Negative feedback through mRNA provides the best control of gene-expression noise

A metallothionein from disk abalone (Haliotis discus discus): Insights into its functional roles in immune response, metal tolerance, and oxidative stress

Metallothioneins (MTs) are cysteine-rich metal-binding proteins whose expression is induced by exposure to essential and non-essential metals, making them potential biological markers for assessing metal pollution in various biomonitoring programs. However, the functional properties of these proteins are yet to be comprehensively characterized in most marine invertebrates. In this study, we identified and characterized an MT homolog from the disk abalone (Haliotis discus discus), referred to as disk abalone MT (AbMT). AbMT exhibited the same primary structural features as MTs from other mollusks containing two β-domains (β2β1-form). AbMT protein demonstrated metal-binding and detoxification abilities against Zn, Cu, and Cd, as evidenced by Escherichia coli growth kinetics, metal tolerance analysis, and UV absorption spectrum. Transcriptional analysis revealed that AbMT was ubiquitously expressed in all analyzed tissues and upregulated in gill tissue following challenge with Vibrio parahaemolyticus, Listeria monocytogenes, and viral hemorrhagic septicemia virus (VHSV). Additionally, overexpression of AbMT suppressed LPS-induced NO production in RAW264.7 macrophages, protected cells against H2O2-induced oxidative stress, and promoted macrophage polarization toward the M1 phase. Conclusively, these findings suggest an important role for AbMT in environmental stress protection and immune regulation in disk abalone.

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

ATP6V1B1 regulates ovarian cancer progression and cisplatin sensitivity through the mTOR/autophagy pathway

Early detection and effective chemotherapy for ovarian cancer, a serious gynecological malignancy, require further progress. This study aimed to investigate the molecular mechanism of ATPase H+-Transporting V1 Subunit B1 (ATP6V1B1) in ovarian cancer development and chemoresistance. Our data show that ATP6V1B1 is upregulated in ovarian cancer and correlated with decreased progression-free survival. Gain- and loss-of-function experiments demonstrated that ATP6V1B1 promotes the proliferation, migration, and invasion of ovarian cancer cells in vitro, while ATP6V1B1 knockout inhibits tumor growth in vivo. In addition, knocking down ATP6V1B1 increases the sensitivity of ovarian cancer cells to cisplatin. Mechanistic studies showed that ATP6V1B1 regulates the activation of the mTOR/autophagy pathway. Overall, our study confirmed the oncogenic role of ATP6V1B1 in ovarian cancer and revealed that ATP6V1B1 promotes ovarian cancer progression via the mTOR/autophagy axis.

Cell-type specific effects of mineralocorticoid receptor gene expression suggest intercellular communication regulating fibrosis in skeletal muscle disease

Introduction: Duchenne muscular dystrophy (DMD) is a fatal striated muscle degenerative disease. DMD is caused by loss of dystrophin protein, which results in sarcolemmal instability and cycles of myofiber degeneration and regeneration. Pathology is exacerbated by overactivation of infiltrating immune cells and fibroblasts, which leads to chronic inflammation and fibrosis. Mineralocorticoid receptors (MR), a type of nuclear steroid hormone receptors, are potential therapeutic targets for DMD. MR antagonists show clinical efficacy on DMD cardiomyopathy and preclinical efficacy on skeletal muscle in DMD models. Methods: We have previously generated myofiber and myeloid MR knockout mouse models to dissect cell-specific functions of MR within dystrophic muscles. Here, we compared skeletal muscle gene expression from both knockouts to further define cell-type specific signaling downstream from MR. Results: Myeloid MR knockout increased proinflammatory and profibrotic signaling, including numerous myofibroblast signature genes. Tenascin C was the most highly upregulated fibrotic gene in myeloid MR-knockout skeletal muscle and is a component of fibrosis in dystrophic skeletal muscle. Surprisingly, lysyl oxidase (Lox), canonically a collagen crosslinker, was increased in both MR knockouts, but did not localize to fibrotic regions of skeletal muscle. Lox localized within myofibers, including only a region of quadriceps muscles. Lysyl oxidase like 1 (Loxl1), another Lox family member, was increased only in myeloid MR knockout muscle and localized specifically to fibrotic regions. Discussion: This study suggests that MR signaling in the dystrophic muscle microenvironment involves communication between contributing cell types and modulates inflammatory and fibrotic pathways in muscle disease.

Mitigating transcription noise via protein sharing in syncytial cells

Bursty transcription allows nuclei to concentrate the work of transcribing mRNA into short, intermittent intervals, potentially reducing transcriptional interference. However, bursts of mRNA production can increase noise in protein abundances. Here, we formulate models for gene expression in syncytia, or multinucleate cells, showing that protein abundance noise may be mitigated locally via spatial averaging of diffuse proteins. Our modeling shows a universal reduction in protein noise, which increases with the average number of nuclei per cell and persists even when the number of nuclei is itself a random variable. Experimental data comparing distributions of a cyclin mRNA that is conserved between brewer's yeast and a closely related filamentous fungus Ashbya gossypii confirm that syncytism is permissive of greater levels of transcriptional noise. Our findings suggest that division of transcriptional labor between nuclei allows syncytia to sidestep tradeoffs between efficiency and precision of gene expression.

Syncytial Assembly Lines: Consequences of Multinucleate Cellular Compartments for Fungal Protein Synthesis

Fast growth and prodigious cellular outputs make fungi powerful tools in biotechnology. Recent modeling work has exposed efficiency gains associated with dividing the labor of transcription over multiple nuclei, and experimental innovations are opening new windows on the capacities and adaptations that allow nuclei to behave autonomously or in coordination while sharing a single, common cytoplasm. Although the motivation of our review is to motivate and connect recent work toward a greater understanding of fungal factories, we use the analogy of the assembly line as an organizing idea for studying coordinated gene expression, generally.

Noise properties of adaptation-conferring biochemical control modules

A key goal of synthetic biology is to develop functional biochemical modules with network-independent properties. Antithetic integral feedback (AIF) is a recently developed control module in which two control species perfectly annihilate each other's biological activity. The AIF module confers robust perfect adaptation to the steady-state average level of a controlled intracellular component when subjected to sustained perturbations. Recent work has suggested that such robustness comes at the unavoidable price of increased stochastic fluctuations around average levels. We present theoretical results that support and quantify this trade-off for the commonly analyzed AIF variant in the idealized limit with perfect annihilation. However, we also show that this trade-off is a singular limit of the control module: Even minute deviations from perfect adaptation allow systems to achieve effective noise suppression as long as cells can pay the corresponding energetic cost. We further show that a variant of the AIF control module can achieve significant noise suppression even in the idealized limit with perfect adaptation. This atypical configuration may thus be preferable in synthetic biology applications.

