Specific Regulatory Networks Research Articles

Whole mount in situ hybridization techniques for analysis of the spatial distribution of mRNAs in sea urchin embryos and early larvae.

A critical process in embryonic development is the activation and spatial localization of mRNAs to specific cells and territories of the embryo. Revealing the spatial distribution of mRNAs and how it changes during development is a vital piece of information that aids in understanding the signaling and regulatory genes driving specific gene regulatory networks. In the laboratory, a cost-efficient, reliable method to determine the spatial distribution of mRNAs in embryos is in situ hybridization. This sensitive and straightforward method employs exogenous antisense RNA probes to find specific and complementary sequences in fixed embryos. Antigenic moieties conjugated to the ribonucleotides incorporated in the probe cross-react with antibodies, and numerous staining methods can be subsequently employed to reveal the spatial distribution of the targeted mRNA. The quality of the data produced by this method is equivalent to the experience of the researcher, and thus a thorough understanding of the numerous steps comprising this method is important for obtaining high quality data. Here we compile and summarize several protocols that have been employed chiefly on five sea urchin species in numerous laboratories around the world. Whereas the protocols can vary for the different species, the overarching steps are similar and can be readily mastered. When properly and carefully undertaken, in situ hybridization is a powerful tool providing unambiguous data for which there currently is no comparable substitute and will continue to be an important method in the era of big data and beyond.

Constructing tissue-specific transcriptional regulatory networks via a Markov random field

BackgroundRecent advances in sequencing technologies have enabled parallel assays of chromatin accessibility and gene expression for major human cell lines. Such innovation provides a great opportunity to decode phenotypic consequences of genetic variation via the construction of predictive gene regulatory network models. However, there still lacks a computational method to systematically integrate chromatin accessibility information with gene expression data to recover complicated regulatory relationships between genes in a tissue-specific manner.ResultsWe propose a Markov random field (MRF) model for constructing tissue-specific transcriptional regulatory networks via integrative analysis of DNase-seq and RNA-seq data. Our method, named CSNets (cell-line specific regulatory networks), first infers regulatory networks for individual cell lines using chromatin accessibility information, and then fine-tunes these networks using the MRF based on pairwise similarity between cell lines derived from gene expression data. Using this method, we constructed regulatory networks specific to 110 human cell lines and 13 major tissues with the use of ENCODE data. We demonstrated the high quality of these networks via comprehensive statistical analysis based on ChIP-seq profiles, functional annotations, taxonomic analysis, and literature surveys. We further applied these networks to analyze GWAS data of Crohn’s disease and prostate cancer. Results were either consistent with the literature or provided biological insights into regulatory mechanisms of these two complex diseases. The website of CSNets is freely available at http://bioinfo.au.tsinghua.edu.cn/jianglab/CSNETS/.ConclusionsCSNets demonstrated the power of joint analysis on epigenomic and transcriptomic data towards the accurate construction of gene regulatory network. Our work provides not only a useful resource of regulatory networks to the community, but also valuable experiences in methodology development for multi-omics data integration.

Clinical Significance and Transcriptional Profiling of Persistent Minimal Residual Disease (MRD) in Multiple Myeloma (MM) Patients with Standard-Risk (SR) and High-Risk (HR) Cytogenetics

Clinical Significance and Transcriptional Profiling of Persistent Minimal Residual Disease (MRD) in Multiple Myeloma (MM) Patients with Standard-Risk (SR) and High-Risk (HR) Cytogenetics

Type 1 Diabetes Mellitus-Associated Genetic Variants Contribute to Overlapping Immune Regulatory Networks.

