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Enzymes in the Cholesterol Synthesis Pathway: Interactomics in the Cancer Context

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Enzymes in the Cholesterol Synthesis Pathway: Interactomics in the Cancer Context biomedicines Article Enzymes in the Cholesterol Synthesis Pathway: Interactomics in the Cancer Context Pavel Ershov * , Leonid Kaluzhskiy , Yuri Mezentsev, Evgeniy Yablokov , Oksana Gnedenko and Alexis Ivanov Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia; [email protected] (L.K.); [email protected] (Y.M.); [email protected] (E.Y.); [email protected] (O.G.); [email protected] (A.I.) * Correspondence: [email protected]; Tel.: +7-499-246-71-15 Abstract: A global protein interactome ensures the maintenance of regulatory, signaling and struc- tural processes in cells, but at the same time, aberrations in the repertoire of protein–protein interac- tions usually cause a disease onset. Many metabolic enzymes catalyze multistage transformation of cholesterol precursors in the cholesterol biosynthesis pathway. Cancer-associated deregulation of these enzymes through various molecular mechanisms results in pathological cholesterol accumula- tion (its precursors) which can be disease risk factors. This work is aimed at systematization and bioinformatic analysis of the available interactomics data on seventeen enzymes in the cholesterol pathway, encoded by HMGCR, MVK, PMVK, MVD, FDPS, FDFT1, SQLE, LSS, DHCR24, CYP51A1, TM7SF2, MSMO1, NSDHL, HSD17B7, EBP, SC5D, DHCR7 genes. The spectrum of 165 unique and 21 common protein partners that physically interact with target enzymes was selected from sev- eral interatomic resources. Among them there were 47 modifying proteins from different protein kinases/phosphatases and ubiquitin-protein ligases/deubiquitinases families. A literature search, Citation: Ershov, P.; Kaluzhskiy, L.; enrichment and gene co-expression analysis showed that about a quarter of the identified protein Mezentsev, Y.; Yablokov, E.; partners was associated with cancer hallmarks and over-represented in cancer pathways. Our re- Gnedenko, O.; Ivanov, A. Enzymes in sults allow to update the current fundamental view on protein–protein interactions and regulatory the Cholesterol Synthesis Pathway: aspects of the cholesterol synthesis enzymes and annotate of their sub-interactomes in term of Interactomics in the Cancer Context. possible involvement in cancers that will contribute to prioritization of protein targets for future Biomedicines 2021, 9, 895. https:// drug development. doi.org/10.3390/biomedicines9080895 Keywords: cholesterol; cholesterol precursors; pathway; enzymes; protein partners; interactome; Academic Editor: Ciro Isidoro cancer; tumor; TCGA; protein–protein interaction Received: 1 July 2021 Accepted: 22 July 2021 Published: 26 July 2021 1. Introduction Publisher’s Note: MDPI stays neutral In normal cells, cholesterol is a crucial component of biomembranes. It plays a regu- with regard to jurisdictional claims in latory and signaling role interacting with cholesterol-binding proteins. Cholesterol is the published maps and institutional affil- precursor of steroid hormones and bile acid biosynthesis pathways [1]. In the last ten years, iations. cholesterol emerged as a modulator of tumor progression [2–6]. Cholesterol accumula- tion is a characteristic feature for cancer cells [5,7]. Cholesterol-containing membrane raft domains regulate certain types of proteins, including various cell signaling ones that are critical to tumor cell survival and invasiveness. This suggests that membrane rafts have a regulatory role in tumor progression [8]. Exogenous cholesterol intake was positively Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. associated with higher incidence of stomach, kidney, pancreas, lung, breast, bladder, colon, This article is an open access article rectum cancers and lymphoma [7]. distributed under the terms and Enzymes of cholesterol synthesis pathway represent molecular targets for drugs, conditions of the Creative Commons mainly, statins. Although the number of registered clinical trials of statin therapy in cancer Attribution (CC BY) license (https:// treatment has reached about two hundred (keywords: disease—cancer; other terms— creativecommons.org/licenses/by/ statin; (https://clinicaltrials.gov/, accessed on 1 June 2021), statins efficacy remains quite 4.0/). controversial due to their pleiotropic effects [9,10]. Statin application was associated with Biomedicines 2021, 9, 895. https://doi.org/10.3390/biomedicines9080895 https://www.mdpi.com/journal/biomedicines Biomedicines 2021, 9, 895 2 of 28 reduction of cancer risk by 20% [11]. However, the addition of statins to standard anticancer therapy does not improve overall survival [12,13]. Involvement of enzymes of cholesterol synthesis pathway (cholesterol synthesis en- zymes) in