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Finding confidence level of miRNA disease associations

Finding confidence level of miRNA disease associations



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I'm an undergraduate computer engineering student, and I have a project about bioinformatics. In this manner, I need to find prediction( or association I'm not sure the correct terminology) confidence of miRNA disease relationships. Let me make more clear my question with an example. For instance, in one of the bioinformatics related databases( HMDD) it is said that, miRNA X is related with disease Y. I want to find that what is the confidence. I mean you're 100% sure about such releationship? Do you know any database for that purpose? In HMDD I can only find miRNA - disease name couples it does not give any statistics.


You can start off by studying how an miRNA regulates the functioning of a gene. it basically binds to the 3'UTR and 5'UTR of an mRNA and restricts its translation. There will be genes associated with the disease of your interest, find out the miRNA's related to those genes. You can use micro array data to find out the up-regulated genes. Target scan human, miRDB, are various databases for retrieving the miRNA's related with the genes. The confidence level for finding out the association of certain miRNA's with the disease can never be 100%. but a relationship can be established by simply viewing the complementarity between the miRNA and the mRNA expressed during the diseased state.


MiRNA

MiRNA Profiling

Kits procedures for miRNA isolation are similar in format to kits used to the isolation of larger cellular RNA species. These procedures often contain guanidinium isothiocynate-based lysis buffers and chloroform for phase separation. They work by trapping of RNA on spin columns that consist of silica beads or glass-fiber filters, followed by elution in a minimum volume. In cases where exosomes (and the RNA they carry) are desired, kits usually have an added step which enriches for vesicle-bound nucleic acids by sequestering them from most blood plasma components before lysing them. When collected from blood, it is important to avoid the anticoagulant heparin, commonly found in the blood collection tubes used by phlebotomists, because the reverse transcriptase step may be compromised if the sample becomes heparin-tainted.

It is worth noting that the retention of small RNAs, including miRNA, is highly ethanol dependent. A minimum of a 50% ethanol is needed to retain molecules less than 200 nts on the column, whereas these small RNAs elute readily at an ethanol concentration of 35% or lower. In terms of a practical application, the subpopulation of RNA to be purified depends in large measure on the concentration of alcohol in the wash buffers. In order to provide the investigator with the greatest latitude, some RNA isolation products feature a two-column system through which the larger RNAs can be purified independently from smaller RNAs from the same biological source. Still other products are magnetic bead-separation based, the bind to and elution from are pH-dependent. Enhancements may include altered wash and elution buffers as well as the use of fractionation-type columns or devices specifically designed to prevent small RNA flow-through.

Purified miRNAs can be profiled in any of a number of ways, including array analysis, qPCR, and RNA-seq, and even modified Northern analysis using a stiff polyacrylamide gel. In terms of assay, qPCR is the most sensitive technique for profiling miRNA. One popular method for rapidly profiling miRNA expression is the use of PCR plates that are preloaded with primers for the assay of specific miRNAs (e.g., Profiler miRNome Arrays Qiagen). A PCR reaction mix is added to the wells of a plate followed by real-time PCR. Various formats are available that will accommodate different thermocycler platforms. A major advantage of this approach is that primers have been optimized and validated for this precise application. As with traditional microarrays, miRNA-prepared plates are themed as well, examples of which include various types of cancer, stem cells, diabetes, apoptosis, cell differentiation, tumor suppressor miRNAs, and many others.

Two additional suggestions: first, another worthwhile strategy to profile miRNA is to perform an Argonaute complex pull-down to discover what types of miRNAs area associated with them second, since Drosha-processing is a prerequisite for pre-miRNA export, knocking out Drosha will cause the accumulation of large amounts of unprocessed pri-miRNAs in the nucleus. For laboratories that would like to jump on the miRNA bandwagon, several companies offer outsourcing services for miRNA profiling: you send in your sample, they report back which miRNAs are being expressed. As with the outsourcing of DNA sequencing when a laboratory does not have that capability, this is a great way to get a lot of data quickly and add another dimension to one’s research enterprise.

A logical next step in the characterization of miRNA expression is the implementation of functional analysis studies pertaining to one or more miRNA species, the expression of which are found to be modulated. These types of studies do not differ markedly from the strategies that have been used to characterize other genes. They include gene inhibition or downregulation to discover loss-of-function consequences, gene induction or upregulation to discover gain-of-function consequences, or influencing other elements in an expression pathway to discover the implications for the natural expression of the miRNAs of interest.


Background

MicroRNAs are small length (

22nt) non-coding RNAs that inhibit the expression of a target mRNA by binding to its 3'-UTR through complimentary base pairing [1] and therefore, these miRNAs act as negative regulators of the gene expression [2–4]. A mature miRNA regulates the post transcriptional gene expression by targeting certain mRNAs, subsequent to which, it modulates multiple signaling pathways, biological processes and patho-physiologies. However, it has also been evidenced that in some cases, miRNAs act as positive regulators of gene expression [5, 6]. Hence, analysis and in-depth exploration of the precise mechanism through which the regulatory mechanism of miRNA exerts its functionality is crucial. Identifying and predicting miRNA and disease associations, has been extensively researched in the past few years [7–10]. However, the precise mechanisms of miRNAs regulating diseases are still unclear. A major portion of the problem persists because about 60% of the molecular bases of diseases are yet unknown [11]. Furthermore, models to predict or determine disease-miRNA associations with high accuracy are very few [12]. Hence, gathering valuable evidence regarding identification of miRNAs influencing human diseases has become a widespread interest in arena of biomedical research with a future looking towards the enhancement of human medicine [13]. In this paper, we investigate the miRNA-disease network from a graph theoretical perspective and devise network scientific models of maximum weighted matching and motif-based analyses, to prioritize disease candidates in a miRNA-disease network. This work also presents a tool, DISMIRA that can perform these analyses and display the network visualization of the results, thereby providing an insight into the nature of networking between miRNAs and their associated diseases.

