such specialist information. Our multidisciplinary research team that combines expertise in modelling, statistics, chemical biology and bioinformatics will ensure the success of our research programme and facilitate the dissemination of its results to a wide community.

EPSRC : Adan Benito : EP/S022694/1 The invention of the electric guitar at the beginning of the 20th century sparked a musical and cultural revolution that changed, not only how music was made and listened to, but also how new instruments were designed. However, new trends in popular music and in both electronic and digital advances, could be causing what many media outlets have called the demise of the guitar as a driver for cultural change. Although the electric guitar has widely been valued as a vehicle for musical expression, many of the efforts that have been carried out to bring the instrument up to date with the era of digital music fail to capture that richness. Attempts at creating interfaces based on the guitar using either sensors or analysis of the sound produced by the instrument have been carried out in the past but, given the complexity of the analysis of musical gestures and the translation of expressive meaning, these methods have never been widely adopted. With this research we propose to validate an augmented instrument prototype based on a guitar with sensors and an hexaphonic pickup to detect different kinds of string bending, a technique commonly used by guitarist to raise the pitch of the string to add articulation to their playing. This gesture has been popularly said to provide singing and talking qualities to guitar playing. We will extract information from both the audio signals produced by the instrument and sensors that detect string movement to obtain enhanced information enhanced information on how guitarists use this resource. We will then analyse how performers interact with the instrument during performance to assess the importance of this technique and to generate data that would later be used to better understand the characteristics of string bending as an expressive tool.