Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit

Mitigating off-target effects of small RNAs: conventional approaches, network theory and artificial intelligence

Three types of highly promising small RNA therapeutics, namely, small interfering RNAs (siRNAs), microRNAs (miRNAs) and the RNA subtype of antisense oligonucleotides (ASOs), offer advantages over small-molecule drugs. These small RNAs can target any gene product, opening up new avenues of effective and safe therapeutic approaches for a wide range of diseases. In preclinical research, synthetic small RNAs play an essential role in the investigation of physiological and pathological pathways as silencers of specific genes, facilitating discovery and validation of drug targets in different conditions. Off-target effects of small RNAs, however, could make it difficult to interpret experimental results in the preclinical phase and may contribute to adverse events of small RNA therapeutics. Out of the two major types of off-target effects we focused on the hybridization-dependent, especially on the miRNA-like off-target effects. Our main aim was to discuss several approaches, including sequence design, chemical modifications and target prediction, to reduce hybridization-dependent off-target effects that should be considered even at the early development phase of small RNA therapy. Because there is no standard way of predicting hybridization-dependent off-target effects, this review provides an overview of all major state-of-the-art computational methods and proposes new approaches, such as the possible inclusion of network theory and artificial intelligence (AI) in the prediction workflows. Case studies and a concise survey of experimental methods for validating in silico predictions are also presented. These methods could contribute to interpret experimental results, to minimize off-target effects and hopefully to avoid off-target-related adverse events of small RNA therapeutics.

Circular RNAs in Cell Cycle Regulation of Cancers

Cancer has been one of the most problematic health issues globally. Typically, all cancers share a common characteristic or cancer hallmark, such as sustaining cell proliferation, evading growth suppressors, and enabling replicative immortality. Indeed, cell cycle regulation in cancer is often found to be dysregulated, leading to an increase in aggressiveness. These dysregulations are partly due to the aberrant cellular signaling pathway. In recent years, circular RNAs (circRNAs) have been widely studied and classified as one of the regulators in various cancers. Numerous studies have reported that circRNAs antagonize or promote cancer progression through the modulation of cell cycle regulators or their associated signaling pathways, directly or indirectly. Mostly, circRNAs are known to act as microRNA (miRNA) sponges. However, they also hold additional mechanisms for regulating cellular activity, including protein binding, RNA-binding protein (RBP) recruitment, and protein translation. This review will discuss the current knowledge of how circRNAs regulate cell cycle-related proteins through the abovementioned mechanisms in different cancers.

MiR-199a-5p inhibits dermal papilla cells proliferation by regulating VEGFA expression in cashmere goat

Hair follicles undergo a renewal cycle consisting of anagen, telogen and catagen stages. MicroRNA (miRNA) plays a crucial role in this process. Recent studies have shown that miR-199a-5p, which exhibits differential expression between anagen and telogen stages in the hair follicle cycle of cashmere goats, inhibits the proliferation of various cell types, including skin keratinocytes and vascular endothelial cells. Since the proliferation of dermal papilla cells (DPCs) is a key factor in the hair follicle cycle, we utilized DPCs to investigate the function and molecular mechanism of miR-199a-5p in cashmere goats. Our functional analysis revealed that miR-199a-5p significantly suppressed cell viability and proliferation of DPCs, as evidenced by MTT, EdU and RT-qPCR methods. Subsequently, we investigated the regulatory mechanism of miR-199a-5p. Through bioinformatics analysis, a potential correlation between lnc102173187 and miR-199a-5p was predicted. However, the dual luciferase reporter assay revealed no interaction between lnc102173187 and miR-199a-5p. Further investigation using dual-luciferase reporter assay, RT-qPCR, and western blot results confirmed that VEGFA was the target gene of miR-199a-5p from. The functional experiment demonstrated that VEGFA promoted the proliferation of DPCs, and antagonized the inhibitory effect of miR-199a-5p on DPCs proliferation. Taken together, this research revealed the role of miR-199a-5p and VEGFA on the proliferation of dermal papilla cells in cashmere goat, which would enrich the theoretical basis for hair follicle development, and could also serve as a marker cofactor to play an important reference and guidance role in the breeding, improvement and optimization of cashmere goat breeds.

RNA-RNA competitive interactions: a molecular civil war ruling cell physiology and diseases

The idea that proteins are the main determining factors in the functioning of cells and organisms, and their dysfunctions are the first cause of pathologies, has been predominant in biology and biomedicine until recently. This protein-centered view was too simplistic and failed to explain the physiological and pathological complexity of the cell. About 80% of the human genome is dynamically and pervasively transcribed, mostly as non-protein-coding RNAs (ncRNAs), which competitively interact with each other and with coding RNAs generating a complex RNA network regulating RNA processing, stability, and translation and, accordingly, fine-tuning the gene expression of the cells. Qualitative and quantitative dysregulations of RNA-RNA interaction networks are strongly involved in the onset and progression of many pathologies, including cancers and degenerative diseases. This review will summarize the RNA species involved in the competitive endogenous RNA network, their mechanisms of action, and involvement in pathological phenotypes. Moreover, it will give an overview of the most advanced experimental and computational methods to dissect and rebuild RNA networks.

Collective Behaviors of Active Matter Learning from Natural Taxes Across Scales

Taxis orientation is common in microorganisms, and it provides feasible strategies to operate active colloids as small-scale robots. Collective taxes involve numerous units that collectively perform taxis motion, whereby the collective cooperation between individuals enables the group to perform efficiently, adaptively, and robustly. Hence, analyzing and designing collectives is crucial for developing and advancing microswarm toward practical or clinical applications. In this review, natural taxis behaviors are categorized and synthetic microrobotic collectives are discussed as bio-inspired realizations, aiming at closing the gap between taxis strategies of living creatures and those of functional active microswarms. As collective behaviors emerge within a group, the global taxis to external stimuli guides the group to conduct overall tasks, whereas the local taxis between individuals induces synchronization and global patterns. By encoding the local orientations and programming the global stimuli, various paradigms can be introduced for coordinating and controlling such collective microrobots, from the viewpoints of fundamental science and practical applications. Therefore, by discussing the key points and difficulties associated with collective taxes of different paradigms, this review potentially offers insights into mimicking natural collective behaviors and constructing intelligent microrobotic systems for on-demand control and preassigned tasks.

