Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs

Advanced computational predictive models of miRNA-mRNA interaction efficiency

The modeling of miRNA-mRNA interactions holds significant implications for synthetic biology and human health. However, this research area presents specific challenges due to the multifaceted nature of mRNA downregulation by miRNAs, influenced by numerous factors including competition or synergism among miRNAs and mRNAs. In this study, we present an improved computational model for predicting miRNA-mRNA interactions, addressing aspects not previously modeled. Firstly, we integrated a novel set of features that significantly enhanced the predictor's performance. Secondly, we demonstrated the cell-specific nature of certain aspects of miRNA-mRNA interactions, highlighting the importance of designing models tailored to specific cell types for improved accuracy. Moreover, we introduce a miRNA binding site interaction model (miBSIM) that, for the first time, accounts for both the distribution of miRNA binding sites along the mRNA and their respective strengths in regulating mRNA stability. Our analysis suggests that distant miRNA sites often compete with each other, revealing the intricate interplay of binding site interactions. Overall, our new predictive model shows a significant improvement of up to 6.43% over previous models in the field. The code of our model is available at https://www.cs.tau.ac.il/~tamirtul/miBSIM.

Specific circulating miRNAs are associated with plasma lipids in a healthy American cohort

Low-density lipoprotein cholesterol (LDL-c) is both a therapeutic target and a risk factor for cardiovascular disease (CVD). MicroRNA (miRNA) has been shown to regulate cholesterol homeostasis, and miRNA in blood circulation has been linked to hypercholesterolemia. However, few studies to date have associated miRNA with phenotypes like LDL-c in a healthy population. To this end, we analyzed circulating miRNA in relation to LDL-c in a healthy cohort of 353 participants using two separate bioinformatic approaches. The first approach found that miR-15b-5p and miR-16-5p were upregulated in individuals with at-risk levels of LDL-c. The second approach identified two miRNA clusters, one that positively and a second that negatively correlated with LDL-c. Included in the cluster that positively correlated with LDL-c were miR-15b-5p and miR-16-5p, as well as other miRNA from the miR-15/107, miR-30, and let-7 families. Cross-species analyses suggested that several miRNAs that associated with LDL-c are conserved between mice and humans. Finally, we examined the influence of diet on circulating miRNA. Our results robustly linked circulating miRNA with LDL-c, suggesting that miRNA could be used as biomarkers for hypercholesterolemia or targets for developing cholesterol-lowering drugs.NEW & NOTEWORTHY This study explored the association between circulating microRNA (miRNA) and low-density lipoprotein cholesterol (LDL-c) in a healthy population of 353 participants. Two miRNAs, miR-15b-5p and miR-16-5p, were upregulated in individuals with at-risk LDL-c levels. Several miRNA clusters were positively and negatively correlated with LDL-c and are known to target mRNA involved in lipid metabolism. The study also investigated the influence of diet on circulating miRNA, suggesting potential biomarkers for hypercholesterolemia.

Pillars of theoretical biology: "Biochemical systems analysis, I, II and III"

Michael Savageau's Biochemical Systems Analysis I, II, IIIpapers, published in volumes 25 and 26 of the journal,kickstarted a research programme that originated many of the core concepts and tools of Systems Biology. This article briefly summarizes these papers anddiscusses the most relevant developments in Biochemical Systems Theory since their publication.

Overview of Computational and Experimental Methods to Identify Tissue-Specific MicroRNA Targets

As ubiquitous posttranscriptional regulators of gene expression, microRNAs (miRNAs) play key roles in cell physiology and function across taxa. In the last two decades, we have gained a good understanding about miRNA biogenesis pathways, modes of action, and consequences of miRNA-mediated gene regulation. More recently, research has focused on exploring causes for miRNA dysregulation, miRNA-mediated crosstalk between genes and signaling pathways, and the role of miRNAs in disease.This chapter discusses methods for the identification of miRNA-target interactions and causes for tissue-specific miRNA-target regulation. Computational approaches for predicting miRNA target sites and assessing tissue-specific target regulation are discussed. Moreover, there is an emphasis on features that affect miRNA target recognition and how high-throughput sequencing protocols can help in assessing miRNA-mediated gene regulation on a genome-wide scale. In addition, this chapter introduces some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA-target interactions.

IFIH1/IRF1/STAT1 promotes sepsis associated inflammatory lung injury via activating macrophage M1 polarization

BACKGROUND

A growing body of research has shown that the phenotypic change in macrophages from M0 to M1 is essential for the start of the inflammatory process in septic acute respiratory distress syndrome (ARDS). Potential treatment targets might be identified with more knowledge of the molecular regulation of M1 macrophages in septic ARDS.

METHODS

A multi-microarray interrelated analysis of high-throughput experiments from ARDS patients and macrophage polarization was conducted to identify the hub genes associated with macrophage M1 polarization and septic ARDS. Lipopolysaccharide (LPS) and Poly (I:C) were utilized to stimulate bone marrow-derived macrophages (BMDMs) for M1-polarized macrophage model construction. Knock down of the hub genes on BMDMs via shRNAs was used to screen the genes regulating macrophage M1 polarization in vitro. The cecal ligation and puncture (CLP) mouse model was constructed in knockout (KO) mice and wild-type (WT) mice to explore whether the screened genes regulate macrophage M1 polarization in septic ARDS in vivo. ChIP-seq and further experiments on BMDMs were performed to investigate the molecular mechanism.

RESULTS

The bioinformatics analysis of gene expression profiles from a clinical cohort of 26 ARDS patients and macrophage polarization found that the 5 hub genes (IFIH1, IRF1, STAT1, IFIT3, GBP1) may have a synergistic effect on macrophage M1 polarization in septic ARDS. Further in vivo investigations indicated that IFIH1, STAT1 and IRF1 contribute to macrophage M1 polarization. The histological evaluation and immunohistochemistry of the lungs from the IRF1^-/- and WT mice indicated that knockout of IRF1 markedly alleviated CLP-induced lung injury and M1-polarized infiltration. Moreover, the molecular mechanism investigations indicated that knockdown of IFIH1 markedly promoted IRF1 translocation into the nucleus. Knockout of IRF1 significantly decreases the expression of STAT1. ChIP-seq and PCR further confirmed that IRF1, as a transcription factor of STAT1, binds to the promoter region of STAT1.

CONCLUSION

IRF1 was identified as the key molecule that regulates macrophage M1polarization and septic ARDS development in vivo and in vitro. Moreover, as the adaptor in response to infection mimics irritants, IFIH1 promotes IRF1 (transcription factor) translocation into the nucleus to initiate STAT1 transcription.

