Search tips
Search criteria

Results 1-25 (1242075)

Clipboard (0)

Related Articles

1.  Quantitative analysis of regulatory flexibility under changing environmental conditions 
Day length changes with the seasons in temperate latitudes, affecting the many biological rhythms that entrain to the day/night cycle: we measure these effects on the expression of Arabidopsis clock genes, using RNA and reporter gene readouts, with a new method of phase analysis.Dusk sensitivity is proposed as a simple, natural and general mathematical measure to analyse and manipulate the changing phase of a clock output relative to the change in the day/night cycle.Dusk sensitivity shows how increasing the numbers of feedback loops in the Arabidopsis clock models allows more flexible regulation, consistent with a previously-proposed, general operating principle of biological networks.The Arabidopsis clock genes show flexibility of regulation that is characteristic of a three-loop clock model, validating aspects of the model and the operating principle, but some clock output genes show greater flexibility arising from direct light regulation.
The analysis of dynamic, non-linear regulation with the aid of mechanistic models is central to Systems Biology. This study compares the predictions of mechanistic, mathematical models of the circadian clock with molecular time-series data on rhythmic gene expression in the higher plant Arabidopsis thaliana. Analysis of the models helps us to understand (explain and predict) how the clock gene circuit balances regulation by external and endogenous factors to achieve particular behaviours. Such multi-factorial regulation is ubiquitous in, and characteristic of, living systems.
The Earth's rotation causes predictable changes in the environment, notably in the availability of sunlight for photosynthesis. Many biological processes are driven by the environmental input via sensory pathways, for example, from photoreceptors. Circadian clocks provide an alternative strategy. These endogenous, 24-h rhythms can drive biological processes that anticipate the regular environmental changes, rather than merely responding. Many rhythmic processes have both light and clock control. Indeed, the clock components themselves must balance internal timing with external inputs, because circadian clocks are reset daily through light regulation of one or more clock components. This process of entrainment is complicated by the change in day length. When the times of dawn and dusk move apart in summer, and closer together in winter, does the clock track dawn, track dusk or interpolate between them?
In plants, the clock controls leaf and petal movements, the opening and closing of stomatal pores, the discharge of floral fragrances, and many metabolic activities, especially those associated with photosynthesis. Centuries of physiological studies have shown that these rhythms can behave differently. Flowering in Ipomoea nil (Pharbitis nil, Japanese morning glory) is controlled by a rhythm that tracks the time of dusk, to give a classic example. We showed that two other rhythms associated with vegetative growth track dawn in this species (Figure 5A), so the clock system allows flexible regulation.
The relatively small number of components involved in the circadian clockwork makes it an ideal candidate for mathematical modelling. Molecular genetic studies in a variety of model eukaryotes have shown that the circadian rhythm is generated by a network of 6–20 genes. These genes form feedback loops generating a rhythm in mRNA production. A single negative feedback loop in which a gene encodes a protein that, after several hours, turns off transcription is capable of generating a circadian rhythm, in principle. A single light input can entrain the clock to ‘local time', synchronised with a light–dark cycle. However, real circadian clocks have proven to be more complicated than this, with multiple light inputs and interlocked feedback loops.
We have previously argued from mathematical analysis that multi-loop networks increase the flexibility of regulation (Rand et al, 2004) and have shown that appropriately deployed flexibility can confer functional robustness (Akman et al, 2010). Here we test whether that flexibility can be demonstrated in vivo, in the model plant, A. thaliana. The Arabidopsis clock mechanism comprises a feedback loop in which two partially redundant, myb transcription factors, LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), repress the expression of their activator, TIMING OF CAB EXPRESSION 1 (TOC1). We previously modelled this single-loop circuit and showed that it was not capable of recreating important data (Locke et al, 2005a). An extended, two-loop model was developed to match observed behaviours, incorporating a hypothetical gene Y, for which the best identified candidate was the GIGANTEA gene (GI) (Locke et al, 2005b). Two further models incorporated the TOC1 homologues PSEUDO-RESPONSE REGULATOR (PRR) 9 and PRR7 (Locke et al, 2006; Zeilinger et al, 2006). In these circuits, a morning oscillator (LHY/CCA1–PRR9/7) is coupled to an evening oscillator (Y/GI–TOC1) via the original LHY/CCA1–TOC1 loop.
These clock models, like those for all other organisms, were developed using data from simple conditions of constant light, darkness or 12-h light–12-h dark cycles. We therefore tested how the clock genes in Arabidopsis responded to light–dark cycles with different photoperiods, from 3 h light to 18 h light per 24-h cycle (Edinburgh, 56° North latitude, has 17.5 h light in midsummer). The time-series assays of mRNA and in vivo reporter gene images showed a range of peak times for different genes, depending on the photoperiod (Figure 5C). A new data analysis method, mFourfit, was introduced to measure the peak times, in the Biological Rhythms Analysis Software Suite (BRASS v3.0). None of the genes showed the dusk-tracking behaviour characteristic of the Ipomoea flowering rhythm. The one-, two- and three-loop models were analysed to understand the observed patterns. A new mathematical measure, dusk sensitivity, was introduced to measure the change in timing of a model component versus a change in the time of dusk. The one- and two-loop models tracked dawn and dusk, respectively, under all conditions. Only the three-loop model (Figure 5B) had the flexibility required to match the photoperiod-dependent changes that we found in vivo, and in particular the unexpected, V-shaped pattern in the peak time of TOC1 expression. This pattern of regulation depends on the structure and light inputs to the model's evening oscillator, so the in vivo data supported this aspect of the model. LHY and CCA1 gene expression under short photoperiods showed greater dusk sensitivity, in the interval 2–6 h before dawn, than the three-loop model predicted, so these data will help to constrain future models.
The approach described here could act as a template for experimental biologists seeking to understand biological regulation using dynamic, experimental perturbations and time-series data. Simulation of mathematical models (despite known imperfections) can provide contrasting hypotheses that guide understanding. The system's detailed behaviour is complex, so a natural and general measure such as dusk sensitivity is helpful to focus on one property of the system. We used the measure to compare models, and to predict how this property could be manipulated. To enable additional analysis of this system, we provide the time-series data and experimental metadata online.
The circadian clock controls 24-h rhythms in many biological processes, allowing appropriate timing of biological rhythms relative to dawn and dusk. Known clock circuits include multiple, interlocked feedback loops. Theory suggested that multiple loops contribute the flexibility for molecular rhythms to track multiple phases of the external cycle. Clear dawn- and dusk-tracking rhythms illustrate the flexibility of timing in Ipomoea nil. Molecular clock components in Arabidopsis thaliana showed complex, photoperiod-dependent regulation, which was analysed by comparison with three contrasting models. A simple, quantitative measure, Dusk Sensitivity, was introduced to compare the behaviour of clock models with varying loop complexity. Evening-expressed clock genes showed photoperiod-dependent dusk sensitivity, as predicted by the three-loop model, whereas the one- and two-loop models tracked dawn and dusk, respectively. Output genes for starch degradation achieved dusk-tracking expression through light regulation, rather than a dusk-tracking rhythm. Model analysis predicted which biochemical processes could be manipulated to extend dusk tracking. Our results reveal how an operating principle of biological regulators applies specifically to the plant circadian clock.
PMCID: PMC3010117  PMID: 21045818
Arabidopsis thaliana; biological clocks; dynamical systems; gene regulatory networks; mathematical models; photoperiodism
2.  A model of yeast cell-cycle regulation based on multisite phosphorylation 
Multisite phosphorylation of CDK target proteins provides the requisite nonlinearity for cell cycle modeling using elementary reaction mechanisms.Stochastic simulations, based on Gillespie's algorithm and using realistic numbers of protein and mRNA molecules, compare favorably with single-cell measurements in budding yeast.The role of transcription–translation coupling is critical in the robust operation of protein regulatory networks in yeast cells.
Progression through the eukaryotic cell cycle is governed by the activation and inactivation of a family of cyclin-dependent kinases (CDKs) and auxiliary proteins that regulate CDK activities (Morgan, 2007). The many components of this protein regulatory network are interconnected by positive and negative feedback loops that create bistable switches and transient pulses (Tyson and Novak, 2008). The network must ensure that cell-cycle events proceed in the correct order, that cell division is balanced with respect to cell growth, and that any problems encountered (in replicating the genome or partitioning chromosomes to daughter cells) are corrected before the cell proceeds to the next phase of the cycle. The network must operate robustly in the context of unavoidable molecular fluctuations in a yeast-sized cell. With a volume of only 5×10−14 l, a yeast cell contains one copy of the gene for each component of the network, a handful of mRNA transcripts of each gene, and a few hundreds to thousands of protein molecules carrying out each gene's function. How large are the molecular fluctuations implied by these numbers, and what effects do they have on the functioning of the cell-cycle control system?
To answer these questions, we have built a new model (Figure 1) of the CDK regulatory network in budding yeast, based on the fact that the targets of CDK activity are typically phosphorylated on multiple sites. The activity of each target protein depends on how many sites are phosphorylated. The target proteins feedback on CDK activity by controlling cyclin synthesis (SBF's role) and degradation (Cdh1's role) and by releasing a CDK-counteracting phosphatase (Cdc14). Every reaction in Figure 1 can be described by a mass-action rate law, with an accompanying rate constant that must be estimated from experimental data. As the transcription and translation of mRNA molecules have major effects on fluctuating numbers of protein molecules (Pedraza and Paulsson, 2008), we have included mRNA transcripts for each protein in the model.
To create a deterministic model, the rate laws are combined, according to standard principles of chemical kinetics, into a set of 60 differential equations that govern the temporal dynamics of the control system. In the stochastic version of the model, the rate law for each reaction determines the probability per unit time that a particular reaction occurs, and we use Gillespie's stochastic simulation algorithm (Gillespie, 1976) to compute possible temporal sequences of reaction events. Accurate stochastic simulations require knowledge of the expected numbers of mRNA and protein molecules in a single yeast cell. Fortunately, these numbers are available from several sources (Ghaemmaghami et al, 2003; Zenklusen et al, 2008). Although the experimental estimates are not always in good agreement with each other, they are sufficiently reliable to populate a stochastic model with realistic numbers of molecules.
By simulating thousands of cells (as in Figure 5), we can build up representative samples for computing the mean and s.d. of any measurable cell-cycle property (e.g. interdivision time, size at division, duration of G1 phase). The excellent fit of simulated statistics to observations of cell-cycle variability is documented in the main text and Supplementary Information.
