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Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray Study
Journal of Genetics and Genomics (Formerly Acta Genetica Sinica) November 2007, 34(11): 974-983
Research Article
Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray Study Volodymyr Dvornyk 1, 2, *, Yaozhong Liu 3, 4, *, Yan Lu 3, Hui Shen 3, Joan M Lappe 3, Shufeng Lei 1, 4, Robert R Recker 3, Hongwen Deng 1, 3, 4,
①
1. Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, China; 2. Department of Biological Sciences, Kent State University, Kent, OH 44242, USA; 3. Osteoporosis Research Center, Creighton University Medical Center, Omaha, NE 68131, USA; 4. Departments of Basic Medical Science, School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
Abstract: Menopause is one of the key physiological events in the female life and can increase the risk for a number of complex autoimmune, neurodegenerative, metabolic, and cardiovascular disorders. Circulating monocytes can differentiate into various cell types and play an important role in tissue morphogenesis and immune response. We studied gene expression profiles of peripheral blood monocytes in healthy pre- and postmenopausal women using Affymetrix Human U133A GeneChip array that contains probes for ~14,500 genes. Comparative analyses between the samples showed that 20 genes were up- and 20 were down-regulated. Of these genes, 28 were classified into six major GO categories relevant to such biological processes as the cell proliferation, immune response, cellular metabolism, and the others. The remaining 12 genes have yet unidentified biological functions. Our results support the hypothesis that functional state of circulating monocytes is indeed affected by menopause, and resulting changes may be determined through the genomewide gene expression profiling. Several differentially expressed genes identified in this study may be candidates for further studies of menopause-associated systemic autoimmune, neurodegenerative, and cardiovascular disorders. Our study is only the first attempt in this direction, but it lays a basis for further research. Keywords: menopause; monocytes; microarrays; differential gene expression
Natural menopause is associated with substantial biochemical and metabolic changes, e.g., decrease in estrogen, increase in low-density lipoprotein, and decrease in high-density lipoprotein cholesterol, which are well-known risk factors for health, particularly in the elderly. Because of these changes, menopause is associated with an increase of risk for a number of complex disorders [1, 2]. In particular, early menopause has been shown to increase risks for ovarian, breast, and colorectal cancers, cardiovascular diseases, and osteoporosis [3–7]. Menopause- and age-related endo-
crinal changes have also been associated with increased incidence of some autoimmune diseases, e.g., diabetes mellitus [8]. Physiologically, menopause is directly related to the cessation of ovarian function and respective decrease of estrogen production by follicles. Estrogen deficiency has been shown as a risk factor for a variety of potential postmenopausal health problems, such as decline in cardiovascular, skeletal, and genitourinary system function [1, 2, 9]. The broad effect of estrogen depletion on various peripheral tissues may be
Received: 2007−02−28; Accepted: 2007−08−09 * These authors contributed equally to the work. ① Corresponding author. E-mail: [email protected]; Tel&Fax: +86-731-8872 791 www.jgenetgenomics.org
Volodymyr Dvornyk et al.: Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray…
explained by its function as a systemic regulatory (hormonal) factor. Peripheral blood monocytes constitute 3% to 7% of circulating white blood cells and represent a population of cells in transit from bone marrow to their ultimate location in tissues, where they can be differentiated into various cell types [10]. Recently, it was hypothesized that monocytes and T cells play a dominant role in aging [11, 12]. There are abundant data about menopause- and age-related metabolic changes in these cells
[13−15]
. With menopause and aging, the [16, 17]
. number of these cells increases Molecular and genetic mechanisms of these processes and changes remain largely unknown. There is strong evidence that estrogen level is a primary factor influencing the mononuclear cells and their functions [14, 18, 19]. Hormone dysfunction may result in autoimmunity after natural menopause [20]. Given that menopause is associated with other hormonal changes [21, 22], which also affect mononuclear cells [23], this may implicate these cells as contributing to postmenopausal health problems. Despite the potential importance of monocytes for postmenopausal health, no attempts have been made to study how menopause influences the functional status of these cells. We hypothesized that circulating monocytes of postmenopausal and premenopausal women are functionally different, and this difference can be reflected in their gene expression profiles.
1 1. 1
Materials and Methods Subjects
This study was approved by the Creighton University Institutional Review Board. All the study subjects signed informed consent documents before entering the project. The study subjects came from an expanding database being created for genetic studies of osteoporosis and obesity that are underway in the Osteoporosis Research Center of Creighton Univewww.jgenetgenomics.org
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rsity Medical Center. Premenopausal subjects were defined as those having at least 10 regular menses during the last 12 months. Menopause was defined as the date of the last menses followed by 12 months of no menses. This is a common criterion having been used for the definition of menopause in the studies of traits associated with menopause [24−26]. Nine premenopausal (50.0 ± 3.1 years old) and 10 postmenopausal (52.0 ± 1.7 years old) healthy women were recruited for this study. The recruited subjects were of about the same age that eliminated potential age-related confounding factors. To avoid the potential effect of diseases or conditions other than menopause, which may lead to gene expression changes in circulating monocytes, we implemented the following very strict exclusion criteria: 1) autoimmune or autoimmune-related diseases: systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, Graves disease, Hashimoto’s thyroiditis, myasthenia gravis, Addison’s disease, dermatomyositis, Sjögren’s syndrome, Reiter’s syndrome; 2) immune-deficiency conditions: AIDS, severe malnutrition, spleenectomy, other conditions that may result in an immune-deficiency state (ataxia-telangiectasia, DiGeorge syndrome, Chediak-Higashi syndrome, job syndrome, leukocyte adhesion defects, panhypogammaglobulinemia, selective deficiency of IgA, combined immunodeficiency disease, Wiscott-Aldrich syndrome, complement deficiencies); 3) hematopoietic and lymphoreticular malignancies: leukaemias, lymphomas (Hodgkin’s disease, non-Hodgkin’s disease), myeloma, Waldenström’s macroglobulinaemia, heavy chain disease, others (leukaemic reticuloendotheliosis, mastocytosis, malignant histiocytosis); 4) other diseases: viral infection (influenza), allergy (active periods of asthma), COPD (chronic obstructive pulmonary disease); 5) any treatment or disease that would be an apparent factor affecting gene expression in these cells (e.g., smoking, chronic alcohol consumption, hormonal or other medications, etc.).
