The yeast galactose network as a quantitative model for cellular memory

Rapidly Inducible Yeast Surface Display for Antibody Evolution with OrthoRep

We recently developed "autonomous hypermutation yeast surface display" (AHEAD), a technology that enables the rapid generation of potent and specific antibodies in yeast. AHEAD pairs yeast surface display with an error-prone orthogonal DNA replication system (OrthoRep) to continuously and rapidly mutate surface-displayed antibodies, thereby enabling enrichment for stronger binding variants through repeated rounds of cell growth and fluorescence-activated cell sorting. AHEAD currently utilizes a standard galactose induction system to drive the selective display of antibodies on the yeast surface. However, achieving maximal display levels can require up to 48 h of induction. Here we report an updated version of the AHEAD platform that utilizes a synthetic β-estradiol-induced gene expression system to regulate the surface display of antibodies and find that induction is notably faster in achieving surface display for both our AHEAD system and traditional yeast surface display from nuclear plasmids that do not hypermutate. The updated AHEAD platform was fully functional in repeated rounds of evolution to drive the rapid evolution of antibodies.

A correlative study of the genomic underpinning of virulence traits and drug tolerance of Candida auris

Candida auris is an opportunistic fungal pathogen with high mortality rates which presents a clear threat to public health. The risk of C. auris infection is high because it can colonize the body, resist antifungal treatment, and evade the immune system. The genetic mechanisms for these traits are not well known. Identifying them could lead to new targets for new treatments. To this end, we present an analysis of the genetics and gene expression patterns of C. auris carbon metabolism, drug resistance, and macrophage interaction. We chose to study two C. auris isolates simultaneously, one drug sensitive (B11220 from Clade II) and one drug resistant (B11221 from Clade III). Comparing the genomes, we confirm the previously reported finding that B11220 was missing a 12.8 kb region on chromosome VI. This region contains a gene cluster encoding proteins related to alternative sugar utilization. We show that B11221, which has the gene cluster, readily assimilates and utilizes D-galactose and L-rhamnose as compared to B11220, which harbors the deletion. B11221 exhibits increased adherence and drug resistance compared to B11220 when grown in these sugars. Transcriptomic analysis of both isolates grown on glucose or galactose showed that the gene cluster was upregulated when grown on D-galactose. These findings reinforce growing evidence of a link between metabolism and drug tolerance. B11221 resists phagocytosis by macrophages and exhibits decreased β-1,3-glucan exposure, a key determinant that allows Candida to evade the host immune system, as compared to B11220. In a transcriptomic analysis of both isolates co-cultured with macrophages, we find upregulation of genes associated with transport and transcription factors in B11221. Our studies show a positive correlation between membrane composition and immune evasion, alternate sugar utilization, and drug tolerance in C. auris.

The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis

Complex living agents consist of cells, which are themselves competent sub-agents navigating physiological and metabolic spaces. Behaviour science, evolutionary developmental biology and the field of machine intelligence all seek to understand the scaling of biological cognition: what enables individual cells to integrate their activities to result in the emergence of a novel, higher-level intelligence with large-scale goals and competencies that belong to it and not to its parts? Here, we report the results of simulations based on the TAME framework, which proposes that evolution pivoted the collective intelligence of cells during morphogenesis of the body into traditional behavioural intelligence by scaling up homeostatic competencies of cells in metabolic space. In this article, we created a minimal in silico system (two-dimensional neural cellular automata) and tested the hypothesis that evolutionary dynamics are sufficient for low-level setpoints of metabolic homeostasis in individual cells to scale up to tissue-level emergent behaviour. Our system showed the evolution of the much more complex setpoints of cell collectives (tissues) that solve a problem in morphospace: the organization of a body-wide positional information axis (the classic French flag problem in developmental biology). We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve the target morphology as well as the ability to recover from perturbation (robustness) and long-term stability (even though neither of these was directly selected for). Moreover, we observed an unexpected behaviour of sudden remodelling long after the system stabilizes. We tested this prediction in a biological system-regenerating planaria-and observed a very similar phenomenon. We propose that this system is a first step towards a quantitative understanding of how evolution scales minimal goal-directed behaviour (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.