Of particular interest to us are observations of Di Talia et al (2007) of the timing of a crucial G1 event (export of Whi5 protein from the nucleus) in a population of budding yeast cells growing at a specific growth rate α=ln2/(mass-doubling time). Whi5 export is a consequence of Whi5 phosphorylation, and it occurs simultaneously with the release (activation) of SBF (see Figure 1). Using fluorescently labeled Whi5, Di Talia et al could easily measure (in individual yeast cells) the time, T1, from cell birth to the abrupt loss of Whi5 from the nucleus. Correlating T1 to the size of the cell at birth, Vbirth, they found that, for a sample of daughter cells, αT1 versus ln(Vbirth) could be fit with two straight lines of slope −0.7 and −0.3. Our simulation of this experiment (Figure 7 of the main text) compares favorably with Figure 3d and e in Di Talia et al (2007).
The major sources of noise in our model (and in protein regulatory networks in yeast cells, in general) are related to gene transcription and the small number of unique mRNA transcripts. As each mRNA molecule may instruct the synthesis of dozens of protein molecules, the coefficient of variation of molecular fluctuations at the protein level (CVP) may be dominated by fluctuations at the mRNA level, as expressed in the formula (Pedraza and Paulsson, 2008) where NM, NP denote the number of mRNA and protein molecules, respectively, and ρ=τM/τP is the ratio of half-lives of mRNA and protein molecules. For a yeast cell, typical values of NM and NP are 8 and 800, respectively (Ghaemmaghami et al, 2003; Zenklusen et al, 2008). If ρ=1, then CVP≈25%. Such large fluctuations in protein levels are inconsistent with the observed variability of size and age at division in yeast cells, as shown in the simplified cell-cycle model of Kar et al (2009) and as we have confirmed with our more realistic model. The size of these fluctuations can be reduced to a more acceptable level by assuming a shorter half-life for mRNA (say, ρ=0.1).
There must be some mechanisms whereby yeast cells lessen the protein fluctuations implied by transcription–translation coupling. Following Pedraza and Paulsson (2008), we suggest that mRNA gestation and senescence may resolve this problem. Equation (3) is based on a simple, one-stage, birth–death model of mRNA turnover. In Supplementary Appendix 1, we show that a model of mRNA processing, with 10 stages each of mRNA gestation and senescence, gives reasonable fluctuations at the protein level (CVP≈5%), even if the effective half-life of mRNA is 10 min. A one-stage model with τM=1 min gives comparable fluctuations (CVP≈5%). In the main text, we use a simple birth–death model of mRNA turnover with an ‘effective' half-life of 1 min, in order to limit the computational complexity of the full cell-cycle model.
In order for the cell's genome to be passed intact from one generation to the next, the events of the cell cycle (DNA replication, mitosis, cell division) must be executed in the correct order, despite the considerable molecular noise inherent in any protein-based regulatory system residing in the small confines of a eukaryotic cell. To assess the effects of molecular fluctuations on cell-cycle progression in budding yeast cells, we have constructed a new model of the regulation of Cln- and Clb-dependent kinases, based on multisite phosphorylation of their target proteins and on positive and negative feedback loops involving the kinases themselves. To account for the significant role of noise in the transcription and translation steps of gene expression, the model includes mRNAs as well as proteins. The model equations are simulated deterministically and stochastically to reveal the bistable switching behavior on which proper cell-cycle progression depends and to show that this behavior is robust to the level of molecular noise expected in yeast-sized cells (∼50 fL volume). The model gives a quantitatively accurate account of the variability observed in the G1-S transition in budding yeast, which is governed by an underlying sizer+timer control system.
PMCID: PMC2947364  PMID: 20739927
bistability; cell-cycle variability; size control; stochastic model; transcription–translation coupling
3.  Disorder, promiscuity, and toxic partnerships 
Cell  2009;138(1):16-18.
Many genes are toxic when overexpressed, but general mechanisms for this toxicity have proven elusive. Vavouri et al. (2009) find that intrinsic protein disorder and promiscuous molecular interactions are strong determinants of dosage sensitivity, explaining in part the toxicity of dosage-sensitive oncogenes in mice and humans.
PMCID: PMC2848715  PMID: 19596229
4.  Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template 
Variable gene dosage is a major source of fluctuations in gene expression in both endogenous and synthetic circuits. Synthetic incoherent feedforward regulatory motifs using RNA interference are shown to robustly adapt to changes in DNA template amounts in mammalian cells.
Variable gene dosage is a major source of fluctuations in gene product levels in both endogenous and synthetic circuits.To mitigate gene expression variability, we designed, simulated, constructed, and tested regulatory circuits, implementing an incoherent feedforward motif.A number of control mechanisms including transcription and post-transcriptional regulation were tested in mammalian cells.Feedforward regulation displayed better adaptation than negative feedback, and circuits based on RNA interference were the most robust to variation in DNA template amounts.
Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network-coding genes. Indeed, changes in gene dosage are clearly a major source of variation in cells, and yet those changes are very common in both normal processes (sex determination, ploidy change) and disease (gene amplification in cancer). In synthetic networks, the problem is exacerbated due to commonly used transient delivery methods that result in very large cell-to-cell variability in gene dosage. The basic question on gene dosage compensation in nature (Veitia et al, 2008; Acar et al, 2010) and a practical challenge of overcoming sensitivity to DNA copy number in synthetic circuits prompted us to investigate mechanisms to reduce this variability using sophisticated internal regulatory mechanisms. Indeed, the baseline expression unit in many synthetic circuits is an open-loop promoter-ORF combination. We hypothesized that some sort of internal regulation will result in ‘expression units' whose gene product (i.e. protein) output will depend only mildly on the intracellular concentration of its DNA template. In other words, we searched for architecture that would lead to ‘adaptation' of the gene product to the amount of DNA template.
By examining large body of published work, we found frequent reference to a so-called ‘incoherent feedforward' network motif (Mangan and Alon, 2003). The canonical three-node incoherent loop contains input, auxiliary regulator, and output nodes. The output is controlled directly by the input and the auxiliary regulator. The latter is also controlled by the input, introducing an additional indirect effect of the input on the output. In incoherent loops, the input controls the auxiliary regulator node in such a way that input's overall indirect action on the output via this node counteracts its direct effect. In a motif named ‘type I incoherent feedforward loop' (I1-FFL), the input's direct effect is activating, as is its control of the auxiliary node, while the auxiliary node controls the output through repression. One of the most studied properties of such motifs is their transient response to persistent stimulus, that is visually characterized as a ‘bump' or ‘pulse' (hence the name ‘pulse generator') that then goes back to the original steady state of the system (Basu et al, 2004). We hypothesized that changing DNA amount could serve as an input to an incoherent circuit if the auxiliary regulator and the output nodes are coexpressed from this DNA; in other words DNA can be viewed as an ‘activator' of both the regulator and the output. We conjectured that this might lead to adaptation to changes in DNA template.
We designed and simulated in silico a number of network architectures that all exhibit incoherent feedforward connectivity. We also compared them with the well-studied feedback loop circuit that in theory weakens but does not eliminate gene product dependency on the DNA template amount. The schematics of the circuits are shown in Figure 1.
Experimental measurement of input–output response of these circuits, or their transfer function, indeed uncovered adaptation of the output to DNA template abundance. Such adaptation has not been observed with feedback loop, as expected. Among various architectures, the post-transcriptional circuits showed faster adaptation, higher absolute expression levels and lower ‘noise' (Figure 4).
We also simulated and measured stochastic variability in the circuits by collecting all the cells with similar input values and statistically analyzing output values in those cells. We found that substantial noise component could not be accounted for by known noise sources, and concluded that the very step of negative regulation, both by a repressor LacI and by a microRNA, significantly increases cell-to-cell variability. This needs to be addressed in further studies. We also found that the negative feedback loop did not result in reduced noise as we expected, yet it did not result in noise increase as in the incoherent motif. This means that there may be effective noise reduction but it is not sufficient to produce narrow distributions of outputs for a given input.
We conclude that expression units that incorporate incoherent feedforward control of the gene product provide adaptation to the amount of DNA template and can be superior to simple combinations of constitutive promoters with ORFs. We also emphasize the relevance of our findings to the long-standing question of gene dosage compensation in cells, and note that similar incoherent architectures with microRNA negative regulators have been found in cells, suggesting that their physiological role is to curb variability in gene dosage and/or promoter strength.
Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network-coding genes. Gene product levels could potentially be decoupled from these changes via built-in adaptation mechanisms, thereby boosting network reliability. Here, we show that a mechanism based on an incoherent feedforward motif enables adaptive gene expression in mammalian cells. We modeled, synthesized, and tested transcriptional and post-transcriptional incoherent loops and found that in all cases the gene product adapts to changes in DNA template abundance. We also observed that the post-transcriptional form results in superior adaptation behavior, higher absolute expression levels, and lower intrinsic fluctuations. Our results support a previously hypothesized endogenous role in gene dosage compensation for such motifs and suggest that their incorporation in synthetic networks will improve their robustness and reliability.
PMCID: PMC3202791  PMID: 21811230
feedforward motifs; gene dosage and noise; mammalian cells; microRNAs; negative autoregulation
5.  Fragilities Caused by Dosage Imbalance in Regulation of the Budding Yeast Cell Cycle 
PLoS Genetics  2010;6(4):e1000919.
Cells can maintain their functions despite fluctuations in intracellular parameters, such as protein activities and gene expression levels. This commonly observed biological property of cells is called robustness. On the other hand, these parameters have different limitations, each reflecting the property of the subsystem containing the parameter. The budding yeast cell cycle is quite fragile upon overexpression of CDC14, but is robust upon overexpression of ESP1. The gene products of both CDC14 and ESP1 are regulated by 1∶1 binding with their inhibitors (Net1 and Pds1), and a mathematical model predicts the extreme fragility of the cell cycle upon overexpression of CDC14 and ESP1 caused by dosage imbalance between these genes. However, it has not been experimentally shown that dosage imbalance causes fragility of the cell cycle. In this study, we measured the quantitative genetic interactions of these genes by performing combinatorial “genetic tug-of-war” experiments. We first showed experimental evidence that dosage imbalance between CDC14 and NET1 causes fragility. We also showed that fragility arising from dosage imbalance between ESP1 and PDS1 is masked by CDH1 and CLB2. The masking function of CLB2 was stabilization of Pds1 by its phosphorylation. We finally modified Chen's model according to our findings. We thus propose that dosage imbalance causes fragility in biological systems.
