Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

Frequency of change determines effectiveness of microbial response strategies

Nature challenges microbes with change at different frequencies and demands an effective response for survival. Here, we used controlled laboratory experiments to investigate the effectiveness of different response strategies, such as post-translational modification, transcriptional regulation, and specialized versus adaptable metabolisms. For this, we inoculated replicated chemostats with an enrichment culture obtained from sulfidic stream microbiomes 16 weeks prior. The chemostats were submitted to alternatingly oxic and anoxic conditions at three frequencies, with periods of 1, 4 and 16 days. The microbial response was recorded with 16S rRNA gene amplicon sequencing, shotgun metagenomics, transcriptomics and proteomics. Metagenomics resolved provisional genomes of all abundant bacterial populations, mainly affiliated with Proteobacteria and Bacteroidetes. Almost all these populations maintained a steady growth rate under both redox conditions at all three frequencies of change. Our results supported three conclusions: (1) Oscillating oxic/anoxic conditions selected for generalistic species, rather than species specializing in only a single condition. (2) A high frequency of change selected for strong codon usage bias. (3) Alignment of transcriptomes and proteomes required multiple generations and was dependent on a low frequency of change.

Phenotype similarities in automatically grouped T2D patients by variation-based clustering of IL-1β gene expression

Background

Analyzing longitudinal gene expression data is extremely challenging due to limited prior information, high dimensionality, and heterogeneity. Similar difficulties arise in research of multifactorial diseases such as Type 2 Diabetes. Clustering methods can be applied to automatically group similar observations. Common clinical values within the resulting groups suggest potential associations. However, applying traditional clustering methods to gene expression over time fails to capture variations in the response. Therefore, shape-based clustering could be applied to identify patient groups by gene expression variation in a large time metabolic compensatory intervention.

Objectives

To search for clinical grouping patterns between subjects that showed similar structure in the variation of IL-1β gene expression over time.

Methods

A new approach for shape-based clustering by IL-1β expression behavior was applied to a real longitudinal database of Type 2 Diabetes patients. In order to capture correctly variations in the response, we applied traditional clustering methods to slopes between measurements.

Results

In this setting, the application of K-Medoids using the Manhattan distance yielded the best results for the corresponding database. Among the resulting groups, one of the clusters presented significant differences in many key clinical values regarding the metabolic syndrome in comparison to the rest of the data.

Conclusions

The proposed method can be used to group patients according to variation patterns in gene expression (or other applications) and thus, provide clinical insights even when there is no previous knowledge on the subject clinical profile and few timepoints for each individual.

Timed Pulses in DNA Strand Displacement Reactions

Inspired by naturally occurring regulatory mechanisms that allow complex temporal pulse features with programmable delays, we demonstrate here a strategy to achieve temporally programmed pulse output signals in DNA-based strand displacement reactions (SDRs). To achieve this, we rationally designed input strands that, once bound to their target duplex, can be gradually degraded, resulting in a pulse output signal. We also designed blocker strands that suppress strand displacement and determine the time at which the pulse reaction is generated. We show that by controlling the degradation rate of blocker and input strands, we can finely control the delayed pulse output over a range of 10 h. We also prove that it is possible to orthogonally delay two different pulse reactions in the same solution by taking advantage of the specificity of the degradation reactions for the input and blocker strands. Finally, we show here two possible applications of such delayed pulse SDRs: the time-programmed pulse decoration of DNA nanostructures and the sequentially appearing and self-erasing formation of DNA-based patterns.

Inferring cell cycle phases from a partially temporal network of protein interactions

The temporal organization of biological systems is key for understanding them, but current methods for identifying this organization are often ad hoc and require prior knowledge. We present Phasik, a method that automatically identifies this multiscale organization by combining time series data (protein or gene expression) and interaction data (protein-protein interaction network). Phasik builds a (partially) temporal network and uses clustering to infer temporal phases. We demonstrate the method's effectiveness by recovering well-known phases and sub-phases of the cell cycle of budding yeast and phase arrests of mutants. We also show its general applicability using temporal gene expression data from circadian rhythms in wild-type and mutant mouse models. We systematically test Phasik's robustness and investigate the effect of having only partial temporal information. As time-resolved, multiomics datasets become more common, this method will allow the study of temporal regulation in lesser-known biological contexts, such as development, metabolism, and disease.

Functional network motifs defined through integration of protein-protein and genetic interactions

Cells are enticingly complex systems. The identification of feedback regulation is critically important for understanding this complexity. Network motifs defined as small graphlets that occur more frequently than expected by chance have revolutionized our understanding of feedback circuits in cellular networks. However, with their definition solely based on statistical over-representation, network motifs often lack biological context, which limits their usefulness. Here, we define functional network motifs (FNMs) through the systematic integration of genetic interaction data that directly inform on functional relationships between genes and encoded proteins. Occurring two orders of magnitude less frequently than conventional network motifs, we found FNMs significantly enriched in genes known to be functionally related. Moreover, our comprehensive analyses of FNMs in yeast showed that they are powerful at capturing both known and putative novel regulatory interactions, thus suggesting a promising strategy towards the systematic identification of feedback regulation in biological networks. Many FNMs appeared as excellent candidates for the prioritization of follow-up biochemical characterization, which is a recurring bottleneck in the targeting of complex diseases. More generally, our work highlights a fruitful avenue for integrating and harnessing genomic network data.

Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.

Effective connectivity determines the critical dynamics of biochemical networks

Living systems comprise interacting biochemical components in very large networks. Given their high connectivity, biochemical dynamics are surprisingly not chaotic but quite robust to perturbations-a feature C.H. Waddington named canalization. Because organisms are also flexible enough to evolve, they arguably operate in a critical dynamical regime between order and chaos. The established theory of criticality is based on networks of interacting automata where Boolean truth values model presence/absence of biochemical molecules. The dynamical regime is predicted using network connectivity and node bias (to be on/off) as tuning parameters. Revising this to account for canalization leads to a significant improvement in dynamical regime prediction. The revision is based on effective connectivity, a measure of dynamical redundancy that buffers automata response to some inputs. In both random and experimentally validated systems biology networks, reducing effective connectivity makes living systems operate in stable or critical regimes even though the structure of their biochemical interaction networks predicts them to be chaotic. This suggests that dynamical redundancy may be naturally selected to maintain living systems near critical dynamics, providing both robustness and evolvability. By identifying how dynamics propagates preferably via effective pathways, our approach helps to identify precise ways to design and control network models of biochemical regulation and signalling.

Multiscale models quantifying yeast physiology: towards a whole-cell model

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.