MiRNA disease database - miRegulome

To facilitate this, an in-house database, miRegulome (freely available at [14]) has been created. This database provides substantial details about the entire regulatory modules of a miRNA curated from PubMed indexed literature. It contains the upstream regulators and chemicals which regulate a miRNA, the downstream targets of a miRNA, miRNA-regulated pathways, functions and diseases along with their associated PubMed IDs. Currently, miRegulome contains information pertaining to 613 miRNAs, 156 diseases, 305 pathways and 96 chemicals. This data has been curated from 3298 PubMed IDs. miRegulome currently has 3751 unique miRNA- disease associations with supporting PubMed IDs.

The work presented in this research uses the data gathered in miRegulome database.

Complex networks

Identification of miRNA-disease associations through experimental laboratory methods are time consuming and expensive [7]. Hence, a large interest has been devoted towards finding important underlying associations through various computational models.

A network of miRNAs and diseases underlain with TFs and target genes is a very dense network and thereby poses a very complex network problem. Complex networks offer a unique perspective to explore relationships among homogeneous and heterogeneous entities. These entities can be biological molecules, diseases, genes etc. Hence, graph theoretic concept is very apt to model and mine important miRNA-disease associations. In our research, almost all the observed miRNA- disease networks, such as miRegulome, mir2Disease [15], miRNA-disease association network (MDAN) [1] and Human MicroRNA Disease Database (HMDD) [16] are scale-free meaning few nodes i.e miRNAs have the highest impact on other nodes, thereby acting as hubs. Hence, a miRNA-disease network follows the topological characteristics of scale-free networks. For e.g. Figure 1 shows a scale free network of miRNA-disease association network of HMDD. Further details about the topological metrics of the scale-free nature of these miRNA-disease networks are elaborated in the Section Motif-Based Analysis.

Network of miRNA-disease associations in HMDD. Blue circles represent miRNAs and red triangles represent diseases.

Literature

There have been many approaches to predict and determine associations between miRNAs and diseases. One of such preliminary works in developing miRNA-disease prediction models demonstrates that miRNAs related to same diseases tend to work together as miRNA groups [15]. This is an significant observation. It necessitates that any model of miRNA-disease association/prediction which claims to be effective considers this dynamic nature of miRNA. Jiang, et al., 2010 [9] uses the same approach and further derives a functional similarity between disease-related miR- NAs and phenotype similarities to derive a score which evaluates the likelihood of association of a miRNA and the disease. Jiang, et al., 2010 [17] uses the disease-gene associations to develop a N aïve − Bayes model, which prioritizes candidate miRNAs based on their genomic distribution. This model relies heavily on the associations between gene-disease and interactions of miRNA and target. However, both these models have high false-positives and high false-negatives in their predictions [1]. This limitation was however, addressed [7], by training a support vector machine classifier based on the input set of features extracted from false-positives and false-negative predicted associations. As demonstrated by Lu, et al., 2008 [16], miRNA-set families tend to closely work towards certain diseases. Hence, implicitly diseases tend to affect the working of other diseases too. This has also been researched [18], where specifically prostate cancer and non-prostate cancer miRNAs are distinguished by the usage of topological features. Here, a prioritization of disease candidate was performed using a network-centric method. Apart from using disease-gene information, few models have used the assumption that miRNA loci and Online Mendelian Inheritance in Man (OMIM) disease loci may contain significant overlaps [19]. This significance score is calculated and used to identify potential associations between miRNAs and OMIM diseases. Chen, et al., 2012 [1] uses global network similarity measure as compared to local network information to implement a random walk on a functionally similar miRNA network, which prioritizes candidate miRNAs for specified diseases. Xuan, et al., 2013 [8] improvises the miRNA functionality estimated approach by appending disease phenotype similarity information and content of disease terms to the existing method. This is used to assign weight to miRNA-disease associations and a weighted k-most similar neighbor based prediction method is deployed. Global network similarity is also used in the inference methods presented [10], where apart from miRNA-similarity and phenotype-similarity inferences, a network based inference model is used. In this model [10], the miRNAs related to queried miRNA are ranked and associated with ranked disease phenotypes associated with target phenotype, thereby relying on known gene-phenotype associations. Graph theory has been extensively used to model and analyze such biological networks [16] and especially bipartite graph modeling has been used to model the miRNA-disease network [1, 10, 12, 16]. Recently, Chen, et al., 2014 [20] has tried to overcome the limitations posed through various previous works, by developing an algorithm of Regularized Least Squares for miRNA-disease association (RLSMDA). Previous models like that of Chen, et al., 2012 [1] which although demonstrate high accuracy in prediction based on their case studies and cross-validation, cannot work in scenarios where associations between the diseases and miRNAs are unknownn and hence cannot predict novel miRNA- disease associations. Chen and Zhang [10] addressed this in their work, which could predict novel associations between diseases and miRNAs, with no prior knowledge of their association. However, its performance was inferior to that of Chen, et al. [1] based on cross-validation results [20]. The work presented by Chen, et al. [20] uses the miRNA functional similarity and disease functional similarity [21] and devises an optimization formulation to generate a continuous classification function which calculates the probablity score of each miRNA to a given disease [20]. Using graph theory, some network inference based prediction algorithms have also been used, as in [22]. In this case, three networks: environmental factors (EF)-miRNA, EF-disease and miRNA-disease were modeled into bipartite networks and three methods, i.e. network based inference (NFI) algorithm [23], EF structure similarity- based inference model and disease phenotype similarity-based inference models were was used to generate an EF-miRNA-disease association model which is validated via 10-fold cross validation. The cases studies presented display impressive results. However, this work too, can predict associations between EF-miRNA-disease which are known in prior and does not predict novel associations [22]. Our work does not present miRNA-disease predictions, rather performs a maximum matching in a set of miRNAs and diseases to determine and prioritize diseases with highest cumulative impact. Hence, the resulting diseases, each of them have valid PubMed literature supporting it, and thereby accurate association with miRNAs. This gives the user complete confidence in the results, he/she is provided with. Further more, all other previous tools are prediction models, predicting a miRNA-disease edge/association. These models do not produce associations between set of miRNAs onto a set of diseases, thereby not exploring the overall dynamics of multi-level interaction of a miRNA-disease network. Our model which acts as an extension to the existing body of work in this field, works on a set of miRNAs and produces an output of a set of associated diseases, taking into account the impact and association of every miRNA in the set with every disease in the set.

