extfieldsofstudy: Medicine Biology

text:
Development of an Immunochromatographic Assay Based on Dense Granule Protein 7 for Serological Detection of Toxoplasma gondii Infection

ABSTRACT Dense granule antigen proteins derived from Toxoplasma gondii (TgGRAs) are potential antigens for the development of diagnostic tools. TgGRA7 and TgGRA14 were detected in the peritoneal fluid of T. gondii-infected mice, suggesting that TgGRAs may be highly antigenic proteins. Here, TgGRA7 and TgGRA14 were evaluated as candidates for the development of a marker for a rapid diagnostic test. The specificity and sensitivity of purified recombinant proteins of TgGRA7 and TgGRA14 were compared in an indirect enzyme-linked immunosorbent assay (iELISA) using a series of serum samples from T. gondii-experimentally infected mice and using recombinant T. gondii major surface antigen 2 (TgSAG2) as a reference control. The iELISA with TgGRA7 showed the greatest diagnostic accuracy and could detect anti-TgGRA7 antibody in acute and chronic infections. A total of 59 field samples from pigs were also examined by the iELISAs, and the results compared with those of the latex agglutination test (LAT). Among the three recombinant antigens, TgGRA7 had the highest rates of positivity, with significant concordance (88.14) and kappa value (0.76) in comparison with the results using LAT. Furthermore, an immunochromatographic test (ICT) based on recombinant TgGRA7 was developed for rapid detection of antibodies to the infection. The ICT differentiated clearly between sera from T. gondii-infected mice and uninfected or Neospora caninum-infected mice. Pig sera were examined with the ICT, and the results compared favorably with those of LAT and iELISA for TgGRA7, with kappa values of 0.66 and 0.70 to 0.79, respectively. These data suggest that the ICT based on TgGRA7 is a promising diagnostic tool for routine testing in the clinic and mass screening of samples in the field.

T oxoplasma gondii is an obligate intracellular protozoan parasite that is widely prevalent in humans and animals. The infection is characterized by significant morbidity and mortality in immunocompromised patients, such as those with AIDS and serious congenital birth defects, among both humans and livestock (1). Cats and other felids are the only definitive hosts of T. gondii that excrete oocysts. These appear 10 days after ingestion of infected tissue and continue to be excreted for 2 weeks. Transmission to intermediate hosts can occur through ingesting infectious oocysts from the environment or cysts in undercooked or uncooked meat (2). Transplacental transmission may also occur when the tachyzoites pass to the fetus via the placenta (3). The infection has a major economic impact on the livestock industry, resulting in reproductive losses, abortion, fetal resorption, and stillbirths (1,3,4). Accurate diagnosis of Toxoplasma gondii infection is crucial for the proper management of the infection in humans and animals.
Diagnosis of toxoplasmosis can be achieved using a number of different approaches, including microscopic examination, animal inoculation, in vitro culture, and molecular and serological methods. Animal inoculation, in vitro culture, and microscopic examination of parasites by inspection of blood smears, tissue specimens, feces, lymph node aspirates, and cerebrospinal fluid are not appropriate for routine mass screening (5). The latex agglutination test (LAT) is to date considered the gold standard method for diagnosis of T. gondii infection; however, the test has poor specificity (5,6). Enzyme-linked immunosorbent assays (ELISAs) with purified recombinant proteins are widely used for routine diagnostic testing and in seroepidemiological investigations (7)(8)(9). However, the test is expensive, needs specialized facilities in the laboratory, and cannot be used in the field and clinic. The immu-nochromatographic test (ICT) is rapid and does not require expensive equipment and expertise (10,11). A previous study has shown a promising application of ICT based on recombinant T. gondii major surface antigen 2 (TgSAG2) for the detection of antibody to the infection in cats (10). A more recent report has documented limitations of TgSAG2 in the detection of antibody in early and acute stages of infection (12). Therefore, the development of a new ICT with a more sensitive and specific antigen for detecting the infection in animals and humans is required for preventive and control programs for toxoplasmosis.
Dense granule antigen proteins of T. gondii (TgGRAs) are identified as secretory proteins found abundantly within the parasitophorous vacuole (PV) surrounding the parasites (13). Members of this family are potential antigens for the development of a diagnostic tool for toxoplasmosis. TgGRA7 is a member of this family that was earlier identified by serological immunoscreening of a T. gondii cDNA library using infected human sera (14). After host cell invasion, this protein is secreted into the vacuolar network, the parasitophorous vacuole membrane (PVM), and extensions protruding into the cytoplasm. TgGRA7 accumulates within the PV and PVM in tachyzoite-infected cells and in the cytoplasm of bradyzoite-infected cells (13,15). Several reports have demon-strated the usefulness of TgGRA7 as a serodiagnostic marker of toxoplasmosis. Indeed, the indirect ELISA (iELISA) based on TgGRA7 showed overall sensitivity of 81 to 88% and specificity of 98 to 100% with sera from patients (16,17). Moreover, the iELISA with TgGRA7 had the highest positivity rates in comparison with the positivity rates of other T. gondii proteins, including the rhoptry (TgROP1) and matrix antigens (TgMAG1), the major surface antigen (TgSAG1), and TgGRA8 (18). Another member of the dense granule proteins, TgGRA14, was recently identified within the PV and PVM surrounding the parasite, although its antigenicity was unknown (19).
In the present study, the diagnostic performances of recombinant proteins of TgGRA7 were evaluated in the iELISA and ICT using sera from experimentally and naturally infected animals. Our data show that the ICT based on TgGRA7 is a better diagnostic tool for routine testing of T. gondii infection in the clinic and mass screening of samples in the field.