Clinical Ageing

Ageing is generally characterised by the declining ability to respond to stress, increasing homeostatic imbalance, and increased risk of ageing-associated diseases . Mechanistically, the lifelong accumulation of a wide range of molecular and cellular impairments leads to organismal senescence. The aging population poses a severe medical concern due to the burden it places on healthcare systems and the general public as well as the prevalence of diseases and impairments associated with old age. In this chapter, we discuss organ failure during ageing as well as ageing of the hypothalamic-pituitary-adrenal axis and drugs that can regulate it. A much-debated subject is about ageing and regeneration. With age, there is a gradual decline in the regenerative properties of most tissues. The goal of regenerative medicine is to restore cells, tissues, and structures that are lost or damaged after disease, injury, or ageing. The question arises as to whether this is due to the intrinsic ageing of stem cells or, rather, to the impairment of stem-cell function in the aged tissue environment. The risk of having a stroke event doubles each decade after the age of 55. Therefore, it is of great interest to develop neurorestorative therapies for stroke which occurs mostly in elderly people. Initial enthusiasm for stimulating restorative processes in the ischaemic brain with cell-based therapies has meanwhile converted into a more balanced view, recognising impediments related to survival, migration, differentiation, and integration of therapeutic cells in the hostile aged brain environment. Therefore, a current lack of understanding of the fate of transplanted cells means that the safety of cell therapy in stroke patients is still unproven. Another issue associated with ischaemic stroke is that patients at risk for these sequels of stroke are not duly diagnosed and treated due to the lack of reliable biomarkers. However, recently neurovascular unit-derived exosomes in response to Stroke and released into serum are new plasma genetic and proteomic biomarkers associated with ischaemic stroke. The second valid option, which is also more economical, is to invest in prevention.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

miRCoop: Identifying Cooperating miRNAs via Kernel Based Interaction Tests

Although miRNAs can cause widespread changes in expression programs, single miRNAs typically induce mild repression on their targets. Cooperativity among miRNAs is reported as one strategy to overcome this constraint. Expanding the catalog of synergistic miRNAs is critical for understanding gene regulation and for developing miRNA-based therapeutics. In this study, we develop miRCoop to identify synergistic miRNA pairs that have weak or no repression on the target mRNA individually, but when act together, induce strong repression. miRCoop uses kernel-based statistical interaction tests, together with miRNA and mRNA target information. We apply our approach to patient data of two different cancer types. In kidney cancer, we identify 66 putative triplets. For 64 of these triplets, there is at least one common transcription factor that potentially regulates all participating RNAs of the triplet, supporting a functional association among them. Furthermore, we find that identified triplets are enriched for certain biological processes that are relevant to kidney cancer. Some of the synergistic miRNAs are very closely encoded in the genome, hinting a functional association among them. In applying the method on tumor data with the primary liver site, we find 3105 potential triplet interactions. We believe miRCoop can aid our understanding of the complex regulatory interactions in different health and disease states of the cell and can help in designing miRNA-based therapies. Matlab code for the methodology is provided in https://github.com/guldenolgun/miRCoop.

microRNA-Mediated Encoding and Decoding of Time-Dependent Signals in Tumorigenesis

microRNAs, pivotal post-transcriptional regulators of gene expression, in the past decades have caught the attention of researchers for their involvement in different biological processes, ranging from cell development to cancer. Although lots of effort has been devoted to elucidate the topological features and the equilibrium properties of microRNA-mediated motifs, little is known about how the information encoded in frequency, amplitude, duration, and other features of their regulatory signals can affect the resulting gene expression patterns. Here, we review the current knowledge about microRNA-mediated gene regulatory networks characterized by time-dependent input signals, such as pulses, transient inputs, and oscillations. First, we identify the general characteristic of the main motifs underlying temporal patterns. Then, we analyze their impact on two commonly studied oncogenic networks, showing how their dysfunction can lead to tumorigenesis.

Gene-specific MicroRNA antagonism protects against HIV Tat and TGF-β-mediated suppression of CFTR mRNA and function

BACKGROUND

MicroRNAs play an important role in health and disease. TGF-β signaling, upregulated by HIV Tat, and in chronic airway diseases and smokers upregulates miR-145-5p to suppress cystic fibrosis transmembrane conductance regulator (CFTR). CFTR suppression in chronic airway diseases like Cystic Fibrosis, COPD and smokers has been associated with suppressed MCC and recurrent lung infections and inflammation. This can explain the emergence of recurrent lung infections and inflammation in people living with HIV.

METHODS

Tat-induced aberrant microRNAome was identified by miRNA expression analysis. microRNA mimics and antagomirs were used to validate the identified miRNAs involved in Tat mediated CFTR mRNA suppression. CRISPR-based editing of the miRNA target sites in CFTR 3'UTR was used to determine rescue of CFTR mRNA and function in airway epithelial cell lines and in primary human bronchial epithelial cells exposed to TGF-β and Tat.

FINDINGS

HIV Tat upregulates miR-145-5p and miR-509-3p. The two miRNAs demonstrate co-operative effects in suppressing CFTR. CRISPR-based editing of the miRNA target site preserves CFTR mRNA and function in airway epithelial cells INTERPRETATION: Given the important roles of TGF-β signaling and the multitude of genes regulated by miRNAs, we demonstrate that CRISPR-based gene-specific microRNA antagonism approach can preserve CFTR mRNA and function in the context of HIV Tat and TGF-β signaling without suppressing expression of other genes regulated by miR-145-5p.

Systemic CLIP-seq analysis and game theory approach to model microRNA mode of binding

microRNAs (miRNAs) associate with Ago proteins to post-transcriptionally silence gene expression by targeting mRNAs. To characterize the modes of miRNA-binding, we developed a novel computational framework, called optiCLIP, which considers the reproducibility of the identified peaks among replicates based on the peak overlap. We identified 98 999 binding sites for mouse and human miRNAs, from eleven Ago2 CLIP-seq datasets. Clustering the binding preferences, we found heterogeneity of the mode of binding for different miRNAs. Finally, we set up a quantitative model, named miRgame, based on an adaptation of the game theory. We have developed a new algorithm to translate the miRgame into a score that corresponds to a miRNA degree of occupancy for each Ago2 peak. The degree of occupancy summarizes the number of miRNA-binding sites and miRNAs targeting each binding site, and binding energy of each miRNA::RNA heteroduplex in each peak. Ago peaks were stratified accordingly to the degree of occupancy. Target repression correlates with higher score of degree of occupancy and number of miRNA-binding sites within each Ago peak. We validated the biological performance of our new method on miR-155-5p. In conclusion, our data demonstrate that miRNA-binding sites within each Ago2 CLIP-seq peak synergistically interplay to enhance target repression.

