Crosstalk between transcription factors and microRNAs in human protein interaction network

The emerging role of miRNA clusters in breast cancer progression

Micro RNAs (miRNAs) are small non-coding RNAs that are essential for regulation of gene expression of the target genes. Large number of miRNAs are organized into defined units known as miRNA clusters (MCs). The MCs consist of two or more than two miRNA encoding genes driven by a single promoter, transcribed together in the same orientation, that are not separated from each other by a transcription unit. Aberrant miRNA clusters expression is reported in breast cancer (BC), exhibiting both pro-tumorogenic and anti-tumorigenic role. Altered MCs expression facilitates to breast carcinogenesis by promoting the breast cells to acquire the various hallmarks of the cancer. Since miRNA clusters contain multiple miRNA encoding genes, targeting cluster may be more attractive than targeting individual miRNAs. Besides targeting dysregulated miRNA clusters in BC, studies have focused on the mechanism of action, and its contribution to the progression of the BC. The present review provides a comprehensive overview of dysregulated miRNA clusters and its role in the acquisition of cancer hallmarks in BC. More specifically, we have presented the regulation, differential expression, classification, targets, mechanism of action, and signaling pathways of miRNA clusters in BC. Additionally, we have also discussed the potential utility of the miRNA cluster as a diagnostic and prognostic indicator in BC.

Single nucleotide alterations in MicroRNAs and human cancer-A not fully explored field

MicroRNAs are ~20 nt long small noncoding RNAs that are processed from stem-looped precursors and function mainly as posttranscriptional regulators of protein coding genes through binding to 3'-untranslated regions of messenger RNAs to inhibit the translation or cause RNA degradation. It is predicted microRNAs could regulate up to half of all human genes and are proved to play important roles in human diseases including cancer. They bind to target mRNAs based on complementary binding which is dominated by the so-called "seed" region which are the 5' 2-8 bases of the microRNA. Due to the small size in nature, even a single nucleotide variation in the precursor region especially those located in the seed regions could show big influence. Here, I summarized and reviewed the current knowledge of these single nucleotide alterations in microRNAs in human cancer including (i) common SNPs in the precursor region, (ii) isomiRs, (iii) somatic mutations of microRNAs. Briefly, this is an underexploited field and clearly, warrants further studies to reveal their biological and clinical significances. I believe they will be key to advancing personalized medicine.

Comparing progression molecular mechanisms between lung adenocarcinoma and lung squamous cell carcinoma based on genetic and epigenetic networks: big data mining and genome-wide systems identification

Non-small-cell lung cancer (NSCLC) is the predominant type of lung cancer in the world. Lung adenocarcinoma (LADC) and lung squamous cell carcinoma (LSCC) are subtypes of NSCLC. We usually regard them as different disease due to their unique molecular characteristics, distinct cells of origin and dissimilar clinical response. However, the differences of genetic and epigenetic progression mechanism between LADC and LSCC are complicated to analyze. Therefore, we applied systems biology approaches and big databases mining to construct genetic and epigenetic networks (GENs) with next-generation sequencing data of LADC and LSCC. In order to obtain the real GENs, system identification and system order detection are conducted on gene regulatory networks (GRNs) and protein-protein interaction networks (PPINs) for each stage of LADC and LSCC. The core GENs were extracted via principal network projection (PNP). Based on the ranking of projection values, we got the core pathways in respect of KEGG pathway. Compared with the core pathways, we found significant differences between microenvironments, dysregulations of miRNAs, epigenetic modifications on certain signaling transduction proteins and target genes in each stage of LADC and LSCC. Finally, we proposed six genetic and epigenetic multiple-molecule drugs to target essential biomarkers in each progression stage of LADC and LSCC, respectively.

A Small RNA-Mediated Regulatory Network in Arabidopsis thaliana Demonstrates Connectivity Between phasiRNA Regulatory Modules and Extensive Co-Regulation of Transcription by miRNAs and phasiRNAs

Gene regulation involves the orchestrated action of multiple regulators to fine-tune the expression of genes. Hierarchical interactions and co-regulation among regulators are commonly observed in biological systems, leading to complex regulatory networks. Small RNA (sRNAs) have been shown to be important regulators of gene expression due to their involvement in multiple cellular processes. In plants, microRNA (miRNAs) and phased small interfering RNAs (phasiRNAs) correspond to two well-characterized types of sRNAs involved in the regulation of posttranscriptional gene expression, although information about their targets and interactions with other gene expression regulators is limited. We describe an extended sRNA-mediated regulatory network in Arabidopsis thaliana that provides a reference frame to understand sRNA biogenesis and activity at the genome-wide level. This regulatory network combines a comprehensive evaluation of phasiRNA production and sRNA targets supported by degradome data. The network includes ~17% of genes in the A. thaliana genome, representing ~50% annotated gene ontology (GO) functional categories. Approximately 14% of genes with GO annotations corresponding to regulation of gene expression were found to be under sRNA control. The unbiased bioinformatic approach used to produce the network was able to detect 107 PHAS loci (regions of phasiRNA production), 5,047 active phasiRNAs (~70% of which were non-canonical), and reconstruct 17 regulatory modules resulting from complex regulatory interactions between different sRNA-regulatory pathways. Known regulatory modules like miR173-TAS-PPR/TPR and miR390-TAS3-ARF/F-box were faithfully reconstructed and expanded, illustrating the accuracy and sensitivity of the methods and providing confidence for the validity of findings of previously unrecognized modules. The network presented here includes a 2X increase in the number of identified PHAS loci, a large complement (~70%) of non-canonical phasiRNAs, and the most comprehensive evaluation of sRNA cleavage activity in A. thaliana to date. Structural analysis showed similarities to networks of other biological systems and demonstrated connectivity between phasiRNA regulatory modules with extensive co-regulation of transcripts by miRNAs and phasiRNAs. The described regulatory network provides a reference that will facilitate global analyses of individual plant regulatory programs such as those that control homeostasis, development, and responses to biotic and abiotic environmental changes.