Public good-driven release of heterogeneous resources leads to genotypic diversification of an isogenic yeast population

Understanding the basis of biological diversity remains a central problem in evolutionary biology. Using microbial systems, adaptive diversification has been studied in (a) spatially heterogeneous environments, (b) temporally segregated resources, and (c) resource specialization in a homogeneous environment. However, it is not well understood how adaptive diversification can take place in a homogeneous environment containing a single resource. Starting from an isogenic population of yeast Saccharomyces cerevisiae, we report rapid adaptive diversification, when propagated in an environment containing melibiose as the carbon source. The diversification is driven due to a public good enzyme α-galactosidase, which hydrolyzes melibiose into glucose and galactose. The diversification is driven by mutations at a single locus, in the GAL3 gene in the S. cerevisiae GAL/MEL regulon. We show that metabolic co-operation involving public resources could be an important mode of generating biological diversity. Our study demonstrates sympatric diversification of yeast starting from an isogenic population and provides detailed mechanistic insights into the factors and conditions responsible for generating and maintaining the population diversity.

Ecological memory of prior nutrient exposure in the human gut microbiome

Many ecosystems have been shown to retain a memory of past conditions, which in turn affects how they respond to future stimuli. In microbial ecosystems, community disturbance has been associated with lasting impacts on microbiome structure. However, whether microbial communities alter their response to repeated stimulus remains incompletely understood. Using the human gut microbiome as a model, we show that bacterial communities retain an "ecological memory" of past carbohydrate exposures. Memory of the prebiotic inulin was encoded within a day of supplementation among a cohort of human study participants. Using in vitro gut microbial models, we demonstrated that the strength of ecological memory scales with nutrient dose and persists for days. We found evidence that memory is seeded by transcriptional changes among primary degraders of inulin within hours of nutrient exposure, and that subsequent changes in the activity and abundance of these taxa are sufficient to enhance overall community nutrient metabolism. We also observed that ecological memory of one carbohydrate species impacts microbiome response to other carbohydrates, and that an individual's habitual exposure to dietary fiber was associated with their gut microbiome's efficiency at digesting inulin. Together, these findings suggest that the human gut microbiome's metabolic potential reflects dietary exposures over preceding days and changes within hours of exposure to a novel nutrient. The dynamics of this ecological memory also highlight the potential for intra-individual microbiome variation to affect the design and interpretation of interventions involving the gut microbiome.

Altered expression response upon repeated gene repression in single yeast cells

Cells must continuously adjust to changing environments and, thus, have evolved mechanisms allowing them to respond to repeated stimuli. While faster gene induction upon a repeated stimulus is known as reinduction memory, responses to repeated repression have been less studied so far. Here, we studied gene repression across repeated carbon source shifts in over 1,500 single Saccharomyces cerevisiae cells. By monitoring the expression of a carbon source-responsive gene, galactokinase 1 (Gal1), and fitting a mathematical model to the single-cell data, we observed a faster response upon repeated repressions at the population level. Exploiting our single-cell data and quantitative modeling approach, we discovered that the faster response is mediated by a shortened repression response delay, the estimated time between carbon source shift and Gal1 protein production termination. Interestingly, we can exclude two alternative hypotheses, i) stronger dilution because of e.g., increased proliferation, and ii) a larger fraction of repressing cells upon repeated repressions. Collectively, our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression. The computational results of our study can serve as the starting point for experimental follow-up studies.

Cells have to continuously adjust to their environment and cope with changing temperature, stress conditions, or metabolic resources. Yeast cells exposed to repeated carbon source shifts have shown to be “primed” by their first exposure, exhibiting enhanced gene expression of specific genes later on. However, how cells respond to a repeated repressive stimulus, e.g., withdrawal of metabolic resources, has been so far much less studied.

For this, we investigated the expression kinetics of a carbon source-responsive gene across repeated repressions. We measured single-cell expression and used mathematical modeling to evaluate potential causes underlying an observed faster repression response upon a repeated stimulus. Specifically, we investigated whether i) an increased dilution due to e.g., proliferation, ii) an increased fraction of repressing cells, or iii) different kinetic parameters in the repeated repression cause the observed faster response in the second repression. Leveraging quantitative mathematical model comparison, we discovered that the faster response is mediated by a shortened estimated time between carbon source shift and protein production termination at the single-cell level.

