Predicting cellular growth from gene expression signatures

Selected humanization of yeast U1 snRNP leads to global suppression of pre-mRNA splicing and mitochondrial dysfunction in the budding yeast

The recognition of 5' splice site (5' ss) is one of the earliest steps of pre-mRNA splicing. To better understand the mechanism and regulation of 5' ss recognition, we selectively humanized components of the yeast U1 snRNP to reveal the function of these components in 5' ss recognition and splicing. We targeted U1C and Luc7, two proteins that interact with and stabilize the yeast U1 (yU1) snRNA and the 5' ss RNA duplex. We replaced the Zinc-Finger (ZnF) domain of yU1C with its human counterpart, which resulted in cold-sensitive growth phenotype and moderate splicing defects. Next, we added an auxin-inducible degron to yLuc7 protein and found that Luc7-depleted yU1 snRNP resulted in the concomitant loss of PRP40 and Snu71 (two other essential yeast U1 snRNP proteins), and further biochemical analyses suggest a model of how these three proteins interact with each other in the U1 snRNP. The loss of these proteins resulted in a significant growth retardation accompanied by a global suppression of pre-mRNA splicing. The splicing suppression led to mitochondrial dysfunction as revealed by a release of Fe 2+ into the growth medium and an induction of mitochondrial reactive oxygen species. Together, these observations indicate that the human U1C ZnF can substitute that of yeast, Luc7 is essential for the incorporation of the Luc7-Prp40-Snu71 trimer into yeast U1 snRNP, and splicing plays a major role in the regulation of mitochondria function in yeast.

Advances in S. cerevisiae Engineering for Xylose Fermentation and Biofuel Production: Balancing Growth, Metabolism, and Defense

Genetically engineering microorganisms to produce chemicals has changed the industrialized world. The budding yeast Saccharomyces cerevisiae is frequently used in industry due to its genetic tractability and unique metabolic capabilities. S. cerevisiae has been engineered to produce novel compounds from diverse sugars found in lignocellulosic biomass, including pentose sugars, like xylose, not recognized by the organism. Engineering high flux toward novel compounds has proved to be more challenging than anticipated since simply introducing pathway components is often not enough. Several studies show that the rewiring of upstream signaling is required to direct products toward pathways of interest, but doing so can diminish stress tolerance, which is important in industrial conditions. As an example of these challenges, we reviewed S. cerevisiae engineering efforts, enabling anaerobic xylose fermentation as a model system and showcasing the regulatory interplay's controlling growth, metabolism, and stress defense. Enabling xylose fermentation in S. cerevisiae requires the introduction of several key metabolic enzymes but also regulatory rewiring of three signaling pathways at the intersection of the growth and stress defense responses: the RAS/PKA, Snf1, and high osmolarity glycerol (HOG) pathways. The current studies reviewed here suggest the modulation of global signaling pathways should be adopted into biorefinery microbial engineering pipelines to increase efficient product yields.

PKA regulatory subunit Bcy1 couples growth, lipid metabolism, and fermentation during anaerobic xylose growth in Saccharomyces cerevisiae

Organisms have evolved elaborate physiological pathways that regulate growth, proliferation, metabolism, and stress response. These pathways must be properly coordinated to elicit the appropriate response to an ever-changing environment. While individual pathways have been well studied in a variety of model systems, there remains much to uncover about how pathways are integrated to produce systemic changes in a cell, especially in dynamic conditions. We previously showed that deletion of Protein Kinase A (PKA) regulatory subunit BCY1 can decouple growth and metabolism in Saccharomyces cerevisiae engineered for anaerobic xylose fermentation, allowing for robust fermentation in the absence of division. This provides an opportunity to understand how PKA signaling normally coordinates these processes. Here, we integrated transcriptomic, lipidomic, and phospho-proteomic responses upon a glucose to xylose shift across a series of strains with different genetic mutations promoting either coupled or decoupled xylose-dependent growth and metabolism. Together, results suggested that defects in lipid homeostasis limit growth in the bcy1Δ strain despite robust metabolism. To further understand this mechanism, we performed adaptive laboratory evolutions to re-evolve coupled growth and metabolism in the bcy1Δ parental strain. The evolved strain harbored mutations in PKA subunit TPK1 and lipid regulator OPI1, among other genes, and evolved changes in lipid profiles and gene expression. Deletion of the evolved opi1 gene partially reverted the strain's phenotype to the bcy1Δ parent, with reduced growth and robust xylose fermentation. We suggest several models for how cells coordinate growth, metabolism, and other responses in budding yeast and how restructuring these processes enables anaerobic xylose utilization.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Multimodal regularised linear models with flux balance analysis for mechanistic integration of omics data

MOTIVATION

High-throughput biological data, thanks to technological advances, have become cheaper to collect, leading to the availability of vast amounts of omic data of different types. In parallel, the in silico reconstruction and modelling of metabolic systems is now acknowledged as a key tool to complement experimental data on a large scale. The integration of these model- and data-driven information is therefore emerging as a new challenge in systems biology, with no clear guidance on how to better take advantage of the inherent multi-source and multi-omic nature of these data types while preserving mechanistic interpretation.