Using the graph theoretical network model, in this work we aim to find the most impacted diseases upon action/altercation of specified miRNAs. Here, we present a model that determines a prioritized set of diseases which are most definitely influenced upon the cumulative action/altercation of specified miRNAs. These associations are determined by a pipeline process of applying the maximum-weighted- maximum-matching algorithm to the network model in Section Maximum Weighted Matching Inference model, calculating cumulative weights per disease in Section Prioritization of disease candidates, and applying the disease ranking scheme in Section Disease ranking scheme. A preliminary version of this work has been presented [24]. Furthermore, none of the previous work have presented any work on the motif analysis of miRNA-disease networks. In this paper, we analyze the topological features of several miRNA-disease networks, especially the motifs in these networks and also the cumulative impact of a set of miRNAs onto a set of diseases. The motif-based analyses is presented in Section Motif-Based Analysis. The visualization of these results and their topological perspective is elaborated in the Section Visualization.


Background

The concept of information diffusion in a network has been widely deployed in the field of social network theory to study spread of ideas, rumors and product adoption between the individuals in the network via the word of mouth effect 28,29,30 . There are essentially two fundamental models of information propagation in social networks - linear threshold (LT) and independent cascade (IC) model. Every other model proposed in the literature is a derivative of these canonical models. Although, this concept has been applied in the field of sociology to study the various behavioral phenomena, such as the spread of a new concept 31 , it has also been extended to understand the dynamics of spreading of diseases 32,33,34 . However, understanding influence diffusion in a complex network of miRNAs has never been attempted before and is challenging due to the multi-level nature of interactions. In this work, considering that miRNAs of similar diseases tend to act cooperatively 24 , we focus on the social nature of miRNAs related to a class of diseases. We deploy an information diffusion model, through which a miRNA’s influence on its neighboring miRNAs is analyzed and quantified. Social influence can affect a range of behaviors in networks such as dissemination of information/influence, communication and in this case, even mutation. In both the LT and IC model, the nodes (i.e. the miRNAs) in the network can be in one of the two states - active or inactive. The activated nodes spread their influence by activating their neighboring inactive nodes based on a certain criteria or effect. Garnovetter et al. 35 proposed the LT model by applying a specific threshold in each of the nodes of the network. Therein, each node is activated only by its neighbor(s) depending upon the cumulative weight of the incoming edges to the node. The node becomes active when the cumulative sum of the weight of the incoming edges from an active neighboring node crosses its threshold value. Once activated, the node remains active and tries to activate its neighbor, thereby propagating its influence. On the contrary, the IC model uses edge probability to determine the information diffusion. In this model, an active node has a single opportunity to activate its neighbors. The edge weights represent the activation probability or likelihood of information propagation in between two nodes. Hence, upon activation, an active neighbor is likely to choose a neighbor with the highest edge weight to activate next.

The miRNA-miRNA interaction network in DMINs used in this study have probability scores as edge weights. These scores act as activation probabilities. Using the IC model, upon an activation of a certain miRNA, based on the edge weights between its neighbors, we can determine the next miRNA that is likely to be activated. In this context, activation implies having a causative effect on another miRNA’s expression level. This effect may be direct (when a miRNA directly controls the expression of another one) or indirect (when such regulation can be due to intermediate genes/proteins that these miRNAs regulate). Following this pattern, the information flow or the spread of influence across the miRNAs can be detected. Hence, the pattern of influence across miRNAs in a disease can be identified and studied. Further, we integrate different DMINs belonging to the same category profile, (e.g. ‘gastrointestinal cancers’) and detect the spread of influence among miRNA-miRNA interaction networks belonging to this profile. Subsequently, we determine the key miRNAs playing an influential role among all the diseases within a certain profile.