Author Summary
Normal cell functioning is dependent on balance between protein interactions and gene regulations. Although the balance is often perturbed by environmental changes, mutations, and noise in biochemical reactions, cellular systems can maintain their function despite these perturbations. This property of cells, called robustness, is now considered to be a design principle of biological systems and has become a central theme for systems biology. We previously developed an experimental method designated “genetic tug-of-war,” in which we assessed the robustness of cellular systems upon overexpression of certain genes, especially that of the budding yeast cell cycle. Although the yeast cell cycle can be maintained despite significant overexpression of most genes within the system, the cell cycle halts upon just two-fold overexpression of M phase phosphatase CDC14. In this study, we experimentally showed that this fragility is caused by dosage imbalance between CDC14 and NET1. Interestingly, fragility of regulation of separase gene ESP1, potentially caused by dosage imbalance, was masked by regulation of other factors such as CDH1 and CLB2. We thus propose that dosage imbalance causes fragility in biological systems.
PMCID: PMC2858678  PMID: 20421994
6.  The auxin signalling network translates dynamic input into robust patterning at the shoot apex 
We provide a comprehensive expression map of the different genes (TIR1/AFBs, ARFs and Aux/IAAs) involved in the signalling pathway regulating gene transcription in response to auxin in the shoot apical meristem (SAM).We demonstrate a relatively simple structure of this pathway using a high-throughput yeast two-hybrid approach to obtain the Aux/IAA-ARF full interactome.The topology of the signalling network was used to construct a model for auxin signalling and to predict a role for the spatial regulation of auxin signalling in patterning of the SAM.We used a new sensor to monitor the input in the auxin signalling pathway and to confirm the model prediction, thus demonstrating that auxin signalling is essential to create robust patterns at the SAM.
The plant hormone auxin is a key morphogenetic signal involved in the control of cell identity throughout development. A striking example of auxin action is at the shoot apical meristem (SAM), a population of stem cells generating the aerial parts of the plant. Organ positioning and patterning depends on local accumulations of auxin in the SAM, generated by polar transport of auxin (Vernoux et al, 2010). However, it is still unclear how auxin is distributed at cell resolution in tissues and how the hormone is sensed in space and time during development. A complex ensemble of 29 Aux/IAAs and 23 ARFs is central to the regulation of gene transcription in response to auxin (for review, see Leyser, 2006; Guilfoyle and Hagen, 2007; Chapman and Estelle, 2009). Protein–protein interactions govern the properties of this transduction pathway (Del Bianco and Kepinski, 2011). Limited interaction studies suggest that, in the absence of auxin, the Aux/IAA repressors form heterodimers with the ARF transcription factors, preventing them from regulating target genes. In the presence of auxin, the Aux/IAA proteins are targeted to the proteasome by an SCF E3 ubiquitin ligase complex (Chapman and Estelle, 2009; Leyser, 2006). In this process, auxin promotes the interaction between Aux/IAA proteins and the TIR1 F-box of the SCF complex (or its AFB homologues) that acts as an auxin co-receptor (Dharmasiri et al, 2005a, 2005b; Kepinski and Leyser, 2005; Tan et al, 2007). The auxin-induced degradation of Aux/IAAs would then release ARFs to regulate transcription of their target genes. This includes activation of most of the Aux/IAA genes themselves, thus establishing a negative feedback loop (Guilfoyle and Hagen, 2007). Although this general scenario provides a framework for understanding gene regulation by auxin, the underlying protein–protein network remains to be fully characterized.
In this paper, we combined experimental and theoretical analyses to understand how this pathway contributes to sensing auxin in space and time (Figure 1). We first analysed the expression patterns of the ARFs, Aux/IAAs and TIR1/AFBs genes in the SAM. Our results demonstrate a general tendency for most of the 25 ARFs and Aux/IAAs detected in the SAM: a differential expression with low levels at the centre of the meristem (where the stem cells are located) and high levels at the periphery of the meristem (where organ initiation takes place). We also observed a similar differential expression for TIR1/AFB co-receptors. To understand the functional significance of the distribution of ARFs and Aux/IAAs in the SAM, we next investigated the global structure of the Aux/IAA-ARF network using a high-throughput yeast two-hybrid approach and uncover a rather simple topology that relies on three basic generic features: (i) Aux/IAA proteins interact with themselves, (ii) Aux/IAA proteins interact with ARF activators and (iii) ARF repressors have no or very limited interactions with other proteins in the network.
The results of our interaction analysis suggest a model for the Aux/IAA-ARF signalling pathway in the SAM, where transcriptional activation by ARF activators would be negatively regulated by two independent systems, one involving the ARF repressors, the other the Aux/IAAs. The presence of auxin would remove the inhibitory action of Aux/IAAs, but leave the ARF repressors to compete with ARF activators for promoter-binding sites. To explore the regulatory properties of this signalling network, we developed a mathematical model to describe the transcriptional output as a function of the signalling input that is the combinatorial effect of auxin concentration and of its perception. We then used the model and a simplified view of the meristem (where the same population of Aux/IAAs and ARFs exhibit a low expression at the centre and a high expression in the peripheral zone) for investigating the role of auxin signalling in SAM function. We show that in the model, for a given ARF activator-to-repressor ratio, the gene induction capacity increases with the absolute levels of ARF proteins. We thus predict that the differential expression of the ARFs generates differences in auxin sensitivities between the centre (low sensitivity) and the periphery (high sensitivity), and that the expression of TIR1/AFB participates to this regulation (prediction 1). We also use the model to analyse the transcriptional response to rapidly changing auxin concentrations. By simulating situations equivalent either to the centre or the periphery of our simplified representation of the SAM, we predict that the signalling pathway buffers its response to the auxin input via the balance between ARF activators and repressors, in turn generated by their differential spatial distributions (prediction 2).
To test the predictions from the model experimentally, we needed to assess both the input (auxin level and/or perception) and the output (target gene induction) of the signalling cascade. For measuring the transcriptional output, the widely used DR5 reporter is perfectly adapted (Figure 5) (Ulmasov et al, 1997; Sabatini et al, 1999; Benkova et al, 2003; Heisler et al, 2005). For assaying pathway input, we designed DII-VENUS, a novel auxin signalling sensor that comprises a constitutively expressed fusion of the auxin-binding domain (termed domain II or DII) (Dreher et al, 2006; Tan et al, 2007) of an IAA to a fast-maturating variant of YFP, VENUS (Figure 5). The degradation patterns from DII-VENUS indicate a high auxin signalling input both in flower primordia and at the centre of the SAM. This is in contrast to the organ-specific expression pattern of DR5::VENUS (Figure 5). These results indicate that the signalling pathway limits gene activation in response to auxin at the meristem centre and confirm the differential sensitivity to auxin between the centre and the periphery (prediction 1). We further confirmed the buffering capacities of the signalling pathway (prediction 2) by carrying out live imaging experiments to monitor DII-VENUS and DR5::VENUS expression in real time (Figure 5). This analysis reveals the presence of important temporal variations of DII-VENUS fluorescence, while DR5::VENUS does not show such global variations. Our approach thus provides evidence that the Aux/IAA-ARF pathway has a key role in patterning in the SAM, alongside the auxin transport system. Our results illustrate how the tight spatio-temporal regulation of both the distribution of a morphogenetic signal and the activity of the downstream signalling pathway provides robustness to a dynamic developmental process.
A comprehensive expression and interaction map of auxin signalling factors in the Arabidopsis shoot apical meristem is constructed and used to derive a mathematical model of auxin signalling, from which key predictions are experimentally confirmed.
The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.
PMCID: PMC3167386  PMID: 21734647
auxin; biosensor; live imaging; ODE; signalling
7.  The Epigenome of Evolving Drosophila Neo-Sex Chromosomes: Dosage Compensation and Heterochromatin Formation 
PLoS Biology  2013;11(11):e1001711.
This study shows how young sex chromosomes have altered their chromatin structure in Drosophila, and what genomic changes have led to silencing of the Y, and hyper-transcription of the X.
Sex chromosomes originated from autosomes but have evolved a highly specialized chromatin structure. Drosophila Y chromosomes are composed entirely of silent heterochromatin, while male X chromosomes have highly accessible chromatin and are hypertranscribed as a result of dosage compensation. Here, we dissect the molecular mechanisms and functional pressures driving heterochromatin formation and dosage compensation of the recently formed neo-sex chromosomes of Drosophila miranda. We show that the onset of heterochromatin formation on the neo-Y is triggered by an accumulation of repetitive DNA. The neo-X has evolved partial dosage compensation and we find that diverse mutational paths have been utilized to establish several dozen novel binding consensus motifs for the dosage compensation complex on the neo-X, including simple point mutations at pre-binding sites, insertion and deletion mutations, microsatellite expansions, or tandem amplification of weak binding sites. Spreading of these silencing or activating chromatin modifications to adjacent regions results in massive mis-expression of neo-sex linked genes, and little correspondence between functionality of genes and their silencing on the neo-Y or dosage compensation on the neo-X. Intriguingly, the genomic regions being targeted by the dosage compensation complex on the neo-X and those becoming heterochromatic on the neo-Y show little overlap, possibly reflecting different propensities along the ancestral chromosome that formed the sex chromosome to adopt active or repressive chromatin configurations. Our findings have broad implications for current models of sex chromosome evolution, and demonstrate how mechanistic constraints can limit evolutionary adaptations. Our study also highlights how evolution can follow predictable genetic trajectories, by repeatedly acquiring the same 21-bp consensus motif for recruitment of the dosage compensation complex, yet utilizing a diverse array of random mutational changes to attain the same phenotypic outcome.