Physicochemical cues are not potent regulators of human dermal fibroblast trans-differentiation

Due to their inherent plasticity, dermal fibroblasts hold great promise in regenerative medicine. Although biological signals have been well-established as potent regulators of dermal fibroblast function, it is still unclear whether physiochemical cues can induce dermal fibroblast trans-differentiation. Herein, we evaluated the combined effect of surface topography, substrate rigidity, collagen type I coating and macromolecular crowding in human dermal fibroblast cultures. Our data indicate that tissue culture plastic and collagen type I coating increased cell proliferation and metabolic activity. None of the assessed in vitro microenvironment modulators affected cell viability. Anisotropic surface topography induced bidirectional cell morphology, especially on more rigid (1,000 kPa and 130 kPa) substrates. Macromolecular crowding increased various collagen types, but not fibronectin, deposition. Macromolecular crowding induced globular extracellular matrix deposition, independently of the properties of the substrate. At day 14 (longest time point assessed), macromolecular crowding downregulated tenascin C (in 9 out of the 14 groups), aggrecan (in 13 out of the 14 groups), osteonectin (in 13 out of the 14 groups), and collagen type I (in all groups). Overall, our data suggest that physicochemical cues (such surface topography, substrate rigidity, collagen coating and macromolecular crowding) are not as potent as biological signals in inducing dermal fibroblast trans-differentiation.

Out-of-equilibrium gene expression fluctuations in the presence of extrinsic noise

Cell-to-cell variability in protein concentrations is strongly affected by extrinsic noise, especially for highly expressed genes. Extrinsic noise can be due to fluctuations of several possible cellular factors connected to cell physiology and to the level of key enzymes in the expression process. However, how to identify the predominant sources of extrinsic noise in a biological system is still an open question. This work considers a general stochastic model of gene expression with extrinsic noise represented as fluctuations of the different model rates, and focuses on the out-of-equilibrium expression dynamics. Combining analytical calculations with stochastic simulations, we characterize how extrinsic noise shapes the protein variability during gene activation or inactivation, depending on the prevailing source of extrinsic variability, on its intensity and timescale. In particular, we show that qualitatively different noise profiles can be identified depending on which are the fluctuating parameters. This indicates an experimentally accessible way to pinpoint the dominant sources of extrinsic noise using time-coarse experiments.

The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian

Gene expression inherently gives rise to stochastic variation ("noise") in the production of gene products. Minimizing noise is crucial for ensuring reliable cellular functions. However, noise cannot be suppressed below a certain intrinsic limit. For constitutively expressed genes, this limit is typically assumed to be Poissonian noise, wherein the variance in mRNA numbers is equal to their mean. Here, we demonstrate that several cell division genes in fission yeast exhibit mRNA variances significantly below this limit. The reduced variance can be explained by a gene expression model incorporating multiple transcription and mRNA degradation steps. Notably, in this sub-Poissonian regime, distinct from Poissonian or super-Poissonian regimes, cytoplasmic noise is effectively suppressed through a higher mRNA export rate. Our findings redefine the lower limit of eukaryotic gene expression noise and uncover molecular requirements for achieving ultralow noise, which is expected to be important for vital cellular functions.

NMT1 sustains ICAM-1 to modulate adhesion and migration of tumor cells

Protein modifications have significant effects on tumorigenesis. N-Myristoylation is one of the most important lipidation modifications, and N-myristoyltransferase 1 (NMT1) is the main enzyme required for this process. However, the mechanism underlying how NMT1 modulates tumorigenesis remains largely unclear. Here, we found that NMT1 sustains cell adhesion and suppresses tumor cell migration. Intracellular adhesion molecule 1 (ICAM-1) was a potential functional downstream effector of NMT1, and its N-terminus could be N-myristoylated. NMT1 prevented ubiquitination and proteasome degradation of ICAM-1 by inhibiting Ub E3 ligase F-box protein 4, which prolonged the half-life of ICAM1 protein. Correlations between NMT1 and ICAM-1 were observed in liver and lung cancers, which were associated with metastasis and overall survival. Therefore, carefully designed strategies focusing on NMT1 and its downstream effectors might be helpful to treat tumors.

The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian

Stochastic variation in gene products ("noise") is an inescapable by-product of gene expression. Noise must be minimized to allow for the reliable execution of cellular functions. However, noise cannot be suppressed beyond an intrinsic lower limit. For constitutively expressed genes, this limit is believed to be Poissonian, meaning that the variance in mRNA numbers cannot be lower than their mean. Here, we show that several cell division genes in fission yeast have mRNA variances significantly below this limit, which cannot be explained by the classical gene expression model for low-noise genes. Our analysis reveals that multiple steps in both transcription and mRNA degradation are essential to explain this sub-Poissonian variance. The sub-Poissonian regime differs qualitatively from previously characterized noise regimes, a hallmark being that cytoplasmic noise is reduced when the mRNA export rate increases. Our study re-defines the lower limit of eukaryotic gene expression noise and identifies molecular requirements for ultra-low noise which are expected to support essential cell functions.

Development of three-layer collagen scaffolds to spatially direct tissue-specific cell differentiation for enthesis repair

Enthesis repair remains a challenging clinical indication. Herein, a three-layer scaffold composed of a tendon-like layer of collagen type I, a fibrocartilage-like layer of collagen type II and a bone-like layer of collagen type I and hydroxyapatite, was designed to recapitulate the matrix composition of the enthesis. To aid tenogenic and fibrochondrogenic differentiation, bioactive molecules were loaded in the tendon-like layer or the fibrocartilage-like layer and their effect was assessed in in vitro setting using human bone marrow derived mesenchymal stromal cells and in an ex vivo model. Seeded human bone marrow mesenchymal stromal cells infiltrated and homogeneously spread throughout the scaffold. As a response to the composition of the scaffold, cells differentiated in a localised manner towards the osteogenic lineage and, in combination with differentiation medium, towards the fibrocartilage lineage. Whilst functionalisation of the tendon-like layer did not improve tenogenic cell commitment within the time frame of this work, relevant fibrochondrogenic markers were detected in the fibrocartilage-like layer when scaffolds were functionalised with bone morphogenetic protein 2 or non-functionalised at all, in vitro and ex vivo, respectively. Altogether, our data advocate the use of compartmentalised scaffolds for the repair and regeneration of interfacial tissues, such as enthesis.

