MOLECULAR MEDICINE REPORTS 18: 3569-3576, 2018 Identification of protein complexes associated with myocardial infarction using a bioinformatics approach NIANHUI JIAO1, YONGJIE QI1, CHANGLI LV2, HONGJUN LI3 and FENGYONG YANG1 1Intensive Care Unit; 2Emergency Department, Laiwu People's Hospital, Laiwu, Shandong 271199; 3Emergency Department, The Central Hospital of Tai'an, Tai'an, Shandong 271000, P.R. China Received November 3, 2016; Accepted January 3, 2018 DOI: 10.3892/mmr.2018.9414 Abstract. Myocardial infarction (MI) is a leading cause of clinical outcomes regarding high-risk MI. For example, muta- mortality and disability worldwide. Determination of the tions in the myocardial infarction-associated transcript have molecular mechanisms underlying the disease is crucial for been reported to cause susceptibility to MI (5). In addition, it identifying possible therapeutic targets and designing effective was demonstrated that mutations in the oxidized low-density treatments. On the basis that MI may be caused by dysfunc- lipoprotein receptor 1 gene may significantly increase the tional protein complexes rather than single genes, the present risk of MI (6). Although some MI-related genes have been study aimed to use a bioinformatics approach to identifying detected, many were identified independently and functional complexes that may serve important roles in the develop- associations among the genes have rarely been explored. ment of MI. By investigating the proteins involved in these Therefore, it is necessary to investigate MI from a systematic identified complexes, numerous proteins have been reported perspective, as the complex disease was reported to occur due that are related to MI, whereas other proteins interacted to the dysregulation of functional gene sets (7). A previous with MI-related proteins, which implied that these protein study reported that the examination of protein complexes may complexes may indeed be related to the development of MI. provide a better understanding not only of cellular functions, The protein complexes detected in the present study may aid in but also human diseases (8). For instance, the BRAFT protein our understanding of the molecular mechanisms that underlie complex was reported to be involved in Fanconi anemia and MI pathogenesis. Bloom Syndrome (9). The mammalian target of rapamycin complex 1 serves a crucial role in hematopoiesis, hemato- Introduction poietic differentiation and leukemogenesis (10). In addition, one previous study revealed that the proteins in complexes Cardiovascular disease (CVD) is a leading cause of mortality may be responsible for diseases (11). Therefore, identification worldwide, and the rates will continue to increase in the of the dysfunctional protein complexes may aid our under- coming decades (1). One typical CVD, myocardial infarction standing of the molecular mechanisms of MI. However, the (MI; also known as heart attack), causes heart failure or cardiac protein complexes associated with MI have not been fully arrest (2), and leads to millions of mortalities every year in investigated. developing countries. Epidemiological studies have shown that The present study proposed a bioinformatics approach high blood pressure, smoking and obesity are leading factors to identify protein complexes associated with MI develop- in MI development (3,4). However, the molecular mechanisms ment and recurrence (Fig. 1). Based on the gene expression of MI, especially its recurrence, remain unclear. Therefore, profiles associated with MI, dysfunctional complexes that elucidation of the molecular mechanisms underlying MI is may be involved in MI were identified, followed by functional crucial for reducing the risk of recurrence. enrichment analysis on the protein complexes detected. Advances in biotechnology have allowed for the successful Combined with previous data, the present study revealed that identification of the genes associated with biomarkers and some proteins from the complexes were related to MI, which suggested an important role for these protein complexes in the molecular mechanism of MI. Materials and methods Correspondence to: Dr Fengyong Yang, Intensive Care Unit, Laiwu People's Hospital, 1 Xuehu Street, Laiwu, Shandong 271199, P. R. Ch i na Data set. MI gene expression data set (GSE48060) was E-mail: [email protected] obtained from the Gene Expression Omnibus depository (12). The data set contains 52 samples, comprising 21 normal, Key words: protein complex, myocardial infarction, gene 26 nonrecurrent and 5 recurrent samples. The normal samples expression, bioinformatics had no previous history of cardiac diseases or other comorbidi- ties, the nonrecurrent samples are the first‑time patients with MI, whereas recurrence referred to those patients