Author Summary
Sex chromosomes differ from non-sex chromosomes (“autosomes”) at the genomic, transcriptomic, and epigenomic level, yet the X and Y share a common evolutionary origin. The Drosophila Y chromosome is gene-poor and associated with a compact and transcriptionally inactive form of genetic material called heterochromatin. The X, in contrast, is enriched for activating chromatin marks and is consequently hyper-transcribed, a process thought to be an adaptation to decay and silencing of genes on the Y, resulting in “dosage compensation.” How sex chromosomes have altered their chromatin structure, and what genomic changes led to this dramatically different epigenetic makeup, however, has remained a mystery. By studying the genome, epigenome, and transcriptome of a species with a very recently evolved pair of sex chromosomes (the neo-X and neo-Y of a fruit fly, Drosophila miranda), we here recapitulate how both dosage compensation and heterochromatin formation evolve in Drosophila and establish several novel and important principles governing the evolution of chromatin structure. We dissect the evolutionary history of over 60 novel binding sites for the dosage compensation complex that evolved by natural selection on the neo-X within the last one million years. We show that the 21-bp consensus motifs for recruiting the dosage compensation complex were acquired by diverse molecular mechanisms along the neo-X, while the onset of heterochromatin formation is triggered by the accumulation of transposable elements, leading to silencing of adjacent neo-Y genes. We find that spreading of these chromatin modifications results in massive mis-expression of neo-sex linked genes, and that little correspondence exists between functional activity of genes on the neo-Y and whether they are dosage-compensated on the neo-X. Intriguingly, the genomic regions being targeted by the dosage compensation complex on the neo-X and those that are heterochromatic on the neo-Y show little overlap, possibly reflecting different propensities of the ancestral chromosome that formed the sex chromosome to evolve active versus repressive chromatin configurations. These findings have broad implications for current models of sex chromosome evolution.
PMCID: PMC3825665  PMID: 24265597
8.  Human Frame Shift Mutations Affecting the Carboxyl Terminus of Perilipin Increase Lipolysis by Failing to Sequester the Adipose Triglyceride Lipase (ATGL) Coactivator AB-hydrolase-containing 5 (ABHD5)* 
The Journal of Biological Chemistry  2011;286(40):34998-35006.
Perilipin (PLIN1) is a constitutive adipocyte lipid droplet coat protein. N-terminal amphipathic helices and central hydrophobic stretches are thought to anchor it on the lipid droplet, where it appears to function as a scaffold protein regulating lipase activity. We recently identified two different C-terminal PLIN1 frame shift mutations (Leu-404fs and Val-398fs) in patients with a novel subtype of partial lipodystrophy, hypertriglyceridemia, severe insulin resistance, and type 2 diabetes (Gandotra, S., Le Dour, C., Bottomley, W., Cervera, P., Giral, P., Reznik, Y., Charpentier, G., Auclair, M., Delépine, M., Barroso, I., Semple, R. K., Lathrop, M., Lascols, O., Capeau, J., O'Rahilly, S., Magré, J., Savage, D. B., and Vigouroux, C. (2011) N. Engl. J. Med. 364, 740–748.) When overexpressed in preadipocytes, both mutants fail to inhibit basal lipolysis. Here we used bimolecular fluorescence complementation assays to show that the mutants fail to bind ABHD5, permitting its constitutive coactivation of ATGL, resulting in increased basal lipolysis. siRNA-mediated knockdown of either ABHD5 or ATGL expression in the stably transfected cells expressing mutant PLIN1 reduced basal lipolysis. These insights from naturally occurring human variants suggest that the C terminus sequesters ABHD5 and thus inhibits basal ATGL activity. The data also suggest that pharmacological inhibition of ATGL could have therapeutic potential in patients with this rare but metabolically serious disorder.
PMCID: PMC3186430  PMID: 21757733
Cell Biology; Lipase; Lipid Droplets; Lipid Metabolism; Lipodystrophy
9.  PdhR, the Pyruvate Dehydrogenase Repressor, Does not Regulate Lipoic Acid Synthesis 
Research in microbiology  2014;165(6):429-438.
Lipoic acid is a covalently-bound enzyme cofactor required for central metabolism all three domains of life. In the last 20 years the pathway of lipoic acid synthesis and metabolism has been established in Escherichia coli. Expression of the genes of the lipoic acid biosynthesis pathway was believed to be constitutive. However, in 2010 Kaleta and coworkers (BMC Syst. Biol. 4:116) predicted a binding site for the pyruvate dehydrogenase operon repressor, PdhR (referred to lipA site 1) upstream of lipA, the gene encoding lipoic acid synthase and concluded that PdhR regulates lipA transcription. We report in vivo and in vitro evidence that lipA is not controlled by PdhR and that the putative regulatory site deduced by the prior workers is nonfunctional and physiologically irrelevant. E. coli PdhR was purified to homogeneity and used for electrophoretic mobility shift assays. The lipA site 1 of Kaleta and coworkers failed to bind PdhR. The binding detected by these workers is due to another site (lipA site 3) located far upstream of the lipA promoter. Relative to the canonical PdhR binding site lipA site 3 is a half-palindrome and as expected had only weak PdhR binding ability. Manipulation of lipA site 3 to construct a palindrome gave significantly enhanced PdhR binding affinity. The native lipA promoter and the version carrying the artificial lipA3 palindrome were transcriptionally fused to a LacZ reporter gene to directly assay lipA expression. Deletion of pdhR gave no significant change in lipA promoter-driven β-galactosidase activity with either the native or constructed palindrome upstream sequences, indicating that PdhR plays no physiological role in regulation of lipA expression.
PMCID: PMC4134263  PMID: 24816490
PdhR; LipA. Lipoic acid; pyruvate dehydrogenase; 2-oxoglutarate dehydrogenase; Electrophoretic mobility shift assays (EMSA)
10.  Hypolipidemic effects of lactic acid bacteria fermented cereal in rats 
The objectives of the present study were to investigate the efficacy of the mixed culture of Lactobacillus acidophilus (DSM 20242), Bifidobacterium bifidum (DSM 20082) and Lactobacillus helveticus (CK60) in the fermentation of maize and the evaluation of the effect of the fermented meal on the lipid profile of rats.
Rats were randomly assigned to 3 groups and each group placed on a Diet A (high fat diet into which a maize meal fermented with a mixed culture of Lb acidophilus (DSM 20242), B bifidum (DSM 20082) and Lb helveticus (CK 60) was incorporated), B (unfermented high fat diet) or C (commercial rat chow) respectively after the first group of 7 rats randomly selected were sacrificed to obtain the baseline data. Thereafter 7 rats each from the experimental and control groups were sacrificed weekly for 4 weeks and the plasma, erythrocytes, lipoproteins and organs of the rats were assessed for cholesterol, triglyceride and phospholipids.
Our results revealed that the mixed culture of Lb acidophilus (DSM 20242), B bifidum (DSM 20082) and Lb helveticus (CK 60) were able to grow and ferment maize meal into ‘ogi’ of acceptable flavour. In addition to plasma and hepatic hypercholesterolemia and hypertriglyceridemia, phospholipidosis in plasma, as well as cholesterogenesis, triglyceride constipation and phospholipidosis in extra-hepatic tissues characterized the consumption of unfermented hyperlipidemic diets. However, feeding the animals with the fermented maize diet reversed the dyslipidemia.
The findings of this study indicate that consumption of mixed culture lactic acid bacteria (Lb acidophilus (DSM 20242), Bifidobacterium bifidum (DSM 20082) and Lb helveticus (CK 60) fermented food results in the inhibition of fat absorption. It also inhibits the activity of HMG CoA reductase. This inhibition may be by feedback inhibition or repression of the transcription of the gene encoding the enzyme via activation of the sterol regulatory element binding protein (SREBP) transcription factor. It is also possible that consumption of fermented food enhances conversion of cholesterol to bile acids by activating cholesterol-7α-hydroxylase.
PMCID: PMC3548745  PMID: 23231860
Dyslipidemia; Lactic acid bacteria; Probiotics; Cereals; Fermentation
11.  Arthropod Phylogenetics in Light of Three Novel Millipede (Myriapoda: Diplopoda) Mitochondrial Genomes with Comments on the Appropriateness of Mitochondrial Genome Sequence Data for Inferring Deep Level Relationships 
PLoS ONE  2013;8(7):e68005.
Arthropods are the most diverse group of eukaryotic organisms, but their phylogenetic relationships are poorly understood. Herein, we describe three mitochondrial genomes representing orders of millipedes for which complete genomes had not been characterized. Newly sequenced genomes are combined with existing data to characterize the protein coding regions of myriapods and to attempt to reconstruct the evolutionary relationships within the Myriapoda and Arthropoda.
The newly sequenced genomes are similar to previously characterized millipede sequences in terms of synteny and length. Unique translocations occurred within the newly sequenced taxa, including one half of the Appalachioria falcifera genome, which is inverted with respect to other millipede genomes. Across myriapods, amino acid conservation levels are highly dependent on the gene region. Additionally, individual loci varied in the level of amino acid conservation. Overall, most gene regions showed low levels of conservation at many sites. Attempts to reconstruct the evolutionary relationships suffered from questionable relationships and low support values. Analyses of phylogenetic informativeness show the lack of signal deep in the trees (i.e., genes evolve too quickly). As a result, the myriapod tree resembles previously published results but lacks convincing support, and, within the arthropod tree, well established groups were recovered as polyphyletic.
The novel genome sequences described herein provide useful genomic information concerning millipede groups that had not been investigated. Taken together with existing sequences, the variety of compositions and evolution of myriapod mitochondrial genomes are shown to be more complex than previously thought. Unfortunately, the use of mitochondrial protein-coding regions in deep arthropod phylogenetics appears problematic, a result consistent with previously published studies. Lack of phylogenetic signal renders the resulting tree topologies as suspect. As such, these data are likely inappropriate for investigating such ancient relationships.
PMCID: PMC3712015  PMID: 23869209
12.  Synthetic in vitro transcriptional oscillators 
A fundamental goal of synthetic biology is to understand design principles through engineering biochemical systems.Three in vitro synthetic transcriptional oscillators were constructed and analyzed: a two-node-negative feedback oscillator, an amplified negative-feedback oscillator, and a three-node ring oscillator.The in vitro oscillators are governed by similar design principles as previous theoretical studies and synthetic oscillators in vivo.Because of unintended reactions that arise even without the complexity of living cells, several challenges remain for predictive and robust oscillator performance.
Fundamental goals for synthetic biology are to understand the principles of biological circuitry from an engineering perspective and to establish engineering methods for creating biochemical circuitry to control molecular processes—both in vitro and in vivo (Benner and Sismour, 2005; Adrianantoandro et al, 2006). Here, we make use of a previously proposed class of in vitro biochemical systems, transcriptional circuits, that can be modularly wired into arbitrarily complex networks by changing the regulatory and coding sequence domains of DNA templates (Kim et al, 2006; Subsoontorn et al 2011). Using design motifs for inhibitory and excitatory regulations, three different oscillator designs were constructed and characterized: a two-switch negative-feedback oscillator, loosely analogous to the p53–Mdm2-feedback loop (Bar-Or et al, 2000); the same oscillator augmented with a positive-feedback loop, loosely analogous to a synthetic relaxation oscillator (Atkinson et al, 2003); and a three-switch ring oscillator analogous to the repressilator (Elowitz and Leibler, 2000).
