Genomic expression profiling of human inflammatory cardiomyopathy (DCMi) suggests novel therapeutic targets

The clinical phenotype of human dilated cardiomyopathy (DCM) encompasses a broad spectrum of etiologically distinct disorders. As targeting of etiology-related pathogenic pathways may be more efficient than current standard heart failure treatment, we obtained the genomic expression profile of a DCM subtype characterized by cardiac inflammation to identify possible new therapeutic targets in humans. In this inflammatory cardiomyopathy (DCMi), a distinctive cardiac expression pattern not described in any previous study of cardiac disorders was observed. Two significantly altered gene networks of particular interest and possible interdependence centered around the cysteine-rich angiogenic inducer 61 (CYR61) and adiponectin (APN) gene. CYR61 overexpression, as in human DCMi hearts in situ, was similarly induced by inflammatory cytokines in vascular endothelial cells in vitro. APN was strongly downregulated in DCMi hearts and completely abolished cytokine-dependent CYR61 induction in vitro. Dysbalance between the CYR61 and APN networks may play a pathogenic role in DCMi and contain novel therapeutic targets. Multiple immune cell-associated genes were also deregulated (e.g., chemokine ligand 14, interleukin-17D, nuclear factors of activated T cells). In contrast to previous investigations in patients with advanced or end-stage DCM where etiology-related pathomechanisms are overwhelmed by unspecific processes, the deregulations detected in this study occurred at a far less severe and most probably fully reversible disease stage.
Introduction
The term dilated cardiomyopathy (DCM) describes a cardiac phenotype characterized by dilation and dysfunction of the ventricles. From an etiologic perspective, DCM encompasses a spectrum of disease entities, some of which are due to monogenic defects in cardiac expressed genes [1], others triggered by exogenous factors including cardiotropic viruses [2]. Targeting of etiology-related pathogenic pathways in etiologically distinct DCM subtypes may be more efficient than current standard treatment protocols employed in heart failure of any origin. One important DCM subtype characterized by cardiac inflammation is designated as inflammatory cardiomyopathy (DCMi), often found in conjunction with cardiac viral infections. Spontaneous elimination of cardiac viruses was associated with significant hemodynamic improvement for various viruses [3], and interferon-β (IFN-β) therapy was useful to promote virus elimination and improve cardiac function and clinical status [4]. A search for new therapeutic targets in DCMi seems worthwhile due to the high prevalence of this condition and its still unsatisfactory long-term outcome. The current study combined in vivo and in vitro methods to identify possible DCMi-related gene regulatory networks that might be exploited to expand the therapeutic repertoire beyond the standard heart failure drugs currently used. The study does not address individual gene deregulations, but searches for disturbances of regulatory gene networks reflected by anomalous cardiac expression patterns. Genomic screening strategies require precise phenotyping of the study groups, as this will minimize interindividual differences and increase the likelihood of revealing distinctive pathomechanisms. Because, in advanced stages of DCM, such processes are most likely overwhelmed by unspecific reactions of the failing heart long after the initiating process has burnt out, we investigated patients with moderately severe disease in contrast to most previous microarray work in the field [5]. The standard clinical characterization was refined by endomyocardial biopsy (EMB)-based immunohistological and molecular virological analyses. In contrast to multiple fields of cancer research, restrictive disease phenotyping of this kind is rarely employed in cardiovascular research, mostly due to the difficult access to affected cardiovascular tissues in humans in vivo. The microarray part of our study was entirely based on RNA isolated from EMBs thus providing an approach suitable for follow-up investigations of clinical courses in relation to cardiac expression profiles. We report on a distinctive cardiac expression pattern in DCMi and two significantly altered gene networks centered around the cysteine-rich angiogenic inducer 61 (CYR61) and adiponectin (APN) gene.
Patients and methods
Characterization of study groups
The patients investigated by microarray analysis of cardiac gene expression displayed the distinct DCM subtype of DCMi as defined by the World Health Organization and characterized by its association with cardiac inflammation. The DCMi group of this study was derived from a large series of patients initially submitted to our clinic with symptoms and signs of DCM; in all of whom, EMBs from the right ventricular septum were obtained by standard procedure for histological, immunohistological, and molecular virological analyses. All patients had given written informed consent. After standard clinical, morphological, and functional patient characterization, their EMBs were assessed for myocardial inflammation by histological and immunohistological methods. Their cardiac virological status was assessed by polymerase chain reaction (PCR)/real-time polymerase chain reaction (RT-PCR) for the following viral genomes: enteroviruses, adenoviruses (ADV), Epstein–Barr virus, human herpesvirus 6 (HHV-6), and parvovirus B19 (PVB19). Eight DCMi patients with cardiac inflammation in association with cardiac PVB19 were selected for the microarray study (DCMi group, Table 1). This group was compared to a control group of four individuals submitted to our clinic and diagnosed to clarify a clinically suspected cardiomyopathy, in whom the full differential diagnostic workup finally revealed that their complaints were noncardiac in origin (Cont group, Table 1). They had normal cardiac morphology and function, were negative for cardiac inflammation or viral genomes, and were subjected to microarray analysis, too. The DCMi group patients were on standard heart failure medication and in New York Heart Association functional class II (Table 1).
Table 1Characterization of study groupsCodeGroupAge (years)EF (%)LVEDD (mm)INFLAMPVB19EVAdVHHV6EBVGA-1DCMi315671++−−−−HI-2DCMi393161++−−−−KA-3DCMi693660++−−−−PS-4DCMi603476++−−−−BA-5DCMi674359++−−−−DE-6DCMi315257++−−−−CH-7DCMi625173++−−−−GH-8DCMi453462++−−−−52.5 ± 17 (31–69)42.0 ± 9.9 (31–56)62.8 ± 7.8 (57–73)RS-14Cont366847−−−−−−SM-15Cont466149−−−−−−KE-16Cont267447−−−−−−TU-17Cont366450−−−−−−36.0 ± 8 (26–46)66.8 ± 5.6 (61–74)48.3 ± 1.2 (47–50)EF ejection fraction; LVEDD left ventricular end-diastolic diameter; INFLAM cardiac inflammation; PVB19 parvovirus B19; EV enterovirus; AdV adenovirus; HHV6 human herpesvirus 6; EBV Epstein–Barr virus
High density microarray studies of human cardiac biopsies
cDNA was synthesized from 200 ng of total RNA isolated from EMBs. The minimal amount of total RNA routinely available from human EMBs was 200 ng. cRNA targets were generated using the small sample protocol of Affymetrix. Briefly, in vitro transcribed unlabeled cRNA from the first round of amplification was subjected to a second round of amplification. cRNA was transcribed into cDNA using random primers. After RNAse H-mediated removal of surplus cRNA, a second strand was synthesized using a T7 primer. cRNA amount was determined by ultraviolet spectroscopy, and distribution of cRNA fragment sizes of both cRNA and fragmentation products was checked on a BioAnalyzer. Each cRNA sample was hybridized for 16 h at 45°C to an Affymetrix Human Genome U133 Plus 2.0 GeneChip Array. Chips were washed and stained with streptavidin–phycoerythrin using a fluidics station according to Affymetrix protocols. Probe arrays were scanned at 3-/1.56-μm resolution using the Affymetrix GeneChip System Confocal Scanner 3000. Raw data were analyzed with Affymetrix GeneChip Operating Software. The detection p value of a transcript determines the detection call indicating if the transcript is reliably detected (p < 0.05; present) or not detected (absent). To enable the comparison between chips, data were normalized to a global intensity of 500. The expression data can be obtained from the Gene Expression Omnibus (GEO) website http://www.ncbi.nlm.nih.gov/projects/geo/, GEO experimental series number: GSE4172. For TaqMan experiments to verify candidate genes, see next chapter.
Quantitative real-time PCR analysis of human cardiac biopsies
Total RNA was extracted using TRIzol reagent (Invitrogen). Quality and quantity was checked on a Agilent 2100 Bioanalyzer. A minimal amount of only 200 ng of total RNA was routinely available from human EMBs for cDNA synthesis to become reverse transcribed using the High Capacity cDNA Archive Kit (Applied) according to the manufacturers instructions. For determination of gene expression, quantitative PCR assays were performed on an ABI PRISM 7900 Sequence Detector (Applied) with TaqMan Universal PCR Master Mix and TaqMan Gene expression assays (Applied) consisting of a FAM™ dye-labeled TaqMan MGB probe and two PCR primers. The following oligonucleotide sets purchased from Applied Biosystems were used: human genes encoding APN (Hs00605917_m1), CYR61 (Hs00155479_m1), chemokine ligand 14 (CXCL14; Hs00171135_m1), Ras-responsive element protein 1 (RREB1; Hs00171486_m1), GTP binding protein overexpressed in skeletal muscle (GEM; Hs00170633_m1), membrane protein, palmitoylated 7 (MPP7; Hs00299584_m1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH; Hs99999905_m1), and hypoxanthine phosphoribosyltransferase 1 (HPRT1; Hs99999909_m1). Quantitative RT-PCR reactions were performed using standard cycle parameters determined by the company. Normalization and fold change were calculated with the ΔΔCt method with GAPDH and HPRT1 as reference mRNA species according to ABI Prism 7900 manufacturer’s instructions [6]. The values given in Supplementary Fig. 2 are the means ± SEM of two independent quantitative TaqMan PCRs performed on RNA isolated from the same EMB that was also used for the microarray analysis.