Type 1 diabetes (T1D) is a chronic metabolic disorder characterized by the autoimmune destruction of insulin-producing pancreatic islet beta cells in genetically predisposed individuals. Genome-wide association studies (GWAS) have identified over 60 risk regions across the human genome, marked by single nucleotide polymorphisms (SNPs), which confer genetic predisposition to T1D. There is increasing evidence that disease-associated SNPs can alter gene expression through spatial interactions that involve distal loci, in a tissue- and development-specific manner. Here, we used three-dimensional (3D) genome organization data to identify genes that physically co-localized with DNA regions that contained T1D-associated SNPs in the nucleus. Analysis of these SNP-gene pairs using the Genotype-Tissue Expression database identified a subset of SNPs that significantly affected gene expression. We identified 246 spatially regulated genes including HLA-DRB1, LAT, MICA, BTN3A2, CTLA4, CD226, NOTCH1, TRIM26, PTEN, TYK2, CTSH, and FLRT3, which exhibit tissue-specific effects in multiple tissues. We observed that the T1D-associated variants interconnect through networks that form part of the immune regulatory pathways, including immune-cell activation, cytokine signaling, and programmed cell death protein-1 (PD-1). Our results implicate T1D-associated variants in tissue and cell-type specific regulatory networks that contribute to pancreatic beta cell inflammation and destruction, adaptive immune signaling, and immune-cell proliferation and activation. A number of other regulatory changes we identified are not typically considered to be central to the pathology of T1D. Collectively, our data represent a novel resource for the hypothesis-driven development of diagnostic, prognostic, and therapeutic interventions in T1D.

Sexual Inequality in the Cancer Cell.

Investigating sex differences in cancer will improve therapy for both sexes and discover sex-specific protective mechanisms. Two recent analyses by Lopes-Ramos and colleagues and Li and colleagues point to specific gene regulatory networks and genomic alterations associated with sex differences in tumor incidence and progression. Integrating this information with emerging concepts about sex biases in the genome may help focus attention on factors that shift the odds for tumor growth. Cancer Res; 78(19); 5504-5. ©2018 AACR See related articles by Li et al., p. 5527, and Lopes-Ramos et al., p. 5538.

Integration of Multi-omics Data for Gene Regulatory Network Inference and Application to Breast Cancer.

Underlying a cancer phenotype is a specific gene regulatory network that represents the complex regulatory relationships between genes. However, it remains a challenge to find cancer-related gene regulatory network because of insufficient sample sizes and complex regulatory mechanisms in which gene is influenced by not only other genes but also other biological factors. With the development of high-throughput technologies and the unprecedented wealth of multi-omics data give us a new opportunity to design machine learning method to investigate underlying gene regulatory network. In this paper, we propose an approach, which use biweight midcorrelation to measure the correlation between factors and make use of nonconvex penalty based sparse regression for gene regulatory network inference (BMNPGRN). BMNCGRN incorporates multi-omics data (including DNA methylation and copy number variation) and their interactions in gene regulatory network model. The experimental results on synthetic datasets show that BMNPGRN outperforms popular and state-of-the-art methods (including DCGRN, ARACNE and CLR) under false positive control. Furthermore, we applied BMNPGRN on breast cancer (BRCA) data from The Cancer Genome Atlas database and provided gene regulatory network.

Abstract 371: Cardiac Endocrinology: Biology of a Heart-derived Hormone That Regulates Body Growth

The endocrine system is crucial for maintaining whole-body homeostasis. Little is known regarding endocrine hormones secreted by the heart other than ANP and BNP discovered over 30 years ago. We recently identified growth differentiation factor 15 (GDF15) as a heart-derived hormone that regulates body growth. Pediatric heart disease induces GDF15 synthesis and secretion by cardiomyocytes. Circulating GDF15 in turn acts on the liver to inhibit growth hormone (GH) signaling and body growth. Blocking cardiomyocyte production of GDF15 normalizes circulating GDF15 level and restores liver GH signaling, establishing GDF15 as a bona fide heart-derived hormone that regulates pediatric body growth. Importantly, plasma GDF15 is further increased in children with concomitant heart disease and failure to thrive. In addition, we used massively parallel single-nucleus RNA-Seq to decipher cardiac cell type specific gene regulatory network that induces GDF15 transcription in heart disease condition. We further determined how GDF15 precursor is processed into mature hormone. Together these studies reveal a new endocrine mechanism (cardiac endocrinology) by which the heart coordinates cardiac function and body growth.

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium, and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of four putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways’ relationship to T2D.

