Search

Regnase-1-mediated mRNA decay (RMD), in which inflammatory mRNAs harboring specific stem–loop structures are degraded, is a critical part of proper immune homeostasis. Prior to initial translation, Regnase-1 associates with target stem–loops but does not carry out endoribonucleolytic cleavage. Single molecule imaging revealed that UPF1 is required to first unwind the stem–loops, thus licensing Regnase-1 to proceed with RNA degradation. Following translation, Regnase-1 physically associates with UPF1 using two distinct points of interaction: The Regnase-1 RNase domain binds to SMG1-phosphorylated residue T28 in UPF1; in addition, an intrinsically disordered segment in Regnase-1 binds to the UPF1 RecA domain, enhancing the helicase activity of UPF1. The SMG1-UPF1–Regnase-1 axis targets pioneer rounds of translation and is critical for rapid resolution of inflammation through restriction of the number of proteins translated by a given mRNA. Furthermore, small-molecule inhibition of SMG1 prevents RNA unwinding in dendritic cells, allowing post-transcriptional control of innate immune responses., 炎症が制御される新たなRNA分解メカニズムを解明 --新たな免疫賦活化法の開発に道筋--. 京都大学プレスリリース. 2019-07-25.

Whole-body metabolic homeostasis is tightly controlled by hormone-like factors with systemic or paracrine effects that are derived from nonendocrine organs, including adipose tissue (adipokines) and liver (hepatokines). Fibroblast growth factor 21 (FGF21) is a hormone-like protein, which is emerging as a major regulator of whole-body metabolism and has therapeutic potential for treating metabolic syndrome. However, the mechanisms that control FGF21 levels are not fully understood. Herein, we demonstrate that FGF21 production in the liver is regulated via a posttranscriptional network consisting of the CCR4–NOT deadenylase complex and RNA-binding protein tristetraprolin (TTP). In response to nutrient uptake, CCR4–NOT cooperates with TTP to degrade AU-rich mRNAs that encode pivotal metabolic regulators, including FGF21. Disruption of CCR4–NOT activity in the liver, by deletion of the catalytic subunit CNOT6L, increases serum FGF21 levels, which ameliorates diet-induced metabolic disorders and enhances energy expenditure without disrupting bone homeostasis. Taken together, our study describes a hepatic CCR4–NOT/FGF21 axis as a hitherto unrecognized systemic regulator of metabolism and suggests that hepatic CCR4–NOT may serve as a target for devising therapeutic strategies in metabolic syndrome and related morbidities.

Patient heterogeneity precludes cancer treatment and drug development; hence, development of methods for finding prognostic markers for individual treatment are urgently required. Here, we present Pasmopy (Patient-Specific Modeling in Python), a computational framework for stratification of patients using in silico signaling dynamics. Pasmopy converts texts and sentences on gene regulation into an executable mathematical model. Using this framework, we built a model of the ErbB receptor signaling network, trained in cultured cell lines, and performed in silico simulation of 377 breast cancer patients using The Cancer Genome Atlas (TCGA) transcriptome datasets. The temporal dynamics of Akt, Extracellular Signal-Regulated Kinase (ERK), and c-Myc in each patient were able to accurately predict the difference in prognosis and sensitivity to kinase inhibitors in triple-negative breast cancer (TNBC). Our model applies to any type of signaling network and facilitates the network-based use of prognostic markers and prediction of drug response.

Adenosine deaminases acting on RNA (ADARs) catalyze the deamination of adenosine (A) to inosine (I). A-to-I RNA editing targets double-stranded RNA (dsRNA), and increases the complexity of gene regulation by modulating base pairing-dependent processes such as splicing, translation, and microRNA (miRNA)-mediated gene silencing. This study investigates the genome-wide binding preferences of the nuclear constitutive isoforms ADAR1-p110 and ADAR2 on human miRNA species by RNA immunoprecipitation of ADAR-bound small RNAs (RIP-seq). Our results suggest that secondary structure predicted by base-pairing probability in the mainly double-stranded region of a pre-miRNA or mature miRNA duplex may determine ADAR isoform preference for binding distinct subpopulations of miRNAs. Furthermore, we identify 31 unique editing sites with statistical significance, 19 sites of which are novel editing sites. Editing sites are enriched in the seed region responsible for target recognition by miRNAs, and isoform-specific nucleotide motifs in the immediate vicinity and opposite of editing sites are consistent with previous studies, and further reveal that ADAR2 may edit A/C bulges more frequently than ADAR1-p110 in the context of miRNA.