Reconfiguration of metabolic fluxes in Pseudomonas putida as a response to sub-lethal oxidative stress

As a frequent inhabitant of sites polluted with toxic chemicals, the soil bacterium and plant-root colonizer Pseudomonas putida can tolerate high levels of endogenous and exogenous oxidative stress. Yet, the ultimate reason of such phenotypic property remains largely unknown. To shed light on this question, metabolic network-wide routes for NADPH generation-the metabolic currency that fuels redox-stress quenching mechanisms-were inspected when P. putida KT2440 was challenged with a sub-lethal H₂O₂ dose as a proxy of oxidative conditions. ¹³C-tracer experiments, metabolomics, and flux analysis, together with the assessment of physiological parameters and measurement of enzymatic activities, revealed a substantial flux reconfiguration in oxidative environments. In particular, periplasmic glucose processing was rerouted to cytoplasmic oxidation, and the cyclic operation of the pentose phosphate pathway led to significant NADPH-forming fluxes, exceeding biosynthetic demands by ~50%. The resulting NADPH surplus, in turn, fueled the glutathione system for H₂O₂ reduction. These properties not only account for the tolerance of P. putida to environmental insults-some of which end up in the formation of reactive oxygen species-but they also highlight the value of this bacterial host as a platform for environmental bioremediation and metabolic engineering.

The Pentose Phosphate Pathway in Yeasts-More Than a Poor Cousin of Glycolysis

The pentose phosphate pathway (PPP) is a route that can work in parallel to glycolysis in glucose degradation in most living cells. It has a unidirectional oxidative part with glucose-6-phosphate dehydrogenase as a key enzyme generating NADPH, and a non-oxidative part involving the reversible transketolase and transaldolase reactions, which interchange PPP metabolites with glycolysis. While the oxidative branch is vital to cope with oxidative stress, the non-oxidative branch provides precursors for the synthesis of nucleic, fatty and aromatic amino acids. For glucose catabolism in the baker’s yeast Saccharomyces cerevisiae, where its components were first discovered and extensively studied, the PPP plays only a minor role. In contrast, PPP and glycolysis contribute almost equally to glucose degradation in other yeasts. We here summarize the data available for the PPP enzymes focusing on S. cerevisiae and Kluyveromyces lactis, and describe the phenotypes of gene deletions and the benefits of their overproduction and modification. Reference to other yeasts and to the importance of the PPP in their biotechnological and medical applications is briefly being included. We propose future studies on the PPP in K. lactis to be of special interest for basic science and as a host for the expression of human disease genes.

The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling

The ability to map causal interactions underlying genetic control and cellular signaling has led to increasingly accurate models of the complex biochemical networks that regulate cellular function. These network models provide deep insights into the organization, dynamics, and function of biochemical systems: for example, by revealing genetic control pathways involved in disease. However, the traditional representation of biochemical networks as binary interaction graphs fails to accurately represent an important dynamical feature of these multivariate systems: some pathways propagate control signals much more effectively than do others. Such heterogeneity of interactions reflects canalization-the system is robust to dynamical interventions in redundant pathways but responsive to interventions in effective pathways. Here, we introduce the effective graph, a weighted graph that captures the nonlinear logical redundancy present in biochemical network regulation, signaling, and control. Using 78 experimentally validated models derived from systems biology, we demonstrate that 1) redundant pathways are prevalent in biological models of biochemical regulation, 2) the effective graph provides a probabilistic but precise characterization of multivariate dynamics in a causal graph form, and 3) the effective graph provides an accurate explanation of how dynamical perturbation and control signals, such as those induced by cancer drug therapies, propagate in biochemical pathways. Overall, our results indicate that the effective graph provides an enriched description of the structure and dynamics of networked multivariate causal interactions. We demonstrate that it improves explainability, prediction, and control of complex dynamical systems in general and biochemical regulation in particular.

Quorum Sensing-Mediated and Growth Phase-Dependent Regulation of Metabolic Pathways in Hafnia alvei H4

Quorum sensing (QS) is a widespread regulatory mechanism in bacteria used to coordinate target gene expression with cell density. Thus far, little is known about the regulatory relationship between QS and cell density in terms of metabolic pathways in Hafnia alvei H4. In this study, transcriptomics analysis was performed under two conditions to address this question. The comparative transcriptome of H. alvei H4 wild-type at high cell density (OD₆₀₀ = 1.7) relative to low cell density (OD₆₀₀ = 0.3) was considered as growth phase-dependent manner (GPDM), and the transcriptome profile of luxI/R deletion mutant (ΔluxIR) compared to the wild-type was considered as QS-mediated regulation. In all, we identified 206 differentially expressed genes (DEGs) mainly presented in chemotaxis, TCA cycle, two-component system, ABC transporters and pyruvate metabolism, co-regulated by the both density-dependent regulation, and the results were validated by qPCR and swimming phenotypic assays. Aside from the co-regulated DEGs, we also found that 59 DEGs, mediated by density-independent QS, function in pentose phosphate and histidine metabolism and that 2084 cell-density-dependent DEGs involved in glycolysis/gluconeogenesis and phenylalanine metabolism were influenced only by GPDM from significantly enriched analysis of transcriptome data. The findings provided new information about the interplay between two density-dependent metabolic regulation, which could assist with the formulation of control strategies for this opportunistic pathogen, especially at high cell density.

Learning causal networks using inducible transcription factors and transcriptome-wide time series

We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors (TFs) and measuring timecourses of the resulting gene expression responses in budding yeast. Each experiment captures a regulatory cascade connecting a single induced regulator to the genes it causally regulates. We discuss the regulatory cascade of a single TF, Aft1, in detail; however, IDEA contains > 200 TF induction experiments with 20 million individual observations and 100,000 signal‐containing dynamic responses. As an application of IDEA, we integrate all timecourses into a whole‐cell transcriptional model, which is used to predict and validate multiple new and underappreciated transcriptional regulators. We also find that the magnitudes of coefficients in this model are predictive of genetic interaction profile similarities. In addition to being a resource for exploring regulatory connectivity between TFs and their target genes, our modeling approach shows that combining rapid perturbations of individual genes with genome‐scale time‐series measurements is an effective strategy for elucidating gene regulatory networks.

Using optimal control to understand complex metabolic pathways

BACKGROUND

Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria.

RESULTS

Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments.

CONCLUSIONS

We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks.

Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling

The quantification of the kinetic rates of RNA synthesis, processing, and degradation are largely based on the integrative analysis of total and nascent transcription, the latter being quantified through RNA metabolic labeling. We developed INSPEcT−, a computational method based on the mathematical modeling of premature and mature RNA expression that is able to quantify kinetic rates from steady-state or time course total RNA-seq data without requiring any information on nascent transcripts. Our approach outperforms available solutions, closely recapitulates the kinetic rates obtained through RNA metabolic labeling, improves the ability to detect changes in transcript half-lives, reduces the cost and complexity of the experiments, and can be adopted to study experimental conditions in which nascent transcription cannot be readily profiled. Finally, we applied INSPEcT− to the characterization of post-transcriptional regulation landscapes in dozens of physiological and disease conditions. This approach was included in the INSPEcT Bioconductor package, which can now unveil RNA dynamics from steady-state or time course data, with or without the profiling of nascent RNA.

Redox metabolism: the hidden player in carbon and nitrogen signaling?

