Quin Xie recently joined the Nuffield Department of Clinical Neurosciences on the Novo Nordisk-Oxford Fellowship scheme. In this interview, Quin discusses her research on the links between metabolic disorders and obesity-related changes in the brain and the longer-term implications of her work. She also reflects on her first few months here in Oxford and shares her impressions of research at the university.
Welcome to Oxford Quin. Tell us a little about the research you are doing as a Novo-Nordisk Oxford Fellow.
My current research project aims to identify brain changes associated with obesity and how that might be related to risk of metabolic disorders. When I mention obesity, most people would immediate think of BMI by way of definition, but it is actually incredibly diverse. Some people with a high BMI are metabolically healthy and doing great clinically. For others, it’s really easy to gain weight no matter how they control their diet, and the accumulation of visceral fat can have severe metabolic consequences. My research approaches this problem from the perspective of the brain, which might sound surprising. But we know that one of the ways GLP-1 agonists take effect is by regulating our appetite. I want to understand how specific changes in the brain of individuals with obesity might be linked to, or even cause these very different metabolic risks.
That sounds really interesting, but must come with some challenges.
Yes, indeed. One of the major challenges in our field is that science is conducted in silos. On one hand, we have massive amounts of human data, where we use non-invasive technologies such as MRI scans to characterize brains change. On the other hand, we have well-controlled experiments in mouse models looking at the genetic risk factors for obesity. But there has been limited success in translating findings across these fields to advance interventions to the preclinical stage and benefit people in need. So in my current project, we hope to bridge this gap by combining analysis of the UK biobank data with comparative analysis of brain imaging data from mouse models, as well as in vitro validation in cell lines.
What are the longer-term implications of your research?
The increased risk of non-communicable diseases associated with obesity will continue to be a critical public health concern over the next few decades. A Lancet study published a few years back showed that since the 1970s, there has been an eight-fold increase in obesity worldwide among children, with major contributions from lower- and middle-income countries. This trend is accelerating, and it acts as the primary driver for a host of metabolic dysfunctions—notably Type 2 Diabetes, which is also rising at a striking rate in young people (I had a review published on this earlier this year).
By examining the heterogeneity of obesity, and comparing data between human and mouse models, we can separate the genetic risk from the modifiable environmental factors. If we can map out these specific subgroups and understand why some people seem to have less favorable outcome when living with obesity, and how the brain plays a role in that, we can potentially introduce new therapeutic targets for managing obesity and come up with more personalized combination of interventions to reverse metabolic risk.
Your work brings together metabolism, obesity, neuroscience, and public health. How did your academic background and research interests lead you to the Novo Nordisk–Oxford Fellowship?
My PhD investigated the role of gut microbiota in Type 1 and Type 2 diabetes, focusing on the crosstalk between the host immune system and the gut microbes, so I consider myself much more of an immunologist. I had the privilege of working directly with human patient samples—mostly young children with Type 1 and adolescents with Type 2 diabetes. My day-to-day involved running immunological assays to characterize the gut microbiota or host responses and analyze the data to find markers of disease risk.
During my PhD work I became increasingly aware of the fact that both subtypes are heavily influences by the social determinants of health. Studies from different public health institutions show that individuals from less favorable socioeconomic backgrounds, and populations in lower-income countries, suffer worse outcomes. We know that part of the metabolic disease risk is modifiable, but we need interventions that actually reach people in need.
My work on the disease risk and prevention of diabetes brought me to where I am today with the Novo Nordisk Fellowship. It is the perfect bridge. I get to take my previous knowledge on diabetes and my experience working with human samples and data, while also expand my expertise into neuroscience, which is a completely new field to me. I hope this collaboration with industry will help accelerate the identification and validation of therapeutic targets, starting from identifying risk in the brain, how it communicates with other systems in the body, to reversing the risk in a clinic setting.
Can you tell us a little about the team you are working with, and the different areas of expertise within the group?
We are a team of about 15 people, and even though I started recently and still meeting new folks, it’s clear that we have a dynamic mix of engineers, experimental specialists, neuroscientists, and so it’s very exciting to be able to work in an intellectually stimulating environment where everyone has different expertise. I myself did not come from a neuroscience background – I was working in an immunology lab during my PhD – so I also find it reassuring that we all come from different backgrounds, and we all have a lot to learn.
