Understanding the regulation of glucose sensing and transport in budding yeast using dynamic inputs

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Biological Sciences

Abstract

Glucose is the preferred source of energy for nearly all cells, from microbes to plants to animals, but we do not understand how cells sense and respond to changes in glucose's availability. We propose to study the sensing and uptake of glucose in budding yeast, one of the simplest organisms that has a similar structure to our own cells. Yeast has seven main transporters for glucose, and our focus is to understand why so many are necessary and how only the appropriate transporters are present at the appropriate time. To do so, we will study both populations of and individual cells and probe their behaviour in fluctuating levels of glucose to determine if particular transporters increase in number in response to either the concentration of extracellular glucose or to how that extracellular concentration changes with time. We will thus determine under which conditions each transporter is optimum and has its highest levels. Further, using mutations to disrupt sensing and mathematical modelling of our results, we will discover how the underlying biochemistry works to ensure a suitable rate of uptake of glucose through changing the types of transporters present. Our results will both address the fundamental question of how cells can regulate their import of glucose and provide quantitative measurements of the levels of the transporters that can be exploited by the biotechnology industry to, for example, increase production of valuable biological compounds.

Technical Summary

Cells live in changing environments and understanding how cells sense and respond to dynamic signals is thus of great importance. Yet we do not know what general aspects of such signals can be sensed by cells or how such sensing is performed biochemically. By studying the response of budding yeast to glucose, we will study both questions. Indeed, understanding how cells respond to glucose is fundamental because glucose is the preferred carbon and energy source for many cells. But even in highly-studied yeast, we do not know why seven major transporters for glucose exist. We propose that some transporters respond to the dynamics of glucose rather than its level and will test this hypothesis with a systems approach. First, we will survey and quantify the levels of the seven transporters at different stages of growth and with differing initial levels of glucose using plate readers and so create an "atlas" of expression. Second, we will use fluorescence microscopy and microfluidics to measure the levels of the transporters both in controlled dynamic environments and in mutants where components are deleted from the signalling network that both senses glucose and regulates the transporters. Such time-lapse data are ideal for fitting and discriminating between mathematical models, and we will develop and experimentally verify a mathematical model of glucose sensing that captures the logic driving the regulation of the transporters. Finally, we will use the model to predict the importance of each transporter for particular and dynamic extracellular conditions and confirm these predictions through the effect of that transporter on growth using competition experiments. Our results will address both how cells respond to dynamic signals and regulate their import of glucose as well as providing quantitative characterisation and mathematical models of promoters that drive expression at different stages of growth and so are of use for biotechnology and synthetic biology.

Planned Impact

We see two main groups of beneficiaries:

(i) industry producing customised gene expression;

(ii) the general public through increasing their awareness and understanding of the interdisciplinary nature of today's bioscience.

First, budding yeast has long been a standard organism in industry from traditional food manufacture to synthetic biology-based start-ups. Many of these applications exploit yeast's metabolic capabilities or indeed extend these capabilities using exogenous genes.

A challenge then for industry is determining suitable promoters to drive expression of the exogenous genes of interest, which are typically enzymes involved in the synthesis of industrially relevant chemicals.

Our research, with its emphasis on the characterisation, both experimentally and mathematically, of seven promoters that are expected to activate at different stages of the yeast growth curve can meet this challenge. Working with Prof.\ Susan Rosser, a colleague at the University of Edinburgh, who is collaborating with Croda, a U.K.-based speciality chemicals company and a member of the FTSE 100 Index, we will use our knowledge of HXT expression to enable budding yeast to efficiently synthesize saponins, chemicals of interest to Croda and which can be used as surfactants. Saponins can be toxic to yeast, and our results will be used to design promoters that express the enzymes for synthesizing saponins only once the diauxic phase of growth has been reached when numbers of cells, and so potential yields, are high. If these enzymes are produced too early in the growth curve, the toxicity of the saponins prohibitively limits both growth and yields.

Second, our research has the capacity to engage the public with its focus on fluorescence and time-lapse microscopy, which provide powerful visual tools for education and increasing scientific awareness.

One goal is to demonstrate the interdisciplinary approaches necessary to understand biological phenomena. Our research uses techniques from mathematics and informatics, as well as cell and molecular biology. Most high school and many undergraduates are not aware of interdisciplinary research with curricula still typically following traditional silos. Yet breakthroughs often come from those working at the edge of disciplines, and we aim to encourage both those interested in the physical sciences to consider research in biology and those already interested in biology to realise the value of training in the physical sciences and mathematics.

A second goal is to illustrate the importance of "blue skies" research, particularly that studies on yeast can inform on our own physiology. For example, growth of yeast in low glucose can prolong life (number of replications) in yeast cells, and such dietary restriction reduces ageing in many organisms, including primates. Another example is the study of cell-to-cell heterogeneity, which appears at first sight to be of only academic interest, but is now recognised to be, for example, fundamental for understanding anti-microbial resistance. Finally, taken together, these illustrations will underpin the importance of having an evolutionary perspective for comprehending and manipulating our world.