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Isolation of monocytes
Monocytes were isolated from 50 mL peripheral blood by negative immunomagnetic bead precipitation using a Monocyte Negative Isolation Kit (Dynal Biotech Inc., Lake Success, NY, USA) and following the protocol recommended by the manufacturer. Monocytes remained untouched, naïve and free of the surface-bound antibody and beads. This is of particular importance as binding antibody-coagulated beads to the cell surface may activate the cells and thus change their gene expression profiles. According to flow cytometry results, average monocyte purity in our samples was 85.8 ± 3.6% (Fig. 1).
遗传学报 Vol. 34 No. 11 2007
ried out according to the manufacturer’s recommendations [27]. The Bioconductor’s Affy package [28] was used to process the probe-level data (raw data) and transform them into expression data. The transformation was performed using the algorithm implemented in the Affymetrix Microarray Analysis Suite v. 5.0 software. After chip hybridization and initial data analysis, the expression values for ~14,500 genes represented on the chips were compared between pre- and postmenopausal subjects by using a nonpaired Student’s t test. Hierarchical clustering (centroid linkage) of the differentially expressed genes (DEGs) was performed by using CLUSTER and visualized by using TREEVIEW [29]. All the raw microarray data were submitted to the NCBI’s Gene Expression Omnibus data repository under accession number GSE2208.
2
Results
On the basis of the analysis performed with the Bioconductor’s Affy package, an average 39.69% of a total of 22,283 probe sets in the array were called “present” for our samples. Of the preselected 8,623 probe sets, we identified the differential expression of 40 genes between the pre- and postmenopausal subFig. 1 Flow-cytometry plot for the sample of isolated monocytes
1.3
RNA isolation, Affymetrix oligonucleotide array hybridization, and data analysis
Total RNA from the samples was isolated using the RNeasy Mini Kit (Qiagen, Inc., Valencia, CA, USA) following the protocol recommended by the manufacturer. Equal aliquots of total RNA from each of the subjects in each group used for biotin labeling as described in the Affymetrix technical bulletin. Hybridization, washing, scanning, and analysis of the Affymetrix GeneChip Human U133A microarrays (Affymetrix, Inc., Santa Clara, CA, USA) were car-
jects (P ≤ 0.001). Among these genes, 20 were up-regulated and 20 were down-regulated in the postmenopausal females as compared to the premenopausal ones. According to the results of the cluster analysis, pre- and postmenopausal women had quite distinct patterns of gene expression and therefore were clearly clustered into two well-defined groups (Fig. 2). One expected result of microarray experiments gives a better understanding of the causes of phenotypic differences between the groups of subjects. To address this issue, we performed an ontological analysis of these 40 DEGs. The analysis allowed us to divide the genes into several classes based on their biological www.jgenetgenomics.org
Volodymyr Dvornyk et al.: Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray…
functions as annotated by the gene ontology (GO) consortium [30]. This classification is shown in Table 1. These results suggest that, of the 40 DEGs, 28 were classified into six major GO categories. These functional categories were relevant to such biological processes as the cell proliferation, immune response, cellular metabolism, and the others. However, many of the genes may be assigned to several GO subcategories that may serve as evidence of their pleiotropy. For example, MAPK1 may contribute to various biological processes and thus belongs to seven GO classes: protein amino acid phosphorylation (GO: 0006468), induction of apoptosis (GO: 0006917), chemotaxis (GO: 0006935), response to stress (GO: 0006950), cell cycle (GO: 0007049), signal transduction (GO: 0007165), and synaptic transmission (GO:0007268). Likewise, PGF is classified into six GO categories according to its biological functions. The remaining 12 genes have yet unidentified biological functions (Table 1). The absolute values of expression significantly varied between the genes. Among the genes up-regulated in postmenopausal females, the most highly expressed were SEPT2, ANP32A, TNFSF13, MTHFD2, Table 1
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Fig. 2 Gene expression profiles of circulating monocytes from 9 premenopausal and 10 postmenopausal healthy women Shown are hierarchical clustering results of microarray data for 40 genes that distinguish premenopausal (blue) from postmenopausal (yellow). Red indicates up-regulated genes, and green represents down-regulated genes.