While decades of research have considered redox metabolism as purely defensive, recent results show that reactive oxygen species (ROS) are necessary for growth and development. Close relationships have been found between the regulation of nitrogen metabolism and ROS in response to both carbon and nitrogen availability. Root nitrate uptake and nitrogen metabolism have been shown to be regulated by a signal from the oxidative pentose phosphate pathway (OPPP) in response to carbon signaling. As a major source of NADP(H), the OPPP is critical to maintaining redox balance under stress situations. Furthermore, recent results suggest that at least part of the regulation of the root nitrate transporter by nitrogen signaling is also linked to the redox status of the plant. This leads to the question of whether there is a more general role of redox metabolism in the regulation of nitrogen metabolism by carbon and nitrogen. This review highlights the role of the OPPP in carbon signaling and redox metabolism, and the interaction between redox and nitrogen metabolism. We discuss how redox metabolism could be an important player in the regulation of nitrogen metabolism in response to carbon/nitrogen interaction and the implications for plant adaptation to extreme environments and future crop development.

Fluconazole resistance in Candida albicans is induced by Pseudomonas aeruginosa quorum sensing

Microorganisms employ quorum sensing (QS) mechanisms to communicate with each other within microbial ecosystems. Emerging evidence suggests that intraspecies and interspecies QS plays an important role in antimicrobial resistance in microbial communities. However, the relationship between interkingdom QS and antimicrobial resistance is largely unknown. Here, we demonstrate that interkingdom QS interactions between a bacterium, Pseudomonas aeruginosa and a yeast, Candida albicans, induce the resistance of the latter to a widely used antifungal fluconazole. Phenotypic, transcriptomic, and proteomic analyses reveal that P. aeruginosa's main QS molecule, N-(3-Oxododecanoyl)-L-homoserine lactone, induces candidal resistance to fluconazole by reversing the antifungal's effect on the ergosterol biosynthesis pathway. Accessory resistance mechanisms including upregulation of C. albicans drug-efflux, regulation of oxidative stress response, and maintenance of cell membrane integrity, further confirm this phenomenon. These findings demonstrate that P. aeruginosa QS molecules may confer protection to neighboring yeasts against azoles, in turn strengthening their co-existence in hostile polymicrobial infection sites.

Effects of overexpression of STB5 in Saccharomyces cerevisiae on fatty acid biosynthesis, physiology and transcriptome

Microbial conversion of biomass to fatty acids (FA) and products derived thereof is an attractive alternative to the traditional oleochemical production route from animal and plant lipids. This study examined if NADPH-costly FA biosynthesis could be enhanced by overexpressing the transcription factor Stb5 in Saccharomyces cerevisiae. Stb5 activates expression of multiple genes encoding enzymes within the pentose phosphate pathway (PPP) and other NADPH-producing reactions. Overexpression of STB5 led to a decreased growth rate and an increased free fatty acid (FFA) production during growth on glucose. The improved FFA synthetic ability in the glucose phase was shown to be independent of flux through the oxidative PPP. RNAseq analysis revealed that STB5 overexpression had wide-ranging effects on the transcriptome in the batch phase, and appeared to cause a counterintuitive phenotype with reduced flux through the oxidative PPP. During glucose limitation, when an increased NADPH supply is likely less harmful, an overall induction of the proposed target genes of Stb5 (eg. GND1/2, TAL1, ALD6, YEF1) was observed. Taken together, the strategy of utilizing STB5 overexpression to increase NADPH supply for reductive biosynthesis is suggested to have potential in strains engineered to have strong ability to consume excess NADPH, alleviating a potential redox imbalance.

Promoter Activity Buffering Reduces the Fitness Cost of Misregulation

Organisms regulate gene expression through changes in the activity of transcription factors (TFs). In yeast, the response of genes to changes in TF activity is generally assumed to be encoded in the promoter. To directly test this assumption, we chose 42 genes and, for each, replaced the promoter with a synthetic inducible promoter and measured how protein expression changes as a function of TF activity. Most genes exhibited gene-specific TF dose-response curves not due to differences in mRNA stability, translation, or protein stability. Instead, most genes have an intrinsic ability to buffer the effects of promoter activity. This can be encoded in the open reading frame and the 3' end of genes and can be implemented by both autoregulatory feedback and by titration of limiting trans regulators. We show experimentally and computationally that, when misexpression of a gene is deleterious, this buffering insulates cells from fitness defects due to misregulation.

Reserve Flux Capacity in the Pentose Phosphate Pathway by NADPH Binding Is Conserved across Kingdoms

All organisms evolved defense mechanisms to counteract oxidative stress and buildup of reactive oxygen species (ROS). To test whether a potentially conserved mechanism exists for the rapid response, we investigated immediate metabolic dynamics of Escherichia coli, yeast, and human dermal fibroblasts to oxidative stress that we found to be conserved between species. To elucidate the regulatory mechanisms that implement this metabolic response, we developed mechanistic kinetic models for each organism's central metabolism and systematically tested activation and inactivation of each irreversible reaction by each metabolite. This ensemble modeling predicts in vivo relevant metabolite-enzyme interactions based on their ability to quantitatively describe metabolite dynamics. All three species appear to inhibit their oxidative pentose phosphate pathway during normal growth by the redox cofactor NADPH and relieve this inhibition to increase the pathway flux for detoxification of ROS during stress, with the sole exception of yeast when exposed to high levels of stress.

Redox rebalance against genetic perturbations and modulation of central carbon metabolism by the oxidative stress regulation

The micro-aerophilic organisms and aerobes as well as yeast and higher organisms have evolved to gain energy through respiration (via oxidative phosphorylation), thereby enabling them to grow much faster than anaerobes. However, during respiration, reactive oxygen species (ROSs) are inherently (inevitably) generated, and threaten the cell's survival. Therefore, living organisms (or cells) must furnish the potent defense systems to keep such ROSs at harmless level, where the cofactor balance plays crucial roles. Namely, NADH is the source of energy generation (catabolism) in the respiratory chain reactions, through which ROSs are generated, while NADPH plays important roles not only for the cell synthesis (anabolism) but also for detoxifying ROSs. Therefore, the cell must rebalance the redox ratio by modulating the fluxes of the central carbon metabolism (CCM) by regulating the multi-level regulation machinery upon genetic perturbations and the change in the growth conditions. Here, we discuss about how aerobes accomplish such cofactor homeostasis against redox perturbations. In particular, we consider how single-gene mutants (including pgi, pfk, zwf, gnd and pyk mutants) modulate their metabolisms in relation to cofactor rebalance (and also by adaptive laboratory evolution). We also discuss about how the overproduction of NADPH (by the pathway gene mutation) can be utilized for the efficient production of useful value-added chemicals such as medicinal compounds, polyhydroxyalkanoates, and amino acids, all of which require NADPH in their synthetic pathways. We then discuss about the metabolic responses against oxidative stress, where αketoacids play important roles not only for the coordination between catabolism and anabolism, but also for detoxifying ROSs by non-enzymatic reactions, as well as for reducing the production of ROSs by repressing the activities of the TCA cycle and respiration (via carbon catabolite repression). Thus, we discuss about the mechanisms (basic strategies) that modulate the metabolism from respiration to respiro-fermentative metabolism causing overflow, based on the role of Pyk activity, affecting the NADPH production at the oxidative pentose phosphate (PP) pathway, and the roles of αketoacids for the change in the source of energy generation from the oxidative phosphorylation to the substrate level phosphorylation.