DNA and RNA hybridization reactions (Figure 1B) can be assembled to create either an inhibitable switch (Figure 1A, right and bottom) with a threshold set by the total concentration of its DNA activator strand (Figure 1C, bottom), or an activatable switch (Figure 1A, left and top) with a threshold set by its DNA inhibitor strand concentration (Figure 1C, top). This threshold mechanism is analogous to biological threshold mechanisms such as ‘inhibitor ultrasensitivity' (Ferrell, 1996) and ‘molecular titration' (Buchler and Louis, 2008). Using these design motifs, we constructed a two-switch negative-feedback oscillator (Figure 1A, inset): RNA activator rA1 activates the production of RNA inhibitor rI2 by modulating switch Sw21, while RNA inhibitor rI2, in turn, inhibits the production of RNA activator rA1 by modulating switch Sw12. A total of seven DNA strands are used, in addition to the two enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H. The fact that such a negative-feedback loop can lead to temporal oscillations can be seen from a mathematical model of transcriptional networks. Experimental results showed qualitative agreement with predicted oscillator behavior from simple model simulations.
The fully optimized system revealed five complete oscillation cycles with a nearly 50% amplitude swing (Figure 3A) until, after ∼20 h, the production rate could no longer be sustained in the batch reaction. Gel measurements verified oscillations in RNA concentrations and switch states (Figure 3B and C). However to our surprise, rather than oscillations with constant amplitude and constant mean, the RNA inhibitor concentration builds up after each cycle. An extended mathematical model that incorporated an interference reaction from ‘waste' product (Figure 3B and C) could qualitatively capture this behavior.
Using a new autoregulatory switch Sw11, we added a positive-feedback loop to the two-node oscillator to make an amplified negative feedback oscillator (Design II, Figure 1D). Further, we replaced the excitatory connection of Sw21 by a chain of two inhibitory connections, Sw23 and Sw31, to construct a three-switch ring oscillator (Design III, Figure 1D). All three oscillator designs could be tuned to reach the oscillatory regime in parameter space.
Reassuringly, our in vitro oscillators exhibit several design principles previously observed in vivo. (1) Introducing delay in a simple negative-feedback loop can help achieve stable oscillation (Novák and Tyson, 2008; Stricker et al, 2008). (2) The addition of a positive-feedback self-loop to a negative-feedback oscillator provides access to rich dynamics and improved tunability (Tsai et al, 2008). (3) Oscillations in biochemical ring oscillators (such as the repressilator) are sensitive to parameter asymmetry among individual components (Tuttle et al, 2005). (4) The saturation of degradation machinery and the management of waste products could play an important role.
However, several significant difficulties remain for predictive and robust oscillator performances: limited lifetime of closed batch reactions, interference from waste products, and asymmetry of switch components make quantitative modeling and predictio difficult. As a complementary approach to top-down view of systems biology, cell-free in vitro systems offer a valuable training ground to create and explore increasingly interesting and powerful information-based chemical systems (Simpson, 2006). In vitro oscillators could be used to orchestrate other chemical processes such as DNA nanomachines (Dittmer and Simmel, 2004) and to provide embedded controllers within prototype artificial cells (Noireaux and Libchaber, 2004; Griffiths and Tawfik, 2006).
The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplified analog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platform for the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally, a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells.
PMCID: PMC3063688  PMID: 21283141
cell free; in vitro; oscillation; synthetic biology; transcriptional circuits
13.  Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology 
PLoS Computational Biology  2007;3(2):e15.
The topology of cellular circuits (the who-interacts-with-whom) is key to understand their robustness to both mutations and noise. The reason is that many biochemical parameters driving circuit behavior vary extensively and are thus not fine-tuned. Existing work in this area asks to what extent the function of any one given circuit is robust. But is high robustness truly remarkable, or would it be expected for many circuits of similar topology? And how can high robustness come about through gradual Darwinian evolution that changes circuit topology gradually, one interaction at a time? We here ask these questions for a model of transcriptional regulation networks, in which we explore millions of different network topologies. Robustness to mutations and noise are correlated in these networks. They show a skewed distribution, with a very small number of networks being vastly more robust than the rest. All networks that attain a given gene expression state can be organized into a graph whose nodes are networks that differ in their topology. Remarkably, this graph is connected and can be easily traversed by gradual changes of network topologies. Thus, robustness is an evolvable property. This connectedness and evolvability of robust networks may be a general organizational principle of biological networks. In addition, it exists also for RNA and protein structures, and may thus be a general organizational principle of all biological systems.
Author Summary
Living things are astonishingly complex, yet unlike houses of cards they are also highly robust. That is, they have persisted for billions of years, despite being exposed to an endless stream of environmental stressors and random mutations. Is this robustness an evolvable property? Do different biological systems vary in their robustness? Has natural selection shaped this robustness? These questions are very difficult to answer experimentally for most systems, be they proteins or large gene networks. Here we address these questions with a model of the transcription regulation networks that regulate both cellular functions and embryonic development in many organisms. We examine millions of such networks that differ in the topology or architecture of their regulatory interactions, that is, in the “who interacts with whom” of a network. We find that radically different network architectures can show the same gene expression pattern. The networks' robustness to both mutations and gene expression noise shows a broad distribution: some network architectures are highly robust, whereas others are quite fragile. Importantly, the entire space of network architectures can be traversed through small changes of individual regulatory interactions, without changing a network's gene expression pattern. This means that high robustness in gene expression can evolve through gradual and neutral evolution in the space of network architectures. Our results show that the robustness of transcriptional regulation networks is an evolvable trait that natural selection can change like any other trait.
PMCID: PMC1794322  PMID: 17274682
14.  Comments on sequence normalization of tiling array expression 
Bioinformatics  2009;25(17):2171-2173.
Motivation: Methods to improve tiling array expression signals are needed to accurately detect genome features. Royce et al. provide statistical normalizations of tile signal based on probe sequence content that promises improved accuracy, and should be independently verified.
Results: Assessment of the sequence content normalization methods identified a problem: confounding of probe sequence content with gene structure (intron/exon) sequence content. Normalization obscured tile signal changes at gene structure boundaries. This and other evidence suggests that simple sequence normalization does not improve detection of genes from tile expression data.
PMCID: PMC2800354  PMID: 19578171
15.  Network Features of the Mammalian Circadian Clock 
PLoS Biology  2009;7(3):e1000052.
The mammalian circadian clock is a cell-autonomous system that drives oscillations in behavior and physiology in anticipation of daily environmental change. To assess the robustness of a human molecular clock, we systematically depleted known clock components and observed that circadian oscillations are maintained over a wide range of disruptions. We developed a novel strategy termed Gene Dosage Network Analysis (GDNA) in which small interfering RNA (siRNA)-induced dose-dependent changes in gene expression were used to build gene association networks consistent with known biochemical constraints. The use of multiple doses powered the analysis to uncover several novel network features of the circadian clock, including proportional responses and signal propagation through interacting genetic modules. We also observed several examples where a gene is up-regulated following knockdown of its paralog, suggesting the clock network utilizes active compensatory mechanisms rather than simple redundancy to confer robustness and maintain function. We propose that these network features act in concert as a genetic buffering system to maintain clock function in the face of genetic and environmental perturbation.
Author Summary
The circadian clock is the biological clock found throughout the body that coordinates the timing of molecular and cellular processes on a 24-hour rhythm. It is composed of numerous transcription factors that feed back and control their own expression. To explore how the clock functions in the face of genetic perturbations, we disrupted its function by knocking down gene expression of known clock genes in a dose-dependent fashion. We measured the expression of clock genes following knockdown and constructed perturbation-based network models to describe, visualize, and mine the results. We reported several novel network features, such as signal propagation through interacting genetic modules and proportional responses whereby levels of expression are altered commensurately with changing levels of the gene. We also observed several examples where a gene is up-regulated following knockdown of its paralog, suggesting the clock network utilizes active compensatory mechanisms rather than simple redundancy to confer robustness and maintain function. We propose that the network features we observe act in concert as a genetic buffering system to maintain clock function in the face of genetic and environmental perturbation.
How does the circadian clock maintain function in the face of genetic perturbation? The authors construct gene dosage perturbation networks and uncover several underlying principles contributing to genetic buffering of the clock.
PMCID: PMC2653556  PMID: 19278294
16.  Design principles of nuclear receptor signaling: how complex networking improves signal transduction 
Nuclear receptors often function in the cytoplasm.A triple conveyor belt pumps ligand (signal) into the nucleus and onto the DNA.The active export of importins enhances signaling to the nucleus.Sharing a single nuclear pore may reduce rather than increase crosstalk.
Nuclear receptors (NRs) derive their family name from the early observation that they are located in the nucleus, despite responding to extracellular signals such as hormones (e.g., cortisol) (Fanestil and Edelman, 1966). According to the ‘classical' paradigm of NR signaling, the NR resides in the nucleus, attached to a DNA response element, waiting for its ligand to bind. The actual systems have multiple additional features (reviewed in Cutress et al, 2008; Cao et al, 2009; Levin, 2009a; Bunce and Campbell, 2010), such as that NRs shuttle between the nucleus and the cytoplasm (Von Knethen et al, 2010) and ligand addition changes receptor location dynamically (Pratt et al, 1989; Liu and DeFranco, 2000; Kumar et al, 2004, 2006; Tanaka et al, 2005; Heitzer et al, 2007; Prüfer and Boudreaux, 2007; Ricketson et al, 2007; Cutress et al, 2008): Figure 1 summarizes the current understanding of the topology of the reaction networks involved in NR signaling, in systems biological graphical notation (SBGN), with NR activation, importin-α and -β binding, nuclear pore complex (NPC)-mediated import, recycling of importins, NR binding to target promoter sequences, exportin-mediated nuclear export of the NR, exportin cycling and free energy-driven Ran recycling. This topology is surprisingly complex when compared with the ‘classical' paradigm. To address to what extent this extra complexity is just detail or contributes essential functionality, we have simulated the dynamics of the NR transcriptional response in maximally realistic mathematical models of increasingly complex designs.
