The Adenomatous polyposis coli (APC) tumour suppressor is found in multiple discrete subcellular locations, which may reflect sites of distinct functions. In Drosophila epithelial cells, the predominant APC relative (E-APC) is concentrated at the apicolateral adherens junctions. Genetic analysis indicates that this junctional association is critical for the function of E-APC in Wnt signalling and in cellular adhesion. Here, we ask whether the junctional association of E-APC is stable, or whether E-APC shuttles between the plasma membrane and the cytoplasm.We generated a Drosophila strain that expresses E-APC (dAPC2) tagged with green fluorescent protein (GFP-E-APC) and we analysed its junctional association with fluorescence recovery after photobleaching (FRAP) experiments in live embryos. This revealed that the junctional association of GFP-E-APC in epithelial cells is highly dynamic, and is far less stable than that of the structural components of the adherens junctions, E-cadherin, α-catenin and Armadillo. The shuttling of GFP-E-APC to and from the plasma membrane is unaltered in mutants of Drosophila glycogen synthase kinase 3 (GSK3), which mimic constitutive Wingless signalling. However, the stability of E-APC is greatly reduced in these mutants, explaining their apparent delocalisation from the plasma membrane as previously observed. Finally, we show that GFP-E-APC forms dynamic patches at the apical plasma membrane of late embryonic epidermal cells that form denticles, and that it shuttles up and down the axons of the optic lobe.We conclude that E-APC is a highly mobile protein that shuttles constitutively between distinct subcellular locations. The Adenomatous polyposis coli (APC) protein is an important tumour suppressor in the colonic epithelium . A key fHowever, APC proteins have additional functions in connection with the actin and microtubule cytoskeletons that appear to be separate from their function in controlling Wnt signalling ,9. One oIn order to explore this mechanism, we asked whether Drosophila E-APC  might have a structural role at AJs. If so, E-APC would be expected to be stably associated with AJs, similarly to the structural components of the adhesive complex. As in mammalian epithelia ,16, the We used the GAL4 system to express GFP-E-APC throughout the embryo, and found that its subcellular distribution is very similar to that of endogenous E-APC in fixed embryos. In particular, GFP-E-APC is concentrated underneath the plasma membrane in apicolateral regions of embryonic epithelial cells Fig. ; Fig. 2bNext, we conducted fluorescence recovery after photobleaching (FRAP) experiments in live embryos, to examine how stably GFP-E-APC is associated with adherens junctions. We bleached the fluorescence in a defined square centred over the junctional region of an epithelial cell with a short laser pulse, and examined the recovery of the fluorescence within this square over time Fig. . This reWe also conducted FRAP experiments with structural AJ components, namely E-cadherin-GFP, Armadillo-GFP and α-catenin-GFP. In these cases, we can only recover a small fraction of the initial fluorescence within the time frame of the experiment Fig. . This alWe conclude that E-APC is significantly more mobile than the structural AJ components. This suggests that E-APC shuttles either within the cortex, along the zonula adherens, or that it shuttles from the cytoplasm to the plasma membrane . IntereIn late embryonic stages, GFP-E-APC forms striking patches underneath the apical plasma membrane of epidermal cells that are in the process of forming denticle extrusions Fig. . These sIt has been reported that E-APC and Armadillo are required for anchoring mitotic spindles in the cortex of dividing blastoderm cells in the early Drosophila embryo . We cannWe also expressed GFP-E-APC in eye imaginal discs, to examine its subcellular distribution within a larval epithelial sheet. We thus noticed striking puncta of green fluorescence within the axons of the optic stalk that connects these discs to the larval brain Fig. . These pshaggy/zeste white3, or sgg, mutants) therefore mimic constitutive and sustained Wingless signalling [sgg mutant embryos [sgg mutant embryos, the levels of membrane-associated GFP-E-APC are also noticeably reduced . Taken together with our results from the sgg mutants, this suggests that the kinetic association of GFP-E-APC with the plasma membrane is unaffected by Wingless signalling.The subcellular distribution of E-APC and its accumulation at the adherens junctions is unchanged in other mutants of the Wingless signalling pathway  were gezw3M11-1 and dshv26 mutant embryos lacking maternal and zygotic gene function were generated as described [sgg mutants (identified with an RFP-marked X chromosome [sgg mutant embryos were hand-picked (from timed egg collections) under the dissecting microscope, and separated into GFP-positive and GFP-negative embryos; unfertilised embryos were discarded.escribed . We did romosome ). For WeAntibody staining of fixed embryos and analysis by confocal microscopy were described previously . The