Circulating miRNAs as potential biomarkers in cardiovascular disease

Due to their robust stability and reasonable ease of detectability in the bloodstream, circulating miRNAs are emerging as attractive diagnostic biomarker candidates in a wide range of cardiovascular diseases ( Table 1 ). Of these circulating miRNAs, cardiac-enriched miRNAs, such as miR-1, miR-133, miR-208, and miR-499, have become the most extensively investigated miRNAs, particularly for the diagnosis of acute coronary syndrome (ACS) and acute myocardial infarction (AMI), as compared with conventional markers of myocardial damage such as creatine kinase (CK) or cardiac troponin [10, 53]. For example, cardiac-specific miR-208a, which is exclusively expressed in the heart, was reported as an attractive candidate miRNA consistently observed in a rat myocardial injury model [54] and a study of human AMI cases [55]. However, characterization of a single miRNA as a reliable cardiac biomarker can be challenging for instance, in other studies, plasma miR-208a concentration was too low to be detected either at baseline or after myocardial injury [53, 56, 57]. Furthermore, in the recent large-scale studies performed in suspected ACS patients, the diagnostic accuracy of using single miRNAs for detecting MI was lower than that of cardiac troponin T [58, 59]. In contrast, the use of a panel of multiple miRNAs or a combination of miRNAs with cardiac troponin has been reported to improve the discriminatory power in ACS diagnosis [60, 61]. The kinetics of release of these miRNAs may also allow for early diagnosis of AMI as compared with traditional biomarkers. Recently, Libetrau et al. [62] demonstrated that serum miR-1 and miR-133a are released within 15 minutes after induction of AMI in patients with hypertrophic cardiomyopathy who underwent transcoronary ablation for septal hypertrophy. These findings are consistent with other studies reporting the release of cardiac-specific miRNAs prior to CK or troponin after prolonged aerobic exercise (i.e., marathon run) [63]. Together, these data suggest the usefulness of circulating cardiac miRNAs in the early diagnosis of AMI and myocardial injury. Finally, to confirm the source of release of miRNAs from cardiac tissue, De Rosa et al. [64] demonstrated the presence of a transcoronary concentration gradient of cardiac-enriched miRNAs proportional to the degree of myocardial injury in ACS, thereby indicating that damaged myocardium was the likely source of released miRNAs. Gidl཯ et al. [58] also supported this idea by demonstrating the presence of miR-208b and miR-499-5p in the coronary sinus immediately after, but not before, cardioplegia in patients undergoing coronary artery bypass graft surgery. These findings suggested that those miRNAs are released from injured myocardium. However, it is still unclear whether these released miRNAs are just by-product from damaged myocardium or has additional roles as intercellular messengers.

Table 1

Circulating miRNAs as diagnostic biomarkers for cardiovascular disease

Circulating
miRNAs
Expression in cardiovascular diseaseReferences
miR-1Up-regulation in AMI[91, 125, 126]
Up-regulation in ACS[60]
miR-16Down-regulation in CAD[127]
miR-17Down-regulation in CAD[128]
miR-19aUp-regulation in AMI[129]
miR-21Up-regulation in NSTEMI in elderly[130]
Up-regulation in ACS[60]
miR-22Up-regulation in HF[131]
miR-23bUp-regulation in PH[74]
miR-26aDown-regulation in PAH[77]
miR-30aUp-regulation in AMI[132]
miR-30bDown-regulation in HF vs. non-HF
dyspnea or control
[66]
miR-31Down-regulation in CAD[127]
miR-92aDown-regulation in CAD[128]
miR-92bUp-regulation in HF[131]
miR-103Down-regulation in HF vs. non-HF
dyspnea or control
[66]
miR-122Down-regulation in AMI[53]
miR-125bDown-regulation in AMI[133]
miR-126Down-regulation in AMI[126]
Down-regulation in CAD[128]
miR-130aUp-regulation in PH[74]
miR-132Down-regulation in UA[61]
miR-133aUp-regulation in AMI[53, 134]
Up-regulation in ACS[57, 64]
Up-regulation in CAD[134]
miR-133bUp-regulation in AMI[53]
miR-134Up-regulation in AMI[135]
Up-regulation in APE vs. non-APE or
control
[78]
miR-142-3pDown-regulation in HF vs. non-HF
dyspnea or control
[66]
miR-145Down-regulation in CAD[127, 128]
miR-150Down-regulation in UA[61]
Down-regulation in PAH[73]
Down-regulation in atrial fibrillation[80]
miR-155Down-regulation in CAD[128]
miR-181aDown-regulation in CAD[127]
miR-186Up-regulation in UA[61]
miR-191Up-regulation in PH[74]
miR-195Up-regulation in AMI[132]
miR-208aUp-regulation in AMI[55]
Up-regulation in ACS[64]
miR-208bUp-regulation in AMI[10, 58-60,
136]
miR-320aUp-regulation in AMI[59]
Up-regulation in HF[131]
miR-320bDown-regulation in AMI[133]
miR-323-3pUp-regulation in ACS[87]
miR-328Up-regulation in AMI[135]
miR-342-3pDown-regulation in HF vs. non-HF
dyspnea or control
[66]
miR-375Down-regulation in AMI[53]
miR-423-5pUp-regulation in HF vs. non-HF dyspnea
or control
[65]
Up-regulation in HF[131]
miR-433Up-regulation in CAD[137]
miR-451Down-regulation in PH[74]
miR-485-3pUp-regulation in CAD[137]
miR-499Up-regulation in AMI[10, 53, 58, 59,
136]
Up-regulation in NSTEMI in elderly[130]
Up-regulation in ACS[60, 64]
Up-regulation in HF[136]
miR-1246Down-regulation in PH[74]
let-7bDown-regulation in AMI[132]
Down-regulation in CTEPH[79]

ACS, Acute coronary syndrome AMI, acute myocardial infarction APE, acute pulmonary embolism CAD, coronary artery disease CTEPH, chronic thromboembolic pulmonary hypertension HF, heart failure NSTEMI, non-ST-segment elevation myocardial infarction PAH, pulmonary arterial hypertension PH, pulmonary hypertension UA, unstable angina.