Bioinspired artificial exosomes based on lipid nanoparticles carrying let-7b-5p promote angiogenesis in vitro and in vivo

MicroRNAs (miRNAs) regulate gene expression by post-transcriptional inhibition of target genes. Proangiogenic small extracellular vesicles (sEVs; popularly identified with the name "exosomes") with a composite cargo of miRNAs are secreted by cultured stem cells and present in human biological fluids. Lipid nanoparticles (LNPs) represent an advanced platform for clinically approved delivery of RNA therapeutics. In this study, we aimed to (1) identify the miRNAs responsible for sEV-induced angiogenesis; (2) develop the prototype of bioinspired "artificial exosomes" (AEs) combining LNPs with a proangiogenic miRNA, and (3) validate the angiogenic potential of the bioinspired AEs. We previously reported that human sEVs from bone marrow (BM)-CD34⁺ cells and pericardial fluid (PF) are proangiogenic. Here, we have shown that sEVs secreted from saphenous vein pericytes and BM mesenchymal stem cells also promote angiogenesis. Analysis of miRNA datasets available in-house or datamined from GEO identified the let-7 family as common miRNA signature of the proangiogenic sEVs. LNPs with either hsa-let-7b-5p or cyanine 5 (Cy5)-conjugated Caenorhabditis elegans miR-39 (Cy5-cel-miR-39; control miRNA) were prepared using microfluidic micromixing. let-7b-5p-AEs did not cause toxicity and transferred functionally active let-7b-5p to recipient endothelial cells (ECs). let-7b-AEs also improved EC survival under hypoxia and angiogenesis in vitro and in vivo. Bioinspired proangiogenic AEs could be further developed into innovative nanomedicine products targeting ischemic diseases.

IFIH1 Contributes to M1 Macrophage Polarization in ARDS

Accumulated evidence has demonstrated that the macrophage phenotypic switch from M0 to M1 is crucial in the initiation of the inflammatory process of acute respiratory distress syndrome (ARDS). Better insight into the molecular control of M1 macrophages in ARDS may identify potential therapeutic targets. In the current study, 36 candidate genes associated with the severity of ARDS and simultaneously involved in M1-polarized macrophages were first screened through a weighted network algorithm on all gene expression profiles from the 26 ARDS patients and empirical Bayes analysis on the gene expression profiles of macrophages. STAT1, IFIH1, GBP1, IFIT3, and IRF1 were subsequently identified as hub genes according to connectivity degree analysis and multiple external validations. Among these candidate genes, IFIH1 had the strongest connection with ARDS through the RobustRankAggreg algorithm. It was selected as a crucial gene for further investigation. For in vitro validation, the RAW264.7 cell line and BMDMs were transfected with shIFIH1 lentivirus and plasmid expression vectors of IFIH1. Cellular experimental studies further confirmed that IFIH1 was a novel regulator for promoting M1 macrophage polarization. Moreover, gene set enrichment analysis (GSEA) and in vitro validations indicated that IFIH1 regulated M1 polarization by activating IRF3. In addition, previous studies demonstrated that activation of IFIH1-IRF3 was stimulated by viral RNAs or RNA mimics. Surprisingly, the current study found that LPS could also induce IFIH1-IRF3 activation via a MyD88-dependent mechanism. We also found that only IFIH1 expression without LPS or RNA mimic stimulation could not affect IRF3 activation and M1 macrophage polarization. These findings were validated on two types of macrophages, RAW264.7 cells and BMDMs, which expanded the knowledge on the inflammatory roles of IFIH1 and IRF3, suggesting IFIH1 as a potential target for ARDS treatment.

Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic

In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today's level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment.

Network- and systems-based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy

Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Methods: Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses. Results: Through RNA sequencing of IKKβ-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates, with miR-15a and miR-16 as the top ones. The results of our analysis are presented in a database that constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential (https://www.synmirapy.net/dc-optimization). Conclusions: Our approach enables the systematic analysis and identification of functional miRNA-gene interactions that can be experimentally tested for improving DC immunogenic potency.

Identification of genuine and novel miRNAs in Amaranthus hypochondriacus from high-throughput sequencing data

Amaranth has been proposed as an exceptional alternative for food security and climate change mitigation. Information about the distribution, abundance, or specificity of miRNAs in amaranth species is scare. Here, small RNAs from seedlings under control, drought, heat, and cold stress conditions of the Amaranthus hypocondriacus variety "Gabriela" were sequenced and miRNA loci identified in the amaranth genome using the ShortStack software. Fifty-three genuine miRNA clustersthirty-nine belonging to conserved families, and fourteen novel, were identified. Identification of their target genes suggests that conserved amaranth miRNAs are involved in growth and developmental processes, as well as stress responses. MiR0005, an amaranth-specific miRNA, exhibited an unusual high level of expression, akin to that of conserved miRNAs. Overall, our results broaden our knowledge regarding the distribution, abundance and expression of miRNAs in amaranth, providing the basis for future research on miRNAs and their functions in this important species.

Identification of two microRNA nodes as potential cooperative modulators of liver metabolism

AIM

Hepatic insulin resistance is a hallmark of type 2 diabetes and non-alcoholic fatty liver disease. Dysregulation of microRNA (miRNA) expression in insulin-resistant livers might coordinate impaired hepatic metabolic function. Here, we aimed to discover miRNAs and their downstream targets involved in hepatic insulin resistance.

METHODS

We determined miRNA expression profiles by small RNA sequencing of two mouse models of impaired hepatic insulin action: high-fat diet-induced obesity and liver-specific insulin receptor knockout. Conversely, we assessed the hepatic miRNA expression profile after treatment with the antidiabetic hormone, fibroblast growth factor 21 (FGF21). Ontology analysis of predicted miRNA gene targets was performed to identify regulated gene pathways. Target enrichment analysis and miRNA mimic overexpression in vitro were used to identify unified protein targets of nodes of regulated miRNAs.

RESULTS

We identified an array of miRNA species regulated by impaired liver insulin action or after fibroblast growth factor 21 treatment. Ontology analysis of predicted miRNA gene targets identified pathways controlling hepatic energy metabolism and insulin sensitivity. We identified a node of two miRNAs downregulated in the livers of liver-specific insulin receptor knockout mice, miR-883b and miR-205, which positively regulate the expression of transcription factor zinc finger E-box-binding homeobox 1 (ZBED1). We found another node of two miRNAs upregulated in the livers of fibroblast growth factor 21-treated mice, miR-155-3p and miR-1968-5p, which canonically downregulates the caveola component, polymerase I and transcript release factor (PTRF), a gene previously implicated in hepatic energy metabolism.