Identification of cancer prognosis-associated lncRNAs based on the miRNA-TF co-regulatory motifs and dosage sensitivity

By integrating dosage sensitivity and motif regulation data, we established a framework and identified a total of 33 cancer prognosis-associated lncRNAs.

microRNA-31 inhibition partially ameliorates the deficiency of bone marrow stromal cells from cleidocranial dysplasia

BACKGROUND

Cleidocranial dysplasia (CCD) in humans is an autosomal-dominant skeletal dysplasia caused by heterozygous mutations of the runt-related transcription factor 2 (RUNX2) and significantly increases the risk of osteoporosis. Increasing evidence demonstrates that the dysfunction of bone marrow stromal cells from CCD patients (BMSCs-CCD) contributes to the bone deficiency, but the characteristics of BMSCs-CCD and the mechanisms that underlie their properties remain undefined.

METHODS

The clinical manifestations of three CCD patients were collected and the mutations of RUNX2 were analyzed. The properties of proliferation, osteogenesis, stemness, and senescence of BMSCs-CCD were compared with that of BMSCs from healthy donors. The expression of microRNA-31 ( miR-31) between BMSCs-CCD and BMSCs was measured and lentivirus-carried miR-31 inhibitor was used to determine the role of miR-31 in BMSCs-CCD both in vitro and in vivo. The molecular mechanisms underlying RUNX2-miR31 and miR-31 targeting stemness and senescence of BMSCs-CCD were also explored.

RESULTS

We identified two mutation sites of RUNX2 via exome sequencing from 2 of 3 Chinese CCD patients with typical clinical presentations. Compared with BMSCs from healthy donors, BMSCs-CCD displayed significantly attenuated proliferation, osteogenesis and stemness, and enhanced senescence. Meanwhile, miR-31 knockdown could ameliorate these deficiency phenotypes of BMSCs-CCD by regulating SATB2, BMI1, CDKN, and SP7. Mechanistically, RUNX2 directly repressed miR-31 expression, and therefore RUNX2 haploinsufficiency in CCD leading to miR-31 upregulation contributed to the deficiency of BMSCs-CCD. miR-31 inhibition in BMSCs-CCD showed enhanced osteogenesis through heterotopic subcutaneous implantation in the nude mice.

CONCLUSIONS

Our results show the functional deficiencies of BMSCs-CCD and a potential role of miR-31 in BMSCs-CCD deficiencies. The application of miR-31 inhibitor in BMSCs-CCD might lend hope for developing BMSC-based therapeutic approaches against CCD-associated skeletal diseases.

Integrated analysis reveals candidate genes and transcription factors in lung adenocarcinoma

Lung adenocarcinoma is the most common type of non‑small cell lung cancer in Asia. Therefore, it is important to improve understanding of the underlying transcriptional regulatory mechanisms involved. The present study aimed to identify potential candidate genes and transcription factors (TFs) associated with the disease. Four gene expression profiles were downloaded from the Gene Expression Omnibus database, which included 141 lung adenocarcinoma patients and 191 healthy controls. The differentially expressed genes (DEGs) were screened out and functional annotation was performed. In addition, TFs were identified and a global transcriptional regulatory network was constructed. Integrated analysis gave rise to a total of 1,238 DEGs in lung adenocarcinoma when compared with healthy tissues, including 970 upregulated and 268 downregulated DEGs. The six overexpressed outlier genes of ceruloplasmin, heparan sulfate 6‑O‑sulfotransferase 2, transmembrane protease serine 4, anillin actin binding protein, cellular retinoic acid binding protein 2 and cystatin SN may serve important roles in the development of lung adenocarcinoma. In addition, the downregulation of carbonic anhydrase 4 and S100 calcium binding protein A12 may render these effective diagnostic biomarkers. The results of the transcriptional regulatory network demonstrated that the hub nodes were sex determining region Y‑box 10, Spi‑B transcription factor and nuclear receptor subfamily 4 group A member 2. The four TFs, forkhead box D1, E74‑like ETS transcription factor 5, homeobox A5 and kruppel‑like factor 5, may warrant future investigations into their function in disease development. In conclusion, the present study provided for further studies a list of candidate genes and TFs for the detection and treatment of lung adenocarcinoma.

