Rate Of mRNA Degradation Research Articles

Finite-time boundary synchronization of space-time discretized stochastic fuzzy genetic regulatory networks with time delays

<p>This paper presents an investigation into the phenomenon of global mean-squared finite-time synchronization within the context of two distinct schemes: The asymptotic and exponential forms. The subject matter encompasses space-time discrete stochastic fuzzy genetic regulatory networks, wherein Dirichlet controlled boundary values and time delays are taken into account. The findings presented therein pertain to global mean-squared finite-time synchronization for the aforementioned discrete stochastic fuzzy networks, which incorporate the Lyapunov-Krasovskii functional with a double sum representing the delay-dependent components. In addition, this study demonstrates that improved global mean-squared finite-time synchronization of space-time discrete stochastic fuzzy genetic regulatory networks with boundary controls can be achieved by optimizing the small diffusion intensities, the small fuzzy MIN and MAX parameters, and the large degradation rates of mRNA and proteins. It was unexpected to discover that the sizes of the time lags exert a direct influence on the value of the convergent rate of global mean-squared finite-time exponential synchronization of the networks. This paper presents a framework for exploring the issues of global mean-squared finite-time asymptotic and exponential synchronization for space-time discrete stochastic fuzzy genetic regulatory networks via the Dirichlet controlled boundaries. To conclude, an illustrative example is provided to demonstrate the efficacy of the aforementioned method.</p>

Minimal Perturbation Analysis of mRNA Degradation Rates with Tet-Off and RT-qPCR.

Messenger RNA stability is an important variable in gene expression and its dynamics. High stability ensures a constant level of synthesized protein, whereas mRNA instability can be critical for regulatory processes in which protein production needs to be stopped, such as development, inflammation, or adaptation to stress. Accurate measurements of RNA degradation rates are important for understanding how RNA features and RNA binding proteins affect the posttranscriptional life of an mRNA. As an alternative to global transcriptional inhibition methods, the use of a Tet-off repressible promoter has the advantage that cells are minimally perturbed by the addition of doxycyclin during the assay. We illustrate the use of a reporter mRNA expressed from a plasmid in Saccharomyces cerevisiae cells, but similar methods can be applied to other regulated promoters, on plasmids or by genome editing, and in other organisms. RNA levels are measured by reverse transcription followed by quantitative PCR. An exponential decay law is then used to estimate how well the measurements follow this expected trend for the simplest possible mechanism of RNA degradation, where the decay is proportional to the amount of RNA present at any given time.

Decoupled degradation and translation enables noise modulation by poly(A) tails

Poly(A) tails are crucial for mRNA translation and degradation, but the exact relationship between tail length and mRNA kinetics remains unclear. Here, we employ a small library of identical mRNAs that differ only in their poly(A)-tail length to examine their behavior in human embryonic kidney cells. We find that tail length strongly correlates with mRNA degradation rates but is decoupled from translation. Interestingly, an optimal tail length of ∼100 nt displays the highest translation rate, which is identical to the average endogenous tail length measured by nanopore sequencing. Furthermore, poly(A)-tail length variability-a feature of endogenous mRNAs-impacts translation efficiency but not mRNA degradation rates. Stochastic modeling combined with single-cell tracking reveals that poly(A) tails provide cells with an independent handle to tune gene expression fluctuations by decoupling mRNA degradation and translation. Together, this work contributes to the basic understanding of gene expression regulation and has potential applications in nucleic acid therapeutics.