In addition to the diagnosis of coronary artery disease, alterations in the expression of several circulating miRNAs including miR-423-5p have been reported to discriminate heart failure (HF) from dyspnea of different etiologies [65, 66]. Notably, circulating miR-423-5p was not helpful in identifying patients with right HF [67], suggesting that perhaps any miRNA-dependent regulatory mechanism for right HF likely differs from those of left HF. Circulating miRNAs have begun to be assessed in patients with HF with preserved ejection fraction (EF) [68, 69]. Although no single miRNA was better than B-natriuretic peptide (BNP) in discriminating HF with preserved EF from HF with reduced EF, the assessment of multiple plasma miRNAs or combination of miRNAs with BNP significantly improved the discriminative power compared with BNP alone. Recently, circulating miRNAs have been evaluated in the response to treatment in patients with end-stage HF. Akat et al. [70] demonstrated that increased levels of certain myomiRs such as miR-208a, miR-208b, miR-499, and miR-1, were nearly completely reversed after the initiation of a left ventricular assist device (LVAD) in advanced HF, suggesting the usefulness of these myomiRs as biomarkers monitoring cardiac injury. Morley-Smith et al. [71] also found that miR-1202 was useful to discriminate between good and poor response to LVAD placement. In addition, a differential expression of certain miRNAs was observed in allograft rejection, suggesting their potential utility to monitor progress after heart transplantation [72].

Other vascular diseases such as pulmonary hypertension have also been studied in this regard. Rhodes et al. reported that circulating levels of miR-150 were reduced in patients with pulmonary arterial hypertension (PAH), and reduced plasma miR-150 levels were associated with poor survival in these patients [73]. Wei et al. [74] demonstrated that a certain set of circulating miRNAs were dysregulated in patients with pulmonary hypertension (PH) and were proportional to the degree of PH determined by mean pulmonary arterial pressure. Other miRNAs such as the miR-130/301 family [75] and miR-210 [76], which have known causative actions in the pathogenesis of PH have been reported to be elevated in the pulmonary circulation of PH patients. Circulating miR-26a has also been identified to be reduced in PAH and directly related with functional severity of this disease [77]. Differential expression of plasma miRNAs has been reported in acute pulmonary embolism [78] and chronic thromboembolic PH [79].

Presence of arrhythmias such as atrial fibrillation (AF) has been associated with alterations of circulating miRNAs. Liu et al. [80] reported that plasma miR-150 levels were significantly lower in patients with AF compared with healthy controls. These findings were independently validated in a larger population by McManus et al. [81]. Interestingly, in this study, miR-21 and miR-150 levels were lower in persistent AF than in paroxysmal AF, and increased after catheter ablation of AF [81]. Moreover, several attempts have been made to create miRNAs signature using circulating miRNAs for various cardiovascular disease including peripheral arterial disease [82] or congenital heart disease such as ventricular septal defect [83].

In addition to their roles as putative diagnostic biomarkers, the prognostic value of circulating miRNAs in cardiovascular disease has also been investigated with mixed utility ( Table 2 ). It has been demonstrated that the circulating levels of miR-133a and miR-208b were related with all-cause mortality at 6 months in ACS patients [84]. However, both miRNAs added little incremental prognostic value to high-sensitive troponin. In accordance with this result, Eitel et al. [85] also reported that circulating concentration of miR-133a could not independently predict cardiovascular events in ST segment elevation MI patients after adjustment for traditional markers. The association of increased levels of miR-208b and miR-499-5p with increased risk of mortality or HF in MI patients was lost after adjustment for troponin T [58]. On the other hand, low concentration of circulating miR-150 was reported to predict LV remodeling after AMI and outperformed N-terminal proBNP in this regard [86]. Pilbrow et al. [87] reported that lower levels of miR-652 can independently predict HF after AMI. Moreover, the use of a panel of multiple miRNAs or a combination of miRNAs with existing prognostic markers such as BNP or cardiac troponin appeared to improve risk stratification in this context [87-89].