Classification of 40 genes differentially expressed between pre- and postmenopausal females by ontological groups
Gene name and ontology class
Fold change (post/pre)
Gene symbol
Probe set ID
P value
Septin 2
SEPT2
200778_s_at
0.001
1.31
Mitogen-activated protein kinase 1
MAPK1
208351_s_at
0.0003
1.50
Tumor necrosis factor (ligand) superfamily, member 13; tumor necrosis factor (ligand) superfamily, member 12-member 13 Placental growth factor, vascular endothelial growth factor-related protein
TNFSF13; TNFSF12TNFS-F13
211495_x_at
0.0005
1.24
PGF
215179_x_at
3.4×10-6
0.76
RAB6A ANP32A
201048_x_at 201051_at
0.0003 0.0006
1.50 1.11
SLC2A1
201250_s_at
7.5×10-5
1.30
NDUFA9
208969_at
6.4×10-5
0.85
Cell proliferation
Transport RAB6A, member RAS oncogene family Acidic (leucine-rich) nuclear phosphoprotein 32 family, member A Solute carrier family 2 (facilitated glucose transporter), member 1 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 9, 39 kDa
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(Continued) Gene symbol
Probe set ID
P value
Fold change (post/pre)
MTHFD2
201761_at
0.0005
1.24
HEAB LYPLA3 PPP1R3D PTPN22
204370_at 204458_at 204554_at 206060_s_at
0.0003 0.0004 0.0005 0.001
1.20 1.32 1.45 1.43
GTF2I MAN1C1 EIF2S1
210892_s_at 218918_at 201142_at
4.2×10−5 0.0002 0.0003
1.51 1.23 0.79
XRCC4
210813_s_at
0.001
0.77
PIN4
214225_at
0.0005
0.77
TAOK1
216310_at
0.0002
0.57
Ribonuclease T2
RNASET2
217984_at
0.0003
0.79
Polymerase (RNA) I polypeptide B, 128 kDa
POLR1B
220113_x_at
0.0005
0.80
PDLIM5
211681_s_at
0.001
1.30
Gene name and ontology class Cellular metabolism Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2, methenyltetrahydrofolate cyclohydrolase ATP/GTP-binding protein Lysophospholipase 3 (lysosomal phospholipase A2) Protein phosphatase 1, regulatory subunit 3D Protein tyrosine phosphatase, non-receptor type 22 (lymphoid) GSeneral transcription factor II, i Mannosidase, alpha, class 1C, member 1 Eukaryotic translation initiation factor 2, subunit 1 alpha, 35 kDa X-ray repair complementing defective repair in Chinese hamster cells 4 Protein (peptidyl-prolyl cis/trans isomerase) NIMA-interacting, 4 (parvulin) TAO kinase 1
Morphogenesis PDZ and LIM domain 5 Ninjurin 2
NINJ2
219594_at
0.001
0.72
Cytosolic ovarian carcinoma antigen 1
COVA1
32042_at
0.001
0.72
WDR1
210935_s_at
0.0009
1.33
PSME1
200814_at
0.0004
0.84
FCGR1A LOC440607 FCGR1A
214511_x_at
0.001
0.73
216950_s_at
0.001
0.71
FKSG17 FLJ12151 NUBP2
211445_x_at 211452_x_at 215373_x_at 218227_at
0.0002 0.0004 1.1×10−5 8.1×10−5
0.74 0.70 0.69 0.71
C1GALT1C1 ZNF16 FLJ11301 M17S2
219283_at 219548_at 221535_at 201383_s_at
0.0002 0.001 0.0003 0.0007
0.81 0.71 0.79 1.41
RBMX2 SERPINB10
204098_at 214539_at
3.0×10−5 0.0003
1.58 1.53
CG018 TMEM39A
214906_x_at 218615_s_at
0.0005 0.0003
1.35 1.39
Response to external stimulus WD repeat domain 1 Immune response Proteasome (prosome, macropain) activator subunit 1 (PA28 alpha) Fc fragment of IgG, high affinity Ia, receptor (CD64) Fc-gamma receptor I B2 Fc fragment of IgG, high affinity Ia, receptor (CD64) Unclassified FKSG17 Putative protein Hypothetical protein FLJ12151 Nucleotide binding protein 2 (MinD homolog, E. coli) C1GALT1-specific chaperone 1 Zinc finger protein 16 (KOX 9) Hypothetical protein FLJ11301 Membrane component, chromosome 17, surface marker 2 (ovarian carcinoma antigen CA125) RNA binding motif protein, X-linked 2 Serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 10 Hypothetical gene CG018 Transmembrane protein 39A
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Volodymyr Dvornyk et al.: Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray…
MAPK1, WDR1, and CG018. The last is a hypothetical gene with yet-to-be-identified function. The three of these highly expressed genes, SEPT2, TNFSF13, and MAPK1, regulate cell proliferation. The first two are the positive regulators, whereas MAPK1 is an inducer of apoptosis. Among the genes down-regulated in postmenopausal subjects, the most highly expressed were RNASET2, PSME1, NDUFA9, FKSG17, two FCGR1A homologs, and an unidentified transcript 211452_x_at. Interestingly, the latter had the highest expression level among all genes on the array. This transcript manifests the closest homology to the hamster’s TRIP, a gene encoding a double stranded RNA binding protein which interacts with the leucine rich repeat of flightless I [31]. Three of the down-regulated genes, PSME1 and the FCGR1A homologs participate in immune response. These genes are particularly interesting with respect to the menopause-related autoimmune diseases.