MATERIALS AND METHODS
Parasites. T. gondii (PLK and RH strains) and Neospora caninum tachyzoites (Nc-1 strain) were maintained in African green monkey kidney (Vero) cells cultured in Eagle's minimum essential medium (EMEM; Sigma, St. Louis, MO) supplemented with 8% heat-inactivated fetal bovine serum. For purification of tachyzoites, the infected cells were washed with cold phosphate-buffered saline (PBS) to remove host cell debris. Cell pellets were resuspended in medium and passed through a 27-gauge needle and then through a 5.0-m-pore filter (Millipore, Bedford, MA).
Mice. C57BL/6, BALB/c, and ICR female mice (6 to 7 weeks of age) were obtained from Clea Japan (Tokyo, Japan) and housed in the animal facility of the National Research Center for Protozoan Diseases under the Guiding Principles for the Care and Use of Research Animals promulgated by the Obihiro University of Agriculture and Veterinary Medicine.
Parasite infection of mice. For preparation of peritoneal fluid from the infected mice, C57BL/6 mice were intraperitoneally infected with 1 ϫ 10 3 tachyzoites of T. gondii PLK or RH strain. Mice were then sacrificed 0, 3, or 6 days postinfection. The peritoneal cells were collected from the peritoneal cavity of naïve or parasite-infected mice by peritoneal washing with 5 ml of cold PBS. After harvesting, cells were centrifuged at 800 ϫ g for 10 min and suspended in cold PBS. The supernatant was stored at Ϫ30°C for the further use of measuring TgGRA7 and TgGRA14 by the double-antibody sandwich ELISA (DAS-ELISA). For collection of serum samples from mice, BALB/c mice were intraperitoneally infected with 1 ϫ 10 3 tachyzoites of PLK or Nc-1. Serum (20 l) was obtained from mice at 7, 14, 21, and 28 days after the infection via tail vein for measuring antigen-specific antibodies by the iELISA. To confirm the lack of an antibody response in uninfected mice, control sera were taken from all animals on day zero before the parasite infection.
Preparation of the recombinant proteins. Total RNA extracted from the RH strain of T. gondii using TRI reagent (Sigma) was reverse transcribed using the SuperScript first strand synthesis system for reverse transcription (RT)-PCR (Invitrogen, Carlsbad, CA) and then used as a template to amplify the target genes. The cDNAs encoding TgGRA7 (corresponding to amino acids 27 to 236, GenBank accession number JX045574.1) and TgGRA14 (corresponding to amino acids 36 to 282, GenBank accession number FJ015061) were amplified using PCR with specific primer sets designed from the available sequences in GenBank, as follows: 5= GCG GAT CCG CCA CCG CGT CAG ATG 3= includes a BamH I restriction enzyme site and 5= GTG AAT TCC TAC TGG CGG GCA TCC TC 3= includes an EcoRI restriction enzyme site for TgGRA7, and 5= CGA ATT CGC CAG TTT GGA GCA GAC AAT T 3= includes a EcoRI restriction enzyme site and 5= TCT CGA GTT ACT TCG TGG TTT CGG AGG T 3= includes a XhoI restriction enzyme site for TgGRA14 (restriction enzyme sites are in boldface). PCR products were digested with the appropriate restriction enzymes and then ligated into the gluta-thione S-transferase (GST) fusion protein in the Escherichia coli expression vector pGEX (GE Healthcare, Buckinghamshire, United Kingdom), which had been digested with the same set of restriction enzymes. Plasmid nucleotide sequences were determined using an ABI 3100 DNA sequencer (Applied Biosystems, Foster City, CA). The cloning of truncated TgSAG2 was performed as described previously (20). Recombinant proteins of TgGRA7, TgGRA14, and TgSAG2 were expressed as glutathione S-transferase (GST) fusion proteins in the E. coli DH5␣ strain (TaKaRa Bio, Inc., Japan). GST tags of the recombinant proteins were removed with thrombin protease (GE Healthcare) according to the manufacturer's instructions. As shown by the results in Figure 1, the purity and quantity of the recombinant proteins of TgGRA7 (29 kDa), TgGRA14 (30 kDa), and TgSAG2 (20 kDa) were confirmed with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) followed by Coomassie brilliant blue R250 staining (MP Biomedicals, Inc., France). The protein concentrations were measured using a bicinchoninic acid (BCA) protein assay kit (Thermo Fisher Scientific, Inc., Rockford, IL).
Production of anti-TgGRA7 or anti-TgGRA14 serum and purification of IgG. One hundred micrograms of each purified recombinant protein in Freund's complete adjuvant (Sigma) were intraperitoneally injected into 6-week-old female ICR mice (n ϭ 4) on day zero. The same protein in Freund's incomplete adjuvant (Sigma) was also intraperitoneally injected into the mice on days 14, 28, and 42. The sera were collected from the mice at day 56. Serum that exhibited a higher sensitivity and specificity to T. gondii from all the inoculated mice was used in this study. One milligram of each purified recombinant protein in Freund's complete adjuvant was also intradermally injected into a female Japanese white rabbit on day zero. The same protein in Freund's incomplete adjuvant was further intradermally injected into the rabbit on days 14, 28, and 42. Immunoglobulin G (IgG) was purified from 2 ml of the rabbit serum that was collected 7 days after the last immunization, using protein A chromatography columns (Bio-Rad Laboratories, CA), following the manufacturer's instructions. Fractions containing IgG were pooled and resolved by SDS-PAGE to test protein purity and quantity. The protein concentrations of purified rabbit polyclonal IgG were measured using a BCA protein assay kit (Thermo Fisher Scientific, Inc.).
iELISA. Fifty-microliter amounts of purified TgGRA7 (29 kDa), TgGRA14 (30 kDa), and TgSAG2 (20 kDa) at a final concentration of 0.1 M were coated onto the ELISA plates (Nunc, Denmark) overnight at 4°C with a carbonate-bicarbonate buffer (pH 9.6). Plates were washed once with PBS containing 0.05% Tween 20 (PBS-T) and blocked with PBS containing 3% skimmed milk (PBS-SM) for 1 h at 37°C before the plates were washed once with PBS-T, and 50-l amounts of serum samples diluted at 1:100 with PBS-SM were added to duplicate wells. The plates were incubated at 37°C for 1 h. After washing six times with PBS-T, the plates were incubated with horseradish peroxidase (HRP)-conjugated anti-mouse IgG or anti-pig total IgG or IgM (Bethyl Laboratories, United States) diluted at 1:4,000 with PBS-SM at 37°C for 1 h. Plates were further washed six times before the substrate solution [0.1 M citric acid, 0.2 M sodium phosphate, 0.003% H 2 O 2 , and 0.3 mg/ml 2,2=-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid); Sigma-Aldrich] was added to each well in 100-l aliquots. The absorbance at 415 nm after 1 h of incubation at room temperature was measured using an ELISA reader (Corona microplate reader MTP-120; Corona, Tokyo, Japan). The cutoff point for pig sera was determined as the mean value at an optical density of 415 nm (OD 415 ) for specific pathogen-free (SPF) pig sera kept in our laboratory (n ϭ 10) plus three standard deviations (IgG, 0.201 for anti-TgGRA7, 0.181 for anti-TgGRA14, and 0.107 for anti-TgSAG2, and IgM, 0.087 for anti-TgGRA7, 0.095 for anti-TgGRA14, and 0.112 for anti-TgSAG2). All samples were examined at least twice to obtain reproducible data.
DAS-ELISA. Rabbit anti-TgGRA7 or TgGRA14 IgG was diluted to 20 g/ml in a 0.05 M carbonate buffer (pH 9.6). One microgram of the IgG per well was used as the capture antibody to coat microtiter plates at 4°C overnight. Blocking was performed with a blocking solution (3% skimmed milk in PBS) at 37°C for 1 h. Plates were incubated at 37°C for 30 min with test samples in duplicate. After washing six times with a washing solution (0.05% Tween 20 in PBS), anti-TgGRA7 or anti-TgGRA14 mouse serum diluted at 1:2,000 in blocking solution was added to each well and the plate was incubated at 37°C for 1 h. After a further six washes, the plates were incubated with HRP-conjugated anti-mouse IgG diluted at 1:5,000 in blocking solution at 37°C. Binding was visualized with the substrate solution [0.1 M citric acid, 0.2 M sodium phosphate, 0.003% H 2 O 2 , and 0.3 mg/ml 2,2=-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)]. The absorbance at 415 nm was measured using an MTP-500 microplate reader. The concentration of TgGRA7 or TgGRA14 was calculated for each sample by standardization with the purified recombinant protein of TgGRA7 or TgGRA14. Standard curves based on recombinant TgGRA7 and TgGRA14 were generated to quantify the secreted proteins in peritoneal fluids and showed coefficients of determination (R 2 ) of 0.9948 and 0.9974, respectively.
LAT. The latex agglutination test (LAT) was performed according to the manufacturer's instructions (Toxocheck-MT; Eiken Chemical, Tokyo, Japan). Samples were considered positive when agglutination was observed at dilutions of 1:32 and greater.
Field sera. Blood samples from 59 pigs with hemorrhagic or necrotic lesions were collected at an abattoir in Japan. The serum samples were then prepared and stored at Ϫ30°C for further use.
ICT. The ICT strips, based on gold colloid-conjugated TgGRA7, were prepared as described previously (10). Briefly, 0.5 mg/ml recombinant TgGRA7 was gently mixed with gold colloids (1:10, vol/vol) and incubated at room temperature for 15 min. The conjugated particles were then blocked with 0.05% polyethylene glycol (PEG) 20,000 (Sigma) and 1% bovine serum albumin (BSA; Sigma). The conjugated gold colloid was pelleted at 18,000 ϫ g for 30 min, and the precipitates were suspended with 0.05% PEG and 0.5% BSA in PBS. The mixture was centrifuged again at 18,000 ϫ g for 30 min, and the pellet was suspended in an appropriate volume of solution containing 10 mM Tris-HCl (pH 8.2) with 5% sucrose. The conjugate was then soaked in borosilicate fiberglass paper (Schleicher and Schuell, Keene, NH) and dried in a vacuum overnight. Recombinant protein was immobilized onto a nitrocellulose membrane (Schleicher and Schuell) using a BioDot BioJet 3050 quanti-dispenser (BioDot, Inc., Irvine, CA) to assess its efficiency as a capture reagent for the test. The purified rabbit anti-TgGRA7 IgG antibody (1.5 mg/ml) was sprayed onto the nitrocellulose membrane at a 4-mm distance from the test line to serve as a procedural control. The strips were assembled on a plastic card (Schleicher and Schuell) and then cut into 2-mm-wide strips using a BioDot cutter (BioDot Inc.). The assay was performed by adding 30 l of the optimal dilution of the serum samples (1:1 in PBS) to the sample pad. The results were judged within 15 min on the basis of the presence or absence of the reaction at the test line. All samples were examined at least twice to obtain reproducible data.
Statistical analysis. The results of LAT, iELISA, and ICT were estimated by the percentage of agreement, the sensitivity and specificity, and the kappa values with 95% confidence interval (http://vassarstats.net/). The strength of agreement was graded with kappa values of fair (0.21 to 0.40), moderate (0.41 to 0.60), and substantial (0.61 to 0.80). The results under various assay conditions were evaluated using Student's t test or analysis of variance (ANOVA) followed by Tukey's multiple comparison. P values of Ͻ0.05 were considered significant.