Our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression.

Do microbes have a memory? History-dependent behavior in the adaptation to variable environments

Microbes are constantly confronted with changes and challenges in their environment. A proper response to these environmental cues is needed for optimal cellular functioning and fitness. Interestingly, past exposure to environmental cues can accelerate or boost the response when this condition returns, even in daughter cells that have not directly encountered the initial cue. Moreover, this behavior is mostly epigenetic and often goes hand in hand with strong heterogeneity in the strength and speed of the response between isogenic cells of the same population, which might function as a bet-hedging strategy. In this review, we discuss examples of history-dependent behavior (HDB) or “memory,” with a specific focus on HDB in fluctuating environments. In most examples discussed, the lag time before the response to an environmental change is used as an experimentally measurable proxy for HDB. We highlight different mechanisms already implicated in HDB, and by using HDB in fluctuating carbon conditions as a case study, we showcase how the metabolic state of a cell can be a key determining factor for HDB. Finally, we consider possible evolutionary causes and consequences of such HDB.

Multiple molecular events underlie stochastic switching between 2 heritable cell states in fungi

Eukaryotic transcriptional networks are often large and contain several levels of feedback regulation. Many of these networks have the ability to generate and maintain several distinct transcriptional states across multiple cell divisions and to switch between them. In certain instances, switching between cell states is stochastic, occurring in a small subset of cells of an isogenic population in a seemingly homogenous environment. Given the scarcity and unpredictability of switching in these cases, investigating the determining molecular events is challenging. White-opaque switching in the fungal species Candida albicans is an example of stably inherited cell states that are determined by a complex transcriptional network and can serve as an experimentally accessible model system to study characteristics important for stochastic cell fate switching in eukaryotes. In standard lab media, genetically identical cells maintain their cellular identity (either "white" or "opaque") through thousands of cell divisions, and switching between the states is rare and stochastic. By isolating populations of white or opaque cells, previous studies have elucidated the many differences between the 2 stable cell states and identified a set of transcriptional regulators needed for cell type switching and maintenance of the 2 cell types. Yet, little is known about the molecular events that determine the rare, stochastic switching events that occur in single cells. We use microfluidics combined with fluorescent reporters to directly observe rare switching events between the white and opaque states. We investigate the stochastic nature of switching by beginning with white cells and monitoring the activation of Wor1, a master regulator and marker for the opaque state, in single cells and throughout cell pedigrees. Our results indicate that switching requires 2 stochastic steps; first an event occurs that predisposes a lineage of cells to switch. In the second step, some, but not all, of those predisposed cells rapidly express high levels of Wor1 and commit to the opaque state. To further understand the rapid rise in Wor1, we used a synthetic inducible system in Saccharomyces cerevisiae into which a controllable C. albicans Wor1 and a reporter for its transcriptional control region have been introduced. We document that Wor1 positive autoregulation is highly cooperative (Hill coefficient > 3), leading to rapid activation and producing an "all or none" rather than a graded response. Taken together, our results suggest that reaching a threshold level of a master regulator is sufficient to drive cell type switching in single cells and that an earlier molecular event increases the probability of reaching that threshold in certain small lineages of cells. Quantitative molecular analysis of the white-opaque circuit can serve as a model for the general understanding of complex circuits.

Microbial Adaptation to Enhance Stress Tolerance

Microbial cell factories have been widely used in the production of various chemicals. Although synthetic biology is useful in improving the cell factories, adaptation is still widely applied to enhance its complex properties. Adaptation is an important strategy for enhancing stress tolerance in microbial cell factories. Adaptation involves gradual modifications of microorganisms in a stressful environment to enhance their tolerance. During adaptation, microorganisms use different mechanisms to enhance non-preferred substrate utilization and stress tolerance, thereby improving their ability to adapt for growth and survival. In this paper, the progress on the effects of adaptation on microbial substrate utilization capacity and environmental stress tolerance are reviewed, and the mechanisms involved in enhancing microbial adaptive capacity are discussed.

Probabilistic Inference with Polymerizing Biochemical Circuits

Probabilistic inference-the process of estimating the values of unobserved variables in probabilistic models-has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially "predictive" behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing.

Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds

Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.

