Abstract
Comorbid states of diseases significantly complicate diagnosis and treatment. Molecular mechanisms of comorbid states of asthma and hypertension are still poorly understood. Prioritization is a way for identifying genes involved in complex phenotypic traits. Existing methods of prioritization consider genetic, expression and evolutionary data, molecular-genetic networks and other. In the case of molecular-genetic networks, as a rule, protein-protein interactions and KEGG networks are used. ANDSystem allows reconstructing associative gene networks, which include more than 20 types of interactions, including protein-protein interactions, expression regulation, transport, catalysis, etc. In this work, a set of genes has been prioritized to find genes potentially involved in asthma and hypertension comorbidity. The prioritization was carried out using well-known methods (ToppGene and Endeavor) and a cross-talk centrality criterion, calculated by analysis of associative gene networks from ANDSystem. The identified genes, including IL1A, CD40LG, STAT3, IL15, FAS, APP, TLR2, C3, IL13 and CXCL10, may be involved in the molecular mechanisms of comorbid asthma/hypertension. An analysis of the dynamics of the frequency of mentioning the most priority genes in scientific publications revealed that the top 100 priority genes are significantly enriched with genes with increased positive dynamics, which may be a positive sign for further studies of these genes.
1 Introduction
Asthma is a chronic inflammatory disease of the respiratory tract, the main characteristics of which are the hypersensitivity of the airways to various stimuli and reversible airflow obstruction [1]. More than 300 million patients worldwide are suffering from this disease [2]. Another widespread disease in the world is hypertension, which is characterized by a persistent increase in blood pressure from 140/90 mmHg. Art. and higher [3]. Annually about nine million people die due to complications associated with hypertension [4]. A number of studies have shown that asthma is related to an increase in the incidence of cardiovascular diseases [5], [6]. In this regard, the combination of asthma and arterial hypertension (comorbidity) in a large percentage of clinical cases is not surprising. Prevalence of hypertension in asthma patients is high with odds ratio = 1.66 [147], [188], p-value < 0.00001 [7]. This may not be a simple coincidence, because standard asthma mechanisms have been implicated in key processes of initiation of hypertension. Good examples of this are the establishment of the role of arachidonic acid-leukotriene B4 production in spontaneously hypertensive rat [8] or the value of Th17 and IL17 in arterial hypertension [9]. Therapeutic control of allergic inflammation leads to better control of blood pressure [10]. The important role of the transcription factor STAT3, which is involved in signal transduction for a variety of cytokines and takes an active part in allergic inflammation [11], [12] and vascular remodeling [13], is well-known. On the basis on this information, it can be assumed that the imbalance between the pro-inflammatory and anti-inflammatory factors in the body creates the optimal situation for the realization of a hereditary predisposition to both asthma and arterial hypertension. In addition to the important role played by immune responses and control of inflammation in the pathogenesis of asthma and hypertension, it is also possible to assume the involvement of other mechanisms underlying the comorbidity of these diseases. For example, β-adrenoblockers and ACE inhibitors, which for a long time were contraindicated for patients with bronchial asthma due to possible bronchospasm, are widely used in the treatment of hypertension. Polymorphism of β-adrenoreceptor genes is associated with the risk of hypertension and bronchial asthma [14], [15]. It is assumed that mutations in the SLC26A4 gene can play an important role in the pathogenesis of bronchial asthma and hypertension, and therefore in the comorbidity of these diseases [16], [17], [18]. However, despite the accumulated data on the participation of specific genes simultaneously in asthma and hypertension, the mechanisms of interaction of these two diseases and the formation of their comorbid state are still poorly understood. Previously, we performed the prioritization of genes that are associated with asthma and hypertension simultaneously, to find the candidates which can potentially be involved in the mechanisms of comorbidity of these two diseases [19].