Fast-Time Stability of Temporal Boolean Networks

In real systems, most of the biological functionalities come from the fact that the connections are not active all the time. Based on the fact, temporal Boolean networks (TBNs) are proposed in this paper, and the fast-time stability is analyzed via semi-tensor product (STP) of matrices and incidence matrices. First, the algebraic form of a TBN is obtained based on the STP method, and one necessary and sufficient condition for global fast-time stability is presented. Moreover, incidence matrices are used to obtain several sufficient conditions, which reduce the computational complexity from O(n2ⁿ) (exponential type) to O(n⁴) (polynomial type) compared with the STP method. In addition, the global fast-time stabilization of TBNs is considered, and pinning controllers are designed based on the neighbors of controlled nodes rather than all the nodes. Finally, the local fast-time stability of TBNs is considered based on the incidence matrices as well. Several examples are provided to illustrate the effectiveness of the obtained results.

Nanotube-mediated cross-feeding couples the metabolism of interacting bacterial cells

Bacteria frequently engage in cross-feeding interactions that involve an exchange of metabolites with other micro- or macroorganisms. The often obligate nature of these associations, however, hampers manipulative experiments, thus limiting our mechanistic understanding of the ecophysiological consequences that result for the organisms involved. Here we address this issue by taking advantage of a well-characterized experimental model system, in which auxotrophic genotypes of E. coli derive essential amino acids from prototrophic donor cells using intercellular nanotubes. Surprisingly, donor-recipient cocultures revealed that the mere presence of auxotrophic genotypes was sufficient to increase amino acid production levels of several prototrophic donor genotypes. Our work is consistent with a scenario, in which interconnected auxotrophs withdraw amino acids from the cytoplasm of donor cells, which delays feedback inhibition of the corresponding amino acid biosynthetic pathway and, in this way, increases amino acid production levels. Our findings indicate that in newly established mutualistic associations, an intercellular regulation of exchanged metabolites can simply emerge from the architecture of the underlying biosynthetic pathways, rather than requiring the evolution of new regulatory mechanisms.

Which Genes in a Typical Intertidal Seagrass (Zostera japonica) Indicate Copper-, Lead-, and Cadmium Pollution?

Healthy seagrasses are considered a prime indicator of estuarine and coastal ecosystem function; however, as the only group of flowering plants recolonizing the sea, seagrasses are frequently exposed to anthropogenic heavy metal pollutants, which are associated with high levels of molecular damage. To determine whether biologically relevant concentrations of heavy metals cause systematic alterations in RNA expression patterns, we performed a gene expression study using transcriptome analyses (RNA-seq). We exposed the typical intertidal seagrass Zostera japonica to 0 and 50 μM of copper (Cu), lead (Pb), and cadmium (Cd) under laboratory conditions. A total of 18,266 differentially expressed genes (DEGs) were identified, of which 2001 co-expressed genes directly related by Cu, Pb, and Cd stress. We also examined the effects of short-term heavy metal Cu, Pb, and Cd pulses on the accumulation of metals in Z. japonica and showed metal concentrations were higher in the shoots than in roots. Twelve differentially expressed genes were further analyzed for expression differences using real-time quantitative polymerase chain reaction (RT-qPCR). Our data suggest that as coastal seawater pollution worsens, the sensitive genes identified in this study may be useful biomarkers of sublethal effects and provide fundamental information for Z. japonica resistant gene engineering.

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

A challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.

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The proteome of kinase knockouts is dominated by enzyme abundance changes

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The enzyme expression profiles of kinase knockouts are non-redundant

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Metabolism is regulated by many expression changes acting in concert

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Machine learning accurately predicts the metabolome from enzyme abundance

CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks

Logical models offer a simple but powerful means to understand the complex dynamics of biochemical regulation, without the need to estimate kinetic parameters. However, even simple automata components can lead to collective dynamics that are computationally intractable when aggregated into networks. In previous work we demonstrated that automata network models of biochemical regulation are highly canalizing, whereby many variable states and their groupings are redundant (Marques-Pita and Rocha, 2013). The precise charting and measurement of such canalization simplifies these models, making even very large networks amenable to analysis. Moreover, canalization plays an important role in the control, robustness, modularity and criticality of Boolean network dynamics, especially those used to model biochemical regulation (Gates and Rocha, 2016; Gates et al., 2016; Manicka, 2017). Here we describe a new publicly-available Python package that provides the necessary tools to extract, measure, and visualize canalizing redundancy present in Boolean network models. It extracts the pathways most effective in controlling dynamics in these models, including their effective graph and dynamics canalizing map, as well as other tools to uncover minimum sets of control variables.

Capacity for instantaneous catabolism of preferred and non-preferred carbon sources in Escherichia coli and Bacillus subtilis

Making the right choice for nutrient consumption in an ever-changing environment is a key factor for evolutionary success of bacteria. Here we investigate the regulatory mechanisms that enable dynamic adaptation between non-preferred and preferred carbon sources for the model Gram-negative and -positive species Escherichia coli and Bacillus subtilis, respectively. We focus on the ability for instantaneous catabolism of a gluconeogenic carbon source upon growth on a glycolytic carbon source and vice versa. By following isotopic tracer dynamics on a 1–2 minute scale, we show that flux reversal from the preferred glucose to non-preferred pyruvate as the sole carbon source is primarily transcriptionally regulated. In the opposite direction, however, E. coli can reverse its flux instantaneously by means of allosteric regulation, whereas in B. subtilis this flux reversal is transcriptionally regulated. Upon removal of transcriptional regulation, B. subtilis assumes the ability of instantaneous glucose catabolism. Using an approach that combines quantitative metabolomics and kinetic modelling, we then identify the additionally necessary key metabolite-enzyme interactions that implement the instantaneous flux reversal in the transcriptionally deregulated B. subtilis, and validate the most relevant allosteric interactions.

Sporadic cases of lumpy skin disease among cattle in Sharkia province, Egypt: Genetic characterization of lumpy skin disease virus isolates and pathological findings

BACKGROUND AND AIM

Lumpy skin disease (LSD) is a highly infectious viral disease upsetting cattle, caused by LSD virus (LSDV) within the family Poxviridae. Sporadic cases of LSD have been observed in cattle previously vaccinated with the Romanian sheep poxvirus (SPPV) vaccine during the summer of 2016 in Sharkia province, Egypt. The present study was undertaken to perform molecular characterization of LSDV strains which circulated in this period as well as investigate their phylogenetic relatedness with published reference capripoxvirus genome sequences.

