Alternative-splicing-mediated gene expression

Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments

The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. Answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a continuous stochastic transcription rate driving a discrete RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. We introduce a framework for numerically and analytically studying such models, and apply Bayesian model selection to identify candidate genes that show signatures of each model in single-cell transcriptomic data from mouse glutamatergic neurons.

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.

Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention

Transcription involves gene activation, nuclear RNA export (NRE) and RNA nuclear retention (RNR). All these processes are multistep and biochemical. A multistep reaction process can create memories between reaction events, leading to non-Markovian kinetics. This raises an unsolved issue: how does molecular memory affect stochastic transcription in the case that NRE and RNR are simultaneously considered? To address this issue, we analyze a non-Markov model, which considers multistep activation, multistep NRE and multistep RNR can interpret many experimental phenomena. In order to solve this model, we introduce an effective transition rate for each reaction. These effective transition rates, which explicitly decode the effect of molecular memory, can transform the original non-Markov issue into an equivalent Markov one. Based on this technique, we derive analytical results, showing that molecular memory can significantly affect the nuclear and cytoplasmic mRNA mean and noise. In addition to the results providing insights into the role of molecular memory in gene expression, our modeling and analysis provide a paradigm for studying more complex stochastic transcription processes.

Kinetic characteristics of transcriptional bursting in a complex gene model with cyclic promoter structure

While transcription often occurs in a bursty manner, various possible regulations can lead to complex promoter patterns such as promoter cycles, giving rise to an important question: How do promoter kinetics shape transcriptional bursting kinetics? Here we introduce and analyze a general model of the promoter cycle consisting of multi-OFF states and multi-ON states, focusing on the effects of multi-ON mechanisms on transcriptional bursting kinetics. The derived analytical results indicate that burst size follows a mixed geometric distribution rather than a single geometric distribution assumed in previous studies, and ON and OFF times obey their own mixed exponential distributions. In addition, we find that the multi-ON mechanism can lead to bimodal burst-size distribution, antagonistic timing of ON and OFF, and diverse burst frequencies, each further contributing to cell-to-cell variability in the mRNA expression level. These results not only reveal essential features of transcriptional bursting kinetics patterns shaped by multi-state mechanisms but also can be used to the inferences of transcriptional bursting kinetics and promoter structure based on experimental data.

Silent transcription intervals and translational bursting lead to diverse phenotypic switching

For complex process of gene expression, we use theoretical analysis and stochastic simulations to study the phenotypic diversity induced by silent transcription intervals and translational bursting.

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.

Steady-state joint distribution for first-order stochastic reaction kinetics

While the analytical solution for the marginal distribution of a stochastic chemical reaction network has been extensively studied, its joint distribution, i.e., the solution of a high-dimensional chemical master equation, has received much less attention. Here we develop an alternative method of computing the exact joint distributions of a wide class of first-order stochastic reaction systems in steady-state conditions. The effectiveness of our method is validated by applying it to four gene expression models of biological significance, including models with 2A peptides, nascent mRNA, gene regulation, translational bursting, and alternative splicing.

Targeting Alternative Splicing as a Potential Therapy for Episodic Ataxia Type 2

Episodic ataxia type 2 (EA2) is an autosomal dominant neurological disorder characterized by paroxysmal attacks of ataxia, vertigo, and nausea that usually last hours to days. It is caused by loss-of-function mutations in CACNA1A, the gene encoding the pore-forming α1 subunit of P/Q-type voltage-gated Ca2+ channels. Although pharmacological treatments, such as acetazolamide and 4-aminopyridine, exist for EA2, they do not reduce or control the symptoms in all patients. CACNA1A is heavily spliced and some of the identified EA2 mutations are predicted to disrupt selective isoforms of this gene. Modulating splicing of CACNA1A may therefore represent a promising new strategy to develop improved EA2 therapies. Because RNA splicing is dysregulated in many other genetic diseases, several tools, such as antisense oligonucleotides, trans-splicing, and CRISPR-based strategies, have been developed for medical purposes. Here, we review splicing-based strategies used for genetic disorders, including those for Duchenne muscular dystrophy, spinal muscular dystrophy, and frontotemporal dementia with Parkinsonism linked to chromosome 17, and discuss their potential applicability to EA2.

Identification of novel isoforms of dairy goat EEF1D and their mRNA expression characterization

Eukaryotic translation elongation factor 1 delta (EEF1D) gene encodes guanine nucleotide exchange protein eEF1Bδ, which participates in the eukaryotic protein synthesis, and plays important roles in regulating cell cycling and milk production. This study firstly focused on detecting the isoforms of dairy goat EEF1D gene and their mRNA expression characterization. Herein, two novel isoforms, EEF1Da and EEF1Dc, were identified in dairy goat. The entire coding sequences of EEF1Da and EEF1Dc isoforms were 843bp and 267bp in length, respectively. Goat EEF1Da had complete conserved domains of elongation factor 1 (EF1) family, and the evolution of goat EEF1Da isoform was agreed with the evolution of species. Expression pattern analysis of different isoforms revealed relatively ubiquitous expression of EEF1D and EEF1Da. While EEF1Dc only expressed in heart, lung, kidney, adipose and muscle. Combining with the analysis results of cloning, qRT-PCR and bioinformatics, EEF1Da is the major alternative splicing form of EEF1D gene. Interestingly, qRT-PCR result showed that the highest expression of EEF1D was in adipose, which is the major component of mammary. This result was consistent with the early research that EEF1D expressed highly in the mammary, which indicated that EEF1D played a potential key role in regulating adipose development and milk production. All these findings would provide a foundation for the further research of EEF1D gene and development of dairy goat industry.