Interactive cooperation and hierarchical operation of microRNA and transcription factor crosstalk in human transcriptional regulatory network

Transcriptional regulation of gene expression is an essential cellular process that is arranged by transcription factors (TFs), microRNAs (miRNA) and their target genes through a variety of mechanisms. Here, we set out to reconstruct a comprehensive transcriptional regulatory network of Homo sapiens consisting of experimentally verified regulatory information on miRNAs, TFs and their target genes. We have performed topological analyses to elucidate the transcriptional regulatory roles of miRNAs and TFs. When we thoroughly investigated the network motifs, different gene regulatory scenarios were observed; whereas, mutual TF-miRNA regulation (interactive cooperation) and hierarchical operation where miRNAs were the upstream regulators of TFs came into prominence. Otherwise, biological process specific subnetworks were also constructed and integration of gene and miRNA expression data on ovarian cancer was achieved as a case study to observe dynamic patterns of the gene expression. Meanwhile, both co-operation and hierarchical operation types were determined in active ovarian cancer and process-specific subnetworks. In addition, the analysis showed that multiple signals from miRNAs were integrated by TFs. Our results demonstrate new insights on the architecture of the human transcriptional regulatory network, and here we present some lessons we gained from deciphering the reciprocal interplay between miRNAs, TFs and their target genes.

Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAs

Analysis of transcription regulatory networks has revealed many principal features that govern gene expression regulation. MicroRNAs (miRNAs) have emerged as another major class of gene regulators that influence gene expression post-transcriptionally, but there remains a need to assess quantitatively their global roles in gene regulation. Here, we have constructed an integrated gene regulatory network comprised of transcription factors (TFs), miRNAs, and their target genes and analyzed the effect of regulation on target mRNA expression, target protein expression, protein-protein interaction, and disease association. We found that while target genes regulated by the same TFs tend to be co-expressed, co-regulation by miRNAs does not lead to co-expression assessed at either mRNA or protein levels. Analysis of interacting protein pairs in the regulatory network revealed that compared to genes co-regulated by miRNAs, a higher fraction of genes co-regulated by TFs encode proteins in the same complex. Although these results suggest that genes co-regulated by TFs are more functionally related than those co-regulated by miRNAs, genes that share either TF or miRNA regulators are more likely to cause the same disease. Further analysis on the interplay between TFs and miRNAs suggests that TFs tend to regulate intramodule/pathway clusters, while miRNAs tend to regulate intermodule/pathway clusters. These results demonstrate that although TFs and miRNAs both regulate gene expression, they occupy distinct niches in the overall regulatory network within the cell.

Dissecting the regulation rules of cancer-related miRNAs based on network analysis

miRNAs (microRNAs) are a set of endogenous and small non-coding RNAs which specifically induce degradation of target mRNAs or inhibit protein translation to control gene expression. Obviously, aberrant miRNA expression in human cells will lead to a serious of changes in protein-protein interaction network (PPIN), thus to activate or inactivate some pathways related to various diseases, especially carcinogenesis. In this study, we systematically constructed the miRNA-regulated co-expressed protein-protein interaction network (CePPIN) for 17 cancers firstly. We investigated the topological parameters and functional annotation for the proteins in CePPIN, especially for those miRNA targets. We found that targets regulated by more miRNAs tend to play a more important role in the forming process of cancers. We further elucidated the miRNA regulation rules in PPIN from a more systematical perspective. By GO and KEGG pathway analysis, miRNA targets are involved in various cellular processes mostly related to cell cycle, such as cell proliferation, growth, differentiation, etc. Through the Pfam classification, we found that miRNAs belonging to the same family tend to have targets from the same family which displays the synergistic function of these miRNAs. Finally, the case study on miR-519d and miR-21-regulated sub-network was performed to support our findings.

Identification of rifampin-regulated functional modules and related microRNAs in human hepatocytes based on the protein interaction network

BACKGROUND

In combination with gene expression profiles, the protein interaction network (PIN) constructs a dynamic network that includes multiple functional modules. Previous studies have demonstrated that rifampin can influence drug metabolism by regulating drug-metabolizing enzymes, transporters, and microRNAs (miRNAs). Rifampin induces gene expression, at least in part, by activating the pregnane X receptor (PXR), which induces gene expression; however, the impact of rifampin on global gene regulation has not been examined under the molecular network frameworks.

METHODS

In this study, we extracted rifampin-induced significant differentially expressed genes (SDG) based on the gene expression profile. By integrating the SDG and human protein interaction network (HPIN), we constructed the rifampin-regulated protein interaction network (RrPIN). Based on gene expression measurements, we extracted a subnetwork that showed enriched changes in molecular activity. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we identified the crucial rifampin-regulated biological pathways and associated genes. In addition, genes targeted by miRNAs that were significantly differentially expressed in the miRNA expression profile were extracted based on the miRNA-gene prediction tools. The miRNA-regulated PIN was further constructed using associated genes and miRNAs. For each miRNA, we further evaluated the potential impact by the gene interaction network using pathway analysis. RESULTS AND DISCCUSSION: We extracted the functional modules, which included 84 genes and 89 interactions, from the RrPIN, and identified 19 key rifampin-response genes that are associated with seven function pathways that include drug response and metabolism, and cancer pathways; many of the pathways were supported by previous studies. In addition, we identified that a set of 6 genes (CAV1, CREBBP, SMAD3, TRAF2, KBKG, and THBS1) functioning as gene hubs in the subnetworks that are regulated by rifampin. It is also suggested that 12 differentially expressed miRNAs were associated with 6 biological pathways.