Detection of TgGRA7 and TgGRA14 in peritoneal fluids of infected mice.
To examine the release of TgGRA7 and TgGRA14, the peritoneal fluids of mice infected with a type I strain (RH) or type II strain (PLK) of T. gondii were analyzed by DAS-ELISA (Fig.  2). The DAS-ELISAs demonstrated no reaction with the noninfected mouse samples but reacted strongly with samples from T. gondii-infected mice. However, the levels of TgGRA7 and TgGRA14 in PLK-infected mice were significantly lower than those in RH-infected mice. More importantly, the quantities of TgGRA7 released were significantly higher (P Ͻ 0.0001) than those of TgGRA14 at days 3 and 6 postinfection. These data suggested that the quantity of TgGRA7 released from the parasites is more abundant than that of TgGRA14 in the body fluids during the course of the infection.
Evaluation of the diagnostic performance of TgGRA7 and TgGRA14. Recombinant proteins were used as antigens for the iELISAs and then validated with sera from experimentally infected mice. A previously established iELISA with TgSAG2 was used as a reference control (20). The iELISAs based on these recombinant proteins succeeded in clearly differentiating between T. gondiiinfected mouse sera and both the SPF and Neospora caninuminfected mouse sera (Fig. 3A). Although the recombinant proteins showed good specificity with experimentally infected mouse sera, TgGRA7 performed best, as evidenced by the higher OD values of sera from T. gondii-infected mice (Fig. 3A). Sera from T. gondiiinfected mice were also sampled serially postinfection to determine the sensitivity of the iELISAs with recombinant proteins (Fig. 3B). Interestingly, the iELISA with TgGRA7 could detect specific antibody as early as 14 days after the infection with levels that were significantly higher than those of anti-TgGRA14 or anti-TgSAG2 antibody. These data demonstrate that TgGRA7 is a better diagnostic marker than TgGRA14 and TgSAG2 in detecting T. gondii antibody in acute and chronic infections. Furthermore, the diagnostic performance of the iELISAs with recombinant proteins was evaluated with 59 field sera from pigs. Among the antigens, iELISA with TgGRA7 showed the highest positive rates for the detection of T. gondii-specific IgG and IgM antibodies (Table 1). Next, the results of the iELISA were compared with those of LAT as a reference test (Table 2). Of the 59 field samples, 32 samples were positive by LAT and 33, 28, and 25 were positive by iELISA with TgGRA7, TgGRA14, and TgSAG2, respectively. As shown by the results in Table 3, the iELISA with TgGRA7 had the highest level of performance, demonstrating a higher kappa value (0.76). Although TgGRA7 showed the highest concordance among the recombinant proteins, four samples detected as negative by LAT were positive by iELISA-TgGRA7, exhibiting low OD values (Ͻ0.3), and three samples that were positive by LAT were negative by iELISA-TgGRA7, exhibiting low OD values near the cutoff. These results support the conclusion that TgGRA7 is a promising diagnostic antigen for the detection of specific antibodies against T. gondii.
Development of ICT based on recombinant TgGRA7. The ICT based on the gold colloid-conjugated TgGRA7 antigen was then developed and validated with sera from experimentally infected mice and pig field samples (Fig. 4A). Consistent with the iELISA results, positive reactions were observed only in the T. gondii-infected mouse sera and not in the sera from SPF mice or N. caninum-infected mice (data not shown). Next, the 59 pig serum samples were tested with the developed ICT and the results were compared with the results of LAT and iELISA with TgGRA7. The ICT detected a total of 24 positive samples, including 1 sample that was negative by LAT but positive by iELISA. Nine samples that were negative by ICT were positive by LAT ( Table 4). The positivity of ICT was significantly correlated with high OD values in iELISA (Fig. 4B). The ICT thus demonstrated substantial and acceptable concordance with LAT and iELISA results, as evidenced by kappa values of 0.66 and 0.70 to 0.79, respectively (Table 5). These data suggest that the developed ICT is a practical and reliable test for the diagnosis of toxoplasmosis.

DISCUSSION
The performances of recombinant TgGRA7, TgGRA14, and TgSAG2 were evaluated by iELISA using sera from experimentally T. gondii-infected mice and field pigs. Strikingly, the iELISA with TgGRA7 succeeded in the detection of antibody in acute and gondii-infected (TG) mice at 30 days after infection were examined by iELISA with TgGRA7, TgGRA14, and TgSAG2 as the antigens (n ϭ 10). (B) Reactivities of recombinant antigens with sera collected serially from experimentally T. gondii-infected mice (n ϭ 12). Each asterisk indicates a significant difference by ANOVA test followed by Tukey's multiple comparison. Differences of P Ͻ 0.05 were considered significant.    (7,14,15,17). Moreover, Pfrepper and colleagues (18) have documented that IgG antibodies against TgGRA7 appeared significantly earlier than those against TgSAG1 and TgMAG1 in human sera. Likewise, other reports have shown that TgGRA7 is more sensitive with serum samples from recently and acutely infected individuals than with samples from those with chronic infection (21,22). The antigenic properties of TgGRA7 can be explained by the facts that this protein is expressed by all infectious stages of T. gondii and is abundant on the surface of host cells and within the PV lumen, the PVM, and the host cell cytosol (23,24). The TgGRA7 released from tachyzoites and bradyzoites after the rupture of their host cells allows the antigen to have direct contact with the host immune system; this can trigger strong antibody response in both early and late stages of infection (14,17,23). On the other hand, the fact that the antigenicity of the recombinant TgGRA14 is lower than that of TgGRA7 is most probably due to its lower level of release from the infected host. Thus, the high expression levels and exposure to the host immune system result in greater antigenicity of TgGRA7.
The excellent antigenic properties of recombinant TgGRA7 and its easy preparation from E. coli in purified form were encouraging for the development of a rapid ICT based on this antigen. Using field pig sera, the ICT results with TgGRA7 were substantially concordant with the results of LAT and iELISA with TgGRA7. However, samples that showed low OD values (Ͻ0.3) in iELISA with TgGRA7 were negative in the ICT because the low titer of antibody could not capture sufficient conjugated antigen to cause a colored reaction on the membrane. Our data indicate that the current ICT with TgGRA7 is a reliable test for rapid diagnosis of swine toxoplasmosis, producing results similar to those of traditional serological tests.
Toxoplasmosis is one of the most important food-borne diseases in the world. The major sources of infection for humans are undercooked meat, especially pork and lamb, and contaminated fruit and vegetables (2,3,8,9). Accurate diagnostic assays for the detection of the T. gondii infection in infected animals are crucial as a step toward management and control of the disease. So far, a large number of different recombinant antigens have been produced in E. coli and their potentials as diagnostic markers have been validated using ELISA in both humans and animals (7,22,25,26). Although this approach is very sensitive and can be incorporated into an automated procedure, it is time consuming, and it requires laboratory procedures, skilled technicians, and special facilities. The current lack of a routine test for screening animals in the field and slaughterhouse makes the development of a rapid, simple, and more practical assay for detection of the infection highly desirable. In this study, we have successfully developed a highly specific and sensitive ICT with TgGRA7 for the detection of antibody against T. gondii infection in pigs. The current ICT has several advantages over traditional serological tests, including its speed, simplicity, and low cost of preparation. These advantages show an important diagnostic potential for ICT in clinical practice, providing a primary screening tool for routine tests and mass screening programs in areas where toxoplasmosis is endemic. Application of the ICT in the field and slaughterhouse prior to meat processing should be considered a control program to decrease the spread of the infection, as well as the risk of infection to humans.


extfieldsofstudy: Chemistry Medicine

s2fieldsofstudy: Biology Medicine

text:
Plasminogen activator inhibitor (PAI)-1 suppresses inhibition of gastric emptying by cholecystokinin (CCK) in mice☆

The intestinal hormone cholecystokinin (CCK) delays gastric emptying and inhibits food intake by actions on vagal afferent neurons. Recent studies suggest plasminogen activator inhibitor (PAI)-1 suppresses the effect of CCK on food intake. In this study we asked whether PAI-1 also modulated CCK effects on gastric emptying. Five minute gastric emptying of liquid test meals was studied in conscious wild type mice (C57BL/6) and in transgenic mice over-expressing PAI-1 in gastric parietal cells (PAI-1H/Kβ mice), or null for PAI-1. The effects of exogenous PAI-1 and CCK8s on gastric emptying were studied after ip administration. Intragastric peptone delayed gastric emptying in C57BL/6 mice by a mechanism sensitive to the CCK-1 receptor antagonist lorglumide. Peptone did not delay gastric emptying in PAI-1-H/Kβ mice. Exogenous CCK delayed gastric emptying of a control test meal in C57BL/6 mice and this was attenuated by administration of PAI-1; exogenous CCK had no effect on emptying in PAI-1-H/Kβ mice. Prior administration of gastrin to increase gastric PAI-1 inhibited CCK-dependent effects on gastric emptying in C57BL/6 mice but not in PAI-1 null mice. Thus, both endogenous and exogenous PAI-1 inhibit the effects of CCK (whether exogenous or endogenous) on gastric emptying. The data are compatible with emerging evidence that gastric PAI-1 modulates vagal effects of CCK.