Comparative mRNA and miRNA expression in European mouflon (Ovis musimon) and sheep (Ovis aries) provides novel insights into the genetic mechanisms for female reproductive success

Prolific breeds of domestic sheep (Ovis aries) are important genetic resources due to their reproductive performance, which is characterized by multiple lambs per birth and out-of-season breeding. However, the lack of a comprehensive understanding of the genetic mechanisms underlying the important reproductive traits, particularly from the evolutionary genomics perspective, has impeded the efficient advancement of sheep breeding. Here, for the first time, by performing RNA-sequencing we built a de novo transcriptome assembly of ovarian and endometrial tissues in European mouflon (Ovis musimon) and performed an mRNA–miRNA integrated expression profiling analysis of the wild species and a highly prolific domestic sheep breed, the Finnsheep. We identified several novel genes with differentially expressed mRNAs (e.g., EREG, INHBA, SPP1, AMH, TDRD5, and ZP2) between the wild and domestic sheep, which are functionally involved in oocyte and follicle development and fertilization, and are significantly (adjusted P-value < 0.05) enriched in the Gene Ontology (GO) terms of various reproductive process, including the regulation of fertilization, oogenesis, ovarian follicle development, and sperm–egg recognition. Additionally, we characterized 58 differentially expressed miRNAs and 210 associated target genes that are essential for the regulation of female reproduction cycles through specific regulatory networks [e.g., (miR-136, miR-374a, miR-9-5p)-(EREG, INHBA)]. Furthermore, our integrated mRNA and miRNA expression profiling analysis elucidated novel direct and indirect miRNA/mRNA causal regulatory relationships related to the reproductive traits of the Ovis species. This study provides in-depth insights into the genomic evolution underlying the reproductive traits of the Ovis species and valuable resources for ovine genomics.

P-61 - Integration of oxidized lipids in pro-inflammatory metabolic networks targeting obesity and related complications

Pathogenesis of obesity and related metabolic diseases is closely associated with redox disbalance and production of oxidized lipids. Oxidized lipids affect all main cell populations in adipose tissue, their adipokine secretion and themselves can act as an active lipokines triggering autocrine and paracrine signaling. Obesity associate adipocyte hypertrophy and M2 to M1 adipose tissue macrophage (ATMs) shift was associated with oxidized lipids signaling. Despite large amount of data on the role of oxidized lipids and especially eicosanoids in pro-inflammatory signaling, no unified metabolic networks required for systems medicine data integration are available till now. Based on the literature meta-study, networks of enzymatic and free-radical-driven oxidation were reconstructed for the most abundant polyunsaturated fatty acids (PUFAs) and included 570 reactions covering 480 oxidized lipid species. Using networks of PUFA oxidation as a scaffold, adipocytes and ATMs specific regulatory metabolic networks covering four eicosanoids, namely prostaglandin E2, lipoxin A4, 6,15-deoxy prostaglandin J3, and epoxyeicosatetraenoic acids were reconstructed and enriched to uncover their roles in obesity related adipose tissue lipolysis, cytokines/adipokine production and secretion.

Modulation of dynamic modes by interplay between positive and negative feedback loops in gene regulatory networks.

A positive and a negative feedback loop can induce bistability and oscillation, respectively, in biological networks. Nevertheless, they are frequently interlinked to perform more elaborate functions in many gene regulatory networks. Coupled positive and negative feedback loops may exhibit either oscillation or bistability depending on the intensity of the stimulus in some particular networks. It is less understood how the transition between the two dynamic modes is modulated by the positive and negative feedback loops. We developed an abstract model of such systems, largely based on the core p53 pathway, to explore the mechanism for the transformation of dynamic behaviors. Our results show that enhancing the positive feedback may promote or suppress oscillations depending on the strength of both feedback loops. We found that the system oscillates with low amplitudes in response to a moderate stimulus and switches to the on state upon a strong stimulus. When the positive feedback is activated much later than the negative one in response to a strong stimulus, the system exhibits long-term oscillations before switching to the on state. We explain this intriguing phenomenon using quasistatic approximation. Moreover, early switching to the on state may occur when the system starts from a steady state in the absence of stimuli. The interplay between the positive and negative feedback plays a key role in the transitions between oscillation and bistability. Of note, our conclusions should be applicable only to some specific gene regulatory networks, especially the p53 network, in which both oscillation and bistability exist in response to a certain type of stimulus. Our work also underscores the significance of transient dynamics in determining cellular outcome.