The synergistic effect of physicochemical in vitro microenvironment modulators in human bone marrow stem cell cultures

Modern bioengineering utilises biomimetic cell culture approaches to control cell fate during in vitro expansion. In this spirit, herein we assessed the influence of bidirectional surface topography, substrate rigidity, collagen type I coating and macromolecular crowding (MMC) in human bone marrow stem cell cultures. In the absence of MMC, surface topography was a strong modulator of cell morphology. MMC significantly increased extracellular matrix deposition, albeit in a globular manner, independently of the surface topography, substrate rigidity and collagen type I coating. Collagen type I coating significantly increased cell metabolic activity and none of the assessed parameters affected cell viability. At day 14, in the absence of MMC, none of the assessed genes was affected by surface topography, substrate rigidity and collagen type I coating, whilst in the presence of MMC, in general, collagen type I α1 chain, tenascin C, osteonectin, bone sialoprotein, aggrecan, cartilage oligomeric protein and runt-related transcription factor were downregulated. Interestingly, in the presence of the MMC, the 1000 kPa grooved substrate without collagen type I coating upregulated aggrecan, cartilage oligomeric protein, scleraxis homolog A, tenomodulin and thrombospondin 4, indicative of tenogenic differentiation. This study further supports the notion for multifactorial bioengineering to control cell fate in culture.

The circadian transcription factor ARNTL2 is regulated by weight-loss interventions in human white adipose tissue and inhibits adipogenesis

Misalignment of physiological circadian rhythms promotes obesity which is characterized by white adipose tissue (WAT) expansion. Differentiation of Adipose stem/progenitor cells (ASCs) contributes to WAT increase but the importance of the cellular clock in this process is incompletely understood. In the present study, we reveal the role of the circadian transcription factor Aryl hydrocarbon receptor nuclear translocator-like 2 (ARNTL2) in human ASCs, isolated from subcutaneous (s)WAT samples of patients undergoing routine elective plastic abdominal surgery. We show that circadian synchronization by serum-shock or stimulation with adipogenic stimuli leads to a different expression pattern of ARNTL2 relative to its well-studied paralogue ARNTL1. We demonstrate that ARNTL2 mRNA is downregulated in ASCs upon weight-loss (WL) whereas ARNTL2 protein is rapidly induced in the course of adipogenic differentiation and highly abundant in adipocytes. ARNTL2 protein is maintained in ASCs cooperatively by mechanistic Target of Rapamycin (mTOR) and Mitogen-activated Protein Kinase (MAPK) signalling pathways while ARNTL2 functions as an inhibitor on both circuits, leading to a feedback mechanism. Consistently, ectopic overexpression of ARNTL2 repressed adipogenesis by facilitating the degradation of ARNTL1, inhibition of Kruppel-Like Factor 15 (KLF15) gene expression and down-regulation of the MAPK-CCAAT/enhancer-binding protein β (C/EBPβ) axis. Western blot analysis of sWAT samples from normal-weight, obese and WL donors revealed that ARNTL2 protein was solely elevated by WL compared to ARNTL1 which underscores unique functions of both transcription factors. In conclusion, our study reveals ARNTL2 to be a WL-regulated inhibitor of adipogenesis which might provide opportunities to develop strategies to ameliorate obesity.

Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis

Intracellular reaction rates depend on concentrations and hence their levels are often regulated. However classical models of stochastic gene expression lack a cell size description and cannot be used to predict noise in concentrations. Here, we construct a model of gene product dynamics that includes a description of cell growth, cell division, size-dependent gene expression, gene dosage compensation, and size control mechanisms that can vary with the cell cycle phase. We obtain expressions for the approximate distributions and power spectra of concentration fluctuations which lead to insight into the emergence of concentration homeostasis. We find that (i) the conditions necessary to suppress cell division-induced concentration oscillations are difficult to achieve; (ii) mRNA concentration and number distributions can have different number of modes; (iii) two-layer size control strategies such as sizer-timer or adder-timer are ideal because they maintain constant mean concentrations whilst minimising concentration noise; (iv) accurate concentration homeostasis requires a fine tuning of dosage compensation, replication timing, and size-dependent gene expression; (v) deviations from perfect concentration homeostasis show up as deviations of the concentration distribution from a gamma distribution. Some of these predictions are confirmed using data for E. coli, fission yeast, and budding yeast.

Frequency response in splicing regulation under mRNA auto-depletion control

Nowadays, there exists a huge literature about stochastic model of transcriptional and translational control in gene networks. However, results related to post-transcriptional regulation via splicing and its connection with transcriptional and translational regulation are almost missing in the current literature and only related to the steady state moments investigation. Nowadays, it is becoming of paramount importance the need for modeling post-transcriptional regulation via splicing especially for DNA viruses or retroviruses. However, there exists only few studies in the literature about splicing regulation and none of them investigate its behavior in the frequency domain that can unveil important features of dynamical stochastic systems that cannot be revealed by the sole steady state moment investigation. The aim of this work is to theoretically investigate a simple gene network subject to splicing regulation with negative feedback control, implemented through mRNA auto-depletion under a frequency domain perspective. This study showed the pivotal role of the burst size, enhancing the noise power spectrum, as well as the splicing conversion rates capable to increase and decrease the noise power spectrum in the pre-mRNA and mRNA, respectively, for high values of conversion rates. Importantly, it shows the capability of the mRNA autodepletion control to modulate the noise as a frequency-dependent amplifying control as a function of the negative feedback strengths.

Double-edged role of resource competition in gene expression noise and control

Despite extensive investigation demonstrating that resource competition can significantly alter the deterministic behaviors of synthetic gene circuits, it remains unclear how resource competition contributes to the gene expression noise and how this noise can be controlled. Utilizing a two-gene circuit as a prototypical system, we uncover a surprising double-edged role of resource competition in gene expression noise: competition decreases noise through introducing a resource constraint but generates its own type of noise which we name as "resource competitive noise." Utilization of orthogonal resources enables retainment of the noise reduction conferred by resource constraint while removing the added resource competitive noise. The noise reduction effects are studied using three negative feedback types: negatively competitive regulation (NCR), local, and global controllers, each having four placement architectures in the protein biosynthesis pathway (mRNA or protein inhibition on transcription or translation). Our results show that both local and NCR controllers with mRNA-mediated inhibition are efficacious at reducing noise, with NCR controllers demonstrating a superior noise-reduction capability. We also find that combining feedback controllers with orthogonal resources can improve the local controllers. This work provides deep insights into the origin of stochasticity in gene circuits with resource competition and guidance for developing effective noise control strategies.

