Predicting cellular growth from gene expression signatures

Optimal homeostasis necessitates bistable control

Bistability is a fundamental phenomenon in nature. In biology, a number of fine properties of bistability have been identified. However, these properties are only consequences of bistability at the physiological level, which do not explain why it had to emerge during evolution. Using optimal homeostasis as the first principle, I find that bistability emerges as an indispensable control mechanism. It is the only solution to a dilemma in glucose homeostasis: high insulin efficiency is required to confer rapidness in plasma glucose clearance, whereas an insulin sparing state is required to guarantee the brain's safety during fasting. The optimality consideration renders a clear correspondence between the molecular and physiological levels. This new perspective can illuminate studies on the twin epidemics of obesity and diabetes and the corresponding intervening strategies. For example, overnutrition and sedentary lifestyle may represent sudden environmental changes that cause the lose of optimality, which may contribute to the marked rise of obesity and diabetes in our generation. Because this bistability result is independent of the parameters of the mathematical model (for which the result is quite general), some other biological systems may also use bistability to control homeostasis.

A conserved cell growth cycle can account for the environmental stress responses of divergent eukaryotes

Transitions between the two phases of the cell growth cycle can account for the environmental stress response, the growth-rate response, and the cross-protection between slow growth and various types of stress factors. It is suggested that this mechanism is conserved across budding and fission yeast and normal human cells.

The respiratory metabolic cycle in budding yeast (Saccharomyces cerevisiae) consists of two phases that are most simply defined phenomenologically: low oxygen consumption (LOC) and high oxygen consumption (HOC). Each phase is associated with the periodic expression of thousands of genes, producing oscillating patterns of gene expression found in synchronized cultures and in single cells of slowly growing unsynchronized cultures. Systematic variation in the durations of the HOC and LOC phases can account quantitatively for well-studied transcriptional responses to growth rate differences. Here we show that a similar mechanism—transitions from the HOC phase to the LOC phase—can account for much of the common environmental stress response (ESR) and for the cross-protection by a preliminary heat stress (or slow growth rate) to subsequent lethal heat stress. Similar to the budding yeast metabolic cycle, we suggest that a metabolic cycle, coupled in a similar way to the ESR, in the distantly related fission yeast, Schizosaccharomyces pombe, and in humans can explain gene expression and respiratory patterns observed in these eukaryotes. Although metabolic cycling is associated with the G0/G1 phase of the cell division cycle of slowly growing budding yeast, transcriptional cycling was detected in the G2 phase of the division cycle in fission yeast, consistent with the idea that respiratory metabolic cycling occurs during the phases of the cell division cycle associated with mass accumulation in these divergent eukaryotes.

Signal Peptidase Complex Subunit 1 and Hydroxyacyl-CoA Dehydrogenase Beta Subunit Are Suitable Reference Genes in Human Lungs

Lung cancer is a common cancer, and expression profiling can provide an accurate indication to advance the medical intervention. However, this requires the availability of stably expressed genes as reference. Recent studies had shown that genes that are stably expressed in a tissue may not be stably expressed in other tissues suggesting the need to identify stably expressed genes in each tissue for use as reference genes. DNA microarray analysis has been used to identify those reference genes with low fluctuation. Fourteen datasets with different lung conditions were employed in our study. Coefficient of variance, followed by NormFinder, was used to identify stably expressed genes. Our results showed that classical reference genes such as GAPDH and HPRT1 were highly variable; thus, they are unsuitable as reference genes. Signal peptidase complex subunit 1 (SPCS1) and hydroxyacyl-CoA dehydrogenase beta subunit (HADHB), which are involved in fundamental biochemical processes, demonstrated high expression stability suggesting their suitability in human lung cell profiling.

Genome-wide studies of mRNA synthesis and degradation in eukaryotes

In recent years, the use of genome-wide technologies has revolutionized the study of eukaryotic transcription producing results for thousands of genes at every step of mRNA life. The statistical analyses of the results for a single condition, different conditions, different transcription stages, or even between different techniques, is outlining a totally new landscape of the eukaryotic transcription process. Although most studies have been conducted in the yeast Saccharomyces cerevisiae as a model cell, others have also focused on higher eukaryotes, which can also be comparatively analyzed. The picture which emerges is that transcription is a more variable process than initially suspected, with large differences between genes at each stage of the process, from initiation to mRNA degradation, but with striking similarities for functionally related genes, indicating that all steps are coordinately regulated. This article is part of a Special Issue entitled: Nuclear Transport and RNA Processing.