Transcriptional and Epigenetic Control of Mammalian Olfactory Epithelium Development.

The postnatal mammalian olfactory epithelium (OE) represents a major aspect of the peripheral olfactory system. It is a pseudostratified tissue that originates from the olfactory placode and is composed of diverse cells, some of which are specialized receptor neurons capable of transducing odorant stimuli to afford the perception of smell (olfaction). The OE is known to offer a tractable miniature model for studying the systematic generation of neurons and glia that typify neural tissue development. During OE development, stem/progenitor cells that will become olfactory sensory neurons and/or non-neuronal cell types display fine spatiotemporal expression of neuronal and non-neuronal genes that ensures their proper proliferation, differentiation, survival, and regeneration. Many factors, including transcription and epigenetic factors, have been identified as key regulators of the expression of such requisite genes to permit normal OE morphogenesis. Typically, specific interactive regulatory networks established between transcription and epigenetic factors/cofactors orchestrate histogenesis in the embryonic and adult OE. Hence, investigation of these regulatory networks critical for OE development promises to disclose strategies that may be employed in manipulating the stepwise transition of olfactory precursor cells to become fully differentiated and functional neuronal and non-neuronal cell types. Such strategies potentially offer formidable means of replacing injured or degenerated neural cells as therapeutics for nervous system perturbations. This review recapitulates the developmental cellular diversity of the olfactory neuroepithelium and discusses findings on how the precise and cooperative molecular control by transcriptional and epigenetic machinery is indispensable for OE ontogeny.

Identification of cancer biomarkers of prognostic value using specific gene regulatory networks (GRN): a novel role of RAD51AP1 for ovarian and lung cancers.

To date, microarray analyses have led to the discovery of numerous individual 'molecular signatures' associated with specific cancers. However, there are serious limitations for the adoption of these multi-gene signatures in the clinical environment for diagnostic or prognostic testing as studies with more power need to be carried out. This may involve larger richer cohorts and more advanced analyses. In this study, we conduct analyses-based on gene regulatory network-to reveal distinct and common biomarkers across cancer types. Using microarray data of triple-negative and medullary breast, ovarian and lung cancers applied to a combination of glasso and Bayesian networks (BNs), we derived a unique network-containing genes that are uniquely involved: small proline-rich protein 1A (SPRR1A), follistatin like 1 (FSTL1), collagen type XII alpha 1 (COL12A1) and RAD51 associated protein 1 (RAD51AP1). RAD51AP1 and FSTL1 are significantly overexpressed in ovarian cancer patients but only RAD51AP1 is upregulated in lung cancer patients compared with healthy controls. The upregulation of RAD51AP1 was mirrored in the bloods of both ovarian and lung cancer patients, and Kaplan-Meier (KM) plots predicted poorer overall survival (OS) in patients with high expression of RAD51AP1. Suppression of RAD51AP1 by RNA interference reduced cell proliferation in vitro in ovarian (SKOV3) and lung (A549) cancer cells. This effect appears to be modulated by a decrease in the expression of mTOR-related genes and pro-metastatic candidate genes. Our data describe how an initial in silico approach can generate novel biomarkers that could potentially support current clinical practice and improve long-term outcomes.

Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks.