folThe following primary and secondary antibodies were used for Western blotting: rabbit anti-E-APC ; mouse mFor live imaging, embryos were dechorionated in 50% bleach for 1–2 minutes and washed. Embryos were transferred to a moistened black filter (Schleicher and Schüll). Embryos were adhered to coverslips with heptane glue, made by mixing heptane and clear sellotape (Sellotape Ltd). Embryos were mounted in Voltalef oil (10S). For short term imaging (<30 minutes), embryos were mounted on a glass slide with small coverslips as supports. For longer term imaging, e.g. for bleaching of pre-denticle patches, embryos were mounted in oil and placed on Bio-foil gas permeable membrane (Sartorius Ltd) mounted on a perspex frame .FRAP experiments were performed using a Bio-Rad Radiance confocal microscope with a 40× NA 1.3 objective lense. Imaging was performed with a 488 nm argon laser at 5% laser power and the following confocal settings: iris at 4 mm, 50% gain, zoom 10, scan speed 500 lps, box size 512 × 512 pixels. These conditions were found to give minimal photobleaching over the observed time.For each FRAP experiment, a pre-bleach image was recorded by selecting a focal plane and taking a Z-series, consisting of 3 0.5 μm steps either side of the desired focal plane (from -1.5 μm to +1.5 μm). The LaserSharp software was used to define several regions of interest (ROIs) for bleaching. A maximum of one bleach ROI was placed in any cell and several cells were always left unbleached. Typically, 3–5 ROIs were bleached in one field of view on one embryo. These regions were bleached at 100% laser power (scanning at 500 lps). 10 bleach scans were found to produce the best results for all constructs. After bleaching, a Z-series was recorded every 15 seconds for 5 minutes. At the time of these experiments, the LaserSharp software did not contain a function for performing this type of 4D bleaching experiment. This problem was overcome by manually switching between imaging and bleaching settings and manually saving pre bleach images and starting the time course. As a result of this, there was usually a 30–60 second delay between the pre-bleach image and the post-bleach images.Data sets were analysed with the Bio-Rad LaserPix software. For each time point, the total pixel intensity distribution was compared to the pre-bleach image to select the corresponding region. The two images were then compared by eye to confirm that they did correspond to the same focal plane. The coordinates for the bleach ROIs were used to accurately locate the bleach spots on the pre bleach image, and the mean fluorescence intensity for each ROI was calculated. Several equivalent sized ROIs were also placed on unbleached cells to measure any change in fluorescence due to photobleaching or movement.To track movement of the cells, an acetate sheet was placed over the computer monitor and each ROI was marked on it as well as the shapes of the cells surrounding it. By aligning the sheet with the appropriate cell shapes, the ROI could be appropriately positioned for each time point. This process was used to position each ROI on the appropriate image for each time point.Once all ROIs had been placed on the image, the mean fluorescence intensities were calculated for each ROI, and their positions were saved on a copy of the image See Fig . Data waData sets were discarded for any of the following reasons. First, if movement of the embryo in the Z axis took the sample outside the range of the Z-series in any time point. Second, if movement in the X/Y axis was sufficient to move significant numbers of the bleach boxes outside of the observed region. Third, if an ROI ever left the field of view, all data points for that ROI was discarded. Fourth, all data sets were discarded if the intensities of the control ROIs changed dramatically at any point in the experiment, or showed a large general increase or decrease.Pre-denticle structures were bleached in a similar manner to junctional E-APC described above.GFP-E-APC was expressed in eye imaginal discs by the GAL4 system, using the driver line GMR.GAL4 (described in FlyBase). Eye discs and brains were dissected from crawling third instar larvae in PBS. Eye discs were teased away from the brain and inverted to reveal the optic stalk. Whole disc/brains were mounted in a drop of PBS under a cover slip, supported by two smaller cover slips. Each disc was observed for no more than 30 minutes.FRAP experiments were performed using a Bio-Rad Radiance confocal microscope and Bio-Rad LaserSharp software, using the 100× NA 1.4 objective lens. A narrow strip was bleached across the whole field of view by adjusting the size of the scanning area. These experiments were performed before a FRAP program was available for LaserSharp so bleaching was performed manually, leading to somewhat variable intervals between each stage of the experiment. The region was bleached with the 488 nm line of an argon laser for approximately 20 scans. Time courses were recorded after each bleaching experiment for 5 minutes.sgg mutant embryos, including the Western blots; M.B. directed the study, helped with the microscopy and drafted the manuscript. All authors read and approved the final manuscript.A.C. developed and conducted most of the FRAP experiments; J.M. completed the analysis of GFP-E-APC in live and fixed FRAP of GFP-E-APC in early embryonic epithelial cells. Example of a FRAP experiment of GFP-E-APC, as described in Figure 3.Click here for fileFRAP of GFP-E-APC in the larval optical stalk. Example of a FRAP experiment of GFP-E-APC, as described in Figure 6.Click here for file