Genomic insights into the different layers of gene regulation in yeast

The model organism Saccharomyces cerevisiae has allowed the development of new functional genomics techniques devoted to the study of transcription in all its stages. With these techniques, it has been possible to find interesting new mechanisms to control gene expression that act at different levels and for different gene sets apart from the known cis-trans regulation in the transcription initiation step. Here we discuss a method developed in our laboratory, Genomic Run-On, and other new methods that have recently appeared with distinct technical features. A comparison between the datasets generated by them provides interesting genomic insights into the different layers of gene regulation in eukaryotes.

Adaptation as a genome-wide autoregulatory principle in the stress response of yeast

The gene expression response of yeast to various types of stresses/perturbations shows a common functional and dynamical pattern for the vast majority of genes, characterised by a quick transient peak (affecting primarily short genes) followed by a return to the pre-stimulus level. Kinetically, this process of adaptation following the transient excursion can be modelled using a genome-wide autoregulatory mechanism by means of which yeast aims at maintaining a preferential concentration in its mRNA levels. The resulting feedback system explains well the different time constants observable in the transient response, while being in agreement with all the known experimental dynamical features. For example, it suggests that a very rapid transient can be induced also by a slowly varying concentration of the gene products.

Cell growth and cell cycle in Saccharomyces cerevisiae: basic regulatory design and protein-protein interaction network

In this review we summarize the major connections between cell growth and cell cycle in the model eukaryote Saccharomyces cerevisiae. In S. cerevisiae regulation of cell cycle progression is achieved predominantly during a narrow interval in the late G1 phase known as START (Pringle and Hartwell, 1981). At START a yeast cell integrates environmental and internal signals (such as nutrient availability, presence of pheromone, attainment of a critical size, status of the metabolic machinery) and decides whether to enter a new cell cycle or to undertake an alternative developmental program. Several signaling pathways, that act to connect the nutritional status to cellular actions, are briefly outlined. A Growth & Cycle interaction network has been manually curated. More than one fifth of the edges within the Growth & Cycle network connect Growth and Cycle proteins, indicating a strong interconnection between the processes of cell growth and cell cycle. The backbone of the Growth & Cycle network is composed of middle-degree nodes suggesting that it shares some properties with HOT networks. The development of multi-scale modeling and simulation analysis will help to elucidate relevant central features of growth and cycle as well as to identify their system-level properties. Confident collaborative efforts involving different expertises will allow to construct consensus, integrated models effectively linking the processes of cell growth and cell cycle, ultimately contributing to shed more light also on diseases in which an altered proliferation ability is observed, such as cancer.

Elemental profiling of single bacterial cells as a function of copper exposure and growth phase

The elemental composition of single cells of Nitrosomonas europaea 19718 was studied via synchrotron X-ray fluorescence microscopy (XFM) as a function of inhibition by divalent copper (Cu(II)) and batch growth phase. Based on XFM, the intracellular Cu concentrations in exponential phase cultures of N. europaea exposed to Cu(II) were statistically higher than in stationary phase cultures at the 95% confidence interval (α = 0.05). However, the impact of Cu inferred from specific oxygen uptake rate (sOUR) measurements at the two physiological states was statistically not dissimilar at the Cu(II) doses tested, except at 1000 µM Cu(II), at which exponential phase cultures were significantly more inhibited. Furthermore, the elemental composition in uninhibited exponential and stationary phase N. europaea cultures was similar. Notably, the molar fractions of Cu and Fe, relative to other elements in N. europaea cultures were statistically higher than those recently reported in Pseudomonas fluorescens possibly owing to the preponderance of metal cofactor rich catalytic enzymes (such as ammonia monooxygenase) and electron transport mechanisms in N. europaea.