Introduction
The delivery of liquid gastric contents to the duodenum is determined by their chemical composition. In particular, gastric emptying is delayed by protein and fat content, osmolarity, viscosity and acid. A number of different neuro-endocrine control mechanisms have been identified that act either to change the pressure difference across the pylorus, for example by relaxation of the gastric corpus, or by changing the resistance to flow across the pylorus. The role of the intestinal hormone cholecystokinin (CCK) as a physiological regulator of the gastric emptying of protein and fat has been relatively well studied since its discovery in the mid-1970s [1][2][3][4][5].
Dietary protein and fatty acids with a chain length greater than C12 release CCK which in turn regulates protein and lipid digestion by stimulating delivery to the duodenum of bile salts (through stimulation of gall bladder contraction) and pancreatic enzymes (through stimulation of pancreatic acinar cell exocytosis) [6]. At the same time CCK inhibits gastric emptying and food intake thereby matching delivery of food to the duodenum with the capacity for its digestion. There is now considerable evidence that the latter actions of CCK are mediated via stimulation of vagal afferent neurons which express the CCK-1 receptor [7]. In particular, inhibition of gastric emptying occurs through activation of vago-vagal reflexes leading to relaxation of the gastric corpus, and perhaps also increased resistance to flow across the pylorus [2,3]. Recent work suggests a number of different mechanisms that modulate vagal responses to CCK, including potentiation by leptin and inhibition by ghrelin [8][9][10].
Plasminogen activator inhibitor (PAI)-1 is normally expressed in gastric parietal cells and ECL cells and there is evidence of increased expression in response to high plasma gastrin concentrations [11] and Helicobacter pylori infection [12,13]. In order to study the function of PAI-1 in the stomach, we recently reported the generation of transgenic mice over-expressing PAI-1 in gastric parietal cells using a promoter sequence of approximately 1 kb of the H/K-ATPase β-subunit. Interestingly, these mice were moderately obese, hyperphagic and insensitive to the satiety action of CCK; moreover experiments in rat nodose ganglia in vitro indicated that the excitatory effect of CCK on vagal afferent neurons was inhibited by PAI-1 [14]. On the basis of these findings, we have now hypothesised that PAI-1 also inhibits the effect of CCK on gastric emptying. We report here that PAI-1-H/Kβ mice exhibit resistance to the effects of both endogenous and exogenous CCK in delaying gastric emptying in mice.

Mice
Male mice, 10-13 weeks of age, were used for all studies. They were maintained on a 12:12 h light/dark cycle. Transgenic mice on a C57BL/6 background exhibiting targeted expression of PAI-1 in gastric partial cells using 1.1 kb of the proximal promote of H + /K + ATPase β-subunit coupled to the coding sequence of mouse PAI-1 (i.e. PAI-1-H/ Kβ mice) have previously been described [14]. Wild type C57BL/6 mice were obtained from Charles River (MA, USA) and mice null for PAI-1 were obtained from Jackson Laboratories (Maine, USA). All experiments were approved by the University of Liverpool Animal Welfare Committee and were conducted in compliance with the UK Animals (Scientific Procedures) Act, 1986.

Gastric emptying
Mice were fasted overnight and water was removed 1 h prior to procedures. Animals received intraperitoneal injections (100 μl; CCK8s or PAI-1 at 2.5 nmol/kg in saline, or saline alone) 5 min prior to gastric emptying studies. In some experiments mice received gastrin (20 nmol/kg) 6 h prior to gastric emptying studies. Mice received liquid test meals of either methyl cellulose (1.5% w/v in distilled water containing 50 mg/l phenol red) or peptone (4.5% w/v in methyl cellulose solution) at a volume of 600 μl by gavage. In studies of FOY305, animals were pretreated by gavage (100 μl, 100 mg/kg) 5 min before the test meal. All test meals were pre-warmed to 37°C. Mice were culled 5 min after gavage by rising CO 2 followed by cervical dislocation. An abdominal incision was made, the pylorus and oesophageal junctions were ligated, the stomach securely removed and the gastric contents collected into Eppendorf tubes and centrifuged (12,000 rpm, 5 min). The supernatant was collected, the volume measured and samples alkalinised with NaOH and absorbance determined at 550 nm. Samples of methyl cellulose and peptone test meals were used as controls. Gastric emptying was calculated as described by Debas et al. [1].

PAI-1 ELISA
Blood was obtained from fasted C57BL/6 mice, or PAI-1 null mice, between 15.00 and 16.00 h via cardiac puncture, collected in 0.1 M tri-sodium citrate, centrifuged and concentrations of plasma PAI-1 were determined by ELISA (Molecular Innovations, MI, USA) according to manufacturer's instructions. The assay was validated by demonstrating (a) undetectable PAI-1 in plasma from PAI-1 null mice, and (b) parallel dilution curves for standard PAI-1 and samples from C57BL/6 mice with high endogenous PAI-1.

Statistics
Data are expressed as means ± S.E.; comparisons made by t test for experiments involving a single experimental variable, or ANOVA for multiple comparisons using Bonferroni correction, and were considered significant at p b 0.05.

Peptone delays gastric emptying in C57BL/6 but not PAI-1-H/Kβ mice
In initial experiments, we sought to adapt methods to the mouse that had previously used to study the role of endogenous CCK on gastric emptying in rats [3,16]. Thus over 85% of a control test meal of methyl cellulose emptied in 5 min following intragastric administration. In contrast, peptone dissolved in the methyl cellulose solution significantly delayed gastric emptying compared with methyl cellulose alone in C57BL/6 mice (Fig. 1A). The action of peptone was reversed by prior administration of the CCK-1 receptor antagonist lorglumide, consistent with a role for endogenous CCK in mediating the action of peptone on gastric emptying in wild type mice (Fig. 1A). However, in PAI-1-H/Kβ mice, peptone did not inhibit gastric emptying compared with methyl cellulose, and in fact emptied slightly more rapidly (Fig. 1B). Moreover, a different type of CCK-releasing meal, namely prior administration of FOY305 [17], also significantly delayed gastric emptying of methyl cellulose in C57BL/6 mice but not PAI-1H/Kβ mice compatible with the conclusion that over-expression of PAI-1 in gastric parietal cells inhibits delayed gastric emptying by endogenous CCK (Fig. 1C).

PAI-1 attenuates the action of exogenous CCK on gastric emptying
To determine whether PAI-1-H/Kβ mice were also resistant to the effects of exogenous CCK, we then studied the action of ip CCK8s (2.5 nmol/kg) on gastric emptying of a control test meal. At the dose used, there was approximately 50% inhibition of emptying of methyl cellulose in C57BL/6 mice. However, in PAI-1-H/Kβ mice there was no significant difference in the emptying of the control test meal after CCK8s compared with ip saline (Fig. 2A). We then asked whether exogenous PAI-1 also inhibited the effect of CCK on gastric emptying. In C57BL/6 mice, the inhibition of emptying of methyl cellulose in response to CCK8s was partially reversed by prior administration of PAI-1 (2.5 nmol/kg) (Fig. 2B). Moreover, ip administration of PAI-1 to C57BL/6 mice also accelerated the emptying of a peptone test meal compatible with inhibition of endogenous as well as exogenous CCK on gastric emptying (ip saline: 0.32 ± 0.02 ml of peptone emptied; ip PAI-1: 0.55 ± 0.02 ml emptied; t test, p b 0.05).

Increases in wild type PAI-1 reverse CCK-delayed gastric emptying
It is known that PAI-1 expression is highly regulated, and elevated plasma gastrin is associated with increased expression in gastric epithelial cells notably parietal cells and ECL cells [11]. We therefore asked whether prior administration of gastrin to increase gastric PAI-1 might modulate CCK-effects on gastric emptying. There was increased plasma PAI-1 in C57BL/6 mice 6 h following ip administration of G17ns (20 nmol/kg) compared with saline (ip saline: 4.6 ± 0.6 ng/ml; G17ns: 9.8 ± 1.0 ng/ml; t test, p b 0.05); there was no significant difference in plasma PAI-1 after acute administration of CCK. In C57BL/6 mice, prior administration of G17ns significantly reversed the effect of subsequent administration of CCK8s in delaying emptying of methyl cellulose (Fig. 3A). Strikingly, however, in PAI-1 null mice, CCK inhibition of gastric emptying was maintained after prior treatment with G17 indicating a role for endogenous PAI-1 in mediating the effect of gastrin (Fig. 3B).

