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The importance of post‐transcriptional regulation by small non‐coding RNAs has recently been recognized in both pro‐ and eukaryotes. Small RNAs (sRNAs) regulate gene expression post‐transcriptionally by base pairing with the mRNA. Here we use dynamical simulations to characterize this regulation mode in comparison to transcriptional regulation mediated by protein–DNA interaction and to post‐translational regulation achieved by protein–protein interaction. We show quantitatively that regulation by sRNA is advantageous when fast responses to external signals are needed, consistent with experimental data about its involvement in stress responses. Our analysis indicates that the half‐life of the sRNA–mRNA complex and the ratio of their production rates determine the steady‐state level of the target protein, suggesting that regulation by sRNA may provide fine‐tuning of gene expression. We also describe the network of regulation by sRNA in Escherichia coli, and integrate it with the transcription regulation network, uncovering mixed regulatory circuits, such as mixed feed‐forward loops. The integration of sRNAs in feed‐forward loops provides tight repression, guaranteed by the combination of transcriptional and post‐transcriptional regulations. Synopsis Living cells are self‐regulated by interactions between different molecules. Until very recently, most research has focused on transcription regulation interactions and on protein–protein interactions, which in many cases are involved in post‐translational regulation. During the last years it has become evident that another type of interaction plays a prominent role in the regulation of cellular processes, manifested by small RNA (sRNA) molecules that base pair with the mRNA and regulate gene expression post‐transcriptionally, influencing translation or mRNA stability. This mode of regulation was found in both pro‐ and eukaryotes (for review see Storz et al, 2005). In this paper, we focus on bacterial sRNAs, and in particular on regulatory interactions found in Escherichia coli, for which most experimental data on sRNAs are available. At present there are about 80 known sRNAs in E. coli (for review see Gottesman, 2005; Storz et al, 2005). These molecules are 50–400 nucleotides long and many of them are evolutionary conserved (Hershberg et al, 2003). Most of those for which some functional knowledge has been acquired were often shown to act as inhibitors of translation by base pairing with the mRNA in the ribosome‐binding site (for review see Gottesman, 2005). We describe the network of sRNA–target regulatory interactions in E. coli, and study the kinetics of this regulation mechanism in comparison to transcription regulation mediated by protein–DNA interaction and post‐translational regulation mediated by protein–protein interaction. For this we describe the three regulatory mechanisms by mathematical models, followed by simulations using average kinetic parameters based on experimental data (Altuvia et al, 1997; Altuvia and Wagner, 2000; Alon, 2006). We show that there are measurable qualitative differences between the three regulation mechanisms, both in response time and in effectiveness. In regard to effectiveness, we find that transcriptional regulation is generally the most effective mechanism in the activation or repression of the target gene. It requires the least copies of the regulating molecule in order to achieve the same level of regulation, as achieved by the two other mechanisms. The regulation by sRNA has stoichiometric properties. We found that it may be effective for the regulation of a few dozens of genes, depending on the relative production rates of the sRNA and target RNA molecules. Our analysis indicates that the half‐life of the sRNA–mRNA complex and the ratio of their production rates determine the steady‐state level of the target protein, suggesting that regulation by sRNA may provide fine‐tuning of gene expression. In regard to response time, protein–protein interaction provides the fastest response to external stimuli, transcription regulation provides the slowest response and sRNA regulation is intermediate. However, when the regulator's synthesis is induced upon an external signal, there is a time interval where regulation by sRNA provides the fastest response to external stimuli (Figure 1). This stems from its relatively quick synthesis relative to protein synthesis. We also analyzed the recovery time upon shutoff of the external signal. Two parameters determine the recovery time in case of regulation by sRNA: (1) the ratio between the production rates of the regulatory sRNA and target mRNA and (2) the degradation rate of the sRNA. For a wide range of these parameters, our analysis indicates that regulation by sRNA leads to a fast recovery. Thus, for stimuli that require fast responses in a short time interval, regulation by sRNA may be advantageous, as, for example, under transient stress conditions. Indeed, most of the regulatory sRNAs with known function are induced in response to various stress conditions. As some of the sRNAs are known to regulate the translation of transcriptional regulators, and the production of the sRNAs themselves is transcriptionally regulated, it is conceivable that there are regulatory modules that integrate these two levels of regulation. Indeed, our integrative analysis of the transcription regulation network and post‐transcriptional regulation network identified interesting combinations of the two levels of regulation in regulatory circuits (Figure 6). These include mixed feed‐forward loops (Figure 6A) and mixed feedback loops (Figure 6B). It is intriguing to understand the advantage of a regulatory circuit that involves the two levels of regulation in comparison to an equivalent circuit that involves only transcription regulation. To this end we examined the feed‐forward loop OmpR‐MicF‐ompF (Figure 6) in comparison to an equivalent feed‐forward loop containing only transcriptional regulators. In this feed‐forward loop, a regulator A activates a second regulator B, and they both repress the target gene c. In loops composed of transcriptional regulation, both A and B are transcriptional regulators, while in the mixed feed‐forward loop found in the E. coli network A is a transcriptional regulator (OmpR) and B is an sRNA (MicF). The mixed feed‐forward loop provides tighter regulation, because it blocks both transcription and translation of the target gene. Thus, if a few transcripts escape the repression by the transcriptional repressor, they will be blocked post‐transcriptionally by the sRNA. It is advantageous also in regard to response time, because for a wide range of parameters it provides both a fast shutdown of the target gene upon an external signal and a fast recovery when the signal has terminated. These properties make this circuit a preferred regulatory module when fast responses to changes in the environment are needed, and further emphasize the advantages of sRNAs in responses to stress conditions. We use dynamical simulations to characterize post‐transcriptional regulation by small non‐coding RNAs (sRNAs), and compare it to transcriptional regulation mediated by protein‐DNA interaction, and to post‐translational regulation achieved by protein‐protein interaction. We show quantitatively that regulation by sRNA is advantageous when fast responses to external signals are needed, consistent with experimental data about its involvement in stress responses. We find that the halflife of the sRNA‐mRNA complex and the ratio of their production rates determine the steady state level of the target protein, suggesting that regulation by sRNA may provide fine‐tuning of gene expression. We describe the network of regulation by sRNA in Escherichia coli and integrate it with the transcription regulation network, uncovering mixed regulatory circuits, such as mixed feed‐forward loops, which provide tight repression, guaranteed by the combination of transcriptional and post‐transcriptional regulations.