RESULTS

Here we investigate different regularisation techniques for high-dimensional data derived from the integration of gene expression profiles with metabolic flux data, extracted from strain-specific metabolic models, to improve cellular growth rate predictions. To this end, we propose ad-hoc extensions of previous regularisation frameworks including group, view-specific and principal component regularisation, and experimentally compare them using data from 1,143 Saccharomyces cerevisiae strains. We observe a divergence between methods in terms of regression accuracy and integration effectiveness based on the type of regularisation employed. In multi-omic regression tasks, when learning from experimental and model-generated omic data, our results demonstrate the competitiveness and ease of interpretation of multimodal regularised linear models compared to data-hungry methods based on neural networks.

AVAILABILITY

All data, models, and code produced in this work are available on GitHub at https://github.com/Angione-Lab/HybridGroupIPFLasso_pc2Lasso.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Paralogs in the PKA Regulon Traveled Different Evolutionary Routes to Divergent Expression in Budding Yeast

Functional divergence of duplicate genes, or paralogs, is an important driver of novelty in evolution. In the model yeast Saccharomyces cerevisiae, there are 547 paralog gene pairs that survive from an interspecies Whole Genome Hybridization (WGH) that occurred ~100MYA. In this work, we report that ~1/6th (110) of these WGH paralogs pairs (or ohnologs) are differentially expressed with a striking pattern upon Protein Kinase A (PKA) inhibition. One member of each pair in this group has low basal expression that increases upon PKA inhibition, while the other has moderate and unchanging expression. For these genes, expression of orthologs upon PKA inhibition in the non-WGH species Kluyveromyces lactis and for PKA-related stresses in other budding yeasts shows unchanging expression, suggesting that lack of responsiveness to PKA was likely the typical ancestral phenotype prior to duplication. Promoter sequence analysis across related budding yeast species further revealed that the subsequent emergence of PKA-dependence took different evolutionary routes. In some examples, regulation by PKA and differential expression appears to have arisen following the WGH, while in others, regulation by PKA appears to have arisen in one of the two parental lineages prior to the WGH. More broadly, our results illustrate the unique opportunities presented by a WGH event for generating functional divergence by bringing together two parental lineages with separately evolved regulation into one species. We propose that functional divergence of two ohnologs can be facilitated through such regulatory divergence.

Genome-scale reconstruction of Gcn4/ATF4 networks driving a growth program

Growth and starvation are considered opposite ends of a spectrum. To sustain growth, cells use coordinated gene expression programs and manage biomolecule supply in order to match the demands of metabolism and translation. Global growth programs complement increased ribosomal biogenesis with sufficient carbon metabolism, amino acid and nucleotide biosynthesis. How these resources are collectively managed is a fundamental question. The role of the Gcn4/ATF4 transcription factor has been best studied in contexts where cells encounter amino acid starvation. However, high Gcn4 activity has been observed in contexts of rapid cell proliferation, and the roles of Gcn4 in such growth contexts are unclear. Here, using a methionine-induced growth program in yeast, we show that Gcn4/ATF4 is the fulcrum that maintains metabolic supply in order to sustain translation outputs. By integrating matched transcriptome and ChIP-Seq analysis, we decipher genome-wide direct and indirect roles for Gcn4 in this growth program. Genes that enable metabolic precursor biosynthesis indispensably require Gcn4; contrastingly ribosomal genes are partly repressed by Gcn4. Gcn4 directly binds promoter-regions and transcribes a subset of metabolic genes, particularly driving lysine and arginine biosynthesis. Gcn4 also globally represses lysine and arginine enriched transcripts, which include genes encoding the translation machinery. The Gcn4 dependent lysine and arginine supply thereby maintains the synthesis of the translation machinery. This is required to maintain translation capacity. Gcn4 consequently enables metabolic-precursor supply to bolster protein synthesis, and drive a growth program. Thus, we illustrate how growth and starvation outcomes are both controlled using the same Gcn4 transcriptional outputs that function in distinct contexts.

Taming fitness: Organism-environment interdependencies preclude long-term fitness forecasting

Fitness is a central but notoriously vexing concept in evolutionary biology. The propensity interpretation of fitness is often regarded as the least problematic account for fitness. It ties an individual's fitness to a probabilistic capacity to produce offspring. Fitness has a clear causal role in evolutionary dynamics under this account. Nevertheless, the propensity interpretation faces its share of problems. We discuss three of these. We first show that a single scalar value is an incomplete summary of a propensity. Second, we argue that the widespread method of "abstracting away" environmental idiosyncrasies by averaging over reproductive output in different environments is not a valid approach when environmental changes are irreversible. Third, we point out that expanding the range of applicability for fitness measures by averaging over more environments or longer time scales (so as to ensure environmental reversibility) reduces one's ability to distinguish selectively relevant differences among individuals because of mutation and eco-evolutionary feedbacks. This series of problems leads us to conclude that a general value of fitness that is both explanatory and predictive cannot be attained. We advocate for the use of propensity-compatible methods, such as adaptive dynamics, which can accommodate these difficulties.

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants

Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the 630 genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.

