Reconstructing dynamic microRNA-regulated interaction networks Marcel H. Schulza, Kusum V. Panditb, Christian L. Lino Cardenasb, Namasivayam Ambalavananc, Naftali Kaminskib, and Ziv Bar-Josepha,d,1 aRay and Stephanie Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213; bDorothy P. and Richard P. Simmons Center for Interstitial Lung Disease, Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213; cDepartments of Pediatrics, Centers for Free Radical Biology and Pulmonary Injury Repair Center, University of Alabama at Birmingham, Birmingham, AL 35249; and dMachine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 Edited by Howard Y. Chang, Stanford University, Stanford, CA, and accepted by the Editorial Board July 31, 2013 (received for review February 20, 2013) The regulation of gene expression in cells, including by microRNAs dynamics of miRNA-regulated networks. Specifically, although (miRNAs), is a dynamic process. Current methods for identifying these methods look for global relationships between the expression miRNA targets by combining sequence and miRNA and mRNA levels of an miRNA and its targets, they may miss important tem- expression data do not adequately use the temporal information poral aspects. Similar to other causal events (11), shifts in miRNA and thus miss important miRNAs and their targets. We developed and mRNA profiles in a specific developmental stage or disease the MIRna Dynamic Regulatory Events Miner (mirDREM), a proba- state can happen in a sequential manner. Static, correlation-based bilistic modeling method that uses input–output hidden Markov analysis may miss key regulatory miRNAs when these change at models to reconstruct dynamic regulatory networks that explain an earlier stage and so their global expression levels do not how temporal gene expression is jointly regulated by miRNAs and correlate with their targets. transcription factors. We measured miRNA and mRNA expression To address this problem, we developed the MIRna Dynamic Regulatory Events Miner (mirDREM), which reconstructs dy- for postnatal lung development in mice and used mirDREM to namic regulatory networks that model the effects of transcription study the regulation of this process. The reconstructed dynamic fi factors (TFs) and miRNAs on their targets over time. Such dy- network correctly identi ed known miRNAs and transcription namic network models have been used for studying TF activity factors. The method has also provided predictions about addi- fi (12, 13); however, they have only used the TF data as static in- tional miRNAs regulating this process and the speci c develop- formation (i.e., whether a TF can bind a gene or not). Although TF mental phases they regulate, several of which were experimentally activity is often posttranscriptionally regulated (12), miRNA ex- validated. Our analysis uncovered links between miRNAs involved pression levels are an excellent indicator of their activity level and in lung development and differentially expressed miRNAs in socanbeusedtodetermineifaspecific miRNA is actively regu- idiopathic pulmonary fibrosis patients, some of which we have lating genes. We have developed computational methods for using experimentally validated using proliferation assays. These dynamic activity information and for restricting the assignments results indicate that some disease progression pathways in id- of miRNAs to targets based on their expected inhibitory effects. iopathic pulmonary fibrosis may represent partial reversal of To test our method, we generated expression data for lung lung differentiation. development in mice. When applied to these data, mirDREM was able to identify several miRNAs that were controlling major systems biology | network modeling developmental stages, several of which we have experimentally validated. any experiments now routinely profile both mRNAs and Results MmicroRNAs (miRNAs) when studying various conditions, The Dynamic Regulatory Events Miner (DREM). mirDREM extends diseases, and biological processes. Most miRNAs target the the dynamic regulatory events miner (DREM) (12, 13) to model genes they regulate by either degrade them or preventing them regulation by miRNAs. DREM integrates time series gene ex- from being translated (1). It is estimated that miRNAs regulate pression data with protein–DNA interaction data (from ChIP- the expression of half of the genes in the human genome (2). CHIP, ChIP-Sequencing, or motif information). DREM uses an Although a single miRNA can target hundreds of genes, miR- input–output hidden Markov model (IOHMM) to identify bi- NAs often have overlapping targets and are acting combinato- furcation events, which are places in the time series where a set rially, thus creating a rich and complex regulatory network (3, 4). of genes that were previously coexpressed diverges. These split Several computational methods were developed to identify events are annotated with TFs that are predicted to regulate miRNA targets using sequence analysis (1). Because of the small genes in the outgoing upward and/or downward paths, allowing number of bases in the interface, such predictions often contain both false positives and false negatives (5) and, because they are context-independent, are less suitable to the analysis of specific Author contributions: M.H.S., K.V.P., C.L.L.C., N.A., N.K., and Z.B.