Pseudogenes and the associated ceRNA network as potential prognostic biomarkers for colorectal cancer

Colorectal cancer (CRC) is one of the most common and malignant carcinomas. Many long noncoding RNAs (lncRNAs) have been reported to play important roles in the tumorigenesis of CRC by influencing the expression of some mRNAs via competing endogenous RNA (ceRNA) networks and interacting with miRNAs. Pseudogene is one kind of lncRNA and can act as RNA sponges for miRNAs and regulate gene expression via ceRNA networks. However, there are few studies about pseudogenes in CRC. In this study, 31 differentially expressed (DE) pseudogenes, 17 DE miRNAs and 152 DE mRNAs were identified by analyzing the expression profiles of colon adenocarcinoma obtained from The Cancer Genome Atlas. A ceRNA network was constructed based on these RNAs. Kaplan-Meier analysis showed that 7 pseudogenes, 4 miRNAs and 30 mRNAs were significantly associated with overall survival. Then multivariate Cox regression analysis of the ceRNA-related DE pseudogenes was performed and a 5-pseudogene signature with the greatest prognostic value for CRC was identified. Moreover, the results were validated by the Gene Expression Omnibus database, and quantitative real-time PCR in 113 pairs of CRC tissues and colon cancer cell lines. This study provides a pseudogene-associated ceRNA network, 7 prognostic pseudogene biomarkers, and a 5-pseudogene prognostic risk signature that may be useful for predicting the survival of CRC patients.

Mechanosensor Piezo1 mediates bimodal patterns of intracellular calcium and FAK signaling

Piezo1 belongs to mechano-activatable cation channels serving as biological force sensors. However, the molecular events downstream of Piezo1 activation remain unclear. In this study, we used biosensors based on fluorescence resonance energy transfer (FRET) to investigate the dynamic modes of Piezo1-mediated signaling and revealed a bimodal pattern of Piezo1-induced intracellular calcium signaling. Laser-induced shockwaves (LIS) and its associated shear stress can mechanically activate Piezo1 to induce transient intracellular calcium (Ca_[i] ) elevation, accompanied by an increase in FAK activity. Interestingly, multiple pulses of shockwave stimulation caused a more sustained calcium increase and a decrease in FAK activity. Similarly, tuning the degree of Piezo1 activation by titrating either the dosage of Piezo1 ligand Yoda1 or the expression level of Piezo1 produced a similar bimodal pattern of FAK responses. Further investigations revealed that SHP2 serves as an intermediate regulator mediating this bimodal pattern in Piezo1 sensing and signaling. These results suggest that the degrees of Piezo1 activation induced by both mechanical LIS and chemical ligand stimulation may determine downstream signaling characteristics.

In silico and in vitro protocols for quantifying gene expression noise modulated by microRNAs

Characterizing the noise modulation pattern of microRNA is valuable for both microRNA function analysis and synthetic biology applications. Here we propose a coarse-grained model to simulate how the properties of microRNAs, competing RNAs, and microRNA response elements affect gene expression noise. We also detail an experimental protocol based on synthetic gene circuits and flow cytometry to quantify the noise. This framework is easy-to-use for the study and application of both microRNA and gene expression noise.

For complete details on the use and execution of this protocol, please refer to Wei et al. (2021).

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Simulate how microRNA modulates gene expression noise

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Consider the impact of competing RNAs and microRNA target composition to noise

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Quantify gene expression noise by synthetic gene circuits and flow cytometer

Illuminating lncRNA Function Through Target Prediction

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

Towards the First Principles in Biology and Cancer: New Vistas in Computational Systems Biology of Cancer

These days many leading scientists argue for a new paradigm for cancer research and propose a complex systems-view of cancer supported by empirical evidence. As an example, Thea Newman (2021) has applied "the lessons learned from physical systems to a critique of reductionism in medical research, with an emphasis on cancer". It is the understanding of this author that the mesoscale constructs that combine the bottom-up as well as top-down approaches, are very close to the concept of emergence. The mesoscale constructs can be said to be those effective components through which the system allows itself to be understood. A short list of basic concepts related to life/biology fundamentals are first introduced to demonstrate a lack of emphasis on these matters in literature. It is imperative that physical and chemical approaches are introduced and incorporated in biology to make it more conceptually sound, quantitative, and based on the first principles. Non-equilibrium thermodynamics is the only tool currently available for making progress in this direction. A brief outline of systems biology, the discovery of emergent properties, and metabolic modeling are introduced in the second part. Then, different cancer initiation concepts are reviewed, followed by application of non-equilibrium thermodynamics in the metabolic and genomic analysis of initiation and development of cancer, stressing the endogenous network hypothesis (ENH). Finally, extension of the ENH is suggested to include a cancer niche (exogenous network hypothesis). It is expected that this will lead to a unifying systems-biology approach for a future combination of the analytical and synthetic arms of two major hypotheses of cancer models (SMT and TOFT).

Predicting miRNA-disease associations using an ensemble learning framework with resampling method

MOTIVATION

Accumulating evidences have indicated that microRNA (miRNA) plays a crucial role in the pathogenesis and progression of various complex diseases. Inferring disease-associated miRNAs is significant to explore the etiology, diagnosis and treatment of human diseases. As the biological experiments are time-consuming and labor-intensive, developing effective computational methods has become indispensable to identify associations between miRNAs and diseases.

