Validation of a mathematical model of gene transcription in aggregated cellular systems: application to l1 retrotransposition

Expression of LINE-1 retrotransposon in early human spontaneous abortion tissues

BACKGROUND

The aim of this study is to investigate a new mechanism that may affect spontaneous abortions (SA): Can long interspersed nuclear element-1 (LINE-1) insertions in embryo cells lead to early SA?

METHODS

The method involves prospective study on new mechanism of human early SA. Twenty SA tissues and 10 induced abortion (IA) tissues were utilized for this experiment. Western Blot, Immunohistochemistry (IHC), and reverse transcription-polymerase chain reaction were used to analyze different LINE-1 proteins and mRNA expression between early SA tissues and early IA tissues. SPSS software version 21.0 was used for statistical analysis.

RESULTS

Western Blot demonstrated that the LINE-1 protein expression in SA tissues (Mean: 60.2%) is higher than in IA tissues (Mean: 30.3%) in 91% of the compared samples. reverse transcription-polymerase chain reaction showed that LINE-1 mRNA expression in SA tissues (Mean: 64.2%) is higher than in IA tissues (Mean: 29.2%) in 6 primer pairs in 89% of the compared samples. IHC showed that the LINE-1 protein expression in SA tissues (Mean: 59.2%) is higher than in IA tissues (Mean: 28.8%) in 83% of the compared samples.

CONCLUSIONS

Expression of LINE-1 in early SA tissues is higher than in IA tissues, LINE-1 may lead to early SA and LINE-1 plays a role in early SA, this shows that a new mechanism may be involved in SA.

Computational modeling of RNase, antisense ORF0 RNA, and intracellular compartmentation and their impact on the life cycle of the line retrotransposon

Nearly half of the human genome is occupied by repetitive sequences of ancient virus-like genetic elements. The largest class, comprising 17% of the genome, belong to the type 1 Long INterspersed Elements (LINE-1) and are the only class capable of autonomous propagation in the genome. When epigenetic silencing mechanisms of LINE-1 fail, the proteins encoded by LINE-1 engage in reverse transcription to make new copies of their own or other DNAs that are pasted back into the genome. To elucidate how LINE-1 is dysregulated as a result of carcinogen exposure, we developed a computational model of key elements in the LINE-1 lifecycle, namely, the role of cytosolic ribonuclease (RNase), RNA interference (RNAi) by the antisense ORF0 RNA, and sequestration of LINE-1 products into stress granules and multivesicular structures.

The model showed that when carcinogen exposure is represented as either a sudden increase in LINE-1 mRNA count, or as an increase in mRNA transcription rate, the retrotransposon copy number exhibits a distinct threshold behavior above which LINE-1 enters a positive feedback loop that allows the cDNA copy number to grow exponentially. We also found that most of the LINE-1 RNA was degraded via the RNAase pathway and that neither ORF0 RNAi, nor the sequestration of LINE-1 products into granules and multivesicular structures, played a significant role in regulating the retrotransposon’s life cycle. Several aspects of the prediction agree with experimental results and indicate that the model has significant potential to inform future experiments related to LINE-1 activation.

Bootstrapping least-squares estimates in biochemical reaction networks

The paper proposes new computational methods of computing confidence bounds for the least-squares estimates (LSEs) of rate constants in mass action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large-volume limit of a reaction network, to network's partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large-volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods.

Model discrimination in dynamic molecular systems: application to parotid de-differentiation network

In modern systems biology the modeling of longitudinal data, such as changes in mRNA concentrations, is often of interest. Fully parametric, ordinary differential equations (ODE)-based models are typically developed for the purpose, but their lack of fit in some examples indicates that more flexible Bayesian models may be beneficial, particularly when there are relatively few data points available. However, under such sparse data scenarios it is often difficult to identify the most suitable model. The process of falsifying inappropriate candidate models is called model discrimination. We propose here a formal method of discrimination between competing Bayesian mixture-type longitudinal models that is both sensitive and sufficiently flexible to account for the complex variability of the longitudinal molecular data. The ideas from the field of Bayesian analysis of computer model validation are applied, along with modern Markov Chain Monte Carlo (MCMC) algorithms, in order to derive an appropriate Bayes discriminant rule. We restrict attention to the two-model comparison problem and present the application of the proposed rule to the mRNA data in the de-differentiation network of three mRNA concentrations in mammalian salivary glands as well as to a large synthetic dataset derived from the model used in the recent DREAM6 competition.