MATERIALS AND METHODS

A total of 82 skin nodules, as well as 5 lymph nodes, were collected from suspect LSD cases, and the virus was isolated in embryonated chicken eggs (ECEs). LSD was confirmed by polymerase chain reactions amplification of the partial and full-length sequences of the attachment and G-protein-coupled chemokine receptor (GPCR) genes, respectively, as well as a histopathological examination of the lesions. Molecular characterization of the LSDV isolates was conducted by sequencing the GPCR gene.

RESULTS

Characteristic skin nodules that covered the whole intact skin, as well as lymphadenopathy, were significant clinical signs in all suspected cases. LSDV isolation in ECEs revealed the characteristic focal white pock lesions dispersed on the chorioallantoic membranes. Histopathologic examination showed characteristic eosinophilic intracytoplasmic inclusion bodies within inflammatory cell infiltration. Phylogenetic analysis revealed that the LSDV isolates were clustered together with other African and European LSDV strains. In addition, the LSDV isolates have a unique signature of LSDVs (A11, T12, T34, S99, and P199).

CONCLUSION

LSDV infections have been detected in cattle previously vaccinated with Romanian SPPV vaccine during the summer of 2016 and making the evaluation of vaccine efficacy under field conditions necessary.

Reserve Flux Capacity in the Pentose Phosphate Pathway Enables Escherichia coli's Rapid Response to Oxidative Stress

To counteract oxidative stress and reactive oxygen species (ROS), bacteria evolved various mechanisms, primarily reducing ROS through antioxidant systems that utilize cofactor NADPH. Cells must stabilize NADPH levels by increasing flux through replenishing metabolic pathways like pentose phosphate (PP) pathway. Here, we investigate the mechanism enabling the rapid increase in NADPH supply by exposing Escherichia coli to hydrogen peroxide and quantifying the immediate metabolite dynamics. To systematically infer active regulatory interactions governing this response, we evaluated ensembles of kinetic models of glycolysis and PP pathway, each with different regulation mechanisms. Besides the known inactivation of glyceraldehyde 3-phosphate dehydrogenase by ROS, we reveal the important allosteric inhibition of the first PP pathway enzyme by NADPH. This NADPH feedback inhibition maintains a below maximum-capacity PP pathway flux under non-stress conditions. Relieving this inhibition instantly increases PP pathway flux upon oxidative stress. We demonstrate that reducing cells' capacity to rapidly reroute their flux through the PP pathway increases their oxidative stress sensitivity.

Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis

During embryogenesis, cells acquire distinct fates by transitioning through transcriptional states. To uncover these transcriptional trajectories during zebrafish embryogenesis, we sequenced 38,731 cells and developed URD, a simulated diffusion-based computational reconstruction method. URD identified the trajectories of 25 cell types through early somitogenesis, gene expression along them, and their spatial origin in the blastula. Analysis of Nodal signaling mutants revealed that their transcriptomes were canalized into a subset of wild-type transcriptional trajectories. Some wild-type developmental branch points contained cells that express genes characteristic of multiple fates. These cells appeared to trans-specify from one fate to another. These findings reconstruct the transcriptional trajectories of a vertebrate embryo, highlight the concurrent canalization and plasticity of embryonic specification, and provide a framework with which to reconstruct complex developmental trees from single-cell transcriptomes.

Nontargeted Metabolomics Reveals the Multilevel Response to Antibiotic Perturbations

Microbes have shown a remarkable ability in evading the killing actions of antimicrobial agents, such that treatment of bacterial infections represents once more an urgent global challenge. Understanding the initial bacterial response to antimicrobials may reveal intrinsic tolerance mechanisms to antibiotics and suggest alternative and less conventional therapeutic strategies. Here, we used mass spectrometry-based metabolomics to monitor the immediate metabolic response of Escherichia coli to a variety of antibiotic perturbations. We show that rapid metabolic changes can reflect drug mechanisms of action and reveal the active role of metabolism in mediating the first stress response to antimicrobials. We uncovered a role for ammonium imbalance in aggravating chloramphenicol toxicity and the essential function of deoxythymidine 5'-diphosphate (dTDP)-rhamnose synthesis for the immediate transcriptional upregulation of GyrA in response to quinolone antibiotics. Our results suggest bacterial metabolism as an attractive target to interfere with the early bacterial response to antibiotic treatments and reduce the probability for survival and eventual evolution of antibiotic resistance.

High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds

Rapidly spreading antibiotic resistance and the low discovery rate of new antimicrobial compounds demand more effective strategies for early drug discovery. One bottleneck in the drug discovery pipeline is the identification of the modes of action (MoAs) of new compounds. We have developed a rapid systematic metabolome profiling strategy to classify the MoAs of bioactive compounds. The method predicted MoA-specific metabolic responses in the nonpathogenic bacterium Mycobacterium smegmatis after treatment with 62 reference compounds with known MoAs and different metabolic and nonmetabolic targets. We then analyzed a library of 212 new antimycobacterial compounds with unknown MoAs from a drug discovery effort by the pharmaceutical company GlaxoSmithKline (GSK). More than 70% of these new compounds induced metabolic responses in M. smegmatis indicative of known MoAs, seven of which were experimentally validated. Only 8% (16) of the compounds appeared to target unconventional cellular processes, illustrating the difficulty in discovering new antibiotics with different MoAs among compounds used as monotherapies. For six of the GSK compounds with potentially new MoAs, the metabolome profiles suggested their ability to interfere with trehalose and lipid metabolism. This was supported by whole-genome sequencing of spontaneous drug-resistant mutants of the pathogen Mycobacterium tuberculosis and in vitro compound-proteome interaction analysis for one of these compounds. Our compendium of drug-metabolome profiles can be used to rapidly query the MoAs of uncharacterized antimicrobial compounds and should be a useful resource for the drug discovery community.

Real Talk: The Inter-play Between the mTOR, AMPK, and Hexosamine Biosynthetic Pathways in Cell Signaling

O-linked N-acetylglucosamine, better known as O-GlcNAc, is a sugar post-translational modification participating in a diverse range of cell functions. Disruptions in the cycling of O-GlcNAc mediated by O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), respectively, is a driving force for aberrant cell signaling in disease pathologies, such as diabetes, obesity, Alzheimer's disease, and cancer. Production of UDP-GlcNAc, the metabolic substrate for OGT, by the Hexosamine Biosynthetic Pathway (HBP) is controlled by the input of amino acids, fats, and nucleic acids, making O-GlcNAc a key nutrient-sensor for fluctuations in these macromolecules. The mammalian target of rapamycin (mTOR) and AMP-activated protein kinase (AMPK) pathways also participate in nutrient-sensing as a means of controlling cell activity and are significant factors in a variety of pathologies. Research into the individual nutrient-sensitivities of the HBP, AMPK, and mTOR pathways has revealed a complex regulatory dynamic, where their unique responses to macromolecule levels coordinate cell behavior. Importantly, cross-talk between these pathways fine-tunes the cellular response to nutrients. Strong evidence demonstrates that AMPK negatively regulates the mTOR pathway, but O-GlcNAcylation of AMPK lowers enzymatic activity and promotes growth. On the other hand, AMPK can phosphorylate OGT leading to changes in OGT function. Complex sets of interactions between the HBP, AMPK, and mTOR pathways integrate nutritional signals to respond to changes in the environment. In particular, examining these relationships using systems biology approaches might prove a useful method of exploring the complex nature of cell signaling. Overall, understanding the complex interactions of these nutrient pathways will provide novel mechanistic information into how nutrients influence health and disease.

