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

Divergence in the Saccharomyces Species' Heat Shock Response Is Indicative of Their Thermal Tolerance

The Saccharomyces species have diverged in their thermal growth profile. Both Saccharomyces cerevisiae and Saccharomyces paradoxus grow at temperatures well above the maximum growth temperature of Saccharomyces kudriavzevii and Saccharomyces uvarum but grow more poorly at lower temperatures. In response to thermal shifts, organisms activate a stress response that includes heat shock proteins involved in protein homeostasis and acquisition of thermal tolerance. To determine whether Saccharomyces species have diverged in their response to temperature, we measured changes in gene expression in response to a 12 °C increase or decrease in temperature for four Saccharomyces species and their six pairwise hybrids. To ensure coverage of subtelomeric gene families, we sequenced, assembled, and annotated a complete S. uvarum genome. In response to heat, the cryophilic species showed a stronger stress response than the thermophilic species, and the hybrids showed a mixture of parental responses that depended on the time point. After an initial strong response indicative of high thermal stress, hybrids with a thermophilic parent resolved their heat shock response to become similar to their thermophilic parent. Within the hybrids, only a small number of temperature-responsive genes showed consistent differences between alleles from the thermophilic and cryophilic species. Our results show that divergence in the heat shock response is mainly a consequence of a strain's thermal tolerance, suggesting that cellular factors that signal heat stress or resolve heat-induced changes are relevant to thermal divergence in the Saccharomyces species.

Consequences of gaining an extra chromosome

Mistakes in chromosome segregation leading to aneuploidy are the primary cause of miscarriages in humans. Excluding sex chromosomes, viable aneuploidies in humans include trisomies of chromosomes 21, 18, or 13, which cause Down, Edwards, or Patau syndromes, respectively. While individuals with trisomy 18 or 13 die soon after birth, people with Down syndrome live to adulthood but have intellectual disabilities and are prone to multiple diseases. At the cellular level, mistakes in the segregation of a single chromosome leading to a cell losing a chromosome are lethal. In contrast, the cell that gains a chromosome can survive. Several studies support the hypothesis that gaining an extra copy of a chromosome causes gene-specific phenotypes and phenotypes independent of the identity of the genes encoded within that chromosome. The latter, referred to as aneuploidy-associated phenotypes, are the focus of this review. Among the conserved aneuploidy-associated phenotypes observed in yeast and human cells are lower viability, increased gene expression, increased protein synthesis and turnover, abnormal nuclear morphology, and altered metabolism. Notably, abnormal nuclear morphology of aneuploid cells is associated with increased metabolic demand for de novo synthesis of sphingolipids. These findings reveal important insights into the possible pathological role of aneuploidy in Down syndrome. Despite the adverse effects on cell physiology, aneuploidy is a hallmark of cancer cells. Understanding how aneuploidy affects cell physiology can reveal insights into the selective pressure that aneuploid cancer cells must overcome to support unlimited proliferation.

Divergence in the Saccharomyces species' heat shock response is indicative of their thermal tolerance

The Saccharomyces species have diverged in their thermal growth profile. Both S. cerevisiae and S. paradoxus grow at temperatures well above the maximum growth temperature of S. kudriavzevii and S. uvarum, but grow more poorly at lower temperatures. In response to thermal shifts, organisms activate a stress response that includes heat shock proteins involved in protein homeostasis and acquisition of thermal tolerance. To determine whether Saccharomyces species have diverged in their response to temperature we measured changes in gene expression in response to a 12°C increase or decrease in temperature for four Saccharomyces species and their six pairwise hybrids. To ensure coverage of subtelomeric gene families we sequenced, assembled and annotated a complete S. uvarum genome. All the strains exhibited a stronger response to heat than cold treatment. In response to heat, the cryophilic species showed a stronger response than the thermophilic species. The hybrids showed a mixture of parental stress responses depending on the time point. After the initial response, hybrids with a thermophilic parent were more similar to S. cerevisiae and S. paradoxus, and the S. cerevisiae × S. paradoxus hybrid showed the weakest heat shock response. Within the hybrids a small subset of temperature responsive genes showed species specific responses but most were also hybrid specific. Our results show that divergence in the heat shock response is indicative of a strain's thermal tolerance, suggesting that cellular factors that signal heat stress or resolve heat induced changes are relevant to thermal divergence in the Saccharomyces species.