Collective dynamics of gene expression in cell populations

The phenotypic state of the cell is commonly thought to be determined by the set of expressed genes. However, given the apparent complexity of genetic networks, it remains open what processes stabilize a particular phenotypic state. Moreover, it is not clear how unique is the mapping between the vector of expressed genes and the cell's phenotypic state. To gain insight on these issues, we study here the expression dynamics of metabolically essential genes in twin cell populations. We show that two yeast cell populations derived from a single steady-state mother population and exhibiting a similar growth phenotype in response to an environmental challenge, displayed diverse expression patterns of essential genes. The observed diversity in the mean expression between populations could not result from stochastic cell-to-cell variability, which would be averaged out in our large cell populations. Remarkably, within a population, sets of expressed genes exhibited coherent dynamics over many generations. Thus, the emerging gene expression patterns resulted from collective population dynamics. It suggests that in a wide range of biological contexts, gene expression reflects a self-organization process coupled to population-environment dynamics.

The Awesome Power of Yeast Evolutionary Genetics: New Genome Sequences and Strain Resources for the Saccharomyces sensu stricto Genus

High-quality, well-annotated genome sequences and standardized laboratory strains fuel experimental and evolutionary research. We present improved genome sequences of three species of Saccharomyces sensu stricto yeasts: S. bayanus var. uvarum (CBS 7001), S. kudriavzevii (IFO 1802^T and ZP 591), and S. mikatae (IFO 1815^T), and describe their comparison to the genomes of S. cerevisiae and S. paradoxus. The new sequences, derived by assembling millions of short DNA sequence reads together with previously published Sanger shotgun reads, have vastly greater long-range continuity and far fewer gaps than the previously available genome sequences. New gene predictions defined a set of 5261 protein-coding orthologs across the five most commonly studied Saccharomyces yeasts, enabling a re-examination of the tempo and mode of yeast gene evolution and improved inferences of species-specific gains and losses. To facilitate experimental investigations, we generated genetically marked, stable haploid strains for all three of these Saccharomyces species. These nearly complete genome sequences and the collection of genetically marked strains provide a valuable toolset for comparative studies of gene function, metabolism, and evolution, and render Saccharomyces sensu stricto the most experimentally tractable model genus. These resources are freely available and accessible through www.SaccharomycesSensuStricto.org.

Bacterial growth laws and their applications

Quantitative empirical relationships between cell composition and growth rate played an important role in the early days of microbiology. Gradually, the focus of the field began to shift from growth physiology to the ever more elaborate molecular mechanisms of regulation employed by the organisms. Advances in systems biology and biotechnology have renewed interest in the physiology of the cell as a whole. Furthermore, gene expression is known to be intimately coupled to the growth state of the cell. Here, we review recent efforts in characterizing such couplings, particularly the quantitative phenomenological approaches exploiting bacterial 'growth laws.' These approaches point toward underlying design principles that can guide the predictive manipulation of cell behavior in the absence of molecular details.

Coupling among growth rate response, metabolic cycle, and cell division cycle in yeast

We discovered that the relative durations of the phases of the yeast metabolic cycle change with the growth rate. These changes can explain mechanistically the transcriptional growth-rate responses of all yeast genes (25% of the genome) that we find to be the same across all studied nutrient limitations in either ethanol or glucose media.

We studied the steady-state responses to changes in growth rate of yeast when ethanol is the sole source of carbon and energy. Analysis of these data, together with data from studies where glucose was the carbon source, allowed us to distinguish a “universal” growth rate response (GRR) common to all media studied from a GRR specific to the carbon source. Genes with positive universal GRR include ribosomal, translation, and mitochondrial genes, and those with negative GRR include autophagy, vacuolar, and stress response genes. The carbon source–specific GRR genes control mitochondrial function, peroxisomes, and synthesis of vitamins and cofactors, suggesting this response may reflect the intensity of oxidative metabolism. All genes with universal GRR, which comprise 25% of the genome, are expressed periodically in the yeast metabolic cycle (YMC). We propose that the universal GRR may be accounted for by changes in the relative durations of the YMC phases. This idea is supported by oxygen consumption data from metabolically synchronized cultures with doubling times ranging from 5 to 14 h. We found that the high oxygen consumption phase of the YMC can coincide exactly with the S phase of the cell division cycle, suggesting that oxidative metabolism and DNA replication are not incompatible.

