Canada's contributions to RNA research: past, present, and future perspectives

The field of RNA research has provided profound insights into the basic mechanisms modulating the function and adaption of biological systems. RNA has also been at the center stage in the development of transformative biotechnological and medical applications, perhaps most notably was the advent of mRNA vaccines that were critical in helping humanity through the Covid-19 pandemic. Unbeknownst to many, Canada boasts a diverse community of RNA scientists, spanning multiple disciplines and locations, whose cutting-edge research has established a rich track record of contributions across various aspects of RNA science over many decades. Through this position paper, we seek to highlight key contributions made by Canadian investigators to the RNA field, via both thematic and historical viewpoints. We also discuss initiatives underway to organize and enhance the impact of the Canadian RNA research community, particularly focusing on the creation of the not-for-profit organization RNA Canada ARN. Considering the strategic importance of RNA research in biology and medicine, and its considerable potential to help address major challenges facing humanity, sustained support of this sector will be critical to help Canadian scientists play key roles in the ongoing RNA revolution and the many benefits this could bring about to Canada.

The probability of edge existence due to node degree: a baseline for network-based predictions

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

Incorporating Clinical, Chemical and Biological Information for Predicting Small Molecule-microRNA Associations Based on Non-Negative Matrix Factorization

Small molecule(SM) drugs can affect the expression of miRNAs, which plays crucial roles in many important biological processes. The chemical structure and clinical information of small molecule can simultaneously incorporate information such as anatomical distribution, therapeutic effects and structural characteristics. It is necessary to develop a novel model that incorporates small molecule chemical structure and clinical information to reveal the unknown small molecule-miRNA associations. In this study, we developed a new framework based on non-negative matrix factorization, called SMANMF, to discover the potential small molecules-miRNAs associations. First, the functional similarity of two miRNAs can be obtained by computing the overlap of the target gene sets in which the miRNAs interact together, and we integrated two types of small molecule similarities, including chemical similarity and clinical similarity. Then, we utilized a non-negative matrix factorization model to discover the unknown relationship between small molecules and miRNAs. The evaluation results indicate that our model can achieve superior prediction performance compared with previous approaches in 5-fold cross-validation. At the same time, the results of case studies also reveal that the SMANMF model has good predictive performance for predicting the potential association between small molecules and miRNAs.

Heterogeneous information network and its application to human health and disease

The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.

Construction of a TF-miRNA-gene feed-forward loop network predicts biomarkers and potential drugs for myasthenia gravis

Myasthenia gravis (MG) is an autoimmune disease and the most common type of neuromuscular disease. Genes and miRNAs associated with MG have been widely studied; however, the molecular mechanisms of transcription factors (TFs) and the relationship among them remain unclear. A TF–miRNA–gene network (TMGN) of MG was constructed by extracting six regulatory pairs (TF–miRNA, miRNA–gene, TF–gene, miRNA–TF, gene–gene and miRNA–miRNA). Then, 3/4/5-node regulatory motifs were detected in the TMGN. Then, the motifs with the highest Z-score, occurring as 3/4/5-node composite feed-forward loops (FFLs), were selected as statistically significant motifs. By merging these motifs together, we constructed a 3/4/5-node composite FFL motif-specific subnetwork (CFMSN). Then, pathway and GO enrichment analyses were performed to further elucidate the mechanism of MG. In addition, the genes, TFs and miRNAs in the CFMSN were also utilized to identify potential drugs. Five related genes, 3 TFs and 13 miRNAs, were extracted from the CFMSN. As the most important TF in the CFMSN, MYC was inferred to play a critical role in MG. Pathway enrichment analysis showed that the genes and miRNAs in the CFMSN were mainly enriched in pathways related to cancer and infections. Furthermore, 21 drugs were identified through the CFMSN, of which estradiol, estramustine, raloxifene and tamoxifen have the potential to be novel drugs to treat MG. The present study provides MG-related TFs by constructing the CFMSN for further experimental studies and provides a novel perspective for new biomarkers and potential drugs for MG.

Review of tools and algorithms for network motif discovery in biological networks

Network motifs are recurrent and over-represented patterns having biological relevance. This is one of the important local properties of biological networks. Network motif discovery finds important applications in many areas such as functional analysis of biological components, the validity of network composition, classification of networks, disease discovery, identification of unique subunits etc. The discovery of network motifs is a computationally challenging task due to the large size of real networks, and the exponential increase of search space with respect to network size and motif size. This problem also includes the subgraph isomorphism check, which is Nondeterministic Polynomial (NP)-complete. Several tools and algorithms have been designed in the last few years to address this problem with encouraging results. These tools and algorithms can be classified into various categories based on exact census, mapping, pattern growth, and so on. In this study, critical aspects of network motif discovery, design principles of background algorithms, and their functionality have been reviewed with their strengths and limitations. The performances of state-of-art algorithms are discussed in terms of runtime efficiency, scalability, and space requirement. The future scope, research direction, and challenges of the existing algorithms are presented at the end of the study.