Integrated TORC1 and PKA signaling control the temporal activation of glucose-induced gene expression in yeast

The growth rate of a yeast cell is controlled by the target of rapamycin kinase complex I (TORC1) and cAMP-dependent protein kinase (PKA) pathways. To determine how TORC1 and PKA cooperate to regulate cell growth, we performed temporal analysis of gene expression in yeast switched from a non-fermentable substrate, to glucose, in the presence and absence of TORC1 and PKA inhibitors. Quantitative analysis of these data reveals that PKA drives the expression of key cell growth genes during transitions into, and out of, the rapid growth state in glucose, while TORC1 is important for the steady-state expression of the same genes. This circuit design may enable yeast to set an exact growth rate based on the abundance of internal metabolites such as amino acids, via TORC1, but also adapt rapidly to changes in external nutrients, such as glucose, via PKA.

Predicting growth rate from gene expression

Growth rate is one of the most important and most complex phenotypic characteristics of unicellular microorganisms, which determines the genetic mutations that dominate at the population level, and ultimately whether the population will survive. Translating changes at the genetic level to their growth-rate consequences remains a subject of intense interest, since such a mapping could rationally direct experiments to optimize antibiotic efficacy or bioreactor productivity. In this work, we directly map transcriptional profiles to growth rates by gathering published gene-expression data from Escherichia coli and Saccharomyces cerevisiae with corresponding growth-rate measurements. Using a machine-learning technique called k-nearest-neighbors regression, we build a model which predicts growth rate from gene expression. By exploiting the correlated nature of gene expression and sparsifying the model, we capture 81% of the variance in growth rate of the E. coli dataset, while reducing the number of features from >4,000 to 9. In S. cerevisiae, we account for 89% of the variance in growth rate, while reducing from >5,500 dimensions to 18. Such a model provides a basis for selecting successful strategies from among the combinatorial number of experimental possibilities when attempting to optimize complex phenotypic traits like growth rate.

Predicting bacterial growth conditions from mRNA and protein abundances

Cells respond to changing nutrient availability and external stresses by altering the expression of individual genes. Condition-specific gene expression patterns may thus provide a promising and low-cost route to quantifying the presence of various small molecules, toxins, or species-interactions in natural environments. However, whether gene expression signatures alone can predict individual environmental growth conditions remains an open question. Here, we used machine learning to predict 16 closely-related growth conditions using 155 datasets of E. coli transcript and protein abundances. We show that models are able to discriminate between different environmental features with a relatively high degree of accuracy. We observed a small but significant increase in model accuracy by combining transcriptome and proteome-level data, and we show that measurements from stationary phase cells typically provide less useful information for discriminating between conditions as compared to exponentially growing populations. Nevertheless, with sufficient training data, gene expression measurements from a single species are capable of distinguishing between environmental conditions that are separated by a single environmental variable.

Data integration and predictive modeling methods for multi-omics datasets

We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.

Cell Size and Growth Rate Are Modulated by TORC2-Dependent Signals

The size of all cells, from bacteria to vertebrates, is proportional to the growth rate set by nutrient availability, but the underlying mechanisms are unknown. Here, we show that nutrients modulate cell size and growth rate via the TORC2 signaling network in budding yeast. An important function of the TORC2 network is to modulate synthesis of ceramide lipids, which play roles in signaling. TORC2-dependent control of ceramide signaling strongly influences both cell size and growth rate. Thus, cells that cannot make ceramides fail to modulate their growth rate or size in response to changes in nutrients. PP2A associated with the Rts1 regulatory subunit (PP2A^Rts1) is embedded in a feedback loop that controls TORC2 signaling and helps set the level of TORC2 signaling to match nutrient availability. Together, the data suggest a model in which growth rate and cell size are mechanistically linked by ceramide-dependent signals arising from the TORC2 network.

The Genotype-Phenotype Relationships in the Light of Natural Selection

Although any genotype–phenotype relationships are a result of evolution, little is known about how natural selection and neutral drift, two distinct driving forces of evolution, operate to shape the relationships. By analyzing ∼500 yeast quantitative traits, we reveal a basic “supervisor–worker” gene architecture underlying a trait. Supervisors are often identified by “perturbational” approaches (such as gene deletion), whereas workers, which usually show small and statistically insignificant deletion effects, are tracked primarily by “observational” approaches that examine the correlation between gene activity and trait value across a number of conditions. Accordingly, supervisors provide most of the genetic understandings of the trait whereas workers provide rich mechanistic understandings. Further analyses suggest that most observed supervisor–worker interactions may evolve largely neutrally, resulting in pervasive between-worker epistasis that suppresses the tractability of workers. In contrast, a fraction of supervisors are recruited/maintained by natural selection to build worker co-expression, boosting the tractability of workers. Thus, by revealing a supervisor–worker gene architecture underlying complex traits, the opposite roles of natural selection versus neutral drift in shaping the gene architecture, and the complementary strengths of the perturbational and observational research strategies in characterizing the gene architecture, this study may lay a new conceptual foundation for understanding the molecular basis of complex traits.

Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies

Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted proliferation rates are associated with patient survival and cancer stage and show a strong heterogeneity in proliferative capacity within and across different cancer panels. We also show how the obtained proliferation rates can be incorporated into genome-scale metabolic reconstructions to obtain the metabolic fluxes for more than 3000 cancer samples that identified specific metabolic liabilities for nine cancer panels. Here we found that affected pathways coincided with the literature, with pentose phosphate pathway, retinol, and branched-chain amino acid metabolism being the most panel-specific alterations and fatty acid metabolism and ROS detoxification showing homogeneous metabolic activities across all cancer panels. The presented strategy has potential applications in personalized medicine since it can leverage gene expression signatures for cell line based prediction of additional metabolic properties which might help in constraining personalized metabolic models and improve the identification of metabolic alterations in cancer for individual patients.

Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery.

Multi-omics data integration is a great challenge. Here, the authors compile a database of E. coli proteomics, transcriptomics, metabolomics and fluxomics data to train models of recurrent neural network and constrained regression, enabling prediction of bacterial responses to perturbations.

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.

Rapid evolutionary adaptation to growth on an 'unfamiliar' carbon source

Background

Cells constantly adapt to changes in their environment. When environment shifts between conditions that were previously encountered during the course of evolution, evolutionary-programmed responses are possible. Cells, however, may also encounter a new environment to which a novel response is required. To characterize the first steps in adaptation to a novel condition, we studied budding yeast growth on xylulose, a sugar that is very rarely found in the wild.

Results

We previously reported that growth on xylulose induces the expression of amino acid biosynthesis genes in multiple natural yeast isolates. This induction occurs despite the presence of amino acids in the growth medium and is a unique response to xylulose, not triggered by naturally available carbon sources. Propagating these strains for ~300 generations on xylulose significantly improved their growth rate. Notably, the most significant change in gene expression was the loss of amino acid biosynthesis gene induction. Furthermore, the reduction in amino-acid biosynthesis gene expression on xylulose was tightly correlated with the improvement in growth rate, suggesting that internal depletion of amino-acids presented a major bottleneck limiting growth in xylulose.

Conclusions

We discuss the possible implications of our results for explaining how cells maintain the balance between supply and demand of amino acids during growth in evolutionary ‘familiar’ vs. ‘novel’ conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3010-x) contains supplementary material, which is available to authorized users.

Efflux Pump Control Alters Synthetic Gene Circuit Function

Synthetic biology aims to design new biological systems for predefined purposes, such as the controlled secretion of biofuels, pharmaceuticals, or other chemicals. Synthetic gene circuits regulating an efflux pump from the ATP-binding cassette (ABC) protein family could achieve this. However, ABC efflux pumps can also drive out intracellular inducer molecules that control the gene circuits. This will introduce an implicit feedback that could alter gene circuit function in ways that are poorly understood. Here, we used two synthetic gene circuits inducible by tetracycline family molecules to regulate the expression of a yeast ABC pump (Pdr5p) that pumps out the inducer. Pdr5p altered the dose-responses of the original gene circuits substantially in Saccharomyces cerevisiae. While one aspect of the change could be attributed to the efflux pumping function of Pdr5p, another aspect remained unexplained. Quantitative modeling indicated that reduced regulator gene expression in addition to efflux pump function could fully explain the altered dose-responses. These predictions were validated experimentally. Overall, we highlight how efflux pumps can alter gene circuit dynamics and demonstrate the utility of mathematical modeling in understanding synthetic gene circuit function in new circumstances.

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.

Steady-state and dynamic gene expression programs in Saccharomyces cerevisiae in response to variation in environmental nitrogen

Steady-state and transiently perturbed nitrogen-limited chemostats show that nitrogen abundance is a primary signal controlling nitrogen-responsive gene expression. When cells experience an increase in nitrogen, some transcripts are rapidly degraded, suggesting that accelerated mRNA degradation contributes to remodeling of gene expression.

Cell growth rate is regulated in response to the abundance and molecular form of essential nutrients. In Saccharomyces cerevisiae (budding yeast), the molecular form of environmental nitrogen is a major determinant of cell growth rate, supporting growth rates that vary at least threefold. Transcriptional control of nitrogen use is mediated in large part by nitrogen catabolite repression (NCR), which results in the repression of specific transcripts in the presence of a preferred nitrogen source that supports a fast growth rate, such as glutamine, that are otherwise expressed in the presence of a nonpreferred nitrogen source, such as proline, which supports a slower growth rate. Differential expression of the NCR regulon and additional nitrogen-responsive genes results in >500 transcripts that are differentially expressed in cells growing in the presence of different nitrogen sources in batch cultures. Here we find that in growth rate–controlled cultures using nitrogen-limited chemostats, gene expression programs are strikingly similar regardless of nitrogen source. NCR expression is derepressed in all nitrogen-limiting chemostat conditions regardless of nitrogen source, and in these conditions, only 34 transcripts exhibit nitrogen source–specific differential gene expression. Addition of either the preferred nitrogen source, glutamine, or the nonpreferred nitrogen source, proline, to cells growing in nitrogen-limited chemostats results in rapid, dose-dependent repression of the NCR regulon. Using a novel means of computational normalization to compare global gene expression programs in steady-state and dynamic conditions, we find evidence that the addition of nitrogen to nitrogen-limited cells results in the transient overproduction of transcripts required for protein translation. Simultaneously, we find that that accelerated mRNA degradation underlies the rapid clearing of a subset of transcripts, which is most pronounced for the highly expressed NCR-regulated permease genes GAP1, MEP2, DAL5, PUT4, and DIP5. Our results reveal novel aspects of nitrogen-regulated gene expression and highlight the need for a quantitative approach to study how the cell coordinates protein translation and nitrogen assimilation to optimize cell growth in different environments.