3
Discussion In normal conditions, circulating monocytes are
mature cells of finite differentiation stage, functionally stable, and homogeneous in phenotype until entering peripheral tissues
[10]
. Therefore, we expected
that in healthy women, the differences of gene expression levels in monocytes between pre- and postmenopausal subjects should be generally small in magnitude. The obtained results support this assumption. Indeed, the maximum differences between the groups under study were in the range of 1.3–1.5-fold. On the other hand, such minor functional differences of monocytes may magnify exponentially in the tissue microenvironment, where many specific local factors can interact and act either synergistically or antagonistically, thus amplifying their summary effect. Some of the DEGs identified in this study may be of potential importance for further studies of complex menopause-related diseases. For example, Alzheimer’s disease (AD) is common among elderly. www.jgenetgenomics.org
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SEPT2 (Nedd5) was reported in neurofibrillary tangles of patients with AD [32]. In addition, overexpression of this gene was associated with various brain tumors [33, 34]. PDLIM5 was identified quite recently; it is a homolog of AD-associated neuronal thread protein and was reported to be abundantly expressed in blood and brain [35]. Ninjuin2 was implicated in the regeneration of neuronal tissue and, similar to our results, found highly expressed in hematopoietic tissues [36]. Another evidence for potential contribution of the DEGs to the postmenopausal and age-related systemic disorders comes from the previous genetic linkage studies. For example, several disorders of unknown etiology have been mapped on human chromosome 12p13, including inflammatory bowel disease [37], multiple sclerosis [38], and type 2 diabetes [39]. Among the DEGs, the ninjurin2 and NDUFA9 genes are located in this region. Recently, the 14q24 region was suggested as harboring genes causing AD with an earlier age at onset [40] and multiple sclerosis [41]. In addition to PSEN1, a gene is previously associated with early onset of AD, this region harbors PGF, which is down regulated in postmenopausal females. PGF belongs to the superfamily of vascular endothelial growth factors and plays an important role in angiogenesis (see Reference 42 for review) and hematopoiesis [43]. Peripheral blood monocytes play an important role in immune response and are thought to contribute to autoimmune diseases [44, 45]. Several DEGs identified in this study are located in regions, which were thought to be linked to autoimmune diseases. For example, PTPN22, TNFSF13, and SEPRINB10 map to 1p13.3, 17p13, and 18q21, respectively, which are all candidates for rheumatoid arthritis [46]. In addition, 17p13 was linked to vitiligo and systemic lupus erythematosus [47]. Down-regulation of the proteasome activator PA28 (PSME1) results in abnormal proteasome activation and has been implicated in the development of
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intimal hyperplasia in animal models [48] and atherosclerosis in humans [49]. In addition, this gene is important for immune response [50] and was reported to be down-regulated by induction of diabetes mellitus [51]. The region, which harbors this gene (14q11.2) was linked to several autoimmune diseases: type 2 diabetes [52], atopic dermatitis [53], and inflammatory bowel disease [54]. Postmenopausal estrogen depletion was thought to be one of the major risk factors for potential health problems, including those of cardiovascular, skeletal, and genitourinary systems [1, 2, 9]. Our results suggest that the estrogen deficiency may not necessarily be the primary factor of this functional decline, and other yet-to-be-identified factors likely play an important role too. For example, SLC2A1 was reported as positively regulated by estrogen [55]. It implies this gene should be under-expressed in postmenopausal women that is, in fact, not the case. Microarray profiling of circulating blood cells becomes increasingly popular for searching gene signatures of various systemic disorders [56−59]. However, no such studies have been done so far on females, who are healthy and differ only by their menopausal status. Our results are in favor of our hypothesis that menopause indeed affects the functional state of circulating monocytes, and resulting differences may be determined through the genomewide gene expression profiling. Several DEGs identified in this study may be candidate for further studies of menopause- and age-associated systemic autoimmune, neurodegenerative, and cardiovascular disorders. Our work is only the first step in this direction, but it lays a basis for further research. References
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and hsp90 in major histocompatibility complex class I antigen processing. J Exp Med, 2002, 196: 185−196. 51 Merforth S, Kuehn L, Osmers A, Dahlmann B. Alteration of 20S proteasome-subtypes and proteasome activator PA28 in skeletal muscle of rat after induction of diabetes mellitus. Int J Biochem Cell Biol, 2003, 35: 740−748. 52 Hsueh WC, St Jean PL, Mitchell BD, Pollin TI, Knowler WC, Ehm MG, Bell CJ, Sakul H, Wagner MJ, Burns DK, Shuldiner AR. Genome-wide and fine-mapping linkage studies of type 2 diabetes and glucose traits in the Old Order Amish: evidence for a new diabetes locus on chromosome 14q11 and confirmation of a locus on chromosome 1q21-q24. Diabetes, 2003, 52: 550−557. 53 Söderhäll C, Bradley M, Kockum I, Wahlgren CF, Luthman H, Nordenskjöld M. Linkage and association to candidate regions in Swedish atopic dermatitis families. Hum Genet, 2001, 109: 129−135. 54 Duerr RH, Barmada MM, Zhang L, Pfutzer R, Weeks DE. High-density genome scan in Crohn disease shows confirmed linkage to chromosome 14q11-12. Am J Hum Genet, 2000, 66: 1857−1862. 55 Wang DY, McKague B, Liss SN, Edwards EA. Gene expression profiles for detecting and distinguishing potential endocrine-disrupting compounds in environmental samples. Environ Sci Technol, 2004, 38: 6396−6406. 56 Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ, Shark KB, Grande WJ, Hughes KM, Kapur V, Gregersen PK, Behrens TW. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci USA, 2003; 100: 2610−2615. 57 Tang Y, Gilbert DL, Glauser TA, Hershey AD, Sharp FR. Blood gene expression profiling of neurologic diseases: a pilot microarray study. Arch Neurol, 2005, 62: 210−215. 58 Olsen N, Sokka T, Seehorn CL, Kraft B, Maas K, Moore J, Aune TM. A gene expression signature for recent onset rheumatoid arthritis in peripheral blood mononuclear cells. Ann Rheum Dis, 2004, 63: 1387−1392. 59 Liu YZ, Dvornyk V, Lu Y, Shen H, Lappe JM, Recker RR, Deng HW. A novel pathophysiological mechanism for osteoporosis suggested by an in vivo gene expression study of circulating monocytes. J Biol Chem, 2005, 280: 29011−29016.
