Single-cell study links metabolism with nutrient signaling and reveals sources of variability

The tricalbin family of membrane contact site tethers is involved in the transcriptional responses of Saccharomyces cerevisiae to glucose

Cellular organelles maintain areas of close apposition with other organelles at which the cytosolic gap in between them is reduced to a minimum. These membrane contact sites (MCS) are vital for organelle communication and are formed by molecular tethers that physically connect opposing membranes. Although many regulatory pathways are known to converge at MCS, a link between MCS and transcriptional regulation-the primary mechanism through which cells adapt their metabolism to environmental cues-remains largely elusive. In this study, we performed RNA-sequencing on Saccharomyces cerevisiae cells lacking tricalbin proteins (Tcb1, Tcb2, and Tcb3), a family of tethering proteins that connect the endoplasmic reticulum with the plasma membrane and Golgi, to investigate if gene expression is altered when MCS are disrupted. Our results indicate that in the tcb1Δ2Δ3Δ strain, pathways responsive to a high-glucose environment, including glycolysis, fermentation, amino acid synthesis, and low-affinity glucose uptake, are upregulated. Conversely, pathways crucial during glucose depletion, such as the tricarboxylic acid cycle, respiration, high-affinity glucose uptake, and amino acid uptake are downregulated. In addition, we demonstrate that the altered gene expression of tcb1Δ2Δ3Δ in glucose metabolism correlates with increased growth, glucose consumption, CO2 production, and ethanol generation. In conclusion, our findings reveal that tricalbin protein deletion induces a shift in gene expression patterns mimicking cellular responses to a high-glucose environment. This suggests that MCS play a role in sensing and signaling pathways that modulate gene transcription in response to glucose availability.

Exploring carbon source related localization and phosphorylation in the Snf1/Mig1 network using population and single cell-based approaches

The AMPK/SNF1 pathway governs energy balance in eukaryotic cells, notably influencing glucose de-repression. In S. cerevisiae, Snf1 is phosphorylated and hence activated upon glucose depletion. This activation is required but is not sufficient for mediating glucose de-repression, indicating further glucose-dependent regulation mechanisms. Employing fluorescence recovery after photobleaching (FRAP) in conjunction with non-linear mixed effects modelling, we explore the spatial dynamics of Snf1 as well as the relationship between Snf1 phosphorylation and its target Mig1 controlled by hexose sugars. Our results suggest that inactivation of Snf1 modulates Mig1 localization and that the kinetic of Snf1 localization to the nucleus is modulated by the presence of non-fermentable carbon sources. Our data offer insight into the true complexity of regulation of this central signaling pathway in orchestrating cellular responses to fluctuating environmental cues. These insights not only expand our understanding of glucose homeostasis but also pave the way for further studies evaluating the importance of Snf1 localization in relation to its phosphorylation state and regulation of downstream targets.

Nuclear Functions of KaeA, a Subunit of the KEOPS Complex in Aspergillus nidulans

Kae1 is a subunit of the highly evolutionarily conserved KEOPS/EKC complex, which is involved in universal (t6A₃₇) tRNA modification. Several reports have discussed the participation of this complex in transcription regulation in yeast and human cells, including our previous observations of KaeA, an Aspergillus nidulans homologue of Kae1p. The aim of this project was to confirm the role of KaeA in transcription, employing high-throughput transcriptomic (RNA-Seq and ChIP-Seq) and proteomic (LC-MS) analysis. We confirmed that KaeA is a subunit of the KEOPS complex in A. nidulans. An analysis of kaeA19 and kaeA25 mutants showed that, although the (t6A₃₇) tRNA modification is unaffected in both mutants, they reveal significantly altered transcriptomes compared to the wild type. The finding that KaeA is localized in chromatin and identifying its protein partners allows us to postulate an additional nuclear function for the protein. Our data shed light on the universal bi-functional role of this factor and proves that the activity of this protein is not limited to tRNA modification in cytoplasm, but also affects the transcriptional activity of a number of nuclear genes. Data are available via the NCBI’s GEO database under identifiers GSE206830 (RNA-Seq) and GSE206874 (ChIP-Seq), and via ProteomeXchange with identifier PXD034554 (proteomic).