The calculations revealed significant disadvantages of the classical and simplest mechanism for endocrine NR-mediated signaling, i.e., the one with localization of the NR exclusively on the DNA (design 1 in Figure 2A): the transcriptional response was very low (Figure 2B). A high concentration of free NR in the nucleus (design 2) improved sensitivity, but made the responsiveness much slower (Figure 2B). If the NR was equally distributed between the nucleus and the cytoplasm without the NR being able to traverse the nucleocytoplasmic membrane (design 3), then, although the NR diffuses more slowly than the much smaller ligand molecule, the higher concentration of the NR increased flux from the plasma membrane to the nuclear membrane; the steady state was reached faster (Figure 2B and C; compare design 3 relative to design 2). Enabling the NR to traverse the nucleocytoplasmic membrane (design 4), further accelerated the response (Figure 2B and C).
Designs 1–4 considered the permeation of the NR through the nuclear membrane to be passive, implying an import/export activity ratio of 1. When varying the import to export activity ratio (design 5), a trade-off between the fast responsiveness of design 4 and the high sensitivity of design 2 was calculated (Figure 2B). In order to maximize responsiveness, core-NR should be concentrated in the cytoplasm, whereas to gain sensitivity, liganded NR should be concentrated in the nucleus. This suggested that performance could be improved by making nuclear import and export selective for liganded over unliganded NR (design 6; Figure 2A). Indeed, retention of core-NR in the cytoplasm provided high influx of ligand into the nucleus (Figure 2D), and also the highest concentration of ligand in the nucleus (Figure 2C): Apart from its classical receptor role in transcription regulation, the NR may function as (part of) an active pump for its ligand, resembling a triple conveyor belt: importins and exportins cycle as conveyor belts and drive the cycling of the third conveyor belt consisting of the NR that pumps ligand into the nucleus.
Two other striking features of the NR signaling network (Figure 1) are related to the facts that the energy of GTP hydrolysis is coupled to an active export of importins rather than to direct active import of NR and that the same NPC is used for all transport processes. At first sight, the former may waste free energy and the latter might cause fragility due to interferences between different NRs and other signaling pathways. However, our models show that active nuclear export of importins is a design preventing NR sequestration in the nucleus by nuclear importins and, equally paradoxically, the transport of all cargo through the same NPC makes the transport of any particular cargo robust with respect to perturbations in the availability of any other cargo.
Our calculations also predict that there is an optimal ratio of nuclear to cytoplasmic fractions of the NR (Figure 2G) that depends on the specific properties of the ligand and on the transcription activation requirements. This may help to explain the observation that different NRs have different predominant intracellular localizations. Our model calculations are thereby in line with many experimental observations, but specific cases of NR signaling may only exhibit a subset of all features. Our models can aid in identifying which subsets are important in any particular case of NR signaling, as we demonstrate for an example.
In this study, we have shown that complex networks of biochemical and signaling reactions can harbor subtle design principles that can be understood rationally in terms of simplified but not simple models (which are available to the reader).
The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of ‘design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic ‘nuclear' receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP-free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal-flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR-mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands.
PMCID: PMC3018161  PMID: 21179018
biochemical network; kinetic model; nuclear receptor; signaling; systems biology
17.  An analysis of a ‘community-driven’ reconstruction of the human metabolic network 
Metabolomics  2013;9(4):757-764.
Following a strategy similar to that used in baker’s yeast (Herrgård et al. Nat Biotechnol 26:1155–1160, 2008). A consensus yeast metabolic network obtained from a community approach to systems biology (Herrgård et al. 2008; Dobson et al. BMC Syst Biol 4:145, 2010). Further developments towards a genome-scale metabolic model of yeast (Dobson et al. 2010; Heavner et al. BMC Syst Biol 6:55, 2012). Yeast 5—an expanded reconstruction of the Saccharomyces cerevisiae metabolic network (Heavner et al. 2012) and in Salmonella typhimurium (Thiele et al. BMC Syst Biol 5:8, 2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonellatyphimurium LT2 (Thiele et al. 2011), a recent paper (Thiele et al. Nat Biotechnol 31:419–425, 2013). A community-driven global reconstruction of human metabolism (Thiele et al. 2013) described a much improved ‘community consensus’ reconstruction of the human metabolic network, called Recon 2, and the authors (that include the present ones) have made it freely available via a database at and in SBML format at Biomodels ( This short analysis summarises the main findings, and suggests some approaches that will be able to exploit the availability of this model to advantage.
PMCID: PMC3715687  PMID: 23888127
Metabolism; Modelling; Systems biology; Networks; Metabolic networks
18.  Comments on the mechanisms of action of radiation protective agents: basis components and their polyvalence 
SpringerPlus  2014;3:414.
These comments suggest a division of radiation protective agents on the grounds of their mechanism of action that increase the radio resistance of an organism.
Given below is the division of radiation protective agents on the basis of their mechanism of action into 3 groups: 1) Radiation protective agents, with the implementation of radiation protective action taking place at the cellular level in the course of rapidly proceeding radiation-chemical reactions. At the same time, when the ionizing radiation energy is absorbed, these agents partially neutralize the “oxygen effect” as a radiobiological phenomenon, especially in the radiolysis of DNA; 2) Radiation protective agents that exert their effect at the system level by accelerating the post-radiation recovery of radiosensitive tissues through activation of a number of pro-inflammatory signaling pathways and an increase in the secretion of hematopoietic growth factors, including their use as mitigators in the early period after irradiation prior to the clinical development of acute radiation syndrome (ARS). 3) Radiomodulators including drugs and nutritional supplements that can elevate the resistance of the organism to adverse environmental factors, including exposure to ionization by means of modulating the gene expression through a hormetic effect of small doses of stressors and a “substrate” maintenance of adaptive changes, resulting in an increased antioxidant protection of the organism. Radiation protective agents having polyvalence in implementation of their action may simultaneously induce radioprotective effect by various routes with a prevalence of basis mechanisms of the action.
PMCID: PMC4132458  PMID: 25133093
Radioprotector; Radiomitigator; Radiomodulator; Mechanism of action
19.  Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network 
In the paper we present a metabolic reconstruction and flux-balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network of the parasite accounts for 1001 reactions and 616 metabolites. Enzyme–gene associations were established for 366 genes and 75% of all enzymatic reactions.The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. The model also can be used to efficiently integrate mRNA-expression data to improve the accuracy of metabolic predictions.Using FBA of the reconstructed metabolic network, we identified 40 enzymatic drug targets (i.e. in silico essential genes) with no or very low sequence identity to human proteins.We experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor.
Malaria remains one of the most severe public health challenges worldwide (WHO, 2008). Although several available drugs have been successful in controlling malaria in the past, most of them are rapidly losing efficacy due to acquired drug resistance in the most lethal causative agent, Plasmodium falciparum (Mackinnon and Marsh, 2010). This creates an urgent need for new drugs and treatments, as well as better understanding of the parasite physiology. With this in mind, we built a genome-scale flux-balance model of the P. falciparum metabolism. Given the complex life cycle of Plasmodium, the flux-balanced model is of direct relevance to the ongoing search to identify new therapeutic drug targets. The model can be used to explore diverse metabolic states of the parasite and identify essential metabolic genes in the context of known alternative pathways (Oberhardt et al, 2009).
The reconstructed model, which is based on Plasmodium-specific databases, genomic annotations, and literature reports, includes 366 genes, 1001 reactions, 616 metabolic species, and 4 cellular compartments. We applied flux-balance analysis (FBA) (Orth et al, 2010) to identify the genes and reactions that are required to produce a set of necessary biomass components. Interestingly, compared with the yeast metabolic network (Duarte et al, 2004), a model eukaryote with a similar genome size, the Plasmodium network has a significantly higher proportion of essential genes; we confirmed this result using a comparative analysis of known gene knockouts in the two microbes. This low level of genetic robustness, which is likely due to the parasitic lifestyle, suggests that many metabolic genes of the parasite can be used as effective drug targets. Indeed, based on the in silico analysis we identified 40 essential P. falciparum genes with no or very little sequence identity to their human homologs.
We used a recently described small-molecule inhibitor (compound 1_03; Sorci et al, 2009) to experimentally verify one of the enzymes identified as essential: nicotinate mononucleotide adenylyltransferase (NMNAT; Figure 2A). This enzyme, and the corresponding NAD synthesis and recycling pathway, have been recently used for anti-microbial development (Magni et al, 2009). However, to the best of our knowledge, they have not been used against P. falciparum. The compound 1_03 was able to completely block host cell escape and reinvasion by arresting parasites in the trophozoite growth stage (Figure 2B). These results demonstrate that the inhibitory compound may be a good starting lead for new anti-malarials.
Importantly, the metabolic model of the parasite can be also used to integrate various genomic data, such as gene expression (Oberhardt et al, 2009). To illustrate these possibilities, we applied gene-expression data as constraints for the flux-balance model (Colijn et al, 2009) in order to predict changes in metabolic exchange fluxes. We found that the model was able to correctly predict the changes in external metabolite concentrations (Olszewski et al, 2009) with about 70% accuracy (Figure 3). The availability of a human metabolic network reconstruction (Duarte et al, 2007) would allow, in the future, to analyze the combined parasite–host network, which would deepen understanding of the P. falciparum metabolic vulnerabilities.
Future improvements of the presented P. falciparum metabolic model, for example incorporation of missing activities and yet undiscovered pathways, will lead to a better understanding of parasite physiology. Ultimately, the improved understanding should significantly accelerate the identification and development of desperately needed new drugs against this devastating disease.
Genome-scale metabolic reconstructions can serve as important tools for hypothesis generation and high-throughput data integration. Here, we present a metabolic network reconstruction and flux-balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network accounts for 1001 reactions and 616 metabolites. Enzyme–gene associations were established for 366 genes and 75% of all enzymatic reactions. Compared with other microbes, the P. falciparum metabolic network contains a relatively high number of essential genes, suggesting little redundancy of the parasite metabolism. The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. Moreover, using constraints based on gene-expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy. Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins. To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor.
PMCID: PMC2964117  PMID: 20823846
flux-balance analysis; Plasmodium falciparum metabolism; systems biology
20.  Ethical Standards to Guide the Development of Obesity Policies and Programs Comment on “Ethical Agreement and Disagreement about Obesity Prevention Policy in the United States” 
The recent report by Barnhill and King about obesity prevention policy raises important issues for discussion and analysis. In response, this article raises four points for further consideration. First, a distinction between equality and justice needs to be made and consistently maintained. Second, different theories of justice highlight one additional important source of disagreement about the ethical propriety of the proposed obesity prevention policies. Third, another point of contention arises with respect to different understandings of the principle of respect for autonomy due to its often-mistaken equation with simple, unfettered freedom. Finally, based on a more robust definition of autonomy, the key issues in obesity prevention policies can be suitably re-framed in terms of whether they advance just social conditions that enable people to realize human capabilities to the fullest extent possible.
