cancers Article Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions Kalyani B. Karunakaran 1, Naveena Yanamala 2, Gregory Boyce 2, Michael J. Becich 3 and Madhavi K. Ganapathiraju 3,4,* 1 Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India; [email protected] 2 Exposure Assessment Branch, National Institute of Occupational Safety and Health, Center for Disease Control, Morgantown, WV 26506, USA; [email protected] (N.Y.); [email protected] (G.B.) 3 Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15206, USA; [email protected] 4 Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15213, USA * Correspondence: [email protected] Simple Summary: Internal organs like the heart and lungs, and body cavities like the thoracic and abdominal cavities, are covered by a thin, slippery layer called the mesothelium. Malignant pleural mesothelioma (MPM) is an aggressive cancer of the lining of the lung, where genetics and asbestos exposure play a role. It is not diagnosable until it becomes invasive, offering only a short survival time to the patient. To help understand the role of the genes that relate to this disease most of which are poorly understood, we constructed the ‘MPM interactome’, including in it the protein-protein Citation: Karunakaran, K.B.; interactions that we predicted computationally and those that are previously known in the literature. Yanamala, N.; Boyce, G.; Becich, M.J.; Five novel protein-protein interactions (PPIs) were tested and validated experimentally. 85.65% of the Ganapathiraju, M.K. Malignant interactome is supported by genetic variant, transcriptomic, and proteomic evidence. Comparative Pleural Mesothelioma Interactome transcriptome analysis revealed 5 repurposable drugs targeting the interactome proteins. We make with 364 Novel Protein-Protein the interactome available on a freely accessible web application, Wiki-MPM. Interactions. Cancers 2021, 13, 1660. https://doi.org/10.3390/ Abstract: Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the outer lining cancers13071660 of the lung, with a median survival of less than one year. We constructed an ‘MPM interactome’ with over 300 computationally predicted protein-protein interactions (PPIs) and over 2400 known PPIs of Academic Editors: Daniel 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes L. Pouliquen and Joanna Kopecka were derived from Biological General Repository for Interaction Datasets (BioGRID) and Human Received: 28 February 2021 Protein Reference Database (HPRD). Novel PPIs were predicted by applying the HiPPIP algorithm, Accepted: 22 March 2021 which computes features of protein pairs such as cellular localization, molecular function, biological Published: 1 April 2021 process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. Publisher’s Note: MDPI stays neutral We validated five novel predicted PPIs experimentally. The interactome is significantly enriched with regard to jurisdictional claims in with genes differentially ex-pressed in MPM tumors compared with normal pleura and with other published maps and institutional affil- thoracic tumors, genes whose high expression has been correlated with unfavorable prognosis in iations. lung cancer, genes differentially expressed on crocidolite exposure, and exosome-derived proteins identified from malignant mesothelioma cell lines. 28 of the interactors of MPM proteins are targets of 147 U.S. Food and Drug Administration (FDA)-approved drugs. By comparing disease-associated versus drug-induced differential expression profiles, we identified five potentially repurposable Copyright: © 2021 by the authors. drugs, namely cabazitaxel, primaquine, pyrimethamine, trimethoprim and gliclazide. Preclinical Licensee MDPI, Basel, Switzerland. studies may be con-ducted in vitro to validate these computational results. Interactome analysis of This article is an open access article disease-associated genes is a powerful approach with high translational impact. It shows how MPM- distributed under the terms and associated genes identified by various high throughput studies are functionally linked, leading to conditions of the Creative Commons clinically translatable results such as repurposed drugs. The PPIs are made available on a webserver Attribution (CC BY) license (https:// with interactive user interface, visualization and advanced search capabilities. creativecommons.org/licenses/by/ 4.0/). Cancers 2021, 13, 1660. https://doi.org/10.3390/cancers13071660 https://www.mdpi.com/journal/cancers Cancers 2021, 13, 1660 2 of 29 Keywords: malignant mesothelioma; protein-protein interactions; systems biology; network analysis; drug repurposing 1. Introduction Internal organs such as heart and lung, and body cavities such as thoracic and ab- dominal cavities, are covered by a thin slippery layer of cells called the “mesothelium”. This protective layer prevents organ adhesion and plays a number of important roles in inflammation and tissue repair [1]. The mesothelia that line the heart, lung and abdominal cavity are called pericardium, pleura and peritoneum, respectively. Mesothelioma is the cancer that originates from this lining (described in detail in a recent review article [2]). Most types of mesothelioma metastasize to different locations in the body [3]. Pleural mesotheliomas account for ~90% of malignant mesotheliomas and have a short median survival, of less than 1 year [4]. Malignant pleural mesothelioma (MPM) is associated with exposure to asbestos; it has a long latency period after exposure and is conclusively diagnosable only after reaching the invasive phase [3]. It tends to cluster in families and occurs only in a small fraction of the population exposed to asbestos, suggesting the involvement of a genetic component [5]. These factors necessitate expeditious discovery of genetic predispositions, molecular mechanisms and therapeutics for the disease. The molecular mechanisms of disease are often revealed by the protein-protein inter- actions (PPIs) of disease-associated genes. For example, the involvement of transcriptional deregulation in MPM pathogenesis was identified through mutations detected in BAP1 and its interactions with proteins such as HCF1, ASXL1, ASXL2, ANKRD1, FOXK1 and FOXK2 [6]. PPI of BAP1 with BRCA1 was central to understanding the role of BAP1 in growth-control pathways and cancer; BAP1 was suggested to play a role in BRCA1 stabiliza- tion [7,8]. Studies on BAP1 and BRCA1 later led to clinical trials of the drug vinorelbine as a second line therapy for MPM patients, and the drug was shown to have rare or moderate effects in MPM patients [9,10]. BAP1 expression was shown to be necessary for vinorelbine activity; 40% of MPM patients in a study showed low BRCA1 expression and vinorelbine resistance [11–13]. Further, 60% of the disease-associated missense mutations perturb PPIs in human genetic disorders [14]. Despite their importance, only about 10–15% of expected PPIs in the human protein interactome are currently known; for nearly half of the human proteins, not even a single PPI is currently known [15]. Due to the sheer number of PPIs remaining to be discovered in the human interactome, it becomes imperative that biological discovery be accelerated by computational and high-throughput biotechnological methods. We developed a com- putational model, called HiPPIP (high-precision protein-protein interaction prediction) that is deemed accurate by computational evaluations and experimental validations of 18 predicted PPIs, where all the tested pairs were shown to be true PPIs ([16,17] and current work, and other unpublished works). HiPPIP computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model [16]. Though each of the features by itself is not an indicator of an interaction, a machine learning model was able to use the combined features to make predictions with high precision. The threshold of HiPPIP to classify a protein-pair as “a PPI” was set high in such a way that it yields very high-precision predictions, even if low recall. Novel PPIs predicted using this model are making translational impact. For example, they highlighted the role of cilia and mitochondria in congenital heart disease [18,19], that oligoadenylate synthetase-like protein (OASL) activates host response during viral infections through RIG-I signaling via its PPI with retinoic acid-inducible gene I (RIG-I)[17], and led to the identification of drugs Cancers 2021, 13, 1660 3 of 29 potentially repurposable for schizophrenia [20], one of which is currently under clinical trials. In this work, we studied MPM-associated genes and their PPIs assembled with HiPPIP and analyzed the MPM interactome to draw translatable results. We demonstrate the various ways in which systems-level analysis of this interactome could lead to biologically insightful and clinically translatable results. We made the interactome available to the cancer biology research community on a webserver with comprehensive annotations, so as