Genome-Wide Analysis of Yeast Metabolic Cycle through Metabolic Network Models Reveals Superiority of Integrated ATAC-seq Data over RNA-seq Data

Saccharomyces cerevisiae undergoes robust oscillations to regulate its physiology for adaptation and survival under nutrient-limited conditions. Environmental cues can induce rhythmic metabolic alterations in order to facilitate the coordination of dynamic metabolic behaviors. Of such metabolic processes, the yeast metabolic cycle enables adaptation of the cells to varying nutritional status through oscillations in gene expression and metabolite production levels. In this process, yeast metabolism is altered between diverse cellular states based on changing oxygen consumption levels: quiescent (reductive charging [RC]), growth (oxidative [OX]), and proliferation (reductive building [RB]) phases. We characterized metabolic alterations during the yeast metabolic cycle using a variety of approaches. Gene expression levels are widely used for condition-specific metabolic simulations, whereas the use of epigenetic information in metabolic modeling is still limited despite the clear relationship between epigenetics and metabolism. This prompted us to investigate the contribution of epigenomic information to metabolic predictions for progression of the yeast metabolic cycle. In this regard, we determined altered pathways through the prediction of regulated reactions and corresponding model genes relying on differential chromatin accessibility levels. The predicted metabolic alterations were confirmed via data analysis and literature. We subsequently utilized RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data sets in the contextualization of the yeast model. The use of ATAC-seq data considerably enhanced the predictive capability of the model. To the best of our knowledge, this is the first attempt to use genome-wide chromatin accessibility data in metabolic modeling. The preliminary results showed that epigenomic data sets can pave the way for more accurate metabolic simulations.

IMPORTANCE Dynamic chromatin organization mediates the emergence of condition-specific phenotypes in eukaryotic organisms. Saccharomyces cerevisiae can alter its metabolic profile via regulation of genome accessibility and robust transcriptional oscillations under nutrient-limited conditions. Thus, both epigenetic information and transcriptomic information are crucial in the understanding of condition-specific metabolic behavior in this organism. Based on genome-wide alterations in chromatin accessibility and transcription, we investigated the yeast metabolic cycle, which is a remarkable example of coordinated and dynamic yeast behavior. In this regard, we assessed the use of ATAC-seq and RNA-seq data sets in condition-specific metabolic modeling. To our knowledge, this is the first attempt to use chromatin accessibility data in the reconstruction of context-specific metabolic models, despite the extensive use of transcriptomic data. As a result of comparative analyses, we propose that the incorporation of epigenetic information is a promising approach in the accurate prediction of metabolic dynamics.

Analyzing Ribosome Remodeling in Health and Disease

Increasing evidence suggests that ribosomes actively regulate protein synthesis. However, much of this evidence is indirect, leaving this layer of gene regulation largely unexplored, in part due to methodological limitations. Indeed, evidence is reviewed demonstrating that commonly used methods, such as transcriptomics, are inadequate because the variability in mRNAs coding for ribosomal proteins (RP) does not necessarily correspond to RP variability. Thus protein remodeling of ribosomes should be investigated by methods that allow direct quantification of RPs, ideally of isolated ribosomes. Such methods are reviewed, focusing on mass spectrometry and emphasizing method-specific biases and approaches to control these biases. It is argued that using multiple complementary methods can help reduce the danger of interpreting reproducible systematic biases as evidence for ribosome remodeling.

Single-cell protein analysis by mass spectrometry

Human physiology and pathology arise from the coordinated interactions of diverse single cells. However, analyzing single cells has been limited by the low sensitivity and throughput of analytical methods. DNA sequencing has recently made such analysis feasible for nucleic acids but single-cell protein analysis remains limited. Mass spectrometry is the most powerful method for protein analysis, but its application to single cells faces three major challenges: efficiently delivering proteins/peptides to mass spectrometry detectors, identifying their sequences, and scaling the analysis to many thousands of single cells. These challenges have motivated corresponding solutions, including SCoPE design multiplexing and clean, automated, and miniaturized sample preparation. Synergistically applied, these solutions enable quantifying thousands of proteins across many single cells and establish a solid foundation for further advances. Building upon this foundation, the SCoPE concept will enable analyzing subcellular organelles and posttranslational modifications, while increases in multiplexing capabilities will increase the throughput and decrease cost.