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Volodymyr Dvornyk et al.: Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray…
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绝经对血液单核细胞基因表达谱的影响：基因芯片初步研究 Dvornyk Volodymyr 1, 2, *, 刘耀中3, 4, *, 陆 燕3, 沈 汇3, Lappe Joan M 3, 雷署丰1, 4, Recker Robert R 3, 邓红文1, 3, 4 1. 湖南师范大学生命科学学院分子与统计遗传学研究室，长沙 2. 肯特州立大学生物科学系，肯特，俄亥俄州
410081；
44242，美国；
3. 克瑞屯大学骨质疏松研究中心，奥马哈，内不拉斯加州
68131，美国；
4. 密苏里堪萨斯城大学医学院基础医学部，堪萨斯城，密苏里州
64108，美国
摘 要：绝经是女性一生中很重要的生理现象之一，它能增加一系列复杂免疫、神经退化、新陈代谢和心血管方面的疾病。 血液单核细胞能分化成各种各样的细胞，这些细胞在组织形态发生和免疫应答方面起着很重要的作用。本研究中采用了包 含大约 14,500 个基因探针的 Affymetrix Human U133A 基因芯片来研究健康的绝经前和绝经后女性外周血液单核细胞中的 基因表达谱。样本之间的对比分析表明有 20 个基因上调，20 个基因下调。其中的 28 个基因根据它们的生物过程如细胞繁 殖、免疫应答、细胞代谢等等被分成了 6 个主要的 GO 类别；剩下的 12 个基因其生物学功能还没有被鉴定。研究结果支持 了我们的假设：血液单核细胞的功能状态确实受到绝经的影响，而且由此带来的改变可能是由全基因组范围的基因表达谱 而决定的。本研究中鉴定的一些差异表达基因有可能作为以后研究与绝经相关的系统免疫、神经退化和心血管疾病的候选 基因研究。此工作是这个研究方向的第一次尝试，为将来的进一步研究奠定了基础。 关键词：绝经；单核细胞；芯片；差异基因表达 作者简介：Dvornyk Volodymyr (1964 −)，男，助理教授，分子与统计遗传学专业。E-mail: [email protected] * 表示并列第一作者。
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Research Article
Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray Study Volodymyr Dvornyk 1, 2, *, Yaozhong Liu 3, 4, *, Yan Lu 3, Hui Shen 3, Joan M Lappe 3, Shufeng Lei 1, 4, Robert R Recker 3, Hongwen Deng 1, 3, 4,
①
1. Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, China; 2. Department of Biological Sciences, Kent State University, Kent, OH 44242, USA; 3. Osteoporosis Research Center, Creighton University Medical Center, Omaha, NE 68131, USA; 4. Departments of Basic Medical Science, School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
Abstract: Menopause is one of the key physiological events in the female life and can increase the risk for a number of complex autoimmune, neurodegenerative, metabolic, and cardiovascular disorders. Circulating monocytes can differentiate into various cell types and play an important role in tissue morphogenesis and immune response. We studied gene expression profiles of peripheral blood monocytes in healthy pre- and postmenopausal women using Affymetrix Human U133A GeneChip array that contains probes for ~14,500 genes. Comparative analyses between the samples showed that 20 genes were up- and 20 were down-regulated. Of these genes, 28 were classified into six major GO categories relevant to such biological processes as the cell proliferation, immune response, cellular metabolism, and the others. The remaining 12 genes have yet unidentified biological functions. Our results support the hypothesis that functional state of circulating monocytes is indeed affected by menopause, and resulting changes may be determined through the genomewide gene expression profiling. Several differentially expressed genes identified in this study may be candidates for further studies of menopause-associated systemic autoimmune, neurodegenerative, and cardiovascular disorders. Our study is only the first attempt in this direction, but it lays a basis for further research. Keywords: menopause; monocytes; microarrays; differential gene expression
Natural menopause is associated with substantial biochemical and metabolic changes, e.g., decrease in estrogen, increase in low-density lipoprotein, and decrease in high-density lipoprotein cholesterol, which are well-known risk factors for health, particularly in the elderly. Because of these changes, menopause is associated with an increase of risk for a number of complex disorders [1, 2]. In particular, early menopause has been shown to increase risks for ovarian, breast, and colorectal cancers, cardiovascular diseases, and osteoporosis [3–7]. Menopause- and age-related endo-
crinal changes have also been associated with increased incidence of some autoimmune diseases, e.g., diabetes mellitus [8]. Physiologically, menopause is directly related to the cessation of ovarian function and respective decrease of estrogen production by follicles. Estrogen deficiency has been shown as a risk factor for a variety of potential postmenopausal health problems, such as decline in cardiovascular, skeletal, and genitourinary system function [1, 2, 9]. The broad effect of estrogen depletion on various peripheral tissues may be
Received: 2007−02−28; Accepted: 2007−08−09 * These authors contributed equally to the work. ① Corresponding author. E-mail: [email protected]; Tel&Fax: +86-731-8872 791 www.jgenetgenomics.org
Volodymyr Dvornyk et al.: Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray…
explained by its function as a systemic regulatory (hormonal) factor. Peripheral blood monocytes constitute 3% to 7% of circulating white blood cells and represent a population of cells in transit from bone marrow to their ultimate location in tissues, where they can be differentiated into various cell types [10]. Recently, it was hypothesized that monocytes and T cells play a dominant role in aging [11, 12]. There are abundant data about menopause- and age-related metabolic changes in these cells
[13−15]
. With menopause and aging, the [16, 17]
. number of these cells increases Molecular and genetic mechanisms of these processes and changes remain largely unknown. There is strong evidence that estrogen level is a primary factor influencing the mononuclear cells and their functions [14, 18, 19]. Hormone dysfunction may result in autoimmunity after natural menopause [20]. Given that menopause is associated with other hormonal changes [21, 22], which also affect mononuclear cells [23], this may implicate these cells as contributing to postmenopausal health problems. Despite the potential importance of monocytes for postmenopausal health, no attempts have been made to study how menopause influences the functional status of these cells. We hypothesized that circulating monocytes of postmenopausal and premenopausal women are functionally different, and this difference can be reflected in their gene expression profiles.