Deciphering the Origin, Evolution, and Physiological Function of the Subtelomeric Aryl-Alcohol Dehydrogenase Gene Family in the Yeast Saccharomyces cerevisiae

Homology searches indicate that Saccharomyces cerevisiae strain BY4741 contains seven redundant genes that encode putative aryl-alcohol dehydrogenases (AAD). Yeast AAD genes are located in subtelomeric regions of different chromosomes, and their functional role(s) remain enigmatic. Here, we show that two of these genes, AAD4 and AAD14, encode functional enzymes that reduce aliphatic and aryl-aldehydes concomitant with the oxidation of cofactor NADPH, and that Aad4p and Aad14p exhibit different substrate preference patterns. Other yeast AAD genes are undergoing pseudogenization. The 5′ sequence of AAD15 has been deleted from the genome. Repair of an AAD3 missense mutation at the catalytically essential Tyr⁷³ residue did not result in a functional enzyme. However, ancestral-state reconstruction by fusing Aad6 with Aad16 and by N-terminal repair of Aad10 restores NADPH-dependent aryl-alcohol dehydrogenase activities. Phylogenetic analysis indicates that AAD genes are narrowly distributed in wood-saprophyte fungi and in yeast that occupy lignocellulosic niches. Because yeast AAD genes exhibit activity on veratraldehyde, cinnamaldehyde, and vanillin, they could serve to detoxify aryl-aldehydes released during lignin degradation. However, none of these compounds induce yeast AAD gene expression, and Aad activities do not relieve aryl-aldehyde growth inhibition. Our data suggest an ancestral role for AAD genes in lignin degradation that is degenerating as a result of yeast's domestication and use in brewing, baking, and other industrial applications.

IMPORTANCE Functional characterization of hypothetical genes remains one of the chief tasks of the postgenomic era. Although the first Saccharomyces cerevisiae genome sequence was published over 20 years ago, 22% of its estimated 6,603 open reading frames (ORFs) remain unverified. One outstanding example of this category of genes is the enigmatic seven-member AAD family. Here, we demonstrate that proteins encoded by two members of this family exhibit aliphatic and aryl-aldehyde reductase activity, and further that such activity can be recovered from pseudogenized AAD genes via ancestral-state reconstruction. The phylogeny of yeast AAD genes suggests that these proteins may have played an important ancestral role in detoxifying aromatic aldehydes in ligninolytic fungi. However, in yeast adapted to niches rich in sugars, AAD genes become subject to mutational erosion. Our findings shed new light on the selective pressures and molecular mechanisms by which genes undergo pseudogenization.

Dynamics of sequestration-based gene regulatory cascades

Gene regulatory cascades are ubiquitous in biology. Because regulatory cascades are integrated within complex networks, their quantitative analysis is challenging in native systems. Synthetic biologists have gained quantitative insights into the properties of regulatory cascades by building simple circuits, but sequestration-based regulatory cascades remain relatively unexplored. Particularly, it remains unclear how the cascade components collectively control the output dynamics. Here, we report the construction and quantitative analysis of the longest sequestration-based cascade in Escherichia coli. This cascade consists of four Pseudomonas aeruginosa protein regulators (ExsADCE) that sequester their partner. Our computational analysis showed that the output dynamics are controlled in a complex way by the concentration of the unbounded transcriptional activator ExsA. By systematically varying the cascade length and the synthesis rate of each regulator, we experimentally verified the computational prediction that ExsC plays a role in rapid circuit responses by sequestering the anti-activator ExsD, while ExsD increases response times by decreasing the free ExsA concentration. In contrast, when additional ExsD was introduced to the cascade via indirect negative feedback, the response time was significantly reduced. Sequestration-based regulatory cascades with negative feedback are often found in biology, and thus our finding provides insights into the dynamics of this recurring motif.

Reciprocal Prioritization to Dietary Glycans by Gut Bacteria in a Competitive Environment Promotes Stable Coexistence

When presented with nutrient mixtures, several human gut Bacteroides species exhibit hierarchical utilization of glycans through a phenomenon that resembles catabolite repression. However, it is unclear how closely these observed physiological changes, often measured by altered transcription of glycan utilization genes, mirror actual glycan depletion. To understand the glycan prioritization strategies of two closely related human gut symbionts, Bacteroides ovatus and Bacteroides thetaiotaomicron, we performed a series of time course assays in which both species were individually grown in a medium with six different glycans that both species can degrade. Disappearance of the substrates and transcription of the corresponding polysaccharide utilization loci (PULs) were measured. Each species utilized some glycans before others, but with different priorities per species, providing insight into species-specific hierarchical preferences. In general, the presence of highly prioritized glycans repressed transcription of genes involved in utilizing lower-priority nutrients. However, transcriptional sensitivity to some glycans varied relative to the residual concentration in the medium, with some PULs that target high-priority substrates remaining highly expressed even after their target glycan had been mostly depleted. Coculturing of these organisms in the same mixture showed that the hierarchical orders generally remained the same, promoting stable coexistence. Polymer length was found to be a contributing factor for glycan utilization, thereby affecting its place in the hierarchy. Our findings not only elucidate how B. ovatus and B. thetaiotaomicron strategically access glycans to maintain coexistence but also support the prioritization of carbohydrate utilization based on carbohydrate structure, advancing our understanding of the relationships between diet and the gut microbiome.IMPORTANCE The microorganisms that reside in the human colon fulfill their energy requirements mainly from diet- and host-derived complex carbohydrates. Members of this ecosystem possess poorly understood strategies to prioritize and compete for these nutrients. Based on direct carbohydrate measurements and corresponding transcriptional analyses, our findings showed that individual bacterial species exhibit different preferences for the same set of glycans and that this prioritization is maintained in a competitive environment, which may promote stable coexistence. Such understanding of gut bacterial glycan utilization will be essential to eliciting predictable changes in the gut microbiota to improve health through the diet.

Deciphering the regulation of metabolism with dynamic optimization: an overview of recent advances

Understanding optimality principles shaping the evolution of regulatory networks controlling metabolism is crucial for deriving a holistic picture of how metabolism is integrated into key cellular processes such as growth, adaptation and pathogenicity. While in the past the focus of research in pathway regulation was mainly based on stationary states, more recently dynamic optimization has proved to be an ideal tool to decipher regulatory strategies for metabolic pathways in response to environmental cues. In this short review, we summarize recent advances in the elucidation of optimal regulatory strategies and identification of optimal control points in metabolic pathways. We discuss biological implications of the discovered optimality principles on genome organization and provide examples how the derived knowledge can be used to identify new treatment strategies against pathogens. Furthermore, we briefly discuss the variety of approaches for solving dynamic optimization problems and emphasize whole-cell resource allocation models as an important emerging area of research that will allow us to study the regulation of metabolism on the whole-cell level.