RESULTS

We present an Ensemble learning framework with Resampling method for MiRNA-Disease Association (ERMDA) prediction to discover potential disease-related miRNAs. Firstly, the resampling strategy is proposed for building multiple different balanced training subsets to address the challenge of sample imbalance within the database. Then, ERMDA extracts miRNA and disease feature representations by integrating miRNA-miRNA similarities, disease-disease similarities and experimentally verified miRNA-disease association information. Next, the feature selection approach is applied to reduce the redundant information and increase the diversity among these subsets. Lastly, ERMDA constructs an individual learner on each subset to yield primitive outcomes, and the soft voting method is introduced for making the final decision based on the prediction results of individual learners. A series of experimental results demonstrates that ERMDA outperforms other state-of-the-art methods on both balanced and unbalanced testing sets. Besides, case studies conducted on the three human diseases further confirm the ERMDA's prediction capability for identifying potential disease-related miRNAs. In conclusion, these experimental results demonstrate that our method can serve as an effective and reliable tool for researchers to explore the regulatory role of miRNAs in complex diseases.

ceRNAs in Cancer: Mechanism and Functions in a Comprehensive Regulatory Network

Noncoding RNAs have been shown with powerful ability in post-transcriptional regulation, enabling intertwined RNA crosstalk and global molecular interaction in a large amount of dysfunctional conditions including cancer. Competing endogenous RNAs (ceRNAs) are those competitively binding with shared microRNAs (miRNAs), freeing their counterparts from miRNA-induced degradation, thus actively influencing and connecting with each other. Constantly updated analytical approaches boost outstanding advancement achieved in this burgeoning hotspot in multilayered intracellular communication, providing new insights into pathogenesis and clinical treatment. Here, we summarize the mechanisms and correlated factors under this RNA interplay and deregulated transcription profile in neoplasm and tumor progression, underscoring the great significance of ceRNAs for diagnostic values, monitoring biomarkers, and prognosis evaluation in cancer.

Single-cell Transcriptomes Reveal Characteristics of MicroRNA in Gene Expression Noise Reduction

Isogenic cells growing in identical environments show cell-to-cell variations because of the stochasticity in gene expression. High levels of variation or noise can disrupt robust gene expression and result in tremendous consequences for cell behaviors. In this work, we showed evidence from single-cell RNA sequencing data analysis that microRNAs (miRNAs) can reduce gene expression noise at the mRNA level in mouse cells. We identified that the miRNA expression level, number of targets, target pool abundance, and miRNA–target interaction strength are the key features contributing to noise repression. miRNAs tend to work together in cooperative subnetworks to repress target noise synergistically in a cell type-specific manner. By building a physical model of post-transcriptional regulation and observing in synthetic gene circuits, we demonstrated that accelerated degradation with elevated transcriptional activation of the miRNA target provides resistance to extrinsic fluctuations. Together, through the integrated analysis of single-cell RNA and miRNA expression profiles, we demonstrated that miRNAs are important post-transcriptional regulators for reducing gene expression noise and conferring robustness to biological processes.

Characterizing microRNA-mediated modulation of gene expression noise and its effect on synthetic gene circuits

MicroRNAs (miRNAs) have been shown to modulate gene expression noise, but less is known about how miRNAs with different properties may regulate noise differently. Here, we investigate the role of competing RNAs and the composition of miRNA response elements (MREs) in modulating noise. We find that weak competing RNAs could introduce lower noise than strong competing RNAs. In comparison with a single MRE, both repetitive and composite MREs can reduce the noise at low expression, but repetitive MREs can elevate the noise remarkably at high expression. We further observed the behavior of a synthetic cell-type classifier with miRNAs as inputs and find that miRNAs and MREs that could introduce higher noise tend to enhance cell state transition. These results provide a systematic and quantitative understanding of the function of miRNAs in controlling gene expression noise and the utilization of miRNAs to modulate the behavior of synthetic gene circuits.

The Cajal body protein coilin is a regulator of the miR-210 hypoxamiR and influences MIR210HG alternative splicing

Hypoxia is a severe stressor to cellular homeostasis. At the cellular level, low oxygen triggers the transcription of a variety of genes supporting cell survival and oxygen homeostasis mediated by transcription factors, such as hypoxia-inducible factors (HIFs). Among many determinants dictating cell responses to hypoxia and HIFs are microRNAs (miRNAs). Cajal bodies (CBs), subnuclear structures involved in ribonucleoprotein biogenesis, have been recently proven to contribute to miRNA processing and biogenesis but have not been studied under hypoxia. Here, we show, for the first time, a hypoxia-dependent increase in CB number in WI-38 primary fibroblasts, which normally have very few CBs. Additionally, the CB marker protein coilin is upregulated in hypoxic WI-38 cells. However, the hypoxic coilin upregulation was not seen in transformed cell lines. Furthermore, we found that coilin is needed for the hypoxic induction of a well-known hypoxia-induced miRNA (hypoxamiR), miR-210, as well as for the hypoxia-induced alternative splicing of the miR-210 host gene, MIR210HG. These findings provide a new link in the physiological understanding of coilin, CBs and miRNA dysregulation in hypoxic pathology.

Competing Endogenous RNA Networks in Glioma

Gliomas are the most common and malignant primary brain tumors. Various hallmarks of glioma, including sustained proliferation, migration, invasion, heterogeneity, radio- and chemo-resistance, contribute to the dismal prognosis of patients with high-grade glioma. Dysregulation of cancer driver genes is a leading cause for these glioma hallmarks. In recent years, a new mechanism of post-transcriptional gene regulation was proposed, i.e., "competing endogenous RNA (ceRNA)." Long non-coding RNAs, circular RNAs, and transcribed pseudogenes act as ceRNAs to regulate the expression of related genes by sponging the shared microRNAs. Moreover, coding RNA can also exert a regulatory role, independent of its protein coding function, through the ceRNA mechanism. In the latest glioma research, various studies have reported that dysregulation of certain ceRNA regulatory networks (ceRNETs) accounts for the abnormal expression of cancer driver genes and the establishment of glioma hallmarks. These achievements open up new avenues to better understand the hidden aspects of gliomas and provide new biomarkers and potential efficient targets for glioma treatment. In this review, we summarize the existing knowledge about the concept and logic of ceRNET and highlight the emerging roles of some recently found ceRNETs in glioma progression.