High-Throughput Gene Mutagenesis Screening Using Base Editing

Base editing is a CRISPR-Cas9 genome engineering tool that allows programmable mutagenesis without the creation of double-stranded breaks. Here, we describe the design and execution of large-scale base editing screens using the Target-AID base editor in yeast. Using this approach, thousands of sites can be mutated simultaneously. The effects of these mutations on fitness can be measured using a pooled growth competition assay followed by DNA sequencing of gRNAs as barcodes.

Protein acetylation regulates xylose metabolism during adaptation of Saccharomyces cerevisiae

BACKGROUND

As the second most abundant polysaccharide in nature, hemicellulose can be degraded to xylose as the feedstock for bioconversion to fuels and chemicals. To enhance xylose conversion, the engineered Saccharomyces cerevisiae with xylose metabolic pathway is usually adapted with xylose as the carbon source in the laboratory. However, the mechanism under the adaptation phenomena of the engineered strain is still unclear.

RESULTS

In this study, xylose-utilizing S. cerevisiae was constructed and used for the adaptation study. It was found that xylose consumption rate increased 1.24-fold in the second incubation of the yYST12 strain in synthetic complete-xylose medium compared with the first incubation. The study figured out that it was observed at the single-cell level that the stagnation time for xylose utilization was reduced after adaptation with xylose medium in the microfluidic device. Such transient memory of xylose metabolism after adaptation with xylose medium, named "xylose consumption memory", was observed in the strains with both xylose isomerase pathway and xylose reductase and xylitol dehydrogenase pathways. In further, the proteomic acetylation of the strains before and after adaptation was investigated, and it was revealed that H4K5 was one of the most differential acetylation sites related to xylose consumption memory of engineered S. cerevisiae. We tested 8 genes encoding acetylase or deacetylase, and it was found that the knockout of the GCN5 and HPA2 encoding acetylases enhanced the xylose consumption memory.

CONCLUSIONS

The behavior of xylose consumption memory in engineered S. cerevisiae can be successfully induced with xylose in the adaptation. H4K5Ac and two genes of GCN5 and HPA2 are related to xylose consumption memory of engineered S. cerevisiae during adaptation. This study provides valuable insights into the xylose adaptation of engineered S. cerevisiae.

Gene regulatory networks exhibit several kinds of memory: quantification of memory in biological and random transcriptional networks

Gene regulatory networks (GRNs) process important information in developmental biology and biomedicine. A key knowledge gap concerns how their responses change over time. Hypothesizing long-term changes of dynamics induced by transient prior events, we created a computational framework for defining and identifying diverse types of memory in candidate GRNs. We show that GRNs from a wide range of model systems are predicted to possess several types of memory, including Pavlovian conditioning. Associative memory offers an alternative strategy for the biomedical use of powerful drugs with undesirable side effects, and a novel approach to understanding the variability and time-dependent changes of drug action. We find evidence of natural selection favoring GRN memory. Vertebrate GRNs overall exhibit more memory than invertebrate GRNs, and memory is most prevalent in differentiated metazoan cell networks compared with undifferentiated cells. Timed stimuli are a powerful alternative for biomedical control of complex in vivo dynamics without genomic editing or transgenes.

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Gene regulatory networks' dynamics are modified by transient stimuli

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GRNs have several different types of memory, including associative conditioning

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Evolution favored GRN memory, and differentiated cells have the most memory capacity

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Training GRNs offers a novel biomedical strategy not dependent on genetic rewiring

Construction and characterization of a zinc-inducible gene expression vector in fission yeast

Gene expression vectors are useful and important tools that are commonly used in a variety of experiments, including expression of foreign genes, functional analysis of genes of interest and complementation experiments. In this study, a hybrid promoter, combining the adh1⁺ upstream activating sequence (UAS) of fission yeast and the GAL10 core promoter of budding yeast, was constructed to enable high level expression depending on the presence of zinc in culture medium for fission yeast. When the hybrid promoter was cloned on the multicopy plasmid, it was fully induced and repressed within 10 h in the presence and absence of zinc, respectively. The kinetics of induction and reduction were similar to those of the endogenous adh1⁺ mRNA. In contrast, native adh1⁺ promoter lost its tight repression in zinc-depleted condition when it was cloned on the plasmid. Because adh1⁺ UAS-specific transcription factors have not yet been identified, we identified UAS elements involved in zinc sensing by characterizing this hybrid promoter. We also found that the expression level increased by the TATA box mutation, GATAA, in the presence of zinc.