Are short synthetic peptides the key to developing cancer vaccines? And what are the obstacles in the way? CTLs specifically recognize and lyse targets through the interaction of T cell receptors (TCRs) on the surface of the T lymphocyte with protein fragments (peptides) presented on the surface of target cells, in association with major histocompatibility complex (MHC) class I molecules. When a particular CTL interacts with a target cell, it rapidly divides to form a clonal population of T cells with the identical TCR.CD8Townsend and colleagues first elucidated the molecular basis of target cell recognition by CTLs in 1985 [+ T cell response  that is specific for several of these antigens. The development of such responses, however, requires a large tumor load, occurs late in the disease, and probably does not cause the efficient destruction of the tumor cells [Furthermore, before Boon and colleagues cloned the first antigen recognized by tumor-reactive CTLs in 1991 , it was or cells . Thus, aUnfortunately, some synthetic peptides, including some corresponding to immunodominant epitopes (those which cause the biggest part of the immune response) from tumor antigens, only seem to bind MHC class I molecules with medium to low affinity and/or are recognized by specific T cells with relatively low avidity. These characteristics are the likely cause of the poor immune reaction generated by these peptides . One strSolinger and colleagues were the first to describe antigen variants producing T cell responses that were stronger than those elicited by the parental sequences . Some hePLoS Medicine [In a study by Lee and colleagues in this issue of Medicine , despiteMedicine ,11. In aMedicine , into anIt is increasingly clear that even the smallest alteration in the structure of the MHC peptide complex can result in significant changes in which TCRs are selected after vaccination. Thus, manipulating the immune T cell repertoire in vivo through the use of heteroclitic tumor antigen peptide variants could be harder than anticipated. As the field moves rapidly towards the use of new vaccine adjuvants with high immunogenic potential , reasses


There is a continued need to develop more effective cancer immunotherapy strategies. Exosomes, cell-derived lipid vesicles that express high levels of a narrow spectrum of cell proteins represent a novel platform for delivering high levels of antigen in conjunction with costimulatory molecules. We performed this study to test the safety, feasibility and efficacy of autologous dendritic cell (DC)-derived exosomes (DEX) loaded with the MAGE tumor antigens in patients with non-small cell lung cancer (NSCLC).This Phase I study enrolled HLA A2+ patients with pre-treated Stage IIIb (N = 4) and IV (N = 9) NSCLC with tumor expression of MAGE-A3 or A4. Patients underwent leukapheresis to generate DC from which DEX were produced and loaded with MAGE-A3, -A4, -A10, and MAGE-3DPO4 peptides. Patients received 4 doses of DEX at weekly intervals.Thirteen patients were enrolled and 9 completed therapy. Three formulations of DEX were evaluated; all were well tolerated with only grade 1–2 adverse events related to the use of DEX (injection site reactions (N = 8), flu like illness (N = 1), and peripheral arm pain (N = 1)). The time from the first dose of DEX until disease progression was 30 to 429+ days. Three patients had disease progression before the first DEX dose. Survival of patients after the first DEX dose was 52–665+ days. DTH reactivity against MAGE peptides was detected in 3/9 patients. Immune responses were detected in patients as follows: MAGE-specific T cell responses in 1/3, increased NK lytic activity in 2/4.Production of the DEX vaccine was feasible and DEX therapy was well tolerated in patients with advanced NSCLC. Some patients experienced long term stability of disease and activation of immune effectors Vaccine immunotherapy as an approach to cancer treatment has evolved over the last 10 years as the basic biology of the immune response has been elucidated. Tumor-associated antigens that are capable of eliciting cytotoxic T cell responses have been identified. Among the most frequently expressed across many malignancies are the MAGE antigens, originally described in melanoma, but expressed by other tumors including non-small cell lung cancer (NSCLC) . Immune in vitro and eradicate established murine tumors [A variety of cells have been shown to release exosomes including dendritic cells , B lymphe tumors . In vitre tumors ,18.Human dexosomes are enriched in the components necessary to function as an antigen-presenting