Discussion
The main finding of this study is that PAI-1 suppresses the inhibition of gastric emptying by CCK in mice. It is well recognised that CCK stimulates vagal afferent neurons resulting in delayed nutrient delivery to the small intestine by inhibition of both gastric emptying and food intake [7]. Previous studies have shown that PAI-1 suppresses CCK-inhibition of food intake, and have provided evidence that it also inhibits CCK-stimulation of vagal afferent neurons [14]. The present study was therefore undertaken to examine the hypothesis that PAI-1 also suppressed CCK inhibition of gastric emptying. The data derived from studies in transgenic mice over-expressing PAI-1 in the stomach, in PAI-1 null mice, and in wild type mice receiving exogenous CCK or PAI-1, all indicate that PAI-1 (whether exogenous or endogenous) inhibits the effect of CCK (both endogenous and exogenous) on gastric emptying. Thus PAI-1 should now be considered a putative modulator of CCK effects on gastric emptying in health and disease.
There is widespread expression of PAI-1 in many different cells including platelets, endothelial cells, hepatocytes, macrophages, monocytes, adipocytes and adipose stromal cells. Expression is increased in response to inflammation, sepsis, TGFβ and plasma concentrations are elevated in obesity [18][19][20]. In the stomach, there is expression of PAI-1 in both epithelial and stromal cells, and increased expression in parietal and ECL cells has been reported in response to gastrin [11] and H. pylori [12,13]. In PAI-1-H/Kβ mice targeted expression of PAI-1 to parietal cells was achieved using a promoter sequence of approximately 1 kb of the H/K-ATPase β-subunit which has been well characterised for this purpose. In qPCR, primers specific for the transgene sequence confirm selective expression in the stomach, while qPCR primers measuring total tissue PAI-1 mRNA abundance (i.e. transgene plus wild-type product) indicated an approximately 3-fold increase in gastric mRNA abundance. These animals exhibit approximately 25% higher food intake and moderate life-long obesity; moreover the hyperphagia in these animals is at least partly a consequence of insensitivity to CCK [14]. The increase in gastric PAI-1 mRNA abundance in PAI-1-H/Kβ mice is comparable to the changes seen in hypergastrinaemia, so that the insensitivity exhibited by these mice to CCK is presumably functionally meaningful.
One of the major actions of PAI-1 is inhibition of the tissue and urokinase plasminogen activators (tPA, uPA) that convert plasminogen to plasmin which in turn digests fibrin, so that PAI-1 is an important player in fibrinolysis. In addition, PAI-1 binds vitronectin, and disrupts interactions with α v β 3 integrins and the uPA receptor (uPAR) leading to biological properties that are independent of tPA or uPA [20]. Previously, uPAR was shown to be expressed by vagal afferent neurons, and knockdown of uPAR expression was associated with decreased capacity of PAI-1 to inhibit CCK effects in vitro and in vivo [14]. In PAI-1H/Kβ mice there are relatively small changes in plasma PAI-1 compared with C57BL/6 mice, and although not examined directly in the present study, it seems reasonable to suppose that PAI-1 released from gastric epithelial cells in these mice acts locally at uPAR on vagal afferent fibres to decrease sensitivity to CCK in inhibiting gastric emptying.
In obesity, there is evidence for attenuation of the action of CCK in inhibiting gastric emptying [21,22]. Various mechanisms have been discussed including changes in the sensitivity of vagal afferent neurons to CCK with possible roles for the microbiota, interactions with other factors including leptin and ghrelin and neurochemical changes in receptor and neuropeptide gene expression [23][24][25][26][27]. Since there is increased plasma PAI-1 in obesity [18,28] it seems possible that this may also account for depression of responses to CCK. The different proposed mechanisms may not, of course, be mutually exclusive and future work will benefit from systematic dose-response studies. Moreover, in this context it is worth noting that there are multiple populations of vagal afferent neurons that respond to CCK and the precise populations mediating effects on food intake and on gastric emptying remain incompletely characterised [29]. Thus while the neuronal populations targeted by PAI-1 remain to be identified this should be seen in the context of a wider need for the functional characterisation of sub-diaphragmatic vagal afferent neurons.
Multiple gut signals regulate gastric emptying including inhibitory roles for several gut hormones in addition to CCK, e.g. GLP-1 and PYY 3-36 as well the lipid amide oleylethanolamide, and stimulatory roles for regulatory peptides such as ghrelin [30]. The inhibitory action of PAI-1 against CCK does not preclude interactions with other mediators that could be studied in the future. It is notable that intestinal PAI-1 may be increased after radiation damage [31], in experimental colitis [32], and in intestinal neurons in Crohn's disease [33]. Given that some effects of endogenous CCK on stomach function appear to be mediated by paracrine actions on intestinal vagal afferent fibres while others are mediated by actions of circulating CCK on gastric vagal afferent fibres [29], it would not be surprising if intestinal PAI-1 also played a role in modulating gastric emptying by acting locally on vagal afferent nerve fibres. In some of the circumstances that are associated with increased PAI-1 there are also increases in proinflammatory mediators such as IL1β that enhance vagal responses to CCK [34,35]. The present data therefore suggest a role for gastric PAI-1 in maintaining nutrient delivery to the small intestine in health and disease by counteracting interactions at vagal afferent neurons between CCK and factors that enhance its effects.


extfieldsofstudy: Biology Medicine

text:
Elucidating the Landscape of Aberrant DNA Methylation in Hepatocellular Carcinoma

Background Hepatocellular carcinoma (HCC) is one of the most common cancers and frequently presents with an advanced disease at diagnosis. There is only limited knowledge of genome-scale methylation changes in HCC. Methods and Findings We performed genome-wide methylation profiling in a total of 47 samples including 27 HCC and 20 adjacent normal liver tissues using the Illumina HumanMethylation450 BeadChip. We focused on differential methylation patterns in the promoter CpG islands as well as in various less studied genomic regions such as those surrounding the CpG islands, i.e. shores and shelves. Of the 485,577 loci studied, significant differential methylation (DM) was observed between HCC and adjacent normal tissues at 62,692 loci or 13% (p<1.03e-07). Of them, 61,058 loci (97%) were hypomethylated and most of these loci were located in the intergenic regions (43%) or gene bodies (33%). Our analysis also identified 10,775 differentially methylated (DM) loci (17% out of 62,692 loci) located in or surrounding the gene promoters, 4% of which reside in known Differentially Methylated Regions (DMRs) including reprogramming specific DMRs and cancer specific DMRs, while the rest (10,315) involving 4,106 genes could be potential new HCC DMR loci. Interestingly, the promoter-related DM loci occurred twice as frequently in the shores than in the actual CpG islands. We further characterized 982 DM loci in the promoter CpG islands to evaluate their potential biological function and found that the methylation changes could have effect on the signaling networks of Cellular development, Gene expression and Cell death (p = 1.0e-38), with BMP4, CDKN2A, GSTP1, and NFATC1 on the top of the gene list. Conclusion Substantial changes of DNA methylation at a genome-wide level were observed in HCC. Understanding epigenetic changes in HCC will help to elucidate the pathogenesis and may eventually lead to identification of molecular markers for liver cancer diagnosis, treatment and prognosis.

Introduction
Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers in the world. The disease represents a significant health burden to our communities due to the lack of effective treatment [1,2]. Chronic infection with hepatitis B virus (HBV) and hepatitis C virus (HCV), alcohol abuse, environmental and occupational toxins, as well as certain metabolic and immune disorders are risk factors of HCC [3,4].
Sequential changes of normal hepatocytes to malignant cells are associated with altered gene expression reflecting accumulated genetic alterations, such as chromosomal instability (CIN) and DNA rearrangements, which have been extensively studied [5]. Nevertheless, the molecular pathogenesis of HCC remains poorly understood. Methylation of DNA cytosine residues at the carbon 5 position (5 mC) in cytosine-guanine dinucleotides (CpG sites) is a common epigenetic mechanism in eukaryotic DNA, playing an important role in regulation of gene activities. Aberrant DNA methylation is observed in many human cancers including HCC [6,7,8], in which global hypomethylation and specific promoter hypermethylation have been found as typical epigenetic changes involved in genomic instability and silencing of tumor suppressor genes, respectively [9,10]. Furthermore, changes in cytosine methylation may also serve as molecular markers for prediction of tumor progression [11]. In HCC, aberrant hypermethylation of candidate genes such as GSTP1, RASSF1A, APC and CDH-1 has been detected using methylation specific polymerase chain reaction (MSP), combined bisulfite restriction analysis (COBRA), and bisulfite sequencing techniques [12,13,14]. However, because the techniques for genome-wide analysis of methylation were introduced only recently, most studies have been of a limited scope with the analyses focusing on hypermethylation of tumor suppressor genes or on hypomethylation of oncogenes or methylation of repetitive elements such as LINE1 despite the fact that there are many more genes and DNA sequences in cancer that may develop aberrant methylation. Recently, genome-wide DNA methylation profiling in HCC was performed using the Illumina HumanMethylation27 BeadChip [14,15], and these two studies identified many previously unknown regions and genes to be differentially methylated in HCC. However, further genomewide analyses of DNA methylation with even higher resolution are still needed for better understanding of aberrant methylation in hepatocarcinogenesis.
In this study we used the latest version of the Illumina methylation microarray chip, the HumanMethylation450 Bead-Chip (Methylation450 BeadChip) to quantitatively analyze over 485,000 CpG sites in 27 HCC samples and 20 adjacent normal tissues. The analysis allowed us to confirm differential methylation in various known differentially methylated regions (DMRs) such as reprogramming specific DMRs (rDMRs), cancer specific DMRs (cDMRs) [7] and Enhancers (in-silico determined by Illumina), as well as to find potential new HCC DM loci. We also identified differential methylation in the genomic regions surrounding the promoter CpG islands, such as the shores and shelves.

Patient Specimens
The study was reviewed and approved by the Committee on Human Studies (Institutional Review Board) of the University of Hawaii at Manoa. All patients participating in the study gave written informed consent. Liver tissues were removed from the patients as part of a partial hepatectomy or liver transplant which was performed for hepatocellular cancer by a hepatobiliary/liver transplant surgeon (LW). No additional procedures were done other than what was clinically indicated for each patient. All operative procedures were performed at Hawaii Medical Center-East between 2009 and 2011. A total of 27 HCC tissues and 20 adjacent normal (non-malignant) tissues, including 19 matched pairs, were obtained. Histopathological evaluation was performed for each patient to confirm tumor content in the tissue. Relevant demographic and clinical information including patient age, HCC AJCC stage, sex, HBV and HCV infection status and alcohol use is shown in Table 1.