In the present work, a prioritization of an extended set of genes, which are associated either with asthma or with hypertension, was carried out. In addition, a new criterion for prioritization, based on the assessment of cross-talk centrality, previously used by us for the prioritization of neuronal apoptosis genes in the associative gene network of Parkinson’s disease [20], was introduced. When assessing the quality of prioritization, this criterion was of the best significance. Among the most significant genes were cytokines (CXCL10, CD40LG, IL1A, IL13 and IL15), toll-like receptor TLR2, death domain receptor FAS, transcription factor STAT3, complement factor C3 and amyloid beta precursor protein APP. An analysis of the overrepresentation of Gene Ontology biological processes for the top 100 highest-priority genes has shown the potential importance of the response to hypoxia, the inflammatory and immune responses and regulation of the apoptotic process in the mechanism of interaction of asthma and hypertension. In addition, we analyzed the dynamics of interest in genes by the frequency of their mention in scientific publications. It appeared that genes simultaneously associated with asthma and hypertension are enriched with genes that have a high positive dynamic of interest in them from researchers. This fact may indicate the involvement of these genes in many mechanisms of manifestation of various phenotypic traits that can determine the importance of their role in the development of comorbidity of diseases. An analysis of the most priority genes also showed significant enrichment with genes that have a high dynamic of interest in them. This may indicate their involvement in multiple phenotypic signs and, accordingly, in the comorbidity of asthma and hypertension. The genes identified as a result of prioritization can be used in the planning of experiments on genotyping and search for associations with the comorbid state of asthma and hypertension.
2 Methods
The sets of genes associated with asthma and hypertension were extracted from the ANDSystem knowledge base [21], [22]. The set of genes associated with asthma included 749 genes. The set of genes associated with hypertension included 706 genes. The number of genes simultaneously presented in the first and second sets was 202. A test set of genes, used for prioritization, was obtained by combining sets of asthma and hypertension genes, minus the 202 genes gained by the intersection of these two sets. The total number of genes in the test set was 1051.
The over-represented Gene Ontology biological processes were identified using the DAVID 6.8 service [23] with the following parameters: the organism – "Homo sapiens", Gene_Ontology – "GOTERM_BP_DIRECT".
The reconstruction of associative gene networks was carried out with ANDSystem tool [21], [22].
To prioritize the genes we used four criteria, which we previously discussed in the analysis of asthma, hypertension and Parkinson’s disease [19], [20]. Criteria 1 and 2 were based on the well-known methods of prioritization: ToppGene [24] (https://toppgene.cchmc.org/prioritization.jsp) and Endeavor [25] version 3.71 (https://endeavour.esat.kuleuven.be/), respectively. The ToppGene and Endeavor programs allow one to perform a ranking of a test set of genes by a training set of genes, according to specific criteria characterizing the proximity of genes from the test set to the genes from the training sample. The genetic information (co-localization in the genome), the functional properties of genes (participation in the same GO categories), etc., can be used by these programs as properties of the vertices of the graph of protein-protein networks. As a training set for each of these programs, a list of genes associated with asthma and hypertension, described above, was provided, while a list of 1051 genes, which is also described above, was used as a test set.
For the ToppGene in the Training parameters section, all features were selected. The "Rank" indicator of the ToppGene program was used to rank the genes. In the case of the Endeavor system, all settings were set by default. The ranking of genes was carried out according to the "p-value" indicator of the Endeavor program. The lowest rank received genes with the lowest values of "p-value", and the largest rank – genes with the highest values of "p-value".
Criterion 3 reflects the specificity of association of biological processes in which the analyzed gene participates with comorbidity mechanisms. Criterion 3 was calculated by formula N1/N2, where N1 is a number of the Gene Ontology biological processes, overrepresented for a set of genes associated with asthma and hypertension; N2 is a number of all Gene Ontology biological processes in which the analyzed gene was involved. Information indicating that the gene is involved in the Gene Ontology biological process was obtained from the AmiGO system [26]. Ranks for genes were determined by sorting the list of genes by descending the number of over-represented Gene Ontology biological processes. Thus, the lowest rank was attributed to the gene for which the number of the over-represented Gene Ontology biological processes was the highest.
To calculate criterion 4, an associative gene network which describes the interactions between the analyzed gene/protein and the genes/proteins from the training set was used. The network was automatically reconstructed by ANDSystem. Criterion 4 was based on the use of the cross-talk centrality (CTC), calculated using the “Intelligent Filtration” function of the “ANDVisio” program. Within this criterion, the CTC value was calculated separately for vertices in the gene network corresponding to the gene and its product (protein). Thus, for the gene, two indices (CTCgene and CTCprotein) were determined.