CONCLUSIONS

This study identifies two nodes of coregulated miRNAs that might coordinately control hepatic energy metabolism in states of insulin resistance.

Computational methods in the study of self-entangled proteins: a critical appraisal

The existence of self-entangled proteins, the native structure of which features a complex topology, unveils puzzling, and thus fascinating, aspects of protein biology and evolution. The discovery that a polypeptide chain can encode the capability to self-entangle in an efficient and reproducible way during folding, has raised many questions, regarding the possible function of these knots, their conservation along evolution, and their role in the folding paradigm. Understanding the function and origin of these entanglements would lead to deep implications in protein science, and this has stimulated the scientific community to investigate self-entangled proteins for decades by now. In this endeavour, advanced experimental techniques are more and more supported by computational approaches, that can provide theoretical guidelines for the interpretation of experimental results, and for the effective design of new experiments. In this review we provide an introduction to the computational study of self-entangled proteins, focusing in particular on the methodological developments related to this research field. A comprehensive collection of techniques is gathered, ranging from knot theory algorithms, that allow detection and classification of protein topology, to Monte Carlo or molecular dynamics strategies, that constitute crucial instruments for investigating thermodynamics and kinetics of this class of proteins.

Systems biology-based investigation of cooperating microRNAs as monotherapy or adjuvant therapy in cancer

MicroRNAs (miRNAs) are short, noncoding RNAs that regulate gene expression by suppressing mRNA translation and reducing mRNA stability. A miRNA can potentially bind many mRNAs, thereby affecting the expression of oncogenes and tumor suppressor genes as well as the activity of whole pathways. The promise of miRNA therapeutics in cancer is to harness this evolutionarily conserved mechanism for the coordinated regulation of gene expression, and thus restoring a normal cell phenotype. However, the promiscuous binding of miRNAs can provoke unwanted off-target effects, which are usually caused by high-dose single-miRNA treatments. Thus, it is desirable to develop miRNA therapeutics with increased specificity and efficacy. To achieve that, we propose the concept of miRNA cooperativity in order to exert synergistic repression on target genes, thus lowering the required total amount of miRNAs. We first review miRNA therapies in clinical application. Next, we summarize the knowledge on the molecular mechanism and biological function of miRNA cooperativity and discuss its application in cancer therapies. We then propose and discuss a systems biology approach to investigate miRNA cooperativity for the clinical setting. Altogether, we point out the potential of miRNA cooperativity to reduce off-target effects and to complement conventional, targeted, or immune-based therapies for cancer.

Genome wide analysis of 3' UTR sequence elements and proteins regulating mRNA stability during maternal-to-zygotic transition in zebrafish

Posttranscriptional regulation plays a crucial role in shaping gene expression. During the maternal-to-zygotic transition (MZT), thousands of maternal transcripts are regulated. However, how different cis-elements and trans-factors are integrated to determine mRNA stability remains poorly understood. Here, we show that most transcripts are under combinatorial regulation by multiple decay pathways during zebrafish MZT. By using a massively parallel reporter assay, we identified cis-regulatory sequences in the 3′ UTR, including U-rich motifs that are associated with increased mRNA stability. In contrast, miR-430 target sequences, UAUUUAUU AU-rich elements (ARE), CCUC, and CUGC elements emerged as destabilizing motifs, with miR-430 and AREs causing mRNA deadenylation upon genome activation. We identified trans-factors by profiling RNA–protein interactions and found that poly(U)-binding proteins are preferentially associated with 3′ UTR sequences and stabilizing motifs. We show that this activity is antagonized by C-rich motifs and correlated with protein binding. Finally, we integrated these regulatory motifs into a machine learning model that predicts reporter mRNA stability in vivo.

A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration

A lot of research studies have shown that many complex human diseases are associated not only with microRNAs (miRNAs) but also with long noncoding RNAs (lncRNAs). However, most of the current existing studies focus on the prediction of disease-related miRNAs or lncRNAs, and to our knowledge, until now, there are few literature studies reported to pay attention to the study of impact of miRNA-lncRNA pairs on diseases, although more and more studies have shown that both lncRNAs and miRNAs play important roles in cell proliferation and differentiation during the recent years. The identification of disease-related genes provides great insight into the underlying pathogenesis of diseases at a system level. In this study, a novel model called PADLMHOOI was proposed to predict potential associations between diseases and lncRNA-miRNA pairs based on the higher-order orthogonal iteration, and in order to evaluate its prediction performance, the global and local LOOCV were implemented, respectively, and simulation results demonstrated that PADLMHOOI could achieve reliable AUCs of 0.9545 and 0.8874 in global and local LOOCV separately. Moreover, case studies further demonstrated the effectiveness of PADLMHOOI to infer unknown disease-related lncRNA-miRNA pairs.

Modulation and Evolution of Animal Development through microRNA Regulation of Gene Expression

microRNAs regulate gene expression by blocking the translation of mRNAs and/or promoting their degradation. They, therefore, play important roles in gene regulatory networks (GRNs) by modulating the expression levels of specific genes and can tune GRN outputs more broadly as part of feedback loops. These roles for microRNAs provide developmental buffering on one hand but can facilitate evolution of development on the other. Here we review how microRNAs can modulate GRNs during animal development as part of feedback loops and through their individual or combinatorial targeting of multiple different genes in the same network. We then explore how changes in the expression of microRNAs and consequently targets can facilitate changes in GRNs that alter development and lead to phenotypic evolution. The reviewed studies exemplify the key roles played by microRNAs in the regulation and evolution of gene expression during developmental processes in animals.

Sensory Neuropathy Affects Cardiac miRNA Expression Network Targeting IGF-1, SLC2a-12, EIF-4e, and ULK-2 mRNAs

Background: Here we examined myocardial microRNA (miRNA) expression profile in a sensory neuropathy model with cardiac diastolic dysfunction and aimed to identify key mRNA molecular targets of the differentially expressed miRNAs that may contribute to cardiac dysfunction. Methods: Male Wistar rats were treated with vehicle or capsaicin for 3 days to induce systemic sensory neuropathy. Seven days later, diastolic dysfunction was detected by echocardiography, and miRNAs were isolated from the whole ventricles. Results: Out of 711 known miRNAs measured by miRNA microarray, the expression of 257 miRNAs was detected in the heart. As compared to vehicle-treated hearts, miR-344b, miR-466b, miR-98, let-7a, miR-1, miR-206, and miR-34b were downregulated, while miR-181a was upregulated as validated also by quantitative real time polymerase chain reaction (qRT-PCR). By an in silico network analysis, we identified common mRNA targets (insulin-like growth factor 1 (IGF-1), solute carrier family 2 facilitated glucose transporter member 12 (SLC2a-12), eukaryotic translation initiation factor 4e (EIF-4e), and Unc-51 like autophagy activating kinase 2 (ULK-2)) targeted by at least three altered miRNAs. Predicted upregulation of these mRNA targets were validated by qRT-PCR. Conclusion: This is the first demonstration that sensory neuropathy affects cardiac miRNA expression network targeting IGF-1, SLC2a-12, EIF-4e, and ULK-2, which may contribute to cardiac diastolic dysfunction. These results further support the need for unbiased omics approach followed by in silico prediction and validation of molecular targets to reveal novel pathomechanisms.

Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases

MicroRNAs (miRs) are endogenous non-coding RNA molecules that play important roles in human health and disease by regulating gene expression and cellular processes. In recent years, with the increasing scientific knowledge and new discovery of miRs and their gene targets, as well as the plentiful experimental evidence that shows dysregulation of miRs in a wide variety of human diseases, the computational modeling approach has emerged as an effective tool to help researchers identify novel functional associations between differential miR expression and diseases, dissect the phenotypic expression patterns of miRs in gene regulatory networks, and elucidate the critical roles of miRs in the modulation of disease pathways from mechanistic and quantitative perspectives. Here we will review the recent systems biology studies that employed different kinetic modeling techniques to provide mechanistic insights relating to the regulatory function and therapeutic potential of miRs in human diseases. Some of the key computational aspects to be discussed in detail in this review include (i) models of miR-mediated network motifs in the regulation of gene expression, (ii) models of miR biogenesis and miR⁻target interactions, and (iii) the incorporation of such models into complex disease pathways in order to generate mechanistic, molecular- and systems-level understanding of pathophysiology. Other related bioinformatics tools such as computational platforms that predict miR-disease associations will also be discussed, and we will provide perspectives on the challenges and opportunities in the future development and translational application of data-driven systems biology models that involve miRs and their regulatory pathways in human diseases.

Tools for Understanding miRNA-mRNA Interactions for Reproducible RNA Analysis

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. The use of RNA data in gene expression analysis has become increasingly important to gain insights into the regulatory mechanisms behind miRNA-mRNA interactions. As a result, we are confronted with a growing landscape of tools, while standards for reproducibility and benchmarking lag behind. This work identifies the challenges for reproducible RNA analysis, and highlights best practices on the processing and dissemination of scientific results. We found that the success of a tool does not solely depend on its performances: equally important is how a tool is received, and then supported within a community. This leads us to a detailed presentation of the RNA workbench, a community effort for sharing workflows and processing tools, built on top of the Galaxy framework. Here, we follow the community guidelines to extend its portfolio of RNA tools with the integration of the TriplexRNA ( https://triplexrna.org ). Our findings provide the basis for the development of a recommendation system, to guide users in the choice of tools and workflows.

Quantitative Characteristic of ncRNA Regulation in Gene Regulatory Networks

RNA is mostly known for its role in protein synthesis, where it encodes information for protein sequence in its messenger RNA (mRNA) form (translation). Yet, RNA molecules regulate several cellular processes other than translation. Here, we present an overview of several mathematical models that help understanding and characterizing the role of noncoding RNA molecules (ncRNAs) in regulating gene expression and protein synthesis. First, we discuss relatively simple models where ncRNAs can modulate protein synthesis via targeting a mRNA. Then, we consider the case of feedback interactions between ncRNAs and their target proteins, and discuss several biological applications where these feedback architectures modulate a cellular phenotype and control the levels of intrinsic and extrinsic noise. Building from these simple circuit motifs, we examine feed-forward circuit motifs involving ncRNAs that generate precise spatial and temporal patterns of protein expression. Further, we investigate the competition between ncRNAs and other endogenous RNA molecules and show that the cross talk between coding and noncoding RNAs can form large genetic circuits that involve up to hundreds of chemical species. Finally, we discuss the role of ncRNAs in modulating cell-cell signaling pathways and therefore the dynamics of spatiotemporal pattern formation in a tissue.

An Integrated Approach for Identification of Functionally Similar MicroRNAs in Colorectal Cancer

Colorectal cancer (CRC) is one of the most prevalent cancers around the globe. However, the molecular reasons for pathogenesis of CRC are still poorly understood. Recently, the role of microRNAs or miRNAs in the initiation and progression of CRC has been studied. MicroRNAs are small, endogenous noncoding RNAs found in plants, animals, and some viruses, which function in RNA silencing and posttranscriptional regulation of gene expression. Their role in CRC development is studied and they are found to be potential biomarkers in diagnosis and treatment of CRC. Therefore, identification of functionally similar CRC related miRNAs may help in the development of a prognostic tool. In this regard, this paper presents a new algorithm, called μSim. It is an integrative approach for identification of functionally similar miRNAs associated with CRC. It integrates judiciously the information of miRNA expression data and miRNA-miRNA functionally synergistic network data. The functional similarity is calculated based on both miRNA expression data and miRNA-miRNA functionally synergistic network data. The effectiveness of the proposed method in comparison to other related methods is shown on four CRC miRNA data sets. The proposed method selected more significant miRNAs related to CRC as compared to other related methods.

MicroRNA interactome analysis predicts post-transcriptional regulation of ADRB2 and PPP3R1 in the hypercholesterolemic myocardium

Little is known about the molecular mechanism including microRNAs (miRNA) in hypercholesterolemia-induced cardiac dysfunction. We aimed to explore novel hypercholesterolemia-induced pathway alterations in the heart by an unbiased approach based on miRNA omics, target prediction and validation. With miRNA microarray we identified forty-seven upregulated and ten downregulated miRNAs in hypercholesterolemic rat hearts compared to the normocholesterolemic group. Eleven mRNAs with at least 4 interacting upregulated miRNAs were selected by a network theoretical approach, out of which 3 mRNAs (beta-2 adrenergic receptor [Adrb2], calcineurin B type 1 [Ppp3r1] and calcium/calmodulin-dependent serine protein kinase [Cask]) were validated with qRT-PCR and Western blot. In hypercholesterolemic hearts, the expression of Adrb2 mRNA was significantly decreased. ADRB2 and PPP3R1 protein were significantly downregulated in hypercholesterolemic hearts. The direct interaction of Adrb2 with upregulated miRNAs was demonstrated by luciferase reporter assay. Gene ontology analysis revealed that the majority of the predicted mRNA changes may contribute to the hypercholesterolemia-induced cardiac dysfunction. In summary, the present unbiased target prediction approach based on global cardiac miRNA expression profiling revealed for the first time in the literature that both the mRNA and protein product of Adrb2 and PPP3R1 protein are decreased in the hypercholesterolemic heart.