DNA Extraction and Bisulfite Conversion
DNA was extracted from the tissue specimens using the AllPrep DNA/RNA Mini kit (Qiagen Inc, Valencia, CA). The quality and quantity of extracted DNA was examined by the NanoDrop-2000 (Thermo Scientific, Wilmington, DE). Bisulfite conversion of 500 ng of DNA was performed on each sample according to the manufacturer's recommendations for the Methylation450 Bead-Chip using the EZ DNA Methylation kit (Zymo Research, Irvine, CA). The treatment protocol included 16 cycles of denaturing at 95uC for 30 sec and incubation at 50uC for 60 min, as well as a final step of holding at 4uC.

Methylation Analysis
HumanMethylation450 BeadChip (Illumina, San Diego, CA) was used to analyze the genome-wide DNA methylation profiles across 485,577 loci. These loci cover 96% of known CpG islands and 99% of NCBI Reference Sequence (RefSeq) genes, with an average of 17 CpG sites per gene distributed across the upstream of the transcription start sites (TSS)1500, TSS200, 59 untranslated regions (UTR), first exon, gene body, and the 39UTR. The 485,577 cytosine positions in the genome include 482,421 (99.35%) CpG dinucleotides, 3,091 (0.64%) CNG targets, and 65 (0.01%) SNP sites. Four ml of bisulfite-converted DNA was used for hybridization onto the Methylation450 BeadChip, following the Illumina Infinium HD Methylation protocol. This consisted of a whole genome amplification step followed by enzymatic endpoint fragmentation, precipitation and resuspension. The resuspended samples were hybridized onto the BeadChips for 16 hours at 48uC. After hybridization, the unhybridized and non-specifically hybridized DNA was washed away, followed by single nucleotide extension using the hybridized bisulfite-treated DNA as a template. The Illumina iScan SQ scanner was used to create images of the single arrays and the intensities of the images were extracted using GenomeStudio (v.2011.1) Methylation module (v.1.9.0) software.
The data was normalized using the 'Background Subtraction' and 'Normalization to Internal Controls' methods offered by the Genome Studio software. First, the background subtraction value was derived from the signals of built-in negative control bead types for each channel, setting the background level at the 5% percentile of the negative controls in the given channel. Background was then subtracted from probe intensities in the same channel. If intensity becomes negative, it is set to 0. Secondly, the internal control probe pairs on the Methylation450 BeadChip were utilized for normalization. The normalization control probe pairs (over 90 of them) are designed to target the same region within housekeeping genes and have no underlying CpG sites in the probe. For normalization, probe intensity in the given sample was multiplied by a constant normalization factor (for all samples) and divided by the average of normalization controls in the probe's channel in the given sample.
The methylation score for each CpG site was represented by a Beta value calculated according to the normalized probe fluorescence intensity ratios between methylated and unmethylated signals. Beta values vary between 0 (fully unmethylated) and 1 (fully methylated). Every Beta value on the Methylation450 BeadChip is accompanied by a detection p-value indicating signals significantly greater than background and these detection p-values were used to remove noise from the data. Average Delta-beta values indicating the differential methylation between HCC and the adjacent normal tissue were calculated by subtracting the average Beta value of pooled HCC samples from that of pooled adjacent normal tissues. A subset of differentially methylated (DM) loci (n = 8) found by the Methylation450 BeadChip analysis was selected for validation using Pyrosequencing. Bisulfite treated DNA was first amplified by PCR in the regions of interest using the PyroMark PCR kit (Qiagen) following the suggested protocol. Information on the predesigned and custom Pyrosequencing assays is in Table S5. One of the PCR primers was biotinylated to enable capture by Streptavidin Sepharose later in the protocol. The Pyrosequencing assay was performed on a PyroMark Q24 instrument (Qiagen) using the PyroMark Gold Q24 Reagents (Qiagen). Purification and subsequent processing of the biotinylated single-stranded DNA were performed according to the manufacturer's recommendations. The sequencing results were analyzed using the PyroMark Q24 software (Qiagen).

Statistical Analysis of Differential Methylation
Partek Genomics Suite (Partek Inc., St. Louis, MO) was used for the statistical analysis of differential methylation. First, data quality control (QC) analyses on the normalized average Beta values generated by the GenomeStudio were performed. These included Principal component analysis (PCA) (Figure S1A), and graphing the sample histograms for signal distributions (data not shown). For the actual differential methylation analysis, a multivariate ANOVA, including factors such as tissue (tumor vs. normal), sex (male vs. female), virus infection (HBV or HCV positive vs. both negative), alcohol use and scan date, was performed to evaluate the contribution of these factors to differential methylation ( Figure S1B). Since the 6-way ANOVA suggested tissue type to be the main significant contributor to differential methylation, we used 1-way ANOVA for tissue type by contrasting pooled tumor samples with pooled normals to determine significant differential methylation (DM) loci with unadjusted p-value cut-off as ,1.03e-07 (corresponding to pvalue ,0.05 after Bonferroni adjustment). Heat maps were created using the Partek Genomics Suite. The Euclidian distance between the two groups of samples (tumors and normals) was calculated by the average linkage.

Ingenuity Pathway Analysis (IPA)
The Ingenuity Pathway Analysis (IPA) program (http://www. ingenuity.com/index.html) was used to identifythe possibly affected gene networks, functional categories and canonical pathways related to HCC and liver function. IPA ranks gene networks by a score (-log (p-value)) that takes into account of the number of focus genes and the size of the network.

Methylation Analysis
To explore the landscape of aberrant DNA methylation in HCC, we analyzed 27 HCC and 20 adjacent normal tissues (with 19 matched pairs) using the Methylation450 BeadChip. A total of five chips were used to profile 47 samples in three batches, and tumor-normal sample pairs were analyzed on the same chip to minimize experimental variation. After normalization of the data with internal controls and the background subtraction, Principal Component Analysis (PCA) was performed on the Beta values of the 485,577 loci to show methylation signal clustering of the samples by tissue type (shown as ellipses in Figure S1A). The results showed distinct overall methylation patterns between HCC and the adjacent normals. Moreover, the tumor group was less tightly clustered than the normal group, suggesting some heterogeneity in the methylation profiles among HCC. Next, we performed a 6-way ANOVA analysis with tissue type (HCC or adjacent normal), age, sex, alcohol, viral status and scan date as variables to assess the sources of variation in the methylation analysis. The most significant source of variation among these variables in the entire data of 485,577 loci was tissue type with other factors having a negligent effect ( Figure S1B). Based on these results, 1-way ANOVA by tissue type was used for the final analysis.

Global Methylation Profile in HCC
Significant DM loci between HCC and adjacent normal tissues were observed in 13% of the CpG sites analyzed (62,692/485,577) (with unadjusted p,1.03e-07). Of these significant DM loci, 61,058 (97%) were hypomethylated ( Figure 1A), compared to 1,634 (3%) loci that were hypermethylated. We further analyzed functional location of the DM loci in hyper-or hypomethylation groups with the notion that a CpG site can be in more than one functional location since a locus can reside in several transcript variants of the same gene or in different genes (therefore the total number of hyper-or hypomethylated loci and the sum of the loci in different functional locations don't match in Figure 1). Among the 1,634 hypermethylated DM loci, most (37%, a significant difference) were localized in the promoter regions which correspond to TSS1500, TSS200, 59UTR and 1 st Exon, followed by 32% and 29% in the gene bodies and intergenic regions, respectively ( Figure 1B). In contrast, hypomethylated DM loci (61,058) were mostly localized in the intergenic regions (43%) and gene bodies (33%) and were less likely to be in the promoter regions (21%) ( Figure 1C). Another interesting observation of the hypermethylated loci is that they tend to locate in more than one functional location. The 1,634 hypermethylated genomic loci are located on 3000 possible functional loci, with an average of 1.84 functional loci for each genomic locus. The functional/genomic locus ratio for hypomethylated loci was only 1.03.
To understand how the observed DM distribution depends on the underlying normal patterns of DNA methylation, we characterized the patterns of DNA methylation in HCC and adjacent normal tissues separately ( Figure S2). A heatmap showing methylation levels for 62,692 DM loci was generated by unsupervised hierarchical clustering ( Figure S2A) and the methylation events for the 62,692 DM loci were further characterized by functional location (Promoter, Body, 39UTR, or Intergenic) ( Figure S2B). When looking at all DM loci (heat map) or by the functional DM loci, the normal tissue DM loci, with an exception of the promoter CpG islands, show consistently higher methylation levels in normals than tumors which contributes in apparent general hypomethylation in 97% of the DM loci in tumors.