Directed evolution of the transcription factor Gal4 for development of an improved transcriptional regulation system in Saccharomyces cerevisiae

As a well-characterized eukaryotic DNA binding transcription factor, Gal4 has been extensively employed to construct controllable gene expression systems in Saccharomyces cerevisiae. The problem of insufficient inducibility arises in the constructs with multiples genes under control of GAL promoters due to the low expression level and activity of the native Gal4. In order to obtain improved transcriptional regulation systems for multi-gene pathways, Gal4 mutants with improved activity were created by directed evolution. During the preliminary screening, five positive Gal4 variants were isolated based on the lycopene-indicated high-throughput screening method. Analysis of the mutation sites revealed that the majority of positive mutations are localized in the middle homology region with unspecified function, suggesting an important role of this domain in transactivation of Gal4. Through combinatorial site-directed mutagenesis, the best variant Gal4^T406A/V413A was obtained, which successfully increased the transcription of P_GAL-driven lycopene pathway genes and led to 48 % higher lycopene accumulation relative to the wild-type Gal4. This study demonstrates the viability of modifying Gal4 activity by directed evolution for elevated expression of P_GAL-driven genes and therefore enhanced production of the target metabolite.

The Computational Boundary of a "Self": Developmental Bioelectricity Drives Multicellularity and Scale-Free Cognition

All epistemic agents physically consist of parts that must somehow comprise an integrated cognitive self. Biological individuals consist of subunits (organs, cells, and molecular networks) that are themselves complex and competent in their own native contexts. How do coherent biological Individuals result from the activity of smaller sub-agents? To understand the evolution and function of metazoan creatures' bodies and minds, it is essential to conceptually explore the origin of multicellularity and the scaling of the basal cognition of individual cells into a coherent larger organism. In this article, I synthesize ideas in cognitive science, evolutionary biology, and developmental physiology toward a hypothesis about the origin of Individuality: "Scale-Free Cognition." I propose a fundamental definition of an Individual based on the ability to pursue goals at an appropriate level of scale and organization and suggest a formalism for defining and comparing the cognitive capacities of highly diverse types of agents. Any Self is demarcated by a computational surface - the spatio-temporal boundary of events that it can measure, model, and try to affect. This surface sets a functional boundary - a cognitive "light cone" which defines the scale and limits of its cognition. I hypothesize that higher level goal-directed activity and agency, resulting in larger cognitive boundaries, evolve from the primal homeostatic drive of living things to reduce stress - the difference between current conditions and life-optimal conditions. The mechanisms of developmental bioelectricity - the ability of all cells to form electrical networks that process information - suggest a plausible set of gradual evolutionary steps that naturally lead from physiological homeostasis in single cells to memory, prediction, and ultimately complex cognitive agents, via scale-up of the basic drive of infotaxis. Recent data on the molecular mechanisms of pre-neural bioelectricity suggest a model of how increasingly sophisticated cognitive functions emerge smoothly from cell-cell communication used to guide embryogenesis and regeneration. This set of hypotheses provides a novel perspective on numerous phenomena, such as cancer, and makes several unique, testable predictions for interdisciplinary research that have implications not only for evolutionary developmental biology but also for biomedicine and perhaps artificial intelligence and exobiology.

Reverse Evolution of a Classic Gene Network in Yeast Offers a Competitive Advantage