There is currently much discussion about sample (patient)-specific gene regulatory network identification, since the efficiently constructed sample-specific gene networks lead to effective personalized cancer therapy. Although statistical approaches have been proposed for inferring gene regulatory networks, the methods cannot reveal sample-specific characteristics because the existing methods, such as an L1-type regularization, provide averaged results for all samples. Thus, we cannot reveal sample-specific characteristics in transcriptional regulatory networks. To settle on this issue, the NetworkProfiler was proposed based on the kernel-based L1-type regularization. The NetworkProfiler imposes a weight on each sample based on the Gaussian kernal function for controlling effect of samples on modeling a target sample, where the amount of weight depends on similarity of cancer characteristics between samples. The method, however, cannot perform gene regulatory network identification well for a target sample in a sparse region (i.e., for a target sample, there are only a few samples having a similar characteristic of the target sample, where the characteristic is considered as a modulator in sample-specific gene network construction), since a constant bandwidth in the Gaussian kernel function cannot effectively group samples for modeling a target sample in sparse region. The cancer characteristics, such as an anti-cancer drug sensitivity, are usually nonuniformly distributed, and thus modeling for samples in a sparse region is also a crucial issue. We propose a novel kernel-based L1-type regularization method based on a modified k-nearest neighbor (KNN)-Gaussian kernel function, called an adaptive NetworkProfiler. By using the modified KNN-Gaussian kernel function, our method provides robust results against the distribution of modulators, and properly groups samples according to a cancer characteristic for sample-specific analysis. Furthermore, we propose a sample-specific generalized cross-validation for choosing the sample-specific tuning parameters in the kernel-based L1-type regularization method. Numerical studies demonstrate that the proposed adaptive NetworkProfiler effectively performs sample-specific gene network construction. We apply the proposed statistical strategy to the publicly available Sanger Genomic data analysis, and extract anti-cancer drug sensitivity-specific gene regulatory networks.

Influence of 5-amino-3-methyl-4-isoxazolecarbohydrazide on selective gene expression in Caco-2 cultured cells

The 5-amino-3-methyl-4-isoxazolecarboxylic acid hydrazide (HIX) is a synthetic isoxazole derivative with a potential for development as an anti-inflammatory drug candidate. The goal of this study was to explore in vitro autoimmune and inflammatory gene modulation by HIX in human Caco-2 cultured cells. The effect of low dose of HIX was tested on the expression level of RNA in 24 h Caco-2 cultures using the QIAGEN Th17 for Autoimmunity & Inflammation RT2 Profiler PCR Array. We choose the PCR technology as the most reliable and sensitive gene expression profiling method for analyzing specific gene regulatory networks. In all experiments, Leflunomide (5-methyl-N-[4-(trifluoromethyl)phenyl]-4-isoxazolecarboxamide), an immuno-suppressive disease-modifying antirheumatic drug was used, as a reference to clinical utility of the isoxazole derivatives. Changes in RNA levels were analyzed and differentially expressed genes with at least 2-fold change were identified. For the majority of genes tested, the effects of HIX and Leflunomide were similar, including up-regulation of CX3CL1 and IL-17F, and down-regulation of IL-10 and TLR4. However twelve genes were were differently regulated by the two compounds: interleukins (IL) IL-1B, IL-6 and a chemokine CCL22 were upregulated by HIX and significantly supressed by Leflunomide. In contrary, IL-2 and IL-27 were upregulated by Leflunomide and suppressed by HIX. The network search by Ingenuity Pathway Analysis showed, that majority of differentially expressed genes were involved in cellular inflammatory responses. These results suggest that 5-amino-3-methyl-4-isoxazolecarbohydrazide has a potential for future clinical developments with structure modification as a disease modifying agent in different than Leflunomide applications.

The αA‐crystallin gene expression in differentiating lens fiber cells, FGF signaling, and transcriptional factories

PurposeEmbryonic lens development requires intricate temporal and spatial control of gene expression that is executed through specific gene regulatory networks regulated by FGF signaling. The αA‐crystallin is the most abundant mammalian lens protein; transcriptional studies of this gene provide detailed insights into the molecular mechanisms governing lens fiber cell differentiation.MethodsDynamic changes in chromatin structure and gene expression during lens developments were evaluated by a combination of RNA‐seq and ATAC‐seq. Expression of αA‐crystallin (ch1) and other crystallins (Cryga, Crybb1, and Crybb3, localized on chromosomes 1 and 5, respectively) in individual nuclei of the developing mouse lens were analyzed by RNA‐FISH. Transgenic mice with c‐Maf promoter were generated and expression of EGFP was evaluated in mouse embryos. Localization of c‐Jun, c‐Maf, Etv5/ERM, and Pax6 was determined by ChIP using the c‐Maf and Cryaa loci and mouse lens chromatin. Co‐transfection experiments with c‐Jun and Etv5 were conducted using c‐Maf and αA‐crystallin reporters.ResultsTranscriptional factories, a subset of topologically associating domains (TADs), are formed between different crystallin loci in lens cell nuclei. These specific TADs are enriched by RNA polymerase II and other proteins. Novel FGF2‐responsive region in the c‐Maf promoter was found. Both c‐Maf and Cryaa regulatory regions contain arrays of AP‐1 and Ets‐binding sites determined by ChIP assays.ConclusionsCollectively, these studies show that the lens fiber cell nuclei spatially organize crystallin loci into TADs, and that FGF signaling up‐regulates expression of αA‐crystallin both directly and indirectly via up‐regulation of c‐Maf. These molecular mechanisms are applicable for other crystallins and genes highly expressed in terminally differentiating lens fibers.