1 1. 1
Materials and Methods Subjects
This study was approved by the Creighton University Institutional Review Board. All the study subjects signed informed consent documents before entering the project. The study subjects came from an expanding database being created for genetic studies of osteoporosis and obesity that are underway in the Osteoporosis Research Center of Creighton Univewww.jgenetgenomics.org
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rsity Medical Center. Premenopausal subjects were defined as those having at least 10 regular menses during the last 12 months. Menopause was defined as the date of the last menses followed by 12 months of no menses. This is a common criterion having been used for the definition of menopause in the studies of traits associated with menopause [24−26]. Nine premenopausal (50.0 ± 3.1 years old) and 10 postmenopausal (52.0 ± 1.7 years old) healthy women were recruited for this study. The recruited subjects were of about the same age that eliminated potential age-related confounding factors. To avoid the potential effect of diseases or conditions other than menopause, which may lead to gene expression changes in circulating monocytes, we implemented the following very strict exclusion criteria: 1) autoimmune or autoimmune-related diseases: systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, Graves disease, Hashimoto’s thyroiditis, myasthenia gravis, Addison’s disease, dermatomyositis, Sjögren’s syndrome, Reiter’s syndrome; 2) immune-deficiency conditions: AIDS, severe malnutrition, spleenectomy, other conditions that may result in an immune-deficiency state (ataxia-telangiectasia, DiGeorge syndrome, Chediak-Higashi syndrome, job syndrome, leukocyte adhesion defects, panhypogammaglobulinemia, selective deficiency of IgA, combined immunodeficiency disease, Wiscott-Aldrich syndrome, complement deficiencies); 3) hematopoietic and lymphoreticular malignancies: leukaemias, lymphomas (Hodgkin’s disease, non-Hodgkin’s disease), myeloma, Waldenström’s macroglobulinaemia, heavy chain disease, others (leukaemic reticuloendotheliosis, mastocytosis, malignant histiocytosis); 4) other diseases: viral infection (influenza), allergy (active periods of asthma), COPD (chronic obstructive pulmonary disease); 5) any treatment or disease that would be an apparent factor affecting gene expression in these cells (e.g., smoking, chronic alcohol consumption, hormonal or other medications, etc.).
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Isolation of monocytes
Monocytes were isolated from 50 mL peripheral blood by negative immunomagnetic bead precipitation using a Monocyte Negative Isolation Kit (Dynal Biotech Inc., Lake Success, NY, USA) and following the protocol recommended by the manufacturer. Monocytes remained untouched, naïve and free of the surface-bound antibody and beads. This is of particular importance as binding antibody-coagulated beads to the cell surface may activate the cells and thus change their gene expression profiles. According to flow cytometry results, average monocyte purity in our samples was 85.8 ± 3.6% (Fig. 1).
遗传学报 Vol. 34 No. 11 2007
ried out according to the manufacturer’s recommendations [27]. The Bioconductor’s Affy package [28] was used to process the probe-level data (raw data) and transform them into expression data. The transformation was performed using the algorithm implemented in the Affymetrix Microarray Analysis Suite v. 5.0 software. After chip hybridization and initial data analysis, the expression values for ~14,500 genes represented on the chips were compared between pre- and postmenopausal subjects by using a nonpaired Student’s t test. Hierarchical clustering (centroid linkage) of the differentially expressed genes (DEGs) was performed by using CLUSTER and visualized by using TREEVIEW [29]. All the raw microarray data were submitted to the NCBI’s Gene Expression Omnibus data repository under accession number GSE2208.
2
Results
On the basis of the analysis performed with the Bioconductor’s Affy package, an average 39.69% of a total of 22,283 probe sets in the array were called “present” for our samples. Of the preselected 8,623 probe sets, we identified the differential expression of 40 genes between the pre- and postmenopausal subFig. 1 Flow-cytometry plot for the sample of isolated monocytes
1.3
RNA isolation, Affymetrix oligonucleotide array hybridization, and data analysis
Total RNA from the samples was isolated using the RNeasy Mini Kit (Qiagen, Inc., Valencia, CA, USA) following the protocol recommended by the manufacturer. Equal aliquots of total RNA from each of the subjects in each group used for biotin labeling as described in the Affymetrix technical bulletin. Hybridization, washing, scanning, and analysis of the Affymetrix GeneChip Human U133A microarrays (Affymetrix, Inc., Santa Clara, CA, USA) were car-
jects (P ≤ 0.001). Among these genes, 20 were up-regulated and 20 were down-regulated in the postmenopausal females as compared to the premenopausal ones. According to the results of the cluster analysis, pre- and postmenopausal women had quite distinct patterns of gene expression and therefore were clearly clustered into two well-defined groups (Fig. 2). One expected result of microarray experiments gives a better understanding of the causes of phenotypic differences between the groups of subjects. To address this issue, we performed an ontological analysis of these 40 DEGs. The analysis allowed us to divide the genes into several classes based on their biological www.jgenetgenomics.org
Volodymyr Dvornyk et al.: Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray…
functions as annotated by the gene ontology (GO) consortium [30]. This classification is shown in Table 1. These results suggest that, of the 40 DEGs, 28 were classified into six major GO categories. These functional categories were relevant to such biological processes as the cell proliferation, immune response, cellular metabolism, and the others. However, many of the genes may be assigned to several GO subcategories that may serve as evidence of their pleiotropy. For example, MAPK1 may contribute to various biological processes and thus belongs to seven GO classes: protein amino acid phosphorylation (GO: 0006468), induction of apoptosis (GO: 0006917), chemotaxis (GO: 0006935), response to stress (GO: 0006950), cell cycle (GO: 0007049), signal transduction (GO: 0007165), and synaptic transmission (GO:0007268). Likewise, PGF is classified into six GO categories according to its biological functions. The remaining 12 genes have yet unidentified biological functions (Table 1). The absolute values of expression significantly varied between the genes. Among the genes up-regulated in postmenopausal females, the most highly expressed were SEPT2, ANP32A, TNFSF13, MTHFD2, Table 1
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Fig. 2 Gene expression profiles of circulating monocytes from 9 premenopausal and 10 postmenopausal healthy women Shown are hierarchical clustering results of microarray data for 40 genes that distinguish premenopausal (blue) from postmenopausal (yellow). Red indicates up-regulated genes, and green represents down-regulated genes.