Least squares estimation in stochastic biochemical networks

The paper presents results on the asymptotic properties of the least-squares estimates (LSEs) of the reaction constants in mass-action, stochastic, biochemical network models. LSEs are assumed to be based on the longitudinal data from partially observed trajectories of a stochastic dynamical system, modeled as a continuous-time, pure jump Markov process. Under certain regularity conditions on such a process, it is shown that the vector of LSEs is jointly consistent and asymptotically normal, with the asymptotic covariance structure given in terms of a system of ordinary differential equations (ODE). The derived asymptotic properties hold true as the biochemical network size (the total species number) increases, in which case the stochastic dynamical system converges to the deterministic mass-action ODE. An example is provided, based on synthetic as well as RT-PCR data from the retro-transcription network of the LINE1 gene.

HPV E7 viral oncoprotein disrupts transcriptional regulation of L1Md retrotransposon

Murine L1Md-A5 retrotransposon is a redox-inducible element regulated by Nrf-2/JunD and E2F/Rb-binding sites within its promoter (5'-UTR). Because the human papillomavirus (HPV) oncoprotein E7 interacts with retinoblastoma (pRb) and members of the AP1 family, studies were conducted to examine functional interactions between HPV E7, pRb, and histone deacetylase 2 (HDAC2) in the regulation of L1Md-A5. Using a transient heterologous transcription system we found that HPV E7 alone, or in combination with HDAC2, disrupted pRb-mediated L1MdA-5 transactivation. HPV E7 also ablated the transcriptional response of L1Md-A5 to genotoxic stress, but did not interfere with basal activity. We conclude that HPV E7 associates with proteins involved in the assembly of macromolecular complexes that regulate antioxidant and E2F/Rb sites within L1MdA-5 to regulate biological activity.

Retinoblastoma family of proteins and chromatin epigenetics: a repetitive story in a few LINEs

The retinoblastoma (RB) protein family in mammals is composed of three members: pRB (or RB1), p107, and p130. Although these proteins do not directly bind DNA, they associate with the E2F family of transcription factors which function as DNA sequence-specific transcription factors. RB proteins alter gene transcription via direct interference with E2F functions, as well as recruitment of transcriptional repressors and corepressors that silence gene expression through DNA and histone modifications. E2F/RB complexes shape the chromatin landscape through recruitment to CpG-rich regions in the genome, thus making E2F/RB complexes function as local and global regulators of gene expression and chromatin dynamics. Recruitment of E2F/pRB to the long interspersed nuclear element (LINE1) promoter enhances the role that RB proteins play in genome-wide regulation of heterochromatin. LINE1 elements are dispersed throughout the genome and therefore recruitment of RB to the LINE1 promoter suggests that LINE1 could serve as the scaffold on which RB builds up heterochromatic regions that silence and shape large stretches of chromatin. We suggest that mutations in RB function might lead to global rearrangement of heterochromatic domains with concomitant retrotransposon reactivation and increased genomic instability. These novel roles for RB proteins open the epigenetic-based way for new pharmacological treatments of RB-associated diseases, namely inhibitors of histone and DNA methylation, as well as histone deacetylase inhibitors.

Reactivation of L1 retrotransposon by benzo(a)pyrene involves complex genetic and epigenetic regulation

Benzo(a)pyrene (BaP), is an environmental pollutant present in tobacco smoke and a byproduct of fossil fuel combustion which likely contributes to the tumorigenic processes in human cancers including lung and esophageal. Long Interspersed Nuclear Element-1 (LINE-1) or L1 is a mobile element within the mammalian genome that propagates via a "copy-and-paste" mechanism using reverse transcriptase and RNA intermediates. L1 is strongly expressed during early embryogenesis and then silenced as cells initiate differentiation programming. Although the complex transcriptional control mechanisms of L1 are not well understood, L1 reactivation has been described in several human cancers and following exposure of mouse or human cells to BaP. In this study we investigated the molecular mechanisms and epigenetic events that regulate L1 reactivation following BaP exposure. We show that challenge of HeLa cells with BaP induces early enrichment of the transcriptionally-active chromatin markers histone H3 trimethylated at lysine 4 (H3K4Me3) and histone H3 acetylated at lysine 9 (H3K9Ac), and reduces association of DNA methyltransferase-1 (DNMT1) with the L1 promoter. These changes are followed by proteasome-dependent decreases in cellular DNMT1 expression and sustained reduction of cytosine methylation within the L1 promoter CpG island. Pharmacological inhibition of the proteasome signaling pathway with the inhibitor MG132 blocks degradation of DNMT1 and alters BaP-mediated histone epigenetic modifications. We conclude that genetic reactivation of L1 by BaP involves an ordered cascade of epigenetic events that begin with nucleosomal histone modifications and is completed with alterations in DNMT1 recruitment to the L1 promoter and reduced DNA methylation of CpG islands.