Cardiovascular cell cultures
Primary culture of neonatal rat cardiomyocytes (NRCMC): Cells were prepared from ventricular tissue of 1- to 3-day-old Wistar rat pups (Charles River) as described previously [7]. Briefly, the removed ventricles of 50–60 animals were placed into ice-cold calcium ion-free phosphate-buffered cell isolation medium (CIM) containing 120 mM NaCl, 4.56 mM KCl, 0.44 mM KH2PO4, 0.42 mM Na2HPO4, 25 mM NaHCO3, and 5.55 mM glucose, pH 7.5, as well as 0.5 mg/ml streptomycin and 5,000 IE/ml penicillin G (Biochrom). Ventricular tissue was transferred to a Petri dish and minced into pieces of approximately 1 mm3 in size using two sterile scalpels. Stepwise disaggregation for 15 min each of tissue pieces into single cells was performed at 37°C under continuous mixing with a magnetic stirrer at 150 rpm in a 50 ml Erlenmeyer flask containing 15-ml CIM supplemented with 0.12% porcine trypsin 1:250 (Belger). The first tissue digest was discarded. The following three to eight supernatants obtained after each 15-min digestion period were poured into single sterile glass centrifuge tubes each containing 4 ml of ice-cold, heat-inactivated fetal calf serum (FCS; Biochrom). Cells were gently sedimented at 400 g for 10 min. The sedimented cells were resuspended in cell growth medium CMRL 1415-ATM (CMRL-ATM, Connaught Medical Research Laboratories-atmospheric) that was supplemented with 10% FCS, 10% horse serum (HS), and 0.02 mg/ml gentamicin (all from Biochrom) and adjusted to pH 7.4 using 1 N NaOH. For enrichment of cardiomyocytes, 20 ml of this suspension containing approximately 1.5 × 108 cardiac cells were incubated in 175 cm2 plastic dishes, Corning culture flask in a water-saturated atmosphere for 90 min at 37°C. During this incubation, most of the nonmuscle cells comprising 30–40% of total cell number attached to the bottom surface of the culture flask. The cardiomyocyte-enriched supernatant was carefully removed and pooled in an Erlenmeyer flask. The number of cells was counted using a light microscope and a hemocytometer. The cell density was adjusted with additional growth medium to 1.2 × 106 cells/ml and the cells seeded to 6-well Nunc culture plates and incubated in a water-saturated atmosphere at 37°C. After 24 h of incubation, the FCS/HS-supplemented growth medium was replaced either by a 10% FCS-supplemented CMRL 1415-ATM or serum-free, hormone-supplemented CMRL 1415-ATM medium containing 2-μM fluorodeoxfyuridine (Sigma) for inhibition of proliferation of contaminating nonmuscle cells. Vascular endothelial cell line: The endothelial cell line EA.hy926 (a hybrid line derived from human umbilical vein endothelial cells and A549 cells) was cultured in Dulbecco’s modified Eagle’s medium supplemented with hypoxanthine–aminopterin–thymidine, 10% FCS, and 1% penicillin and streptomycin.
Quantitative real-time PCR analysis of activated cell cultures
To study regulatory factors of CYR61 and APN in cultured cardiovascular cells (Fig. 2a,b), NRCMCs were grown in 6-well and EA.hy926 cells in 12-well dishes for 48 h. Thereafter, they were incubated for 24 h with the following cytokines (concentrations): IFN-β1b (30 ng/ml), IFN-β1a (30 ng/ml), IFN-γ (30 ng/ml), IL-1β (30 ng/ml), IL-6 (100 ng/ml), tumor necrosis factor (TNF)-α (20 ng/ml), and TNF-β (50 ng/ml). In some experiments (Fig. 2a), EA.hy926 cells were preincubated for 1 h with APN (10 μg/ml) before the addition of the above cytokines. This APN concentration was chosen because the APN plasma range in humans is 3–30 μg/ml. Cells were harvested 24 h after cytokine addition. Of the total RNA, 1 to 3 μg were isolated from cell cultures to become reverse transcribed using the High Capacity cDNA Archive Kit, and TaqMan PCRs were then carried out employing the following primer systems (Applied) for the rat genes encoding APN (Acdc, Rn00595250_m1), CYR61 (Rn00580055_m1), and GAPD (Rn99999916_s1). mRNA amounts were standardized against the housekeeping gene GAPDH. The values given in Fig. 2a,b are the means±SEM of two independent experiments each performed in triplicate.
Network analysis
The networks shown in Fig. 1a,c,e were derived by the Ingenuity Pathways Analysis (IPA; http://www.ingenuity.com) software. The microarray dataset containing gene identifiers and corresponding expression values was uploaded as an Excel spreadsheet using the template provided in the application. Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base (IPKB). A cutoff of fold change ≥1.5 was set to identify genes whose expression was significantly differentially regulated; these were then used as the starting point for generating regulatory networks. Biological functions were assigned to each network or the overall analysis, respectively, by using the findings that have been extracted from the scientific literature and stored in the IPKB. The biological functions assigned to each network or the overall analysis, respectively, are ranked according to the significance of that biological function to the network or the overall analysis, respectively. A Fischer’s exact test is used to calculate a p value determining the probability that the biological function assigned to that network is explained by chance alone. The networks are displayed graphically as nodes (genes/gene products) and edges (the biological relationships between the nodes). Human, mouse, and rat orthologs of a gene are stored as separate objects in the knowledge base but are represented as a single node in the network. The intensity of the node color indicates the degree of up-(red) or down-(green) regulation. Nodes are displayed using various shapes that represent the functional class of the gene product. Edges are displayed with various labels that describe the nature of the relationship between the nodes (e.g., B for binding, T for transcription).
Fig. 1Altered gene expression profile and regulatory networks in DCMi. a, c, e The networks altered in DCMi are displayed graphically as nodes (genes/gene products) and edges (biological relationships between the nodes). Human, mouse, and rat orthologs of a gene are stored as separate objects in the knowledge base but are represented as a single node in the network. The intensity of the node color indicates the degree of up- (red) or down- (green) regulation. Nodes are displayed using various shapes that represent the functional class of the gene product. Continuous edge lines indicate protein–protein interactions, broken edge lines changes at the transcriptional level. The software further displays various labels that describe the nature of the relationship between the nodes (e.g., B for binding, T for transcription). b, d, f Show cluster analyses of the genes within the networks in (a), (c), (e), respectively. Here, analogous to the networks, red color denotes upregulation, green color denotes downregulation of a gene, whereas the brightness of the color indicates the extent of deregulation
Statistical analysis
For statistical analysis of microarray data, we used the significance analysis of microarrays (SAM). SAM statistics identifies significant changes in gene expression by performing a set of gene-specific t-tests. For each gene, a score is calculated on the basis of its change of expression relative to the standard deviation of repeated measurements for that gene. Genes with scores greater than a given threshold Δ are defined as significantly deregulated. Manual adjustment of this threshold Δ allows the identification of smaller or larger cohorts of genes. In addition, a false discovery rate can be estimated based on random permutations of all measurements. With respect to quantitative TaqMan PCRs, Student’s t-test was applied to demonstrate statistical significance. A value of p < 0.05 was considered statistically significant.
Results
Because, in advanced or end-stage DCM, any etiology-related pathomechanisms are most likely overwhelmed by unspecific reactions of the terminally failing myocardium long after the initiating process has burnt out, we studied patients with moderately severe disease (Table 1). The standard clinical characterization was refined by EMB-based immunohistological (Supplementary Fig. 1) and molecular virological analyses. The DCMi group was characterized by myocardial inflammation in association with PVB19. A distinctive expression profile was detected in the DCMi group by microarray analysis of the EMBs. The complete dataset is available from the website http://www.ncbi.nlm.nih.gov/projects/geo/. Genes significantly (q value < 5.0) and ≥1.5-fold deregulated are listed in Supplementary Table 1. Subsequently, a network analysis of the complete dataset with 380 genes ≥1.5-fold down- and 1,038 genes ≥1.5-fold up-regulated was performed as shown in Fig. 1a–f. Several of these genes were validated by quantitative TaqMan PCR (Table 2, Supplementary Fig. 2). Whereas confirmatory PCR analysis was desirable for a major number of DCMi-associated genes, the limited mRNA quantity available from human EMBs confined the number of such analyses. The minimal amount of total RNA available from EMBs for both microarrays and confirmatory PCR was only 200 ng. The genes selected for confirmatory PCRs are shown in Supplementary Fig. 2. They include CYR61, APN, CXCL14, and GEM as novel deregulated genes associated with DCMi, natriuretic peptide precursor A (NPPA), and natriuretic peptide precursor B (NPPB) as standard heart failure-associated genes and confirmed the microarray data in all cases. The microarray q values for each of the genes in Supplementary Fig. 2, the corresponding p values in the confirmatory TaqMan PCRs, and similar regulatory factors are proof of deregulation of these genes of particular interest.