Comparison of partial and full nitrification processes applied for treating high-strength nitrogen wastewaters: microbial ecology through nitrous oxide production

The goal of this study was to compare the microbial ecology, gene expression, biokinetics, and N2O emissions from a lab-scale bioreactor operated sequentially in full-nitrification and partial-nitrification modes. Based on sequencing of 16S rRNA and ammonia monooxygenase subunit A (amoA) genes, ammonia oxidizing bacteria (AOB) populations during full- and partial-nitrification modes were distinct from one another. The concentrations of AOB (XAOB) and their respiration rates during full- and partial-nitrification modes were statistically similar, whereas the concentrations of nitrite oxidizing bacteria (XNOB) and their respiration rates declined significantly after the switch from full- to partial-nitrification. The transition from full-nitrification to partial nitrification resulted in a protracted transient spike of nitrous oxide (N2O) and nitric oxide (NO) emissions, which later stabilized. The trends in N2O and NO emissions correlated well with trends in the expression of nirK and norB genes that code for the production of these gases in AOB. Both the transient and stabilized N2O and NO emissions during partial nitrification were statistically higher than those during steady-state full-nitrification. Based on these results, partial nitrification strategies for biological nitrogen removal, although attractive for their reduced operating costs and energy demand, may need to be optimized against the higher carbon foot-print attributed to their N2O emissions.

Topology of protein interaction network shapes protein abundances and strengths of their functional and nonspecific interactions

How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous nonfunctional interactions? Here we study this problem using a first-principle model of the cell whose phenotypic traits are directly determined from its genome through biophysical properties of protein structures and binding interactions in a crowded cellular environment. The model cell includes three independent prototypical pathways, whose topologies of protein-protein interaction (PPI) subnetworks are different, but whose contributions to the cell fitness are equal. Model cells evolve through genotypic mutations and phenotypic protein copy number variations. We found a strong relationship between evolved physical-chemical properties of protein interactions and their abundances due to a "frustration" effect: Strengthening of functional interactions brings about hydrophobic interfaces, which make proteins prone to promiscuous binding. The balancing act is achieved by lowering concentrations of hub proteins while raising solubilities and abundances of functional monomers. On the basis of these principles we generated and analyzed a possible realization of the proteome-wide PPI network in yeast. In this simulation we found that high-throughput affinity capture-mass spectroscopy experiments can detect functional interactions with high fidelity only for high-abundance proteins while missing most interactions for low-abundance proteins.

Yeast cells can access distinct quiescent states

We conducted a phenotypic, transcriptional, metabolic, and genetic analysis of quiescence in yeast induced by starvation of prototrophic cells for one of three essential nutrients (glucose, nitrogen, or phosphate) and compared those results with those obtained with cells growing slowly due to nutrient limitation. These studies address two related questions: (1) Is quiescence a state distinct from any attained during mitotic growth, and (2) does the nature of quiescence differ depending on the means by which it is induced? We found that either limitation or starvation for any of the three nutrients elicits all of the physiological properties associated with quiescence, such as enhanced cell wall integrity and resistance to heat shock and oxidative stress. Moreover, the starvations result in a common transcriptional program, which is in large part a direct extrapolation of the changes that occur during slow growth. In contrast, the metabolic changes that occur upon starvation and the genetic requirements for surviving starvation differ significantly depending on the nutrient for which the cell is starved. The genes needed by cells to survive starvation do not overlap the genes that are induced upon starvation. We conclude that cells do not access a unique and discrete G(0) state, but rather are programmed, when nutrients are scarce, to prepare for a range of possible future stressors. Moreover, these survival strategies are not unique to quiescence, but are engaged by the cell in proportion to nutrient scarcity.

Interdependence of cell growth and gene expression: origins and consequences

In bacteria, the rate of cell proliferation and the level of gene expression are intimately intertwined. Elucidating these relations is important both for understanding the physiological functions of endogenous genetic circuits and for designing robust synthetic systems. We describe a phenomenological study that reveals intrinsic constraints governing the allocation of resources toward protein synthesis and other aspects of cell growth. A theory incorporating these constraints can accurately predict how cell proliferation and gene expression affect one another, quantitatively accounting for the effect of translation-inhibiting antibiotics on gene expression and the effect of gratuitous protein expression on cell growth. The use of such empirical relations, analogous to phenomenological laws, may facilitate our understanding and manipulation of complex biological systems before underlying regulatory circuits are elucidated.