Genomic features of human limb specific enhancers

To elucidate important cellular and molecular interactions that regulate patterning and skeletal development, vertebrate limbs served as a model organ. A growing body of evidence from detailed studies on a subset of limb regulators like the HOXD cluster or SHH, reveals the importance of enhancers in limb related developmental and disease processes. Exploiting the recent genome-wide availability of functionally confirmed enhancer dataset, this study establishes regulatory interactions for dozens of human limb developmental genes. From these data, it appears that the long-range regulatory interactions are fairly common during limb development. This observation highlights the significance of chromosomal breaks/translocations in human limb deformities. Transcriptional factor (TF) analysis predicts that the differentiation of early nascent limb-bud into future territories entail distinct TF interaction networks. Conclusively, an important motivation for annotating the human limb specific regulatory networks is to pave way for the systematic exploration of their role in disease and evolution.

416 - Identification of novel cancer biomarkers of prognostic value using specific gene regulatory networks

416 - Identification of novel cancer biomarkers of prognostic value using specific gene regulatory networks

Regulation of Tlx3 by Pax6 is required for the restricted expression of Chrnα3 in Cerebellar Granule Neuron progenitors during development.

Homeobox gene Tlx3 is known to promote glutamatergic differentiation and is expressed in post-mitotic neurons of CNS. Contrary to this here, we discovered that Tlx3 is expressed in the proliferating progenitors of the external granule layer in the cerebellum, and examined factors that regulate this expression. Using Pax6−/−Sey mouse model and molecular interaction studies we demonstrate Pax6 is a key activator of Tlx3 specifically in cerebellum, and induces its expression starting at embryonic day (E)15. By Postnatal day (PN)7, Tlx3 is expressed in a highly restricted manner in the cerebellar granule neurons of the posterior cerebellar lobes, where it is required for the restricted expression of nicotinic cholinergic receptor-α3 subunit (Chrnα3) and other genes involved in formation of synaptic connections and neuronal migration. These results demonstrate a novel role for Tlx3 and indicate that Pax6-Tlx3 expression and interaction is part of a region specific regulatory network in cerebellum and its deregulation during development could possibly lead to Autistic spectral disorders (ASD).

Integrated microRNA-mRNA analyses reveal OPLL specific microRNA regulatory network using high-throughput sequencing.

Ossification of the posterior longitudinal ligament (OPLL) is a genetic disorder which involves pathological heterotopic ossification of the spinal ligaments. Although studies have identified several genes that correlated with OPLL, the underlying regulation network is far from clear. Through small RNA sequencing, we compared the microRNA expressions of primary posterior longitudinal ligament cells form OPLL patients with normal patients (PLL) and identified 218 dysregulated miRNAs (FDR < 0.01). Furthermore, assessing the miRNA profiling data of multiple cell types, we found these dysregulated miRNAs were mostly OPLL specific. In order to decipher the regulation network of these OPLL specific miRNAs, we integrated mRNA expression profiling data with miRNA sequencing data. Through computational approaches, we showed the pivotal roles of these OPLL specific miRNAs in heterotopic ossification of longitudinal ligament by discovering highly correlated miRNA/mRNA pairs that associated with skeletal system development, collagen fibril organization, and extracellular matrix organization. The results of which provide strong evidence that the miRNA regulatory networks we established may indeed play vital roles in OPLL onset and progression. To date, this is the first systematic analysis of the micronome in OPLL, and thus may provide valuable resources in finding novel treatment and diagnostic targets of OPLL.