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.

Dominance from the perspective of gene-gene and gene-chemical interactions

In this study, we used genetic interaction (GI) and gene-chemical interaction (GCI) data to compare mutations with different dominance phenotypes. Our analysis focused primarily on Saccharomyces cerevisiae, where haploinsufficient genes (HI; genes with dominant loss-of-function mutations) were found to be participating in gene expression processes, namely, the translation and regulation of gene transcription. Non-ribosomal HI genes (mainly regulators of gene transcription) were found to have more GIs and GCIs than haplosufficient (HS) genes. Several properties seem to lead to the enrichment of interactions, most notably, the following: importance, pleiotropy, gene expression level and gene expression variation. Importantly, after these properties were appropriately considered in the analysis, the correlation between dominance and GI/GCI degrees was still observed. Strikingly, for the GCIs of heterozygous strains, haploinsufficiency was the only property significantly correlated with the number of GCIs. We found ribosomal HI genes to be depleted in GIs/GCIs. This finding can be explained by their high variation in gene expression under different genetic backgrounds and environmental conditions. We observed the same distributions of GIs among non-ribosomal HI, ribosomal HI and HS genes in three other species: Schizosaccharomyces pombe, Drosophila melanogaster and Homo sapiens. One potentially interesting exception was the lack of significant differences in the degree of GIs between non-ribosomal HI and HS genes in Schizosaccharomyces pombe.

Chromatin Dynamics and the RNA Exosome Function in Concert to Regulate Transcriptional Homeostasis

The histone variant H2A.Z is a hallmark of nucleosomes flanking promoters of protein-coding genes and is often found in nucleosomes that carry lysine 56-acetylated histone H3 (H3-K56Ac), a mark that promotes replication-independent nucleosome turnover. Here, we find that H3-K56Ac promotes RNA polymerase II occupancy at many protein-coding and noncoding loci, yet neither H3-K56Ac nor H2A.Z has a significant impact on steady-state mRNA levels in yeast. Instead, broad effects of H3-K56Ac or H2A.Z on RNA levels are revealed only in the absence of the nuclear RNA exosome. H2A.Z is also necessary for the expression of divergent, promoter-proximal non-coding RNAs (ncRNAs) in mouse embryonic stem cells. Finally, we show that H2A.Z functions with H3-K56Ac to facilitate formation of chromosome interaction domains (CIDs). Our study suggests that H2A.Z and H3-K56Ac work in concert with the RNA exosome to control mRNA and ncRNA expression, perhaps in part by regulating higher-order chromatin structures.

Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles

A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (±3.5%) to 98.3% (±2.3%) for the various characteristics. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain) achieved 10.6% (±1.0%) higher performance than any individual models. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

The transcriptional profile of an organism contains clues about the environmental context in which it has evolved and currently lives, its behavior and cellular state. It is yet unclear, however, how much information can be efficiently extracted and how it can be used to classify new samples with respect to their environmental and genetic characteristics. Here, we have constructed an extensive transcriptome compendium of Escherichia coli that we have further enriched via an iterative learning approach. We then apply an ensemble of various machine learning algorithms to infer environmental and cellular information such as strain, growth phase, medium, oxygen level, antibiotic and carbon source. Functional analysis of the most informative genes provides mechanistic insights and palpable hypotheses regarding their role in each environmental or genetic context. Our work argues that genome-scale gene expression can be a multi-purpose marker for identifying latent, heterogeneous cellular and environmental states and that optimal classification can be achieved with a feature set of a couple hundred genes that might not necessarily have the most pronounced differential expression in the respective conditions.

Estimating a structured covariance matrix from multi-lab measurements in high-throughput biology

We consider the problem of quantifying the degree of coordination between transcription and translation, in yeast. Several studies have reported a surprising lack of coordination over the years, in organisms as different as yeast and human, using diverse technologies. However, a close look at this literature suggests that the lack of reported correlation may not reflect the biology of regulation. These reports do not control for between-study biases and structure in the measurement errors, ignore key aspects of how the data connect to the estimand, and systematically underestimate the correlation as a consequence. Here, we design a careful meta-analysis of 27 yeast data sets, supported by a multilevel model, full uncertainty quantification, a suite of sensitivity analyses and novel theory, to produce a more accurate estimate of the correlation between mRNA and protein levels-a proxy for coordination. From a statistical perspective, this problem motivates new theory on the impact of noise, model mis-specifications and non-ignorable missing data on estimates of the correlation between high dimensional responses. We find that the correlation between mRNA and protein levels is quite high under the studied conditions, in yeast, suggesting that post-transcriptional regulation plays a less prominent role than previously thought.

Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria

A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other ‘omics’ studies.