Measuring growth rate in high-throughput growth phenotyping

Growth rate is an important variable and parameter in biology with a central role in evolutionary, functional genomics, and systems biology studies. In this review the pros and cons of the different technologies presently available for high-throughput measurements of growth rate are discussed. Growth rate can be measured in liquid microcultivation of individual strains, in competition between strains, as growing colonies on agar, as division of individual cells, and estimated from molecular reporters. Irrespective of methodology, statistical issues such as spatial biases and batch effects are crucial to investigate and correct for to ensure low false discovery rates. The rather low correlations between studies indicate that cross-laboratory comparison and standardization are pressing issue to assure high-quality and comparable growth-rate data.

Evolution of transcriptional regulatory networks in yeast populations

Saccharomyces cerevisiae is the most thoroughly studied eukaryote at the cellular, molecular, and genetic level. Recent boost in whole-genome sequencing, array-based allelic variation mapping, and genome-wide transcriptional profiling have unprecedentedly advanced knowledge on cell biology and evolution of this organism. It is now possible to investigate how evolution shapes the functional architecture of yeast genomes and how this architecture relates to the evolution of the regulatory networks controlling the expression of genes that make up an organism. A survey of the information on genetic and whole-genome expression variations in yeast populations shows that a significant score of gene expression variation is dependent on genotype-by-environment interaction. In some cases, large trans effects are the result of mutations in the promoters of key master regulator genes. Yet trans-variation in environmental sensor proteins appears to explain the majority of the expression patterns differentiating strains in natural populations. The challenge is now to use this information to model how individual genetic polymorphisms interact in a condition-dependent fashion to produce phenotypic change. In this study, we show how fruitful application of systems biology to the progress of science and medicine requires the use of evolution as a lens to reconstruct the hierarchical structure of regulation of biological systems. The lessons learned in yeast can be of paramount importance in advancing the application of genomics and systems biology to emerging fields including personalized medicine.

Linking community profiles, gene expression and N-removal in anammox bioreactors treating municipal anaerobic digestion reject water

Anaerobic ammonium oxidation (anammox) requires 60% less oxygen and no external organic carbon compared to conventional biological nitrogen removal (BNR). Nevertheless, full-scale installations of anammox are uncommon, primarily owing to the lack of well-established process monitoring and control strategies that result in stable anammox reactor performance. The overarching goal of this study was to develop and apply molecular biomarkers that link microbial community structure and activity to anammox process performance in a bioreactor fed with actual anaerobic digestion centrate from a full-scale operational wastewater treatment facility. Over long-term operation, Candidatus "Brocadia sp. 40" emerged as the dominant anammox population present in the reactor. There was good correspondence between reactor nitrogen removal performance and anammox bacterial concentrations. During the period of reactor operation, there was also a marked shift in biomass morphology from discrete cells to granular aggregates, which was paralleled by a shift also to more stable nitrogen removal and the succession and establishment of bacteria related to the Chlorobi/Bacteroidetes superfamily. Based on batch assays, hydrazine oxidoreductase (hzo) expression and concentrations of the 16S-23S rRNA intergenic spacer region (ISR) were good quantitative biomarkers of oxygen- and nitrite-mediated inhibition. When applied to a continuous anammox reactor, both molecular biomarkers show promise as monitoring tools for "predicting" reactor performance.

Nutrient control of eukaryote cell growth: a systems biology study in yeast

Background

To elucidate the biological processes affected by changes in growth rate and nutrient availability, we have performed a comprehensive analysis of the transcriptome, proteome and metabolome responses of chemostat cultures of the yeast, Saccharomyces cerevisiae, growing at a range of growth rates and in four different nutrient-limiting conditions.

Results

We find significant changes in expression for many genes in each of the four nutrient-limited conditions tested. We also observe several processes that respond differently to changes in growth rate and are specific to each nutrient-limiting condition. These include carbohydrate storage, mitochondrial function, ribosome synthesis, and phosphate transport. Integrating transcriptome data with proteome measurements allows us to identify previously unrecognized examples of post-transcriptional regulation in response to both nutrient and growth-rate signals.