Our third goal is to show an example of the logic of cellular regulation: how a shift in extracellular glucose is sensed by the cell and processed to cause expression of the appropriate transporters for the new glucose environment, which we will illustrate in real time using microfluidics and by following gene expression with fluorescent protein markers.
 
Description Glucose is many organisms' preferred carbon source, and its transport is complex with budding yeast using seven different transporters.

Our results suggest that yeast excels at importing glucose. By having multiple transporters and expressing these transporters in glucose concentrations that match their affinities, yeast likely mitigate a rate-affinity tradeoff inherent to these transporters.

By combining time-lapse microscopy with time-varying concentrations of extracellular glucose, we find that levels of the transporters are history-dependent and are regulated by a push-pull system that comprises two types of repressors. As glucose rises, repression by one type of repressor is weakened or 'pulled' and repression by the other type is strengthened or 'pushed'. In falling glucose, this push-pull is reversed. With mathematical modelling and statistical inference, we show that the sensitivity of each HXT gene to these repressors explains the dynamic behaviour we observe, matching expression not only with the range of glucose concentrations that correspond to the transporter's affinity but also the levels of some transporters to whether glucose is increasing or decreasing.
Exploitation Route A promoter having the ability to respond to a decreasing but not an increasing signal could be of use in synthetic biology and biotechnology.

A better understanding of glucose transport should help in developing better strategies for metabolic engineering.
Sectors Manufacturing, including Industrial Biotechology

 
Title Analysing and meta-analysing time-series data of microbial growth and gene expression from plate readers 
Description Responding to change is a fundamental property of life, making time-series data invaluable in biology. For microbes, plate readers are a popular, convenient means to measure growth and also gene expression using fluorescent reporters. Nevertheless, the difficulties of analysing the resulting data can be a bottleneck, particularly when combining measurements from different wells and plates. Here we present omniplate, a Python module that corrects and normalises plate-reader data, estimates growth rates and fluorescence per cell as function of time, calculates errors, exports in different formats, and enables meta-analysis of multiple plates. The software corrects for autofluorescence, the optical density's non-linear dependence on the number of cells, and the effects of the media. We use omniplate to measure the Monod relationship for growth of budding yeast in raffinose, showing that raffinose is a convenient carbon source for controlling growth rates. Using fluorescent tagging, we study yeast's glucose transport. Our results are consistent with the regulation of the hexose transporter (HXT) genes being approximately bipartite: the medium and high affinity transporters are regulated by both the high affinity glucose sensor Snf3 and the kinase complex SNF1 via the repressors Mth1, Mig1, and Mig2; the low affinity transporters are predominately regulated by the low affinity sensor Rgt2 via the co-repressor Std1. We thus demonstrate that omniplate is a powerful tool for exploiting the advantages offered by time-series data in revealing biological regulation. 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
Impact No impacts yet. 
URL https://datashare.ed.ac.uk/handle/10283/4192
 
Description Developing a statistical approach to select between different mathematical models of biological phenomena 
Organisation Royal College of Surgeons in Ireland
Country Ireland 
Sector Academic/University 
PI Contribution We have provided data and a group of mathematical models to describe that data
Collaborator Contribution They have provided an algorithm for using the data to find the model that best describes the data from the group of models we provided.
Impact We have a preprint publication together on the bioRxiv.
Start Year 2019
 
Title Omniplate: a Python package for analysing time-series data from plate readers 
Description The software corrects and normalises plate-reader data, estimates growth rates and fluorescence per cell as function of time, calculates errors, exports in different formats, and enables meta-analysis of multiple plates. The software corrects for autofluorescence, the optical density's non-linear dependence on the number of cells, and the effects of the media. 
Type Of Technology Software 
Year Produced 2021 
Open Source License? Yes  
Impact No impacts yet. 
URL https://swainlab.bio.ed.ac.uk/software/omniplate
 
Description An interactive exhibit for the Future Health Hackathon in Edinburgh 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact My PDRA developed a 'patient simulator' to illustrate how genetically identical cells are a heterogeneous population of decision makers. Players had to administer the treatment in real time to a simulated infection. Inside the patient simulator is a Raspberry PI. Python code in the PI sends the controller input to an LED output. An Ordinary Differential Equation (ODE) model drives the connection. In it, there are parameters for bacterial reproduction, natural antibiotic degradation/dilution, the antibiotic's killing efficiency and whether the bacteria can inactivate the antibiotic (as in beta-lactam antibiotic resistance). For every game, random sets of parameters are chosen, such that the player never knows at start what the system is, or how the system will respond. For some of them, strikingly high levels of treatment would actually work; for more chronic infections, a temporal pattern of treatment ("a course of treatment") would be most effective to keep the patient healthy.
Year(s) Of Engagement Activity 2019