Table 2Partial cardiac gene expression profile of human DCMi (further data at http://www.ncbi.nlm.nih.gov/projects/geo/ and in Supplementary Table 1)NetworkSymbolRegulationFactorq valueGene NameAPNAPNaDown6.80.605Adiponectin14.4CYRCXCL14Down5.91.943Chemokine (C–X–C motif) ligand 144.4AKTTDGF1Down4.70.605Teratocarcinoma-derived growth factor 1CYRPDGFADown3.60.371Platelet-derived growth factor (PDGF)-α polypeptideAPNSOS1Down3.10.371Son of sevenless homolog 1 (Drosophila)CYRTMSB10Down2.60.371Thymosin-β10APNINSRDown2.50.371Insulin receptorCYREGFRDown2.52.226Epidermal growth factor (EGF) receptorAPNPIK3R1Down2.50.991Phosphoinositide-3-kinase regulatory subunit 1 (p85α)CYRLAMA4Down2.40.371Laminin α4APNLMO4Down2.33.868Lin-11, Isl-1, Mec-3 (LIM) domain only 4CYRHSPG2Down2.12.226Heparan sulfate proteoglycan 2 CYRSOX9Down2.10.371Sex determining region Y (SRY)-box 9APNLIFRDown2.01.632Leukemia inhibitory factor (LIF) receptorCYRTNXBDown2.00.371Tenascin XBAKTTGFBR3Down2.00.895TGF-β receptor III (betaglycan)AKTEIF4EBP1Down2.02.345Eukaryotic translation initiation factor 4E binding protein 1CYRSLIT2Down2.01.667Slit homolog 2 (Drosophila)CYRIGFBP5Down1.90.991Insulin-like growth factor binding protein 5CYRCYR61aUp3.61.943Cysteine-rich, angiogenic inducer, 613.3CYRTHBS1Up3.43.868Thrombospondin 1APNSTAT1Up3.01.222Signal transducer and activator of transcription 1AKTANKRD1Up3.02.226Ankyrin repeat domain 1 (cardiac muscle)CYRITGB1Up2.51.994Integrin β1 = β-subunit of fibronectin receptor (CD29)AKTBDNFUp2.43.641Brain-derived neurotrophic factorAKTMATR3Up2.40.895Matrin 3APNJAK2Up2.33.469Janus kinase 2APNSLC16A1Up2.31.943Solute carrier family 16 member 1APNHBEFGUp2.23.179Heparin-binding EGF-like growth factorCYRCD47Up2.14.108Integrin-associated signal transducerCYRFGF2Up2.12.868Fibroblast growth factor (basic)APNSORT1Up2.11.632SortilinAPNEPHA4Up2.12.345EPH receptor A4AKTSTX16Up2.12.226Syntaxin 16CYRNESUp2.02.487NestinCYRPDGFCUp2.03.469Platelet derived growth factor CCYRPTK2Up2.01.571Protein tyrosine kinase 2APNPTPN11Up2.01.632Protein tyrosine phosphatase non-receptor type 11 AKTGOSR2Up2.01.667Golgi SNAP receptor complex member 2APNNFAT4aUp1.92.345Nuclear factor of activated T cells 4CYRBIRC4Up1.91.943Baculoviral IAP repeat-containing 4CYRHSXIAPA1Up1.92.487XIAP associated factor-1AKTPPP2R1BUp1.93.641Protein phosphatase 2 regulatory subunit A βAPNSMARCA2Up1.92.226SWI/SNF related matrix associated regulator of chromatin AKTMID1Up1.93.175Midline 1AKTPARVAUp1.82.621Parvin-αAPNLPLUp1.82.487Lipoprotein lipaseCYRLAMA2Up1.81.994Laminin-α2AKTGSK3BUp1.80.991Glycogen synthase kinase 3βAKTPPP2R2AUp1.81.222Protein phosphatase 2 regulatory subunit B αAPNSYNCRIPUp1.81.994Synaptotagmin binding RNA interacting proteinAKTPPP2R2AUp1.81.222Protein phosphatase 2 regulatory subunit B αAKTUPF3AUp1.83.173UPF3 regulator of nonsense transcripts A APNADIPOR1Up1.72.868Adiponectin receptor 1CYRTFPIUp1.73.868Tissue factor pathway inhibitorAPNSNX1Up1.74.362Sorting nexin 1APNSNX2Up1.74.467Sorting nexin 2APNRBBP4Up1.72.345Retinoblastoma binding protein 4AKTFBXO32Up1.72.487F-box protein 32AKTVPS33AUp1.72.226Vacuolar protein sorting 33AAPNNFAT1aUp1.65.091Nuclear factor of activated T cells 1AKTNFAT3aUp1.64.108Nuclear factor of activated T cells 3APNJAK1Up1.61.994Janus kinase 1APNSP3Up1.62.226Sp3 transcription factorAKTFRAP1Up1.64.362FK506 binding protein 12AKTSTX6Up1.61.994Syntaxin 6CYRPRELPUp1.64.757Proline arginine-rich end leucine-rich repeat proteinAPNRALBUp1.63.173v-ral simian leukemia viral oncogene homolog BAPNFGF13Up1.64.757Fibroblast growth factor 13APNTGFBR1Up1.64.108Transforming growth factor-β receptor IAKTFTSUp1.63.469Fused toes homologAKTAKT1Up1.54.757v-akt murine thymoma viral oncogene homolog 1AKTPPP2CAUp1.54.362Protein phosphatase 2 catalytic subunit αAll factors of deregulations are derived from the Affymetrix microarray analysis. For certain genes additional TaqMan analyses were conducted, which are then given in italics below the respective Affymetrix values (see also Supplementary Fig. 2).aPlease note the synonyma APN = ADIPOQ, CYR61 = CCN1, NFAT1 = NFATC2IP, NFAT3 = NFATc4, NFAT4 = NFATc3
The network analysis of the microarray data [8] was conducted by the IPA software employing the genes significantly (q value < 5.0) and ≥1.5-fold deregulated. IPA is based on modeled biological networks derived from information of peer-reviewed scientific publications. Analysis of our data revealed several strongly altered regulatory networks. The gene networks shown in Fig. 1a,c,e have not been previously described in former microarray studies of DCM (details in discussion). One of these networks centered around the CYR61 or CCN1 gene (Fig. 1a,b), another one around the APN gene (Fig. 1c,d). These networks are related to cell-to-cell signaling and cellular compromise. Another network around the AKT1 gene (Fig. 1e,f) also deserves consideration because AKT1 is involved in the regulation of both CYR61 [9] and APN [10] and because APN strongly influences CYR61 expression in vitro (Fig. 2b). Table 2 summarizes all member genes of these three networks with full gene names, q values, and fold changes. Those two genes (CYR61, APN) that were among the most strongly deregulated and, at the same time, key components of complex deregulated networks were further investigated in cardiovascular cell cultures in vitro (Fig. 2a,b).
Fig. 2Cytokines regulating of APN and CYR61 in cardiovascular cells. We searched for possible triggers of cardiac APN and CYR61 deregulation in DCMi in both endothelial cells and cardiomyocytes. a, b Summarize real-time quantitative TaqMan PCR data on the regulatory potential of inflammatory cytokines in these cell cultures investigated as first approximation to the far more complex in vivo situation with multiple resident and infiltrating cell types (compare Fig. 3). a Endothelial cells expressed CYR61 and both APN receptors but no detectable amount of APN mRNA (data not shown). The inflammatory cytokines TNF-α and IFN-β induced CYR61 expression in these cells, whereas others showed no effect (IFN-γ) or even reduced CYR61 (TNF-β). To reveal possible interactions between the APN and CYR61 regulatory networks, CYR61 induction experiments were conducted in the presence (asterisksin shaded bars) vs the absence of APN. The effect of APN on CYR61 induction by cytokines was unexpectedly strong: induction of CYR61 by TNF-α or IFN-β was not only abolished but reverted resulting in a net reduction of CYR61 expression below that in untreated controls. The APN concentration at which effect was observed is in the range 3–30 μg/ml as observed in human plasma [14]. Error bars indicate the SEM from two independent experiments each performed in triplicate. b In contrast to endothelial cells, cardiomyocytes expressed APN and its receptors as well as CYR61. Several inflammatory cytokines (TNF-α, IFN-β,γ) significantly reduced APN expression in cardiomyocytes, whereas CYR61 expression was not significantly altered in this cell type (not shown). Error bars as in panel (a)
The APN regulatory network contains significantly deregulated (q value ≤ 5.0, ≥1.5-fold change) genes not previously reported in microarray studies of DCM (full gene names in Table 1): STAT1 3.0-fold up; JAK2 2.3-fold up; HBEGF 2.2-fold up; SORT1 2.1-fold up; PTPN11 2.0-fold up; LPL 1.8-fold up; JAK1 1.6-fold up; SP3 1.6-fold up; APN 6.8-fold down; INSR 2.5-fold down; PIK3R1 2.5-fold down; LIFR 2.0-fold down; TNXB 2.0-fold down; and TGFBR3 2.0-fold down. Three genes encoding the nuclear factors of activated T cells (NFAT), NFAT1 = NFATC2IP (1.6-fold up), NFAT3 = NFATC4 (1.6-fold up), NFAT4 = NFATC3 (1.9-fold up), are also linked to the APN network as negative regulators of APN expression [11]. Similarly, the ADIPOR1 gene (1.7-fold up) encoding the APN receptor 1 gene is part of the APN signal transduction system.
A second regulatory network around CYR61 contains further deregulated genes with q value ≤ 5.0 and ≥1.5-fold change (full gene names in Table 1): CYR61 = CCN1 3.6-fold up; THBS1 3.4-fold up; ITGB1 2.5-fold up; CD47 2.1-fold up; FGF2 2.1-fold up; NES 2.0-fold up; PDGFC 2.0-fold up; PTK2 2.0-fold up; BIRC4 1.9-fold up; HSXIAPA1 1.9-fold up; LAMA2 1.8-fold up; TFPI 1.7-fold up; CXCL14 5.9-fold down; PDGFA 3.6-fold down; TMSB10 2.6-fold down; EGFR 2.1-fold down; LAMA4 2.4-fold down; HSPG2 2.1-fold down; TNXB 2.0-fold; and IGFBP5 1.9-fold down. A recent publication also links CYR61 = CCN1 to Wnt signaling pathways [13] and several Wnt signal pathway genes were deregulated in DCMi: STX16 2.1-fold up; GCLM 2.0-fold up; CSNK1A1 1.9-fold up; PPP2R1B 1.9-fold up; GSK3B 1.8-fold up; INSR 2.5-fold down; FBXO40 2.1-fold down; and CLIC5 2.0-fold down.
In addition to the multifunctional CYR61 and APN genes, several other deregulated genes are known to be involved in immunological and inflammatory processes: The CXCL14 gene was 5.9-fold and the neurturin (NRTN) gene 5.8-fold downregulated in DCMi. The interleukin-17D (IL-17D) gene, a member of IL-17A to F gene family, was 2.6-fold induced. IL-17 serum levels were elevated in DCMi patients (not shown). Altered intracardiac immune regulation is further suggested by upregulation of the immediate early response 3 (IER3, 2.6-fold) and 5 (IER5, 2.3-fold) genes, ubiquitin-specific protease 39 (USP39, 3.4-fold), phospholipase A2-activating protein (PLAA, 2.6-fold), and IFN-related developmental regulator 1 (IFRD1, 2.4-fold). Induction of the neural cell adhesion molecule (NCAM1, 2.0-fold) gene [14] and the coxsackie virus–ADV-receptor (CXADR, 1.7-fold) gene [15] already known from immunohistochemical studies of DCMi hearts were recognized by the microarrays, too.
As a first step to elucidate the molecular mechanisms that deregulate CYR61 and APN expression in human DCMi, cell culture studies were conducted. Both genes were significantly deregulated in DCMi as components of complex gene networks. Figure 2a shows that certain inflammatory cytokines induced CYR61 expression in vascular endothelial cell cultures similarly as observed in the DCMi hearts. This CYR61 induction by TNF-α and IFN-β was completely abolished in the presence of APN (Fig. 2a). The APN plasma range in humans is 3–30 μg/ml [16], and the concentration therefore chosen for the cell culture investigations was 10 μg/ml. At this concentration, APN generally suppressed the CYR61 expression level in this cell type. In cardiomyocytes, TNF-α, IFN-β, IFN-γ, and IL-1β all significantly reduced the expression of APN (Fig. 2b), whereas CYR61 expression was not affected by these cytokines in this cell type. Several other deregulated genes of interest known to be involved in immunological and inflammatory processes were neither expressed in endothelial cells nor in cardiomyocytes, and therefore could not be studied in these cell biological models.