Controllable Cell Deformation Using Acoustic Streaming for Membrane Permeability Modulation

Hydrodynamic force loading platforms for controllable cell mechanical deformation play an essential role in modern cell technologies. Current systems require assistance from specific microstructures thus limiting the controllability and flexibility in cell shape modulation, and studies on real-time 3D cell morphology analysis are still absent. This article presents a novel platform based on acoustic streaming generated from a gigahertz device for cell shape control and real-time cell deformation analysis. Details in cell deformation and the restoration process are thoroughly studied on the platform, and cell behavior control at the microscale is successfully achieved by tuning the treating time, intensity, and wave form of the streaming. The application of this platform in cell membrane permeability modulation and analysis is also exploited. Based on the membrane reorganization during cell deformation, the effects of deformation extent and deformation patterns on membrane permeability to micro- and macromolecules are revealed. This technology has shown its unique superiorities in cell mechanical manipulation such as high flexibility, high accuracy, and pure fluid force operation, indicating its promising prospect as a reliable tool for cell property study and drug therapy development.

The development and controversy of competitive endogenous RNA hypothesis in non-coding genes

As a momentous post-transcriptional regulator, microRNAs (miRNAs) are attracting more and more attention. The classical miRNAs regulated mechanism shows it binds to the targets' 3'UTR thus play the role in post-transcription. Meanwhile, single miRNA can target multiple genes, so those should compete to bind that miRNA. Vice versa, single gene can sponge mass of miRNAs as well. Thus the competitive endogenous RNAs (ceRNAs) hypothesis was put forward in 2011. The ceRNA hypothesis has made huge achievements, in particular in non-coding genes, which including long non-coding RNAs (lncRNAs), circle RNAs (circRNAs) and pseudogenes, even viral transcripts. It also contributed greatly to epigenetics development. However, an increasing number of controversies have occurred with applause. Based on this situation, this review introduces something in detail about the ceRNAs hypothesis achieved in lncRNAs, circRNAs, pseudogenes and viral transcripts, respectively. Meanwhile, it also covers controversy of the ceRNAs hypothesis.

Exploring the potential microRNA sponge interactions of breast cancer based on some known interactions

MicroRNA (miRNA) sponges' regulatory mechanisms play an important role in developing human cancer. Herein, we develop a new method to explore potential miRNA sponge interactions (EPMSIs) for breast cancer. Based on some known interactions, and a matching gene expression profile, EPMSIs explored other potential miRNA sponge interactions for breast cancer. Every interaction is inferred with a value representing interaction intensity. Then, we apply a clustering algorithm called BCPlaid to potential interactions. Ten modules are identified; nine of them are closely associated with biological enrichments. When we employ a classification algorithm to separate normal and tumor samples in each module, each module demonstrates powerful classification performance. Furthermore, EPMSI illustrates a new method to explore the miRNA sponge regulatory network for breast cancer by applying its superior performance.

The Sponge RNAs of bacteria - How to find them and their role in regulating the post-transcriptional network

In bacteria small regulatory RNAs (sRNAs) interact with their mRNA targets through non-consecutive base-pairing. The loose base-pairing specificity allows sRNAs to regulate large numbers of genes, either affecting the stability and/or the translation of mRNAs. Mechanisms enabling post-transcriptional regulation of the sRNAs themselves have also been described involving so-called sponge RNAs. Sponge RNAs modulate free sRNA levels in the cell through RNA-RNA interactions that sequester ("soak up") the sRNA and/or promote degradation of the target sRNA or the sponge RNA-sRNA complex. The development of complex RNA sequencing strategies for the detection of RNA-RNA interactions has enabled identification of several sponge RNAs, as well as previously known regulatory RNAs able to act as both regulators and sponges. This review highlights techniques that have enabled the identification of these sponge RNAs, the origins of sponge RNAs and the mechanisms by which they function in the post-transcriptional network.

Deep belief network-Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that have shown to play a critical role in regulating gene expression. In past decades, cumulative experimental studies have verified that miRNAs are implicated in many complex human diseases and might be potential biomarkers for various types of diseases. With the increase of miRNA-related data and the development of analysis methodologies, some computational methods have been developed for predicting miRNA-disease associations, which are more economical and time-saving than traditional biological experimental approaches. In this study, a novel computational model, deep belief network (DBN)-based matrix factorization (DBN-MF), is proposed for miRNA-disease association prediction. First, the raw interaction features of miRNAs and diseases were obtained from the miRNA-disease adjacent matrix. Second, 2 DBNs were used for unsupervised learning of the features of miRNAs and diseases, respectively, based on the raw interaction features. Finally, a classifier consisting of 2 DBNs and a cosine score function was trained with the initial weights of DBN from the last step. During the training, the miRNA-disease adjacent matrix was factorized into 2 feature matrices for the representation of miRNAs and diseases, and the final prediction label was obtained according to the feature matrices. The experimental results show that the proposed model outperforms the state-of-the-art approaches in miRNA-disease association prediction based on the 10-fold cross-validation. Besides, the effectiveness of our model was further demonstrated by case studies.

Mechanisms of Long Non-Coding RNAs in Cancers and Their Dynamic Regulations

Long non-coding RNA (lncRNA), which is a kind of noncoding RNA, is generally characterized as being more than 200 nucleotide transcripts in length. LncRNAs exhibit many biological activities, including, but not limited to, cancer development. In this review, a search of the PubMed database was performed to identify relevant studies published in English. The term "lncRNA or long non-coding RNA" was combined with a range of search terms related to the core focus of the review: mechanism, structure, regulation, and cancer. The eligibility of the retrieved studies was mainly based on the abstract. The decision as to whether or not the study was included in this review was made after a careful assessment of its content. The reference lists were also checked to identify any other study that could be relevant to this review. We first summarized the molecular mechanisms of lncRNAs in tumorigenesis, including competing endogenous RNA (ceRNA) mechanisms, epigenetic regulation, decoy and scaffold mechanisms, mRNA and protein stability regulation, transcriptional and translational regulation, miRNA processing regulation, and the architectural role of lncRNAs, which will help a broad audience better understand how lncRNAs work in cancer. Second, we introduced recent studies to elucidate the structure of lncRNAs, as there is a link between lncRNA structure and function and visualizing the architectural domains of lncRNAs is vital to understanding their function. Third, we explored emerging evidence for regulators of lncRNA expression, lncRNA turnover, and lncRNA modifications (including 5-methylcytidine, N6-methyladenosine, and adenosine to inosine editing), highlighting the dynamics of lncRNAs. Finally, we used autophagy in cancer as an example to interpret the diverse mechanisms of lncRNAs and introduced clinical trials of lncRNA-based cancer therapies.