Frontiers of high-throughput metabolomics

Large scale metabolomics studies are increasingly used to investigate genetically different individuals and time-dependent responses to environmental stimuli. New mass spectrometric approaches with at least an order of magnitude more rapid analysis of small molecules within the cell's metabolome are now paving the way towards true high-throughput metabolomics, opening new opportunities in systems biology, functional genomics, drug discovery, and personalized medicine. Here we discuss the impact and advantages of the progress made in profiling large cohorts and dynamic systems with high temporal resolution and automated sampling. In both areas, high-throughput metabolomics is gaining traction because it can generate hypotheses on molecular mechanisms and metabolic regulation. We conclude with the current status of the less mature single cell analyses where high-throughput analytics will be indispensable to resolve metabolic heterogeneity in populations and compartmentalization of metabolites.

Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness

Data of gene expression levels across individuals, cell types, and disease states is expanding, yet our understanding of how expression levels impact phenotype is limited. Here, we present a massively parallel system for assaying the effect of gene expression levels on fitness in Saccharomyces cerevisiae by systematically altering the expression level of ∼100 genes at ∼100 distinct levels spanning a 500-fold range at high resolution. We show that the relationship between expression levels and growth is gene and environment specific and provides information on the function, stoichiometry, and interactions of genes. Wild-type expression levels in some conditions are not optimal for growth, and genes whose fitness is greatly affected by small changes in expression level tend to exhibit lower cell-to-cell variability in expression. Our study addresses a fundamental gap in understanding the functional significance of gene expression regulation and offers a framework for evaluating the phenotypic effects of expression variation.

Distinct transcriptional regulation of the two Escherichia coli transhydrogenases PntAB and UdhA

Transhydrogenases catalyse interconversion of the redox cofactors NADH and NADPH, thereby conveying metabolic flexibility to balance catabolic NADPH formation with anabolic or stress-based consumption of NADPH. Escherichia coli is one of the very few microbes that possesses two isoforms: the membrane-bound, proton-translocating transhydrogenase PntAB and the cytosolic, energy-independent transhydrogenase UdhA. Despite their physiological relevance, we have only fragmented information on their regulation and the signals coordinating their counteracting activities. Here we investigated PntAB and UdhA regulation by studying transcriptional responses to environmental and genetic perturbations. By testing pntAB and udhA GFP reporter constructs in the background of WT E. coli and 62 transcription factor mutants during growth on different carbon sources, we show distinct transcriptional regulation of the two transhydrogenase promoters. Surprisingly, transhydrogenase regulation was independent of the actual catabolic overproduction or underproduction of NADPH but responded to nutrient levels and growth rate in a fashion that matches the cellular need for the redox cofactors NADPH and/or NADH. Specifically, the identified transcription factors Lrp, ArgP and Crp link transhydrogenase expression to particular amino acids and intracellular concentrations of cAMP. The overall identified set of regulators establishes a primarily biosynthetic role for PntAB and link UdhA to respiration.

Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments

An important experimental design question for high-throughput time series studies is the number of replicates required for accurate reconstruction of the profiles. Due to budget and sample availability constraints, more replicates imply fewer time points and vice versa. We analyze the performance of dense and replicate sampling by developing a theoretical framework that focuses on a restricted yet expressive set of possible curves over a wide range of noise levels and by analyzing real expression data. For both the theoretical analysis and experimental data, we observe that, under reasonable noise levels, autocorrelations in the time series data allow dense sampling to better determine the correct levels of non-sampled points when compared to replicate sampling. A Java implementation of our framework can be used to determine the best replicate strategy given the expected noise. These results provide theoretical support to the large number of high-throughput time series experiments that do not use replicates.

A Generic and Cell-Type-Specific Wound Response Precedes Regeneration in Planarians

Regeneration starts with injury. Yet how injuries affect gene expression in different cell types and how distinct injuries differ in gene expression remain unclear. We defined the transcriptomes of major cell types of planarians--flatworms that regenerate from nearly any injury--and identified 1,214 tissue-specific markers across 13 cell types. RNA sequencing on 619 single cells revealed that wound-induced genes were expressed either in nearly all cell types or specifically in one of three cell types (stem cells, muscle, or epidermis). Time course experiments following different injuries indicated that a generic wound response is activated with any injury regardless of the regenerative outcome. Only one gene, notum, was differentially expressed early between anterior- and posterior-facing wounds. Injury-specific transcriptional responses emerged 30 hr after injury, involving context-dependent patterning and stem-cell-specialization genes. The regenerative requirement of every injury is different; however, our work demonstrates that all injuries start with a common transcriptional response.

Cellular Growth Arrest and Persistence from Enzyme Saturation

Metabolic efficiency depends on the balance between supply and demand of metabolites, which is sensitive to environmental and physiological fluctuations, or noise, causing shortages or surpluses in the metabolic pipeline. How cells can reliably optimize biomass production in the presence of metabolic fluctuations is a fundamental question that has not been fully answered. Here we use mathematical models to predict that enzyme saturation creates distinct regimes of cellular growth, including a phase of growth arrest resulting from toxicity of the metabolic process. Noise can drive entry of single cells into growth arrest while a fast-growing majority sustains the population. We confirmed these predictions by measuring the growth dynamics of Escherichia coli utilizing lactose as a sole carbon source. The predicted heterogeneous growth emerged at high lactose concentrations, and was associated with cell death and production of antibiotic-tolerant persister cells. These results suggest how metabolic networks may balance costs and benefits, with important implications for drug tolerance.

In bacteria, changes in gene expression, with resulting changes in protein concentration, can drastically change how fast cells and cellular populations grow. This fact has big implications for how we treat infectious disease, which types of organisms make up our microbiomes, and what patterns of gene regulation have undergone evolutionary selection. Here, we show how, in principle, the expression level of a single enzyme can affect bacterial population growth by creating a threshold where cells grow optimally fast just below it, but rapidly reach a state of no growth just above it because metabolic byproducts build up and halt growth. The narrow margin between these two states makes entering either of them possible for the same bacterium because of intrinsic uncertainty, or "noise", in gene expression. The predicted result is a variety of growth rates in a single population of genetically identical cells, manifested as a mix of fast- and slow-growing cells. We created laboratory conditions that reproduce the effect in the model organism E. coli, and showed that there may be a benefit to having slower growing cells, because they can survive antibiotic exposure for longer.

Principles of dynamical modularity in biological regulatory networks

Intractable diseases such as cancer are associated with breakdown in multiple individual functions, which conspire to create unhealthy phenotype-combinations. An important challenge is to decipher how these functions are coordinated in health and disease. We approach this by drawing on dynamical systems theory. We posit that distinct phenotype-combinations are generated by interactions among robust regulatory switches, each in control of a discrete set of phenotypic outcomes. First, we demonstrate the advantage of characterizing multi-switch regulatory systems in terms of their constituent switches by building a multiswitch cell cycle model which points to novel, testable interactions critical for early G2/M commitment to division. Second, we define quantitative measures of dynamical modularity, namely that global cell states are discrete combinations of switch-level phenotypes. Finally, we formulate three general principles that govern the way coupled switches coordinate their function.