Critical microRNAs and regulatory motifs in cleft palate identified by a conserved miRNA-TF-gene network approach in humans and mice

Cleft palate (CP) is the second most common congenital birth defect. The etiology of CP is complicated, with involvement of various genetic and environmental factors. To investigate the gene regulatory mechanisms, we designed a powerful regulatory analytical approach to identify the conserved regulatory networks in humans and mice, from which we identified critical microRNAs (miRNAs), target genes and regulatory motifs (miRNA-TF-gene) related to CP. Using our manually curated genes and miRNAs with evidence in CP in humans and mice, we constructed miRNA and transcription factor (TF) co-regulation networks for both humans and mice. A consensus regulatory loop (miR17/miR20a-FOXE1-PDGFRA) and eight miRNAs (miR-140, miR-17, miR-18a, miR-19a, miR-19b, miR-20a, miR-451a and miR-92a) were discovered in both humans and mice. The role of miR-140, which had the strongest association with CP, was investigated in both human and mouse palate cells. The overexpression of miR-140-5p, but not miR-140-3p, significantly inhibited cell proliferation. We further examined whether miR-140 overexpression could suppress the expression of its predicted target genes (BMP2, FGF9, PAX9 and PDGFRA). Our results indicated that miR-140-5p overexpression suppressed the expression of BMP2 and FGF9 in cultured human palate cells and Fgf9 and Pdgfra in cultured mouse palate cells. In summary, our conserved miRNA-TF-gene regulatory network approach is effective in detecting consensus miRNAs, motifs, and regulatory mechanisms in human and mouse CP.

Identifying lncRNA and mRNA Co-Expression Modules from Matched Expression Data in Ovarian Cancer

Long non-coding RNAs (lncRNAs) have been shown to be involved in multiple biological processes and play critical roles in tumorigenesis. Numerous lncRNAs have been discovered in diverse species, but the functions of most lncRNAs still remain unclear. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood. With the advances of high-throughput technologies, the increasing availability of genomic data creates opportunities for deciphering the molecular mechanism and underlying pathogenesis of human diseases. Here, we develop an integrative framework called JONMF to identify lncRNA-mRNA co-expression modules based on the sample-matched lncRNA and mRNA expression profiles. We formulate the module detection task as an optimization problem with joint orthogonal non-negative matrix factorization that could effectively prevent multicollinearity and produce a good modularity interpretation. The constructed lncRNA-mRNA co-expression network and the gene-gene interaction network are used as the network-regularized constraints to improve the module accuracy, while the sparsity constraints are simultaneously utilized to achieve modular sparse solutions. We applied JONMF to human ovarian cancer dataset and the experiment results demonstrate that the proposed method can effectively discover biologically functional co-expression modules, which may provide insights into the function of lncRNAs and molecular mechanism of human diseases.

Disjoint motif discovery in biological network using pattern join method

The biological network plays a key role in protein function annotation, protein superfamily classification, disease diagnosis, etc. These networks exhibit global properties like small‐world property, power‐law degree distribution, hierarchical modularity, robustness, etc. Along with these, the biological network also possesses some local properties like clustering and network motif. Network motifs are recurrent and statistically over‐represented subgraphs in a target network. Operation of a biological network is controlled by these motifs, and they are responsible for many biological applications. Discovery of network motifs is a computationally hard problem and involves a subgraph isomorphism check which is NP‐complete. In recent years, researchers have developed various tools and algorithms to detect network motifs efficiently. However, it is still a challenging task to discover the network motif within a practical time bound for the large motif. In this study, an efficient pattern‐join based algorithm is proposed to discover network motif in biological networks. The performance of the proposed algorithm is evaluated on the transcription regulatory network of Escherichia coli and the protein interaction network of Saccharomyces cerevisiae. The running time of the proposed algorithm outperforms most of the existing algorithms to discover large motifs.