CONCLUSIONS

Our results suggest that rifampin contributes to changes in the expression of genes by regulating key molecules in the protein interaction networks. This study offers valuable insights into rifampin-induced biological mechanisms at the level of miRNAs, genes and proteins.

Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

Constructing an integrated genetic and epigenetic cellular network for whole cellular mechanism using high-throughput next-generation sequencing data

Background

Epigenetics has been investigated in cancer initiation, and development, especially, since the appearance of epigenomics. Epigenetics may be defined as the mechanisms that lead to heritable changes in gene function and without affecting the sequence of genome. These mechanisms explain how individuals with the same genotype produce phenotypic differences in response to environmental stimuli. Recently, with the accumulation of high-throughput next-generation sequencing (NGS) data, a key goal of systems biology is to construct networks for different cellular levels to explore whole cellular mechanisms. At present, there is no satisfactory method to construct an integrated genetic and epigenetic cellular network (IGECN), which combines NGS omics data with gene regulatory networks (GRNs), microRNAs (miRNAs) regulatory networks, protein-protein interaction networks (PPINs), and epigenetic regulatory networks of methylation using high-throughput NGS data.

Results

We investigated different kinds of NGS omics data to develop a systems biology method to construct an integrated cellular network based on three coupling models that describe genetic regulatory networks, protein–protein interaction networks, microRNA (miRNA) regulatory networks, and methylation regulation. The proposed method was applied to construct IGECNs of gastric cancer and the human immune response to human immunodeficiency virus (HIV) infection, to elucidate human defense response mechanisms. We successfully constructed an IGECN and validated it by using evidence from literature search. The integration of NGS omics data related to transcription regulation, protein-protein interactions, and miRNA and methylation regulation has more predictive power than independent datasets. We found that dysregulation of MIR7 contributes to the initiation and progression of inflammation-induced gastric cancer; dysregulation of MIR9 contributes to HIV-1 infection to hijack CD4+ T cells through dysfunction of the immune and hormone pathways; dysregulation of MIR139-5p, MIRLET7i, and MIR10a contributes to the HIV-1 integration/replication stage; dysregulation of MIR101, MIR141, and MIR152 contributes to the HIV-1 virus assembly and budding mechanisms; dysregulation of MIR302a contributes to not only microvesicle-mediated transfer of miRNAs but also dysfunction of NF-κB signaling pathway in hepatocarcinogenesis.

Conclusion

The coupling dynamic systems of the whole IGECN can allow us to investigate genetic and epigenetic cellular mechanisms via omics data and big database mining, and are useful for further experiments in the field of systems and synthetic biology.

MicroRNAs As Potential Targets for Abiotic Stress Tolerance in Plants

The microRNAs (miRNAs) are small (20-24 nt) sized, non-coding, single stranded riboregulator RNAs abundant in higher organisms. Recent findings have established that plants assign miRNAs as critical post-transcriptional regulators of gene expression in sequence-specific manner to respond to numerous abiotic stresses they face during their growth cycle. These small RNAs regulate gene expression via translational inhibition. Usually, stress induced miRNAs downregulate their target mRNAs, whereas, their downregulation leads to accumulation and function of positive regulators. In the past decade, investigations were mainly aimed to identify plant miRNAs, responsive to individual or multiple environmental factors, profiling their expression patterns and recognizing their roles in stress responses and tolerance. Altered expressions of miRNAs implicated in plant growth and development have been reported in several plant species subjected to abiotic stress conditions such as drought, salinity, extreme temperatures, nutrient deprivation, and heavy metals. These findings indicate that miRNAs may hold the key as potential targets for genetic manipulations to engineer abiotic stress tolerance in crop plants. This review is aimed to provide recent updates on plant miRNAs, their biogenesis and functions, target prediction and identification, computational tools and databases available for plant miRNAs, and their roles in abiotic stress-responses and adaptive mechanisms in major crop plants. Besides, the recent case studies for overexpressing the selected miRNAs for miRNA-mediated enhanced abiotic stress tolerance of transgenic plants have been discussed.

A cross-cancer differential co-expression network reveals microRNA-regulated oncogenic functional modules

MicroRNAs (miRNAs) are small non-coding RNAs that can regulate their target gene expressions at the post-transcriptional level. Moreover, they have been reported as either oncomirs or tumor suppressors and possess therapeutic potential in cancer. In this study, we investigated differential co-expression of miRNAs across four cancer types. We observed that the loss of positive co-expressions among miRNAs frequently occurs in the studied cancer types. This observation suggests that the disruption of positive co-expressions among miRNAs may be prevalent during tumorigenesis. By systematically collecting these lost positive co-expressions among miRNAs in cancer, we constructed a cross-cancer miRNA differential co-expression network. We observed that the influential miRNAs in the proposed network, i.e., hubs or in larger cliques, tended to be involved in more cancer types than other miRNAs. Moreover, we found that miRNAs which lose their positive co-expressions in cancers might co-contribute to cancer development, and even could be used to predict the cancer types in which miRNAs were involved. Finally, we identified two potential miRNA-regulated onco-modules, mitosis and DNA replication, that are associated with poor survival outcomes in patients across multiple cancers. Collectively, our study suggested that the disruption of miRNA positive co-expression in cancer might contribute to cancer development. Our findings also form an important basis for identifying miRNAs with potential co-contribution to carcinogenesis.