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer.

A cell-based probabilistic approach unveils the concerted action of miRNAs

Mature microRNAs (miRNAs) regulate most human genes through direct base-pairing with mRNAs. We investigate the underlying principles of miRNA regulation in living cells. To this end, we overexpressed miRNAs in different cell types and measured the mRNA decay rate under a paradigm of a transcriptional arrest. Based on an exhaustive matrix of mRNA-miRNA binding probabilities, and parameters extracted from our experiments, we developed a computational framework that captures the cooperative action of miRNAs in living cells. The framework, called COMICS, simulates the stochastic binding events between miRNAs and mRNAs in cells. The input of COMICS is cell-specific profiles of mRNAs and miRNAs, and the outcome is the retention level of each mRNA at the end of 100,000 iterations. The results of COMICS from thousands of miRNA manipulations reveal gene sets that exhibit coordinated behavior with respect to all miRNAs (total of 248 families). We identified a small set of genes that are highly responsive to changes in the expression of almost any of the miRNAs. In contrast, about 20% of the tested genes remain insensitive to a broad range of miRNA manipulations. The set of insensitive genes is strongly enriched with genes that belong to the translation machinery. These trends are shared by different cell types. We conclude that the stochastic nature of miRNAs reveals unexpected robustness of gene expression in living cells. By applying a systematic probabilistic approach some key design principles of cell states are revealed, emphasizing in particular, the immunity of the translational machinery vis-a-vis miRNA manipulations across cell types. We propose COMICS as a valuable platform for assessing the outcome of miRNA regulation of cells in health and disease.

Alteration in miRNA expression occurs throughout cell differentiation, inflammation, viral infection, tumorigenesis, and other pathologies. Notwithstanding a rich body of experimental data intended to assess the outcome of miRNA alterations in cells, the underlying design principles remain obscure and fragmented. In this study, we develop a quantitative stochastic model that simulates the mRNA steady-state in view of alteration in miRNAs’ abundance. We systematically analyzed the behavior of miRNA-mRNA regulation and confirm that the stochastic nature of miRNA regulation reveals unexpected robustness of cell behavior across cell types. Specifically, we expose the immunity of the translational machinery towards miRNA regulation. The developed platform, called COMICS compares the results of miRNA regulation across various cell types. Based on stochastic and probabilistic considerations, we provide a dynamic and flexible framework that quantifies the competition of miRNAs within cells in health and disease.

From Endogenous to Synthetic microRNA-Mediated Regulatory Circuits: An Overview

MicroRNAs are short non-coding RNAs that are evolutionarily conserved and are pivotal post-transcriptional mediators of gene regulation. Together with transcription factors and epigenetic regulators, they form a highly interconnected network whose building blocks can be classified depending on the number of molecular species involved and the type of interactions amongst them. Depending on their topology, these molecular circuits may carry out specific functions that years of studies have related to the processing of gene expression noise. In this review, we first present the different over-represented network motifs involving microRNAs and their specific role in implementing relevant biological functions, reviewing both theoretical and experimental studies. We then illustrate the recent advances in synthetic biology, such as the construction of artificially synthesised circuits, which provide a controlled tool to test experimentally the possible microRNA regulatory tasks and constitute a starting point for clinical applications.

Differential network analysis depicts regulatory mechanisms for hepatocellular carcinoma from diverse backgrounds

Aim: To elucidate the integrative combinational gene regulatory network landscape of hepatocellular carcinoma (HCC) molecular carcinogenesis from diverse background. Materials & methods: Modified gene regulatory network analysis was used to prioritize differentially regulated genes and links. Integrative comparisons using bioinformatics methods were applied to identify potential critical molecules and pathways in HCC with different backgrounds. Results: E2F1 with its surrounding regulatory links were identified to play different key roles in the HCC risk factor dysregulation mechanisms. Hsa-mir-19a was identified as showed different effects in the three HCC differential regulation networks, and showed vital regulatory role in HBV-related HCC. Conclusion: We describe in detail the regulatory networks involved in HCC with different backgrounds. E2F1 may serve as a universal target for HCC treatment.

Competing endogenous RNA crosstalk at system level

microRNAs (miRNAs) regulate gene expression at post-transcriptional level by repressing target RNA molecules. Competition to bind miRNAs tends in turn to correlate their targets, establishing effective RNA-RNA interactions that can influence expression levels, buffer fluctuations and promote signal propagation. Such a potential has been characterized mathematically for small motifs both at steady state and away from stationarity. Experimental evidence, on the other hand, suggests that competing endogenous RNA (ceRNA) crosstalk is rather weak. Extended miRNA-RNA networks could however favour the integration of many crosstalk interactions, leading to significant large-scale effects in spite of the weakness of individual links. To clarify the extent to which crosstalk is sustained by the miRNA interactome, we have studied its emergent systemic features in silico in large-scale miRNA-RNA network reconstructions. We show that, although generically weak, system-level crosstalk patterns (i) are enhanced by transcriptional heterogeneities, (ii) can achieve high-intensity even for RNAs that are not co-regulated, (iii) are robust to variability in transcription rates, and (iv) are significantly non-local, i.e. correlate weakly with miRNA-RNA interaction parameters. Furthermore, RNA levels are generically more stable when crosstalk is strongest. As some of these features appear to be encoded in the network's topology, crosstalk may functionally be favoured by natural selection. These results suggest that, besides their repressive role, miRNAs mediate a weak but resilient and context-independent network of cross-regulatory interactions that interconnect the transcriptome, stabilize expression levels and support system-level responses.

A learning-based framework for miRNA-disease association identification using neural networks

MOTIVATION

A microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many biological processes. Lots of studies have shown that miRNAs are implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes.