Classification of 40 genes differentially expressed between pre- and postmenopausal females by ontological groups
Gene name and ontology class
Fold change (post/pre)
Gene symbol
Probe set ID
P value
Septin 2
SEPT2
200778_s_at
0.001
1.31
Mitogen-activated protein kinase 1
MAPK1
208351_s_at
0.0003
1.50
Tumor necrosis factor (ligand) superfamily, member 13; tumor necrosis factor (ligand) superfamily, member 12-member 13 Placental growth factor, vascular endothelial growth factor-related protein
TNFSF13; TNFSF12TNFS-F13
211495_x_at
0.0005
1.24
PGF
215179_x_at
3.4×10-6
0.76
RAB6A ANP32A
201048_x_at 201051_at
0.0003 0.0006
1.50 1.11
SLC2A1
201250_s_at
7.5×10-5
1.30
NDUFA9
208969_at
6.4×10-5
0.85
Cell proliferation
Transport RAB6A, member RAS oncogene family Acidic (leucine-rich) nuclear phosphoprotein 32 family, member A Solute carrier family 2 (facilitated glucose transporter), member 1 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 9, 39 kDa
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(Continued) Gene symbol
Probe set ID
P value
Fold change (post/pre)
MTHFD2
201761_at
0.0005
1.24
HEAB LYPLA3 PPP1R3D PTPN22
204370_at 204458_at 204554_at 206060_s_at
0.0003 0.0004 0.0005 0.001
1.20 1.32 1.45 1.43
GTF2I MAN1C1 EIF2S1
210892_s_at 218918_at 201142_at
4.2×10−5 0.0002 0.0003
1.51 1.23 0.79
XRCC4
210813_s_at
0.001
0.77
PIN4
214225_at
0.0005
0.77
TAOK1
216310_at
0.0002
0.57
Ribonuclease T2
RNASET2
217984_at
0.0003
0.79
Polymerase (RNA) I polypeptide B, 128 kDa
POLR1B
220113_x_at
0.0005
0.80
PDLIM5
211681_s_at
0.001
1.30
Gene name and ontology class Cellular metabolism Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2, methenyltetrahydrofolate cyclohydrolase ATP/GTP-binding protein Lysophospholipase 3 (lysosomal phospholipase A2) Protein phosphatase 1, regulatory subunit 3D Protein tyrosine phosphatase, non-receptor type 22 (lymphoid) GSeneral transcription factor II, i Mannosidase, alpha, class 1C, member 1 Eukaryotic translation initiation factor 2, subunit 1 alpha, 35 kDa X-ray repair complementing defective repair in Chinese hamster cells 4 Protein (peptidyl-prolyl cis/trans isomerase) NIMA-interacting, 4 (parvulin) TAO kinase 1
Morphogenesis PDZ and LIM domain 5 Ninjurin 2
NINJ2
219594_at
0.001
0.72
Cytosolic ovarian carcinoma antigen 1
COVA1
32042_at
0.001
0.72
WDR1
210935_s_at
0.0009
1.33
PSME1
200814_at
0.0004
0.84
FCGR1A LOC440607 FCGR1A
214511_x_at
0.001
0.73
216950_s_at
0.001
0.71
FKSG17 FLJ12151 NUBP2
211445_x_at 211452_x_at 215373_x_at 218227_at
0.0002 0.0004 1.1×10−5 8.1×10−5
0.74 0.70 0.69 0.71
C1GALT1C1 ZNF16 FLJ11301 M17S2
219283_at 219548_at 221535_at 201383_s_at
0.0002 0.001 0.0003 0.0007
0.81 0.71 0.79 1.41
RBMX2 SERPINB10
204098_at 214539_at
3.0×10−5 0.0003
1.58 1.53
CG018 TMEM39A
214906_x_at 218615_s_at
0.0005 0.0003
1.35 1.39
Response to external stimulus WD repeat domain 1 Immune response Proteasome (prosome, macropain) activator subunit 1 (PA28 alpha) Fc fragment of IgG, high affinity Ia, receptor (CD64) Fc-gamma receptor I B2 Fc fragment of IgG, high affinity Ia, receptor (CD64) Unclassified FKSG17 Putative protein Hypothetical protein FLJ12151 Nucleotide binding protein 2 (MinD homolog, E. coli) C1GALT1-specific chaperone 1 Zinc finger protein 16 (KOX 9) Hypothetical protein FLJ11301 Membrane component, chromosome 17, surface marker 2 (ovarian carcinoma antigen CA125) RNA binding motif protein, X-linked 2 Serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 10 Hypothetical gene CG018 Transmembrane protein 39A
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Volodymyr Dvornyk et al.: Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray…
MAPK1, WDR1, and CG018. The last is a hypothetical gene with yet-to-be-identified function. The three of these highly expressed genes, SEPT2, TNFSF13, and MAPK1, regulate cell proliferation. The first two are the positive regulators, whereas MAPK1 is an inducer of apoptosis. Among the genes down-regulated in postmenopausal subjects, the most highly expressed were RNASET2, PSME1, NDUFA9, FKSG17, two FCGR1A homologs, and an unidentified transcript 211452_x_at. Interestingly, the latter had the highest expression level among all genes on the array. This transcript manifests the closest homology to the hamster’s TRIP, a gene encoding a double stranded RNA binding protein which interacts with the leucine rich repeat of flightless I [31]. Three of the down-regulated genes, PSME1 and the FCGR1A homologs participate in immune response. These genes are particularly interesting with respect to the menopause-related autoimmune diseases.