Just as an anecdote, one of the DPhil students in our lab came from an engineer background. For her project, she needed to track over time how much food her mice were consuming. She didn't have the equipment she needed, so she built snack tracker herself that can monitor feeding continuously for 48 hours and map the data to the circadian rhythms. I’m very impressed by the hacker-and-maker mindset, which I rarely see in my peers, and I’m hoping to collaborate when I’m designing my own experiment.
What aspects of the Oxford research environment have stood out to you since arriving?
I am relatively new to the lab and the institution, so I’ll just talk about what I noticed in terms of the values here and how I perceive this to be beneficial in the long run. There seem to be a heavy emphasis on Open Science. Publisher paywalls and institutional subscriptions have imposed some barriers to share and access knowledge, and the science community has been talking about it for some time. Oxford’s push for open access can help democratize our work. There are also platforms to share our data, toolboxes, analyses, so this ensures our science is reproducible, it accelerates collaboration at a larger scale, and it allows anyone with an interest to learn about our findings.
I’m also impressed by Oxford's dedicated teams for Science Communication and Public Engagement. Because so much of our work is publicly funded, I believe that scientists have an obligation to return those findings to the public. Having the channels to help us translate our day-to-day work for the general public is so important. It’s not just about promoting the impact of our research, advancing our careers and the institution reputation, or simply sharing interesting knowledge. It’s also about bridging some of the gaps in health inequities, at least in my field of diabetes research, so we have improved health literacy in the population by telling people what we know about the factors contribution to disease risk and how might we change that. If we place enough emphasis on science communication, maybe it’ll be as important in mitigating disease risk as the experiments we do in the lab
Are there any collaborations you'd like to pursue with colleagues in Oxford?
I am supported by the Novo Nordisk-Oxford fellowship, so the project is designed to be collaborative from the ground up. One of the important motivations for me to apply to this fellowship is that it has a wide scope – we have a bit of epidemiology looking that human data at the population level, a bit of evolutionary biology and comparative biology looking at the mouse brain and perhaps even some behavioral testing, and a bit of cellular/molecular biology with cell culture and multi-omics validation. To be able to put so many pieces together we rely on experts from different fields. Our group works closely with a PI in evolutionary neuroscience who specializes in comparing brain structures across different species. We also have geneticists to guide our analysis to find causal variants, and platforms to help us characterize functions and phenotypes of cells under different treatments. Part of our in vitro validation will be completed directly on-site at the Oxford Research Centre. By gaining exposure to that streamlined, highly efficient environment, I will have the resources and mentorship to take our biological discoveries and actually advance them into the pre-clinical stage. I’m excited about all the collaborative efforts and the many possibilities of carrying out this project!
And finally, what do you see as the biggest challenges and opportunities for medical science over the next decade and how might be overcome them?
One of the biggest challenges for the medical science field over the next decade is to have a systems-level understanding of how different parts of the body's physiological systems interact with each other, and how do we rescue the breakdown of communication as best as possible in disease context. We have had a lot of advances in characterizing biological processes at different levels in recent years like spatial transcriptomics. But even if we arrive at recording, at a sub-molecular level, all the ongoing processes in the whole body, the data is usually limited in sample size. Our current understanding of the biological system, as well as the sample size, are not sufficient to inform our choice of analysis such that we make sense of all their complexities and make biological interpretations. We are going to need new computational methods to extract key features and to address the spatial/temporal dependencies, and build more efficient analytical pipelines, and we will hopefully benefit from the advances in AI models. An additional complication has to do with how we collect and store the data, how we make use of the data, and who will ultimately benefit from the data. In the future we also need to move away from animal models to robust ex vivo human models, such that we understand disease mechanisms and develop treatments in a way that is more specific to human disease contexts. As an example, we have moved from flat, 2D petri dishes to 3D organoids, which have been incredible for studying early development and tumor growth. Ultimately, we need to come up with diverse, interconnected systems that allow us to model and study more complicated diseases like diabetes, the pathophysiology of which is manifested across multiple tissues.