High-throughput, ‘omic’ methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of ‘omic’ data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.

Summary: Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation: Not applicable.

Growing evidence supports a role for the unfolded protein response (UPR) in carcinogenesis; however, the precise molecular mechanisms underlying this phenomenon remain elusive. Herein, we identified the circadian clock PER1 mRNA as a novel substrate of the endoribonuclease activity of the UPR sensor IRE1α. Analysis of the mechanism shows that IRE1α endoribonuclease activity decreased PER1 mRNA in tumor cells without affecting PER1 gene transcription. Inhibition of IRE1α signaling using either siRNA-mediated silencing or a dominant-negative strategy prevented PER1 mRNA decay, reduced tumorigenesis, and increased survival, features that were reversed upon PER1 silencing. Clinically, patients showing reduced survival have lower levels of PER1 mRNA expression and increased splicing of XBP1, a known IRE-α substrate, thereby pointing toward an increased IRE1α activity in these patients. Hence, we describe a novel mechanism connecting the UPR and circadian clock components in tumor cells, thereby highlighting the importance of this interplay in tumor development. Cancer Res; 73(15); 4732–43. ©2013 AACR.

MicroRNAs (miRNAs) regulate diverse biological processes by repressing mRNAs, but their modest effects on direct targets, together with their participation in larger regulatory networks, make it challenging to delineate miRNA-mediated effects. Here, we describe an approach to characterizing miRNA-regulatory networks by systematically profiling transcriptional, post-transcriptional and epigenetic activity in a pair of isogenic murine fibroblast cell lines with and without Dicer expression. By RNA sequencing (RNA-seq) and CLIP (crosslinking followed by immunoprecipitation) sequencing (CLIP-seq), we found that most of the changes induced by global miRNA loss occur at the level of transcription. We then introduced a network modeling approach that integrated these data with epigenetic data to identify specific miRNA-regulated transcription factors that explain the impact of miRNA perturbation on gene expression. In total, we demonstrate that combining multiple genome-wide datasets spanning diverse regulatory modes enables accurate delineation of the downstream miRNA-regulated transcriptional network and establishes a model for studying similar networks in other systems.

Neurofibromatosis type 1 (NF1) is a cancer predisposition disorder that results from inactivation of the tumor suppressor neurofibromin, a negative regulator of RAS signaling. Patients with NF1 present with a wide range of clinical manifestations, and the tumor with highest prevalence is cutaneous neurofibroma (cNF). Most patients harboring cNF suffer greatly from the burden of those tumors, which have no effective medical treatment. Ironically, none of the numerous NF1 mouse models developed so far recapitulate cNF. Here, we discovered that HOXB7 serves as a lineage marker to trace the developmental origin of cNF neoplastic cells. Ablating Nf1 in the HOXB7 lineage faithfully recapitulates both human cutaneous and plexiform neurofibroma. In addition, we discovered that modulation of the Hippo pathway acts as a “modifier” for neurofibroma tumorigenesis. This mouse model opens the doors for deciphering the evolution of cNF to identify effective therapies, where none exist today. Significance: This study provides insights into the developmental origin of cNF, the most common tumor in NF1, and generates the first mouse model that faithfully recapitulates both human cutaneous and plexiform neurofibroma. The study also demonstrates that the Hippo pathway can modify neurofibromagenesis, suggesting that dampening the Hippo pathway could be an attractive therapeutic target. This article is highlighted in the In This Issue feature, p. 1