Emerging functional markers for cancer stem cell-based therapies: Understanding signaling networks for targeting metastasis

Metastasis is one of the most challenging issues in cancer patient management, and effective therapies to specifically target disease progression are missing, emphasizing the urgent need for developing novel anti-metastatic therapeutics. Cancer stem cells (CSCs) gained fast attention as a minor population of highly malignant cells within liquid and solid tumors that are responsible for tumor onset, self-renewal, resistance to radio- and chemotherapies, and evasion of immune surveillance accelerating recurrence and metastasis. Recent progress in the identification of their phenotypic and molecular characteristics and interactions with the tumor microenvironment provides great potential for the development of CSC-based targeted therapies and radical improvement in metastasis prevention and cancer patient prognosis. Here, we report on newly uncovered signaling mechanisms controlling CSC's aggressiveness and treatment resistance, and CSC-specific agents and molecular therapeutics, some of which are currently under investigation in clinical trials, gearing towards decisive functional CSC intrinsic or surface markers. One special research focus rests upon subverted regulatory pathways such as insulin-like growth factor 1 receptor signaling and its interactors in metastasis-initiating cell populations directly related to the gain of stem cell- and EMT-associated properties, as well as key components of the E2F transcription factor network regulating metastatic progression, microenvironmental changes, and chemoresistance. In addition, the study provides insight into systems biology tools to establish complex molecular relationships behind the emergence of aggressive phenotypes from high-throughput data that rely on network-based analysis and their use to investigate immune escape mechanisms or predict clinical outcome-relevant CSC receptor signaling signatures. We further propose that customized vector technologies could drastically enhance systemic drug delivery to target sites, and summarize recent progress and remaining challenges. This review integrates available knowledge on CSC biology, computational modeling approaches, molecular targeting strategies, and delivery techniques to envision future clinical therapies designed to conquer metastasis-initiating cells.

A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network

Motivation

Increasing studies have demonstrated that many human complex diseases are associated with not only microRNAs, but also long-noncoding RNAs (lncRNAs). LncRNAs and microRNA play significant roles in various biological processes. Therefore, developing effective computational models for predicting novel associations between diseases and lncRNA-miRNA pairs (LMPairs) will be beneficial to not only the understanding of disease mechanisms at lncRNA-miRNA level and the detection of disease biomarkers for disease diagnosis, treatment, prognosis, and prevention, but also the understanding of interactions between diseases and LMPairs at disease level.

Results

It is well known that genes with similar functions are often associated with similar diseases. In this article, a novel model named PADLMP for predicting associations between diseases and LMPairs is proposed. In this model, a Disease-LncRNA-miRNA (DLM) tripartite network was designed firstly by integrating the lncRNA-disease association network and miRNA-disease association network; then we constructed the disease-LMPairs bipartite association network based on the DLM network and lncRNA-miRNA association network; finally, we predicted potential associations between diseases and LMPairs based on the newly constructed disease-LMPair network. Simulation results show that PADLMP can achieve AUCs of 0.9318, 0.9090 ± 0.0264, and 0.8950 ± 0.0027 in the LOOCV, 2-fold, and 5-fold cross validation framework, respectively, which demonstrate the reliable prediction performance of PADLMP.

MicroRNA-425 and microRNA-155 cooperatively regulate atrial natriuretic peptide expression and cGMP production

Aims

Atrial natriuretic peptide (ANP), secreted primarily by atrial cardiomyocytes, decreases blood pressure by raising cyclic 3’,5’-guanosine monophosphate (cGMP) levels and inducing vasorelaxation, natriuresis, and diuresis. Raising the level of ANP has been shown to be an effective treatment for hypertension. To advance the future development of an anti-microRNA (miR) approach to increasing expression of ANP, we investigated the regulation of NPPA expression by two miRs: miR-425 and miR-155. We examined whether miR-425 and miR-155 have an additive effect on the expression and function of ANP.

Methods and results

Human embryonic stem cell-derived cardiomyocytes (hESC-CMs) were transfected with miR-425, miR-155, or a combination of the two miRs. Two days later, NPPA expression was measured using real time qPCR. Each of the miRs decreased NPPA expression over a wide range of concentrations, with a significant reduction at concentrations as low as 1 nM. The combination of miR-425 and miR-155 reduced NPPA expression to a greater extent than either miR-425 or miR-155 alone. An in vitro assay was developed to study the potential biological significance of the miR-induced decrease in NPPA expression. The cooperative effect of miR-425 and miR-155 on NPPA expression was associated with a significant decrease in cGMP levels.

Conclusions

These data demonstrate that miR-425 and miR-155 regulate NPPA expression in a cooperative manner. Targeting both miRNAs with anti-miRs (possibly at submaximal concentrations) might prove to be a more effective strategy to modulate ANP levels, and thus blood pressure, than targeting either miRNA alone.

MiR-205-5p and miR-342-3p cooperate in the repression of the E2F1 transcription factor in the context of anticancer chemotherapy resistance

High rates of lethal outcome in tumour metastasis are associated with the acquisition of invasiveness and chemoresistance. Several clinical studies indicate that E2F1 overexpression across high-grade tumours culminates in unfavourable prognosis and chemoresistance in patients. Thus, fine-tuning the expression of E2F1 could be a promising approach for treating patients showing chemoresistance.

Methods: We integrated bioinformatics, structural and kinetic modelling, and experiments to study cooperative regulation of E2F1 by microRNA (miRNA) pairs in the context of anticancer chemotherapy resistance.

Results: We showed that an enhanced E2F1 repression efficiency can be achieved in chemoresistant tumour cells through two cooperating miRNAs. Sequence and structural information were used to identify potential miRNA pairs that can form tertiary structures with E2F1 mRNA. We then employed molecular dynamics simulations to show that among the identified triplexes, miR-205-5p and miR-342-3p can form the most stable triplex with E2F1 mRNA. A mathematical model simulating the E2F1 regulation by the cooperative miRNAs predicted enhanced E2F1 repression, a feature that was verified by in vitro experiments. Finally, we integrated this cooperative miRNA regulation into a more comprehensive network to account for E2F1-related chemoresistance in tumour cells. The network model simulations and experimental data indicate the ability of enhanced expression of both miR-205-5p and miR-342-3p to decrease tumour chemoresistance by cooperatively repressing E2F1.

Conclusions: Our results suggest that pairs of cooperating miRNAs could be used as potential RNA therapeutics to reduce E2F1-related chemoresistance.

