Biology subjects

Swain, P. S.

Publications and source records attributed to Swain, P. S..

13 recordsLinked to original sources

bioRxiv2024

Single-cell metabolic oscillations are pervasive and may alleviate a proteome constraint

Biological rhythms not only coordinate cellular activity with external signals, but may also enable internal coordination. The metabolic cycle in budding yeast is perhaps the most well-studied example. Historically researchers have investigated this cycle in populations growing in chemostats, but more recently time-lapse microscopy has revealed single-cell oscillations in the redox state of enzyme cofactors and in ATP levels. How to relate the results of these two types of assays is however unclear. Here we report single-cell rhythms too in intracellular pH and show that oscillations in the redox state of flavin molecules occur in auxotrophic and prototrophic strains, in nutrients favouring respiration or fermentation, and in deletion mutants for which oscillations in chemostats are either unobservable or disrupted. To explain the pervasiveness of these rhythms, we postulate that cells generate oscillations to alleviate a proteome constraint - amino acids cells use for one class of enzymes are unavailable for others. Using flux balance analysis with an enzyme-constrained genome-scale metabolic model, we show that, with a finite proteome, sequential synthesis of biomass components typically generates a shorter doubling time than synthesising components in parallel. Our results suggest that the metabolic cycle drives growth and is potentially widespread because all cells grow within a proteome constraint.

systems biology↗

bioRxiv2024

Quantifying the nuclear localisation of fluorescently tagged proteins

Cells are highly dynamic, continually responding to intra- and extracellular signals. Monitoring and measuring the response to these signals at the single-cell level is challenging. Signal transduction is fast, but reporters for downstream gene expression are typically slow, requiring fluorescent proteins to be expressed and to mature. An alternative is to fluorescently tag and then monitor the intracellular locations of transcription factors and other effectors. These proteins move in or out of the nucleus in minutes, after upstream signalling modifies their state of phosphorylation. Although such approaches are being used increasingly, there is no consensus on how to quantify the nuclear and cytoplasmic localisation of these proteins. Using budding yeast, we developed a convolutional neural network that quantifies nuclear localisation from fluorescence and, optionally, bright-field images. Focusing on the cellular response to changing glucose, we generated ground-truth data using strains with both a transcription factor and a nuclear protein tagged with fluorescent markers. We then showed that the neural network based approach outperformed seven published methods, particularly when predicting single-cell time series, which are key to determining how cells respond and adapt. Collectively, our results are conclusive -- using machine learning to automatically determine the appropriate image processing consistently outperforms ad hoc methods. Adopting such methods promises to both improve the accuracy and, with transfer learning, the consistency of single-cell analyses.

bioinformatics↗

bioRxiv2024

A co-transcriptional mechanism for tightly controlling RNA homeostasis in yeast

Transcription termination by the Nrd1-Nab3-Sen1 (NNS) complex is key in repressing pervasive transcription in Saccharomyces cerevisiae. Counterintuitively, during starvation, multiple mRNAs that are upregulated are also increasingly bound and prematurely terminated and degraded via NNS. Here we demonstrate that this NNS-mediated attenuation is important for controlling the expression and protein concentration of an evolutionarily conserved mitochondrial transporter, Pic2. Strikingly, we find that even a modest increase in Pic2 protein levels caused by defective NNS regulation has major phenotypical consequences, increasing cell volume and intracellular stress, prolonging cell cycle and decreasing growth rate. Disrupting Nab3 binding to PIC2 globally redistributed Nrd1 binding, changing the levels of other NNS-regulated transcripts. We propose that imbalances in the availability of the subunits constituting the NNS complex underlie the cell volume and cycle anomalies. Collectively our results illustrate that even subtle changes in how RNA-binding proteins interact with a single RNA substrate can cause global defects and they emphasise the crucial role of the NNS complex in preserving microbial fitness during stress. HighlightsO_LINNS regulates the expression and protein concentration of a stress-response protein-coding gene (PIC2), improving cell fitness and adaptability to environmental challenges. C_LIO_LICreating an imbalance in RNA binding of Nab3 and Nrd1 for PIC2 mRNA disturbs the homeostasis of co-regulated transcripts. C_LIO_LIEven a modest defect in NNS regulation of PIC2 elicits severe defects in cell growth, increases cell size and intracellular stress, and prolongs the cell cycle. C_LI

molecular biology↗

bioRxiv2023

The type of carbon source not the growth rate it supports can determine diauxie

How cells choose between potential carbon sources is a classic example of cellular decision-making, and we know that many organisms prioritise glucose. Yet there has been little investigation of whether other sugars are also preferred, blinkering our view of carbon sensing. Here we study eukaryotic budding yeast and its growth on mixtures of palatinose, an isomer of sucrose, with other sugars. We find that yeast prioritise galactose over palatinose, but not sucrose or fructose, despite all three of these sugars being able to support faster growth than palatinose. Our results therefore disfavour carbon flux-sensing as the sole mechanism. By using genetic perturbations and transcriptomics, we show that repression is active and through Gal4, the master regulator of the GAL regulon. Cells enforce their preference for galactose over palatinose by preventing runaway positive feedback in the MAL regulon, whose genes enable palatinose catabolism. They do so both by repressing MAL11, the gene encoding the palatinose transporter, and by first expressing the isomaltases, IMA1 and IMA5, which cleave palatinose and so prevent its intracellular concentration becoming enough to induce further MAL expression. Our results demonstrate that budding yeast actively maintain a preference for carbon sources other than glucose and that such preferences have been selected by more than differences in growth rates. They imply that carbon-sensing strategies even in unicellular organisms are more complex than previously thought.