Discussion
Identification of DCM subtypes
DCM is the term used to describe any clinical phenotype characterized by dilation and dysfunction of the cardiac ventricles. From an etiologic perspective, however, DCM encompasses a diverse spectrum of disease entities, some of which are due to monogenic defects in cardiac expressed genes [1], others triggered by exogenous factors including cardiotropic viruses [2]. Therapies directed at etiology-related pathogenic pathways in these etiologically distinct DCM subtypes may be more efficient than current standard treatment protocols employed in heart failure of any origin. However, etiology-related early pathogenic pathways must be assumed to prevail only in the beginnings of the respective DCM subtype, whereas common unspecific molecular pathomechanisms will predominate in advanced heart failure irrespective of etiology. One important DCM subtype as defined by cardiac inflammation was investigated in the current study to further define it at the molecular level. Whereas previous studies focused on advanced or end-stage DCM [5] where specific pathomechanisms are overwhelmed by unspecific processes, we probed an earlier and restrictively defined phenotype (DCMi) to identify early and etiology-related disease processes. Importantly, our patients were not terminally ill nor on maximum supportive therapy as in previous studies of advanced DCM. Similarly, controls had no severe underlying condition as, e.g., heart transplantation donor hearts often used as controls for expression profiles but were free of cardiac disease after comprehensive exclusion of multiple cardiac differential diagnoses. Further, any search for etiology-related pathomechanisms is more likely successful if the study groups are highly homogenous. An excellent previous study has also defined its patient groups by clinical, morphological, and hemodynamic phenotyping [17] and derived interesting data on common expression profiles in disorders linked by common hemodynamic anomalies. We likewise defined a highly homogeneous study group by sequential stratification of a large DCM patient cohort, first, by standard clinical, morphological, and hemodynamic criteria, second, by biopsy-based detection of cardiac inflammation in conjunction with a cardiotropic virus. Cardiac inflammation in our patients was associated with the presence of PVB19 genomes in the myocardium. We think, however, that the observed anomalous expression pattern probably reflects a general inflammatory pathomechanism that may be triggered by diverse proinflammatory stimuli. After the current pilot study all the key genes of this anomalous pattern need to be further investigated in major patient groups with varying degrees of cardiac inflammation and hemodynamic impairment, to address issues of specificity and dynamic. In vivo biopsies on which the current study was entirely based further provide the perspective for follow-up studies of the clinical courses that would then add a third stratification criterion for patient selection to be used in the future.
Cardiac expression profile of DCMi
The anomalous cardiac expression pattern observed for human DCMi was not described in previous studies of human cardiac diseases or heart failure of any origin [5, 18–27]. Most network genes listed in Table 2 and, in particular, CYR61 and APN have, to our knowledge, not been found to be deregulated in human microarray studies and also not in animal models of DCM. A comprehensive recent review [5] summarizes that the major molecular pathway changes in human DCM, and other human cardiomyopathies encompass sarcomeric, cytoskeletal, extracellular matrix, Ca2+ homeostasis, apoptosis, and energy metabolism genes. Major deregulations of inflammation- and immune system-associated genes as in DCMi were not included. Because we investigated a DCM subtype associated with intracardiac inflammation, a recent microarray study of two giant cell myocarditis patients [28] is of specific interest for comparison. That study addressed an immunohistologically defined inflammatory cardiac phenotype clearly different from that in our study and found deregulated T cell activation genes, including IL-10 receptor-α, β2-integrin, chemokine receptor 4, and chemokine ligands 5, 9, 13, and 18. Remarkably, none of the latter genes was deregulated in DCMi but only CXCL14. Conversely, NFAT1 and NFAT4 gene upregulations and APN gene downregulation as observed in DCMi were not observed in the giant cell myocarditis study. This appears to reflect distinct immunological pathomechanisms.
The CYR61 gene and its associated network showed particularly strong deregulation in DCMi. CYR61 = CCN1 is a member of the CCN gene family [29] (also including connective tissue growth factor [CTGF] = CCN2) and has already attracted attention as a multifunctional, secreted protein able to modulate multiple cell functions. It induces  apoptosis in fibroblasts [30] and mediates cell death in neurons [31] and in virus-infected cells [32]. From a therapeutic perspective, it is interesting that RNA interference-mediated silencing of the CYR61 gene protected cells against cell death, whereas CYR61 overexpression enhanced it [32]. Moreover, CYR61 coordinates genes that control inflammation (IL-1β), extracellular matrix remodeling (e.g., MMP1,3), cell–extracellular matrix interactions, and angiogenesis [33, 34]. CYR61 acts in a cell type- and context-dependent manner through direct binding to at least five distinct integrins, including α6β1 and heparan sulfate proteoglycans as co-receptors [35], integrins αvβ3 and αvβ5 [36], and integrin αMβ2 on blood monocytes [37]. CYR61 is involved in integrin-linked kinase-mediated AKT and β-catenin-T cell factor/Lef signaling [38]. It is also regulated by canonical Wnt signaling and involved in mesenchymal stem cell differentiation [13]. In contrast to this wealth of data on in vitro actions of CYR61, data on its physiological functions and pathogenic potential in humans are sparse. CYR61 upregulation has only recently been described in end-stage ischemic cardiomyopathy [39]. The current work shows, for the first time, CYR61 induction in an inflammatory cardiac disorder with far less severe clinical phenotype, where, most likely, no irreversible cardiac damage has yet occurred. Cardiac CYR61 expression is apparently induced by multiple stimuli [39], including inflammation in vivo and inflammatory cytokines in vitro (this study). Whereas transient CYR61 induction may be a temporarily beneficial response of the heart to various stress factors, chronic stimulation of the auto/paracrine signaling pathways activated by CYR61 may be maladaptive and pathogenic in itself by, e.g., sustaining exuberant tissue inflammatory and remodeling processes [33]. Our observation of CYR61 overexpression in DCMi suggests to investigate further, if modulation of CYR61-dependent auto/paracrine pathways might have therapeutic potential in cardiac diseases associated with local inflammation.
In the current study, we show, for the first time, that APN is synthesized by human hearts in situ, not only by adipose tissues as previously assumed, and that this local synthesis is strongly downregulated in a human cardiac disease. Cultured cardiomyocytes were already known to express APN and both of its receptors and secrete APN [42]. APN has encountered high interest in the cardiovascular field due to pioneering work in APN knockout mice, proving important functions of APN in the heart [40, 41]. These studies assumed, however, that cardioprotective effects were exerted by APN synthesized by adipose tissue and acting via the circulation. APN inhibits apoptosis and TNF-α production in cardiomyocytes, modulates hypertrophic signals in the heart [40], and protects it from ischemic injury in a mouse model [41]. APN regulation has not been previously investigated in human hearts in situ. Previous studies [40, 41] and the present work suggest that cardiac APN is influenced by multiple factors acting on components of the APN gene network. AKT1 is involved in the regulation of both CYR61 [9] and APN [10]. Interestingly, APN abolished CYR61 induction by inflammatory cytokines in endothelial cells (Fig. 2a). Because multiple other endothelial activation markers are induced in DCMi [54, 55], it is of interest to know the whole spectrum controlled by APN, but this is beyond the scope of the current study. In synopsis with a previous report that APN inhibits IL-8 synthesis by endothelial cells [10], these data suggest that local cardiac APN synthesis is required for adequate control of local inflammatory processes. If so, depression of cardiac APN synthesis by various mechanism may render the heart vulnerable to inflammatory challenges such as imposed by infiltrating immune cells in DCMi or under other conditions associated with enhanced cardiac inflammatory cytokine production [43, 44]. Modulation of the local cardiac APN system may therefore have therapeutic potential under certain conditions. Due to the complexities of the biological functions of both CYR61 and APN, it is impossible to predict the effects of their modulation in vivo. Synopsis of previous work and the current study suggests to investigate, however, if modulation of the balance between the CYR61 and APN networks may have therapeutic potential in cardiac diseases associated with local inflammation. One obvious approach would be cardiac APN gene therapy, another one RNA interference-mediated CYR61 suppression by cardiac shRNA expression.
In addition to the multifunctional CYR61 and APN proteins and interacting gene products such as the NFATs, several other deregulated genes are also directly involved in immunological and inflammatory processes. Two genes encoding proteins described as endothelial activation markers in DCMi [14, 15] were upregulated 2.0-fold (NCAM1) and 1.7-fold CXADR. CXCL14 (a component of the CYR61 network) belongs to a family of dendritic cell- and monocyte-attracting chemokine-like proteins [45, 46]. Loss of CXCL14 in tumors was associated with low infiltration by dendritic cells, whereas its restoration caused dendritic cell attraction in vitro and in vivo [47]. The downregulated NRTN gene belongs to the same family as glial cell line-derived neurotrophic factor (GDNF). GDNF and NRTN signaling is mediated by a two-component receptor: the signal-transducing component RET shared by both ligands and the ligand-specific binding components GFR-α1 (GDNF) or GFR-α2 (NRTN). Human T and B cells and monocytes produce RET and NRTN. GFR-α1 and GFR-α2 are transcribed in all immune cell subsets, with GFR-α2 being the dominant ligand-binding chain in T cells, B cells, and monocytes [48]. Therefore, NRTN downregulation may alter immune cell communication, whereas little is known about its functions in other cell types. IL-17D belongs to a family of secreted proteins that play an active role in inflammatory and autoimmune diseases [49]. Because IL-17D stimulates production of IL-6, IL-8, and granulocyte/macrophage colony-stimulating factor (GM-CSF) in human endothelial cells [50], its upregulation may enhance the activation of the endothelium, the primary attachment site for migrating immune cells. IL-17 functionally cooperates with TNF-α [51], enhances growth of vascular endothelial cells [52], and stimulates matrix metallo-proteinase-9 expression [53]. The upregulated PLAA activates mammalian PLA2s involved in inflammatory responses [54]. Thus, PLA2-IB is upregulated during inflammation, stimulates CXCL8 production via extracellular signal-regulated kinase (ERK) and nuclear factor-κB in neutrophils, and modulates inflammatory responses via the PLA2-IB receptor [55].