Spectral neural approximations for models of transcriptional dynamics

The advent of high-throughput transcriptomics provides an opportunity to advance mechanistic understanding of transcriptional processes and their connections to cellular function at an unprecedented, genome-wide scale. These transcriptional systems, which involve discrete stochastic events, are naturally modeled using chemical master equations (CMEs), which can be solved for probability distributions to fit biophysical rates that govern system dynamics. While CME models have been used as standards in fluorescence transcriptomics for decades to analyze single-species RNA distributions, there are often no closed-form solutions to CMEs that model multiple species, such as nascent and mature RNA transcript counts. This has prevented the application of standard likelihood-based statistical methods for analyzing high-throughput, multi-species transcriptomic datasets using biophysical models. Inspired by recent work in machine learning to learn solutions to complex dynamical systems, we leverage neural networks and statistical understanding of system distributions to produce accurate approximations to a steady-state bivariate distribution for a model of the RNA life cycle that includes nascent and mature molecules. The steady-state distribution to this simple model has no closed-form solution and requires intensive numerical solving techniques: our approach reduces likelihood evaluation time by several orders of magnitude. We demonstrate two approaches, whereby solutions are approximated by 1) learning the weights of kernel distributions with constrained parameters or 2) learning both weights and scaling factors for parameters of kernel distributions. We show that our strategies, denoted by kernel weight regression and parameter-scaled kernel weight regression, respectively, enable broad exploration of parameter space and can be used in existing likelihood frameworks to infer transcriptional burst sizes, RNA splicing rates, and mRNA degradation rates from experimental transcriptomic data.

Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq

During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities. Here, we combine single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell types. Our study provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.

Cellular energy regulates mRNA degradation in a codon-specific manner

Codon optimality is a major determinant of mRNA translation and degradation rates. However, whether and through which mechanisms its effects are regulated remains poorly understood. Here we show that codon optimality associates with up to 2-fold change in mRNA stability variations between human tissues, and that its effect is attenuated in tissues with high energy metabolism and amplifies with age. Mathematical modeling and perturbation data through oxygen deprivation and ATP synthesis inhibition reveal that cellular energy variations non-uniformly alter the effect of codon usage. This new mode of codon effect regulation, independent of tRNA regulation, provides a fundamental mechanistic link between cellular energy metabolism and eukaryotic gene expression.

Development of a minimal PBPK-QSP modeling platform for LNP-mRNA based therapeutics to study tissue disposition and protein expression dynamics

Physiologically based pharmacokinetic models have gained significant recognition as effective mathematical models that enable deeper mechanistic investigation of drug delivery and tissue disposition. Here we describe the development of a platform PBPK-quantitative systems pharmacology (QSP) model to study tissue delivery of lipid nanoparticle (LNP) based mRNA therapeutics. The model is calibrated to published data in the context of Crigler-Najjar syndrome. Sensitivity analyses were performed to explore factors that influence protein expression and pharmacodynamic response following LNP-mRNA liver disposition. The most sensitive determinants of protein exposures were mRNA stability, translation, and cellular uptake rate, while the liver influx rate of lipid nanoparticle did not appreciably impact protein expression. Indeed, protein expression level may be tuned by modulation of mRNA degradation rate. However, simulations predicted that when the intrinsic half-life of the translated protein falls below a certain threshold, lowering mRNA degradation rate may not rescue protein exposure, a design feature that should be considered in optimal design of mRNA therapeutics. Additionally, interplay of LNP degradation rate and mRNA escape rate from endosomes was found to be crucial in modulation of protein expression. Simulations predicted that at a given LNP degradation rate, protein exposure varied linearly with mRNA escape rate. We further extended the model by incorporating LNP recycling to identify conditions necessary for observing a second peak in mRNA pharmacokinetics (PK). Simulations predict that with a fast recycling and slow tissue re-uptake rates, a robust second peak is observed in the plasma mRNA concentration curve. The amplitude and timing of the second peak could be tuned with recycling and re-uptake rates. Modeling results indicate that within the context of non-secreted mRNA mediated enzyme replacement therapy, recycling may depress or improve protein exposure depending on the re-uptake rate of the recycled LNP. The model is subsequently used to generate virtual animal cohorts to investigate optimal dosing and schedule of the compound. Virtual instances of the model were then employed to identify design principles that potentially reduce dosing frequency while maintaining efficacy. This study demonstrates the potential applications of coupled PBPK-QSP model for LNP based mRNA therapeutics as a translational platform.

Physical modeling of ribosomes along messenger RNA: Estimating kinetic parameters from ribosome profiling experiments using a ballistic model.