Cancer phenotypes, referred to as hallmarks of cancer, are acquired by dysregulation of biochemical networks, in addition to genetic and epigenetic alterations. For the last decade, systems biology approaches, a combination of mathematical modeling and quantitative experiments, have attempted to reveal the structure and spatiotemporal dynamics of signal transduction networks in cancer. In this review, we discuss recent systems biology studies of dynamic regulation associated with well-studied sub-networks of the hallmarks of cancer: cell cycle regulation abnormalities, resistance to cell death, angiogenesis, invasion and metastasis, dysregulation of immune responses, and metabolism. Using mathematical modeling to understand these dynamic circuitries in the complete cancer network should enable us to formulate basic principles in the hallmarks of cancer.

The tumor microenvironment is fundamental to cancer progression, and the influence of its mechanical properties is increasingly being appreciated. Tamoxifen has been used for many years to treat estrogen‐positive breast cancer. Here we report that tamoxifen regulates the level and activity of collagen cross‐linking and degradative enzymes, and hence the organization of the extracellular matrix, via a mechanism involving both the G protein‐coupled estrogen receptor (GPER) and hypoxia‐inducible factor‐1 alpha (HIF‐1A). We show that tamoxifen reduces HIF‐1A levels by suppressing myosin‐dependent contractility and matrix stiffness mechanosensing. Tamoxifen also downregulates hypoxia‐regulated genes and increases vascularization in PDAC tissues. Our findings implicate the GPER/HIF‐1A axis as a master regulator of peri‐tumoral stromal remodeling and the fibrovascular tumor microenvironment and offer a paradigm shift for tamoxifen from a well‐established drug in breast cancer hormonal therapy to an alternative candidate for stromal targeting strategies in PDAC and possibly other cancers.See also: E Cortes et al (January 2019) andM Pein & T Oskarsson (January 2019)EMBO Reports (2019) 20: e46557

Virus infection induces the development of T follicular helper (TFH) and T helper 1 (TH1) cells. Although TFH cells are important in anti-viral humoral immunity, the contribution of TH1 cells to a protective antibody response remains unknown. We found that IgG2 antibodies predominated in the response to vaccination with inactivated influenza A virus (IAV) and were responsible for protective immunity to lethal challenge with pathogenic H5N1 and pandemic H1N1 IAV strains, even in mice that lacked TFH cells and germinal centers. The cytokines interleukin-21 and interferon-γ, which are secreted from TH1 cells, were essential for the observed greater persistence and higher titers of IgG2 protective antibodies. Our results suggest that TH1 induction could be a promising strategy for producing effective neutralizing antibodies against emerging influenza viruses.

Wnt/β-catenin signaling plays a key role in the tumorigenicity of colon cancer. Furthermore, it has been reported that lncRNAs are dysregulated in several steps of cancer development. Here we show that β-catenin directly activates the transcription of the long noncoding RNA (lncRNA) ASBEL [antisense ncRNA in the ANA (Abundant in neuroepithelium area)/BTG3 (B-cell translocation gene 3) locus] and transcription factor 3 (TCF3), both of which are required for the survival and tumorigenicity of colorectal cancer cells. ASBEL interacts with and recruits TCF3 to the activating transcription factor 3 (ATF3) locus, where it represses the expression of ATF3. Furthermore, we demonstrate that ASBEL-TCF3-mediated down-regulation of ATF3 expression is required for the proliferation and tumorigenicity of colon tumor cells. ATF3, in turn, represses the expression of ASBEL Our results reveal a pathway involving an lncRNA and two transcription factors that plays a key role in Wnt/β-catenin-mediated tumorigenesis. These results may provide insights into the variety of biological and pathological processes regulated by Wnt/β-catenin signaling.