The CTCgene was calculated by the formula:
where Ni – is a number of links of the i-th gene with members of the associative gene network of asthma/hypertension; M – is a number of vertices of the associative gene network of asthma/hypertension. CTCprotein for the i-th protein was determined the same way. The CTCgene and CTCprotein grades were determined by sorting the values in descending order, like in the case of criterion 3.
In the case when several genes had the same value for the criterion under which the ranking was conducted, the rank of these genes was calculated the similar way as the Spearman rank correlation coefficient [27]. Such genes were assigned the same rank, equal to the average arithmetic ranks of these genes, according to their position, in the sorted list of genes.
As the fifth criterion, the average value of the ranks obtained according to the criteria 1–4 was taken like it was done in [19].
For the reconstruction of the dynamics, the information on investigated genes from PubMed publications for the period from 1990 to 2017 were considered. For each year from this period, the number of publications in which the analyzed gene was mentioned was calculated. The marking of gene names in the texts of publications was carried out by using the ANDSystem. The trend reflecting the dynamics of the number of gene references in the articles was calculated using the Pearson correlation coefficient between the year and the total number of articles published this year in which the gene was mentioned. The enrichment of the gene set with genes with increased interest dynamics was calculated using the hypergeometric distribution realized in the hypergeom.sf function of the scipy.stats package of the Python programming language [28]. Genes with high dynamics of interest were genes for which the Pearson correlation coefficient exceeded 0.9.
3 Results and Discussion
To prioritize the genes for their potential simultaneous involvement in asthma and hypertension 749 genes of asthma and 706 genes of hypertension were considered. The training set of genes consisted of 202 genes, which were presented both in asthma and hypertension lists. Prioritization was carried out by using 4 criteria (see Online Appendix 1). The ranking of genes was carried out according to the value calculated as the mean for the ranks of the genes obtained using each of the 4 criteria. The top 10 of the most significant genes is listed in Table 1. Among them were cytokines (CXCL10, CD40LG, IL1A, IL13 and IL15), toll-like receptor TLR2, death domain receptor FAS, transcription factor STAT3, complement component C3 and amyloid beta precursor protein APP. It is interesting to note that all these genes are genes from the group of asthma. At the same time, the ratio of asthma genes to the genes of hypertension in the top 100 of the most significant genes appeared to be 2:1.
Top 10 of the most significant genes.
| Gene symbol | GeneID | Criterion 1 | Criterion 2 | Criterion 3 | Criterion 4 A | Criterion 4 B | Average of criteria 1–4 |
|---|---|---|---|---|---|---|---|
| ToppGene | Endeavour | GO BP | Gene CTC | Protein CTC | |||
| IL1A | 3552 | 17 | 102 | 80.00 | 2 | 11 | 42.4 |
| CD40LG | 959 | 3 | 72 | 113.50 | 23 | 4 | 43.1 |
| STAT3 | 6774 | 50 | 4 | 143.00 | 18 | 2 | 43.4 |
| IL15 | 3600 | 72 | 129 | 38.00 | 13.5 | 36.5 | 57.8 |
| FAS | 355 | 35 | 18 | 52.00 | 177.5 | 20 | 60.5 |
| APP | 351 | 25 | 16 | 263.00 | 5 | 19 | 65.6 |
| TLR2 | 7097 | 6 | 75 | 84.00 | 146 | 22 | 66.6 |
| C3 | 718 | 49 | 67 | 84.00 | 49.5 | 88.5 | 67.6 |
| IL13 | 3596 | 5 | 110 | 33.50 | 177.5 | 14.5 | 68.1 |
| CXCL10 | 3627 | 7 | 92 | 86.00 | 55 | 101.5 | 68.3 |
Figure 1 shows that the top 10 genes are relatively well connected to each other inside the associative gene network, reconstructed with ANDSystem (see Online Appendix 2). In particular, the transcription factor STAT3 regulates CXCL10 [29] and Fas [30]. In turn, TLR2, IL-13 and IL-15 regulate the activity of STAT3 [31], [32], [33].
An associative gene network reconstructed with ANDSystem, illustrating the interactions of the top 10 of most significant genes.