General Methylation Patterns in Promoter CpG islands and Surrounding Areas
10,775 DM loci were located in CpG islands and surrounding areas. CpG islands in promoters as well as in the adjacent regions such as the shores (0-2 kb from the promoter CpG islands) and shelves (2-4 kb from the promoter CpG islands) have been found to be differentially methylated in cancer and the stem cells [7,16,17]. While most of the DM loci found in this study reside in the open seas, interestingly, differential methylation occurred in the shores twice as frequently than in the promoters (1,952 or 18% versus 982 or 9%) ( Figure 2A). The actual methylation levels for each promoter CpG island and the surrounding regions are shown by heatmaps in Figure 2C. The branch lengths in the adjacent normal cluster were generally shorter than the branch lengths in the HCC cluster, indicating more heterogeneity in methylation levels among the tumor tissues, confirming the finding by PCA. The number of hypo-and hypermethylation events within the promoter CpG islands in HCC tissues were similar (45% and 55%, respectively), but significantly less hypermethylation was observed in the shores (8% of DM events), shelves (1%) and open seas (7%); hypomethylation being the dominant event in these regions.

Methylation Patterns in Enhancers and Known and Novel DM Loci
Of the 10,775 promoter-related DM loci, 4% (460 loci) were in known DMRs, rDMRs and cDMRs that were established in colon cancer [7]. The remaining 10,315 DM loci located in 4,106 genes, on average 2.5 DM loci/gene, were newly identified and could potentially be HCC-specific ( Figure 2B and Table S1). These HCC-related DM loci are harbored by genes which may have  (Table S2).
Because there is still limited understanding of the size and content of genomic regions involved in the promoter region epigenetic regulation in HCC, we focused next on the sublocation methylation levels of the 10,775 promoter-related DM loci including the known DMRs, cDMRs, rDMRs, and the potential new DMR loci as stratified by the promoter CpG islands, shores, shelves, and enhancers, but excluding loci in the open seas. The genomic regions were further classified into N_shelves (2-4 kb upstream of CpG islands), N_shores (0-2 kb upstream of CpG islands), CpG islands, S_shores (0-2 kb downstream of CpG islands) and S_ shelves (2-4 kb downstream of CpG islands). This analysis was done in the adjacent normal tissues and tumor tissues separately ( Figure 3 and Table S3). Average Beta values of pooled samples for the DM loci within each region were used to examine the methylation levels. As seen in Figure 3, in the adjacent normal tissues, a higher density (number) of DM loci were observed in the shores than in the promoter CpG islands for all known cDMRs (9.2 fold), rDMRs (4.9 fold), the potential new HCC DMR loci (2 fold) and even for the DM loci within enhancers (1.3 fold), but not for the 148 general DMRs. The average methylation levels (Beta values) in the CpG islands of cDMRs, rDMRs and enhancers were approximately only half the levels of the loci in the shores ( Figure 3A). In contrast, in HCC tissues, methylation levels across the CpG islands, shores and shelves were similar although the average methylation level seems to be slightly higher in the CpG islands than in the surrounding regions ( Figure 3B).

DM Loci in Promoter CpG islands
To further investigate the possible biological implications of differential methylation, we focused on a subset of 982 promoter DM loci (437 hypo-and 545 hypermethylated) that were located in the CpG islands within promoters. The average Beta and Deltabeta values between HCC and the adjacent normal tissues for the 982 loci are shown in Table S4. Among these loci, 80% (348/437) of the hypomethylated and 91% (494/545) of the hypermethylated loci had Delta-beta values $|0.2| which means greater than or equal to 20% methylation differences between tumor and normal tissues. The Methylation450 BeadChip can reliably detect a Delta-beta value of 0.2 with a lower than 1% false positive rate (http:// www.illumina.com), giving confidence on findings with moderate Delta-beta values as well.
The 437 hypo-and 545 hypermethylated promoter loci are colocated with 211 and 279 genes, respectively (Table S4). The DM loci ranked by the largest Delta-beta values are in Table 2, which indicates the gene corresponding to each locus, TargetID provided by Methylation450 BeadChip, Genomic location (GRCh37/ hg19), Functional Location, Delta-beta values, and the Bonferroni adjusted p-values. Genes C15orf60, DCAF4L2, GAB4, OR2B11, ACP1, SH3YL1, and C2CD4D are covered by more than one locus. Moreover, TargetIDs cg06792448, cg10749741, cg25945732, cg03468349, and cg02339682 are localized in more than one gene. A high frequency of loci within TSSs (TSS1500 or TSS200, 12 out of 20 (60%)) was observed among the top 20 hypermethylated loci. In contrast, only 6 loci out of 20 (30%) were located within TSS200 among the top 20 hypomethylated loci. Interestingly, of the top 20 hypermethylated loci, three (genes RIMS2, MYADM, and ZNF397OS) were in known DMRs and four (MTMR7, LPAR2, FOXE3, and SNX31) were in the enhancer regions. MYT1L (adjusted p = 6.9e-14) and FOXE3 (adjusted p = 9.0e-10) were found as the most significant hypo-and hypermethylated genes between the HCC and adjacent normal tissues, respectively, while NFATC1 (Delta-beta 20.6) and RIMS2 (Deltabeta 0.55) showed the biggest methylation level differences between two groups among hypo-and hypermethylated genes, respectively. Overall, there was a high level of inter individual consistency in the differential methylation between the HCC and normal tissues. Beta values for 5 randomly selected hypomethylated and hypermethylated loci among the top 20 DM loci are shown by dot plots in Figure 4. The dot plots show consistent differential methylation of these loci between the HCC and the adjacent normal tissues for all patients except for a few outlier samples.
To explore the biological implications associated with the 982 promoter DM loci, we characterized the associated genes using IPA (Table 3). Twenty-six genes including BMP4, CDKN2A and GSTP1 were involved in the top IPA networks related to'Cellular development, Gene expression, and Cell death' (p = 1.0e-38). Moreover, 15 genes were found to be involved in 'Cellular movement, Cell cycle, Cellular compromise, and Cell-mediated immune response' (p = 1.0e-17), and another 15 genes were identified in 'Cellular development, Cellular function and maintenance' (p = 1.0e-17), all of which were suggested as top networks in IPA. Cell cycle, proliferation, and cancer-related genes were also identified (Table 3).
Interestingly, we found consistent opposite methylation patterns between two adjacent CpG islands in a known imprinted gene (GNAS, guanine nucleotide binding protein, alpha stimulating). There are two separate CpG islands in this gene with 5 hypomethylated loci located on CpG island 1 (at chr20:57,428,473-57,429,858) and 3 hypermethylated ones located on CpG island 2 (chr20:57,465,695-57,465,691) ( Figure S3) in our HCC samples.

Validation of DM loci by Pyrosequencing
Eight DM loci identified by the Methylation450 BeadChip were selected for validation using pyrosequencing. Five hypermethy-lated loci harbored by four genes (GSTP1, TACSTD2, BMP4 and RASSF1) and three hypomethylated loci in three genes (ARHGAP8, GPR35 and DLGAP1) were validated using 7 assays with 12 tumornormal tissue pairs from this study. Two DM loci were validated for GSTP1 by pre-designed Pyrosequencing assays. Since there are limitations in designing assays for methylation loci due to high GC content, pre-designed assays were not always available for analyzing the exactly same locus that was assessed by the Methylation450 BeadChip. Nonetheless, since more than one locus with a significant p-value was observed in the genes, we selected the assays with the closest distance to the DM loci of interest to correlate the methylation results between the Pyrosequencing and the Methylation450 BeadChip. If the locus of interest was not covered by the assay, average methylation values of the analyzed CpG sites by Pyrosequencing were used for the correlation analysis. Five assays pre-designed by Qiagen and 2 previously used by other groups were used. Detailed information on the analyzed genomic region for each assay, the number of analyzed CpG sites by Pyrosequencing as well as the loci that show significantly differential methylation for a given gene on the BeadChip, but could not be technically validated, is shown in Table S5. For 5 of the genes, high correlations ranging from 0.86 to 0.99 were found between the Pyrosequencing and Methyla-tion450 BeadChip results for both the exactly matched CpG sites (GSTP1, RASSF1 and BMP4) and the closest CpG sites (DLGAP1 and GPR35) ( Figure 5, Proximal validatedloci). BMP4 showed the highest concordance between the Pyrosequencing and the  Methylation450 BeadChip (r = 0.99). We also observed that when the site of the Pyrosequencing assay was far away from the DM loci found by the Methylation450 BeadChip (about 0.9 kb for TACSTD2, 31 kb for ARHGAP8), the methylation status was not well correlated, indicating location specificity ( Figure 5, Distal validated loci).