Table 2

Circulating miRNAs as prognostic biomarkers for cardiovascular disease

Circulating
miRNAs
Outcome parameterExpression
associated with
poor outcome
References
miR-10Allograft rejection after heart
transplantation
Down-
regulation
[72]
miR-16LV contractility at 6 months
post-MI
Up-regulation[88]
miR-27aLV contractility at 6 months
post-MI
Up-regulation[88]
miR-29aLVEDV at 90 days post-MIUp-regulation[138]
miR-31Allograft rejection after heart
transplantation
Up-regulation[72]
miR-34aHF within 1 year post-MIUp-regulation[139]
Mortality or HF within 6 months
post-MI
Up-regulation[89]
LVEDD at 1 year post-MIUp-regulation[139]
LVEDV at 6 months post-MIUp-regulation[89]
miR-92aAllograft rejection after heart
transplantation
Up-regulation[72]
miR-101LV contractility at 6 months
post-MI
Down-regulation[88]
miR-126Incident MI within 10 yearsUp-regulation[140]
miR-133aAll-cause mortality within 6
months after ACS
Up-regulation[84]
MACE within 6 months post-MIUp-regulation[85]
miR-133bEarly myocardial injury and
recovery after heart
transplantation
Up-regulation[141]
miR-134Cardiac death or HF within 6
months post-MI
Up-regulation[135]
miR-150LVEDV post-MIDown-
regulation
[86]
LV contractility at 6 months
post-MI
Down-
regulation
[88]
Survival in PAHDown-
regulation
[73]
miR-155Cardiac death within 1 year
post-MI
Up-regulation[142]
Allograft rejection after heart
transplantation
Up-regulation[72]
miR-192HF within 1 year post-MIUp-regulation[139]
miR-194HF within 1 year post-MIUp-regulation[139]
LVEDD at 1 year post-MIUp-regulation[139]
miR-197Incident MI within 10 yearsDown-
regulation
[140]
miR-208bMortality or HF within 30 days
post-MI
Up-regulation[58]
Mortality or HF within 6 months
post-MI
Up-regulation[89]
LVEDV at 6 months post-MIUp-regulation[89]
All-cause mortality within 6
months after ACS
Up-regulation[84]
miR-223Incident MI within 10 yearsDown-
regulation
[140]
miR-328Cardiac death or HF within 6
months post-MI
Up-regulation[135]
miR-380*Cardiac death within 1 year
post-MI
Up-regulation[142]
miR-499-5pMortality or HF within 30 days
post-MI
Up-regulation[58]
miR-652Readmission for HF post-ACSDown-
regulation
[87]
miR-1202Response to LVAD therapyUp-regulation[71]

ACS, Acute coronary syndrome HF, heart failure LV, left ventricle LVAD, left ventricular assist device LVEDD, left ventricular end-diastolic dimension LVEDV, left ventricular end-diastolic volume MACE, major cardiovascular event MI, myocardial infarction PAH, pulmonary arterial hypertension.

Despite the attraction of measuring circulating miRNAs, many challenges exist in proving their utility as ideal diagnostic or prognostic biomarkers in cardiovascular disease ( Fig. 2 ). First, many of these miRNAs are ubiquitous, so the definite source of these miRNAs cannot be identified in most cases, with the exception of some cardiac or muscle-specific miRNAs (so-called “myomiRs”) [6]. In spite of recent efforts to identify more definitively the tissue source of released miRNAs [58, 64], many questions remain such as whether alteration of miRNAs levels in cardiovascular disease can be attributed to increased expression or simple release from damaged tissue, and whether released miRNAs are merely by-products of cell injury or play an actual biologic purpose in disease progression or manifestation. Second, there are numerous examples of the same circulating miRNA or myomiR altered in a variety of clinical situations ( Table 1 ). Thus, a consideration of context specificity will be necessary in the future determination of the utility of these miRNAs as true biomarkers of disease. Third, due to their low expression, some cardiac- and muscle-specific circulating miRNAs can be difficult to detect and quantify accurately with currently available methods [90]. Coupled with the possibility of contamination from blood cells, the measurement of these miRNAs can be fraught with error [42]. As such, a number of the alterations in circulating miRNAs that have previously been reported in a variety of cardiovascular disease conditions ( Tables 1 and ​ and2) 2 ) require independent validation in larger cohorts of patients for real application to clinical practice. Fourth, there are no standardized endogenous controls for normalization. Although the spike-in of exogenous miRNAs (e.g., miRNAs derived from the worm Caenorhabditis elegans) is widely used in such analyses [9], alternative methods for normalization using endogenous miRNA controls [53] or plasma volume [91] have been utilized, often with varying effects on final quantitations. Fifth, inter-individual and intra-individual variations in circulating miRNA expression certainly exist, notably dependent upon time of day, diet or nutrition, or gender [92, 93], as well as activity level or physical fitness [63, 94]. Therefore, there exist no reference values for “normal expression” for facile clinical interpretation. Finally, it is still unclear which type of assay system for miRNA measurements would be ideal for clinical use, with possibilities including quantitative polymerase chain reaction vs. next generation sequencing. Recent advances in both platforms have allowed for the simultaneous evaluation of multiple miRNAs with improved sensitivity. However, the decision on the diagnosis of cardiovascular disease is usually required urgently, especially in ACS. Therefore, more rapid and sensitive quantification techniques may need to be developed in the future if circulating miRNAs are to be useful as clinically applicable biomarkers [95].

Although miRNAs have many attractive features for study in the circulating bloodstream, there are still many challenges for establishing circulating miRNAs as clinically useful biomarkers. These include non-specific tissue- or organ-distribution, low serum/plasma concentration, wide inter- or intra-individual variations, absence of controls for normalization, and absence of standardized quantification methods.


MiRNA-Disease Association Prediction with Collaborative Matrix Factorization

As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, and experimentally verified miRNA-disease associations. Experiments verified that CMFMDA achieves intended purpose and application values with its short consuming-time and high prediction accuracy. In addition, we used CMFMDA on Esophageal Neoplasms and Kidney Neoplasms to reveal their potential related miRNAs. As a result, 84% and 82% of top 50 predicted miRNA-disease pairs for these two diseases were confirmed by experiment. Not only this, but also CMFMDA could be applied to new diseases and new miRNAs without any known associations, which overcome the defects of many previous computational methods.