Global Transcriptional Programs in Archaea Share Features with the Eukaryotic Environmental Stress Response

The environmental stress response (ESR), a global transcriptional program originally identified in yeast, is characterized by a rapid and transient transcriptional response composed of large, oppositely regulated gene clusters. Genes induced during the ESR encode core components of stress tolerance, macromolecular repair, and maintenance of homeostasis. In this review, we investigate the possibility for conservation of the ESR across the eukaryotic and archaeal domains of life. We first re-analyze existing transcriptomics data sets to illustrate that a similar transcriptional response is identifiable in Halobacterium salinarum, an archaeal model organism. To substantiate the archaeal ESR, we calculated gene-by-gene correlations, gene function enrichment, and comparison of temporal dynamics. We note reported examples of variation in the ESR across fungi, then synthesize high-level trends present in expression data of other archaeal species. In particular, we emphasize the need for additional high-throughput time series expression data to further characterize stress-responsive transcriptional programs in the Archaea. Together, this review explores an open question regarding features of global transcriptional stress response programs shared across domains of life.

Quantitative Studies for Cell-Division Cycle Control

The cell-division cycle (CDC) is driven by cyclin-dependent kinases (CDKs). Mathematical models based on molecular networks, as revealed by molecular and genetic studies, have reproduced the oscillatory behavior of CDK activity. Thus, one basic system for representing the CDC is a biochemical oscillator (CDK oscillator). However, genetically clonal cells divide with marked variability in their total duration of a single CDC round, exhibiting non-Gaussian statistical distributions. Therefore, the CDK oscillator model does not account for the statistical nature of cell-cycle control. Herein, we review quantitative studies of the statistical properties of the CDC. Over the past 70 years, studies have shown that the CDC is driven by a cluster of molecular oscillators. The CDK oscillator is coupled to transcriptional and mitochondrial metabolic oscillators, which cause deterministic chaotic dynamics for the CDC. Recent studies in animal embryos have raised the possibility that the dynamics of molecular oscillators underlying CDC control are affected by allometric volume scaling among the cellular compartments. Considering these studies, we discuss the idea that a cluster of molecular oscillators embedded in different cellular compartments coordinates cellular physiology and geometry for successful cell divisions.

Microarray Data Normalization and Robust Detection of Rhythmic Features

Data derived from microarray technologies are generally subject to various sources of noise and accordingly the raw data are pre-processed before formally analysed. Data normalization is a key pre-processing step when dealing with microarray experiments, such as circadian gene-expressions, since it removes systematic variations across arrays. A wide variety of normalization methods are available in the literature. However, from our experience in the study of rhythmic expression patterns in oscillatory systems (e.g. cell-cycle, circadian clock), the choice of the normalization method may substantially impair the identification of rhythmic genes. Hence, the identification of a gene as rhythmic could be just as an artefact of how the data were normalized. Yet, gene rhythmicity detection is crucial in modern toxicological and pharmacological studies, thus a procedure to truly identify rhythmic genes that are robust to the choice of a normalization method is required.To perform the task of detecting rhythmic features, we propose a rhythmicity measure based on bootstrap methodology to robustly identify rhythmic genes in oscillatory systems. Although our methodology can be extended to any high-throughput experiment, in this chapter, we illustrate how to apply it to a publicly available circadian clock microarray gene-expression data and give full details (both statistical and computational) so that the methodology can be used in an easy way. We will show that the choice of normalization method has very little effect on the proposed methodology since the results derived from the bootstrap-based rhythmicity measure are highly rank correlated for any pair of normalization methods considered. This suggests, on the one hand, that the rhythmicity measure proposed is robust to the choice of the normalization method, and on the other hand, that gene rhythmicity detected using this measure is potentially not a mere artefact of the normalization method used. In this way the researcher using this methodology will be protected against the possible effect of different normalizations, as the conclusions obtained will not depend so strongly on them. Additionally, the described bootstrap methodology can also be employed as a tool to simulate gene-expression participating in an oscillatory system from a reference data set.

A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data

Motivation: Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. There are numerous normalization methods available in the literature. Based on our experience, in the context of oscillatory systems, such as cell-cycle, circadian clock, etc., the choice of the normalization method may substantially impact the determination of a gene to be rhythmic. Thus rhythmicity of a gene can purely be an artifact of how the data were normalized. Since the determination of rhythmic genes is an important component of modern toxicological and pharmacological studies, it is important to determine truly rhythmic genes that are robust to the choice of a normalization method.