For better understanding which biological processes can underlie the molecular mechanisms of interaction between asthma and hypertension, an analysis of the overrepresentation of Gene Ontology biological processes on two sets of data was conducted: the training set of genes and the top 100 of the highest priority genes from the table in Online Appendix 1 were analyzed. For the set of genes associated with asthma and hypertension simultaneously, there were found 69 significantly over-represented biological processes (p-value < 0.05 with FDR correction). For the top 100 of the most priority genes, there were 42 such processes. Twenty-two biological processes were over-represented simultaneously for these two sets of genes (see Online Appendix 3). For example, the response to hypoxia had the best relevance for the training set of genes (p-value < 10−12). In the second set of genes, the p-value for this process was <10−8, and the most significant process appeared to be the inflammatory response (p-value < 10−19). The intersection of over-represented processes was more than 50% of the total number of all overrepresented processes for a set of top 100 priority genes. Such a high value was partly related to the 3rd criterion of prioritization, which was based on considering the overrepresented biological processes. But since the ranking was performed by using the rank averaged over all 4 criteria, such intersection indirectly confirms the correctness of the prioritization.
It should be noted that the manual analysis of the full-text articles of PubMed Central showed that for genes from the top 10 there was additional information that was not represented in ANDSystem that speaks in favor of their possible connection with hypertension. For example, for the IL1A gene, it is known that in Chinese Han population genetic variations in IL1A are associated with the risk of preeclampsia, which is characterized by hypertension and proteinuria [34]. In addition in Chinese children with type 1 diabetes the prevalence of hypertension in carriers of T allele in the IL1A gene was 4.2-fold higher than for C allele carriers with p-value < 0.001 [35]. The article [36] stated that the down-regulation of CD40LG might be related to kidney dysfunction in an early phase of hypertension, and [37] discussed that CD40LG was included in pulmonary hypertension gene subnetwork. Gene STAT3 regulate angiogenesis and vasoconstriction and is involved in the development of portal hypertension [38]. In patients with essential hypertension, IL-15 might be involved in the development of cardiovascular diseases [39]. For gene FAS it was shown that its expression was significantly increased in spontaneously hypertensive rats compared to Wistar-Kyoto rats [40]. In [41] APP was identified to undergo differential ubiquitination during pulmonary hypertension pathogenesis. Hypomethylation of the TLR2 gene increased the risk of essential hypertension in the Chinese population [42]. It was shown that C3 is highly expressed in mesenchymal tissues in spontaneously hypertensive rats (SHR) [43] and increased C3 induces salt-sensitive hypertension in SHR [44]. In [45] was discussed that IL-13 was involved in the induction of pulmonary hypertension. CXCL10 appeared to be involved in the pathogenesis of hypertension [46] and its level in serum was elevated in untreated essential hypertension [47].
One of the interesting characteristics of a gene may be the dynamics of increasing researcher’s interest in this gene, which may reflect the prospect of its study. It turned out that the genes from the training set, simultaneously associated with asthma and hypertension, are statistically significantly enriched with genes that have a high positive dynamic of their mention in the scientific publications of PubMed (p-value < 10−15). The total number of genes with a positive growing trend (r > 0.9) was 872 of the 17,868 human genes, presented in the ANDSystem. Among the 202 genes of the training set, it appeared to be 20% of such genes (42 genes). The HMOX1 gene, which encodes heme oxygenase 1, possessed the greatest positive dynamics of interest (r = 0.99). It is interesting to note that the top 100 of most priority genes contained 25% of genes possessing increased positive dynamics of interest. This enrichment was also statistically significant (p-value < 10−11). Analysis of the top 5 genes showed that among them STAT3 belongs to the group of 25% of genes possessing increased positive dynamics of interest (r = 0.96). The differences in the dynamics of interest for genes from the top 5 group are shown in Figure 2.
The dynamics of researcher’s interest for the top 5 most priority genes, according to scientific publications of PubMed.
Gene STAT3 encodes a protein that in response to cytokines and growth factors is phosphorylated and translocated to the nucleus where it starts to act as a transcription activator [48]. STAT3 is involved in many cellular processes including cell growth and apoptosis [49].