Long-Term Functional and Cytoarchitectonic Effects of the Systemic Administration of the Histamine H1 Receptor Antagonist/Inverse Agonist Chlorpheniramine During Gestation in the Rat Offspring Primary Motor Cortex

The transient histaminergic system is among the first neurotransmitter systems to appear during brain development in the rat mesencephalon/rhombencephalon. Histamine increases FOXP2-positive deep-layer neuron differentiation of cortical neural stem cells through H₁ receptor activation in vitro. The in utero or systemic administration of chlorpheniramine (H₁ receptor antagonist/inverse agonist) during deep-layer cortical neurogenesis decreases FOXP2 neurons in the developing cortex, and H₁R- or histidine decarboxylase-knockout mice show impairment in learning and memory, wakefulness and nociception, functions modulated by the cerebral cortex. Due to the role of H₁R in cortical neural stem cell neurogenesis, the purpose of this study was to evaluate the postnatal impact of the systemic administration of chlorpheniramine during deep-layer cortical neuron differentiation (E12–14) in the primary motor cortex (M1) of neonates (P0) and 21-day-old pups (P21). Chlorpheniramine or vehicle were systemically administered (5 mg/kg, i.p.) to pregnant Wistar rats at gestational days 12–14, and the expression and distribution of deep- (FOXP2 and TBR1) and superficial-layer (SATB2) neuronal cortical markers were analyzed in neonates from both groups. The qRT-PCR analysis revealed a reduction in the expression of Satb2 and FoxP2. However, Western blot and immunofluorescence showed increased protein levels in the chlorpheniramine-treated group. In P21 pups, the three markers showed impaired distribution and increased immunofluorescence in the experimental group. The Sholl analysis evidenced altered dendritic arborization of deep-layer neurons, with lower excitability in response to histamine, as evaluated by whole-cell patch-clamp recording, as well as diminished depolarization-evoked [³H]-glutamate release from striatal slices. Overall, these results suggest long-lasting effects of blocking H₁Rs during early neurogenesis that may impact the pathways involved in voluntary motor activity and cognition.

Frequency analysis of splicing regulation

In the past decades, mathematical modelers developed a huge literature to model and analyze gene networks under both deterministic and stochastic formalisms. Such literature is predominantly focused on modeling transcriptional and translational regulation, while the development of proper mathematical frameworks to model and study post-transcriptional regulation via splicing and its connection with transcriptional and translational regulation are almost missing. Nowadays, it is becoming of paramount importance the need for modeling post-transcriptional regulation via splicing especially for bacteria or viruses. However, current literature is focused on investigating splicing regulation at steady state and none of them have the purpose to investigate gene networks behavior in the frequency domain, thus providing only a partial investigation about the system dynamical response. The aim of this work is to theoretically investigate a simple gene network subjects to splicing regulation with/without negative feedback control under a frequency domain perspective. This study showed the pivotal role of the burst size, as well as splicing conversion rates to modulate the noise and the power spectrum response. It also shows an interesting behavior under the frequency domain induced by the merging effect of burst size, splicing conversion rates and negative feedback strength.

A combined physicochemical approach towards human tenocyte phenotype maintenance

During in vitro culture, bereft of their optimal tissue context, tenocytes lose their phenotype and function. Considering that tenocytes in their native tissue milieu are exposed simultaneously to manifold signals, combination approaches (e.g. growth factor supplementation and mechanical stimulation) are continuously gaining pace to control cell fate during in vitro expansion, albeit with limited success due to the literally infinite number of possible permutations. In this work, we assessed the potential of scalable and potent physicochemical approaches that control cell fate (substrate stiffness, anisotropic surface topography, collagen type I coating) and enhance extracellular matrix deposition (macromolecular crowding) in maintaining human tenocyte phenotype in culture. Cell morphology was primarily responsive to surface topography. The tissue culture plastic induced the largest nuclei area, the lowest aspect ratio, and the highest focal adhesion kinase. Collagen type I coating increased cell number and metabolic activity. Cell viability was not affected by any of the variables assessed. Macromolecular crowding intensely enhanced and accelerated native extracellular matrix deposition, albeit not in an aligned fashion, even on the grooved substrates. Gene analysis at day 14 revealed that the 130 kPa grooved substrate without collagen type I coating and under macromolecular crowding conditions positively regulated human tenocyte phenotype. Collectively, this work illustrates the beneficial effects of combined physicochemical approaches in controlling cell fate during in vitro expansion.

Noise suppression in stochastic genetic circuits using PID controllers

Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. In contrast, integral feedback has no effect on the protein noise level from stochastic expression, but significantly minimizes the impact of external disturbances, particularly when the disturbance comes at low frequencies. Counter-intuitively, integral feedback is found to amplify external disturbances at intermediate frequencies. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis using both analytical methods and Monte Carlo simulations reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static input-output sensitivity of the open-loop system. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels.

In the noisy cellular environment, biochemical species such as genes, RNAs and proteins that often occur at low molecular counts, are subject to considerable stochastic fluctuations in copy numbers over time. How cellular biochemical processes function reliably in the face of such randomness is an intriguing fundamental problem. Increasing evidence suggests that random fluctuations (noise) in protein copy numbers play important functional roles, such as driving genetically identical cells to different cell fates. Moreover, many disease states have been attributed to elevated noise levels in specific proteins. Here we systematically investigate design of biochemical systems that function as proportional, integral and derivative-based feedback controllers to suppress protein count fluctuations arising from bursty expression of the protein and external disturbance in protein synthesis. Our results show that different controllers are effective in buffering different noise components, and identify ranges of feedback gain for minimizing deleterious fluctuations in protein levels.

MicroRNA Based Feedforward Control of Intrinsic Gene Expression Noise

Intrinsic noise, which arises in gene expression at low copy numbers, can be controlled by diverse regulatory motifs, including feedforward loops. Here, we study an example of a feedforward control system based on the interaction between an mRNA molecule and an antagonistic microRNA molecule encoded by the same gene, aiming to quantify the variability (or noise) in molecular copy numbers. Using linear noise approximation, we show that the mRNA noise is sub-Poissonian in case of non-bursty transcription, and exhibits a nonmonotonic response both to the species natural lifetime ratio and to the strength of the antagonistic interaction. Additionally, we use the Chemical Reaction Network Theory to prove that the mRNA copy number distribution is Poissonian in the absence of spontaneous mRNA decay channel. In case of transcriptional bursts, we show that feedforward control can attenuate the super-Poissonian gene-expression noise that is due to bursting, and that the effect is more considerable at the protein than at the mRNA level. Our results indicate that the strong coupling between mRNA and microRNA in the sense of burst stoichiometry and also of timing of production events renders the microRNA based feedforward motif an effective mechanism for the control of gene expression noise.

Moment dynamics for gene regulation with rational rate laws

This aim of this paper is mainly to investigate the performance of two typical moment closure schemes in gene regulatory master equations of rational rate laws. When the reaction rate is polynomial, the error bounds between the authentic and approximate moments obtained by schemes of Gaussian moment closure and log-normal moment closure are explicitly given. When the reaction rate is not polynomial, it is shown that the two schemes both behave well in the absence of active-inactive state switch, but in the presence of active-inactive state switch the log-normal closure scheme is far superior to the Gaussian closure scheme in capturing the asymptotic ensemble statistics. Moreover, the accuracy of the log-normal closure method is further confirmed by steady-state analytic results and the conditional Gaussian closure method. It is also disclosed that optimal negative feedback exists in suppressing protein noise in the presence of the on-off switch control.