provoking and maintaining pathological cancer-dependent processes is closely linked with regulation on transcriptional and post-translational levels [1,14]. The last one can be realized through protein–protein interactions (PPIs) and post-translational modifications (PTMs) resulting in a change of protein stability and enzymatic activity. With the growing number of experimentally determined PPIs of cholesterol synthesis enzymes, it is necessary to assess and make comparative analysis of available information on both interactomes of individual enzymes (sub-interactomes) and the group interactome for several enzymes, functioning in a single metabolic pathway. The aim of the work was to systematize interactomics data on the cholesterol synthesis enzymes with respect to the cancer context. This is intended to update the current understanding of the functional and regulatory aspects of the cholesterol synthesis pathway in socially significant diseases. 2. Materials and Methods 2.1. TCGA Datasets Analysis The Cancer Genome Atlas (TCGA) program has generated, analyzed, and made avail- able data on the genomic sequence, expression, methylation, and copy number variation of over 11,000 individuals with over 30 different types of cancer [15,16]. The TCGA database was used to determine the expression patterns of genes, encoding cholesterol synthesis enzymes, as well as to find the associations with patient survival. The Gene Expression Profiling Interactive Analysis (GEPIA2) web server [17](http://gepia2.cancer-pku.cn/, accessed on 23 April 2021) was used to obtain median values of gene expression in tumor and normal tissues from TCGA datasets and to determine the tumor/normal ratio or fold change (FC). The selection of differentially expressed genes (DEGs) was performed according to FC ≥ 3 and cut-off significance level 0.01. A heat map was plotted using web server NG-CHM GUI 2.19.1 [18]. Prognostic value of DEGs and Kaplan–Meier plotting were performed using GEPIA2 with the following settings: significance level 0.05; p-value adjustment by FDR; group cut-off-median. Principal component analysis (PCA) of data matrix with FC values for 17 genes, encoding cholesterol synthesis enzymes, and 30 differ- ent cancers was performed using ClustVis resource [19](https://biit.cs.ut.ee/clustvis/, accessed on 26 April 2021). The assessment of mutational variability (somatic muta- tion frequency) of genes was performed using TCGA PanCancer Atlas Studies dataset (10953 patients/10967 samples) at the cBioPortal (https://www.cbioportal.org/, accessed on 2 May 2021). 2.2. Interactomics Data Acquisition and Processing All possible spectrum of experimentally established (physical interactions) protein partners for each of 17 cholesterol synthesis enzymes (target enzymes) was retrieved with a set of interactome browsers [20–32] (Table1). Common protein partners for target enzymes were obtained using the Venn diagram tool (http://bioinformatics.psb.ugent.be/webtools/ Venn/, accessed on 26 March 2021). Table 1. Interactome browsers for retrieving protein–protein interactions data. No. Name and Ref. Site Notes Evidence type—protein interactions, 1 Funcoup 5.0 [20] http://FunCoup.sbc.su.se (accessed on 15 March 2021) LLR Score > 2, confidence > 0.9, network: “PPI”, “Complex” 2 Mentha [21] https://mentha.uniroma2.it/ (accessed on 15 March 2021) Evidence type: physical interactions 3 CORUM [22] http://mips.helmholtz-muenchen.de/corum/ (accessed on 15 March 2021) - 4 ExoCarta [23] http://www.exocarta.org/ (accessed on 15 March 2021) - Methods (binary, indirect), 5 APID [24] http://apid.dep.usal.es (accessed on 15 March 2021) ≥1 publication Biomedicines 2021, 9, 895 3 of 28 Table 1. Cont. No. Name and Ref. Site Notes Interaction type: association, 6 MINT [25] https://mint.bio.uniroma2.it/ (accessed on 16 March 2021) physical association 7 SIGNOR 2.0 [26] https://signor.uniroma2.it/ (accessed on 16 March 2021) - 8 HuRI [27] http://www.interactome-atlas.org/ (accessed on 16 March 2021) - 9 IID [28] http://iid.ophid.utoronto.ca (accessed on 16 March 2021) Interaction type: experimental Bioplex Explorer 10 https://bioplex.hms.harvard.edu/explorer/ (accessed on 16 March 2021) Interaction probability > 0.9 3.0 [29] 11 Wiki-Pi [30] https://hagrid.dbmi.pitt.edu/wiki-pi/ (accessed on 16 March 2021) - http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/ (accessed on 16 12 HIPPIE 2.0 [31] Score > 0.6 March 2021) 13 HINT [32] http://hint.yulab.org/ (accessed on 16 March 2021) Binary interaction 2.3. Annotation and Enrichment Analysis Functional annotation of the final list of target enzyme protein partners with Gene Ontol- ogy, KEGG, REACTOME, Panther (FDR ≤ 0.05) and Wiki Pathways terms (p-value ≤ 0.05) was performed with over-representation analysis (ORA) on the Web Gestalt server (WEB- based Gene SeT AnaLysis Toolkit) [33].
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