Reconstructing and analysing cellular states, space and time from gene expression profiles of many cells and single cells

Gene expression profiling is a fast, cheap and standardised analysis that provides a high dimensional measurement of the state of a biological sample, including of single cells. Computational methods to reconstruct the composition of samples and spatial and temporal information from expression profiles are described, as well as how they can be used to describe the effects of genetic variation.

Novel distal eQTL analysis demonstrates effect of population genetic architecture on detecting and interpreting associations

Mapping expression quantitative trait loci (eQTL) has identified genetic variants associated with transcription rates and has provided insight into genotype-phenotype associations obtained from genome-wide association studies (GWAS). Traditional eQTL mapping methods present significant challenges for the multiple-testing burden, resulting in a limited ability to detect eQTL that reside distal to the affected gene. To overcome this, we developed a novel eQTL testing approach, " NET: work-based, L: arge-scale I: dentification o F: dis T: al eQTL" (NetLIFT), which performs eQTL testing based on the pairwise conditional dependencies between genes' expression levels. When applied to existing data from yeast segregants, NetLIFT replicated most previously identified distal eQTL and identified 46% more genes with distal effects compared to local effects. In liver data from mouse lines derived through the Collaborative Cross project, NetLIFT detected 5744 genes with local eQTL while 3322 genes had distal eQTL. This analysis revealed founder-of-origin effects for a subset of local eQTL that may contribute to previously described phenotypic differences in metabolic traits. In human lymphoblastoid cell lines, NetLIFT was able to detect 1274 transcripts with distal eQTL that had not been reported in previous studies, while 2483 transcripts with local eQTL were identified. In all species, we found no enrichment for transcription factors facilitating eQTL associations; instead, we found that most trans-acting factors were annotated for metabolic function, suggesting that genetic variation may indirectly regulate multigene pathways by targeting key components of feedback processes within regulatory networks. Furthermore, the unique genetic history of each population appears to influence the detection of genes with local and distal eQTL.

The genomic landscape of compensatory evolution

The Genomic Landscape of Compensatory Evolution Laboratory selection experiment explains how organisms compensate for the loss of genes during evolution, and reveals the deleterious side-effects of this process when adapting to novel environments.

Adaptive evolution is generally assumed to progress through the accumulation of beneficial mutations. However, as deleterious mutations are common in natural populations, they generate a strong selection pressure to mitigate their detrimental effects through compensatory genetic changes. This process can potentially influence directions of adaptive evolution by enabling evolutionary routes that are otherwise inaccessible. Therefore, the extent to which compensatory mutations shape genomic evolution is of central importance. Here, we studied the capacity of the baker's yeast genome to compensate the complete loss of genes during evolution, and explored the long-term consequences of this process. We initiated laboratory evolutionary experiments with over 180 haploid baker's yeast genotypes, all of which initially displayed slow growth owing to the deletion of a single gene. Compensatory evolution following gene loss was rapid and pervasive: 68% of the genotypes reached near wild-type fitness through accumulation of adaptive mutations elsewhere in the genome. As compensatory mutations have associated fitness costs, genotypes with especially low fitnesses were more likely to be subjects of compensatory evolution. Genomic analysis revealed that as compensatory mutations were generally specific to the functional defect incurred, convergent evolution at the molecular level was extremely rare. Moreover, the majority of the gene expression changes due to gene deletion remained unrestored. Accordingly, compensatory evolution promoted genomic divergence of parallel evolving populations. However, these different evolutionary outcomes are not phenotypically equivalent, as they generated diverse growth phenotypes across environments. Taken together, these results indicate that gene loss initiates adaptive genomic changes that rapidly restores fitness, but this process has substantial pleiotropic effects on cellular physiology and evolvability upon environmental change. Our work also implies that gene content variation across species could be partly due to the action of compensatory evolution rather than the passive loss of genes.

While core cellular processes are generally conserved during evolution, the constituent genes differ somewhat between related species with similar lifestyles. Why should this be so? In this work, we propose that gene loss may initially be deleterious, but organisms can recover fitness by the accumulation of compensatory mutations elsewhere in the genome. To investigate this process in the laboratory, we investigated 180 haploid yeast strains, each of which initially displayed slow growth owing to the deletion of a single gene. Laboratory evolutionary experiments revealed that defects in a broad range of molecular processes can readily be compensated during evolution. Genomic analyses and functional assays demonstrated that compensatory evolution generates hidden genetic and physiological variation across parallel evolving lines, which can be revealed when the environment changes. Strikingly, despite nearly full recovery of fitness, the wild-type genomic expression pattern is generally not restored. Based on these results, we argue that genomes undergo major changes not simply to adapt to external conditions but also to compensate for previously accumulated deleterious mutations.

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.

Coordination of microbial metabolism

Beyond fuelling cellular activities with building blocks and energy, metabolism also integrates environmental conditions into intracellular signals. The underlying regulatory network is complex and multifaceted: it ranges from slow interactions, such as changing gene expression, to rapid ones, such as the modulation of protein activity via post-translational modification or the allosteric binding of small molecules. In this Review, we outline the coordination of common metabolic tasks, including nutrient uptake, central metabolism, the generation of energy, the supply of amino acids and protein synthesis. Increasingly, a set of key metabolites is recognized to control individual regulatory circuits, which carry out specific functions of information input and regulatory output. Such a modular view of microbial metabolism facilitates an intuitive understanding of the molecular mechanisms that underlie cellular decision making.