Phenotypic selection through cell death: stochastic modelling of O-6-methylguanine-DNA methyltransferase dynamics

Glioblastoma (GBM) is the most aggressive malignant primary brain tumour with a median overall survival of 15 months. To treat GBM, patients currently undergo a surgical resection followed by exposure to radiotherapy and concurrent and adjuvant temozolomide (TMZ) chemotherapy. However, this protocol often leads to treatment failure, with drug resistance being the main reason behind this. To date, many studies highlight the role of O-6-methylguanine-DNA methyltransferase (MGMT) in conferring drug resistance. The mechanism through which MGMT confers resistance is not well studied-particularly in terms of computational models. With only a few reasonable biological assumptions, we were able to show that even a minimal model of MGMT expression could robustly explain TMZ-mediated drug resistance. In particular, we showed that for a wide range of parameter values constrained by novel cell growth and viability assays, a model accounting for only stochastic gene expression of MGMT coupled with cell growth, division, partitioning and death was able to exhibit phenotypic selection of GBM cells expressing MGMT in response to TMZ. Furthermore, we found this selection allowed the cells to pass their acquired phenotypic resistance onto daughter cells in a stable manner (as long as TMZ is provided). This suggests that stochastic gene expression alone is enough to explain the development of chemotherapeutic resistance.

The synergistic effect of low oxygen tension and macromolecular crowding in the development of extracellular matrix-rich tendon equivalents

Cellular therapies play an important role in tendon tissue engineering, with tenocytes being the most prominent and potent cell population available. However, for the development of a rich extracellular matrix tenocyte-assembled tendon equivalent, prolonged in vitro culture is required, which is associated with phenotypic drift. Recapitulation of tendon tissue microenvironment in vitro with cues that enhance and accelerate extracellular matrix synthesis and deposition, whilst maintaining tenocyte phenotype, may lead to functional cell therapies. Herein, we assessed the synergistic effect of low oxygen tension (enhances extracellular matrix synthesis) and macromolecular crowding (enhances extracellular matrix deposition) in human tenocyte culture. Protein analysis demonstrated that human tenocytes at 2% oxygen tension and with 50 μg ml^-1 carrageenan (macromolecular crowder used) significantly increased synthesis and deposition of collagen types I, III, V and VI. Gene analysis at day 7 illustrated that human tenocytes at 2% oxygen tension and with 50 μg ml^-1 carrageenan significantly increased the expression of prolyl 4-hydroxylase subunit alpha 1, procollagen-lysine 2- oxoglutarate 5-dioxygenase 2, scleraxis, tenomodulin and elastin, whilst chondrogenic (e.g. runt-related transcription factor 2, cartilage oligomeric matrix protein, aggrecan) and osteogenic (e.g. secreted phosphoprotein 1, bone gamma-carboxyglutamate protein) trans-differentiation markers were significantly down-regulated or remained unchanged. Collectively, our data clearly illustrates the beneficial synergistic effect of low oxygen tension and macromolecular crowding in the accelerated development of tissue equivalents.

Synthetic negative feedback circuits using engineered small RNAs

Negative feedback is known to enable biological and man-made systems to perform reliably in the face of uncertainties and disturbances. To date, synthetic biological feedback circuits have primarily relied upon protein-based, transcriptional regulation to control circuit output. Small RNAs (sRNAs) are non-coding RNA molecules that can inhibit translation of target messenger RNAs (mRNAs). In this work, we modelled, built and validated two synthetic negative feedback circuits that use rationally-designed sRNAs for the first time. The first circuit builds upon the well characterised tet-based autorepressor, incorporating an externally-inducible sRNA to tune the effective feedback strength. This allows more precise fine-tuning of the circuit output in contrast to the sigmoidal, steep input–output response of the autorepressor alone. In the second circuit, the output is a transcription factor that induces expression of an sRNA, which inhibits translation of the mRNA encoding the output, creating direct, closed-loop, negative feedback. Analysis of the noise profiles of both circuits showed that the use of sRNAs did not result in large increases in noise. Stochastic and deterministic modelling of both circuits agreed well with experimental data. Finally, simulations using fitted parameters allowed dynamic attributes of each circuit such as response time and disturbance rejection to be investigated.

Autoregulation of bacterial gene expression: lessons from the MazEF toxin-antitoxin system

Autoregulation is the direct modulation of gene expression by the product of the corresponding gene. Autoregulation of bacterial gene expression has been mostly studied at the transcriptional level, when a protein acts as the cognate transcriptional repressor. A recent study investigating dynamics of the bacterial toxin-antitoxin MazEF system has shown how autoregulation at both the transcriptional and post-transcriptional levels affects the heterogeneity of Escherichia coli populations. Toxin-antitoxin systems hold a crucial but still elusive part in bacterial response to stress. This perspective highlights how these modules can also serve as a great model system for investigating basic concepts in gene regulation. However, as the genomic background and environmental conditions substantially influence toxin activation, it is important to study (auto)regulation of toxin-antitoxin systems in well-defined setups as well as in conditions that resemble the environmental niche.

Buffering and Amplifying Transcriptional Noise During Cell Fate Specification

The molecular processes that drive gene transcription are inherently noisy. This noise often manifests in the form of transcriptional bursts, producing fluctuations in gene activity over time. During cell fate specification, this noise is often buffered to ensure reproducible developmental outcomes. However, sometimes noise is utilized as a “bet-hedging” mechanism to diversify functional roles across a population of cells. Studies of bacteria, yeast, and cultured cells have provided insights into the nature and roles of noise in transcription, yet we are only beginning to understand the mechanisms by which noise influences the development of multicellular organisms. Here we discuss the sources of transcriptional noise and the mechanisms that either buffer noise to drive reproducible fate choices or amplify noise to randomly specify fates.

Characterization of noise in multistable genetic circuits reveals ways to modulate heterogeneity

Random fluctuations in the amount of cellular components like mRNA and protein molecules are inevitable due to the stochastic and discrete nature of biochemical reactions. If large enough, this so-called “cellular noise” can lead to random transitions between the expression states of a multistable genetic circuit. That way, heterogeneity within isogenic populations is created. Our aim is to understand which dynamical features of a simple autoregulatory system determine its intrinsic noise level, and how they can be modified in order to regulate state-transitions. To that end, novel mathematical methods for the state-dependent characterization and prediction of noise in multistable systems are developed. First, we introduce the hybrid LNA, a modified version of the Linear Noise Approximation. It yields good predictions on variances of mRNA and protein fluctuations, even for reaction systems comprising low-copy-number components (e.g. mRNA) and highly nonlinear reaction rates. Furthermore, the temporal structure of fluctuations and the skewness of the protein distribution are characterized via state-dependent protein burst sizes and burst frequencies. Based on this mathematical framework, we develop graphical methods which support the intuitive design of regulatory circuits with a desired noise pattern. The methods are then used to predict how overall noise in the system can be adapted, and how state-specific noise modifications are possible that allow, e.g., the generation of unidirectional transitions. Our considerations are validated by stochastic simulations. This way, a design of genetic circuits is possible that takes population heterogeneity into account and is valuable in applications of synthetic biology and biotechnology. Moreover, natural phenomena like the bimodal development of genetic competence can be studied.