-J. designed research; responses and conditions. Because miRNAs often inhibit their M.H.S., K.V.P., C.L.L.C., and N.A. performed research; M.H.S. contributed new reagents/ direct targets, integrating sequence, mRNA and miRNA ex- analytic tools; M.H.S. and K.V.P. analyzed data; and M.H.S., K.V.P., N.K., and Z.B.-J. wrote pression data are one way to improve such an analysis (6–8). the paper. Several methods, mostly based on (anti-) correlation or regression Conflict of interest statement: N.K. is an inventor on use of microRNAs for the diagnosis and treatment of lung fibrosis. analysis, have been developed to use expression data when pre- – This article is a PNAS Direct Submission. H.Y.C. is a guest editor invited by the Editorial dicting targets of miRNAs (8 10). A representative example is Board. GenMIR++ (5, 6), one of the first methods to integrate miRNA fi fi Data deposition: The data reported in this paper have been deposited in the Gene Ex- and mRNA expression pro les in a uni ed probabilistic model. pression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo [accession nos. GSE41412 GenMIR++ employs a generative model in which miRNA ex- (time series expression data) and GSE41607 (gene expression data after transfection)]. pression profiles are used to explain the expression of a putative mirDREM is available from www.sb.cs.cmu.edu/drem. target inferred from sequence analysis. Other methods search for See Commentary on page 15516. anticorrelation (8, 9). 1To whom correspondence should be addressed. E-mail: [email protected]. Although the methods mentioned were successfully applied to This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. study gene regulation by miRNAs, they do not address the temporal 1073/pnas.1303236110/-/DCSupplemental. 15686–15691 | PNAS | September 24, 2013 | vol. 110 | no. 39 www.pnas.org/cgi/doi/10.1073/pnas.1303236110 Downloaded by guest on October 2, 2021 us to associate temporal information (the timing of the splits, has been performed to study various regulatory networks activated Fig. 1) with the often static protein–DNA interaction data. Unlike during lung development (19), relatively little is known about the SEE COMMENTARY standard HMMs, which rely on a transition probability matrix, temporal role miRNAs play in this process. DREM uses an L1 logistic regression classifier to determine tran- We measured lung mRNA and miRNA expression levels at sition probabilities. five developmental time points with four to five biological rep- Although DREM can be used to model regulation by miRNAs licates collected for each time point (0, 4, 7, 14, and 42 d; (using a set of predicted targets as static data), such an approach Materials and Methods). We used mirDREM to combine the does not fully use miRNA expression data. DREM does not use temporal mRNA and miRNA expression data with static TF– the expression levels of TFs because several TFs are posttran- gene interaction data (20) and miRNA–gene interaction pre- scriptionally regulated and so their expression levels do not al- dictions from the Microcosm database [which uses the miRanda ways correspond to their activity level (12). In contrast, miRNAs algorithm (21)]. The reconstructed dynamic network is shown in are transcriptionally regulated and in the majority of cases their Fig. 2. mirDREM identified a network with eight split nodes (SI expression levels directly correspond to their activity. In addition, Appendix, Dataset S1), with the biggest changes occurring in the unlike TFs that can serve as activators or repressors, miRNAs week 1 to week 2 transition. This agrees well with current knowl- directly repress their targets, leading to anticorrelated expression edge regarding lung development. Rapid alveolar septation is levels between miRNAs and their targets (1, 8, 9, 10, 14). Finally, known to occur between postnatal days 7 and 14 (P7–P14), and miRNAs often target fewer genes than TFs and their impact on the majority of alveolar septation is complete by P14 (22), al- each of their targets