Results: In this paper we introduce a rhythmicity measure and a bootstrap methodology to detect rhythmic genes in an oscillatory system. Although the proposed methodology can be used for any high-throughput gene expression data, in this paper we illustrate the proposed methodology using several publicly available circadian clock microarray gene-expression datasets. We demonstrate that the choice of normalization method has very little effect on the proposed methodology. Specifically, for any pair of normalization methods considered in this paper, the resulting values of the rhythmicity measure are highly correlated. Thus it suggests that the proposed measure is robust to the choice of a normalization method. Consequently, the rhythmicity of a gene is potentially not a mere artifact of the normalization method used. Lastly, as demonstrated in the paper, the proposed bootstrap methodology can also be used for simulating data for genes participating in an oscillatory system using a reference dataset.

Availability: A user friendly code implemented in R language can be downloaded from http://www.eio.uva.es/~miguel/robustdetectionprocedure.html

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.

Post-transcriptional regulation across human tissues

Transcriptional and post-transcriptional regulation shape tissue-type-specific proteomes, but their relative contributions remain contested. Estimates of the factors determining protein levels in human tissues do not distinguish between (i) the factors determining the variability between the abundances of different proteins, i.e., mean-level-variability and, (ii) the factors determining the physiological variability of the same protein across different tissue types, i.e., across-tissues variability. We sought to estimate the contribution of transcript levels to these two orthogonal sources of variability, and found that scaled mRNA levels can account for most of the mean-level-variability but not necessarily for across-tissues variability. The reliable quantification of the latter estimate is limited by substantial measurement noise. However, protein-to-mRNA ratios exhibit substantial across-tissues variability that is functionally concerted and reproducible across different datasets, suggesting extensive post-transcriptional regulation. These results caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation to shaping tissue-type-specific proteomes.

Order restricted inference for oscillatory systems for detecting rhythmic signals

MOTIVATION

Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data. There is a great potential for misclassifying non-rhythmic as rhythmic genes and vice versa. This has been a problem of considerable interest in recent years. In this article we develop a constrained inference based methodology called Order Restricted Inference for Oscillatory Systems (ORIOS) to detect rhythmic signals. Instead of using mathematical functions (e.g. sinusoidal) to describe shape of rhythmic signals, ORIOS uses mathematical inequalities. Consequently, it is robust and not limited by the biologist's choice of the mathematical model. We studied the performance of ORIOS using simulated as well as real data obtained from mouse liver, pituitary gland and data from NIH3T3, U2OS cell lines. Our results suggest that, for a broad collection of patterns of gene expression, ORIOS has substantially higher power to detect true rhythmic genes in comparison to some popular methods, while also declaring substantially fewer non-rhythmic genes as rhythmic.

AVAILABILITY AND IMPLEMENTATION

A user friendly code implemented in R language can be downloaded from http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/peddada/index.cfm CONTACT: peddada@niehs.nih.gov.

Growth rate controls mRNA turnover in steady and non-steady states

Gene expression has been investigated in relation with growth rate in the yeast Saccharomyces cerevisiae, following different experimental strategies. The expression of some specific gene functional categories increases or decreases with growth rate. Our recently published results have unveiled that these changes in mRNA concentration with growth depend on the relative alteration of mRNA synthesis and decay, and that, in addition to this gene-specific transcriptomic signature of growth, global mRNA turnover increases with growth rate. We discuss here these results in relation with other previous and concurrent publications, and we add new evidence which indicates that growth rate controls mRNA turnover even under non-steady-state conditions.