Discussion
Aberrant DNA methylation has been shown to play an important role in tumorigenesis and cancer progression in many malignancies including HCC [18,19]. A previous study focusing on DNA methylation profiles in HCC at a few selected loci of tumor suppressor genes and oncogenes found methylation changes in the promoters of 9 cancer-related genes, including SOCS-1, GSTP, APC, E-cadherin, RAR-beta, p14, p15, p16 (CDKN2A), and p73 [19]. New technologies of DNA methylation analysis utilizing microarrays now offer tremendous opportunities to study methylation at a genome scale. This approach can improve our understanding of the epigenetic mechanisms and the effect of DNA methylation on disease-associated molecular networks and pathways, beyond single genes.
In this study, we measured DNA methylation levels at 485,577 loci across 99% of RefSeq genes including 96% of the known CpG islands in 27 HCC tissues and 20 adjacent normals. After Bonferroni adjustment, we found statistically significantly differential methylation levels in 62,692 loci, 97% (61,058 loci) of which were hypomethylated, featuring general (or ''global'') hypomethylation. Aberrant genome-wide hypomethylation is known to be linked to tumorigenesis, and our finding is in agreement with this notion. Previous studies also show lower levels of genome-wide 59methylcytosine contents in HCC tissues [20] and LINE-1 hypomethylation in the blood of HCC patients [21]. Furthermore, our finding of a disproportionate enrichment of hypomethylation events to the intergenic regions and gene bodies, rather than the promoters, bears resemblance to the findings of a previous genome-wide study on colon cancer [7]. Table 3. Biological functions of 10 IPA networks of the genes harboring 982 differentially methylated (DM) loci in the promoter CpG islands. Although several genome-wide methylation studies on HCC have been published recently [15,22,23], these studies have used the Illumina HumanMethylation27 BeadChip that includes much lower numbers of CpG loci than the Methylation450 BeadChip we used in this study. A recent study by Shen et al found methylation at 2,324 CpG loci (representing 548 hyperand 1,290 hypomethylated genes) to be significantly different in 62 Taiwanese HCC cases (mostly HBV positive) compared to the adjacent normal tissues [15]. Among the 20 top ranked genes on their list, 14 of the hypermethylated genes (DAB2IP, BMP4, CDKN2A, TSPYL5, CDKL2, ZNF154, ZNF540, CCDC37, NKX6-2, FOXD2, HIST1H3E, LYPD3, DNM3 and RASSF1) and 18 of the hypomethylated genes (CCL20, AKT3, SCGB1D1, WFDC6, PAX4, GCET2, CD300E, CD1B, MNDA, CD1E, CYP11B1, KRTAP13-1, KLK9, KPNA-1, SPRR1B, SPRR1A, CCR6 and OR51B4) were also found with DM loci in our study. Furthermore, 14 out of the above hypermethylated genes were also identified by Ammerpohl et al. [22]. Since the Methylation450 BeadChip covers more CpG sites than the earlier version of BeadChip, we analyzed approximately sixteen times more loci than the previous studies, and each gene was covered by many more CpG sites (Table S6). Among our top ranked 20 hypo-and 20 hypermethylated loci within promoters (Table 2), two hypermethylated genes (FTMT and GMT) and six hypomethylated genes (FOXE3, SH3YL, RNF135, BMP4, ZNF154 and MYADM) were also identified by Shen et al. [15]. Compared to the total of 1,490 DM loci found by the Illumina HumanMethylation27 BeadChip, we identified 62,692 DM loci using the HumanMethylation450 BeadChip. Locus wise, Our data is in 49% (735/1,490), 46% (685/1,490) and 57% (852/1,490) agreement with the previous studies by Ammerpohl et al. [22], Hernandez-Vargas et al. [23] and Shen et al. [15], respectively. The overlapping loci were 100% consistent in the direction of methylation changes (data not shown).

Molecules in Network
Previous studies reported statistically significant DM loci between HCC and normal tissues, but did not expand the analysis for a closer look at the structure of the DM genomic locations. However, relatively little is currently known about the significance of methylation changes between various genomic sublocations, i.e., promoter CpG islands, shores, shelves and enhancers. Irizarry et al. recently performed a genome-wide methylation study of colon cancer with a surprising finding that 13 times more altered methylation events were observed in the shores than in the promoter CpG islands [7].
In this study, we observed differential promoter methylation levels between the HCC and the adjacent normal tissues in the promoter regions including the TSS1500, TSS200, 59UTR and 1 st Exon loci, methylation of which could directly regulate gene expression based on the current hypotheses. While the numbers of DM loci in the promoter CpG islands were quite similar for hypo-(45%) and hypermethylation (55%), significantly less hypermethylation events were found in the shores (8% of DM events) and shelves (1%) in the HCC compared to the adjacent normal tissues. This indicates that most of the promoter region hypermethylation events occur in the promoter CpG islands in HCC. Fittingly, looking at the level of methylation, normal tissues showed lower methylation levels in the promoter CpG islands than in the shores and shelves. In contrast, promoter CpG island methylation levels were higher than the levels in the shores and shelves in tumor tissues. Of the 10,315 promoter-related DM loci identified in this study, those that are not among the known DMRs, could potentially be novel DMR loci for HCC. The number of DM loci (mostly hypomethylated) was higher in the shores than in the promoter CpG islands for all the known rDMRs, cDMRs, as well as for the DM loci within enhancers and the potential new DMR loci, which bears resemblance to the finding of enrichment of colon cancer DM events on the shores rather than promoter CpG islands by Irizarry [7].
During development and carcinogenesis, methylation at each individual CpG site is established in a regional specific manner, with some regions that remain unmethylated while others become hypermethylated [24]. Similarly, we observed regionally consistent methylation patterns of the CpG loci within promoter CpG islands for the majority of the co-located genes (data not shown). Interestingly, we found one gene (GNAS) that had different methylation patterns in two separate promoter CpG islands. The hypomethylated CpG island is located upstream of a GNAS antisense RNA (GNAS-AS1) which suggests that the imprinting process may be regulated by these different patterns of methylation [25,26].
For the evaluation of the possible biological implications of the aberrant methylation in the HCC tumors, we used IPA in order to identify pathways and cellular processes possibly disturbed by differential methylation of the affected genes. We found that the methylation changes observed in this study could be linked to the signaling networks of 'Cellular development, Gene expression and Cell death' (p = 1.0e-38). These networks include BMP4 [27], CDKN2A [28] and GSTP1 [29], genes that have been well studied in HCC before. Genes related to 'Cell cycle, proliferation, and cancer' were also indicated in our IPA analysis. Methylation at the CpG islands of promoters has been shown to directly correlate with gene expression [10,30]. Our preliminary expression analysis of the 7 genes with promoter DM loci that we attempted to validate by pyrosequencing show at least partial correlation between promoter methylation and expression levels in 16 paired samples (data not shown). Of the 16 tumor-normal pairs with presumed hypermethylated promoter loci in HCC (in genes BMP4, RASSF1,GSTP1, and TACSTD2), 5, 6, 14 and 16 of the 16 HCCs had decreased expression of the gene, respectively. Of the pairs with presumed hypomethylated promoter loci (in ARHGAP8, GPR35, and DLGAP1), 10,11 and 12 of the 16 HCCs had increased expression of the gene, respectively. These results reflect the complexity of epigenetic regulation, since even that the differential methylation patterns were consistent between the tumors and normals for these particular genes (see Figure 5 for 5 of the genes); there was not always a direct correlation between the methylation and expression patterns.
Until recently, the importance of methylation changes beyond TSSs and CpG islands, e.g. in other gene regions such as gene bodies has been overlooked even though dynamic methylation patterns have been observed in these regions [31]. Since we have rich data from the Methylation450 BeadChip that allows simultaneous analysis of more than 480,000 loci on a genomewide scale, we have detailed information on the methylation of CpG islands, TSSs, gene bodies and beyond in HCC. However, the possible heterogeneity of HCC tissue composition and the various risk factors of HCC may have substantial impacts on methylation. Therefore, it will important to evaluate methylation changes stratified by different risk factors of HCC, such as HBV and HCV infection. To gain a more comprehensive understanding of HCC methylation profiles, our future analysis will include specific risk factors of HCC, such as viral infection, alcohol drinking habits, disease stage, histology, and the presence of cirrhotic vs. non-cirrhotic surrounding tissue. We also aim to correlate the DM findings with gene expression beyond the current dogma, i.e. including DM loci that are not on the promoter CpG islands.
In summary, this is the first report to our knowledge to apply the Methylation450 BeadChip in HCC for assessing genome-wide methylation. Our current analysis focused on differential methylation patterns in various regions of the genome and especially in or near the gene promoters. Our main observations are: (1) General or ''global'' hypomethylation was observed in HCC, concentrating in the intergenic regions and gene bodies; (2) Discovery of 10,315 potentially new DMR loci for HCC with higher density in the promoter shores than in the CpG islands; (3) Higher frequency of hypermethylation events in the promoter CpG islands than in the shores and shelves; (4) Enrichment of promoter CpG island DM loci in gene networks of 'Cellular development, Gene expression, Cell death, and Cancer'. Our findings from the genome-wide profiling of HCC may help to define the landscape of aberrant DNA methylation in HCC and give more depth to the observation of altered methylation in the promoter CpG islands and other related gene regulatory regions. Understanding epigenetic changes in HCC will help to elucidate the pathogenesis and eventually may lead to identification of molecular markers for disease diagnosis, treatment and prognosis.

Supporting Information
Figure S1 Principal Component Analysis (PCA)and sources of variation. A. Two-dimensional PCA of DNA methylation data between HCC (red) and adjunct normal tissues (blue). x axis, first principal component (PC1); y axis, second principal component (PC2). B. Statistical significance of the different sources of variation in the methylation data estimated by a 6-way ANOVA model. F-ratio for each factor (source) represents the F-statistics for the factor/F-statistics for error (noise). (TIF)