SummaryAberrant activation of Wnt/β-catenin signaling is a major driving force in colon cancer. Wnt/β-catenin signaling induces the expression of the transcription factor c-Myc, leading to cell proliferation and tumorigenesis. c-Myc regulates multiple biological processes through its ability to directly modulate gene expression. Here, we identify a direct target of c-Myc, termed MYU, and show that MYU is upregulated in most colon cancers and required for the tumorigenicity of colon cancer cells. Furthermore, we demonstrate that MYU associates with the RNA binding protein hnRNP-K to stabilize CDK6 expression and thereby promotes the G1-S transition of the cell cycle. These results suggest that the MYU/hnRNP-K/CDK6 pathway functions downstream of Wnt/c-Myc signaling and plays a critical role in the proliferation and tumorigenicity of colon cancer cells.

This paper proposes a new system model called a positive quadratic system for molecular interaction in a cell, including a signal transduction pathway and a gene regulatory pathway. Although the whole network of these pathways is far more complex than simple chemical reactions, one of the features of the proposed model is that it can capture the quadratic property of molecular interaction as well as the positivity properties of the state variables with a rather simple form. Based on this model, we derive a sufficient condition of the stability in the sense of Lyapunov, and present a method for estimating a positive invariant set depending on the initial state. We show that the proposed method is effective by numerical simulations of two fundamental biological models.

Genetic network inference methods using random forests have shown promise. Some of the random-forest-based inference methods have an ability to analyze both time-series and static gene expression data. We think however that, as the gene expression levels at two adjacent measurements of a time-series data are often similar to each other, the gene expression levels at each measurement in the time-series data are less informative than those in the static data. On the basis of this idea, we proposed a new inference method that relies more on static gene expression data than time-series ones. Through the numerical experiments, we showed that the quality of the inferred genetic network is slightly improved by giving greater importance to static data than time-series ones. Although we develop the new method by modifying the random-forest-based inference method proposed by the authors, we could introduce the idea in this study into any inference method that is capable of analyzing both time-series and static gene expression data.

Acquired drug resistance is the major reason why patients fail to respond to cancer therapies. It is a challenging task to determine the tipping point of endocrine resistance and detect the associated molecules. Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance. We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules. The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients. The results provided the detection for the pre-resistance state or early signs of endocrine resistance. Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.

Paired-end RNA sequencing (RNA-seq) is usually applied to the quantification of long transcripts such as messenger or long non-coding RNAs, in which case overlapping pairs are discarded. In contrast, RNA-seq on short RNAs (≤ 200 nt) is typically carried out in single-end mode, as the additional cost associated with paired-end would only translate into redundant sequence information. Here, we exploit paired-end sequencing of short RNAs as a strategy to filter out sequencing errors and apply this method to the identification of adenosine-to-inosine (A-to-I) RNA editing events on human precursor microRNA (pre-miRNA) and mature miRNA. Combined with RNA immunoprecipitation sequencing (RIP-seq) of A-to-I RNA editing enzymes, this method takes full advantage of deep sequencing technology to identify RNA editing sites with unprecedented resolution in terms of editing efficiency.

A single transcription factor (TF) potentially induces both threshold and graded transcription responses via super enhancers (SEs) and typical enhancers (TEs). However, the mechanism by which a TF binds to different types of enhancers to determine gene expression quantity remains to be elucidated. To clarify the mechanism, we performed an integrated analysis of chromatin accessibility, RelA-DNA binding and single-cell transcription for each enhancer in anti-IgM-stimulated mouse primary B lymphocytes. Here we show that SE activity, defined by acetylated histone H3 lysine 27 (H3K27Ac) modifications, was simultaneously quantitatively controlled by chromatin opening and enriched RelA binding, resulting in threshold transcription. Our analysis indicated that the number of RelA binding in open chromatin regions at SEs and TEs was on average five and one, respectively, and that a mathematical model having these differences in TF numbers as parameters could explain enhancer-mediated quantitative transcription differences of individual genes. This study indicates that highly specific on-off expression of TF-enriched SE-targeted genes results in a highly selective epigenetic landscape for cell specificity, in contract to broadly responsive TE-regulated genes that may be responsible for cellular homeostasis.