Myocardial infarction (MI) remains the leading cause of death worldwide. Cardiac fibroblasts (CFs) are abundant in the heart and are responsible for cardiac repair post-MI. NF-κB-repressing factor (NKRF) plays a significant role in the transcriptional inhibition of various specific genes. However, the NKRF action mechanism in CFs remains unclear in cardiac repair post-MI. This study investigates the NKRF mechanism in cardiac remodeling and dysfunction post-MI by establishing a CF-specific NKRF-knockout (NKRF-CKO) mouse model. NKRF expression is downregulated in CFs in response to pathological cardiac remodeling in vivo and TNF-α in vitro. NKRF-CKO mice demonstrate worse cardiac function and survival and increased infarct size, heart weight, and MMP2 and MMP9 expression post-MI compared with littermates. NKRF inhibits CF migration and invasion in vitro by downregulating MMP2 and MMP9 expression. Mechanistically, NKRF inhibits human antigen R (HuR) transcription by binding to the classical negative regulatory element within the HuR promoter via an NF-κB-dependent mechanism. This decreases HuR-targeted Mmp2 and Mmp9 mRNA stability. This study suggests that NKRF is a therapeutic target for pathological cardiac remodeling.

The majority of breast cancers are primarily hormone‐sensitive and can be managed by endocrine therapy, although therapy‐resistant or hormone‐refractory cancers need alternative treatments. Recently, increasing attention is being paid to RNA‐binding proteins (RBP) in cancer pathophysiology. The precise role of RBP in breast cancer, however, remains to be clarified. We herein show that an RBP non‐POU domain‐containing octamer binding (NONO) plays a critical role in the pathophysiology of breast cancers regardless of their hormone dependency. Clinicopathological and immunohistochemical study of 127 breast cancer cases showed that NONO is a significant independent prognostic factor for breast cancer patients. Notably, siRNA‐mediated NONO knockdown substantially repressed the proliferation of both hormone‐sensitive MCF‐7 and hormone‐refractory MB‐MDA‐231 breast cancer cells. Integrative analysis combined with expression microarray and RIP‐sequencing (RNA immunoprecipitation‐sequencing) showed that NONO post‐transcriptionally regulates the expression of cell proliferation‐related genes by binding to their mRNAs, as exemplified by S‐phase‐associated kinase 2 and E2F transcription factor 8. Overall, these results suggest that NONO is a key regulator for breast cancer proliferation through the pre‐mRNA splicing of cell proliferation‐related genes and could be a potential new diagnostic and therapeutic target for advanced disease. The present study shows that Drosophila behavior human splicing family RNA‐binding protein NONO plays a critical role in breast cancer tumorigenesis. Clinicopathological study defines that NONO immunoreactivity significantly correlates with poor overall and distant disease‐free survival of breast cancer patients. Cell‐based experiments show that NONO contributes to breast cancer proliferation by regulating SKP2 and E2F8 expression at the post‐transcriptional level. Our findings provide a new cancer strategy by applying NONO as a potential diagnostic and therapeutic target for breast cancer.