Gene expression is the synthesis of proteins from the information encoded on DNA. One of the two main steps of gene expression is the translation of messenger RNA (mRNA) into polypeptide sequences of amino acids. Here, by taking into account mRNA degradation, we model the motion of ribosomes along mRNA with a ballistic model where particles advance along a filament without excluded volume interactions. Unidirectional models of transport have previously been used to fit the average density of ribosomes obtained by the experimental ribo-sequencing (Ribo-seq) technique in order to obtain the kinetic rates. The degradation rate is not, however, accounted for and experimental data from different experiments are needed to have enough parameters for the fit. Here, we propose an entirely novel experimental setup and theoretical framework consisting in splitting the mRNAs into categories depending on the number of ribosomes from one to four. We solve analytically the ballistic model for a fixed number of ribosomes per mRNA, study the different regimes of degradation, and propose a criterion for the quality of the inverse fit. The proposed method provides a high sensitivity to the mRNA degradation rate. The additional equations coming from using the monosome (single ribosome) and polysome (arbitrary number) ribo-seq profiles enable us to determine all the kinetic rates in terms of the experimentally accessible mRNA degradation rate.

Mycobacterial RNase E cleaves with a distinct sequence preference and controls the degradation rates of most Mycolicibacterium smegmatis mRNAs

The mechanisms and regulation of RNA degradation in mycobacteria have been subject to increased interest following the identification of interplay between RNA metabolism and drug resistance. Mycobacteria encode multiple ribonucleases predicted to participate in mRNA degradation and/or processing of stable RNAs. RNase E is hypothesized to play a major role in mRNA degradation because of its essentiality in mycobacteria and its role in mRNA degradation in gram-negative bacteria. Here, we defined the impact of RNase E on mRNA degradation rates transcriptome-wide in the nonpathogenic model Mycolicibacterium smegmatis. RNase E played a rate-limiting role in degradation of the transcripts encoded by at least 89% of protein-coding genes, with leadered transcripts often being more affected by RNase E repression than leaderless transcripts. There was an apparent global slowing of transcription in response to knockdown of RNase E, suggesting that M.smegmatis regulates transcription in responses to changes in mRNA degradation. This compensation was incomplete, as the abundance of most transcripts increased upon RNase E knockdown. We assessed the sequence preferences for cleavage by RNase E transcriptome-wide in M.smegmatis and Mycobacterium tuberculosis and found a consistent bias for cleavage in C-rich regions. Purified RNase E had a clear preference for cleavage immediately upstream of cytidines, distinct from the sequence preferences of RNase E in gram-negative bacteria. We furthermore report a high-resolution map of mRNA cleavage sites in M. tuberculosis, which occur primarily within the RNase E-preferred sequence context, confirming that RNase E has a broad impact on the M.tuberculosis transcriptome.

ZC3H13 Accelerates Keloid Formation by Mediating N6-methyladenosine Modification of HIPK2.

Keloids are fibroproliferative skin disorders caused by the improper healing of wounded skin. A growing body of evidence suggests the involvement of N6-Methyladenosine (m6A) modification in various bioprocesses; however, its role in keloid formation has not yet been investigated. The aim of this study was to determine the effect of the m6A regulator zinc finger CCCH domain containing protein 13 (ZC3H13) on the pathogenesis of keloid formation. ZC3H13 and homeodomain-interacting protein kinase 2 (HIPK2) expression was evaluated in healthy skin and keloid tissues, as well as in human dermal fibroblasts and human keloid fibroblasts (HKF), using qRT-PCR and western blotting. The effects of ZC3H13 overexpression and knockdown on the cell function of HKFs were assessed using CCK8, transwell, and flow cytometry. Furthermore, the influence of ZC3H13 on HIPK2 m6A modification was assessed using MeRIP-qPCR and mRNA stability assays. Both ZC3H13 expression and m6A RNA methylation were upregulated in keloid tissues and HKFs. Silencing of ZC3H13 inhibited proliferation and migration, while enhancing apoptosis in HKFs, whereas overexpression had the opposite effect. Furthermore, HIPK2 levels were high in keloid tissues and HKFs, and a positive correlation was observed between ZC3H13 and HIPK2. In HKFs, ZC3H13 overexpression elevated the m6A levels of HIPK2 mRNA and reduced the rate of HIPK2 mRNA degradation. Mechanically, ZC3H13-induced m6A modifications significantly improved HIPK2 mRNA stability. Collectively, ZC3H13 accelerated keloid formation by mediating the m6A modification of HIPK2 mRNA and maintaining its stability.