A dynamic interplay of nucleosome and Msn2 binding regulates kinetics of gene activation and repression following stress

The transcription factor Msn2 mediates a significant proportion of the environmental stress response, in which a common cohort of genes changes expression in a stereotypic fashion upon exposure to any of a wide variety of stresses. We have applied genome-wide chromatin immunoprecipitation and nucleosome profiling to determine where Msn2 binds under stressful conditions and how that binding affects, and is affected by, nucleosome positioning. We concurrently determined the effect of Msn2 activity on gene expression following stress and demonstrated that Msn2 stimulates both activation and repression. We found that some genes responded to both intermittent and continuous Msn2 nuclear occupancy while others responded only to continuous occupancy. Finally, these studies document a dynamic interplay between nucleosomes and Msn2 such that nucleosomes can restrict access of Msn2 to its canonical binding sites while Msn2 can promote reposition, expulsion and recruitment of nucleosomes to alter gene expression. This interplay may allow the cell to discriminate between different types of stress signaling.

Coordination of gene expression and growth-rate in natural populations of budding yeast

Cells adapt to environmental changes through genetic mutations that stabilize novel phenotypes. Often, this adaptation involves regulatory changes which modulate gene expression. In the budding yeast, ribosomal-related gene expression correlates with cell growth rate across different environments. To examine whether the same relationship between gene expression and growth rate is observed also across natural populations, we measured gene expression, growth rate and ethanol production of twenty-four wild type yeast strains originating from diverse habitats, grown on the pentose sugar xylulose. We found that expression of ribosome-related genes did not correlate with growth rate. Rather, growth rate was correlated with the expression of amino acid biosynthesis genes. Searching other databases, we observed a similar correlation between growth rate and amino-acid biosyntehsis genes in a library of gene deletions. We discuss the implications of our results for understanding how cells coordinate their translation capacity with available nutrient resources.

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.

Automated design of bacterial genome sequences

Background

Organisms have evolved ways of regulating transcription to better adapt to varying environments. Could the current functional genomics data and models support the possibility of engineering a genome with completely rearranged gene organization while the cell maintains its behavior under environmental challenges? How would we proceed to design a full nucleotide sequence for such genomes?

Results

As a first step towards answering such questions, recent work showed that it is possible to design alternative transcriptomic models showing the same behavior under environmental variations than the wild-type model. A second step would require providing evidence that it is possible to provide a nucleotide sequence for a genome encoding such transcriptional model. We used computational design techniques to design a rewired global transcriptional regulation of Escherichia coli, yet showing a similar transcriptomic response than the wild-type. Afterwards, we “compiled” the transcriptional networks into nucleotide sequences to obtain the final genome sequence. Our computational evolution procedure ensures that we can maintain the genotype-phenotype mapping during the rewiring of the regulatory network. We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons. Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations.

Conclusions

This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments.

Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates

Countless studies monitor the growth rate of microbial populations as a measure of fitness. However, an enormous gap separates growth-rate differences measurable in the laboratory from those that natural selection can distinguish efficiently. Taking advantage of the recent discovery that transcript and protein levels in budding yeast closely track growth rate, we explore the possibility that growth rate can be more sensitively inferred by monitoring the proteomic response to growth, rather than growth itself. We find a set of proteins whose levels, in aggregate, enable prediction of growth rate to a higher precision than direct measurements. However, we find little overlap between these proteins and those that closely track growth rate in other studies. These results suggest that, in yeast, the pathways that set the pace of cell division can differ depending on the growth-altering stimulus. Still, with proper validation, protein measurements can provide high-precision growth estimates that allow extension of phenotypic growth-based assays closer to the limits of evolutionary selection.

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.

Restricted pleiotropy facilitates mutational erosion of major life-history traits

Radical shifts to new natural and human made niches can make some functions unneeded and thus exposed to genetic degeneration. Here we ask not about highly specialized and rarely used functions but those relating to major life-history traits, rate of growth, and resistance to prolonged starvation. We found that in yeast each of the two traits was visibly impaired by at least several hundred individual gene deletions. There were relatively few deletions affecting negatively both traits and likely none harming one but improving the other. Functional profiles of gene deletions affecting either growth or survival were strikingly different: the first related chiefly to synthesis of macromolecules whereas the second to maintenance and recycling of cellular structures. The observed pattern of gene indispensability corresponds to that of gene induction, providing a rather rare example of agreement between the results of deletion and expression studies. We conclude that transitions to new environments in which the ability to grow at possibly fastest rate or survive under very long starvation become practically unnecessary can result in rapid erosion of these vital functions because they are coded by many genes constituting large mutational targets and because restricted pleiotropy is unlikely to constrain this process.