Live cell microscopy: From image to insight

Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.

Scalable and flexible inference framework for stochastic dynamic single-cell models

Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability.

Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae

Saccharomyces cerevisiae has a sophisticated signalling system that plays a crucial role in cellular adaptation to changing environments. The SNF1 pathway regulates energy homeostasis upon glucose derepression; hence, it plays an important role in various processes, such as metabolism, cell cycle and autophagy. To unravel its behaviour, SNF1 signalling has been extensively studied. However, the pathway components are strongly interconnected and inconstant; therefore, elucidating its dynamic behaviour based on experimental data only is challenging. To tackle this complexity, systems biology approaches have been successfully employed. This review summarizes the progress, advantages and disadvantages of the available mathematical modelling frameworks covering Boolean, dynamic kinetic, single-cell models, which have been used to study processes and phenomena ranging from crosstalks to sources of cell-to-cell variability in the context of SNF1 signalling. Based on the lessons from existing models, we further discuss how to develop a consensus dynamic mechanistic model of the entire SNF1 pathway that can provide novel insights into the dynamics of nutrient signalling.

D-Xylose Sensing in Saccharomyces cerevisiae: Insights from D-Glucose Signaling and Native D-Xylose Utilizers

Extension of the substrate range is among one of the metabolic engineering goals for microorganisms used in biotechnological processes because it enables the use of a wide range of raw materials as substrates. One of the most prominent examples is the engineering of baker's yeast Saccharomyces cerevisiae for the utilization of d-xylose, a five-carbon sugar found in high abundance in lignocellulosic biomass and a key substrate to achieve good process economy in chemical production from renewable and non-edible plant feedstocks. Despite many excellent engineering strategies that have allowed recombinant S. cerevisiae to ferment d-xylose to ethanol at high yields, the consumption rate of d-xylose is still significantly lower than that of its preferred sugar d-glucose. In mixed d-glucose/d-xylose cultivations, d-xylose is only utilized after d-glucose depletion, which leads to prolonged process times and added costs. Due to this limitation, the response on d-xylose in the native sugar signaling pathways has emerged as a promising next-level engineering target. Here we review the current status of the knowledge of the response of S. cerevisiae signaling pathways to d-xylose. To do this, we first summarize the response of the native sensing and signaling pathways in S. cerevisiae to d-glucose (the preferred sugar of the yeast). Using the d-glucose case as a point of reference, we then proceed to discuss the known signaling response to d-xylose in S. cerevisiae and current attempts of improving the response by signaling engineering using native targets and synthetic (non-native) regulatory circuits.

AMPK Phosphorylation Is Controlled by Glucose Transport Rate in a PKA-Independent Manner

To achieve growth, microbial organisms must cope with stresses and adapt to the environment, exploiting the available nutrients with the highest efficiency. In Saccharomyces cerevisiae, Ras/PKA and Snf1/AMPK pathways regulate cellular metabolism according to the supply of glucose, alternatively supporting fermentation or mitochondrial respiration. Many reports have highlighted crosstalk between these two pathways, even without providing a comprehensive mechanism of regulation. Here, we show that glucose-dependent inactivation of Snf1/AMPK is independent from the Ras/PKA pathway. Decoupling glucose uptake rate from glucose concentration, we highlight a strong coordination between glycolytic metabolism and Snf1/AMPK, with an inverse correlation between Snf1/AMPK phosphorylation state and glucose uptake rate, regardless of glucose concentration in the medium. Despite fructose-1,6-bisphosphate (F1,6BP) being proposed as a glycolytic flux sensor, we demonstrate that glucose-6-phosphate (G6P), and not F1,6BP, is involved in the control of Snf1/AMPK phosphorylation state. Altogether, this study supports a model by which Snf1/AMPK senses glucose flux independently from PKA activity, and thanks to conversion of glucose into G6P.