with any 3570 JIAO et al: IDENTIFICATION OF PROTEIN COMPLEXES ASSOCIATED WITH MYOCARDIAL INFARCTION recurrent events within 18-months following the initial treat- Table I. Protein complexes that are significantly different ment. All expression values were pre-processed with Robust among the three myocardial infarction groups. Multi-array Average (13). The expression value of a gene associated with multiple probes was calculated as the average Normal_ Recurrent_ Normal_ expression value of all related probes. Gene expression profiles Recurrent Nonrecurrent Nonrecurrent were normalized with mean 0 and standard deviation 1. The protein complexes and protein-protein interactions COM_1553 COM_1553 COM_1553 were retrieved from the Human Protein Reference Database COM_2750 COM_2750 COM_2750 (HPRD) (14). Functional enrichment analysis of genes in each COM_2322 COM_1422 COM_1648 protein complex was performed by DAVID (15), which is an COM_1422 COM_1427 online tool for understanding biological functions behind a list COM_1427 COM_1426 of genes. COM_970 COM_970 COM_2286 COM_1505 Identification of differentially expressed genes (DEGs). COM_2287 COM_2286 Genes that are differentially expressed between two condi- COM_2302 COM_2287 tions may be related to the condition and therefore may help COM_2296 COM_2322 explain how the differences occurred. In the present study, the COM_2298 COM_3000 data set was divided into three groups: Normal, Nonrecurrent and Recurrent. The differentially expressed genes between COM_1661 COM_3014 the three groups were detected by Student's t‑test with a COM_1688 COM_2296 P-value cutoff of 0.01. As a result, 793 (normal vs. recurrent), COM_1685 COM_2998 871 (normal vs. nonrecurrent) and 423 (recurrent vs. nonre- COM_33 COM_242 current) DEGs and their corresponding t-scores were obtained. COM_2796 COM_1661 COM_2967 COM_33 Identification of MI‑related protein complexes. Protein COM_2796 complexes are groups of proteins that interact with each COM_2967 other, which are fundamental functional units of the macro- molecular systems. 1,521 protein complexes were obtained from the HPRD database with detailed protein annotations. By following the work of Liu et al (16), a score Sc was defined complexes that were significantly different among the three MI for each complex to measure its relevance to the development groups were identified and the functions of those complexes of MI. were also investigated (Fig. 1). Identification of protein complexes associated with MI. Gene expression data and protein complex annotations were used to detect 17, 19 and 3 complexes as significantly different between normal vs. recurrent, recurrent vs. nonrecurrent and where N denotes the number of genes in the complex c, and Ti normal vs. nonrecurrent, respectively. Table I provides infor- represents the t-score of gene i calculated by Student's t‑test mation about the protein complexes that are different among using the gene expression data between two different groups. the distinct groups; protein complexes used in the present To verify that the MI-related complexes were not detected study were named and obtained from the HPRD database. A by chance, for each complex, a gene set was randomly picked Venn diagram of the three sets of protein complexes detected with the same number of genes as that in the complex, and for for each of the three groups was created (Fig. 2A), which each gene set, a score was calculated with the aforementioned revealed that numerous complexes are shared among the three equation. This procedure was repeated 10,000 times and the sets. In addition, DEGs were identified by Student's t‑test with P‑value was defined as the frequency of the gene set score a cutoff of P<0.01. As a result, 793, 871 and 423 genes were larger than Sc for the corresponding complex. Subsequently, detected to be differentially expressed in the comparison the complex was identified as related to MI if P<0.01. In of normal vs. recurrent, normal vs. nonrecurrent and recur- particular, only complexes that have at least 10 proteins were rent vs. nonrecurrent, respectively (Fig. 2B). It was noted that considered in the present study. 31, 9 and 21 DEGs from the three above comparisons, respec- tively, belonged to protein complexes. With the functional Results and Discussion annotations of protein complexes to which the DEGs belong, it was possible to investigate the molecular mechanisms of MI Protein complexes comprising multiple proteins are essential from another perspective. cellular functional units. Instead of focusing on a single gene, The results demonstrated that some protein complexes the present study aimed to identify the protein complexes that are differentially expressed across different stages of MI; may serve important roles