Regulation of alternative splicing at the single-cell level

Alternative splicing is a key cellular mechanism for generating distinct isoforms, whose relative abundances regulate critical cellular processes. It is therefore essential that inclusion levels of alternative exons be tightly regulated. However, how the precision of inclusion levels among individual cells is governed is poorly understood. Using single-cell gene expression, we show that the precision of inclusion levels of alternative exons is determined by the degree of evolutionary conservation at their flanking intronic regions. Moreover, the inclusion levels of alternative exons, as well as the expression levels of the transcripts harboring them, also contribute to this precision. We further show that alternative exons whose inclusion levels are considerably changed during stem cell differentiation are also subject to this regulation. Our results imply that alternative splicing is coordinately regulated to achieve accuracy in relative isoform abundances and that such accuracy may be important in determining cell fate.

Roles of factorial noise in inducing bimodal gene expression

Some gene regulatory systems can exhibit bimodal distributions of mRNA or protein although the deterministic counterparts are monostable. This noise-induced bimodality is an interesting phenomenon and has important biological implications, but it is unclear how different sources of expression noise (each source creates so-called factorial noise that is defined as a component of the total noise) contribute separately to this stochastic bimodality. Here we consider a minimal model of gene regulation, which is monostable in the deterministic case. Although simple, this system contains factorial noise of two main kinds: promoter noise due to switching between gene states and transcriptional (or translational) noise due to synthesis and degradation of mRNA (or protein). To better trace the roles of factorial noise in inducing bimodality, we also analyze two limit models, continuous and adiabatic approximations, apart from the exact model. We show that in the case of slow gene switching, the continuous model where only promoter noise is considered can exhibit bimodality; in the case of fast switching, the adiabatic model where only transcriptional or translational noise is considered can also exhibit bimodality but the exact model cannot; and in other cases, both promoter noise and transcriptional or translational noise can cooperatively induce bimodality. Since slow gene switching and large protein copy numbers are characteristics of eukaryotic cells, whereas fast gene switching and small protein copy numbers are characteristics of prokaryotic cells, we infer that eukaryotic stochastic bimodality is induced mainly by promoter noise, whereas prokaryotic stochastic bimodality is induced primarily by transcriptional or translational noise.

Effects of promoter leakage on dynamics of gene expression

Background

Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes. As network motifs occurring recurrently in complex biological networks, gene auto-regulatory circuits have been extensively studied but gene expression dynamics remain to be fully understood, e.g., how promoter leakage affects expression noise is unclear.

Results

In this work, we analyze a gene model with auto regulation, where the promoter is assumed to have one active state with highly efficient transcription and one inactive state with very lowly efficient transcription (termed as promoter leakage). We first derive the analytical distribution of gene product, and then analyze effects of promoter leakage on expression dynamics including bursting kinetics. Interestingly, we find that promoter leakage always reduces expression noise and that increasing the leakage rate tends to simplify phenotypes. In addition, higher leakage results in fewer bursts.

Conclusions

Our results reveal the essential role of promoter leakage in controlling expression dynamics and further phenotype. Specifically, promoter leakage is a universal mechanism of reducing expression noise, controlling phenotypes in different environments and making the gene produce generate fewer bursts.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0157-z) contains supplementary material, which is available to authorized users.

Division time-based amplifiers for stochastic gene expression

While cell-to-cell variability is a phenotypic consequence of gene expression noise, sources of this noise may be complex – apart from intrinsic sources such as the random birth/death of mRNA and stochastic switching between promoter states, there are also extrinsic sources of noise such as cell division where division times are either constant or random.

Feedback-induced counterintuitive correlations of gene expression noise with bursting kinetics

Previous studies showed that a higher frequency of bursting results in lower expression noise whereas a larger size of bursting leads to higher expression noise. Here, we show counterintuitive correlations of expression noise with bursting kinetics due to the effect of feedback. Specifically, in the case of increasing the negative feedback strength but keeping the mean expression fixed, both the mean burst frequency and the mean burst size are invariant if the off-switching rate decreases, but expression noise is reduced; or the mean burst frequency is invariant and the burst size decreases if the transcription rate increases, but expression noise is amplified. Similarly, in the case of increasing the positive feedback strength but keeping the mean expression fixed, both the mean burst frequency and the mean burst size are invariant if the on-switching rate decreases; or the mean burst frequency increases and the mean burst size is invariant if the leakage rate decreases, but expression noise is amplified. In addition, we find that the previous conclusion that a larger burst size results in the lower noise in burst size needs to be modified in the case of feedback. Our results not only clarify the confusing relationship between feedback and expression noise but also imply that the mRNA or protein noise is no longer a simple sum of the internal noise and the promoter noise as shown in the case of no feedback.