Application of dynamic expansion tree for finding large network motifs in biological networks

Network motifs play an important role in the structural analysis of biological networks. Identification of such network motifs leads to many important applications such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, and protein function annotation. However, identification of large network motifs is a challenging task as it involves the graph isomorphism problem. Although this problem has been studied extensively in the literature using different computational approaches, still there is a lot of scope for improvement. Motivated by the challenges involved in this field, an efficient and scalable network motif finding algorithm using a dynamic expansion tree is proposed. The novelty of the proposed algorithm is that it avoids computationally expensive graph isomorphism tests and overcomes the space limitation of the static expansion tree (SET) which makes it enable to find large motifs. In this algorithm, the embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of a parent node, either by adding a vertex or by adding an edge. This process does not involve any graph isomorphism check. The time complexity of vertex addition and edge addition are O(n) and O(1), respectively. The growth of a dynamic expansion tree (DET) depends on the availability of patterns in the target network. Pruning of branches in the DET significantly reduces the space requirement of the SET. The proposed algorithm has been tested on a protein–protein interaction network obtained from the MINT database. The proposed algorithm is able to identify large network motifs faster than most of the existing motif finding algorithms.

IndeCut evaluates performance of network motif discovery algorithms

Motivation

Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no method to numerically evaluate whether any network motif discovery algorithm performs as intended on realistically sized datasets-thus it was not possible to assess the validity of resulting network motifs.

Results

In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options.

Availability and implementation

The open source software package is available at https://github.com/megrawlab/IndeCut.

Contact

megrawm@science.oregonstate.edu or david.koslicki@math.oregonstate.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

Playing critical roles as post-transcriptional regulators, microRNAs (miRNAs) are a family of short non-coding RNAs that are derived from longer transcripts called precursor miRNAs (pre-miRNAs). Experimental methods to identify pre-miRNAs are expensive and time-consuming, which presents the need for computational alternatives. In recent years, the accuracy of computational methods to predict pre-miRNAs has been increasing significantly. However, there are still several drawbacks. First, these methods usually only consider base frequencies or sequence information while ignoring the information between bases. Second, feature extraction methods based on secondary structures usually only consider the global characteristics while ignoring the mutual influence of the local structures. Third, methods integrating high-dimensional feature information is computationally inefficient. In this study, we have proposed a novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure. In addition, the feature space is smaller than that of most popular methods, which makes our method computationally more efficient than the competitors. Finally, we applied these features to train a support vector machine model to predict pre-miRNAs and compared the results with other popular predictors. As a result, our method outperforms others based on both 5-fold cross-validation and the Jackknife test.

CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer

BACKGROUND

Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. However, the functional roles of vast majority of cancer specific ncRNAs and their combinational regulation patterns are still insufficiently understood.

RESULTS

Here we develop an integrative framework called CeModule to discover lncRNA, miRNA and mRNA-associated regulatory modules. We fully utilize the matched expression profiles of lncRNAs, miRNAs and mRNAs and establish a model based on joint orthogonality non-negative matrix factorization for identifying modules. Meanwhile, we impose the experimentally verified miRNA-lncRNA interactions, the validated miRNA-mRNA interactions and the weighted gene-gene network into this framework to improve the module accuracy through the network-based penalties. The sparse regularizations are also used to help this model obtain modular sparse solutions. Finally, an iterative multiplicative updating algorithm is adopted to solve the optimization problem.

CONCLUSIONS

We applied CeModule to two cancer datasets including ovarian cancer (OV) and uterine corpus endometrial carcinoma (UCEC) obtained from TCGA. The modular analysis indicated that the identified modules involving lncRNAs, miRNAs and mRNAs are significantly associated and functionally enriched in cancer-related biological processes and pathways, which may provide new insights into the complex regulatory mechanism of human diseases at the system level.

BiModule: biclique modularity strategy for identifying transcription factor and microRNA co-regulatory modules

Systematic identification of gene regulatory modules can provide invaluable knowledge towards understanding aberrant transcriptional/post-transcriptional collaborative regulatory (co-regulatory) effects in cancer. Transcription factor (TF) and microRNA (miRNA) are known as two classes of prominent regulators that play crucial roles in gene regulation. Existing studies on gene regulatory modules identification mainly focused on the miRNA-mediated regulatory network, and few considered these two regulators in a co-occurring network. In this current study, we developed a computational method called BiModule for systematically identifying TF-miRNA co-regulatory modules. BiModule operates in two main stages: it first constructs a cancerspecific regulator-mRNA network and then identifies modules based on maximal bicliques by employing biclique modularity strategy, which is a novel flexible method for bipartite graph mining. We applied our model to a cervical cancer dataset. The results showed that the TF-miRNA co-regulatory modules identified by BiModule exhibit denser connections and stronger expression correlations than another existing related method. Moreover, the BiModule-modules exhibit high biological functional enrichment. In addition, based on Kaplan-Meier survival analysis, we found a number of modules with significant prognostic associations.

Analysis of Hypoxiamir-Gene Regulatory Network Identifies Critical MiRNAs Influencing Cell-Cycle Regulation Under Hypoxic Conditions

BACKGROUND

Hypoxia is a pathophysiological condition which arises due to low oxygen concentration in conditions like cardiovascular diseases, inflammation, ascent to higher altitude, malignancies, deep sea diving, prenatal birth, etc. A number of microRNAs (miRNAs), Transcription Factors (TFs) and genes have been studied separately for their role in hypoxic adaptation and controlling cell-cycle progression and apoptosis during this stress.