Paired-end RNA sequencing (RNA-seq) is usually applied to the quantification of long transcripts such as messenger or long non-coding RNAs, in which case overlapping pairs are discarded. In contrast, RNA-seq on short RNAs (≤ 200 nt) is typically carried out in single-end mode, as the additional cost associated with paired-end would only translate into redundant sequence information. Here, we exploit paired-end sequencing of short RNAs as a strategy to filter out sequencing errors and apply this method to the identification of adenosine-to-inosine (A-to-I) RNA editing events on human precursor microRNA (pre-miRNA) and mature miRNA. Combined with RNA immunoprecipitation sequencing (RIP-seq) of A-to-I RNA editing enzymes, this method takes full advantage of deep sequencing technology to identify RNA editing sites with unprecedented resolution in terms of editing efficiency.

Feedback control is a key mechanism in signal transduction, intimately involved in regulating the outcome of the cellular response. Here we report a novel mechanism by which PHLDA1, Pleckstrin homology-like domain, family A, member 1, negatively regulates ErbB receptor signaling by inhibition of receptor oligomerization. We have found that the ErbB3 ligand, heregulin, induces PHILDA1 expression in MCF-7 cells. Transcriptionally-induced PHLDA1 protein directly binds to ErbB3, while knockdown of PHLDA1 increases complex formation between ErbB3 and ErbB2. To provide insight into the mechanism for our time-course and single cell experimental observations, we performed a systematic computational search of network topologies of the mathematical models based on receptor dimer-tetramer formation in the ErbB activation processes. Our results indicate that only a model in which PHLDA1 inhibits formation of both dimers and tetramer can explain the experimental data. Predictions made from this model were further validated by single molecule imaging experiments. Our studies suggest a unique regulatory feature of PHLDA1 to inhibit the ErbB receptor oligomerization process and thereby control the activity of receptor signaling network.

Immunological memory is thought to be mediated exclusively by lymphocytes. However, enhanced innate immune responses caused by a previous infection increase protection against reinfection, which suggests the presence of innate immunological memory. Here we identified an important role for the stress-response transcription factor ATF7 in innate immunological memory. ATF7 suppressed a group of genes encoding factors involved in innate immunity in macrophages by recruiting the histone H3K9 dimethyltransferase G9a. Treatment with lipopolysaccharide, which mimics bacterial infection, induced phosphorylation of ATF7 via the kinase p38, which led to the release of ATF7 from chromatin and a decrease in repressive histone H3K9me2 marks. A partially disrupted chromatin structure and increased basal expression of target genes were maintained for long periods, which enhanced resistance to pathogens. ATF7 might therefore be important in controlling memory in cells of the innate immune system.

Uncaging promoter and enhancer dynamics In order to understand cellular differentiation, it is important to understand the timing of the regulation of gene expression. Arner et al. used cap analysis of gene expression (CAGE) to analyze gene enhancer and promoter activities in a number of human and mouse cell types. The RNA of enhancers was transcribed first, followed by that of transcription factors, and finally by genes that are not transcription factors. Science , this issue p. 1010

Our previous study proposed a positive quadratic system representation for molecular interaction in a cell, including a signal transduction pathway and a gene regulatory network, and also presented a method for estimating a positive invariant set depending on the initial state. As an extension towards wider applications of this approach, this paper proposes a system representation called here a singularly perturbed positive quadratic system, and shows that every positive rational system, which is used as a mathematical model expressing biological behavior, can be approximately represented by a quasi-steady state system of a singularly perturbed positive quadratic system. In addition, we prove that the singularly perturbed positive quadratic system preserves stability at an equilibrium point of the positive rational system.