A novel framework for inferring condition-specific TF and miRNA co-regulation of protein-protein interactions

Recent studies have shown that transcription factors (TFs) and microRNAs (miRNAs), while independently regulate their downstream targets, collaborate with each other to regulate gene expression. However, their synergistic roles in protein-protein interactions (PPIs) remain mostly unknown. In this paper, we present a novel framework (called CoRePPI) for inferring TF and miRNA co-regulation of PPIs. Particularly, CoRePPI is aimed at discovering the co-regulation specific to a condition of interest, by using heterogeneous data, including miRNA and messenger RNA (mRNA) expression profiles, putative miRNA targets, TF targets and PPIs. CoRePPI firstly finds the network motifs indicating the co-regulation of PPIs by TFs and miRNAs in tumor and normal conditions separately. Then by identifying the differential motifs found in one condition but not in the other, it builds the networks consisting of TFs, miRNAs and their co-regulated PPIs specific to different conditions respectively. To validate CoRePPI, we apply it to the Pan-Cancer dataset which includes the expression profiles of 12 cancer types from TCGA. Through network topology analysis, we found that the tumor and normal CoRePPI networks are scale-free. Furthermore, the results of differential and intersected network analysis between the tumor and normal CoRePPI networks suggest that only a small fraction of the regulatory relationships between TFs and miRNAs are conserved in both conditions but they co-regulate different downstream PPIs in tumor and normal conditions; and in different conditions the majority of the regulatory relationships between TFs and miRNAs are different although they may regulate the same PPIs in their respective conditions. The CoRePPI sub-networks constructed for the three types of cancers (breast cancer, lung cancer and ovarian cancer) are all scale-free, and the intersection of these CoRePPI sub-networks can be utilized as the biomarker CoRePPI sub-network of the three types of cancers. The PPI enrichment analyses of the tumor and normal CoRePPI networks suggest that the co-regulating TFs and miRNAs are significantly associated with the specific biological processes, diseases and pathways. In addition, comparing with the two non-condition-specific approaches, the tumor CoRePPI network is found to have the most enriched cancer-related PPIs. Altogether, the results uncover the combined regulatory patterns of TFs and miRNAs on the PPIs, and may provide new insights for research in cancer-associated TFs and miRNAs.

Identification of Biomarker and Co-Regulatory Motifs in Lung Adenocarcinoma Based on Differential Interactions

Changes in intermolecular interactions (differential interactions) may influence the progression of cancer. Specific genes and their regulatory networks may be more closely associated with cancer when taking their transcriptional and post-transcriptional levels and dynamic and static interactions into account simultaneously. In this paper, a differential interaction analysis was performed to detect lung adenocarcinoma-related genes. Furthermore, a miRNA-TF (transcription factor) synergistic regulation network was constructed to identify three kinds of co-regulated motifs, namely, triplet, crosstalk and joint. Not only were the known cancer-related miRNAs and TFs (let-7, miR-15a, miR-17, TP53, ETS1, and so on) were detected in the motifs, but also the miR-15, let-7 and miR-17 families showed a tendency to regulate the triplet, crosstalk and joint motifs, respectively. Moreover, several biological functions (i.e., cell cycle, signaling pathways and hemopoiesis) associated with the three motifs were found to be frequently targeted by the drugs for lung adenocarcinoma. Specifically, the two 4-node motifs (crosstalk and joint) based on co-expression and interaction had a closer relationship to lung adenocarcinoma, and so further research was performed on them. A 10-gene biomarker (UBC, SRC, SP1, MYC, STAT3, JUN, NR3C1, RB1, GRB2 and MAPK1) was selected from the joint motif, and a survival analysis indicated its significant association with survival. Among the ten genes, JUN, NR3C1 and GRB2 are our newly detected candidate lung adenocarcinoma-related genes. The genes, regulators and regulatory motifs detected in this work will provide potential drug targets and new strategies for individual therapy.

miRegulome: a knowledge-base of miRNA regulomics and analysis

miRNAs regulate post transcriptional gene expression by targeting multiple mRNAs and hence can modulate multiple signalling pathways, biological processes, and patho-physiologies. Therefore, understanding of miRNA regulatory networks is essential in order to modulate the functions of a miRNA. The focus of several existing databases is to provide information on specific aspects of miRNA regulation. However, an integrated resource on the miRNA regulome is currently not available to facilitate the exploration and understanding of miRNA regulomics. miRegulome attempts to bridge this gap. The current version of miRegulome v1.0 provides details on the entire regulatory modules of miRNAs altered in response to chemical treatments and transcription factors, based on validated data manually curated from published literature. Modules of miRegulome (upstream regulators, downstream targets, miRNA regulated pathways, functions, diseases, etc) are hyperlinked to an appropriate external resource and are displayed visually to provide a comprehensive understanding. Four analysis tools are incorporated to identify relationships among different modules based on user specified datasets. miRegulome and its tools are helpful in understanding the biology of miRNAs and will also facilitate the discovery of biomarkers and therapeutics. With added features in upcoming releases, miRegulome will be an essential resource to the scientific community.

AVAILABILITY

http://bnet.egr.vcu.edu/miRegulome.