Working model of local cardiac inflammation control
A working model of a novel inflammation-associated cardiac pathomechanism is outlined in Fig. 3. The model proposes the existence and pathogenic relevance of a local inflammation control system in human hearts acting by local synthesis of anti-inflammatory proteins, a representative of which is APN. If local cardiac APN synthesis by cardiomyocytes is absent, as in knockout mice [40, 41], or suppressed, as in DCMi patients, inflammatory challenges, such as imposed by cardiac immune cell infiltrations (Supplementary Fig. 1), may result in grave damage to vascular endothelia and myocardium. The model assumes that cardiac inflammation control is predominantly mediated by auto/paracrine actions of locally synthesized APN, whereas, in the context of cardiac ischemic injury in APN knockout mice, a complementary systemic model of APN action has been proposed, in which APN secreted by adipose tissue acts hormone-like to protect the heart [41] and the endothelia via the circulation [10]. APN acts directly upon immune cells, such as natural killer (NK) cells [12], and on nonimmune cells, such as vascular endothelia [10]. One new target controlled by APN is CYR61 that is locally upregulated in DCMi hearts in situ and whose endothelial induction by cytokines is abolished by APN (Fig. 2a). Although CYR61 induces transient local inflammation and matrix remodeling during healing processes [33], its activity needs to be controlled so that it does not cause chronic tissue damage. In addition to such direct anti-inflammatory actions of APN upon non-immune targets (endothelial cells, cardiomyocytes), a recent study [12] shows that APN is also a potent negative regulator of IL-2-induced NK cell activation. IFN-γ production was suppressed by APN, accompanied by downregulation of the IFN-γ-inducible TNF-related apoptosis inducing ligand and Fas ligands. APN and cellular immunity are further linked by the fact that NFAT transcription factors are key regulators of T cell function [56] and also negative regulators of APN expression [11]. All local cardiac functions of CYR61, APN, and other factors in the model are thought to be mediated via auto/paracrine loops unrelated to the respective circulating plasma levels (3–30 μg/ml for APN in humans, unknown for CYR61).
Fig. 3Working model of local cardiac inflammation control. The model proposes the existence of a inflammation control system in human hearts acting by local synthesis of anti-inflammatory proteins, a representative of which is APN. If local cardiac APN synthesis by cardiomyocytes is genetically absent [38, 39] or suppressed (this study), inflammatory challenges may result in aggravated damage to endothelia and myocardium. This local model assumes that inflammation control and other regulatory functions are mediated by auto/paracrine actions of APN synthesized locally by cardiomyocytes, whereas in the context of APN knockout mice [38, 39], a complementary systemic model of APN action was proposed in which APN secreted by adipose tissue acts hormone-like to protect heart and endothelia via the circulation. APN (downregulated in DCMi) acts directly upon immune cells [10] (right side of figure) and also on nonimmune cells such as vascular endothelia [8] (left side). One new target for control by APN is CYR61 that is locally upregulated in DCMi (center) and whose endothelial induction by cytokines is suppressed by APN (see Fig. 2a). In addition to such direct anti-inflammatory action of APN on vascular endothelia (left side), APN is a potent negative regulator of IL-2-induced NK cell activation (right side) [10]. APN and cellular immunity are further linked by NFATs (upregulated in DCMi), which are key regulators of T cell function [54], and negative regulators of APN expression [9]. The functions of CYR61, APN, and other factors deregulated in DCMi in this local model are thought to be mediated via auto/paracrine loops unrelated to the respective circulating plasma levels. CYR61-associated CXCL14 downregulation in DCMi (right side) may alter immune cell attraction to the heart. IL-17D induction in DCMi (left side) may stimulate IL-6, IL-8, and GM-CSF production by endothelial cells and thus activation of the endothelium, the primary attachment site for migrating immune cells
Limitations and perspective of the study
Gain/loss-of-function experiments are required to clarify which of the gene deregulations in DCMi are primary and causative or secondary and maladaptive. Conventional gain/loss-of-function experiments in cell cultures may contribute to solve these questions but are rather artificial as compared to the complex physiological situation in intact tissues composed of multiple interacting cell types. Cardiac overexpression of deregulated genes and silencing of these genes by shRNA-mediated RNA interference in vivo has recently become feasible, however, by intravenous injection of cardiotropic AAV8 and AAV9 vectors in mice [57, 58]. These systems have considerable potential for gene therapy [59]. Beyond the classical knockout technologies, this novel approach may also help to clarify pathogenic cause–effect relationships arising from genomic and proteomic screening studies in humans such as the current one based on EMBs of human hearts in situ.
Conclusions
Genomic expression profiling evolves as a useful tool to identify novel pathomechanisms in human cardiac disorders [5, 8] because most animal models, if available at all, display multiple crucial differences to the respective human disease. Restrictive disease phenotyping significantly improves the chance of genomic approaches to identify such mechanisms and therapeutic targets even in complex human disorders. The current work shows, for the first time, local APN expression in human hearts in situ and its downregulation in a human cardiac disease. It further demonstrates that APN efficiently inhibits the endothelial expression of CYR61 that is strongly induced in DCMi. Synopsis of previous work and these data suggests to investigate further, if modulation of the balance between the CYR61 and APN network may have therapeutic potential in cardiac diseases associated with inflammation.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Fig. S1
The disease phenotyping of the DCMi patient group (Table 1 and Patients and methods) that were investigated by genomewide Affymetrix microarray expression profiling included (in addition to clinical and molecular virological characterization) an immunohistochemical characterization of cardiac immune cell infiltration and vascular endothelial activation. The immunohistochemical studies were done on EMBs obtained by the same standard procedure as those used for the Affymetrix microarray expression profiling and for the TaqMan quantitative RT-PCRs (264.6 KB) (226.1 KB) (159.6 KB)
Fig. S2
Real-time quantitative TaqMan PCR data on shifts in gene expression in the DCMi patients versus the control group. TaqMan data on the above novel DCMi-associated genes were obtained by analysis of residual RNA extracted from the same EMB as already used for the genomic Affymetrix microarrays and confirmed the respective data from the microarray study reported in detail in Table 2, Fig. 2 (panels a–f), and http://www.ncbi.nlm.nih.gov/projects/geo/ in all cases. Sharp sign denotes p < 0.05 between the study groups. In addition to these new genes, the standard heart failure-associated genes NPPA and NPPB were significantly (p < 0.05) upregulated (18.7-fold and 39.0-fold, respectively) in the DCMi group as assessed by TaqMan PCR, confirming a trend observed in the microarrays (7.5-fold at q = 6.400 and 20.5-fold at q = 5.722; (PDF 22 kb).
Table S1
Extension of Table 2 with all shifts in cardiac gene expression down to 1.5-fold change for DCMi patient group vs Cont control group, as assessed by Affymetrix microarray analysis of RNA extracted from EMB. For further details of the DCMi and Cont groups, see Table 1 and Patients and methods. The complete Affymetrix dataset is available from the GEO website http://www.ncbi.nlm.nih.gov/ projects/geo/. GEO experimental series number was assigned as GSE4172 (PDF 1 mb).

genomic expression profiling
human inflammatory cardiomyopathy
dilated cardiomyopathy
heart failure
molecular pathomechanisms
molecular therapeutic targets


Cytogenetic determinants of male fertility

BACKGROUND Cytogenetic abnormalities have been known to be important causes of male infertility for decades.
Introduction
Infertility is a significant problem, affecting up to 15% of couples of reproductive age (de Kretser, 1997). For many years, it was assumed that most reproductive problems could be attributed to the female partner but research in recent years has demonstrated that ∼30–50% of infertility is caused by a male factor (Lipshultz and Howards, 1997). Treatment of male-factor infertility by in vitro fertilization (IVF) was largely unsuccessful. However, the development of intracytoplasmic sperm injection (ICSI) (Palermo et al., 1992) revolutionized the treatment of male infertility, providing many men the chance to father their own children. ICSI also greatly stimulated research into the causes of male infertility, and our knowledge has increased tremendously in the past decade.
The development of new techniques has provided a wealth of information on the cytogenetics of human sperm. The human sperm–hamster oocyte fusion system allowed the first analysis of human sperm chromosomes (Rudak et al., 1978; Martin et al., 1983; Brandriff et al., 1985; Mikamo et al., 1990; Templado et al., 1996). This technique provides precise karyotypes in which numerical and structural abnormalities can be assessed for each chromosome (Fig. 1). Unfortunately, the technique is very difficult with only a handful of laboratories achieving success; also the data yield, though of great significance, is low.
Figure 1:
Q-banded karyotype of a normal 23,X sperm.
Fluorescence in situ hybridization (FISH) analysis with chromosome-specific DNA probes was developed in the 1990s, providing a faster, cheaper, easier alternative for detecting aneuploidy in human sperm (Martin et al., 1993; Robbins et al., 1993; Wyrobek et al., 1994; Martin et al., 1996). Also, sperm hampered by abnormalities in motility or other aspects of fertilization can be assessed using FISH analysis (Downie et al., 1997; Egozcue et al., 1997; Robbins et al., 1997; Martin et al., 1999a; Hristova et al., 2002). This is a simple technique that has been embraced by many laboratories, but it must be remembered that it is indirect: fluorescent signals, rather than chromosomes, are counted (Fig. 2).
Figure 2:
Disomic XY sperm, with green X signal, red Y signal and a single blue chromosome 1 signal (an internal control to distinguish disomy from diploidy).
Single sperm polymerase chain reaction (PCR) or single sperm typing is a difficult, time-consuming technique but it can be used in a very powerful manner for specific studies on recombination in delimited areas of the genome (Shi et al., 2001, 2002; Tiemann-Boege et al., 2006).
The latest development to shed light on the causes of chromosomal abnormalities and infertility is meiotic analysis of the synaptonemal complex (SC). Various important meiotic structures can be identified by the use of immunofluorescence. Antibodies against SCP1 (transverse elements) or SCP3 (lateral elements) can be used to visualize the SCs (the proteinaceous structure linking homologous chromosomes in prophase of meiosis I). The centromere can be localized with CREST antisera. Most importantly, recent studies have demonstrated that antibodies against the DNA mismatch repair protein MLH1 identify the sites of meiotic exchange in SCs in both mouse (Baker et al., 1996; Anderson et al., 1999) and human spermatocytes and oocytes (Barlow and Hultén, 1998; Lynn et al., 2002; Tease et al., 2002).
The use of a multicentromeric FISH technique in conjunction with SC meiotic analysis is a powerful tool to study recombination and the fidelity of pairing in individual identified chromosomes (Sun et al., 2004b, 2006b, 2007a; Oliver-Bonet et al., 2006) (Fig. 3). These studies have provided some important information on the meiotic abnormalities in infertile men and the association between meiotic recombination and aneuploidy (Gonsalves et al., 2004; Sun et al., 2004a, 2006a, 2007b; Codina-Pascual et al., 2005; Martin, 2005).
Figure 3:
(upper) Human pachytene spermatocyte with SCs shown in red, centromeres in blue and MLH1 foci in yellow. (lower) Subsequent cenM-FISH analysis permits identification of individual chromosomes so that recombination (MLH1) foci can be analyzed for each SC.