PMCID: PMC3937894  PMID: 24596891
Obesity Prevention; Public Health Ethics; Autonomy; Positive and Negative Liberty; Equality; Justice; Capabilities
21.  Inteins, introns, and homing endonucleases: recent revelations about the life cycle of parasitic genetic elements 
Self splicing introns and inteins that rely on a homing endonuclease for propagation are parasitic genetic elements. Their life-cycle and evolutionary fate has been described through the homing cycle. According to this model the homing endonuclease is selected for function only during the spreading phase of the parasite. This phase ends when the parasitic element is fixed in the population. Upon fixation the homing endonuclease is no longer under selection, and its activity is lost through random processes. Recent analyses of these parasitic elements with functional homing endonucleases suggest that this model in its most simple form is not always applicable. Apparently, functioning homing endonuclease can persist over long evolutionary times in populations and species that are thought to be asexual or nearly asexual. Here we review these recent findings and discuss their implications. Reasons for the long-term persistence of a functional homing endonuclease include: More recombination (sexual and as a result of gene transfer) than previously assumed for these organisms; complex population structures that prevent the element from being fixed; a balance between active spreading of the homing endonuclease and a decrease in fitness caused by the parasite in the host organism; or a function of the homing endonuclease that increases the fitness of the host organism and results in purifying selection for the homing endonuclease activity, even after fixation in a local population. In the future, more detailed studies of the population dynamics of the activity and regulation of homing endonucleases are needed to decide between these possibilities, and to determine their relative contributions to the long term survival of parasitic genes within a population. Two outstanding publications on the amoeba Naegleria group I intron (Wikmark et al. BMC Evol Biol 2006, 6:39) and the PRP8 inteins in ascomycetes (Butler et al.BMC Evol Biol 2006, 6:42) provide important stepping stones towards integrated studies on how these parasitic elements evolve through time together with, or despite, their hosts.
PMCID: PMC1654191  PMID: 17101053
22.  Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network 
We present the first gene regulatory network (GRN) that pertains to post-developmental gene expression. Specifically, we mapped a transcription regulatory network of Caenorhabditis elegans metabolic gene promoters using gene-centered yeast one-hybrid assays. We found that the metabolic GRN is enriched for nuclear hormone receptors (NHRs) compared with other gene-centered regulatory networks, and that these NHRs organize into functional network modules.The NHR family has greatly expanded in nematodes; C. elegans has 284 NHRs, whereas humans have only 48. We show that the NHRs in the metabolic GRN have metabolic phenotypes, suggesting that they do not simply function redundantly.The mediator subunit MDT-15 preferentially interacts with NHRs that occur in the metabolic GRN.We describe an NHR circuit that responds to nutrient availability and propose a model for the evolution and organization of NHRs in C. elegans metabolic regulatory networks.
Physical and/or regulatory interactions between transcription factors (TFs) and their target genes are essential to establish body plans of multicellular organisms during development, and these interactions have been studied extensively in the context of GRNs. The precise control of differential gene expression is also of critical importance to maintain physiological homeostasis, and many metabolic disorders such as obesity and diabetes coincide with substantial changes in gene expression. Much work has focused on the GRNs that control metazoan development; however, the design principles and organization of the GRNs that control systems physiology remain largely unexplored.
In this study, we present the first gene-centered GRN that includes ∼70 genes involved in C. elegans metabolism and physiology, 100 TFs and more than 500 protein–DNA interactions between them. The resulting metabolic GRN is enriched for NHRs, compared with other gene-centered regulatory networks. NHRs are well-known regulators of lipid meta-qj;bolism in mammals. The transcriptional activity of NHRs can be modified by diffusible ligands, which allows these TFs to function as molecular sensors and rapidly alter the expression of their target genes. Interestingly, NHRs comprise the largest family of TFs in nematodes; the C. elegans genome encodes 284 NHRs, most of which are uncharacterized. Furthermore, their organization in GRNs has not yet been investigated. In our study, we show that the C. elegans NHRs that we retrieved in the metabolic GRN organize into network modules, and that most of these NHRs function to maintain lipid homeostasis in the nematode. Interestingly, network modularity has been proposed to facilitate rapid and robust changes in gene expression. Our results suggest that the C. elegans metabolic GRN may have evolved by combining NHR family expansion with the specific modular wiring of NHRs to enable the rapid adaptation of the animal to different environmental cues.
NHRs can interact with transcriptional cofactors such as chromatin remodeling complexes and Mediator components. For instance, the C. elegans Mediator subunit, MDT-15, can interact with NHR-49 to regulate the expression of its target genes. To find all the TFs that MDT-15 can interact with, we performed systematic yeast two-hybrid assays with MDT-15 versus 755 full-length TFs. We found that MDT-15 preferentially associates with NHRs, and specifically with those NHRs that confer a metabolic phenotype and that occur in the metabolic GRN. This illustrates the central role of MDT-15 in the regulation of metabolic gene expression.
Using a variety of genetic and biochemical approaches, we characterized NHR-86 in more detail. NHR-86 participates in one of the two NHR modules, and has a high-flux capacity; that is it has both a high incoming and a high outgoing degree. We obtained an nhr-86 mutant and generated an NHR-86 antibody, and showed that NHR-86 functions as an auto-repressor in vivo and that nhr-86 mutant animals store abnormally high levels of body fat.
Finally, we discovered a novel NHR circuit that responds to nutrient availability. In this circuit NHR-45 regulates the activity of nhr-178 promoter in two distinct physiologically important tissues: the intestine and the hypodermis. Both of these NHRs are required to maintain lipid homeostasis in C. elegans. The expression of nhr-178 is responsive to the nutritional status of the animal, which switches between ON and OFF states in the hypodermis. We found that NHR-45 activity is necessary to control this switch in the hypodermis. Interestingly, NHR-45 has opposite effects on the activity of the nhr-178 promoter in these tissues: NHR-45 activates this promoter in the intestine, but represses it in the hypodermis.
Altogether our study leads to a model in which the expansion of the NHR family, TFs that have the capacity to act as fast molecular sensors, is combined with a modular network organization to enable rapid and robust responses to various environmental cues.
Gene regulatory networks (GRNs) provide insights into the mechanisms of differential gene expression at a systems level. GRNs that relate to metazoan development have been studied extensively. However, little is still known about the design principles, organization and functionality of GRNs that control physiological processes such as metabolism, homeostasis and responses to environmental cues. In this study, we report the first experimentally mapped metazoan GRN of Caenorhabditis elegans metabolic genes. This network is enriched for nuclear hormone receptors (NHRs). The NHR family has greatly expanded in nematodes: humans have 48 NHRs, but C. elegans has 284, most of which are uncharacterized. We find that the C. elegans metabolic GRN is highly modular and that two GRN modules predominantly consist of NHRs. Network modularity has been proposed to facilitate a rapid response to different cues. As NHRs are metabolic sensors that are poised to respond to ligands, this suggests that C. elegans GRNs evolved to enable rapid and adaptive responses to different cues by a concurrence of NHR family expansion and modular GRN wiring.
PMCID: PMC2890327  PMID: 20461074
C. elegans; gene regulatory network; metabolism; nuclear hormone receptor; transcription factor
23.  A mechanism for robust circadian timekeeping via stoichiometric balance 
An accurate mathematical model of the mammalian circadian clock provides novel insights into the mechanisms that generate 24-h rhythms. A double-negative feedback loop design is proposed for biological clocks whose period needs to be tightly regulated.
A 1–1 stoichiometric balance and tight binding between activators (PER–CRY) and repressors (BMAL1–CLOCK/NPAS2) is required for sustained rhythmicity.Stoichiometry is balanced by an additional negative feedback loop consisting of a stable activator.Our detailed model can explain more experimental data than previous models.Mathematical analysis of a simple model supports our claims.
Circadian (∼24 h) timekeeping is essential for the lives of many organisms. To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. Our model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. Using this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. Furthermore, we find that an additional slow negative feedback loop preserves this stoichiometric balance and maintains timekeeping with a fixed period. The role of this mechanism in generating robust rhythms is validated by analysis of a simple and general model and a previous model of the Drosophila circadian clock. We propose a double-negative feedback loop design for biological clocks whose period needs to be tightly regulated even with large changes in gene dosage.
PMCID: PMC3542529  PMID: 23212247
biological clocks; circadian rhythms; gene regulatory networks; mathematical model; robustness
24.  Economics of membrane occupancy and respiro-fermentation 
The authors propose that prokaryotic metabolism is fundamentally constrained by the cytoplasmic membrane surface area available for protein expression, and show that this constraint can explain previously puzzling physiological phenomena, including respiro-fermentation.
We propose that prokaryotic cellular metabolism is fundamentally constrained by the finite cytoplasmic membrane surface area available for protein expression.A metabolic model of Escherichia coli updated to include a cytoplasmic membrane constraint is capable of predicting a variety of puzzling phenomena in this organism, including the respiro-fermentation phenomenon.Because the surface area to volume ratio is directly related to the morphology of the cell, this constraint provides a direct link between prokaryotic morphology and physiology.The potential relevance of this constraint to eukaryotes is discussed.
Many heterotrophs can produce ATP through both respiratory and fermentative pathways, allowing them to survive with or without oxygen. Since the molar ATP yield (molar ATP yield: mole of ATP produced/mole of substrate consumed) from respiration is about 15-fold higher than that from fermentation, ATP production via respiration is more efficient. Surprisingly, at high catabolic rate, many facultative aerobic organisms employ fermentative pathways simultaneously with respiration, even in the presence of abundant oxygen to produce ATP (Pfeiffer et al, 2001; Vemuri et al, 2006; Molenaar et al, 2009). This leads to an observable tradeoff between the ATP yield and the catabolic rate (Pfeiffer et al, 2001; Vemuri et al, 2006). This respiro-fermentation physiology is commonly observed in microorganisms, including Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae (Molenaar et al, 2009), as well as cancer cells (Vander Heiden et al, 2009). Despite extensive research, existing theories (Majewski and Domach, 1990; Varma and Palsson, 1994; Pfeiffer et al, 2001; Vazquez et al, 2008; Molenaar et al, 2009) cannot fully explain the respiro-fermentation phenomenon.