1. Introduction

MicroRNAs (miRNAs) are a class of short noncoding RNAs (19

25 nt), which normally regulate gene expression and protein production by targeting messenger RNAs (mRNAs) at the posttranscriptional level [1–9]. Since the first two miRNA lin-4 and let-7 were found in 1993 and 2000 [10, 11], thousands of miRNAs have been detected in eukaryotic organisms ranging from nematodes to humans. The latest venison of miRBase contains 26845 entries and more than 2000 miRNAs have been detected in human [12–14]. With the development of bioinformatics and the progress of miRNA-related projects, researches are gradually focused on the function of miRNAs. Existing studies have shown that miRNAs are involved in many important biological processes [15, 16], like cell differentiation [17], proliferation [18], signal transduction [19], viral infection [20], and so on. Therefore, it is easy to find that miRNAs have close relationship with various human complex diseases [12, 21–26]. For example, researchers found that mir-433 is upregulated in gastric carcinoma by regulating the expression of GRB2, which is a known tumour-associated protein [27]. Mir-126 can not only function as an inhibitor to suppress the growth of colorectal cancer cells by its overexpression, but also can help to differentiate between malignant and normal colorectal tissue [28]. Besides, the change of mir-17

92 miRNA cluster expression has close relationship with kidney cyst growth in polycystic kidney disease [29]. Considering the close relationship between miRNA and disease, we should try all means to excavate all latent associations between miRNA and disease and to facilitate the diagnose, prevention, and treatment human complex disease [30–33]. However, using experimental methods to identify miRNA-disease association is expensive and time-consuming. As the miRNA-related theories are becoming more and more common, such as the prediction model about miRNA and disease, the function of miRNA in biological processes, and signaling pathways, new therapies are urgently needed for the treatment of complex disease it is necessary to develop powerful computational methods to reveal potential miRNA-disease associations [12, 15, 20, 34–40].

Previous studies had shown that functionally similar miRNAs always appear in similar diseases therefore many computational models were proposed to identify novel miRNA-disease associations [13, 41–46]. For example, Jiang et al. [31] analyzed and improved disease-gene prediction model, introduced the principle of hypergeometric distribution and how to use it, and discussed its application in prediction model and its actual effect. In order to realize the prediction function of the improved model, they used different types of dataset including miRNA functional similarity data, disease phenotype similarity data, and the known human disease-miRNA association data. Therefore, the prediction accuracy of this method is greatly impacted by miRNA neighbor information and miRNA-target interaction prediction. Chen et al. [47] reported a new method HGIMDA to identify novel miRNA-disease association by using heterogeneous graph inference. This algorithm can get better prediction accuracy by integrating known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for diseases and miRNAs. In addition, HGIMDA could be applied for new diseases and new miRNAs which do not have any known association. Li et al. [48] proposed the computational model Matrix completion for MiRNA-disease association prediction (MCMDA) to predict miRNA-disease associations. This model only uses known miRNA-disease associations and achieved better prediction performance. The limitation of MCMDA is that it could not be applied for new diseases and new miRNAs which do not have any known association. You et al. [49] developed model Path-Based MiRNA-Disease Association Prediction (PBMDA) to predict miRNA-disease associations by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. Depth-first search algorithm was used in this model to identify novel miRNA-disease associations. Benefiting from effective algorithm and reliable biological datasets, PBMDA has better prediction performance. Furthermore, Xu et al. [50] introduced an approach to identify disease-related miRNAs by the miRNA target-dysregulated network (MTDN). Furthermore, in order to distinguish and identify disease-related miRNAs from candidate, a SVM classifier based on radial basis function and the lib SVM package had been proposed. Researches have shown that miRNAs can functionally interact with environmental factors (EFs) to affect and determine human complex disease. Chen [51] proposed model miREFRWR to predict the association between disease and miRNA-EF interactions. Random walks theory was applied on miRNA similarity network and EF similarity network. In addition, drug chemical structure similarity, miRNA function similarity, and networked-based similarity were also used in miREFRWR. Based on these biological datasets and efficient calculation method, miREFRWR could be an effective tool in computational biology. What is more, Chen et al. [52] also proposed a computational model RKNNMDA to predict the potential associations between miRNA and disease. Four biological datasets, experimentally verified human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases were integrated into RKNNMDA. It can be found that the prediction accuracy of RKNNMDA is excellent. Moreover, RKNNMDA could be applied for new diseases which do not have any known related miRNA information.

Generally speaking, current prediction model on miRNA-disease association is still demonstrating some shortcomings. For example, unreliable datasets have a great influence on the accuracy of prediction model, such as miRNA-target interactions and disease-genes associations. In addition, for miRNAs and diseases which do not have any known associations, we cannot use some of the existing models to predict its relevant information. In other words, we need to design and develop a new effective computational model. According to the assumption that functionally similar miRNAs always appear in similar diseases, we introduce the model of Collaborative Matrix Factorization for MiRNA-Disease Association prediction (CMFMDA) to reveal novel miRNA-disease association by integrating experimentally validated miRNA-disease associations, miRNA functional similarity information, and disease semantic similarity information. For CMFMDA, we can obtain its test results with three different ways: 5-fold CV, Local LOOCV, and global LOOCV. The AUCs of these three methods are 0.8697, 0.8318, and 0.8841, respectively, which suggest that CMFMDA is a reliable and efficient prediction model. And then, we use two cases: Esophageal Neoplasms and Kidney Neoplasms, to evaluate the performance of CMFMDA. In both of these two important diseases, 42 and 41 out of top 50 predicted miRNA-disease associations were confirmed by recent experimental literatures, respectively. In addition, experiments show that CMFMDA can be applied for diseases and miRNAs without any known association.