MicroRNAs are part of the regulatory network that controls EGF induced apoptosis, including elements of the JAK/STAT pathway, in A431 cells

MiRNAs are known to regulate gene expression and in the context of cancer have been shown to regulate metastasis, cell proliferation and cell death. In this report we describe potential miRNA regulatory roles with respect to induction of cell death by pharmacologic dose of Epidermal Growth Factor (EGF). Our previous work suggested that multiple pathways are involved in the induction of apoptosis, including interferon induced genes, cytokines, cytoskeleton and cell adhesion and TP53 regulated genes. Using miRNA time course expression profiling of EGF treated A431 cells and coupling this to our previous gene expression and proteomic data, we have been able to implicate a number of additional miRNAs in the regulation of apoptosis. Specifically we have linked miR-134, miR-145, miR-146b-5p, miR-432 and miR-494 to the regulation of both apoptotic and anti-apoptotic genes expressed as a function of EGF treatment. Whilst additional miRNAs were differentially expressed, these had the largest number of apoptotic and anti-apoptotic targets. We found 5 miRNAs previously implicated in the regulation of apoptosis and our results indicate that an additional 20 miRNAs are likely to be involved based on their correlated expression with targets. Certain targets were linked to multiple miRNAs, including PEG10, BTG1, ID1, IL32 and NCF2. Some miRNAs that target the interferon pathway were found to be down regulated, consistent with a novel layer of regulation of interferon pathway components downstream of JAK/STAT. We have significantly expanded the repertoire of miRNAs that may regulate apoptosis in cancer cells as a result of this work.

Functional evolution of cardiac microRNAs in heart development and functions

MicroRNAs (miRNAs) are a class of endogenous small noncoding RNAs that regulate gene expression either by degrading target mRNAs or by suppressing protein translation. miRNAs have been found to be involved in many biological processes, such as development, differentiation, and growth. However, the evolution of miRNA regulatory functions and networks has not been well studied. In this study, we conducted a cross-species analysis to study the evolution of cardiac miRNAs and their regulatory functions and networks. We found that conserved cardiac miRNA target genes have maintained highly conserved cardiac functions. Additionally, most of cardiac miRNA target genes in human with annotations of cardiac functions evolved from the corresponding homologous targets, which are also involved in heart development-related functions. On the basis of these results, we investigated the functional evolution of cardiac miRNAs and presented a functional evolutionary map. From this map, we identified the evolutionary time at which the cardiac miRNAs became involved in heart development or function and found that the biological processes of heart development evolved earlier than those of heart functions, for example, heart contraction/relaxation or cardiac hypertrophy. Our study of the evolution of the cardiac miRNA regulatory networks revealed the emergence of new regulatory functional branches during evolution. Furthermore, we discovered that early evolved cardiac miRNA target genes tend to participate in the early stages of heart development. This study sheds light on the evolution of developmental features of genes regulated by cardiac miRNAs.

Computational developments in microRNA-regulated protein-protein interactions

Protein-protein interaction (PPI) is one of the most important functional components of a living cell. Recently, researchers have been interested in investigating the correlation between PPI and microRNA, which has been found to be a regulator at the post-transcriptional level. Studies on miRNA-regulated PPI networks will not only facilitate an understanding of the fine tuning role that miRNAs play in PPI networks, but will also provide potential candidates for tumor diagnosis. This review describes basic studies on the miRNA-regulated PPI network in the way of bioinformatics which includes constructing a miRNA-target protein network, describing the features of miRNA-regulated PPI networks and overviewing previous findings based on analysing miRNA-regulated PPI network features.

Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network

Background

Gene regulatory networks (GRNs) are models of molecule-gene interactions instrumental in the coordination of gene expression. Transcription factor (TF)-GRNs are an important subset of GRNs that characterize gene expression as the effect of TFs acting on their target genes. Although such networks can qualitatively summarize TF-gene interactions, it is highly desirable to quantitatively determine the strengths of the interactions in a TF-GRN as well as the magnitudes of TF activities. To our knowledge, such analysis is rare in plant biology. A computational methodology developed for this purpose is network component analysis (NCA), which has been used for studying large-scale microbial TF-GRNs to obtain nontrivial, mechanistic insights. In this work, we employed NCA to quantitatively analyze a plant TF-GRN important in floral development using available regulatory information from AGRIS, by processing previously reported gene expression data from four shoot apical meristem cell types.

Results

The NCA model satisfactorily accounted for gene expression measurements in a TF-GRN of seven TFs (LFY, AG, SEPALLATA3 [SEP3], AP2, AGL15, HY5 and AP3/PI) and 55 genes. NCA found strong interactions between certain TF-gene pairs including LFY → MYB17, AG → CRC, AP2 → RD20, AGL15 → RAV2 and HY5 → HLH1, and the direction of the interaction (activation or repression) for some AGL15 targets for which this information was not previously available. The activity trends of four TFs - LFY, AG, HY5 and AP3/PI as deduced by NCA correlated well with the changes in expression levels of the genes encoding these TFs across all four cell types; such a correlation was not observed for SEP3, AP2 and AGL15.