Conclusions

Our results emphasize the unique properties of carbon metabolism and the carbon substrate, the limitation of which induces significant changes in gene regulation at the transcriptional and post-transcriptional level, as well as altering how many genes respond to growth rate. By comparison, the responses to growth limitation by other nutrients involve a smaller set of genes that participate in specific pathways.

See associated commentary http://www.biomedcentral.com/1741-7007/8/62

Metabolic cycling in single yeast cells from unsynchronized steady-state populations limited on glucose or phosphate

Oscillations in patterns of expression of a large fraction of yeast genes are associated with the "metabolic cycle," usually seen only in prestarved, continuous cultures of yeast. We used FISH of mRNA in individual cells to test the hypothesis that these oscillations happen in single cells drawn from unsynchronized cultures growing exponentially in chemostats. Gene-expression data from synchronized cultures were used to predict coincident appearance of mRNAs from pairs of genes in the unsynchronized cells. Quantitative analysis of the FISH results shows that individual unsynchronized cells growing slowly because of glucose limitation or phosphate limitation show the predicted oscillations. We conclude that the yeast metabolic cycle is an intrinsic property of yeast metabolism and does not depend on either synchronization or external limitation of growth by the carbon source.

Growth landscape formed by perception and import of glucose in yeast

An important challenge in systems biology is to quantitatively describe microbial growth using a few measurable parameters that capture the essence of this complex phenomenon. Two key events at the cell membrane – extracellular glucose sensing and uptake – initiate the budding yeast’s growth on glucose. However, conventional growth models focus almost exclusively on glucose uptake. Here we present results from growth-rate experiments that cannot be explained by focusing on glucose uptake alone. By imposing a glucose uptake rate independent of the sensed extracellular glucose level, we show that despite increasing both the sensed glucose concentration and uptake rate, the cell’s growth rate can decrease or even approach zero. We resolve this puzzle by showing that the interaction between glucose perception and import, not their individual actions, determines the central features of growth and characterize this interaction using a quantitative model. Disrupting this interaction by knocking out two key glucose sensors significantly changes the cell’s growth rate, yet uptake rates are unchanged. This is due to a decrease in burden that glucose perception places on the cells. Our work shows that glucose perception and import are separate and pivotal modules of yeast growth whose interplay can be precisely tuned and measured.

Coordination of gene expression with growth rate: a feedback or a feed-forward strategy?

In the budding yeast, a large fraction of genes is coordinately regulated with growth rate. We argue that this correlation does not reflect a direct feedback from growth rate to gene expression. Rather, what appears to be a response to growth rate is dominated by environmental sensing. External parameters, such as nutrition or temperature, feed-forward to define gene expression pattern that is tuned to the evolutionary-predicted growth rate. While such a feed-forward strategy requires fine-tuning of signaling mechanisms, and is limited in the range of environments that can be monitored, it enables advanced preparation to physiological changes that predictably occur following environmental switching. The capacity to anticipate and prepare for changing conditions was probably a major selection force during yeast evolution.

Harnessing gene expression to identify the genetic basis of drug resistance

The advent of cost-effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants interact with the environment. We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug-free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene-drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug.

Slow growth induces heat-shock resistance in normal and respiratory-deficient yeast

Yeast cells respond to a variety of environmental stresses, including heat shock and growth limitation. There is considerable overlap in these responses both from the point of view of gene expression patterns and cross-protection for survival. We performed experiments in which cells growing at different steady-state growth rates in chemostats were subjected to a short heat pulse. Gene expression patterns allowed us to partition genes whose expression responds to heat shock into subsets of genes that also respond to slow growth rate and those that do not. We found also that the degree of induction and repression of genes that respond to stress is generally weaker in respiratory deficient mutants, suggesting a role for increased respiratory activity in the apparent stress response to slow growth. Consistent with our gene expression results in wild-type cells, we found that cells growing more slowly are cross-protected for heat shock, i.e., better able to survive a lethal heat challenge. Surprisingly, however, we found no difference in cross-protection between respiratory-deficient and wild-type cells, suggesting induction of heat resistance at low growth rates is independent of respiratory activity, even though many of the changes in gene expression are not.