4 Concluding Remarks
It is well known that many diseases are comorbid, which significantly complicates their treatment, and also prevents their proper diagnosis. The study of the molecular mechanisms of comorbid states is an urgent task of the modern biomedicine. Asthma and hypertension are examples of widespread comorbid diseases. In this study, we used prioritization methods, including existing well-known methods (ToppGene and Endeavor), and an approach (cross-talk centrality), based on the analysis of associative gene networks from ANDSystem, to find genes that could potentially be involved in asthma and hypertension simultaneously. Cytokines (CXCL10, CD40LG, IL1A, IL13 and IL15), toll-like receptor TLR2, death domain receptor FAS, transcription factor STAT3, complement factor C3 and amyloid beta precursor protein (APP), appeared to be the most significant. In addition, an analysis of the dynamics of the frequency of mentioning genes in scientific publications was carried out. It turned out that the top 100 of the priority genes are significantly enriched by genes with increased positive dynamics of researcher’s interest in them. This may indicate a promise for further researches on these genes. A further study of the predicted genes, simultaneously involved in asthma and hypertension, can help to illuminate the mechanisms of the comorbid state of these two diseases.
Acknowledgements
Gene prioritization was carried out with the support of the Volkswagen Stiftung Trilateral Partnerships – Cooperation Projects between Scholars and Scientists from Ukraine, Russia, and Germany “In silico screening and experimental validation of new drug targets for the treatment of co-morbid multifactorial diseases” (90.335).
Development of methods and analysis of the dynamics of interest in the area of genomics and medical genetics on the basis of frequency of mentioning of genes in scientific publications was supported by Federal target program “Research and development in priority areas of development of Russia’s scientific and technological complex for 2014–2020”. Agreement on granting a subsidy as of October 23, 2017 No. 14.601.21.0015 between the Ministry of Education and Science of the Russian Federation and Tomsk NRMC on conducting research project titled: Development of a forecast for the implementation of the priority of scientific and technological development defined in paragraph 20c “personalized medicine, high-tech health care and health saving technologies, including the rational use of medicines (primarily antibacterial)” Scientific and technological development strategy of the Russian Federation. The unique identifier of the project is RFMEFI60117X0015. The state agreement identifier is 0000000007417PE10002.
Conflict of interest statement: Authors state no conflict of interest. All authors have read the journal’s Publication ethics and publication malpractice statement available at the journal’s website and hereby confirm that they comply with all its parts applicable to the present scientific work.
References
[1] Lim TK, Ko FW, Benton MJ, Berge MV, Mak J. Year in review 2016: chronic obstructive pulmonary disease and asthma. Respirology 2017;22(4):820–8.10.1111/resp.13037Search in Google Scholar PubMed
[2] Katwa U, Rivera E. Asthma management in the era of smart-medicine: devices, gadgets, apps and telemedicine. Indian J Pediatr 2018;10:1–6.10.1007/s12098-018-2611-6Search in Google Scholar PubMed
[3] Camm AJ, Lüscher TF, Serruys PW, eds. The ESC textbook of cardiovascular medicine. Oxford, UK: OXFORD University Press, 2009:1136.10.1093/med/9780199566990.001.0001Search in Google Scholar
[4] Russo A, Di Gaetano C, Cugliari G, Matullo G. Advances in the genetics of hypertension: the effect of rare variants. Int J Mol Sci 2018;19(3):688.10.3390/ijms19030688Search in Google Scholar PubMed PubMed Central