Conclusions

For the first time, we have reported the use of NCA to quantitatively analyze a plant TF-GRN important in floral development for obtaining nontrivial information about connectivity strengths between TFs and their target genes as well as TF activity. However, since NCA relies on documented connectivity information about the underlying TF-GRN, it is currently limited in its application to larger plant networks because of the lack of documented connectivities. In the future, the identification of interactions between plant TFs and their target genes on a genome scale would allow the use of NCA to provide quantitative regulatory information about plant TF-GRNs, leading to improved insights on cellular regulatory programs.

miRNA regulation in the context of functional protein networks: principles and applications

MicroRNAs (miRNAs) are small non-coding endogenous regulatory RNAs that fine-tune gene expression in a wide range of biological processes and diseases. miRNAs exert their function by targeting mRNAs to trigger their degradation or inhibit protein translation. The proteins encoded by the genes targeted by miRNAs may act as key components of cellular networks, thus the use of biological molecular network information for the purposes of elucidating the role of miRNAs in molecular disease mechanism is a key objective in systems biomedicine. The crosstalk layer between miRNA-target networks and functional protein is rich sources of information to explore the function of miRNAs at the system level. Characterizing the influence of miRNAs in the context of the target (protein interactors of the target) is in the early stages with potential to help better understand how miRNAs function within the cellular networks. In this article, the latest research on the cross-talk between miRNAs and protein networks, particularly physical protein interactions and gene regulatory networks is summarized. This article also covers recent research on understanding the biology of miRNAs at the system level and defines principles of miRNA regulation of protein and gene regulatory networks. The second part of the article highlights the promise of considering the protein context of the miRNA target when searching for functional miRNA-target interactions. Some of the applications of integrating protein networks with miRNA-targets that have clinical and functional utility are described.

Functional analysis of microRNA and transcription factor synergistic regulatory network based on identifying regulatory motifs in non-small cell lung cancer

Background

Lung cancer, especially non-small cell lung cancer, is a leading cause of malignant tumor death worldwide. Understanding the mechanisms employed by the main regulators, such as microRNAs (miRNAs) and transcription factors (TFs), still remains elusive. The patterns of their cooperation and biological functions in the synergistic regulatory network have rarely been studied.

Results

Here, we describe the first miRNA-TF synergistic regulation network in human lung cancer. We identified important regulators (MYC, NFKB1, miR-590, and miR-570) and significant miRNA-TF synergistic regulatory motifs by random simulations. The two most significant motifs were the co-regulation of miRNAs and TFs, and TF-mediated cascade regulation. We also developed an algorithm to uncover the biological functions of the human lung cancer miRNA-TF synergistic regulatory network (regulation of apoptosis, cellular protein metabolic process, and cell cycle), and the specific functions of each miRNA-TF synergistic subnetwork. We found that the miR-17 family exerted important effects in the regulation of non-small cell lung cancer, such as in proliferation and cell cycle regulation by targeting the retinoblastoma protein (RB1) and forming a feed forward loop with the E2F1 TF. We proposed a model for the miR-17 family, E2F1, and RB1 to demonstrate their potential roles in the occurrence and development of non-small cell lung cancer.

Conclusions

This work will provide a framework for constructing miRNA-TF synergistic regulatory networks, function analysis in diseases, and identification of the main regulators and regulatory motifs, which will be useful for understanding the putative regulatory motifs involving miRNAs and TFs, and for predicting new targets for cancer studies.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

FOXC1 in human trabecular meshwork cells is involved in regulatory pathway that includes miR-204, MEIS2, and ITGβ1

Forkhead box C1 (FOXC1) is a transcription factor that affects eye development. FOXC1 is implicated in the etiology of glaucoma because mutations in the gene are among the causes of Axenfeld-Rieger syndrome which is often accompanied by glaucoma. Glaucoma is the second leading cause of blindness. It is a complex disorder whose genetic basis in most patients remains unknown. Microarrays expression analysis was performed to identify genes in human trabecular meshwork (TM) primary cultures that are affected by FOXC1 and genes that may have roles in glaucoma. This represents the first genome wide analysis of FOXC1 target genes in any tissue. FOXC1 knock down by siRNAs affected the expression of 849 genes. Results on selected genes were confirmed by real time PCR, immunoblotting, and dual luciferase reporter assays. Observation of MEIS2 as a FOXC1 target and consideration of FOXC1 as a potential target of miR-204 prompted testing the effect of this micro RNA on expression of FOXC1 and several genes identified by array analysis as FOXC1 target genes. It was observed that miR-204 caused decreased expression of FOXC1 and the FOXC1 target genes CLOCK, PLEKHG5, ITGβ1, and MEIS2 in the TM cultures. Expression of CLOCK, PLEKHG5, ITGβ1 has not previously been reported to be affected by miR-204. The data suggest existence of a complex regulatory pathway in the TM part of which includes interactions between FOXC1, miR-204, MEIS2, and ITGβ1. All these molecules are known to have TM relevant functions, and the TM is strongly implicated in the etiology of glaucoma.

SignaLink 2 - a signaling pathway resource with multi-layered regulatory networks

BACKGROUND

Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor.

DESCRIPTION

We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML). Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org.

CONCLUSIONS

With SignaLink 2 as a single resource, users can effectively analyze signaling pathways, scaffold proteins, modifier enzymes, transcription factors and miRNAs that are important in the regulation of signaling processes. This integrated resource allows the systems-level examination of how cross-talks and signaling flow are regulated, as well as provide data for cross-species comparisons and drug discovery analyses.