The membrane economics theory
We propose the hypothesis that the prokaryotic metabolism is fundamentally constrained by the finite cytoplasmic surface area available for protein expression—in order to maximize fitness, prokaryotic organisms such as E. coli must economically manage the expression of membrane proteins based on the membrane cost and the fitness benefit of the proteins. This hypothesis is proposed based on theoretical considerations (in this work), numerical analysis (Phillips and Milo, 2009), and experimental observation that the overexpression of non-respiratory membrane protein significantly reduces the oxygen consumption rate and induces aerobic fermentation (Wagner et al, 2007). Such a constraint on transmembrane protein expression may have significant physiological consequences in prokaryotes, such as E. coli, at higher catabolic rates. First, since both substrate transporters and respiratory enzymes are localized on the cytoplasmic membrane in prokaryotes, increased substrate uptake rates necessitates a decrease in the respiratory rate. This decrease in the respiratory rate, forces prokaryotes to process the additional substrate through the fermentative pathways, which are not catalyzed by transmembrane proteins, for continued ATP production. Furthermore, since the membrane requirement of an enzyme is inversely related to its turnover rate (see Materials and methods section in the manuscript), the faster and inefficient respiratory enzymes (such as Cyd-I and Cyd-II in E. coli) might be preferred over the slower and efficient enzymes (such as Cyo in E. coli), leading to an altered respiratory stoichiometry at higher catabolic rates. Finally, the absence of the respiratory enzymes under anaerobic conditions explains why the maximum glucose uptake rate (GUR) of E. coli is much higher.
Applying membrane economics theory to E. coli
To illustrate that the ‘membrane economics' theory could satisfactorily explain the physiological changes associated with the respiro-fermentation phenomenon in E. coli, we modified the genome-scale metabolic model of E. coli (Feist et al, 2007) to include a cytoplasmic membrane occupancy constraint. Using ‘relative membrane costs' calculated from experimental data, the new modeling framework—FBA with membrane economics (FBAME)—predicted that wild-type E. coli has a GUR of 10.7 mmol/gdw/h, an oxygen uptake rate (OUR) of 15.8 mmol/gdw/h, and a specific growth rate of 0.69 per hour during aerobic growth with excess glucose. FBAME also predicted that under the same growth condition, an E. coli knockout strain with no cytochromes has a GUR of 18 mmol/gdw/h and growth rate of 0.42. These values agree very well with the reported experimental values for E. coli grown in batch cultures (Vemuri et al, 2006; Portnoy et al, 2008), which supports our hypothesis that the higher GUR of E. coli during glucose-excess anaerobiosis than under aerobic conditions is due to the absence of the respiratory enzymes. We also simulated the aerobic growth of E. coli in glucose-limited chemostat using both conventional FBA and FBAME. FBAME successfully predicted the growth rate and yield changes with respect to increasing GUR (Figure 2A and B), as well as the aerobic production of acetate (Figure 2C) and concomitant repression of oxygen uptake (Figure 2D). On the other hand, traditional FBA significantly overestimated the growth rate and yield at higher GURs (this overestimation cannot be explained by varying the growth-associated maintenance (GAM) energy parameter; Figure 2A), and failed to predict the decrease in yield independent of acetate overflow and reduction in oxygen uptake at higher GURs (Figure 2). In addition, FBAME was able to predict the reduction of the TCA cycle activities at higher uptake rates (Figure 3C and D) as well as the selective expression of Cyo and Cyd-II at lower uptake rates (Figure 3A and B), whereas conventional FBA cannot predict the expression of inefficient Cyd-II. These predictions agree with the gene expression data from glucose-limited chemostat (Figure 3). Given the simplicity of the constraint we imposed, our model predictions agree surprisingly well with experimental observations, lending strong credibility to the membrane economics hypothesis.
Concluding remarks
Although it has been long suggested that cellular evolution are governed by non-adjustable mechanistic constraints (Palsson, 2000; Papin et al, 2005; Novak et al, 2006), to date, most metabolic models rely on empirically derived parameters such as glucose and OUR. In this article, we showed that complex phenomena, such as the respiro-fermentation in E. coli, could be satisfactorily explained and accurately predicted by using constraint-based optimization by introducing a simple mechanistic constraint on membrane enzyme occupancy. Given that the cytoplasmic membrane occupancy constraint is directly related to the surface area to volume (S/V) ratio of the cell, it is possible that this constraint resulted in the evolution of mitochondria in eukaryotes as mitochondria allows for a significantly increased S/V ratio. Further efforts to elucidate such fundamental cellular constraints as well as the underlying design principles could significantly improve our understanding of the regulation and evolution of metabolism.
The simultaneous utilization of efficient respiration and inefficient fermentation even in the presence of abundant oxygen is a puzzling phenomenon commonly observed in bacteria, yeasts, and cancer cells. Despite extensive research, the biochemical basis for this phenomenon remains obscure. We hypothesize that the outcome of a competition for membrane space between glucose transporters and respiratory chain (which we refer to as economics of membrane occupancy) proteins influences respiration and fermentation. By incorporating a sole constraint based on this concept in the genome-scale metabolic model of Escherichia coli, we were able to simulate respiro-fermentation. Further analysis of the impact of this constraint revealed differential utilization of the cytochromes and faster glucose uptake under anaerobic conditions than under aerobic conditions. Based on these simulations, we propose that bacterial cells manage the composition of their cytoplasmic membrane to maintain optimal ATP production by switching between oxidative and substrate-level phosphorylation. These results suggest that the membrane occupancy constraint may be a fundamental governing constraint of cellular metabolism and physiology, and establishes a direct link between cell morphology and physiology.
PMCID: PMC3159977  PMID: 21694717
constraint-based modeling; flux balance analysis; membrane occupancy; overflow metabolism; respiro-fermentation
25.  Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models 
Proteomic and transcriptomic data from wild-type and laboratory-evolved strains of Escherichia coli are consistent with predicted pathway usage from optimal growth rate solutions.In laboratory-evolved strains, there is an upregulation of the pathways in the computed optimal growth states, and downregulation of non-functional pathways.Known regulatory mechanisms are only partially responsible for altered metabolic pathway activity.
When prokaryotes are maintained at early- to mid-log phase growth through serial passaging for hundreds of generations, the strains improve fitness and evolve a higher growth rate (Lenski and Travisano, 1994; Ibarra et al, 2002). This increased growth rate is the result of the appearance of a few causal mutations (Herring et al, 2006; Conrad et al, 2009). In Escherichia coli, these altered growth phenotypes are consistent with predictions from genome-scale models of metabolism (GEMs) (Ibarra et al, 2002; Fong and Palsson, 2004). However, it is still not known (1) whether absolute gene and protein expression levels and expression changes are consistent with optimal growth predictions from in silico GEMs or (2) whether measured expression changes can be linked to physiological changes that are based on known mechanisms or pathways. In this study, we begin to address these questions using constraint-based modeling of E. coli K-12 metabolism (Feist and Palsson, 2008) to analyze omic data that document the expression changes in E. coli under adaptive evolution in three different growth conditions.
Mapping high-throughput data to a network can be useful for interpretation. However, it does not account for upstream and downstream effects of gene and protein expression changes. The analysis of data in the context of GEMs can suggest if predicted activity is consistent with the data. For this work, we used a variant of flux balance analysis (FBA), called Parsimonious enzyme usage FBA (pFBA) (Figure 1), to classify all genes according to whether they are used in the optimal growth solutions. Results from these models were compared with the data to assess whether the data were consistent with genes and proteins within the predicted optimal solutions, and whether the expression changes were consistent with measured physiology. Through this analysis, we find that the data provide a high coverage of genes that contribute to the optimal growth solutions (Figure 1B). In fact, the union of the proteomic and transcriptomic data for non-essential genes provides support for 97.7% of all non-essential gene-associated reactions within the optimal growth predictions. Thus, the spectrum of expressed genes and proteins is consistent with the pathway utilization that is predicted for these optimal growth phenotypes.
Laboratory-evolved strains attain a higher growth rate. This higher growth rate is usually associated with an increased substrate uptake rate (Ibarra et al, 2002; Fong et al, 2005) and in some cases more efficient metabolism (Ibarra et al, 2002). Both of these properties are also witnessed in the strains studied here. It has been reported that in most cases, evolved strain growth phenotype is consistent with GEM predictions (Ibarra et al, 2002; Teusink et al, 2009). Here, we evaluate whether the laboratory-evolved strains adjust the gene and protein expression levels in accordance with pathway usage in the optimal growth predictions. Essential and non-essential genes and proteins within the optimal growth solutions are significantly upregulated (Figure 1B). This suggests that these proteins may be acting as bottlenecks that are relieved through the adaptive process, thereby allowing for a higher substrate uptake rate and growth rate. However, genes and proteins associated with reactions that cannot carry a flux in the given growth conditions are downregulated in the evolved strains (Figure 1B). Furthermore, there is downregulation of genes associated with less efficient pathways (Figure 5C). Thus, the omic data support the emergence of the predicted optimal growth states, consistent with the increased substrate uptake upstream and the increased biomass production downstream of these internal pathways.
Regulatory mechanisms, both known and unknown, are responsible for the changes seen here. Across all data sets, several metabolic regulons are significantly downregulated. However, no known regulons were enriched among upregulated genes or proteins for all but one data set. Aside from just regulating the metabolic pathways directly, these mechanisms lead to additional physiological changes. For example, in the minimal media growth conditions used here, the stringent response normally represses growth while upregulating amino-acid biosynthetic processes. However, evolved strain gene expression shows a suppression of the stringent response, as evolved strain gene expression shows either no expression change or changes opposite to the normal stringent response.
The implications of this work are as follows: (1) genome-scale gene and protein expression data are consistent with FBA computed optimal growth states, and evolved strains reinforce these optimal states; (2) genome-scale models will have an important function bridging the gap between genotype and phenotype; and (3) the development of additional genome-scale models of other growth-related processes such as transcription and translation (Thiele et al, 2009) will have an important function in elucidating the mechanisms that contribute the most to altered phenotypes (Lewis et al, 2009a). In addition, reconstruction of the transcriptional regulation network will aid in identifying the control of expression changes seen in the other systems.
After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild-type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM-computed optimal growth states.
PMCID: PMC2925526  PMID: 20664636
Escherichia coli; genome-scale models; microarray; optimality; proteomics

Results 1-25 (1242075)