Scientific Data, 4, ISSN:2052-4463

Huynh-Thu and colleagues initially introduce the random forest into field of genetic network inference. Their method, GENIE3, has performed well on genetic network inference problems. However, GENIE3 was designed only for analyzing static expression data that were measured under steady-state conditions. In order to infer genetic networks from time-series of gene expression data, this study proposes a new method based on the random forest. The proposed method has an ability to analyze both static and time-series data. When inferring a genetic network only from steady-state gene expression data, however, the proposed method is equivalent to GENIE3. Therefore, the proposed method can be seen as an extension of GENIE3. Through numerical experiments, we showed that the proposed method outperformed the existing inference methods on all of the 5 artificial genetic network inference problems.

The ErbB receptor signaling pathway plays an important role in the regulation of cellular proliferation, survival and differentiation, and dysregulation of the pathway is linked to various types of human cancer. Mathematical models have been developed as a practical complementary approach to deciphering the complexity of ErbB receptor signaling and elucidating how the pathways discriminate between ligands to induce different cell fates. In this study, we developed a simulator to accurately calculate the dynamic sensitivity of extracellular-signal-regulated kinase (ERK) activity (ERK*) and Akt activity (Akt*), downstream of the ErbB receptors stimulated with epidermal growth factor (EGF) and heregulin (HRG). To demonstrate the feasibility of this simulator, we estimated how the reactions critically responsible for ERK* and Akt* change with time and in response to different doses of EGF and HRG, and predicted that only a small number of reactions determine ERK* and Akt*. ERK* increased steeply with increasing HRG dose until saturation, while showing a gently rising response to EGF. Akt* had a gradual wide-range response to HRG and a blunt response to EGF. Akt* was sensitive to perturbations of intracellular kinetics, while ERK* was more robust due to multiple, negative feedback loops. Overall, the simulator predicted reactions that were critically responsible for ERK* and Akt* in response to the dose of EGF and HRG, illustrated the response characteristics of ERK* and Akt*, and estimated mechanisms for generating robustness in the ErbB signaling network.

Phenotypic heterogeneity of cancer cells is caused not only by genetic and epigenetic alterations but also by stochastic variation of intracellular signaling molecules. Using cells that stably express Förster resonance energy transfer (FRET) biosensors, we show here a correlation between a temporal fluctuation in the activity of Rac1 and the invasive properties of C6 glioma cells. By using long-term time-lapse imaging, we found that Rac1 activity in C6 glioma cells fluctuated over a timescale that was substantially longer than that of the replication cycle. Because the relative level of Rac1 activity in each cell was unaffected by a suspension–adhesion procedure, we were able to sort C6 glioma cells according to the levels of Rac1 activity, yielding Rac1high and Rac1low cells. The Rac1high cells invaded more efficiently than did Rac1low cells in a Matrigel invasion assay. We assessed the transcriptional profiles of Rac1high and Rac1low cells and performed gene ontology analysis. Among the 14 genes that were most associated with the term ‘membrane’ (membrane-related genes) in Rac1high cells, we identified four genes that were associated with glioma invasion and Rac1 activity by using siRNA knockdown experiments. Among the transcription factors upregulated in Rac1high cells, Egr2 was found to positively regulate expression of the four membrane-related invasion-associated genes. The identified signaling network might cause the fluctuations in Rac1 activity and the heterogeneity in the invasive capacity of glioma cells.

The inference of genetic networks is a problem to obtain mathematical models that can explain observed time-series of gene expression levels. A number of models have been proposed to describe genetic networks. The S-system model is one of the most studied models among them. Due to its advantageous features, numerous inference algorithms based on the S-system model have been proposed. The number of the parameters in the S-system model is however larger than those of the other well-studied models. Therefore, when trying to infer S-system models of genetic networks, we need to provide a larger amount of gene expression data to the inference method. In order to reduce the amount of gene expression data required for an inference of genetic networks, this study simplifies the S-system model by fixing some of its parameters to 0. In this study, we call this simplified S-system model a reduced S-system model. We then propose a new inference method that estimates the parameters of the reduced S-system model by minimizing two-dimensional functions. Finally, we check the effectiveness of the proposed method through numerical experiments on artificial and actual genetic network inference problems.