Lengthening of 3'UTR increases with morphological complexity in animal evolution

MOTIVATION

Evolutionary expansion of gene regulatory circuits seems to boost morphological complexity. However, the expansion patterns and the quantification relationships have not yet been identified. In this study, we focus on the regulatory circuits at the post-transcriptional level, investigating whether and how this principle may apply.

RESULTS

By analysing the structure of mRNA transcripts in multiple metazoan species, we observed a striking exponential correlation between the length of 3' untranslated regions (3'UTR) and morphological complexity as measured by the number of cell types in each organism. Cellular diversity was similarly associated with the accumulation of microRNA genes and their putative targets. We propose that the lengthening of 3'UTRs together with a commensurate exponential expansion in post-transcriptional regulatory circuits can contribute to the emergence of new cell types during animal evolution.

Systems biology of meridians, acupoints, and chinese herbs in disease

Meridians, acupoints, and Chinese herbs are important components of traditional Chinese medicine (TCM). They have been used for disease treatment and prevention and as alternative and complementary therapies. Systems biology integrates omics data, such as transcriptional, proteomic, and metabolomics data, in order to obtain a more global and complete picture of biological activity. To further understand the existence and functions of the three components above, we reviewed relevant research in the systems biology literature and found many recent studies that indicate the value of acupuncture and Chinese herbs. Acupuncture is useful in pain moderation and relieves various symptoms arising from acute spinal cord injury and acute ischemic stroke. Moreover, Chinese herbal extracts have been linked to wound repair, the alleviation of postmenopausal osteoporosis severity, and anti-tumor effects, among others. Different acupoints, variations in treatment duration, and herbal extracts can be used to alleviate various symptoms and conditions and to regulate biological pathways by altering gene and protein expression. Our paper demonstrates how systems biology has helped to establish a platform for investigating the efficacy of TCM in treating different diseases and improving treatment strategies.

Molecular network analysis of human microRNA targetome: from cancers to Alzheimer's disease

MicroRNAs (miRNAs), a class of endogenous small noncoding RNAs, mediate posttranscriptional regulation of protein-coding genes by binding chiefly to the 3’ untranslated region of target mRNAs, leading to translational inhibition, mRNA destabilization or degradation. A single miRNA concurrently downregulates hundreds of target mRNAs designated “targetome”, and thereby fine-tunes gene expression involved in diverse cellular functions, such as development, differentiation, proliferation, apoptosis and metabolism. Recently, we characterized the molecular network of the whole human miRNA targetome by using bioinformatics tools for analyzing molecular interactions on the comprehensive knowledgebase. We found that the miRNA targetome regulated by an individual miRNA generally constitutes the biological network of functionally-associated molecules in human cells, closely linked to pathological events involved in cancers and neurodegenerative diseases. We also identified a collaborative regulation of gene expression by transcription factors and miRNAs in cancer-associated miRNA targetome networks. This review focuses on the workflow of molecular network analysis of miRNA targetome in silico. We applied the workflow to two representative datasets, composed of miRNA expression profiling of adult T cell leukemia (ATL) and Alzheimer’s disease (AD), retrieved from Gene Expression Omnibus (GEO) repository. The results supported the view that miRNAs act as a central regulator of both oncogenesis and neurodegeneration.

The emerging paradigm of network medicine in the study of human disease

The molecular pathways that govern human disease consist of molecular circuits that coalesce into complex, overlapping networks. These network pathways are presumably regulated in a coordinated fashion, but such regulation has been difficult to decipher using only reductionistic principles. The emerging paradigm of "network medicine" proposes to utilize insights garnered from network topology (eg, the static position of molecules in relation to their neighbors) as well as network dynamics (eg, the unique flux of information through the network) to understand better the pathogenic behavior of complex molecular interconnections that traditional methods fail to recognize. As methodologies evolve, network medicine has the potential to capture the molecular complexity of human disease while offering computational methods to discern how such complexity controls disease manifestations, prognosis, and therapy. This review introduces the fundamental concepts of network medicine and explores the feasibility and potential impact of network-based methods for predicting individual manifestations of human disease and designing rational therapies. Wherever possible, we emphasize the application of these principles to cardiovascular disease.

microRNAs as promising tools for improving stress tolerance in rice

Rice (Oryza sativa) represents one of the most important food crops in the world, since it feeds more than two billion people. The increased rice production can play significant roles in upgrading the economic status of countries like India and China. A great deal of research has been carried out in the recent past on the molecular biology, genomics and biotechnology of rice. By employing recombinant DNA technology, remarkable progress had been made towards production of rice plants with increase yield, improved nutritional quality and resistance to various diseases. Under these circumstances, the study of microRNAs can contribute to new discoveries in this field. The miRNAs are assign to modulate gene expression at the post-transcriptional level. They are small, non-coding, single stranded RNAs that are abundantly found in prokaryotic and eukaryotic cells and can trigger translational repression or gene silencing by binding to complementary sequences on target mRNA transcripts. In the recent years, miRNAs have been reported to control a variety of biological processes, such as plant development, differentiation, signal transduction or stress responses. The present review provides an up-date on microRNAs and their involvement in the stress response in rice. A section is specifically dedicated to the genetic engineering perspectives regarding the miRNAs applications in rice tolerance to stress conditions.