It is clear that cytogenetic abnormalities (both somatic and meiotic) are a major cause of male infertility. This review will summarize our knowledge of somatic chromosome abnormalities and their effects on sperm, susceptibility to sperm chromosome anomalies in chromosomally normal infertile men, and meiotic errors in translocation carriers and infertile men with a normal chromosome constitution.
Materials and Methods
Research publications from 1978 to 2008, from PubMed and the author's personal library, were reviewed. Methods for selecting and synthesizing the data were based on personal experience.
Somatic chromosomal abnormalities
Somatic chromosomal abnormalities are relatively common in humans. These can be numerical chromosomal abnormalities, such as an extra chromosome, or structural abnormalities such as a translocation. It has long been recognized that somatic chromosomal abnormalities are associated with infertility, an increased probability of pregnancy loss and the birth of handicapped children. The frequency of somatic chromosomal abnormalities in infertile men varies from 3% to 19%: 3% in the cases of mild infertility and 19% in men with non-obstructive azoospermia (NOA) (Yoshida et al., 1997). Thus, it is imperative that chromosome karyotyping be performed in all infertile men so that they can be apprised of their risks. Somatic chromosomal abnormalities have definite consequences on the cytogenetic abnormalities observed in sperm and consequently those observed in newborns.
Sex chromosomal abnormalities
47,XYY
Men with a 47,XYY karyotype are generally fertile, but they are seen more frequently in infertile populations. Many 47,XYY men produce normal children, but there has been no systematic analysis of children born to these men. Theoretically, 50% of the sperm cells should be abnormal. In a study of 75 sperm karyotypes from a 47,XYY male, we found no sperm disomic for a sex chromosome (Benet and Martin, 1988). Our results supported the hypothesis that the extra sex chromosome is eliminated during spermatogenesis. FISH analysis on the same male with 10 000 sperm studied demonstrated a small but significant increase for XY disomy to 0.6% (Martin et al., 1999b). Other laboratories have demonstrated increased frequencies of sperm aneuploidy for the sex chromosomes ranging from 0.3% to 15% (Mercier et al., 1996; Chevret et al., 1997; Shi and Martin, 2000b).
The majority of the more-stringent three-color FISH studies have demonstrated a low risk of 24,YY or 24,XY sperm of ∼1%. However, a recent study of two 47,XYY men with severe oligozoospermia demonstrated a higher frequency of aneuploid sperm of 37–38%, with approximately one-half of the abnormalities caused by sex chromosomal aneuploidy (Gonzalez-Merino et al., 2007). This group found that the frequency of sperm aneuploidy was concordant with the frequency of aneuploidy in preimplantation embryos (32%).
Since many 47,XYY men have normal semen parameters, the severe oligozoospermia observed in these men may indicate more perturbations during meiotic pairing, subsequent loss of germ cells and the production of aneuploid sperm.
47,XXY
Patients with Klinefelter syndrome (47,XXY) or mosaic variants of Klinefelter syndrome have greatly impaired spermatogenesis, with severe oligozoospermia or azoospermia. Nevertheless, these men are candidates for ICSI, particularly with the new methods used for recovering testicular spermatozoa. Studies on sperm chromosomes from men with Klinefelter syndrome have also demonstrated that the extra sex chromosome appears to be eliminated during spermatogenesis. FISH analysis has demonstrated that the frequency of aneuploidy for the sex chromosomes varies from 1.5% (Lim et al., 1999) to 7% (Kruse et al., 1998) in sperm from Klinefelter mosaics, and 2% (Rives et al., 2000) to 45% (Estop et al., 1998) in the sperm of men who appear to have a non-mosaic 47,XXY karyotype. The majority of babies born to 47,XXY men have been normal although chromosomally abnormal fetuses have been reported (Ron-el et al., 2000; Friedler et al., 2001). Staessen et al. (2003) studied 113 embryos by preimplantation genetic diagnosis (PGD) and found a significantly increased frequency of autosomal and sex chromosomal abnormalities. Thus, there appears to be a small increased risk for these men.
Structural aberrations
Translocations
Robertsonian translocations: Robertsonian translocation carriers have a fusion of the long arms of two acrocentric chromosomes. The fused short arms (containing redundant DNA) are generally lost so that the carrier has a balanced chromosomal constitution with 45 chromosomes. When the chromosomes pair during meiosis, they do so as a trivalent, and the resulting gametes can be chromosomally normal or aneuploid with an extra or missing chromosome q arm. This can cause translocation Down's syndrome or Patau's syndrome, e.g. sperm karyotyping studies in our laboratory, and others have demonstrated that the actual frequency of unbalanced sperm in seven men is lower than theoretically expected, with 3–27% of sperm being unbalanced because of the translocation (Martin, 1995; Ogawa et al., 2000). Similarly, FISH studies in 71 Robertsonian translocation heterozygotes have demonstrated that 7% to 40% are unbalanced, with a mean of 15% unbalanced (Rousseaux et al., 1995; Blanco et al., 2000; Escudero et al., 2000; Honda et al., 2000; Vegetti et al., 2000; Frydman et al., 2001; Morel et al., 2001; Pellestor et al., 2001; Acar et al., 2002; Baccetti et al., 2002; Anton et al., 2004; Anahory et al., 2005; Douet-Guilbert et al., 2005; Machev et al., 2005; Brugnon et al., 2006; Hatakeyama et al., 2006; Moradkhani et al., 2006a,b; Ogur et al., 2006; Chen et al., 2007; Nishikawa et al., 2008). Thus, all Robertsonian translocations have relatively similar segregation behaviors despite the participation of different acrocentric chromosomes. The risks of chromosomal imbalance at prenatal diagnosis are even lower, with generally only 1–2% of paternally derived Robertsonian translocations being unbalanced (Boué and Gallano, 1984). Even though the risks are low, prospective parents deserve to be informed of them as the abnormalities can be devastating, and trisomy 13 fetuses have been detected after ICSI using sperm from a father with a Robertsonian translocation (In't Veld et al., 1997a). Also, a special category of Robertsonian translocation, if found, would nullify the usefulness of ICSI: a Robertsonian translocation between the same two chromosomes, e.g. two chromosomes 13, would produce only abnormal embryos, trisomy 13 or monosomy 13, with no hope of long-term survival. A case with this particular problem was discovered in the Netherlands after three unsuccessful ICSI attempts (In't Veld et al., 1997b).Reciprocal translocations: reciprocal translocations occur when there are exchanges of chromosome material between any chromosomes. During meiosis, four chromosomes must pair in reciprocal translocation heterozygotes and the resulting segregations have a higher frequency of unbalanced chromosomes than Robertsonian translocations. Sperm karyotyping studies of 37 reciprocal translocation heterozygotes have shown that 19–77% of spermatozoa are unbalanced (Estop et al., 1995; Martin and Spriggs, 1995; Cifuentes et al., 1999). FISH analyses of chromosome segregations in 99 reciprocal translocation heterozygotes have also shown a large range in the frequency of unbalanced sperm, from 37% to 91% (Goldman and Hultén, 1993; Spriggs and Martin, 1994; Rousseaux and Chevret, 1995; Estop et al., 1997, 1998, 1999, 2000; Van Hummelen et al., 1997; Blanco et al., 1998a, 2000; Martini et al., 1998; Mercier et al., 1998; Cifuentes et al., 1999; Giltay et al., 1999; Honda et al., 1999; Vegetti et al., 2000; Oliver-Bonet et al., 2001, 2002, 2004; Pellestor et al., 2001; Cora et al., 2002; Geneix et al., 2002; Trappe et al., 2002; Baccetti et al., 2003; Escudero et al., 2003; Lim et al., 2003; Anton et al., 2004; Morel et al., 2004; Douet-Guilbert et al., 2005; Machev et al., 2005; Brugnon et al., 2006; Midro et al., 2006; Yakut et al., 2006; Perrin et al., 2007; Wiland et al., 2007; Nishikawa et al., 2008; Vozdova et al., 2008). In one study, four male family members of a kindred segregating a chromosome 15;17 translocation were studied by FISH analysis (Cora et al., 2002; Vozdova et al., 2008). The segregation patterns were very similar in all four men, with ∼50% of sperm chromosomally unbalanced. Also, Morel et al. (2004) found similar frequencies of imbalance of 37% and 43% in two brothers heterozygous for a chromosome 7;8 translocation. These studies demonstrate that the risk of meiotic imbalance is primarily determined by the characteristics of the chromosomes involved, and the break-point positions. They also demonstrate the reproducibility of the method. In both karyotyping and FISH studies, the mean frequency of sperm with unbalanced chromosomes is ∼50% in reciprocal translocation carriers. Many of these imbalances are not compatible with survival, and the average frequency of paternally derived translocation imbalances at prenatal diagnosis is 12% (Boué and Gallano, 1984). However, some translocations have higher risks of imbalance and survival, and all have serious consequences of mental and physical handicaps. A number of fetuses with unbalanced segregations of reciprocal translocations have been reported after ICSI (Baschat et al., 1996; Meschede et al., 1997). Because the frequency of chromosome abnormality is very high, some men carrying reciprocal translocations have undergone PGD to implant only chromosomally normal or balanced embryos. Studies comparing the frequency of chromosome abnormalities in sperm and embryos from reciprocal translocation carriers show a close agreement in the abnormality frequencies (Escudero et al., 2003).
Inversions
Inversions occur when two chromosome breaks occur in the same chromosome and the breaks heal in an inverted order. All the genes are present in the correct number but difficulties arise during pairing of homologous chromosomes during meiosis. If a single crossover occurs in the inverted region of the paired chromosomes, offspring with chromosomal duplications and deficiencies can result from recombinant chromosomes.