A Network-Based Integrative Workflow to Unravel Mechanisms Underlying Disease Progression

Unraveling mechanisms underlying diseases has motivated the development of systems biology approaches. The key challenges for the development of mathematical models and computational tool are (1) the size of molecular networks, (2) the nonlinear nature of spatio-temporal interactions, and (3) feedback loops in the structure of interaction networks. We here propose an integrative workflow that combines structural analyses of networks, high-throughput data, and mechanistic modeling. As an illustration of the workflow, we use prostate cancer as a case study with the aim of identifying key functional components associated with primary to metastasis transitions. Analysis carried out by the workflow revealed that HOXD10, BCL2, and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN, and JUNB are playing a central role. The identified key elements of each network are validated using patient survival analysis. The workflow presented here allows experimentalists to use heterogeneous data sources for the identification of diagnostic and prognostic signatures.

Intron retention enhances gene regulatory complexity in vertebrates

Background

While intron retention (IR) is now widely accepted as an important mechanism of mammalian gene expression control, it remains the least studied form of alternative splicing. To delineate conserved features of IR, we performed an exhaustive phylogenetic analysis in a highly purified and functionally defined cell type comprising neutrophilic granulocytes from five vertebrate species spanning 430 million years of evolution.

Results

Our RNA-sequencing-based analysis suggests that IR increases gene regulatory complexity, which is indicated by a strong anti-correlation between the number of genes affected by IR and the number of protein-coding genes in the genome of individual species. Our results confirm that IR affects many orthologous or functionally related genes in granulocytes. Further analysis uncovers new and unanticipated conserved characteristics of intron-retaining transcripts. We find that intron-retaining genes are transcriptionally co-regulated from bidirectional promoters. Intron-retaining genes have significantly longer 3′ UTR sequences, with a corresponding increase in microRNA binding sites, some of which include highly conserved sequence motifs. This suggests that intron-retaining genes are highly regulated post-transcriptionally.

Conclusions

Our study provides unique insights concerning the role of IR as a robust and evolutionarily conserved mechanism of gene expression regulation. Our findings enhance our understanding of gene regulatory complexity by adding another contributor to evolutionary adaptation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1339-3) contains supplementary material, which is available to authorized users.

Systems genetics identifies a co-regulated module of liver microRNAs associated with plasma LDL cholesterol in murine diet-induced dyslipidemia

Chronically altered levels of circulating lipids, termed dyslipidemia, is a significant risk factor for a number of metabolic and cardiovascular morbidities. MicroRNAs (miRNAs) have emerged as important regulators of lipid balance, have been implicated in dyslipidemia, and have been proposed as candidate therapeutic targets in lipid-related disorders including atherosclerosis. A major limitation of most murine studies of miRNAs in lipid metabolic disorders is that they have been performed in just one (or very few) inbred strains, such as C57BL/6. Moreover, although individual miRNAs have been associated with lipid phenotypes, it is well understood that miRNAs likely work together in functional modules. To address these limitations, we implemented a systems genetics strategy using the Diversity Outbred (DO) mouse population. Specifically, we performed gene and miRNA expression profiling in the livers from ~300 genetically distinct DO mice after 18 wk on either a high-fat/high-cholesterol diet or a high-protein diet. Large-scale correlative analysis of these data with a wide range of cardio-metabolic end points revealed a co-regulated module of miRNAs significantly associated with circulating low-density lipoprotein cholesterol (LDL-C) levels. The hubs of this module were identified as miR-199a, miR-181b, miR-27a, miR-21_-_1, and miR-24. In sum, we demonstrate that a high-fat/high-cholesterol diet robustly rewires the miRNA regulatory network, and we identify a small group of co-regulated miRNAs that may exert coordinated effects to control circulating LDL-C.

MicroRNA-mediated regulatory circuits: outlook and perspectives

MicroRNAs have been found to be necessary for regulating genes implicated in almost all signaling pathways, and consequently their dysfunction influences many diseases, including cancer. Understanding of the complexity of the microRNA-mediated regulatory network has grown in terms of size, connectivity and dynamics with the development of computational and, more recently, experimental high-throughput approaches for microRNA target identification. Newly developed studies on recurrent microRNA-mediated circuits in regulatory networks, also known as network motifs, have substantially contributed to addressing this complexity, and therefore to helping understand the ways by which microRNAs achieve their regulatory role. This review provides a summarizing view of the state-of-the-art, and perspectives of research efforts on microRNA-mediated regulatory motifs. In this review, we discuss the topological properties characterizing different types of circuits, and the regulatory features theoretically enabled by such properties, with a special emphasis on examples of circuits typifying their biological significance in experimentally validated contexts. Finally, we will consider possible future developments, in particular regarding microRNA-mediated circuits involving long non-coding RNAs and epigenetic regulators.

Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

BACKGROUND

MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics.

METHODS AND RESULTS

We designed a computational method to detect multi-disease associated co-functional microRNA pairs and conducted cross disease analysis on a reconstructed disease-gene-microRNA (DGR) tripartite network. The construction of the DGR tripartite network is by the integration of newly predicted disease-microRNA associations with those relationships of diseases, microRNAs and genes maintained by existing databases. The prediction method uses a set of reliable negative samples of disease-microRNA association and a pre-computed kernel matrix instead of kernel functions. From this reconstructed DGR tripartite network, multi-disease associated co-functional microRNA pairs are detected together with their common dysfunctional target genes and ranked by a novel scoring method. We also conducted proof-of-concept case studies on cancer-related co-functional microRNA pairs as well as on non-cancer disease-related microRNA pairs.

CONCLUSIONS

With the prioritization of the co-functional microRNAs that relate to a series of diseases, we found that the co-function phenomenon is not unusual. We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases.

Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering

Differences in the expression profiles of miRNAs and mRNAs have been reported in colorectal cancer. Nevertheless, information on important miRNA-mRNA regulatory modules in colorectal cancer is still lacking. In this regard, this study presents an application of the RH-SAC algorithm on miRNA and mRNA expression data for identification of potential miRNA-mRNA modules. First, a set of miRNA rules was generated using the RH-SAC algorithm. The mRNA targets of the selected miRNAs were identified using the miRTarBase database. Next, the expression values of target mRNAs were used to generate mRNA rules using the RH-SAC. Then all miRNA-mRNA rules have been integrated for generating networks. The RH-SAC algorithm unlike other existing methods selects a group of co-expressed miRNAs and mRNAs that are also differentially expressed. In total 17 miRNAs and 141 mRNAs were selected. The enrichment analysis of selected mRNAs revealed that our method selected mRNAs that are significantly associated with colorectal cancer. We identified novel miRNA/mRNA interactions in colorectal cancer. Through experiment, we could confirm that one of our discovered miRNAs, hsa-miR-93-5p, was significantly up-regulated in 75.8% CRC in comparison to their corresponding non-tumor samples. It could have the potential to examine colorectal cancer subtype specific unique miRNA/mRNA interactions.

RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells

Background

Distinct RNA species may compete for binding to microRNAs (miRNAs). This competition creates an indirect interaction between miRNA targets, which behave as miRNA sponges and eventually influence each other’s expression levels. Theoretical predictions suggest that not only the mean expression levels of targets but also the fluctuations around the means are coupled through miRNAs. This may result in striking effects on a broad range of cellular processes, such as cell differentiation and proliferation. Although several studies have reported the functional relevance of this mechanism of interaction, detailed experiments are lacking that study this phenomenon in controlled conditions by mimicking a physiological range.

Results

We used an experimental design based on two bidirectional plasmids and flow cytometry measurements of cotransfected mammalian cells. We validated a stochastic gene interaction model that describes how mRNAs can influence each other’s fluctuations in a miRNA-dependent manner in single cells. We show that miRNA–target correlations eventually lead to either bimodal cell population distributions with high and low target expression states, or correlated fluctuations across targets when the pool of unbound targets and miRNAs are in near-equimolar concentration. We found that there is an optimal range of conditions for the onset of cross-regulation, which is compatible with 10–1000 copies of targets per cell.

Conclusions

Our results are summarized in a phase diagram for miRNA-mediated cross-regulation that links experimentally measured quantities and effective model parameters. This phase diagram can be applied to in vivo studies of RNAs that are in competition for miRNA binding.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1162-x) contains supplementary material, which is available to authorized users.

Khan F, Schmitz U, Wolkenhauer O, Gupta SK. MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression

Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks. One such network was derived for each of the clinical states of prostate cancer based on differentially expressed and significantly correlated gene, miRNA and TF pairs from the patient data. We identified key elements of each network using a network analysis approach and validated our results using patient survival analysis. We observed that HOXD10, BCL2 and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN and JUNB are playing a central role. Benefiting integrative networks our analysis suggests that some of these molecules were targeted by several overexpressed miRNAs which may have a major effect on the dysregulation of these molecules. For example, in the metastatic tumors five miRNAs (miR-671-5p, miR-665, miR-663, miR-512-3p and miR-371-5p) are mainly responsible for the dysregulation of STAT3 and hence can provide an opportunity for early detection of metastasis and development of alternative therapeutic approaches. Our findings deliver new details on key functional components in prostate cancer progression and provide opportunities for the development of alternative therapeutic approaches.

RNA Systems Biology for Cancer: From Diagnosis to Therapy

It is due to the advances in high-throughput omics data generation that RNA species have re-entered the focus of biomedical research. International collaborate efforts, like the ENCODE and GENCODE projects, have spawned thousands of previously unknown functional non-coding RNAs (ncRNAs) with various but primarily regulatory roles. Many of these are linked to the emergence and progression of human diseases. In particular, interdisciplinary studies integrating bioinformatics, systems biology, and biotechnological approaches have successfully characterized the role of ncRNAs in different human cancers. These efforts led to the identification of a new tool-kit for cancer diagnosis, monitoring, and treatment, which is now starting to enter and impact on clinical practice. This chapter is to elaborate on the state of the art in RNA systems biology, including a review and perspective on clinical applications toward an integrative RNA systems medicine approach. The focus is on the role of ncRNAs in cancer.

Deciphering hallmark processes of aging from interaction networks

BACKGROUND

Aging is broadly considered to be a dynamic process that accumulates unfavourable structural and functional changes in a time dependent fashion, leading to a progressive loss of physiological integrity of an organism, which eventually leads to age-related diseases and finally to death.

SCOPE OF REVIEW

The majority of aging-related studies are based on reductionist approaches, focusing on single genes/proteins or on individual pathways without considering possible interactions between them. Over the last few decades, several such genes/proteins were independently analysed and linked to a role that is affecting the longevity of an organism. However, an isolated analysis on genes and proteins largely fails to explain the mechanistic insight of a complex phenotype due to the involvement and integration of multiple factors.

MAJOR CONCLUSIONS

Technological advance makes it possible to generate high-throughput temporal and spatial data that provide an opportunity to use computer-based methods. These techniques allow us to go beyond reductionist approaches to analyse large-scale networks that provide deeper understanding of the processes that drive aging.

GENERAL SIGNIFICANCE

In this review, we focus on systems biology approaches, based on network inference methods to understand the dynamics of hallmark processes leading to aging phenotypes. We also describe computational methods for the interpretation and identification of important molecular hubs involved in the mechanistic linkage between aging related processes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Understanding microRNA-mediated gene regulatory networks through mathematical modelling

The discovery of microRNAs (miRNAs) has added a new player to the regulation of gene expression. With the increasing number of molecular species involved in gene regulatory networks, it is hard to obtain an intuitive understanding of network dynamics. Mathematical modelling can help dissecting the role of miRNAs in gene regulatory networks, and we shall here review the most recent developments that utilise different mathematical modelling approaches to provide quantitative insights into the function of miRNAs in the regulation of gene expression. Key miRNA regulation features that have been elucidated via modelling include: (i) the role of miRNA-mediated feedback and feedforward loops in fine-tuning of gene expression; (ii) the miRNA–target interaction properties determining the effectiveness of miRNA-mediated gene repression; and (iii) the competition for shared miRNAs leading to the cross-regulation of genes. However, there is still lack of mechanistic understanding of many other properties of miRNA regulation like unconventional miRNA–target interactions, miRNA regulation at different sub-cellular locations and functional miRNA variant, which will need future modelling efforts to deal with. This review provides an overview of recent developments and challenges in this field.

Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast

Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments.

miR-638 promotes melanoma metastasis and protects melanoma cells from apoptosis and autophagy

The present study identified miR-638 as one of the most significantly overexpressed miRNAs in metastatic lesions of melanomas compared with primary melanomas. miR-638 enhanced the tumorigenic properties of melanoma cells in vitro and lung colonization in vivo. mRNA expression profiling identified new candidate genes including TP53INP2 as miR-638 targets, the majority of which are involved in p53 signalling. Overexpression of TP53INP2 severely attenuated proliferative and invasive capacity of melanoma cells which was reversed by miR-638. Depletion of miR-638 stimulated expression of p53 and p53 downstream target genes and induced apoptosis and autophagy. miR-638 promoter analysis identified the miR-638 target transcription factor associated protein 2α (TFAP2A/AP-2α) as a direct negative regulator of miR-638, suggestive for a double-negative regulatory feedback loop. Taken together, miR-638 supports melanoma progression and suppresses p53-mediated apoptosis pathways, autophagy and expression of the transcriptional repressor TFAP2A/AP-2α.