[5] Prosser R, Carleton B, Smith A. The comorbidity burden of the treated asthma patient population in British Columbia. Chronic Dis Inj Can 2010;30(2):46–55.10.24095/hpcdp.30.2.03Search in Google Scholar
[6] Tattersall MC, Guo M, Korcarz CE, Gepner AD, Kaufman JD, Liu KJ, et al. Asthma predicts cardiovascular disease events: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Bio 2015;35(6):1520–5.10.1161/ATVBAHA.115.305452Search in Google Scholar PubMed PubMed Central
[7] Su X, Ren Y, Li M, Zhao X, Kong L, Kang J. Prevalence of comorbidities in asthma and nonasthma patients: a meta-analysis. Medicine. 2016;95(22):e3459–e3465.10.1097/MD.0000000000003459Search in Google Scholar PubMed PubMed Central
[8] Waki H, Hendy EB, Hindmarch CC, Gouraud S, Toward M, Kasparov S, et al. Excessive leukotriene B4 in nucleus tractus solitarii is prohypertensive in spontaneously hypertensive rats. Hypertension 2013;61(1):194–201.10.1161/HYPERTENSIONAHA.112.192252Search in Google Scholar PubMed PubMed Central
[9] Solak Y, Afsar B, Vaziri ND, Aslan G, Yalcin CE, Covic A, et al. Hypertension as an autoimmune and inflammatory disease. Hypertens Res 2016;39(8):567–73.10.1038/hr.2016.35Search in Google Scholar PubMed
[10] Magen E, Yosefy C, Viskoper RJ, Mishal J. Treatment of allergic rhinitis can improve blood pressure control. J Hum Hypertens 2006;20(11):888–93.10.1038/sj.jhh.1002088Search in Google Scholar PubMed
[11] Litonjua AA, Tantisira KG, Lake S, Lazarus R, Richter BG, Gabriel S, et al. Polymorphisms in signal transducer and activator of transcription 3 and lung function in asthma. Respir Res 2005;6(1):52.10.1186/1465-9921-6-52Search in Google Scholar PubMed PubMed Central
[12] Simeone-Penney MC, Severgnini M, Tu P, Homer RJ, Mariani TJ, Cohn L, et al. Airway epithelial STAT3 is required for allergic inflammation in a murine model of asthma. J Immunol 2007;178(10):6191–9.10.4049/jimmunol.178.10.6191Search in Google Scholar PubMed
[13] Freeman AF, Avila EM, Shaw PA, Davis J, Hsu AP, Welch P, et al. Coronary artery abnormalities in Hyper-IgE syndrome. J Clin Immunol 2011;31(3):338–45.10.1007/s10875-011-9515-9Search in Google Scholar PubMed PubMed Central
[14] Liu Z, Qi H, Liu B, Liu K, Wu J, Cao H, et al. Genetic susceptibility to salt-sensitive hypertension in a Han Chinese population: a validation study of candidate genes. Hypertens Res 2017;40(10):876–84.10.1038/hr.2017.57Search in Google Scholar PubMed
[15] Xie H, Cheng Y, Huo Y, Huang G, Su J. Association between β2-adrenoceptor gene polymorphisms and asthma risk: an updated meta-analysis. PLoS One 2014;9(7):e101861.10.1371/journal.pone.0101861Search in Google Scholar PubMed PubMed Central
[16] Dossena S, Bizhanova A, Nofziger C, Bernardinelli E, Ramsauer J, Kopp P, et al. Identification of allelic variants of pendrin (SLC26A4) with loss and gain of function. Cell Physiol Biochem 2011;28(3):467–76.10.1159/000335108Search in Google Scholar PubMed PubMed Central
[17] Nofziger C, Vezzoli V, Dossena S, Schönherr T, Studnicka J, Nofziger J, et al. STAT6 links IL‐4/IL‐13 stimulation with pendrin expression in asthma and chronic obstructive pulmonary disease. Clin Pharmacol Ther 2011;90(3):399–405.10.1038/clpt.2011.128Search in Google Scholar PubMed
[18] Kim BG, Yoo TH, Yoo JE, Seo YJ, Jung J, Choi JY. Resistance to hypertension and high Cl− excretion in humans with SLC26A4 mutations. Clin Genet 2017;91(3):448–52.10.1111/cge.12789Search in Google Scholar PubMed
[19] Saik OV, Demenkov PS, Ivanisenko TV, Bragina EY, Freidin MB, Goncharova IA, et al. Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks. BMC Med Genomics 2018;11(1):15.10.1186/s12920-018-0331-4Search in Google Scholar PubMed PubMed Central