•  miR‐21 expression in cancer and other diseases •  Mechanisms of miR‐21 elevation in cancer: multi‐level regulatory control •  Transcriptional control •  Post‐trancriptional regulation •  miR‐21 functions in cancer •  Identification of direct miR‐21 targets •  miR‐21 in gliomas: targeting cell cycle, apoptosis and invasion •  miR‐21 networking and feedback regulation •  miR‐21 as a diagnostic and prognostic marker •  Potential therapeutic target •  Acknowledgements More than 1000 microRNAs (miRNAs) are expressed in human cells, some tissue or cell type specific, others considered as house‐keeping molecules. Functions and direct mRNA targets for some miRNAs have been relatively well studied over the last years. Every miRNA potentially regulates the expression of numerous protein‐coding genes (tens to hundreds), but it has become increasingly clear that not all miRNAs are equally important; diverse high‐throughput screenings of various systems have identified a limited number of key functional miRNAs over and over again. Particular miRNAs emerge as principal regulators that control major cell functions in various physiological and pathophysiological settings. Since its identification 3 years ago as the miRNA most commonly and strongly up‐regulated in human brain tumour glioblastoma [1], miR‐21 has attracted the attention of researchers in various fields, such as development, oncology, stem cell biology and aging, becoming one of the most studied miRNAs, along with let‐7, miR‐17–92 cluster (‘oncomir‐1’), miR‐155 and a few others. However, an miR‐21 knockout mouse has not yet been generated, and the data about miR‐21 functions in normal cells are still very limited. In this review, we summarise the current knowledge of miR‐21 functions in human disease, with an emphasis on its regulation, oncogenic role, targets in human cancers, potential as a disease biomarker and novel therapeutic target in oncology.

Genome integrity must be tightly preserved to ensure cellular survival and to deter the genesis of disease. Endogenous and exogenous stressors that impose threats to genomic stability through DNA damage are counteracted by a tightly regulated DNA damage response (DDR). RNA binding proteins (RBPs) are emerging as regulators and mediators of diverse biological processes. Specifically, RBPs that bind to adenine uridine (AU)-rich elements (AREs) in the 3' untranslated region (UTR) of mRNAs (AU-RBPs) have emerged as key players in regulating the DDR and preserving genome integrity. Here we review eight established AU-RBPs (AUF1, HuR, KHSRP, TIA-1, TIAR, ZFP36, ZFP36L1, ZFP36L2) and their ability to maintain genome integrity through various interactions. We have reviewed canonical roles of AU-RBPs in regulating the fate of mRNA transcripts encoding DDR genes at multiple post-transcriptional levels. We have also attempted to shed light on non-canonical roles of AU-RBPs exploring their post-translational modifications (PTMs) and sub-cellular localization in response to genotoxic stresses by various factors involved in DDR and genome maintenance. Dysfunctional AU-RBPs have been increasingly found to be associated with many human cancers. Further understanding of the roles of AU-RBP in maintaining genomic integrity may uncover novel therapeutic strategies for cancer.

Post‐transcriptional regulation by microRNAs and siRNAs depends not only on characteristics of individual binding sites in target mRNA molecules, but also on system‐level properties such as overall molecular concentrations. We hypothesize that an intracellular pool of microRNAs/siRNAs faced with a larger number of available predicted target transcripts will downregulate each individual target gene to a lesser extent. To test this hypothesis, we analyzed mRNA expression change from 178 microRNA and siRNA transfection experiments in two cell lines. We find that downregulation of particular genes mediated by microRNAs and siRNAs indeed varies with the total concentration of available target transcripts. We conclude that to interpret and design experiments involving gene regulation by small RNAs, global properties, such as target mRNA abundance, need to be considered in addition to local determinants. We propose that analysis of microRNA/siRNA targeting would benefit from a more quantitative definition, rather than simple categorization of genes as ‘target’ or ‘not a target.’ Our results are important for understanding microRNA regulation and may also have implications for siRNA design and small RNA therapeutics.

Long non‐coding RNAs (lncRNAs), as part of the family of non‐protein‐coding transcripts, are implicated in the occurrence and progression of several cardiovascular diseases (CVDs). With recent advances in lncRNA research, these molecules are purported to regulate gene expression at multiple levels, thereby producing beneficial or detrimental biological effects during CVD pathogenesis. At the transcriptional level, lncRNAs affect gene expression by interacting with DNA and proteins, for example, components of chromatin‐modifying complexes, or transcription factors affecting chromatin status. These potential mechanisms suggest that lncRNAs guide proteins to specific gene loci (eg promoter regions), or forestall proteins to specific genomic sites via DNA binding. Additionally, some lncRNAs are required for correct chromatin conformation, which occurs via chromatin looping in enhancer‐like models. At the post‐transcriptional level, lncRNAs interact with RNA molecules, mainly microRNAs (miRNAs) and mRNAs, potentially regulating CVD pathophysiological processes. Moreover, lncRNAs appear to post‐transcriptionally modulate gene expression by participating in mRNA splicing, stability, degradation and translation. Thus, the purpose of this review is to provide a comprehensive summary of lncRNAs implicated in CVD biological processes, with an emphasis on potential mechanisms of action.