RESULTS

We propose a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures interaction features between diseases and miRNAs based on a three-layer network including disease similarity network, miRNA similarity network and protein-protein interaction network. Then, it employs an auto-encoder to identify the essential feature combination for each pair of miRNA and disease automatically. Finally, taking the reduced feature representation as input, it uses a convolutional neural network to predict the final label. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and miRNA-phenotype association prediction.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/Issingjessica/MDA-CNN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The number of titrated microRNA species dictates ceRNA regulation

microRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts-our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were co-regulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor- and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent.

ceRNAs in plants: computational approaches and associated challenges for target mimic research

The competing endogenous RNA hypothesis has gained increasing attention as a potential global regulatory mechanism of microRNAs (miRNAs), and as a powerful tool to predict the function of many noncoding RNAs, including miRNAs themselves. Most studies have been focused on animals, although target mimic (TMs) discovery as well as important computational and experimental advances has been developed in plants over the past decade. Thus, our contribution summarizes recent progresses in computational approaches for research of miRNA:TM interactions. We divided this article in three main contributions. First, a general overview of research on TMs in plants is presented with practical descriptions of the available literature, tools, data, databases and computational reports. Second, we describe a common protocol for the computational and experimental analyses of TM. Third, we provide a bioinformatics approach for the prediction of TM motifs potentially cross-targeting both members within the same or from different miRNA families, based on the identification of consensus miRNA-binding sites from known TMs across sequenced genomes, transcriptomes and known miRNAs. This computational approach is promising because, in contrast to animals, miRNA families in plants are large with identical or similar members, several of which are also highly conserved. From the three consensus TM motifs found with our approach: MIM166, MIM171 and MIM159/319, the last one has found strong support on the recent experimental work by Reichel and Millar [Specificity of plant microRNA TMs: cross-targeting of mir159 and mir319. J Plant Physiol 2015;180:45-8]. Finally, we stress the discussion on the major computational and associated experimental challenges that have to be faced in future ceRNA studies.

Behavior of active filaments near solid-boundary under linear shear flow

The steady-state behavior of a dilute suspension of self-propelled filaments confined between planar walls subjected to Couette-flow is reported herein. The effect of hydrodynamics has been taken into account using a mesoscale simulation approach. We present a detailed analysis of positional and angular probability distributions of filaments with varying propulsive force and shear-flow. The distribution of the centre-of-mass of the filament shows adsorption near the surfaces, which diminishes with the flow. The excess density of filaments decreases with Weissenberg number as Wi-β with an exponent β ≈ 0.8, in the intermediate shear range (1 < Wi < 30). The angular orientational moment also decreases near the wall as Wi-δ with δ ≈ 1/5; the variation in orientational moment near the wall is relatively slower than the bulk. It shows a strong dependence on the propulsive force near the wall, with variation on force as Pe-1/3 for large Pe ≥ 1. The active filament shows orientational preference with flow near the surfaces, which splits into upstream and downstream swimming. The population splitting from a unimodal (propulsive force dominated regime) to bimodal phase (shear dominated regime) is identified in the parameter space of propulsive force and shear flow.

Characteristics of the competition among RNAs for the binding of shared miRNAs

Competing endogenous RNAs (ceRNAs) are RNAs that share common miRNA binding sites and compete with each other for the miRNA association at these sites. The observation of this phenomenon in the cells altered the view of the miRNA target RNAs from molecules that are passively controlled by miRNAs to molecules that also modulate the miRNAs activity. In this review, we build a general profile of ceRNAS characteristics in order to facilitate ceRNAs identification by researchers. The information summarized here contains an actualized list of previously reported ceRNAs and classes of RNAs that can participate in this type of interaction, the expression behavior and characteristics of ceRNAs and miRNAs in the context of competition, the influence of the shared MREs/miRNAs numbers and the miRNA binding strength on the competition, reports on competition between RNAs in different subcellular localizations and the concept that ceRNAs may form a huge regulatory network in the cell.

Interplay Between Long Noncoding RNAs and MicroRNAs in Cancer

In the last decade noncoding RNAs (ncRNAs) have been extensively studied in several biological processes and human diseases including cancer. microRNAs (miRNAs) are the best-known class of ncRNAs. miRNAs are small ncRNAs of around 20-22 nucleotides (nt) and are crucial posttranscriptional regulators of protein coding genes. Recently, new classes of ncRNAs, longer than miRNAs have been discovered. Those include intergenic noncoding RNAs (lincRNAs) and circular RNAs (circRNAs). These novel types of ncRNAs opened a very exciting field in biology, leading researchers to discover new relationships between miRNAs and long noncoding RNAs (lncRNAs), which act together to control protein coding gene expression. One of these new discoveries led to the formulation of the "competing endogenous RNA (ceRNA) hypothesis." This hypothesis suggests that an lncRNA acts as a sponge for miRNAs reducing their expression and causing the upregulation of miRNA targets. In this chapter we first discuss some recent discoveries in this field showing the mutual regulation of miRNAs, lncRNAs, and protein-coding genes in cancer. We then discuss the general approaches for the study of ceRNAs and present in more detail a recent computational approach to explore the ability of lncRNAs to act as ceRNAs in human breast cancer that has been shown to be, among the others, the most precise and promising.

Profile analysis of C. elegans rheotaxis behavior using a microfluidic device

The directed motility of organisms in response to fluid velocity, which is called rheotaxis, is important in the life cycle of C. elegans, enabling them to navigate their environment and maintain their positions in the presence of adverse flow. Thus, to study the mechanism underlying rheotaxis behavior and reveal information on parasitic diseases, the profile analysis of the rheotaxis response in worm populations with high resolution in well-defined fluid environments is highly desirable. In this work, we presented a rapid and robust microfluidic approach to quantitatively analyze the rheotaxis behavior of worms in response to velocity. The flow-based microfluidic chip contained six helical spline microchannels for generating six flow streams with different flow velocities. Since the worms loaded in the chip would swim upstream into channels, the distribution of the worms in response to the different flow velocities was successfully monitored for the quantitative analysis of their rheotaxis behavior using this microfluidic chip. The results indicated that the rate range of around 50 μm s-1 was the most favorable flow velocity for the wild-type worms. Further, we analyzed ASH neuron-blocked worms and found that the functionally defective ASH neurons inhibited their sensitivity to flow rate. In addition, the rheotaxis analysis of the mutant worms indicated that TRP mechanosensory channels and serotonin signals also play a regulatory role in the rheotaxis response of these worms. Thus, our microfluidic method provides a useful platform to study the rheotaxis behaviors in C. elegans and can be further applied for anti-parasitic drug tests.