Network Analysis of Gene Expression

Studies have pointed out that the expression of genes are highly regulated, which result in a cascade of distinct patterns of coexpression forming a network. Identifying and understanding such patterns is crucial in deciphering molecular mechanisms that underlie the pathophysiology of diseases. With the advance of high throughput assay of messenger RNA (mRNA) and high performance computing, reconstructing such network from molecular data such as gene expression is now possible. This chapter discusses an overview of methods of constructing such networks, practical considerations, and an example.

Analyzing the Complex Regulatory Landscape of Hfq - an Integrative, Multi-Omics Approach

The ability of bacteria to respond to environmental change is based on the ability to coordinate, redirect and fine-tune their genetic repertoire as and when required. While we can learn a great deal from reductive analysis of individual pathways and global approaches to gene regulation, a deeper understanding of these complex signaling networks requires the simultaneous consideration of several regulatory layers at the genome scale. To highlight the power of this approach we analyzed the Hfq transcriptional/translational regulatory network in the model bacterium Pseudomonas fluorescens. We first used extensive ‘omics’ analyses to assess how hfq deletion affects mRNA abundance, mRNA translation and protein abundance. The subsequent, multi-level integration of these datasets allows us to highlight the discrete contributions by Hfq to gene regulation at different levels. The integrative approach to regulatory analysis we describe here has significant potential, for both dissecting individual signaling pathways and understanding the strategies bacteria use to cope with external challenges.

Ancient human miRNAs are more likely to have broad functions and disease associations than young miRNAs

Background

microRNAs (miRNAs) are essential to the regulation of gene expression in eukaryotes, and improper expression of miRNAs contributes to hundreds of diseases. Despite the essential functions of miRNAs, the evolutionary dynamics of how they are integrated into existing gene regulatory and functional networks is not well understood. Knowledge of the origin and evolutionary history a gene has proven informative about its functions and disease associations; we hypothesize that incorporating the evolutionary origins of miRNAs into analyses will help resolve differences in their functional dynamics and how they influence disease.

Results

We computed the phylogenetic age of miRNAs across 146 species and quantified the relationship between human miRNA age and several functional attributes. Older miRNAs are significantly more likely to be associated with disease than younger miRNAs, and the number of associated diseases increases with age. As has been observed for genes, the miRNAs associated with different diseases have different age profiles. For example, human miRNAs implicated in cancer are enriched for origins near the dawn of animal multicellularity. Consistent with the increasing contribution of miRNAs to disease with age, older miRNAs target more genes than younger miRNAs, and older miRNAs are expressed in significantly more tissues. Furthermore, miRNAs of all ages exhibit a strong preference to target older genes; 93% of validated miRNA gene targets were in existence at the origin of the targeting miRNA. Finally, we find that human miRNAs in evolutionarily related families are more similar in their targets and expression profiles than unrelated miRNAs.

Conclusions

Considering the evolutionary origin and history of a miRNA provides useful context for the analysis of its function. Consistent with recent work in Drosophila, our results support a model in which miRNAs increase their expression and functional regulatory interactions over evolutionary time, and thus older miRNAs have increased potential to cause disease. We anticipate that these patterns hold across mammalian species; however, comprehensively evaluating them will require refining miRNA annotations across species and collecting functional data in non-human systems.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4073-z) contains supplementary material, which is available to authorized users.

Gene expression noise is affected differentially by feedback in burst frequency and burst size

Inside individual cells, expression of genes is stochastic across organisms ranging from bacterial to human cells. A ubiquitous feature of stochastic expression is burst-like synthesis of gene products, which drives considerable intercellular variability in protein levels across an isogenic cell population. One common mechanism by which cells control such stochasticity is negative feedback regulation, where a protein inhibits its own synthesis. For a single gene that is expressed in bursts, negative feedback can affect the burst frequency or the burst size. In order to compare these feedback types, we study a piecewise deterministic model for gene expression of a self-regulating gene. Mathematically tractable steady-state protein distributions are derived and used to compare the noise suppression abilities of the two feedbacks. Results show that in the low noise regime, both feedbacks are similar in term of their noise buffering abilities. Intriguingly, feedback in burst size outperforms the feedback in burst frequency in the high noise regime. Finally, we discuss various regulatory strategies by which cells implement feedback to control burst sizes of expressed proteins at the level of single cells.

Far-infrared radiation protects viability in a cell model of Spinocerebellar Ataxia by preventing polyQ protein accumulation and improving mitochondrial function

Far infrared radiation (FIR) is currently investigated as a potential therapeutic strategy in various diseases though the mechanism is unknown. Presently, we tested if FIR mediates beneficial effects in a cell model of the neurodegenerative disease spinocerebellar ataxia type 3 (SCA3). SCA3 is caused by a mutation leading to an abnormal polyglutamine expansion (PolyQ) in ataxin-3 protein. The consequent aggregation of mutant ataxin-3 results in disruption of vital cell functions. In this study, neuroblastoma cells (SK-N-SH) was transduced to express either non-pathogenic ataxin-3-26Q or pathogenic ataxin-3-78Q proteins. The cells expressing ataxin-3-78Q demonstrated decreased viability, and increased sensitivity to metabolic stress in the presence rotenone, an inhibitor of mitochondrial respiration. FIR exposure was found to protect against these effects. Moreover, FIR improved mitochondrial respiratory function, which was significantly compromised in ataxin-3-78Q and ataxin-3-26Q expressing cells. This was accompanied by decreased levels of mitochondrial fragmentation in FIR treated cells, as observed by fluorescence microscopy and protein expression analysis. Finally, the expression profile LC3-II, Beclin-1 and p62 suggested that FIR prevent the autophagy inhibiting effects observed in ataxin-3-78Q expressing cells. In summary, our results suggest that FIR have rescuing effects in cells expressing mutated pathogenic ataxin-3, through recovery of mitochondrial function and autophagy.