The Arabidopsis LEAFY (LFY) transcription factor is a key regulator of floral meristem emergence and identity. LFY interacts genetically and physically with UNUSUAL FLORAL ORGANS, a substrate adaptor of CULLIN1–RING ubiquitin ligase complexes (CRL1). The functionally redundant genes BLADE ON PETIOLE1 (BOP1) and -2 (BOP2) are potential candidates to regulate LFY activity and have recently been shown to be substrate adaptors of CULLIN3 (CUL3)–RING ubiquitin ligases (CRL3). We tested the hypothesis that LFY activity is controlled by BOPs and CUL3s in plants and that LFY is a substrate for ubiquitination by BOP-containing CRL3 complexes. When constitutively expressed, LFY activity is fully dependent on BOP2 as well as on CUL3A and B to regulate target genes such as APETALA1 and to induce ectopic flower formation. We also show that LFY and BOP2 proteins interact physically and that LFY-dependent ubiquitinated species are produced in vitro in a reconstituted cell-free CRL3 system in the presence of LFY, BOP2 and CUL3. This new post-translational regulation of LFY activity by CRL3 complexes makes it a unique transcription factor subjected to a positive dual regulation by both CRL1 and CRL3 complexes and suggests a novel mechanism for promoting flower development.

Positive feedback driven by transcriptional regulation has long been considered a key mechanism underlying cell lineage segregation during embryogenesis. Using the developing spinal cord as a paradigm, we found that canonical, transcription‐driven feedback cannot explain robust lineage segregation of motor neuron subtypes marked by two cardinal factors, Hoxa5 and Hoxc8. We propose a feedback mechanism involving elementary microRNA–mRNA reaction circuits that differ from known feedback loop‐like structures. Strikingly, we show that a wide range of biologically plausible post‐transcriptional regulatory parameters are sufficient to generate bistable switches, a hallmark of positive feedback. Through mathematical analysis, we explain intuitively the hidden source of this feedback. Using embryonic stem cell differentiation and mouse genetics, we corroborate that microRNA–mRNA circuits govern tissue boundaries and hysteresis upon motor neuron differentiation with respect to transient morphogen signals. Our findings reveal a previously underappreciated feedback mechanism that may have widespread functions in cell fate decisions and tissue patterning. SYNOPSIS Robust cell fate decision and precise tissue boundary formation are critical for development. This study reports a feedback mechanism involving mRNA‐microRNA interactions during cell lineage segregation in mouse spinal cord development. Robust lineage segregation of mouse Hoxa5+ and Hoxc8+ motor neurons does not require canonical transcriptional feedback loops. Mathematical modeling derives a wide range of biologically plausible parameters that allow bistability to arise from post‐transcriptional networks. An intuitive interpretation of the mathematical analysis reveals a hidden feedback mechanism involving mRNA‐microRNA interactions. In vitro and in vivo experiments validate the critical roles of two microRNAs in lineage segregation and tissue boundary formation. Robust cell fate decision and precise tissue boundary formation are critical for development. This study reports a feedback mechanism involving mRNA‐microRNA interactions during cell lineage segregation in mouse spinal cord development.

Germ‐line mutations in the BRCA1 gene strongly predispose women to breast cancer (lifetime risk up to 80%). Furthermore, the BRCA1 protein is absent or present at very low levels in about one third of sporadic breast cancers. However, the mechanisms underlying BRCA1 somatic inactivation appear multiple and are still not fully understood. We report here the involvement of miR‐146a and miR‐146b‐5p that bind to the same site in the 3′UTR of BRCA1 and down‐regulate its expression as demonstrated using reporter assays. This was further confirmed with the endogenous BRCA1 gene by transfecting microRNA (miRNA) precursors or inhibitors in mammary cell lines. This down‐regulation was accompanied by an increased proliferation and a reduced homologous recombination rate, two processes controlled by BRCA1. Furthermore, we showed that the highest levels of miR‐146a and/or miR‐146b‐5p are found in basal‐like mammary tumour epithelial cell lines and in triple negative breast tumours, which are the closest to tumours arising in carriers of BRCA1 mutations. This work provides further evidence for the involvement of miRNAs in sporadic breast cancer through down‐regulation of BRCA1.