Kinetic Modelling of Competition and Depletion of Shared miRNAs by Competing Endogenous RNAs

Non-coding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting, e.g., the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.

Modeling ncRNA-Mediated Circuits in Cell Fate Decision

Noncoding RNAs (ncRNAs) play critical roles in essential cell fate decisions. However, the exact molecular mechanisms underlying ncRNA-mediated bistable switches remain elusive and controversial. In recent years, systematic mathematical and quantitative experimental analyses have made significant contributions on elucidating the molecular mechanisms of controlling ncRNA-mediated cell fate decision processes. In this chapter, we review and summarize the general framework of mathematical modeling of ncRNA in a pedagogical way and the application of this general framework on real biological processes. We discuss the emerging properties resulting from the reciprocal regulation between mRNA, miRNA, and competing endogenous mRNA (ceRNA), as well as the role of mathematical modeling of ncRNA in synthetic biology. Both the positive feedback loops between ncRNAs and transcription factors and the emerging properties from the miRNA-mRNA reciprocal regulation enable bistable switches to direct cell fate decision.

Circular RNAs as Therapeutic Agents and Targets

It has recently been reported that thousands of covalently linked circular RNAs (circRNAs) are expressed from human genomes. circRNAs emerge during RNA splicing. circRNAs are circularized in a reaction termed "backsplicing," whereby the spliceosome fuses a splice donor site in a downstream exon to a splice acceptor site in an upstream exon. Although a young field of research, first studies indicate that backsplicing is not an erroneous reaction of the spliceosome. Instead, circRNAs are produced in cells with high cell-type specificity and can exert biologically meaningful and specific functions. These observations and the finding that circRNAs are stable against exonucleolytic decay are raising the question whether circRNAs may be relevant as therapeutic agents and targets. In this review, we start out with a short introduction into classification, biogenesis and general molecular mechanisms of circRNAs. We then describe reports, where manipulating circRNA abundance has been shown to have therapeutic value in animal disease models in vivo, with a focus on cardiovascular disease (CVD). Starting from existing approaches, we outline particular challenges and opportunities for future circRNA-based therapeutic approaches that exploit stability and molecular effector functions of native circRNAs. We end with considerations which designer functions could be engineered into artificial therapeutic circular RNAs.

Molecular functions and specific roles of circRNAs in the cardiovascular system

As part of the superfamily of long noncoding RNAs, circular RNAs (circRNAs) are emerging as a new type of regulatory molecules that partake in gene expression control. Here, we review the current knowledge about circRNAs in cardiovascular disease. CircRNAs are not only associated with different types of cardiovascular disease, but they have also been identified as intracellular effector molecules for pathophysiological changes in cardiovascular tissues, and as cardiovascular biomarkers. This evidence is put in the context of the current understanding of general circRNA biogenesis and of known interactions of circRNAs with DNA, RNA, and proteins.

Comparative study of joint bioinformatics analysis of underlying potential of 'neurimmiR', miR-212-3P/miR-132-3P, being involved in epilepsy and its emerging role in human cancer

Considering the critical roles of miR-132/212 participated in central nervous system, many researches started to explored the contributions of miR-132/212 to epilepsy and achieve something worthwhile. Further illuminates all the genes targeted by miR-132/212 may be a valuable means for us to completely understand the working mechanism playing in epilepsy, by which it can influence diverse biological process. This study attempts to establish macrocontrol regulation system and knowledge that miR-212-3p/132-3p effected the epilepsy, for this literature search, miRbase, Vienna RNAfold webserver, Human miRNA tissue atlas, DIANA-TarBase, miRtarbase, STRING, TargetScanhuman, Cytoscape plugin ClueGO + Cluepedia+STRING, DAVID Bioinformatics Resources, Starbase, GeneCards suite and GEO database are comprehensive employed, miR-132-3p/212-3p and its target gene were found have highly expressed in brain and lots of molecular function and metabolic pathways associated with epilepsy may be intervened by it. Meanwhile, the emerging role of miR-132-3p/212-3p being involved in human cancer also been analyzed by several webtools for TCGA data integrative analysis, most remarkably and well worth exploring in our research conclusion that showed miR-132-3p/212-3p may be the core molecular underlying tumor-induced epileptogenesis.

On the role of extrinsic noise in microRNA-mediated bimodal gene expression

Several studies highlighted the relevance of extrinsic noise in shaping cell decision making and differentiation in molecular networks. Bimodal distributions of gene expression levels provide experimental evidence of phenotypic differentiation, where the modes of the distribution often correspond to different physiological states of the system. We theoretically address the presence of bimodal phenotypes in the context of microRNA (miRNA)-mediated regulation. MiRNAs are small noncoding RNA molecules that downregulate the expression of their target mRNAs. The nature of this interaction is titrative and induces a threshold effect: below a given target transcription rate almost no mRNAs are free and available for translation. We investigate the effect of extrinsic noise on the system by introducing a fluctuating miRNA-transcription rate. We find that the presence of extrinsic noise favours the presence of bimodal target distributions which can be observed for a wider range of parameters compared to the case with intrinsic noise only and for lower miRNA-target interaction strength. Our results suggest that combining threshold-inducing interactions with extrinsic noise provides a simple and robust mechanism for obtaining bimodal populations without requiring fine tuning. Furthermore, we characterise the protein distribution's dependence on protein half-life.

Sonoporation-induced cell membrane permeabilization and cytoskeleton disassembly at varied acoustic and microbubble-cell parameters

Sonoporation mediated by microbubbles has being extensively studied as a promising technique to facilitate gene/drug delivery to cells. Previous studies mainly explored the membrane-level responses to sonoporation. To provide in-depth understanding on this process, various sonoporation-induced cellular responses (e.g., membrane permeabilization and cytoskeleton disassembly) generated at different impact parameters (e.g., acoustic driving pressure and microbubble-cell distances) were systemically investigated in the present work. HeLa cells, whose α-tubulin cytoskeleton was labeled by incorporation of a green fluorescence protein (GFP)-α-tubulin fusion protein, were exposed to a single ultrasound pulse (1 MHz, 20 cycles) in the presence of microbubbles. Intracellular transport via sonoporation was assessed in real time using propidium iodide and the disassembly of α-tubulin cytoskeleton was observed by fluorescence microscope. Meanwhile, the dynamics of an interacting bubble-cell pair was theoretically simulated by boundary element method. Both the experimental observations and numerical simulations showed that, by increasing the acoustic pressure or reducing the bubble-cell distance, intensified deformation could be induced in the cellular membrane, which could result in enhanced intracellular delivery and cytoskeleton disassembly. The current results suggest that more tailored therapeutic strategies could be designed for ultrasound gene/drug delivery by adopting optimal bubble-cell distances and/or better controlling incident acoustic energy.