Glucose repression is a central regulatory system in yeast that ensures the utilization of carbon sources in a highly economical manner. The galactose (GAL) metabolism network is stringently regulated by glucose repression in yeast and has been a classic system for studying gene regulation. We show here that a Saccharomyces cerevisiae (S. cerevisiae) lineage in spontaneously fermented milk has swapped all its structural GAL genes (GAL2 and the GAL7-10-1 cluster) with early diverged versions through introgression. The rewired GAL network has abolished glucose repression and conversed from a strictly inducible to a constitutive system through polygenic changes in the regulatory components of the network, including a thymine (T) to cytosine (C) and a guanine (G) to adenine (A) transition in the upstream repressing sequence (URS) sites of GAL1 and GAL4, respectively, which impair Mig1p-mediated repression, loss of function of the repressor Gal80p through a T146I substitution in the protein, and subsequent futility of GAL3. Furthermore, the milk lineage of S. cerevisiae has achieved galactose-utilization rate elevation and galactose-over-glucose preference switch through the duplication of the introgressed GAL2 and the loss of function of the main glucose transporter genes HXT6 and HXT7. In addition, we demonstrate that GAL2 requires GAL7 or GAL10 for its expression, and Gal2p likely requires Gal1p for its transportation function in the milk lineage of S. cerevisiae. We show a clear case of reverse evolution of a classic gene network for ecological adaptation and provide new insights into the regulatory model of the canonical GAL network.

On the duration of the microbial lag phase

When faced with environmental changes, microbes enter a lag phase during which cell growth is arrested, allowing cells to adapt to the new situation. The discovery of the lag phase started the field of gene regulation and led to the unraveling of underlying mechanisms. However, the factors determining the exact duration and dynamics of the lag phase remain largely elusive. Naively, one would expect that cells adapt as quickly as possible, so they can resume growth and compete with other organisms. However, recent studies show that the lag phase can last from several hours up to several days. Moreover, some cells within the same population take much longer than others, despite being genetically identical. In addition, the lag phase duration is also influenced by the past, with recent exposure to a given environment leading to a quicker adaptation when that environment returns. Genome-wide screens in Saccharomyces cerevisiae on carbon source shifts now suggest that the length of the lag phase, the heterogeneity in lag times of individual cells, and the history-dependent behavior are not determined by the time it takes to induce a few specific genes related to uptake and metabolism of a new carbon source. Instead, a major shift in general metabolism, and in particular a switch between fermentation and respiration, is the major bottleneck that determines lag duration. This suggests that there may be a fitness trade-off between complete adaptation of a cell’s metabolism to a given environment, and a short lag phase when the environment changes.

Transition between fermentation and respiration determines history-dependent behavior in fluctuating carbon sources

Cells constantly adapt to environmental fluctuations. These physiological changes require time and therefore cause a lag phase during which the cells do not function optimally. Interestingly, past exposure to an environmental condition can shorten the time needed to adapt when the condition re-occurs, even in daughter cells that never directly encountered the initial condition. Here, we use the molecular toolbox of Saccharomyces cerevisiae to systematically unravel the molecular mechanism underlying such history-dependent behavior in transitions between glucose and maltose. In contrast to previous hypotheses, the behavior does not depend on persistence of proteins involved in metabolism of a specific sugar. Instead, presence of glucose induces a gradual decline in the cells' ability to activate respiration, which is needed to metabolize alternative carbon sources. These results reveal how trans-generational transitions in central carbon metabolism generate history-dependent behavior in yeast, and provide a mechanistic framework for similar phenomena in other cell types.

Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies

Generating a comprehensive map of molecular interactions in living cells is difficult and great efforts are undertaken to infer molecular interactions from large-scale perturbation experiments. Here, we develop the analytical and numerical tools to quantify the fundamental limits for inferring transcriptional networks from gene knockout screens and introduce a network inference method that is unbiased with respect to measurement noise and scalable to large network sizes. We show that network asymmetry, knockout coverage and measurement noise are central determinants that limit prediction accuracy, whereas the knowledge about gene-specific variability among biological replicates can be used to eliminate noise-sensitive nodes and thereby boost the performance of network inference algorithms.

Reliable inference of gene interactions from perturbation experiments remains a challenge. Here, the authors quantify the upper limits of transcriptional network inference from knockout screens, identify the key determinants of accuracy, and introduce an unbiased and scalable inference algorithm.