3
Discussion In normal conditions, circulating monocytes are
mature cells of finite differentiation stage, functionally stable, and homogeneous in phenotype until entering peripheral tissues
[10]
. Therefore, we expected
that in healthy women, the differences of gene expression levels in monocytes between pre- and postmenopausal subjects should be generally small in magnitude. The obtained results support this assumption. Indeed, the maximum differences between the groups under study were in the range of 1.3–1.5-fold. On the other hand, such minor functional differences of monocytes may magnify exponentially in the tissue microenvironment, where many specific local factors can interact and act either synergistically or antagonistically, thus amplifying their summary effect. Some of the DEGs identified in this study may be of potential importance for further studies of complex menopause-related diseases. For example, Alzheimer’s disease (AD) is common among elderly. www.jgenetgenomics.org
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SEPT2 (Nedd5) was reported in neurofibrillary tangles of patients with AD [32]. In addition, overexpression of this gene was associated with various brain tumors [33, 34]. PDLIM5 was identified quite recently; it is a homolog of AD-associated neuronal thread protein and was reported to be abundantly expressed in blood and brain [35]. Ninjuin2 was implicated in the regeneration of neuronal tissue and, similar to our results, found highly expressed in hematopoietic tissues [36]. Another evidence for potential contribution of the DEGs to the postmenopausal and age-related systemic disorders comes from the previous genetic linkage studies. For example, several disorders of unknown etiology have been mapped on human chromosome 12p13, including inflammatory bowel disease [37], multiple sclerosis [38], and type 2 diabetes [39]. Among the DEGs, the ninjurin2 and NDUFA9 genes are located in this region. Recently, the 14q24 region was suggested as harboring genes causing AD with an earlier age at onset [40] and multiple sclerosis [41]. In addition to PSEN1, a gene is previously associated with early onset of AD, this region harbors PGF, which is down regulated in postmenopausal females. PGF belongs to the superfamily of vascular endothelial growth factors and plays an important role in angiogenesis (see Reference 42 for review) and hematopoiesis [43]. Peripheral blood monocytes play an important role in immune response and are thought to contribute to autoimmune diseases [44, 45]. Several DEGs identified in this study are located in regions, which were thought to be linked to autoimmune diseases. For example, PTPN22, TNFSF13, and SEPRINB10 map to 1p13.3, 17p13, and 18q21, respectively, which are all candidates for rheumatoid arthritis [46]. In addition, 17p13 was linked to vitiligo and systemic lupus erythematosus [47]. Down-regulation of the proteasome activator PA28 (PSME1) results in abnormal proteasome activation and has been implicated in the development of
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intimal hyperplasia in animal models [48] and atherosclerosis in humans [49]. In addition, this gene is important for immune response [50] and was reported to be down-regulated by induction of diabetes mellitus [51]. The region, which harbors this gene (14q11.2) was linked to several autoimmune diseases: type 2 diabetes [52], atopic dermatitis [53], and inflammatory bowel disease [54]. Postmenopausal estrogen depletion was thought to be one of the major risk factors for potential health problems, including those of cardiovascular, skeletal, and genitourinary systems [1, 2, 9]. Our results suggest that the estrogen deficiency may not necessarily be the primary factor of this functional decline, and other yet-to-be-identified factors likely play an important role too. For example, SLC2A1 was reported as positively regulated by estrogen [55]. It implies this gene should be under-expressed in postmenopausal women that is, in fact, not the case. Microarray profiling of circulating blood cells becomes increasingly popular for searching gene signatures of various systemic disorders [56−59]. However, no such studies have been done so far on females, who are healthy and differ only by their menopausal status. Our results are in favor of our hypothesis that menopause indeed affects the functional state of circulating monocytes, and resulting differences may be determined through the genomewide gene expression profiling. Several DEGs identified in this study may be candidate for further studies of menopause- and age-associated systemic autoimmune, neurodegenerative, and cardiovascular disorders. Our work is only the first step in this direction, but it lays a basis for further research. References
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绝经对血液单核细胞基因表达谱的影响：基因芯片初步研究 Dvornyk Volodymyr 1, 2, *, 刘耀中3, 4, *, 陆 燕3, 沈 汇3, Lappe Joan M 3, 雷署丰1, 4, Recker Robert R 3, 邓红文1, 3, 4 1. 湖南师范大学生命科学学院分子与统计遗传学研究室，长沙 2. 肯特州立大学生物科学系，肯特，俄亥俄州
410081；
44242，美国；
3. 克瑞屯大学骨质疏松研究中心，奥马哈，内不拉斯加州
68131，美国；
4. 密苏里堪萨斯城大学医学院基础医学部，堪萨斯城，密苏里州
64108，美国
摘 要：绝经是女性一生中很重要的生理现象之一，它能增加一系列复杂免疫、神经退化、新陈代谢和心血管方面的疾病。 血液单核细胞能分化成各种各样的细胞，这些细胞在组织形态发生和免疫应答方面起着很重要的作用。本研究中采用了包 含大约 14,500 个基因探针的 Affymetrix Human U133A 基因芯片来研究健康的绝经前和绝经后女性外周血液单核细胞中的 基因表达谱。样本之间的对比分析表明有 20 个基因上调，20 个基因下调。其中的 28 个基因根据它们的生物过程如细胞繁 殖、免疫应答、细胞代谢等等被分成了 6 个主要的 GO 类别；剩下的 12 个基因其生物学功能还没有被鉴定。研究结果支持 了我们的假设：血液单核细胞的功能状态确实受到绝经的影响，而且由此带来的改变可能是由全基因组范围的基因表达谱 而决定的。本研究中鉴定的一些差异表达基因有可能作为以后研究与绝经相关的系统免疫、神经退化和心血管疾病的候选 基因研究。此工作是这个研究方向的第一次尝试，为将来的进一步研究奠定了基础。 关键词：绝经；单核细胞；芯片；差异基因表达 作者简介：Dvornyk Volodymyr (1964 −)，男，助理教授，分子与统计遗传学专业。E-mail: [email protected] * 表示并列第一作者。
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