Control theory meets synthetic biology

The past several years have witnessed an increased presence of control theoretic concepts in synthetic biology. This review presents an organized summary of how these control design concepts have been applied to tackle a variety of problems faced when building synthetic biomolecular circuits in living cells. In particular, we describe success stories that demonstrate how simple or more elaborate control design methods can be used to make the behaviour of synthetic genetic circuits within a single cell or across a cell population more reliable, predictable and robust to perturbations. The description especially highlights technical challenges that uniquely arise from the need to implement control designs within a new hardware setting, along with implemented or proposed solutions. Some engineering solutions employing complex feedback control schemes are also described, which, however, still require a deeper theoretical analysis of stability, performance and robustness properties. Overall, this paper should help synthetic biologists become familiar with feedback control concepts as they can be used in their application area. At the same time, it should provide some domain knowledge to control theorists who wish to enter the rising and exciting field of synthetic biology.

A biophysical model of supercoiling dependent transcription predicts a structural aspect to gene regulation

Background

Transcription in Escherichia coli generates positive supercoiling in the DNA, which is relieved by the enzymatic activity of gyrase. Recently published experimental evidence suggests that transcription initiation and elongation are inhibited by the buildup of positive supercoiling. It has therefore been proposed that intermittent binding of gyrase plays a role in transcriptional bursting. Considering that transcription is one of the most fundamental cellular processes, it is desirable to be able to account for the buildup and release of positive supercoiling in models of transcription.

Results

Here we present a detailed biophysical model of gene expression that incorporates the effects of supercoiling due to transcription. By directly linking the amount of positive supercoiling to the rate of transcription, the model predicts that highly transcribed genes’ mRNA distributions should substantially deviate from Poisson distributions, with enhanced density at low mRNA copy numbers. Additionally, the model predicts a high degree of correlation between expression levels of genes inside the same supercoiling domain.

Conclusions

Our model, incorporating the supercoiling state of the gene, makes specific predictions that differ from previous models of gene expression. Genes in the same supercoiling domain influence the expression level of neighboring genes. Such structurally dependent regulation predicts correlations between genes in the same supercoiling domain. The topology of the chromosome therefore creates a higher level of gene regulation, which has broad implications for understanding the evolution and organization of bacterial genomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13628-016-0027-0) contains supplementary material, which is available to authorized users.

Conditional Moment Closure Schemes for Studying Stochastic Dynamics of Genetic Circuits

Inside individual cells, stochastic expression drives random fluctuations in gene product copy numbers, which corrupts functioning of both natural and synthetic genetic circuits. Dynamic models of genetic circuits are formulated stochastically using the chemical master equation framework. Since obtaining probability distributions can be computationally expensive in these models, noise is typically investigated through lower-order statistical moments (mean, variance, correlation, skewness, etc.) of mRNA/proteins levels. However, due to the nonlinearities in genetic circuits, this moment dynamics is typically not closed, in the sense that the time derivative of the lower-order statistical moments depends on high-order moments. Moment equations are closed by expressing higher-order moments as nonlinear functions of lower-order moments, a technique commonly referred to as moment closure. We provide a new moment closure scheme for studying stochastic dynamics of genetic circuits, where genes randomly toggle between transcriptionally active and inactive states. The method is based on conditioning protein levels on active states of genes and then expressing higher-order moments as functions of lower-order conditional moments. The conditional closure scheme is illustrated on different circuit motifs and found to outperform existing closure techniques. Rapid computation of stochasticity through closure methods will enable improved characterization and design of synthetic circuits that exhibit robust performance in spite of noisy expression of underlying genes.

An evolutionarily biased distribution of miRNA sites toward regulatory genes with high promoter-driven intrinsic transcriptional noise

Background

miRNAs are a major class of regulators of gene expression in metazoans. By targeting cognate mRNAs, miRNAs are involved in regulating most, if not all, biological processes in different cell and tissue types. To better understand how this regulatory potential is allocated among different target gene sets, we carried out a detailed and systematic analysis of miRNA target sites distribution in the mouse genome.

Results

We used predicted conserved and non-conserved sites for 779 miRNAs in 3′ UTR of 18440 genes downloaded from TargetScan website. Our analysis reveals that 3′ UTRs of genes encoding regulatory proteins harbor significantly greater number of miRNA sites than those of non-regulatory, housekeeping and structural, genes. Analysis of miRNA sites for orthologous 3′UTR’s in 10 other species indicates that the regulatory genes were maintaining or accruing miRNA sites while non-regulatory genes gradually shed them in the course of evolution. Furthermore, we observed that 3′ UTR of genes with higher gene expression variability driven by their promoter sequence content are targeted by many more distinct miRNAs compared to genes with low transcriptional noise.

Conclusions

Based on our results we envision a model, which we dubbed “selective inclusion”, whereby non-regulatory genes with low transcription noise and stable expression profile lost their sites, while regulatory genes which endure higher transcription noise retained and gained new sites. This adaptation is consistent with the requirements that regulatory genes need to be tightly controlled in order to have precise and optimum protein level to properly function.

Mosaic physiology from developmental noise: within-organism physiological diversity as an alternative to phenotypic plasticity and phenotypic flexibility

A key problem in organismal biology is to explain the origins of functional diversity. In the context of organismal biology, functional diversity describes the set of phenotypes, across scales of biological organization and through time, that a single genotype, or genome, or organism, can produce. Functional diversity encompasses many phenomena: differences in cell types within organisms; physiological and morphological differences among tissues and organs; differences in performance; morphological shifts in external phenotype; and changes in behavior. How can single genomes produce so many different phenotypes? Modern biology proposes two general mechanisms. The first is developmental programs, by which single cells and their single genomes diversify, via relatively deterministic processes, into the sets of cell types, tissues and organs that we see in most multicellular organisms. The second general mechanism is phenotypic modification stemming from interactions between organisms and their environments – modifications known either as phenotypic plasticity or as phenotypic flexibility, depending on the time scale of the response and the degree of reversibility. These two diversity-generating mechanisms are related because phenotypic modifications may sometimes arise as a consequence of environments influencing developmental programs. Here, I propose that functional diversity also arises via a third fundamental mechanism: stochastic developmental events giving rise to mosaics of physiological diversity within individual organisms. In biological systems, stochasticity stems from the inherently random actions of small numbers of molecules interacting with one another. Although stochastic effects occur in many biological contexts, available evidence suggests that they can be especially important in gene networks, specifically as a consequence of low transcript numbers in individual cells. I briefly review known mechanisms by which organisms control such stochasticity, and how they may use it to create adaptive functional diversity. I then fold this idea into modern thinking on phenotypic plasticity and flexibility, proposing that multicellular organisms exhibit ‘mosaic physiology’. Mosaic physiology refers to sets of diversified phenotypes, within individual organisms, that carry out related functions at the same time, but that are distributed in space. Mosaic physiology arises from stochasticity-driven differentiation of cells, early during cell diversification, which is then amplified by cell division and growth into macroscopic phenotypic modules (cells, tissues, organs) making up the physiological systems of later life stages. Mosaic physiology provides a set of standing, diversified phenotypes, within single organisms, that raise the likelihood of the organism coping well with novel environmental challenges. These diversified phenotypes can be distinct, akin to polyphenisms at the organismal level; or they can be continuously distributed, creating a kind of standing, simultaneously expressed reaction norm of physiological capacities.