Updated regulation curation model at the Saccharomyces Genome Database

The Saccharomyces Genome Database (SGD) provides comprehensive, integrated biological information for the budding yeast Saccharomyces cerevisiae, along with search and analysis tools to explore these data, enabling the discovery of functional relationships between sequence and gene products in fungi and higher organisms. We have recently expanded our data model for regulation curation to address regulation at the protein level in addition to transcription, and are presenting the expanded data on the ‘Regulation’ pages at SGD. These pages include a summary describing the context under which the regulator acts, manually curated and high-throughput annotations showing the regulatory relationships for that gene and a graphical visualization of its regulatory network and connected networks. For genes whose products regulate other genes or proteins, the Regulation page includes Gene Ontology enrichment analysis of the biological processes in which those targets participate. For DNA-binding transcription factors, we also provide other information relevant to their regulatory function, such as DNA binding site motifs and protein domains. As with other data types at SGD, all regulatory relationships and accompanying data are available through YeastMine, SGD’s data warehouse based on InterMine.

Database URL: http://www.yeastgenome.org

Molecular and cellular bases of adaptation to a changing environment in microorganisms

Environmental heterogeneity constitutes an evolutionary challenge for organisms. While evolutionary dynamics under variable conditions has been explored for decades, we still know relatively little about the cellular and molecular mechanisms involved. It is of paramount importance to examine these molecular bases because they may play an important role in shaping the course of evolution. In this review, we examine the diversity of adaptive mechanisms in the face of environmental changes. We exploit the recent literature on microbial systems because those have benefited the most from the recent emergence of genetic engineering and experimental evolution followed by genome sequencing. We identify four emerging trends: (i) an adaptive molecular change in a pathway often results in fitness trade-off in alternative environments but the effects are dependent on a mutation's genetic background; (ii) adaptive changes often modify transcriptional and signalling pathways; (iii) several adaptive changes may occur within the same molecular pathway but be associated with pleiotropy of different signs across environments; (iv) because of their large associated costs, macromolecular changes such as gene amplification and aneuploidy may be a rapid mechanism of adaptation in the short-term only. The course of adaptation in a variable environment, therefore, depends on the complexity of the environment but also on the molecular relationships among the genes involved and between the genes and the phenotypes under selection.

Proton-solute coupling mechanism of the maltose transporter from Saccharomyces cerevisiae

Mal11 catalyzes proton-coupled maltose transport across the plasma membrane of Saccharomyces cerevisiae. We used structure-based design of mutants and a kinetic analysis of maltose transport to determine the energy coupling mechanism of transport. We find that wildtype Mal11 is extremely well coupled and allows yeast to rapidly accumulate maltose to dangerous levels, resulting under some conditions in self-lysis. Three protonatable residues lining the central membrane-embedded cavity of Mal11 were identified as having potential roles in proton translocation. We probed the mechanistic basis for proton coupling with uphill and downhill transport assays and found that single mutants can still accumulate maltose but with a lower coupling efficiency than the wildtype. Next, we combined the individual mutations and created double and triple mutants. We found some redundancy in the functions of the acidic residues in proton coupling and that no single residue is most critical for proton coupling to maltose uptake, unlike what is usually observed in related transporters. Importantly, the triple mutants were completely uncoupled but still fully active in downhill efflux and equilibrium exchange. Together, these results depict a concerted mechanism of proton transport in Mal11 involving multiple charged residues.

Taking chances and making mistakes: non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments

Natural selection has shaped the strategies for survival and growth of microorganisms. The success of microorganisms depends not only on slow evolutionary tuning but also on the ability to adapt to unpredictable changes in their environment. In principle, adaptive strategies range from purely deterministic mechanisms to those that exploit the randomness intrinsic to many cellular and molecular processes. Depending on the environment and selective pressures, particular strategies can lie somewhere along this continuum. In recent years, non-genetic cell-to-cell differences have received a lot of attention, not least because of their potential impact on the ability of microbial populations to survive in dynamic environments. Using several examples, we describe the origins of spontaneous and induced mechanisms of phenotypic adaptation. We identify some of the commonalities of these examples and consider the potential role of chance and constraints in microbial phenotypic adaptation.

A living vector field reveals constraints on galactose network induction in yeast

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. Cellular memory of long-term glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection-a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.