Functional Characterization of Insulation Effect for Synthetic Gene Circuits in Mammalian Cells

Insulators are noncoding gene regulatory elements in eukaryotic genome, which function as chromatin partitioning boundaries, and block interference across different chromatin domains. To facilitate modular construction of synthetic gene circuit that is usually composed of multiple transcription cassettes, unwanted cross-regulations between different cassettes should be avoided. Here, we developed a quantitative method to characterize the functional effect of three insulators on the cross-regulations of six promoters in mammalian cells. We showed that the unwanted cross-regulations displayed a threshold-like effect, and the threshold position varied along with the context of promoters and insulators. We tested the function of insulators in both cascade and sensory switch circuits assembled in episomal plasmid vectors, and showed that the insulation effect was mainly revealed on the first regulatory layer of the cascade circuit. A deviation on the response curve of the sensory switch circuit with or without insulators was observed, but response intensity of some sensory switch circuits were not affected. Therefore, our results provided a general guide on the selection of insulators with varying promoters in episomal synthetic gene circuits in mammalian cells, which may be useful to reduce the effect of the unwanted cross-regulations.

Mapping the operational landscape of microRNAs in synthetic gene circuits

MicroRNAs are a class of short, noncoding RNAs that are ubiquitous modulators of gene expression, with roles in development, homeostasis, and disease. Engineered microRNAs are now frequently used as regulatory modules in synthetic biology. Moreover, synthetic gene circuits equipped with engineered microRNA targets with perfect complementarity to endogenous microRNAs establish an interface with the endogenous milieu at the single-cell level. The function of engineered microRNAs and sensor systems is typically optimized through extensive trial-and-error. Here, using a combination of synthetic biology experimentation in human embryonic kidney cells and quantitative analysis, we investigate the relationship between input genetic template abundance, microRNA concentration, and output under microRNA control. We provide a framework that employs the complete operational landscape of a synthetic gene circuit and enables the stepwise development of mathematical models. We derive a phenomenological model that recapitulates experimentally observed nonlinearities and contains features that provide insight into the microRNA function at various abundances. Our work facilitates the characterization and engineering of multi-component genetic circuits and specifically points to new insights on the operation of microRNAs as mediators of endogenous information and regulators of gene expression in synthetic biology.

Fundamentals of Laser-Based Hydrogel Degradation and Applications in Cell and Tissue Engineering

The cell and tissue engineering fields have profited immensely through the implementation of highly structured biomaterials. The development and implementation of advanced biofabrication techniques have established new avenues for generating biomimetic scaffolds for a multitude of cell and tissue engineering applications. Among these, laser-based degradation of biomaterials is implemented to achieve user-directed features and functionalities within biomimetic scaffolds. This review offers an overview of the physical mechanisms that govern laser-material interactions and specifically, laser-hydrogel interactions. The influences of both laser and material properties on efficient, high-resolution hydrogel degradation are discussed and the current application space in cell and tissue engineering is reviewed. This review aims to acquaint readers with the capability and uses of laser-based degradation of biomaterials, so that it may be easily and widely adopted.

Independent channels for miRNA biosynthesis ensure efficient static and dynamic control in the regulation of the early stages of myogenesis

Motivated by recent experimental work, we define and study a deterministic model of the complex miRNA-based regulatory circuit that putatively controls the early stage of myogenesis in human. We aim in particular at a quantitative understanding of (i) the roles played by the separate and independent miRNA biosynthesis channels (one involving a miRNA-decoy system regulated by an exogenous controller, the other given by transcription from a distinct genomic locus) that appear to be crucial for the differentiation program, and of (ii) how competition to bind miRNAs can efficiently control molecular levels in such an interconnected architecture. We show that optimal static control via the miRNA-decoy system constrains kinetic parameters in narrow ranges where the channels are tightly cross-linked. On the other hand, the alternative locus for miRNA transcription can ensure that the fast concentration shifts required by the differentiation program are achieved, specifically via non-linear response of the target to even modest surges in the miRNA transcription rate. While static, competition-mediated regulation can be achieved by the miRNA-decoy system alone, both channels are essential for the circuit's overall functionality, suggesting that that this type of joint control may represent a minimal optimal architecture in different contexts.

Exploring genetic associations with ceRNA regulation in the human genome

Competing endogenous RNAs (ceRNAs) are RNA molecules that sequester shared microRNAs (miRNAs) thereby affecting the expression of other targets of the miRNAs. Whether genetic variants in ceRNA can affect its biological function and disease development is still an open question. Here we identified a large number of genetic variants that are associated with ceRNA's function using Geuvaids RNA-seq data for 462 individuals from the 1000 Genomes Project. We call these loci competing endogenous RNA expression quantitative trait loci or 'cerQTL', and found that a large number of them were unexplored in conventional eQTL mapping. We identified many cerQTLs that have undergone recent positive selection in different human populations, and showed that single nucleotide polymorphisms in gene 3΄UTRs at the miRNA seed binding regions can simultaneously regulate gene expression changes in both cis and trans by the ceRNA mechanism. We also discovered that cerQTLs are significantly enriched in traits/diseases associated variants reported from genome-wide association studies in the miRNA binding sites, suggesting that disease susceptibilities could be attributed to ceRNA regulation. Further in vitro functional experiments demonstrated that a cerQTL rs11540855 can regulate ceRNA function. These results provide a comprehensive catalog of functional non-coding regulatory variants that may be responsible for ceRNA crosstalk at the post-transcriptional level.