systems biology↗

bioRxiv2023

Nunchaku: Optimally partitioning data into piece-wise linear segments

When analysing two-dimensional data sets, scientists are often interested in regions where one variable depends linearly on the other. Typically they use an ad hoc method to do so. Here we develop a statistically rigorous, Bayesian approach to infer the optimal partitioning of a data set into contiguous piece-wise linear segments. Our nunchaku algorithm is freely available. Focusing on microbial growth, we use nunchaku to identify the range of optical density where the density is linearly proportional to the number of cells and to automatically find the regions of exponential growth for both Escherichia coli and Saccharomyces cerevisiae. For budding yeast, we consequently are able to infer the Monod constant for growth on fructose. Our algorithm lends itself to automation and high throughput studies, increases reproducibility, and will facilitate data analysis for a broad range of scientists.

bioinformatics↗

bioRxiv2022

A label-free method to track individuals and lineages of budding cells

Much of biochemical regulation ultimately controls growth rate, particularly in microbes. Although time-lapse microscopy visualises cells, determining their growth rates is challenging because cells often overlap in images, particularly for those that divide asymmetrically, like Saccharomyces cerevisiae. Here we present the Birth Annotator for Budding Yeast (BABY), an algorithm to determine single-cell growth rates from label-free images. Using a convolutional neural network, BABY resolves overlaps through separating cells by size and assigns buds to mothers by identifying bud necks. BABY uses machine learning to track cells and determine lineages, estimates growth rates as the rate of change of volumes, and identifies cytokinesis by how growth varies. Using BABY and a microfluidic device, we show that bud growth is first sizer- then timer-controlled, that the nuclear concentration of Sfp1, a regulator of ribosome biogenesis, varies before the growth rate does, and that growth rate can be used for real-time control. Growth rate and fitness are strongly correlated, and BABY should therefore generate much biological insight.

systems biology↗

bioRxiv2021

The proton motive force determines Escherichia coli's robustness to extracellular pH

Maintaining intracellular homeostases is a hallmark of life, and key physiological variables, such as cytoplasmic pH, osmotic pressure, and proton motive force (PMF), are typically interdependent. Using a mathematical model, we argue that near neutral pH homeostasis implies that cells must export ions other than protons to generate physiological electrical potential across their plasma membrane. For Escherichia coli, proton:ion antiporters are the only known cation efflux pumps, and we therefore predict that principal function of antiporters is to generate an out-of-equilibrium plasma membrane potential and so maintain the PMF at the constant levels observed. Consequently, the strength of the PMF determines the range of extracellular pH over which the cell is able to preserve its near neutral cytoplasmic pH, and the non-zero PMF is needed to maintain membrane potential. In support, we concurrently measure the PMF and cytoplasmic pH in single cells and demonstrate both that decreasing the PMFs strength impairs E. colis ability to maintain its pH and that artificially collapsing the PMF destroys the out-of-equilibrium plasma membrane potential. We further predict the observed ranges of extracellular pH for which three of E. colis antiporters are expressed, through defining their cost by the rate at which they divert protons from being imported to generate ATP. Taken together, our results suggest a new perspective on bacterial electrophysiology, where cells regulate the plasma membrane potential to maintain

microbiology↗

bioRxiv2021

A push-pull system of repressors matches levels of glucose transporters to extracellular glucose in budding yeast

A common cellular task is to match gene expression dynamically to a range of concentrations of a regulatory molecule. Studying glucose transport in budding yeast, we determine mechanistically how such matching occurs for seven hexose transporters. By combining time-lapse microscopy with mathematical modelling, we find that levels of transporters are history-dependent and are regulated by a push-pull system comprising two types of repressors. Repression by these two types varies with glucose in opposite ways, and not only matches the expression of transporters by their affinity to a range of glucose concentrations, but also the expression of some to how glucose is changing. We argue that matching is favoured by a rate-affinity trade-off and that the regulatory system allows yeast to import glucose rapidly enough to starve competitors. Matching expression to a pattern of input is fundamental, and we believe that push-pull repression is widespread.

systems biology↗

bioRxiv2018

Optimally designed vs intuition-driven inputs: the study case of promoter activity modelling