Quantifying intrinsic and extrinsic variability in stochastic gene expression models

Genetically identical cell populations exhibit considerable intercellular variation in the level of a given protein or mRNA. Both intrinsic and extrinsic sources of noise drive this variability in gene expression. More specifically, extrinsic noise is the expression variability that arises from cell-to-cell differences in cell-specific factors such as enzyme levels, cell size and cell cycle stage. In contrast, intrinsic noise is the expression variability that is not accounted for by extrinsic noise, and typically arises from the inherent stochastic nature of biochemical processes. Two-color reporter experiments are employed to decompose expression variability into its intrinsic and extrinsic noise components. Analytical formulas for intrinsic and extrinsic noise are derived for a class of stochastic gene expression models, where variations in cell-specific factors cause fluctuations in model parameters, in particular, transcription and/or translation rate fluctuations. Assuming mRNA production occurs in random bursts, transcription rate is represented by either the burst frequency (how often the bursts occur) or the burst size (number of mRNAs produced in each burst). Our analysis shows that fluctuations in the transcription burst frequency enhance extrinsic noise but do not affect the intrinsic noise. On the contrary, fluctuations in the transcription burst size or mRNA translation rate dramatically increase both intrinsic and extrinsic noise components. Interestingly, simultaneous fluctuations in transcription and translation rates arising from randomness in ATP abundance can decrease intrinsic noise measured in a two-color reporter assay. Finally, we discuss how these formulas can be combined with single-cell gene expression data from two-color reporter experiments for estimating model parameters.

Adaptive noise

In biology, noise implies error and disorder and is therefore something which organisms may seek to minimize and mitigate against. We argue that such noise can be adaptive. Recent studies have shown that gene expression can be noisy, noise can be genetically controlled, genes and gene networks vary in how noisy they are and noise generates phenotypic differences among genetically identical cells. Such phenotypic differences can have fitness benefits, suggesting that evolution can shape noise and that noise may be adaptive. For example, gene networks can generate bistable states resulting in phenotypic diversity and switching among individual cells of a genotype, which may be a bet hedging strategy. Here, we review the sources of noise in gene expression, the extent to which noise in biological systems may be adaptive and suggest that applying evolutionary rigour to the study of noise is necessary to fully understand organismal phenotypes.

Consequences of mRNA transport on stochastic variability in protein levels

Homogeneous cell populations can exhibit considerable cell-to-cell variability in protein levels arising from the stochastic nature of the gene-expression process. In particular, transcriptional bursting of mRNAs from the promoter has been implicated as a major source of stochasticity in the expression of many genes. In eukaryotes, transcribed pre-mRNAs have to be exported outside the nucleus and in many cases, export rates can be slow and comparable to mRNA turnover rates. We investigate whether such export processes can be effective mechanisms in buffering protein levels from transcriptional bursting of pre-mRNAs in the nucleus. For a stochastic gene-expression model with both transcriptional bursting and export, we derive an exact solution of the steady-state probability-generating function for both the nuclear and the cytoplasmic mRNA levels. These formulas reveal that decreasing export rates can dramatically reduce variability in cytoplasmic mRNA levels. However, our results also show that decreasing export rates enhance mRNA autocorrelation times, which function to increase heterogeneity in protein levels. Our overall analysis concludes that under physiologically relevant parameter regimes, a pre-mRNA export step can decrease steady-state variability at the mRNA level but not at the protein level. Finally, we reinforce previous observations that saturation in the pre-mRNA transport machinery can be an important mechanism in suppressing protein variability from underlying transcriptional bursts.

MiRNAs confer phenotypic robustness to gene networks by suppressing biological noise

miRNAs are small non-coding RNAs able to modulate target gene expression. It has been postulated that miRNAs confer robustness to biological processes, but clear experimental evidence is still missing. Here, using a synthetic biological approach, we demonstrate that microRNAs provide phenotypic robustness to transcriptional regulatory networks by buffering fluctuations in protein levels. We construct a network motif in mammalian cells exhibiting a 'toggle-switch' phenotype in which two alternative protein expression levels define its ON and OFF states. The motif consists of an inducible transcription factor that self-regulates its own transcription and that of a miRNA against the transcription factor itself. We confirm, using mathematical modelling and experimental approaches, that the microRNA confers robustness to the toggle-switch by enabling the cell to maintain and transmit its state. When absent, a dramatic increase in protein noise level occurs, causing the cell to randomly switch between the two states.

Control by a hair's breadth: the role of microRNAs in the skin

MicroRNAs have continued to attract enormous interest in the scientific community ever since their discovery. Their allure stems from their unique role in posttranscriptional gene expression control as well as their potential application as therapeutic targets in various disease pathologies. While much is known concerning their general biological function, such as their interaction with RNA-induced silencing complexes, many important questions still remain unanswered, especially regarding their functions in the skin. In this review, we summarize our current knowledge of the role of microRNAs in the skin in order to shine new light on our understanding of cutaneous biology and emphasize the significance of these small, single-stranded RNA molecules in the largest organ of the human body. Key events in epidermal and hair follicle biology, including differentiation, proliferation, and pigmentation, all involve microRNAs. We explore the role of microRNAs in several cutaneous processes, such as appendage formation, wound-healing, epithelial-mesenchymal transition, carcinogenesis, immune response, and aging. In addition, we discuss current trends in research and offer suggestions for future studies.

The magnitude and colour of noise in genetic negative feedback systems

The comparative ability of transcriptional and small RNA-mediated negative feedback to control fluctuations or ‘noise’ in gene expression remains unexplored. Both autoregulatory mechanisms usually suppress the average (mean) of the protein level and its variability across cells. The variance of the number of proteins per molecule of mean expression is also typically reduced compared with the unregulated system, but is almost never below the value of one. This relative variance often substantially exceeds a recently obtained, theoretical lower limit for biochemical feedback systems. Adding the transcriptional or small RNA-mediated control has different effects. Transcriptional autorepression robustly reduces both the relative variance and persistence (lifetime) of fluctuations. Both benefits combine to reduce noise in downstream gene expression. Autorepression via small RNA can achieve more extreme noise reduction and typically has less effect on the mean expression level. However, it is often more costly to implement and is more sensitive to rate parameters. Theoretical lower limits on the relative variance are known to decrease slowly as a measure of the cost per molecule of mean expression increases. However, the proportional increase in cost to achieve substantial noise suppression can be different away from the optimal frontier—for transcriptional autorepression, it is frequently negligible.