A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

The interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer, and neurological disorders. Therefore, unraveling the role of nutrients as signaling molecules and metabolites together with their interconnectivity may provide a deeper understanding of how these conditions occur. Both signaling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition, current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism in yeast cells, we developed a hybrid model, combining a Boolean module, describing the main pathways of glucose and nitrogen signaling, and an enzyme-constrained model accounting for the central carbon metabolism of Saccharomyces cerevisiae, using a regulatory network as a link. The resulting hybrid model was able to capture a diverse utalization of isoenzymes and to our knowledge outperforms constraint-based models in the prediction of individual enzymes for both respiratory and mixed metabolism. The model showed that during fermentation, enzyme utilization has a major contribution in governing protein allocation, while in low glucose conditions robustness and control are prioritized. In addition, the model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression, as well as regulatory effects associated with lifespan increase during caloric restriction. Overall, we show that our hybrid model provides a comprehensive framework for the study of the non-trivial effects of the interplay between signaling and metabolism, suggesting connections between the Snf1 signaling pathways and processes that have been related to chronological lifespan of yeast cells.

Fine-Tuning of Energy Levels Regulates SUC2 via a SNF1-Dependent Feedback Loop

Nutrient sensing pathways are playing an important role in cellular response to different energy levels. In budding yeast, Saccharomyces cerevisiae, the sucrose non-fermenting protein kinase complex SNF1 is a master regulator of energy homeostasis. It is affected by multiple inputs, among which energy levels is the most prominent. Cells which are exposed to a switch in carbon source availability display a change in the gene expression machinery. It has been shown that the magnitude of the change varies from cell to cell. In a glucose rich environment Snf1/Mig1 pathway represses the expression of its downstream target, such as SUC2. However, upon glucose depletion SNF1 is activated which leads to an increase in SUC2 expression. Our single cell experiments indicate that upon starvation, gene expression pattern of SUC2 shows rapid increase followed by a decrease to initial state with high cell-to-cell variability. The mechanism behind this behavior is currently unknown. In this work we study the long-term behavior of the Snf1/Mig1 pathway upon glucose starvation with a microfluidics and non-linear mixed effect modeling approach. We show a negative feedback mechanism, involving Snf1 and Reg1, which reduces SUC2 expression after the initial strong activation. Snf1 kinase activity plays a key role in this feedback mechanism. Our systems biology approach proposes a negative feedback mechanism that works through the SNF1 complex and is controlled by energy levels. We further show that Reg1 likely is involved in the negative feedback mechanism.

Generalized size scaling of metabolic rates based on single-cell measurements with freshwater phytoplankton

Kleiber's law describes the scaling of metabolic rate with body size across several orders of magnitude in size and across taxa and is widely regarded as a fundamental law in biology. The physiological origins of Kleiber's law are still debated and generalizations of the law accounting for deviations from the scaling behavior have been proposed. Most theoretical and experimental studies of Kleiber's law, however, have focused on the relationship between the average body size of a species and its mean metabolic rate, neglecting intraspecific variation of these 2 traits. Here, we propose a theoretical characterization of such variation and report on proof-of-concept experiments with freshwater phytoplankton supporting such framework. We performed joint measurements at the single-cell level of cell volume and nitrogen/carbon uptake rates, as proxies of metabolic rates, of 3 phytoplankton species using nanoscale secondary ion mass spectrometry (NanoSIMS) and stable isotope labeling. Common scaling features of the distribution of nutrient uptake rates and cell volume are found to hold across 3 orders of magnitude in cell size. Once individual measurements of cell volume and nutrient uptake rate within a species are appropriately rescaled by a function of the average cell volume within each species, we find that intraspecific distributions of cell volume and metabolic rates collapse onto a universal curve. Based on the experimental results, this work provides the building blocks for a generalized form of Kleiber's law incorporating intraspecific, correlated variations of nutrient-uptake rates and body sizes.