Synthetic biology is an emerging engineering discipline that aims at synthesising logical circuits into cells to accomplish new functions. Despite a thriving community and some notable successes, the basic task of assembling predictable gene circuits is still a key challenge. Mathematical models are uniquely suited to help solve this issue. Yet in biology they are perceived as expensive and laborious to obtain because low-information experiments have often been used to infer model parameters. How much additional information can be gained using optimally designed experiments? To tackle this question we consider a building block in Synthetic Biology, an inducible promoter in yeast S. cerevisiae. Using in vivo data we re-fit a mathematical model for such a system; we then compare in silico the quality of the parameter estimates when model calibration is done using typical (e.g. step inputs) and optimally designed experiments. We find that Optimal Experimental Design leads to ~70% improvement in the predictive ability of the inferred models. We conclude providing suggestions on how optimally designed experiments can be implemented in vivo.

bioengineering↗

bioRxiv2018

Estimating numbers of fluorescent molecules in single cells by analysing fluctuations in photobleaching

The impact of fluorescence microscopy has been limited by the difficulties of express-ing measurements of fluorescent proteins in numbers of molecules. Absolute numbers enable the integration of results from different laboratories, empower mathematical modelling, and are the bedrock for a quantitative, predictive biology. Here we develop a general algorithm to infer numbers of molecules from fluctuations in the photobleaching of proteins tagged with Green Fluorescent Protein. To untangle measurement noise from stochastic fluctuations, we use the linear noise approximation and Kalman filtering within a framework of Bayesian inference. Not only do our results agree with biochemical measurements for multiple proteins in budding yeast, but we also provide a statistically verified model of measurement noise for fluorescence microscopes. The experiments we require are straightforward and use only a wide-field fluorescence microscope. As such, our approach has the potential to become standard for those practising quantitative fluorescence microscopy.

systems biology↗

bioRxiv2017

Distributed and dynamic intracellular organization of extracellular information

Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for ten transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: generalists (Msn2/4, Tod6/Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. Each transcription factor reports differently, and it is only their collective response that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change, and we predict that such multi-dimensional representations are common in cellular decision-making.\n\nSignificance StatementTo thrive in diverse environments, cells must represent extracellular change intracellularly despite stochastic biochemistry. Here we introduce a quantitative framework for investigating the organization of information within a cell. Combining single-cell measurements of intracellular dynamics with a new, scalable methodology for estimating mutual information between time-series and a discrete input, we demonstrate that extracellular information is encoded in the dynamics of the nuclear localization of transcription factors and that information is lost with alternative static statistics. Any one transcription factor is usually insufficient, but the collective dynamics of multiple transcription factors can represent complex extracellular change. We therefore show that a cells internal representation of its environment can be both distributed across diverse proteins and dynamically encoded.

systems biology↗

bioRxiv2017

Predicting metabolic adaptation from networks of mutational paths

Competition for substrates is a ubiquitous selection pressure faced by microbes, yet intracellular trade-offs can prevent cells from metabolizing every type of available substrate. Adaptive evolution is constrained by these trade-offs, but their consequences for the repeatability and predictability of evolution are unclear. Here we develop an eco-evolutionary model with a metabolic trade-off to generate networks of mutational paths in microbial communities and show that these networks have descriptive and predictive information about the evolving communities. We find that long-term outcomes, including community collapse, diversity, and cycling, have characteristic evolutionary dynamics that determine the entropy, or repeatability, of mutational paths. Although reliable prediction of evolutionary outcomes from environmental conditions is difficult, graph-theoretic properties of the mutational networks enable accurate prediction even from incomplete observations. In conclusion, we present a novel methodology for analyzing adaptive evolution and report that the dynamics of adaptation are a key variable for predictive success.

evolutionary biology↗

bioRxiv2017

Morphologically Constrained and Data Informed Cell Segmentation of Budding Yeast

MotivationAlthough high-content image cytometry is becoming increasingly routine, processing the large amount of data acquired during time-lapse experiments remains a challenge. The majority of approaches for automated single-cell segmentation focus on flat, uniform fields of view covered with a single layer of cells. In the increasingly popular microfluidic devices that trap individual cells for long term imaging, these conditions are not met. Consequently, most segmentation techniques perform poorly. Incorporating information about the microfluidic features, media flow and morphology of the cells can substantially improve performance, though it may constrain the generalizability of software.\n\nResultsHere we present DISCO (Data Informed Segmentation of Cell Objects), a framework for using the physical constraints imposed by microfluidic traps, the shape based morphological constraints of budding yeast and temporal information about cell growth and motion, to allow tracking and segmentation of cells in micrflouidic devices. Using manually curated data sets, we demonstrate substantial improvements in both tracking and segmentation for this approach when compared with existing software.\n\nAvailabilityThe MATLAB(R) code for the algorithm and for measuring performance is available at https://github.com/pswain/segmentation-software. The test images and the curated ground truth results used for comparing the algorithms are available at http://swainlab.bio.ed.ac.uk/.

bioinformatics↗