[20] Yankina MA, Saik O, Demenkov PS, Khusnutdinova EK, Rogaev E, Lavrik IN, et al. Analysis of the interactions of neuronal apoptosis genes in the associative gene network of Parkinson’s disease. Vavilovskii Zhurnal Genet Selektsii 2018;22(1):153–60.10.18699/VJ18.343Search in Google Scholar
[21] Demenkov PS, Ivanisenko TV, Kolchanov NA, Ivanisenko VA. ANDVisio: a new tool for graphic visualization and analysis of literature mined associative gene networks in the ANDSystem. In Silico Biol 2012;11(3,4):149–61.10.3233/ISB-2012-0449Search in Google Scholar PubMed
[22] Ivanisenko VA, Saik OV, Ivanisenko NV, Tiys ES, Ivanisenko TV, Demenkov PS, et al. ANDSystem: an Associative Network Discovery System for automated literature mining in the field of biology. BMC Syst Biol 2015;9(2):S2.10.1186/1752-0509-9-S2-S2Search in Google Scholar PubMed PubMed Central
[23] Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2008;4(1):44–57.10.1038/nprot.2008.211Search in Google Scholar PubMed
[24] Chen J, Xu H, Aronow BJ, Jegga AG. Improved human disease candidate gene prioritization using mouse phenotype. BMC Bioinformatics 2007;8(1):392.10.1186/1471-2105-8-392Search in Google Scholar PubMed PubMed Central
[25] Tranchevent LC, Ardeshirdavani A, ElShal S, Alcaide D, Aerts J, Auboeuf D, et al. Candidate gene prioritization with Endeavour. Nucleic Acids Res 2016;44(W1):W117–21.10.1093/nar/gkw365Search in Google Scholar PubMed PubMed Central
[26] Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S, et al. AmiGO: online access to ontology and annotation data. Bioinformatics 2008;25(2):288–9.10.1093/bioinformatics/btn615Search in Google Scholar PubMed PubMed Central
[27] Spearman C. ‘Footrule’ for measuring correlation. Br J Psychol 1904–1920. 1906;2(1):89–108.10.1111/j.2044-8295.1906.tb00174.xSearch in Google Scholar
[28] Oliphant TE. Python for scientific computing. Comput Sci Eng 2007;9(3): 9–12.10.1109/MCSE.2007.58Search in Google Scholar
[29] Liu M, Amodu AS, Pitts S, Patrickson J, Hibbert JM, Battle M, et al. Heme mediated STAT3 activation in severe malaria. PLoS One. 2012;7(3):e34280.10.1371/journal.pone.0034280Search in Google Scholar PubMed PubMed Central
[30] Ivanov VN, Bhoumik A, Krasilnikov M, Raz R, Owen-Schaub LB, Levy D, et al. Cooperation between STAT3 and c-jun suppresses Fas transcription. Mol Cell 2001;7(3):517–28.10.1016/S1097-2765(01)00199-XSearch in Google Scholar PubMed
[31] Kwok SK, Cho ML, Her YM, Oh HJ, Park MK, Lee SY, et al. TLR2 ligation induces the production of IL-23/IL-17 via IL-6, STAT3 and NF-kB pathway in patients with primary Sjogren’s syndrome. Arthritis Res Ther 2012;14(2):R64.10.1186/ar3780Search in Google Scholar PubMed PubMed Central
[32] Umeshita-Suyama R, Sugimoto R, Akaiwa M, Arima K, Yu B, Wada M, et al. Characterization of IL-4 and IL-13 signals dependent on the human IL-13 receptor α chain 1: redundancy of requirement of tyrosine residue for STAT3 activation. Int Immunol 2000;12(11):1499–509.10.1093/intimm/12.11.1499Search in Google Scholar PubMed
[33] Willerslev-Olsen A, Litvinov IV, Fredholm SM, Petersen DL, Sibbesen NA, Gniadecki R, et al. IL-15 and IL-17F are differentially regulated and expressed in mycosis fungoides (MF). Cell Cycle 2014;13(8):1306–12.10.4161/cc.28256Search in Google Scholar PubMed PubMed Central
[34] Li J, Liu M, Zong J, Tan P, Wang J, Wang X, et al. Genetic variations in IL1A and IL1RN are associated with the risk of preeclampsia in Chinese Han population. Sci Rep 2014;4:5250.10.1038/srep05250Search in Google Scholar PubMed PubMed Central
[35] Zhou X, Ca JG, Peng H, Wang JL, Li GM. Association of IL-1alpha gene polymorphism with susceptibility to type 1 diabetes in Chinese children. Genet Mol Res. 2016;15(3). DOI: 10.4238/gmr.15038025.Search in Google Scholar PubMed