Recent advances in microfluidic platforms for single-cell analysis in cancer biology, diagnosis and therapy

Understanding molecular, cellular, genetic and functional heterogeneity of tumors at the single-cell level has become a major challenge for cancer research. The microfluidic technique has emerged as an important tool that offers advantages in analyzing single-cells with the capability to integrate time-consuming and labour-intensive experimental procedures such as single-cell capture into a single microdevice at ease and in a high-throughput fashion. Single-cell manipulation and analysis can be implemented within a multi-functional microfluidic device for various applications in cancer research. Here, we present recent advances of microfluidic devices for single-cell analysis pertaining to cancer biology, diagnostics, and therapeutics. We first concisely introduce various microfluidic platforms used for single-cell analysis, followed with different microfluidic techniques for single-cell manipulation. Then, we highlight their various applications in cancer research, with an emphasis on cancer biology, diagnosis, and therapy. Current limitations and prospective trends of microfluidic single-cell analysis are discussed at the end.

Extreme Osmotolerance and Halotolerance in Food-Relevant Yeasts and the Role of Glycerol-Dependent Cell Individuality

Osmotolerance or halotolerance are used to describe resistance to sugars and salt, or only salt, respectively. Here, a comprehensive screen of more than 600 different yeast isolates revealed that osmosensitive species were equally affected by NaCl and glucose. However, the relative toxicity of salt became increasingly prominent in more osmoresistant species. We confirmed that growth inhibition by glucose in a laboratory strain of Saccharomyces cerevisiae occurred at a lower water activity (A_w) than by salt (NaCl), and pre-growth in high levels of glucose or salt gave enhanced cross-resistance to either. Salt toxicity was largely due to osmotic stress but with an additive enhancement due to effects of the relevant cation. Almost all of the yeast isolates from the screen were also noted to exhibit hetero-resistance to both salt and sugar, whereby high concentrations restricted growth to a small minority of cells within the clonal populations. Rare resistant colonies required growth for up to 28 days to become visible. This cell individuality was more marked with salt than sugar, a possible further reflection of the ion toxicity effect. In both cases, heteroresistance in S. cerevisiae was strikingly dependent on the GPD1 gene product, important for glycerol synthesis. In contrast, a tps1Δ deletant impaired for trehalose showed altered MIC but no change in heteroresistance. Effects on heteroresistance were evident in chronic (but not acute) salt or glucose stress, particularly relevant to growth on low A_w foods. The study reports diverse osmotolerance and halotolerance phenotypes and heteroresistance across an extensive panel of yeast isolates, and indicates that Gpd1-dependent glycerol synthesis is a key determinant enabling growth of rare yeast subpopulations at low A_w, brought about by glucose and in particular salt.

Carbon Catabolite Repression in Filamentous Fungi

Carbon Catabolite Repression (CCR) has fascinated scientists and researchers around the globe for the past few decades. This important mechanism allows preferential utilization of an energy-efficient and readily available carbon source over relatively less easily accessible carbon sources. This mechanism helps microorganisms to obtain maximum amount of glucose in order to keep pace with their metabolism. Microorganisms assimilate glucose and highly favorable sugars before switching to less-favored sources of carbon such as organic acids and alcohols. In CCR of filamentous fungi, CreA acts as a transcription factor, which is regulated to some extent by ubiquitination. CreD-HulA ubiquitination ligase complex helps in CreA ubiquitination, while CreB-CreC deubiquitination (DUB) complex removes ubiquitin from CreA, which causes its activation. CCR of fungi also involves some very crucial elements such as Hexokinases, cAMP, Protein Kinase (PKA), Ras proteins, G protein-coupled receptor (GPCR), Adenylate cyclase, RcoA and SnfA. Thorough study of molecular mechanism of CCR is important for understanding growth, conidiation, virulence and survival of filamentous fungi. This review is a comprehensive revision of the regulation of CCR in filamentous fungi as well as an updated summary of key regulators, regulation of different CCR-dependent mechanisms and its impact on various physical characteristics of filamentous fungi.