Differences in environmental stress response among yeasts is consistent with species-specific lifestyles

Defining how organisms respond to environmental change has always been an important step toward understanding their adaptive capacity and physiology. Variation in transcription during stress has been widely described in model species, especially in the yeast Saccharomyces cerevisiae, which helped to shape general rules regarding how cells cope with environmental constraints, as well as to decipher the functions of many genes. Comparison of the environmental stress response (ESR) across species is essential to obtaining better insight into the common and species-specific features of stress defense. In this context, we explored the transcriptional landscape of the yeast Lachancea kluyveri (formerly Saccharomyces kluyveri) in response to diverse stresses, using RNA sequencing. We investigated variation in gene expression and observed a link between genetic plasticity and environmental sensitivity. We identified the ESR genes in this species and compared them to those already found in S. cerevisiae We observed common features between the two species, as well as divergence in the regulatory networks involved. Of interest, some changes were related to differences in species lifestyle. Thus we were able to decipher how adaptation to stress has evolved among different yeast species. Finally, by analyzing patterns of coexpression, we were able to propose potential biological functions for 42% of genes and also annotate 301 genes for which no function could be assigned by homology. This large data set allowed for the characterization of the evolution of gene regulation and provides an efficient tool for assessing gene function.

The importance of controlling mRNA turnover during cell proliferation

Microbial gene expression depends not only on specific regulatory mechanisms, but also on cellular growth because important global parameters, such as abundance of mRNAs and ribosomes, could be growth rate dependent. Understanding these global effects is necessary to quantitatively judge gene regulation. In the last few years, transcriptomic works in budding yeast have shown that a large fraction of its genes is coordinately regulated with growth rate. As mRNA levels depend simultaneously on synthesis and degradation rates, those studies were unable to discriminate the respective roles of both arms of the equilibrium process. We recently analyzed 80 different genomic experiments and found a positive and parallel correlation between both RNA polymerase II transcription and mRNA degradation with growth rates. Thus, the total mRNA concentration remains roughly constant. Some gene groups, however, regulate their mRNA concentration by uncoupling mRNA stability from the transcription rate. Ribosome-related genes modulate their transcription rates to increase mRNA levels under fast growth. In contrast, mitochondria-related and stress-induced genes lower mRNA levels by reducing mRNA stability or the transcription rate, respectively. We critically review here these results and analyze them in relation to their possible extrapolation to other organisms and in relation to the new questions they open.

Cell cycle Start is coupled to entry into the yeast metabolic cycle across diverse strains and growth rates

Cells have evolved oscillators with different frequencies to coordinate periodic processes. Here we studied the interaction of two oscillators, the cell division cycle (CDC) and the yeast metabolic cycle (YMC), in budding yeast. Previous work suggested that the CDC and YMC interact to separate high oxygen consumption (HOC) from DNA replication to prevent genetic damage. To test this hypothesis, we grew diverse strains in chemostat and measured DNA replication and oxygen consumption with high temporal resolution at different growth rates. Our data showed that HOC is not strictly separated from DNA replication; rather, cell cycle Start is coupled with the initiation of HOC and catabolism of storage carbohydrates. The logic of this YMC-CDC coupling may be to ensure that DNA replication and cell division occur only when sufficient cellular energy reserves have accumulated. Our results also uncovered a quantitative relationship between CDC period and YMC period across different strains. More generally, our approach shows how studies in genetically diverse strains efficiently identify robust phenotypes and steer the experimentalist away from strain-specific idiosyncrasies.

The cellular growth rate controls overall mRNA turnover, and modulates either transcription or degradation rates of particular gene regulons

We analyzed 80 different genomic experiments, and found a positive correlation between both RNA polymerase II transcription and mRNA degradation with growth rates in yeast. Thus, in spite of the marked variation in mRNA turnover, the total mRNA concentration remained approximately constant. Some genes, however, regulated their mRNA concentration by uncoupling mRNA stability from the transcription rate. Ribosome-related genes modulated their transcription rates to increase mRNA levels under fast growth. In contrast, mitochondria-related and stress-induced genes lowered mRNA levels by reducing mRNA stability or the transcription rate, respectively. We also detected these regulations within the heterogeneity of a wild-type cell population growing in optimal conditions. The transcriptomic analysis of sorted microcolonies confirmed that the growth rate dictates alternative expression programs by modulating transcription and mRNA decay.

The regulation of overall mRNA turnover keeps a constant ratio between mRNA decay and the dilution of [mRNA] caused by cellular growth. This regulation minimizes the indiscriminate transmission of mRNAs from mother to daughter cells, and favors the response capacity of the latter to physiological signals and environmental changes. We also conclude that, by uncoupling mRNA synthesis from decay, cells control the mRNA abundance of those gene regulons that characterize fast and slow growth.