2. Materials and Methods

2.1. Human miRNA-Disease Associations

We obtained information about the associations between miRNA and disease from HMDD, including 5430 experimentally confirmed human miRNA-diseases associations about 383 diseases and 495 miRNAs. Adjacency matrix

is proposed to describe the association between miRNA and disease. If miRNA

is associated with disease

is 1, otherwise 0. Furthermore, we declared two variables nm and nd to represent the number of miRNAs and diseases investigated in this paper, respectively.

2.2. MiRNA Functional Similarity

Base on the assumption that miRNAs with similarity functions are regarded to be involved in similar diseases, Wang et al. [42] present a method to calculate the miRNA functional similarity score. We downloaded miRNA functional similarity scores from http://www.cuilab.cn/files/images/cuilab/misim.zip and constructed matrix

to represent the miRNA function similarity network, where the entity represents the functional similarity score between miRNA and .

2.3. Disease Semantic Similarity

In this paper, disease can be described as a Directed Acyclic Graph (DAG) and

was used to describe disease , where is the node set including all ancestor nodes of and itself and is the corresponding links set including the direct edges from parent nodes to child nodes. The semantic value of disease in is defined as follows:


Prediction of miRNA-disease associations based on Weighted K -Nearest known neighbors and network consistency projection

MicroRNAs (miRNA) are a type of non-coding RNA molecules that are effective on the formation and the progression of many different diseases. Various researches have reported that miRNAs play a major role in the prevention, diagnosis, and treatment of complex human diseases. In recent years, researchers have made a tremendous effort to find the potential relationships between miRNAs and diseases. Since the experimental techniques used to find that new miRNA-disease relationships are time-consuming and expensive, many computational techniques have been developed. In this study, Weighted K -Nearest Known Neighbors and Network Consistency Projection techniques were suggested to predict new miRNA-disease relationships using various types of knowledge such as known miRNA-disease relationships, functional similarity of miRNA, and disease semantic similarity. An average AUC of 0.9037 and 0.9168 were calculated in our method by 5-fold and leave-one-out cross validation, respectively. Case studies of breast, lung, and colon neoplasms were applied to prove the performance of our proposed technique, and the results confirmed the predictive reliability of this method. Therefore, reported experimental results have shown that our proposed method can be used as a reliable computational model to reveal potential relationships between miRNAs and diseases.


EBAepidermolysis bullosa acquisita
eQTLExpression quantitative trait loci/locus
lncRNALong non coding RNA
miRNAMicro RNA
QTLQuantitative trait loci/locus
SnoRNASmall nucleolar RNA
SnRNASmall nuclear RNA

Additional file

Flowchart describing the workflow of analysis. The flowchart provides an overview of the analysis performed for understanding regulation of miRNAs and their contribution to the disease phenotype. Figure S2 Interaction network accessed via IPA software for epistasis of miR-501. The graph depicts the interacting genes identified from epistasis scan of miRNA miR-501 in chromosome 1 and chromosome 2. The graph shows all known gene interactions between the two loci where genes colored in yellow are from locus on chromosome 2 and green are from locus on chromosome 1. The red line shows the possible pathway for the regulation of miR-501. Figure S3 qRT-PCR validation of miR-223 expression in EBA and normal murine skin. (ZIP 637 kb)


Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.5015282.

Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

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Discussion

In this work, we developed FCMDAP to predict human disease-related miRNAs. FCMDAP calculates the similarity between miRNAs by using mutual information based on the known miRNA-mRNA interaction information and adds the miRNA family information to construct a miRNA space. FCMDAP integrates disease functional similarity based on the disease-gene interaction and disease semantic similarity based on the DAG from MeSH to construct a disease space. FCMDAP integrates the association scores between miRNA and disease from miRNA and disease spaces. The association scores between miRNA and disease are calculated based on the k most similar neighbor recommendation algorithm, and miRNA cluster information is added into miRNA space. Like NSIM and other method, FCMDAP also predict unknown associations by constructing miRNA network and disease network. However, in the process, the similarity calculation process of miRNA and disease are independent of each other. Multiple types of data including miRNA-mRNA interaction, miRNA family information, disease-gene interaction, DAG from MeSH to calculate miRNA similarity, and disease similarity are considered and the prediction does not only depend on the known miRNA–diseases associations, thereby improving the accuracy of similarity calculations. Using the k most similar neighbor recommendation algorithm and miRNA cluster information makes the prediction results more reasonable, and improves the predictive performance.

LOOCV and case research show that FCMDAP exhibits excellent performance in predicting miRNA–disease associations. FCMDAP shows satisfactory performance in predicting diseases without any related miRNA information and miRNAs without any related disease information. The average AUC of FCMDAP for predicting isolated diseases and isolated miRNAs are 0.8417 and 0.8944, respectively. For isolated lung neoplasms, the prediction accuracy reached 100% in the top 50 predicted miRNAs. For the isolated hsa-mir-93, the prediction accuracy reached 90% in the top 10 diseases.

However, FCMDAP presents the following limitations. miRNA similarity can be further improved if other biomolecules that interact with miRNAs can be considered. As FCMDAP is developed on experimentally verified miRNA–disease associations, miRNA–disease associations can be experimentally verified, thereby improving the performance of FCMDAP.


Watch the video: Gene silencing by microRNAs. miRNA biogenesis. miRNA mechanism. Gene silencing by miRNAs (August 2022).