Noise Minimization in Cell-Free Gene Expression.

Biochemical reactions that involve small numbers of molecules are accompanied by a degree of inherent randomness that results in noisy reaction outcomes. In synthetic biology, the ability to minimize noise particularly during the reconstitution of future synthetic protocells is an outstanding challenge to secure robust and reproducible behavior. Here we show that by encapsulation of a bacterial cell-free gene expression system in water-in-oil droplets, in vitro-synthesized MazF reduces cell-free gene expression noise >2-fold. With stochastic simulations we identify that this noise minimization acts through both increased degradation and the autoregulatory feedback of MazF. Specifically, we find that the expression of MazF enhances the degradation rate of mRNA up to 18-fold in a sequence-dependent manner. This sequence specificity of MazF would allow targeted noise control, making it ideal to integrate into synthetic gene networks. Therefore, including MazF production in synthetic biology can significantly minimize gene expression noise, impacting future design principles of more complex cell-free gene circuits.

An in silico FSHD muscle fiber for modeling DUX4 dynamics and predicting the impact of therapy.

Facioscapulohumeral muscular dystrophy (FSHD) is an incurable myopathy linked to the over-expression of the myotoxic transcription factor DUX4. Targeting DUX4 is the leading therapeutic approach, however, it is only detectable in 0.1-3.8% of FSHD myonuclei. How rare DUX4 drives FSHD and the optimal anti-DUX4 strategy are unclear. We combine stochastic gene expression with compartment models of cell states, building a simulation of DUX4 expression and consequences in FSHD muscle fibers. Investigating iDUX4 myoblasts, scRNAseq, and snRNAseq of FSHD muscle we estimate parameters including DUX4 mRNA degradation, transcription and translation rates, and DUX4 target gene activation rates. Our model accurately recreates the distribution of DUX4 and targets gene-positive cells seen in scRNAseq of FSHD myocytes. Importantly, we show DUX4 drives significant cell death despite expression in only 0.8% of live cells. Comparing scRNAseq of unfused FSHD myocytes to snRNAseq of fused FSHD myonuclei, we find evidence of DUX4 protein syncytial diffusion and estimate its rate via genetic algorithms. We package our model into freely available tools, to rapidly investigate the consequences of anti-DUX4 therapy.

11,12-EET suppressed LPS induced TF expression and thrombus formation by accelerating mRNA degradation rate via strengthening PI3K-Akt signaling pathway and inhibiting p38-TTP pathway

Epoxyeicosatrienoic acids (EETs), which are synthesized from arachidonic acid by cytochrome P450 epoxygenases, function primarily as autocrine and paracrine effectors in the cardiovascular system. So far, most research has focused on the vasodilatory, anti-inflammatory, anti-apoptotic and mitogenic properties of EETs in the systemic circulation. However, whether EETs could suppress tissue factor (TF) expression and prevent thrombus formation remains unknown. Here we utilized in vivo and in vitro models to investigate the effects and underlying mechanisms of exogenously EETs on LPS induced TF expression and inferior vein cava ligation induced thrombosis. We observed that the thrombus formation rate and the size of the thrombus were greatly reduced in 11,12-EET treated mice，accompanied by decreased TF and inflammatory cytokines expression. Further in vitro studies showed that by enhancing p38 MAPK activation and subsequent tristetraprolin (TTP) phosphorylation, LPS strengthened the stability of TF mRNA and induced increased TF expression. However, by strengthening PI3K-dependent Akt phosphorylation, which acted as a negative regulator of p38-TTP signaling pathway，11,12-EET reduced LPS-induced TF expression in monocytes. In addition, 11,12-EET inhibited LPS-induced NF-κB nuclear translocation by activating the PI3K/Akt pathway. Further study indicated that the inhibitory effect of 11,12-EET on TF expression was mediated by antagonizing LPS-induced activation of thromboxane prostanoid receptor. In conclusion, our study demonstrated that 11,12-EET prevented thrombosis by reducing TF expression and targeting the CYP2J2 epoxygenase pathway may represent a novel approach to mitigate thrombosis related diseases.

RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning.

Messenger RNA-based therapeutics have shown tremendous potential, as demonstrated by the rapid development of messenger RNA based vaccines for COVID-19. Nevertheless, distribution of mRNA vaccines worldwide has been hampered by mRNA's inherent thermal instability due to in-line hydrolysis, a chemical degradation reaction. Therefore, predicting and understanding RNA degradation is a crucial and urgent task. Here we present RNAdegformer, an effective and interpretable model architecture that excels in predicting RNA degradation. RNAdegformer processes RNA sequences with self-attention and convolutions, two deep learning techniques that have proved dominant in the fields of computer vision and natural language processing, while utilizing biophysical features of RNA. We demonstrate that RNAdegformer outperforms previous best methods at predicting degradation properties at nucleotide resolution for COVID-19 mRNA vaccines. RNAdegformer predictions also exhibit improved correlation with RNA in vitro half-life compared with previous best methods. Additionally, we showcase how direct visualization of self-attention maps assists informed decision-making. Further, our model reveals important features in determining mRNA degradation rates via leave-one-feature-out analysis.

11,12-Eet Suppressed Lps Induced Tf Expression and Thrombus Formation by Accelerating Mrna Degradation Rate Via Strengthening Pi3k-Akt Signaling Pathway and Inhibiting P38-Ttp Pathway

11,12-Eet Suppressed Lps Induced Tf Expression and Thrombus Formation by Accelerating Mrna Degradation Rate Via Strengthening Pi3k-Akt Signaling Pathway and Inhibiting P38-Ttp Pathway

The genetic and biochemical determinants of mRNA degradation rates in mammals

BackgroundDegradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms.ResultsWe establish a compendium of 39 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). The key novel principle learned by Saluki is that the spatial positioning of splice sites, codons, and RNA-binding motifs within an mRNA is strongly associated with mRNA half-life. Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays.ConclusionsOur work produces a more robust ground truth for transcriptome-wide mRNA half-lives in mammalian cells. Using these revised measurements, we trained Saluki, a model that is over 50% more accurate in predicting half-life from sequence than existing models. Saluki succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome.

NDFIP1 limits cellular TAZ accumulation via exosomal sorting to inhibit NSCLC proliferation.

NDFIP1 has been previously reported as a tumor suppressor in multiple solid tumors, but the function of NDFIP1 in NSCLC and the underlying mechanism are still unknown. Besides, the WW domain containing proteins can be recognized by NDFIP1, resulted in the loading of the target proteins into exosomes. However, whether WW domain-containing transcription regulator 1 (WWTR1, also known as TAZ) can be packaged into exosomes by NDFIP1 and if so, whether the release of this oncogenic protein via exosomes has an effect on tumor development has not been investigated to any extent. Here, we first found that NDFIP1 was low expressed in NSCLC samples and cell lines, which is associated with shorter OS. Then, we confirmed the interaction between TAZ and NDFIP1, and the existence of TAZ in exosomes, which requires NDFIP1. Critically, knockout of NDFIP1 led to TAZ accumulation with no change in its mRNA level and degradation rate. And the cellular TAZ level could be altered by exosome secretion. Furthermore, NDFIP1 inhibited proliferation in vitro and in vivo, and silencing TAZ eliminated the increase of proliferation caused by NDFIP1 knockout. Moreover, TAZ was negatively correlated with NDFIP1 in subcutaneous xenograft model and clinical samples, and the serum exosomal TAZ level was lower in NSCLC patients. In summary, our data uncover a new tumor suppressor, NDFIP1 in NSCLC, and a new exosome-related regulatory mechanism of TAZ.