Paracentric inversions: paracentric inversions occur when both break points are in one chromosome arm. If a single crossover occurs within the inversion pairing loop, one-half of the gametes are normal, one-quarter are acentric (and would be lost) and one-quarter are dicentric (which could lead to a break between the centromeres and chromosome imbalance). Paracentric inversions are rarely reported, since they can only be detected by the use of banding procedures (Pettenati et al., 1995). Some investigators have suggested that paracentric inversions in man are generally harmless (Madan, 1995); however, recombinant chromosomes have been observed in newborns, and the risk of viable recombinants has been estimated to be 3.8% (Pettenati et al., 1995). Only two men with paracentric inversions have been studied by sperm karyotyping (Martin, 1986, 1999). Neither showed any recombinant chromosomes in sperm, suggesting that either an inversion loop was not formed or that crossing over was suppressed within the loop. Similarly, one case has been studied by FISH analysis and 1% of sperm were recombinant, with both dicentric and acentric chromosomes observed (Devine et al., 2000). Thus, the risk for paracentric inversions appears to be small.Pericentric inversions: pericentric inversions occur when the chromosome breaks occur in both chromosome arms and include the centromere in the inversion. Sperm karyotyping studies have been performed in seven men heterozygous for pericentric inversions (Martin, 1999). Four men had no recombinant chromosomes and three men had frequencies of imbalance varying from 11% to 31%. FISH studies have been performed in 24 pericentric inversion carriers with the frequency of recombinant chromosomes varying from 0% to 54% (Anton et al., 2007; Chantot-Bastaraud et al., 2007; Morel et al., 2007). The inversions that produce recombinant chromosomes are, in general, large inversions encompassing more than half of the chromosome length. An overall risk at prenatal diagnosis has been estimated to be 10–15% (Daniel et al., 1989), but it is clear that the risks are dependent on the individual inversion.
Interchromosomal effects
A number of researchers have suggested that there is an increased frequency of chromosomal abnormalities unrelated to the structural abnormality and have termed this an ‘interchromosomal effect’ (Aurias et al., 1978), e.g. an increased frequency of trisomy 21 children born to Robertsonian translocation carriers. Sperm karyotype studies have not shown any support for an interchromosomal effect in translocation (Martin and Spriggs, 1995) or inversion carriers (Martin, 1999), despite the fact that all chromosomes can be analyzed. However, the number of sperm karyotypes is limited with a maximum of 548 sperm cells analyzed in one translocation carrier (Spriggs et al., 1992). Thus, a small interchromosomal effect would be missed by these studies.
Studies employing FISH analysis have the advantage of much larger sample sizes, with a few hundred to several thousand sperm assessed. There is some suggestion for an interchromosomal effect in 58% (21/36 studied for an interchromosomal effect) of Robertsonian translocations and 64% (35/55) of reciprocal translocations studied for the segregation of other chromosomes by FISH analysis, since an increased frequency of numerical abnormalities was observed in at least one of the chromosomes evaluated. For inversions, only one of seven cases demonstrated a possible interchromosomal effect (Amiel et al., 2001). However, it is doubtful whether this is a true interchromosomal effect, since carriers of translocations and inversions are often infertile, with altered sperm profiles. In fact, almost all the Robertsonian translocation carriers studied for an interchromosomal effect had abnormal semen profiles, with only three normospermic men (none of whom demonstrated an interchromosomal effect). In reciprocal translocation heterozygotes, abnormal semen profiles were much more common among the cases demonstrating an interchromosomal effect (67%) compared with those that did not (11%). Infertile men with oligozoospermia, asthenozoospermia or teratozoospermia are known to have an increased frequency of chromosome abnormalities in their sperm (Moosani et al., 1995; Martin, 1996; Aran et al., 1999; Pang et al., 1999; Nishikawa et al., 2000). Therefore, it is possible that the increased frequency of numerical chromosomal abnormalities in some carriers of structural rearrangements may be related to infertility factors rather than the rearrangement. On the other hand, clear examples of translocations with asynaptic segments pairing with sex chromosomes or autosomes suggest that this type of interchromosomal effect may be causing the infertility (Oliver-Bonet et al., 2005a).
PGD studies have demonstrated that embryos of translocation carriers have a high frequency of genetic imbalance. Some studies have suggested an interchromosomal effect for Robertsonian translocation carriers, since there appears to be a higher frequency of aneuploidy (Conn et al., 1998, 1999; Gianaroli et al., 2002), whereas others have not found a significant increase (Scriven et al., 2001; Munné et al., 2005). In summary, an interchromosomal effect may be a reality for some translocations, especially in infertile patients, and sperm chromosome studies may be useful to determine the level of risk.
Y chromosome microdeletions
Tiepolo and Zuffardi (1976) first recognized that six azoospermic men had deletions of the long arm of the Y chromosome, large enough to be recognized by light microscopy. Since that time, it has been determined that the majority are Yq microdeletions and therefore require analysis by molecular means. Most studies demonstrate that 4–14% of azoospermic or severely oligozoospermic men have an Yq microdeletion, making this a major contribution to male infertility (Foresta et al., 2001). Of significance is the fact that all sons are expected to be infertile.
Three regions have been delineated as azoospermic factor (AZF) a, b and c with AZFc holding the best prognosis for viable testicular sperm retrieval (Foresta et al., 2001; Vogt, 2004). An interesting aspect of Y chromosome microdeletions is the instability of the chromosome suggested by recent studies. Patsalis et al. (2002) studied 12 mosaic 46,X/46,XY patients with Turner syndrome trails or sexual ambiguities and 4/12 had Y chromosome microdeletions. Ferlin et al. (2007) performed sperm FISH studies in 11 men with AZFc deletions and found only 33% Y-bearing sperm compared with 49% in controls, and a significant increase in disomic XY sperm and sperm nullisomic for the sex chromosomes. More research is clearly indicated, but this suggests that men with a Y chromosome microdeletion may have an increased risk of 45,X and 47,XXY offspring as well as mosaic offspring because of loss of the Y chromosome.
Germinal mosaics
Men with a normal somatic karyotype may still have an abnormal cell line in their testes. These men are called ‘germinal mosaics’, and it is difficult to discover them without a testicular biopsy. Studies have discovered that 1–17% of infertile men are germinal mosaics (Chandley et al., 1976; Hendry et al., 1976), so this is still a risk after a normal karyotype result, but the risks for abnormal offspring would be lower than those for non-mosaic individuals.
Infertile men with a normal karyotype
With the advent of ICSI, it has become clear that infertile men with a normal somatic karyotype also have an increased risk of chromosomally abnormal sperm. Moosani et al. (1995) were the first to report that infertile men with a normal 46,XY karyotype have an increased risk for autosomal and sex chromosomal abnormalities in their sperm. More than 30 FISH studies have confirmed this association of increased sperm aneuploidy frequencies in 46,XY infertile men (Aran et al., 1999; Pang et al., 1999; Nishikawa et al., 2000). Most studies have reported the increase of sperm chromosome abnormalities in infertile men to be about three times higher than in control donors (Moosani et al., 1995; Lahdetie et al., 1999; Acar et al., 2000). Reports based on prenatal diagnosis of ICSI pregnancies (Van Steirteghem et al., 2002) and newborns (Aboulghar et al., 2001) have indicated the risk of de novo chromosome abnormalities to be ∼2% to 3%, which is 3-fold higher than that in normal pregnancies. Thus, the increased frequency of chromosome abnormalities in ICSI pregnancies and newborns mirrors the increased frequency observed in the sperm of infertile ICSI patients. Furthermore, studies have indicated that these chromosome abnormalities are of paternal origin (Van Opstal et al., 1997), underscoring the fact that chromosomally abnormal sperm in ICSI patients become chromosomally abnormal fetuses and children.
The first studies of sperm chromosomal abnormalities in infertile men tended to lump all types in infertility together. However, it is possible that some subsets of infertility have an elevated risk of sperm chromosomal abnormalities whereas others do not. We have studied men with asthenozoospermia (motility defects; Hristova et al., 2002), teratozoospermia (abnormalities of form; Templado et al., 2002), various degrees of oligozoospermia (low concentration; Martin et al., 2003b) and azoospermia (no sperm in the ejaculate; Martin et al., 2003a). To our surprise, we found that men with any type of infertility had an increased frequency of sperm chromosomal abnormalities, varying from 2 to 10 times higher than that of control donors. Thus, it seems that any perturbation of spermatogenesis confers an increased risk of aneuploid sperm.
One rare type of teratozoospermia appears to confer a very high risk is men with a high percentage of macrocephalic, multinucleated, multiflagellate sperm. A number of studies have reported very high frequencies of aneuploidy and polyploidy in these men (50–100%) (Benzacken et al., 2001; Devillard et al., 2002; Lewis-Jones et al., 2003). It has also been suggested that abnormalities of the centrosome may exist in surgically retrieved sperm, a problem that may lead to increased mitotic non-disjunction and mosaicism in resulting embryos (Silber et al., 2003).
Utility of aneuploidy assessment in sperm
There have been a number of studies which have found a high frequency of sperm aneuploidy in men who subsequently fathered chromosomally abnormal children (Blanco et al., 1998b; Martinez-Pasarell et al., 1999a,b; Moosani et al., 1999; Soares et al., 2001; Nagvenkar et al., 2005). For example, in our original study of infertile men, one male had a frequency of 24,XY sperm that was 9-fold higher than controls (Martin, 1986). This man subsequently had ICSI and fathered a pregnancy that resulted in a 47,XXY fetus (Moosani et al., 1999). Gianaroli et al. (2005) studied sperm aneuploidy and correlated it to results in blastomeres after PGD in couples. They found a higher incidence of monosomies and trisomies in embryos from microepididymal sperm aspiration and testicular sperm extraction sperm, and aneuploidy for the sex chromosomes increased proportionally to the severity of the male-factor condition. These authors suggested that it is important to include sperm FISH analysis in preliminary tests given to infertile couples, especially in the case of repeated IVF failures. These preliminary studies suggest a correlation between sperm aneuploidy frequencies and ICSI outcome. However, unpublished results from our research laboratory demonstrated that only 10% of infertile men with a normal karyotype have a 24,XY sperm frequency that is ≥5 times that of controls, and only 3% have a frequency ≥10 times that of controls. Thus, it appears that a high frequency of sperm aneuploidy is relatively rare in 46,XY infertile men.