Differential Stoichiometry among Core Ribosomal Proteins

Understanding the regulation and structure of ribosomes is essential to understanding protein synthesis and its dysregulation in disease. While ribosomes are believed to have a fixed stoichiometry among their core ribosomal proteins (RPs), some experiments suggest a more variable composition. Testing such variability requires direct and precise quantification of RPs. We used mass spectrometry to directly quantify RPs across monosomes and polysomes of mouse embryonic stem cells (ESC) and budding yeast. Our data show that the stoichiometry among core RPs in wild-type yeast cells and ESC depends both on the growth conditions and on the number of ribosomes bound per mRNA. Furthermore, we find that the fitness of cells with a deleted RP-gene is inversely proportional to the enrichment of the corresponding RP in polysomes. Together, our findings support the existence of ribosomes with distinct protein composition and physiological function.

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Wild-type yeast and mouse cells build ribosomes with different protein composition

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The stoichiometry among ribosomal proteins (RP) correlates to growth rate

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RP stoichiometry depends on the number of ribosomes bound per mRNA

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RP stoichiometry depends on the growth conditions

Determination of Temporal Order among the Components of an Oscillatory System

Oscillatory systems in biology are tightly regulated process where the individual components (e.g. genes) express in an orderly manner by virtue of their functions. The temporal order among the components of an oscillatory system may potentially be disrupted for various reasons (e.g. environmental factors). As a result some components of the system may go out of order or even cease to participate in the oscillatory process. In this article, we develop a novel framework to evaluate whether the temporal order is unchanged in different populations (or experimental conditions). We also develop methodology to estimate the order among the components with a suitable notion of “confidence.” Using publicly available data on S. pombe, S. cerevisiae and Homo sapiens we discover that the temporal order among the genes cdc18; mik1; hhf1; hta2; fkh2 and klp5 is evolutionarily conserved from yeast to humans.

Core oxidative stress response in Aspergillus nidulans

BACKGROUND

The b-Zip transcription factor AtfA plays a key role in regulating stress responses in the filamentous fungus Aspergillus nidulans. To identify the core regulons of AtfA, we examined genome-wide expression changes caused by various stresses in the presence/absence of AtfA using A. nidulans microarrays. We also intended to address the intriguing question regarding the existence of core environmental stress response in this important model eukaryote.

RESULTS

Examination of the genome wide expression changes caused by five different oxidative stress conditions in wild type and the atfA null mutant has identified a significant number of stereotypically regulated genes (Core Oxidative Stress Response genes). The deletion of atfA increased the oxidative stress sensitivity of A. nidulans and affected mRNA accumulation of several genes under both unstressed and stressed conditions. The numbers of genes under the AtfA control appear to be specific to a stress-type. We also found that both oxidative and salt stresses induced expression of some secondary metabolite gene clusters and the deletion of atfA enhanced the stress responsiveness of additional clusters. Moreover, certain clusters were down-regulated by the stresses tested.

CONCLUSION

Our data suggest that the observed co-regulations were most likely consequences of the overlapping physiological effects of the stressors and not of the existence of a general environmental stress response. The function of AtfA in governing various stress responses is much smaller than anticipated and/or other regulators may play a redundant or overlapping role with AtfA. Both stress inducible and stress repressive regulations of secondary metabolism seem to be frequent features in A. nidulans.

Connecting growth with gene expression: of noise and numbers

Growth is a dynamic process whereby cells accumulate mass. Growth rates of single cells are connected to RNA and protein synthesis rates, and therefore with biomolecule numbers. Noise in gene expression depends on these numbers, and is thus linked with cellular growth. Whether these global attributes of the cell participate in gene regulation is still largely unexplored. New experimental and modelling studies suggest that systemic variations in biomolecule numbers can coordinate cellular processes, including growth itself, through global regulatory feedback that acts in addition to genetic regulatory networks. Here, we review these findings and speculate on possible implications of this less appreciated layer of gene regulation for cellular physiology and adaptation to changing environments.