OBJECTIVE

We hypothesize that miRNAs and TFs may act in conjunction to regulate a multitude of genes and play a crucial and combinatorial role during hypoxia-stress-responses and associated cellcycle control mechanisms.

METHOD

We collected a comprehensive and non-redundant list of human hypoxia-responsive miRNAs (also known as hypoxiamiRs). Their experimentally validated gene-targets were retrieved from various databases and a comprehensive hypoxiamiR-gene regulatory network was built.

RESULTS

Functional characterization and pathway enrichment of genes identified phospho-proteins as enriched nodes. The phospho-proteins which were localized both in the nucleus and cytoplasm and could potentially play important role as signaling molecules were selected; and further pathway enrichment revealed that most of them were involved in NFkB signaling. Topological analysis identified several critical hypoxiamiRs and network perturbations confirmed their importance in the network. Feed Forward Loops (FFLs) were identified in the subnetwork of enriched genes, miRNAs and TFs. Statistically significant FFLs consisted of four miRNAs (hsa-miR-182-5p, hsa- miR-146b-5p, hsa-miR-96, hsa-miR-20a) and three TFs (SMAD4, FOXO1, HIF1A) both regulating two genes (NFkB1A and CDKN1A).

CONCLUSION

Detailed BioCarta pathway analysis identified that these miRNAs and TFs together play a critical and combinatorial role in regulating cell-cycle under hypoxia, by controlling mechanisms that activate cell-cycle checkpoint protein, CDKN1A. These modules work synergistically to regulate cell-proliferation, cell-growth, cell-differentiation and apoptosis during hypoxia. A detailed mechanistic molecular model of how these co-regulatory FFLs may regulate the cell-cycle transitions during hypoxic stress conditions is also put forth. These biomolecules may play a crucial and deterministic role in deciding the fate of the cell under hypoxic-stress.

A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations

MOTIVATION

MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information.

RESULTS

In this study, we propose a new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases, especially for new diseases and miRNAs or those diseases and miRNAs with sparse known associations. First, we integrate the disease semantic information and miRNA functional information to estimate disease similarity and miRNA similarity, respectively. Considering that there is no available interaction observed for new diseases or miRNAs, a preprocessing step is developed to construct the interaction score profiles that will assist in prediction. Next, a graph regularized non-negative matrix factorization framework is utilized to simultaneously identify potential associations for all diseases. The results indicated that our proposed method can effectively prioritize disease-associated miRNAs with higher accuracy compared with other recent approaches. Moreover, case studies also demonstrated the effectiveness of GRNMF to infer unknown miRNA-disease associations for those novel diseases and miRNAs.

AVAILABILITY AND IMPLEMENTATION

The code of GRNMF is freely available at https://github.com/XIAO-HN/GRNMF/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Randomization Strategies Affect Motif Significance Analysis in TF-miRNA-Gene Regulatory Networks

Gene-regulatory networks are an abstract way of capturing the regulatory connectivity between transcription factors, microRNAs, and target genes in biological cells. Here, we address the problem of identifying enriched co-regulatory three-node motifs that are found significantly more often in real network than in randomized networks. First, we compare two randomization strategies, that either only conserve the degree distribution of the nodes’ in- and out-links, or that also conserve the degree distributions of different regulatory edge types. Then, we address the issue how convergence of randomization can be measured. We show that after at most 10 × |E| edge swappings, converged motif counts are obtained and the memory of initial edge identities is lost.

The Key Genes of Chronic Pancreatitis which Bridge Chronic Pancreatitis and Pancreatic Cancer Can be Therapeutic Targets

An important question in systems biology is what role the underlying molecular mechanisms play in disease progression. The relationship between chronic pancreatitis and pancreatic cancer needs further exploration in a system view. We constructed the disease network based on gene expression data and protein-protein interaction. We proposed an approach to discover the underlying core network and molecular factors in the progression of pancreatic diseases, which contain stages of chronic pancreatitis and pancreatic cancer. The chronic pancreatitis and pancreatic cancer core network and key factors were revealed and then verified by gene set enrichment analysis of pathways and diseases. The key factors provide the microenvironment for tumor initiation and the change of gene expression level of key factors bridge chronic pancreatitis and pancreatic cancer. Some new candidate genes need further verification by experiments. Transcriptome profiling-based network analysis reveals the importance of chronic pancreatitis genes and pathways in pancreatic cancer development on a system level by computational method and they can be therapeutic targets.

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.