Probabilistic adaptation in changing microbial environments

Microbes growing in animal host environments face fluctuations that have elements of both randomness and predictability. In the mammalian gut, fluctuations in nutrient levels and other physiological parameters are structured by the host's behavior, diet, health and microbiota composition. Microbial cells that can anticipate environmental fluctuations by exploiting this structure would likely gain a fitness advantage (by adapting their internal state in advance). We propose that the problem of adaptive growth in structured changing environments, such as the gut, can be viewed as probabilistic inference. We analyze environments that are "meta-changing": where there are changes in the way the environment fluctuates, governed by a mechanism unobservable to cells. We develop a dynamic Bayesian model of these environments and show that a real-time inference algorithm (particle filtering) for this model can be used as a microbial growth strategy implementable in molecular circuits. The growth strategy suggested by our model outperforms heuristic strategies, and points to a class of algorithms that could support real-time probabilistic inference in natural or synthetic cellular circuits.

An Orthogonal Permease-Inducer-Repressor Feedback Loop Shows Bistability

Feedback loops in biological networks, among others, enable differentiation and cell cycle progression, and increase robustness in signal transduction. In natural networks, feedback loops are often complex and intertwined, making it challenging to identify which loops are mainly responsible for an observed behavior. However, minimal synthetic replicas could allow for such identification. Here, we engineered a synthetic permease-inducer-repressor system in Saccharomyces cerevisiae to analyze if a transport-mediated positive feedback loop could be a core mechanism for the switch-like behavior in the regulation of metabolic gene networks such as the S. cerevisiae GAL system or the Escherichia coli lac operon. We characterized the synthetic circuit using deterministic and stochastic mathematical models. Similar to its natural counterparts, our synthetic system shows bistable and hysteretic behavior, and the inducer concentration range for bistability as well as the switching rates between the two stable states depend on the repressor concentration. Our results indicate that a generic permease-inducer-repressor circuit with a single feedback loop is sufficient to explain the experimentally observed bistable behavior of the natural systems. We anticipate that the approach of reimplementing natural systems with orthogonal parts to identify crucial network components is applicable to other natural systems such as signaling pathways.

The binary response of the GAL/MEL genetic switch of Saccharomyces cerevisiae is critically dependent on Gal80p-Gal4p interaction

Studies on the Saccharomyces cerevisiae GAL/MEL genetic switch have revealed that its bistability is dependent on ultrasensitivity that can be altered or abolished by disabling different combinations of nested feedback loops. In contrast, we have previously demonstrated that weakening of the interaction between Gal80p and Gal4p alone is sufficient to abolish the ultrasensitivity (Das Adhikari et al. 2014). Here, we demonstrate that altering the epistatic interaction between Gal80p and Gal4p also abolishes the bistability, and the switch response to galactose becomes graded instead of binary. However, the GAL/MEL switch of wild-type and epistatically altered strains responded in a graded fashion to melibiose. The properties of the epistatically altered strain resemble Kluyveromyces lactis, which separated from the Saccharomyces lineage 100 mya before whole-genome duplication (WGD). Based on the results reported here, we propose that epistatic interactions played a crucial role in the evolution of the fine regulation of S. cerevisiae GAL/MEL switch following WGD.

A phylogenetic analysis of macroevolutionary patterns in fermentative yeasts

When novel sources of ecological opportunity are available, physiological innovations can trigger adaptive radiations. This could be the case of yeasts (Saccharomycotina), in which an evolutionary novelty is represented by the capacity to exploit simple sugars from fruits (fermentation). During adaptive radiations, diversification and morphological evolution are predicted to slow‐down after early bursts of diversification. Here, we performed the first comparative phylogenetic analysis in yeasts, testing the “early burst” prediction on species diversification and also on traits of putative ecological relevance (cell‐size and fermentation versatility). We found that speciation rates are constant during the time‐range we considered (ca., 150 millions of years). Phylogenetic signal of both traits was significant (but lower for cell‐size), suggesting that lineages resemble each other in trait‐values. Disparity analysis suggested accelerated evolution (diversification in trait values above Brownian Motion expectations) in cell‐size. We also found a significant phylogenetic regression between cell‐size and fermentation versatility (R² = 0.10), which suggests correlated evolution between both traits. Overall, our results do not support the early burst prediction both in species and traits, but suggest a number of interesting evolutionary patterns, that warrant further exploration. For instance, we show that the Whole Genomic Duplication that affected a whole clade of yeasts, does not seems to have a statistically detectable phenotypic effect at our level of analysis. In this regard, further studies of fermentation under common‐garden conditions combined with comparative analyses are warranted.