Lysine acetylation controls local protein conformation by influencing proline isomerization

Gene transcription responds to stress and metabolic signals to optimize growth and survival. Histone H3 (H3) lysine 4 trimethylation (K4me3) facilitates state changes, but how levels are coordinated with the environment is unclear. Here, we show that isomerization of H3 at the alanine 15-proline 16 (A15-P16) peptide bond is influenced by lysine 14 (K14) and controls gene-specific K4me3 by balancing the actions of Jhd2, the K4me3 demethylase, and Spp1, a subunit of the Set1 K4 methyltransferase complex. Acetylation at K14 favors the A15-P16trans conformation and reduces K4me3. Environmental stress-induced genes are most sensitive to the changes at K14 influencing H3 tail conformation and K4me3. By contrast, ribosomal protein genes maintain K4me3, required for their repression during stress, independently of Spp1, K14, and P16. Thus, the plasticity in control of K4me3, via signaling to K14 and isomerization at P16, informs distinct gene regulatory mechanisms and processes involving K4me3.

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H3K14 acetylation influences cis-trans isomerization at the H3A15-P16 peptide bond

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H3A15-P16trans is associated with H3K14ac and reduced global H3K4me3

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A15-P16cis-trans isomerization balances K4me3 (Set1/Spp1) and demethylation (Jhd2)

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K4me3 on RPGs is largely Spp1- and K14/P16-insensitive while ESR genes are dependent

Constant growth rate can be supported by decreasing energy flux and increasing aerobic glycolysis

Fermenting glucose in the presence of enough oxygen to support respiration, known as aerobic glycolysis, is believed to maximize growth rate. We observed increasing aerobic glycolysis during exponential growth, suggesting additional physiological roles for aerobic glycolysis. We investigated such roles in yeast batch cultures by quantifying O2 consumption, CO2 production, amino acids, mRNAs, proteins, posttranslational modifications, and stress sensitivity in the course of nine doublings at constant rate. During this course, the cells support a constant biomass-production rate with decreasing rates of respiration and ATP production but also decrease their stress resistance. As the respiration rate decreases, so do the levels of enzymes catalyzing rate-determining reactions of the tricarboxylic-acid cycle (providing NADH for respiration) and of mitochondrial folate-mediated NADPH production (required for oxidative defense). The findings demonstrate that exponential growth can represent not a single metabolic/physiological state but a continuum of changing states and that aerobic glycolysis can reduce the energy demands associated with respiratory metabolism and stress survival.

MULTI-WAY BLOCKMODELS FOR ANALYZING COORDINATED HIGH-DIMENSIONAL RESPONSES

We consider the problem of quantifying temporal coordination between multiple high-dimensional responses. We introduce a family of multi-way stochastic blockmodels suited for this problem, which avoids pre-processing steps such as binning and thresholding commonly adopted for this type of problems, in biology. We develop two inference procedures based on collapsed Gibbs sampling and variational methods. We provide a thorough evaluation of the proposed methods on simulated data, in terms of membership and blockmodel estimation, predictions out-of-sample, and run-time. We also quantify the effects of censoring procedures such as binning and thresholding on the estimation tasks. We use these models to carry out an empirical analysis of the functional mechanisms driving the coordination between gene expression and metabolite concentrations during carbon and nitrogen starvation, in S. cerevisiae.

Visible light alters yeast metabolic rhythms by inhibiting respiration

Exposure of cells to visible light in nature or in fluorescence microscopy often is considered to be relatively innocuous. However, using the yeast respiratory oscillation (YRO) as a sensitive measurement of metabolism, we find that non-UV visible light has a significant impact on yeast metabolism. Blue/green wavelengths of visible light shorten the period and dampen the amplitude of the YRO, which is an ultradian rhythm of cell metabolism and transcription. The wavelengths of light that have the greatest effect coincide with the peak absorption regions of cytochromes. Moreover, treating yeast with the electron transport inhibitor sodium azide has similar effects on the YRO as visible light. Because impairment of respiration by light would change several state variables believed to play vital roles in the YRO (e.g., oxygen tension and ATP levels), we tested oxygen's role in YRO stability and found that externally induced oxygen depletion can reset the phase of the oscillation, demonstrating that respiratory capacity plays a role in the oscillation's period and phase. Light-induced damage to the cytochromes also produces reactive oxygen species that up-regulate the oxidative stress response gene TRX2 that is involved in pathways that enable sustained growth in bright visible light. Therefore, visible light can modulate cellular rhythmicity and metabolism through unexpectedly photosensitive pathways.

Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both "red" and "white" biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype-phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

Dissociation of the H3K36 demethylase Rph1 from chromatin mediates derepression of environmental stress-response genes under genotoxic stress in Saccharomyces cerevisiae

The H3K36 demethylase Rph1 is a transcriptional repressor for stress-responsive genes in yeast. Rph1-mediated transcriptional repression is relieved by phosphorylation of Rph1, reduced Rph1 level, and dissociation of Rph1 from chromatin with genotoxic stress. Rph1 may function as a regulatory node in different stress-signaling pathways.

Cells respond to environmental signals by altering gene expression through transcription factors. Rph1 is a histone demethylase containing a Jumonji C (JmjC) domain and belongs to the C₂H₂ zinc-finger protein family. Here we investigate the regulatory network of Rph1 in yeast by expression microarray analysis. More than 75% of Rph1-regulated genes showed increased expression in the rph1-deletion mutant, suggesting that Rph1 is mainly a transcriptional repressor. The binding motif 5′-CCCCTWA-3′, which resembles the stress response element, is overrepresented in the promoters of Rph1-repressed genes. A significant proportion of Rph1-regulated genes respond to DNA damage and environmental stress. Rph1 is a labile protein, and Rad53 negatively modulates Rph1 protein level. We find that the JmjN domain is important in maintaining protein stability and the repressive effect of Rph1. Rph1 is directly associated with the promoter region of targeted genes and dissociated from chromatin before transcriptional derepression on DNA damage and oxidative stress. Of interest, the master stress-activated regulator Msn2 also regulates a subset of Rph1-repressed genes under oxidative stress. Our findings confirm the regulatory role of Rph1 as a transcriptional repressor and reveal that Rph1 might be a regulatory node connecting different signaling pathways responding to environmental stresses.

Developmental regulation of nucleolus size during Drosophila eye differentiation

When cell cycle withdrawal accompanies terminal differentiation, biosynthesis and cellular growth are likely to change also. In this study, nucleolus size was monitored during cell fate specification in the Drosophila eye imaginal disc using fibrillarin antibody labeling. Nucleolus size is an indicator of ribosome biogenesis and can correlate with cellular growth rate. Nucleolar size was reduced significantly during cell fate specification and differentiation, predominantly as eye disc cells entered a cell cycle arrest that preceded cell fate specification. This reduction in nucleolus size required Dpp and Hh signaling. A transient enlargement of the nucleolus accompanied cell division in the Second Mitotic Wave. Nucleoli continued to diminish in postmitotic cells following fate specification. These results suggest that cellular growth is regulated early in the transition from proliferating progenitor cells to terminal cell fate specification, contemporary with regulation of the cell cycle, and requiring the same extracellular signals.

Decoupling nutrient signaling from growth rate causes aerobic glycolysis and deregulation of cell size and gene expression

The nutrition and the growth rate of a cell are two interacting factors with pervasive physiological effects. Our experiments decouple these factors and demonstrate the role of a growth rate signal, independent of the actual rate of biomass increase, on gene regulation, the cell division cycle, and the switch to a respiro-fermentative metabolism.

To survive and proliferate, cells need to coordinate their metabolism, gene expression, and cell division. To understand this coordination and the consequences of its failure, we uncoupled biomass synthesis from nutrient signaling by growing, in chemostats, yeast auxotrophs for histidine, lysine, or uracil in excess of natural nutrients (i.e., sources of carbon, nitrogen, sulfur, and phosphorus), such that their growth rates (GRs) were regulated by the availability of their auxotrophic requirements. The physiological and transcriptional responses to GR changes of these cultures differed markedly from the respective responses of prototrophs whose growth-rate is regulated by the availability of natural nutrients. The data for all auxotrophs at all GRs recapitulated the features of aerobic glycolysis, fermentation despite high oxygen levels in the growth media. In addition, we discovered wide bimodal distributions of cell sizes, indicating a decoupling between the cell division cycle (CDC) and biomass production. The aerobic glycolysis was reflected in a general signature of anaerobic growth, including substantial reduction in the expression levels of mitochondrial and tricarboxylic acid genes. We also found that the magnitude of the transcriptional growth-rate response (GRR) in the auxotrophs is only 40–50% of the magnitude in prototrophs. Furthermore, the auxotrophic cultures express autophagy genes at substantially lower levels, which likely contributes to their lower viability. Our observations suggest that a GR signal, which is a function of the abundance of essential natural nutrients, regulates fermentation/respiration, the GRR, and the CDC.