Combination of high-fat diet and cadmium impairs testicular spermatogenesis in an m6A-YTHDF2-dependent manner

Environmental cadmium (Cd) or high-fat diet (HFD) exposure alone are risk factors of male infertility. However, the effect and mechanism of co-exposure to HFD and Cd on sperm quality remain unclear. This study was aimed to explore the combined effects of HFD and Cd on spermatogenesis as well as its m6A-dependent mechanism in vivo and in vitro. As a result, co-exposure of HFD and Cd resulted in a significant decrease in the number of mature testicular seminiferous tubules and epididymis sperm quantity in mice, compared with Cd or HFD exposure alone. Correspondingly, the mRNAs expression of Smc3(spermatocytes marker), Acrv1(round spermatids marker) and Lzumo3(elongated spermatids marker) were downregulated in HFD and Cd group. Furthermore, combined exposure downregulated the expression of meiosis-related proteins (STRA8 and SYCP3), increased the m6A level of Stra8, and upregulated the expression of m6A-related proteins (METTL3 and YTHDF2) in mouse spermatocytes. Mechanistically, the above-mentioned impacts caused by co-exposure were markedly restored by Mettl3 siR and Ythdf2 siR. In addition, RNA stability assay showed that Ythdf2 siR obviously reversed co-exposure-increased Stra8 mRNA degradation rate in actinomycin-D-treated mouse spermatocytes. Meanwhile, excess ROS was observed in combined-exposure group, and a free radical scavenger N-tert-Butyl-α-phenylnitrone (PBN) attenuated co-exposure-upregulated expression of METTL3 and YTHDF2 in mouse spermatocytes. These results suggested that combination of HFD and Cd impaired spermatogenesis by degrading Stra8 in an m6A-YTHDF2-dependent manner via ROS activation.

Mechanisms of cellular mRNA transcript homeostasis.

For most genes, mRNA transcript abundance scales with cell size to ensure a constant concentration. Scaling of mRNA synthesis rates with cell size plays an important role, with regulation of the activity and abundance of RNA polymerase II (Pol II) now emerging as a key point of control. However, there is also considerable evidence for feedback mechanisms that kinetically couple the rates of mRNA synthesis, nuclear export, and degradation to allow cells to compensate for changes in one by adjusting the others. Researchers are beginning to integrate results from these different fields to reveal the mechanisms underlying transcript homeostasis. This will be crucial for moving beyond our current understanding of relative gene expression towards an appreciation of how absolute transcript levels are linked to other aspects of the cellular phenotype.

The rate of thermodynamic cost against adiabatic and nonadiabatic fluctuations of a single gene circuit in Drosophila embryos

We study the stochastic dynamics of the externally regulating gene circuit as an example of an eve-skipped gene stripe in the development of Drosophila. Three gene regulation regimes are considered: an adiabatic phase when the switching rate of the gene from the OFF to ON state is faster than the rate of mRNA degradation; a nonadiabatic phase when the switching rate from the OFF to ON state is slower than that of the mRNA degradation; and a bursting phase when the gene switching is fast and transcription is very fast, while the ON state probability is very low. We found that the rate of thermodynamic cost quantified by the entropy production rate can suppress the fluctuations of the gene circuit. A higher (lower) rate of thermodynamic cost leads to reduced (increased) fluctuations in the number of gene products in the adiabatic (nonadiabatic) regime. We also found that higher thermodynamic cost is often required to sustain the emergence of more gene states and, therefore, more heterogeneity coming from genetic mutations or epigenetics. We also study the stability of the gene state using the mean first passage time from one state to another. We found the monotonic decrease in time, i.e., in the stability of the state, in the transition from the nonadiabatic to adiabatic regimes. Therefore, as the higher rate of thermodynamic cost suppresses the fluctuations, higher stability requires higher thermodynamics cost to maintain.