[36] Batchu SN, Hughson A, Gerloff J, Fowell DJ, Korshunov VA. Role of Axl in early kidney inflammation and progression of salt-dependent hypertension. Hypertension 2013;62(2):302–9.10.1161/HYPERTENSIONAHA.113.01382Search in Google Scholar PubMed PubMed Central
[37] Chun HJ, Bonnet S, Chan SY. Translational advances in the field of pulmonary hypertension. Translating MicroRNA biology in pulmonary hypertension. It will take more than “miR” words. Am J Respir Crit Care Med 2017;195(2):167–78.10.1164/rccm.201604-0886PPSearch in Google Scholar PubMed PubMed Central
[38] Xu W, Liu P, Mu YP. Research progress on signaling pathways in cirrhotic portal hypertension. World J Clin Cases 2018;6(10):335–43.10.12998/wjcc.v6.i10.335Search in Google Scholar PubMed PubMed Central
[39] Kaibe M, Ohishi M, Ito N, Yuan M, Takagi T, Terai M, et al. Serum interleukin-15 concentration in patients with essential hypertension. Am J Hypertens 2005;18(8):1019–25.10.1016/j.amjhyper.2005.02.014Search in Google Scholar PubMed
[40] Grell AS, Frederiksen SD, Edvinsson L, Ansar S. Cerebrovascular gene expression in spontaneously hypertensive rats. PloS One 2017;12(9):e0184233.10.1371/journal.pone.0184233Search in Google Scholar PubMed PubMed Central
[41] Wade BE, Zhao J, Ma J, Hart CM, Sutliff RL. Hypoxia-induced alterations in the lung ubiquitin proteasome system during pulmonary hypertension pathogenesis. Pulm Circ 2018:2045894018788267.10.1177/2045894018788267Search in Google Scholar PubMed PubMed Central
[42] Mao S, Gu T, Zhong F, Fan R, Zhu F, Ren P, et al. Hypomethylation of the Toll-like receptor-2 gene increases the risk of essential hypertension. Mol Med Rep 2017;16(1):964–70.10.3892/mmr.2017.6653Search in Google Scholar PubMed
[43] Lin ZH, Fukuda N, Jin XQ, Yao EH, Ueno T, Endo M, et al. Complement 3 is involved in the synthetic phenotype and exaggerated growth of vascular smooth muscle cells from spontaneously hypertensive rats. Hypertension 2004;44(1):42–7.10.1161/01.HYP.0000129540.83284.caSearch in Google Scholar PubMed
[44] Negishi E, Fukuda N, Otsuki T, Katakawa M, Komatsu K, Chen L, et al. Involvement of complement 3 in the salt-sensitive hypertension by activation of renal renin-angiotensin system in spontaneously hypertensive rats. Am J Physiol Renal Physiol. 2018;315(6). DOI: 10.1152/ajprenal.00370.2018.10.1152/ajprenal.00370.2018Search in Google Scholar PubMed
[45] Park SH, Chen WC, Esmaeil N, Lucas B, Marsh LM, Reibman J, et al. Interleukin 13–and interleukin 17A–induced pulmonary hypertension phenotype due to inhalation of antigen and fine particles from air pollution. Pulm Circ 2014;4(4):654–68.10.1086/678511Search in Google Scholar PubMed PubMed Central
[46] Martynowicz H, Janus A, Nowacki D, Mazur G. The role of chemokines in hypertension. Adv Clin Exp Med 2014;23(3):319–25.10.17219/acem/37123Search in Google Scholar PubMed
[47] Antonelli A, Fallahi P, Ferrari SM, Ghiadoni L, Virdis A, Mancusi C, et al. High serum levels of CXC (CXCL10) and CC (CCL2) chemokines in untreated essential hypertension. Int J Immunopathol Pharmacol 2012;25(2):387–95.10.1177/039463201202500208Search in Google Scholar PubMed
[48] Kim M, Morales L, Jang IS, Cho YY, Kim D. Protein tyrosine phosphatases as potential regulators of STAT3 signaling. Int J Mol Sci 2018;19(9):2708.10.3390/ijms19092708Search in Google Scholar PubMed PubMed Central
[49] Fathi N, Rashdi G, Sharifi S. STAT3 and apoptosis challenges in cancer. Int J Biol Macromol 2018;117:993–1001.10.1016/j.ijbiomac.2018.05.121Search in Google Scholar PubMed
Supplementary Material
The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/jib-2018-0054).
©2018, Olga V. Saik et al., published by Walter de Gruyter GmbH, Berlin/Boston
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.