Recombination analysis by single sperm PCR
Our studies, and those of others, have shown that the frequency of aneuploidy in sperm is elevated for the sex chromosomes compared with the autosomes, for both fertile (Martin et al., 1991; Spriggs et al., 1996; Scarpato et al., 1998) and infertile men (Shi and Martin, 2000a; Hristova et al., 2002). The XY bivalent normally has only one crossover in the pseudoautosomal region during meiosis. If recombination is reduced or absent for these chromosomes, they may be particularly susceptible to non-disjunction. Indeed, it has been shown that 47,XXY of paternal origin is associated with a decreased recombination frequency (Hassold et al., 1991; Lorda-Sanchez et al., 1992). For a direct test of whether recombination is associated with non-disjunction in human sperm, we performed single sperm PCR analysis for a sex specific locus (STS/STS pseudogene) and a pseudoautosomal locus (DXYS15) (Shi and Martin, 2001). Individual unisomic sperm (23,X or Y) were isolated using a FACStarPlus flow cytometer into PCR wells. To identify disomic 24,XY sperm, 3-color FISH analysis was performed with probes for chromosomes X, Y and 1. The 24,XY cells were identified using fluorescence microscopy, each disomic sperm was scraped off the slide using a glass needle attached to a micromanipulator and then put into a PCR well. Hemi-nested PCR analysis of the two markers was performed to determine the frequency of recombination. The frequency of recombination between the two DNA markers was 38% for the normal unisomic sperm compared with 25% for the 24,XY disomic sperm that had undergone non-disjunction. This difference was highly significant, and demonstrates that lack of recombination in the pseudoautosomal region is associated with XY non-disjunction and the production of aneuploid sperm.
SC analysis
The discovery that lack of recombination is associated with non-disjunction is significant because it provides a definite molecular correlate with aneuploidy. The next logical step would be to assess recombination in other chromosomes. However, this is extremely difficult and time-consuming by single sperm PCR analysis. Luckily, new immunocytogenetic techniques allow assessment of recombination and chromosome pairing by visualization of the SC in early meiosis (Barlow and Hultén, 1998; Lynn et al., 2002; Sun et al., 2004a, 2006b, 2007b). The SC can be analyzed throughout the stages of prophase (leptotene, zygotene, pachytene, diplotene) to assess the progress of meiosis and the fidelity of chromosome pairing and synapsis. The frequency and location of recombination sites on individual chromosomes can also be analyzed by use of antibodies to the mismatch repair protein MLH1 (Baker et al., 1996; Marcon and Moens, 2003; Sun et al., 2004b) combined with cenM-FISH to identify individual chromosomes (Fig. 3). This technique provided the first recombination maps for every autosome (Sun et al., 2004b, 2006b).
Studies in a number of laboratories have demonstrated that normal healthy men have ∼50 recombination foci per pachytene cell, with a wide range in the mean number of recombination sites per cell and in the number of sites in individual pachytene cells (Barlow and Hultén, 1998; Lynn et al., 2002; Sun et al., 2004b, 2006b). This number of recombination sites is very similar to chiasma counts at diakinesis (Hultén, 1974; Laurie, 1985; Laurie and Hultén, 1985) and also to the corresponding genetic length obtained from linkage data (Kong et al., 2002).
SC analysis in translocation carriers
Reciprocal translocations are known to generate meiotic disturbances that affect both quantitative and qualitative sperm production. The chromosomes involved in a reciprocal translocation must pair as a quadrivalent, which is clearly visible at the SC level during pachytene (Oliver-Bonet et al., 2005a; Sun et al., 2005b). Meiotic studies of infertile men carrying chromosomal rearrangements have shown that quadrivalent configurations have different degrees of asynapsis around the break points. It has also been observed that these regions sometimes interact with the sex body. Associations between an autosome and the sex body can be visualized as continuous proteinaceous filaments that connect the two together. This association has been suggested to be the cause of infertility in carriers (Gabriel-Robez et al., 1986). It has been hypothesized that this may be due to the activation of X-linked genes, or a spreading of inactivation to the autosomes. Study of the SC permits analysis of these two hypotheses.
The X and Y chromosomes pair in only a small region, with the rest of the chromosomes remaining unpaired. Meiotic sex chromosomes are inactivated with many proteins locating to the sex body (Oliver-Bonet et al., 2005b). It has been hypothesized that the association of an autosome with the sex body causes reversal of the sex body inactivation and allows expression of some genes, with lethal results for the cell (Lifschytz and Lindsley, 1972). Others have suggested a spreading of sex body inactivation toward the autosomes connected to the sex body (Jaafar et al., 1993). Recent studies have shown normal timing and progression of condensation through the pachytene stage for the sex body in a carrier of a t(Y;1) translocation (Sun et al., 2005b) and for the sex body associated with the quadrivalent in a t(13;20) carrier (Oliver-Bonet et al., 2005b). These results, together with the fact that there is a strong relationship between sex body inactivation and XY condensation (Fernandez-Capetillo et al., 2003), do not support the model of gene activation on the X chromosomes. In addition, the observation that autosome arms invading the sex body show gradual heterochromatinization, mimicking the behavior of the sex chromosomes, suggests that spreading of inactivation to the autosome is taking place.
It has been suggested that transcriptional repression might not be an exclusive mechanism of the sex chromosomes, but rather a general mechanism that acts to silence any asynapsed region in the cell (Baarends and Grootegoed, 2003). Such a general mechanism has been described in Neurospora crassa (Shiu et al., 2001), and in the mouse (Turner et al., 2005). It is possible that a similar mechanism is operative in humans and that the spreading of sex body inactivation toward the translocated chromosome is a consequence of asynapsed regions within the quadrivalent attaching to the sex body. In this case, genes important in meiosis might be repressed with resultant destruction of the cell.
The meiotic process in two translocation carriers with different fertility outcomes, one normozoospermic and the other azoospermic, have been compared with interesting results (Oliver-Bonet et al., 2005a). A significant number of quadrivalents were attached to the sex body in the azoospermic carrier, whereas such an association was never detected in the normozoospermic carrier. In addition, this normozoospermic patient displayed heterologous synapsis within the quadrivalent. Thus, it appears that unpaired regions within the quadrivalent are likely to be detected by the pachytene checkpoint, so asynapsed regions seek each other out and try to pair, in order to escape the checkpoint and avoid apoptosis of the cell. The other option to shelter unsynapsed regions from the checkpoint is association with the sex body, but this situation may lead to anomalies disrupting the proper segregation of the chromosomes.
SC analysis in infertile men
Studies of infertile men have demonstrated a number of meiotic errors. Most of these meiotic studies have been performed on men with NOA, but some men with obstructive azoospermia and oligoasthenoteratozoospermia have also been studied. Approximately one-half of the men with NOA have no meiotic cells (Gonsalves et al., 2004; Sun et al., 2005a; Topping et al., 2006, 2007b). In those with meiotic cells, the progression of meiosis is altered, with significantly more cells observed in the early stages of prophase (leptotene and zygotene; Gonsalves et al., 2004; leptotene and zygotene; Ferguson et al., 2007; Sun et al., 2007b). This suggests problems in the development of the lateral and transverse elements of the SC, leading to difficulties in pairing and synapsis of homologous chromosomes.
Sun et al. (2005a,c, 2007b) determined that NOA men have a significant increase in the proportion of pachytene cells with unsynapsed regions. Also, a number of studies have demonstrated that NOA males have a significant reduction in the frequency of MLH1 foci compared with controls (Gonsalves et al., 2004; Sun et al., 2005a, 2007b), although two studies have not observed this reduction (Codina-Pascual et al., 2005; Topping et al., 2006). Achiasmate bivalents (with no crossovers) have been observed with a significantly elevated frequency in NOA males compared with controls (leptotene and zygotene; Gonsalves et al., 2004; Sun et al., 2005a, 2007b; Ferguson et al., 2007). For example, Sun et al. (2007b) observed that 29% of pachytene cells had at least one bivalent with no recombination foci, compared with 5% in controls. This is a dangerous situation, since without a crossover there is no mechanism to ensure orderly chromosome segregation at metaphase I. This could lead to engagement of the pachytene checkpoint and meiotic arrest or sperm aneuploidy.
A minimum number of recombination sites (crossovers) for correct alignment and segregation of the chromosomes is one per chromosome arm (except for the short arms of the acrocentric chromosomes, which rarely recombine). Achiasmate chromosomes (those without a recombination site) are rare in humans, except for the sex chromosome pair, which has a visible recombination site only 56–93% of the time (Codina-Pascual et al., 2005; Sun et al., 2006a; Ferguson et al., 2007). However, when achiasmate chromosomes are observed in autosomes, they occur most frequently for chromosomes 21 and 22 (Codina-Pascual et al., 2006; Sun et al., 2006a). This is interesting, since it parallels data on aneuploidy in human sperm. Both studies on human sperm karyotypes (Martin and Rademaker, 1990) and FISH analysis (Williams et al., 1993; Spriggs et al., 1996; Blanco et al., 1998b; Scarpato et al., 1998) have demonstrated that chromosomes 21 and 22, and the sex chromosomes have the highest frequency of aneuploidy. This strengthens the association between lack of recombination and aneuploidy. However, these studies on meiotic recombination and sperm aneuploidy have not been performed on the same men. A more robust test of the association between meiotic recombination and aneuploidy would be to correlate both analyses in the same men.
When we performed just such a study in vasectomy reversal patients, we found no correlation between meiotic recombination in individual chromosomes and sperm aneuploidy for the same chromosome (Sun et al., 2008). We hypothesized that our population of fertile men may not have reached the threshold of meiotic abnormalities necessary to demonstrate the relationship between recombination and aneuploidy, since achiasmate bivalents were rare in this group of men. Ferguson et al. (2007) found a significant inverse correlation between meiotic recombination for the sex chromosomes and XY disomy in sperm. They also found a significant relationship between the frequency of achiasmate chromosome 21 and sperm disomy 21. This analysis was performed in a mixed group of control donors (vasectomy reversals) and infertile men. When we studied men with NOA, we also demonstrated that a low frequency of meiotic recombination in the sex bivalent was significantly correlated with a high frequency of aneuploidy for the sex chromosomes (unpublished results). Thus, it is possible that men with more dramatic meiotic abnormalities are infertile because of loss of meiotic cells at the pachytene checkpoint, and also face an increased risk of aneuploid sperm because of non-recombinant cells that escape the checkpoint but are still susceptible to errors of chromosome segregation. Egozcue's group in Barcelona has performed elegant research in this area. They suggest that the best candidates for a meiotic study would be: infertile males with a normal karyotype and unexplained infertility, and among them, infertile males with normozoospermia and long-term sterility, or IVF failures (embryonic factor, no fertilization, repeated IVF failure), or infertile males with a severe oligozoospermia (<5 × 106 sperm/ml) or a severe oligoasthenozoospermia (<1.5 × 106 motile sperm/ml) (Egozcue et al., 2005).
Funding
R.H.M. holds the Canada Research Chair in Genetics and her research is funded by the Canadian Institutes of Health Research. Funding to pay the Open Access publication charges for this article was provided by the Canadian Institutes of Health Research.

male infertility
meiosis
sperm aneuploidy
sperm chromosome abnormalities