A new system for comparative functional genomics of Saccharomyces yeasts

Whole-genome sequencing, particularly in fungi, has progressed at a tremendous rate. More difficult, however, is experimental testing of the inferences about gene function that can be drawn from comparative sequence analysis alone. We present a genome-wide functional characterization of a sequenced but experimentally understudied budding yeast, Saccharomyces bayanus var. uvarum (henceforth referred to as S. bayanus), allowing us to map changes over the 20 million years that separate this organism from S. cerevisiae. We first created a suite of genetic tools to facilitate work in S. bayanus. Next, we measured the gene-expression response of S. bayanus to a diverse set of perturbations optimized using a computational approach to cover a diverse array of functionally relevant biological responses. The resulting data set reveals that gene-expression patterns are largely conserved, but significant changes may exist in regulatory networks such as carbohydrate utilization and meiosis. In addition to regulatory changes, our approach identified gene functions that have diverged. The functions of genes in core pathways are highly conserved, but we observed many changes in which genes are involved in osmotic stress, peroxisome biogenesis, and autophagy. A surprising number of genes specific to S. bayanus respond to oxidative stress, suggesting the organism may have evolved under different selection pressures than S. cerevisiae. This work expands the scope of genome-scale evolutionary studies from sequence-based analysis to rapid experimental characterization and could be adopted for functional mapping in any lineage of interest. Furthermore, our detailed characterization of S. bayanus provides a valuable resource for comparative functional genomics studies in yeast.

Inositol pyrophosphates regulate cell growth and the environmental stress response by activating the HDAC Rpd3L

Cells respond to stress and starvation by adjusting their growth rate and enacting stress defense programs. In eukaryotes this involves inactivation of TORC1, which in turn triggers downregulation of ribosome and protein synthesis genes and upregulation of stress response genes. Here we report that the highly conserved inositol pyrophosphate (PP-IP) second messengers (including 1-PP-IP5, 5-PP-IP4, and 5-PP-IP5) are also critical regulators of cell growth and the general stress response, acting in parallel with the TORC1 pathway to control the activity of the class I histone deacetylase Rpd3L. In fact, yeast cells that cannot synthesize any of the PP-IPs mount little to no transcriptional response to osmotic, heat, or oxidative stress. Furthermore, PP-IP-dependent regulation of Rpd3L occurs independently of the role individual PP-IPs (such as 5-PP-IP5) play in activating specialized stress/starvation response pathways. Thus, the PP-IP second messengers simultaneously activate and tune the global response to stress and starvation 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.

Computational design of genomic transcriptional networks with adaptation to varying environments

Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome. Combining transcriptomic and signaling data, we develop an evolutionary computational procedure that allows obtaining alternative genomic transcriptional regulatory network (GTRN) that still maintains its adaptability to dynamic environments. We apply our methodology to an E. coli GTRN and show that it could be rewired to simpler transcriptional regulatory structures. These rewired GTRNs still maintain the global physiological response to fluctuating environments. Rewired GTRNs contain 73% fewer regulated operons. Genes with similar functions and coordinated patterns of expression across environments are clustered into longer regulated operons. These synthetic GTRNs are more sensitive and show a more robust response to challenging environments. This result illustrates that the natural configuration of E. coli GTRN does not necessarily result from selection for robustness to environmental perturbations, but that evolutionary contingencies may have been important as well. We also discuss the limitations of our methodology in the context of the demand theory. Our procedure will be useful as a novel way to analyze global transcription regulation networks and in synthetic biology for the de novo design of genomes.

Managing drug resistance in cancer: lessons from HIV therapy

Drug resistance is a common cause of treatment failure for HIV infection and cancer. The high mutation rate of HIV leads to genetic heterogeneity among viral populations and provides the seed from which drug-resistant clones emerge in response to therapy. Similarly, most cancers are characterized by extensive genetic, epigenetic, transcriptional and cellular diversity, and drug-resistant cancer cells outgrow their non-resistant peers in a process of somatic evolution. Patient-specific combination of antiviral drugs has emerged as a powerful approach for treating drug-resistant HIV infection, using genotype-based predictions to identify the best matched combination therapy among several hundred possible combinations of HIV drugs. In this Opinion article, we argue that HIV therapy provides a 'blueprint' for designing and validating patient-specific combination therapies in cancer.

Detection of transcriptional triggers in the dynamics of microbial growth: application to the respiratorily versatile bacterium Shewanella oneidensis

The capacity of microorganisms to respond to variable external conditions requires a coordination of environment-sensing mechanisms and decision-making regulatory circuits. Here, we seek to understand the interplay between these two processes by combining high-throughput measurement of time-dependent mRNA profiles with a novel computational approach that searches for key genetic triggers of transcriptional changes. Our approach helped us understand the regulatory strategies of a respiratorily versatile bacterium with promising bioenergy and bioremediation applications, Shewanella oneidensis, in minimal and rich media. By comparing expression profiles across these two conditions, we unveiled components of the transcriptional program that depend mainly on the growth phase. Conversely, by integrating our time-dependent data with a previously available large compendium of static perturbation responses, we identified transcriptional changes that cannot be explained solely by internal network dynamics, but are rather triggered by specific genes acting as key mediators of an environment-dependent response. These transcriptional triggers include known and novel regulators that respond to carbon, nitrogen and oxygen limitation. Our analysis suggests a sequence of physiological responses, including a coupling between nitrogen depletion and glycogen storage, partially recapitulated through dynamic flux balance analysis, and experimentally confirmed by metabolite measurements. Our approach is broadly applicable to other systems.