Gene Expression Switching of Receptor Subunits in Human Brain Development

Synaptic receptors in the human brain consist of multiple protein subunits, many of which have multiple variants, coded by different genes, and are differentially expressed across brain regions and developmental stages. The brain can tune the electrophysiological properties of synapses to regulate plasticity and information processing by switching from one protein variant to another. Such condition-dependent variant switch during development has been demonstrated in several neurotransmitter systems including NMDA and GABA. Here we systematically detect pairs of receptor-subunit variants that switch during the lifetime of the human brain by analyzing postmortem expression data collected in a population of donors at various ages and brain regions measured using microarray and RNA-seq. To further detect variant pairs that co-vary across subjects, we present a method to quantify age-corrected expression correlation in face of strong temporal trends. This is achieved by computing the correlations in the residual expression beyond a cubic-spline model of the population temporal trend, and can be seen as a nonlinear version of partial correlations. Using these methods, we detect multiple new pairs of context dependent variants. For instance, we find a switch from GLRA2 to GLRA3 that differs from the known switch in the rat. We also detect an early switch from HTR1A to HTR5A whose trends are negatively correlated and find that their age-corrected expression is strongly positively correlated. Finally, we observe that GRIN2B switch to GRIN2A occurs mostly during embryonic development, presumably earlier than observed in rodents. These results provide a systematic map of developmental switching in the neurotransmitter systems of the human brain.

Synapses change their properties during development affecting information processing and learning. Most synaptic receptors consist of several proteins, each having several variants coded by closely related genes. These protein variants are similar in structure, yet often differ slightly in their biophysical attributes. Switching a synapse from using one variant to another provides the brain with a way to fine-tune electrophysiological properties of synapses and has been described in NMDA and GABA receptors. Here we describe a systematic approach to detect pairs of context-dependent variants at a genome-wide scale based on a set of post-mortem expression measurements taken from brains at multiple ages. We take into account both the profile of expression as it changes along life and also the detrended age-corrected correlation among genes. This method characterizes the landscape of developmental switches in brain transcriptome, putting forward new candidates pairs for deeper analysis. The abundance of switching between context-dependent variants through life suggests that it is a major mechanism by which the brain tunes its plasticity and information processing.

Integrative Model of Oxidative Stress Adaptation in the Fungal Pathogen Candida albicans

The major fungal pathogen of humans, Candida albicans, mounts robust responses to oxidative stress that are critical for its virulence. These responses counteract the reactive oxygen species (ROS) that are generated by host immune cells in an attempt to kill the invading fungus. Knowledge of the dynamical processes that instigate C. albicans oxidative stress responses is required for a proper understanding of fungus-host interactions. Therefore, we have adopted an interdisciplinary approach to explore the dynamical responses of C. albicans to hydrogen peroxide (H₂O₂). Our deterministic mathematical model integrates two major oxidative stress signalling pathways (Cap1 and Hog1 pathways) with the three major antioxidant systems (catalase, glutathione and thioredoxin systems) and the pentose phosphate pathway, which provides reducing equivalents required for oxidative stress adaptation. The model encapsulates existing knowledge of these systems with new genomic, proteomic, transcriptomic, molecular and cellular datasets. Our integrative approach predicts the existence of alternative states for the key regulators Cap1 and Hog1, thereby suggesting novel regulatory behaviours during oxidative stress. The model reproduces both existing and new experimental observations under a variety of scenarios. Time- and dose-dependent predictions of the oxidative stress responses for both wild type and mutant cells have highlighted the different temporal contributions of the various antioxidant systems during oxidative stress adaptation, indicating that catalase plays a critical role immediately following stress imposition. This is the first model to encapsulate the dynamics of the transcriptional response alongside the redox kinetics of the major antioxidant systems during H₂O₂ stress in C. albicans.

Exploring the structure and function of temporal networks with dynamic graphlets

MOTIVATION

With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships.

RESULTS

We base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in static network research. We develop new theory to allow for graphlet-based analyses of temporal networks. Our new notion of dynamic graphlets is different from existing dynamic network approaches that are based on temporal motifs (statistically significant subgraphs). The latter have limitations: their results depend on the choice of a null network model that is required to evaluate the significance of a subgraph, and choosing a good null model is non-trivial. Our dynamic graphlets overcome the limitations of the temporal motifs. Also, when we aim to characterize the structure and function of an entire temporal network or of individual nodes, our dynamic graphlets outperform the static graphlets. Clearly, accounting for temporal information helps. We apply dynamic graphlets to temporal age-specific molecular network data to deepen our limited knowledge about human aging.

AVAILABILITY AND IMPLEMENTATION

http://www.nd.edu/∼cone/DG.

Temporal hierarchy of gene expression mediated by transcription factor binding affinity and activation dynamics

Understanding cellular responses to environmental stimuli requires not only the knowledge of specific regulatory components but also the quantitative characterization of the magnitude and timing of regulatory events. The two-component system is one of the major prokaryotic signaling schemes and is the focus of extensive interest in quantitative modeling and investigation of signaling dynamics. Here we report how the binding affinity of the PhoB two-component response regulator (RR) to target promoters impacts the level and timing of expression of PhoB-regulated genes. Information content has often been used to assess the degree of conservation for transcription factor (TF)-binding sites. We show that increasing the information content of PhoB-binding sites in designed phoA promoters increased the binding affinity and that the binding affinity and concentration of phosphorylated PhoB (PhoB~P) together dictate the level and timing of expression of phoA promoter variants. For various PhoB-regulated promoters with distinct promoter architectures, expression levels appear not to be correlated with TF-binding affinities, in contrast to the intuitive and oversimplified assumption that promoters with higher affinity for a TF tend to have higher expression levels. However, the expression timing of the core set of PhoB-regulated genes correlates well with the binding affinity of PhoB~P to individual promoters and the temporal hierarchy of gene expression appears to be related to the function of gene products during the phosphate starvation response. Modulation of the information content and binding affinity of TF-binding sites may be a common strategy for temporal programming of the expression profile of RR-regulated genes.

IMPORTANCE

A single TF often orchestrates the expression of multiple genes in response to environmental stimuli. It is not clear how different TF-binding sites within the regulon dictate the expression profile. Our studies of Escherichia coli PhoB, a response regulator that controls expression of a core set of phosphate assimilation genes in response to phosphate starvation, showed that expression levels of PhoB-regulated genes are under sophisticated control and do not follow a simple correlation with the binding affinity of PhoB~P to individual promoters. However, the expression timing correlates with the PhoB-binding affinity and gene functions. Genes involved in direct Pi uptake contain high-affinity sites and are transcribed earlier than genes involved in phosphorus scavenging. This illustrates an elaborate mechanism of temporally programmed gene expression, even for nondevelopmental pathways.