entity. Extensive electron microscopic and protein characterization has revealed that dexosomes contain a specific set of proteins that differentiate them from other plasma membrane derived vesicles (such as apoptotic cells for example), including MHC class I and II molecules and CD1a, b, c, d molecules, as well as the co-stimulatory molecule CD86 and several tetraspan proteins (CD9, CD37, CD53, CD63, CD81, and CD82) [Anosys unpublished data, ,20].Dexosomes have been demonstrated to participate in antigen presentation in the following way ,22. Afteex vivo dendritic cell approach for eradication of advanced cancer. Purified dexosomes were shown to be effective in both suppressing tumor growth and eradicating an established tumor in this model. Furthermore, the effect of the dendritic cell-derived dexosome was greater than that of the dendritic cell from which it was produced [Dexosomes have demonstrated significant antitumor activity in a mouse tumor model, suggesting that the use of dexosomes derived from dendritic cells may result in improved efficacy relative to the produced . TherefoWe performed this study to investigate the safety, feasibility, and efficacy of administering autologous dexosomes loaded with tumor antigens (subsequently referred to as DEX) to patients with advanced NSCLC. We also evaluated the immunologic responses in selected patients and monitored the clinical outcomes.This phase I clinical protocol was approved by the Duke University Medical Center Institutional Review Board and conducted in compliance with the Helsinki Declaration and under an IND from the United States Food and Drug Administration held by Anosys Corporation. All subjects provided written informed consent. Patients were eligible for enrollment if they had histologically confirmed, unresectable Stage III A or B or Stage IV NSCLC, were HLA A*0201 positive, at least 18 years of age, and had adequate organ function and a Karnofsky performance status of at least 80%. Patients were required to have been treated with at least one prior standard chemotherapy regimen and have measurable disease. In addition, patients were required to have tumor expressing MAGE A3 or MAGE A4. To avoid performing repeat biopsies, this was achieved by detecting MAGE A3 or MAGE A4 expression in peripheral blood tumor cells by RT-PCR using established methods.The main exclusion criteria were: prior therapy within 4 weeks of the leukapheresis, CNS disease, history of autoimmune disease, concurrent use of systemic steroids, presence of HIV infection or acute or chronic viral hepatitis B or C. Pregnant or lactating women were also excluded.2 atmosphere in the presence of 50 ng/mL GM-CSF (Immunex, Seattle, Washington) and 10 ng/mL of IL-4 (Schering-Plough, Kennilworth, NJ). On the 7th day of culture, the supernatant of the resulting dendritic cell preparation was harvested, filtered, and concentrated. Dexosomes were then isolated by ultracentrifugation on a D2O/sucrose cushion. As described in table Dexosomes were manufactured from peripheral blood mononuclear cells (PBMCs) as previously described . Briefly13 MHC class II molecules in a volume of 3 mL (divided into twoinjections given at two sites on opposite sides of the body) as a combination of subcutaneous (90% of the volume) and intradermal (10%) injections weekly for 4 weeks. No retreatment was allowed. Vital signs were monitored for 1 hour after each injection. Clinical responses were assessed by RECIST criteria. CT scans of the chest through the upper abdomen were obtained at baseline, 1 month following the last dose of DEX and every 3 months after last dose of DEX for 1 year, but scans to confirm responses were not required in this phase I study. All surviving patients have been followed every 6 months for assessment of vital status.Patients were enrolled into three cohorts (A,B,C) that varied in the method of MHC Class I peptide loading and concentration as described in Table Prior to the initial leukapheresis and 1 week after the last dose of DEX, the following peptides were injected intradermally, in addition to the standard recall antigen panel of Candida, Mumps, and tetanus: MAGE-A3(112–120), MAGE-A4(230–239), MAGE-A10(254–262), and MAGE-A3(247–258), each at 10 μg in 0.1 mL saline. The diameter of the induration and erythema was measured 48 hours following the peptide injection.in vitro stimulation of PBMCs with autologous DCs pulsed with the MAGE-A3(112–120), MAGE-A4(230–239), and MAGE-A10(254–262). The number of spots (interferon-gamma-secreting T cells) per 20,000 responding PBMC was reported. The background number of spots against an irrelevant antigen was subtracted from the number of spots for the experimental conditions.Immune response was evaluated at baseline and 1 week following last dose of DEX with cryopreserved PBMCs obtained by leukapheresis. The ELISPOT assay was performed by ImmunoSite, Inc. (Pittsburgh, PA) according to previously reported methods  using boNK cells were isolated from cryopreserved PBMCs using an NK Cell Isolation Kit (Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer's instruction. NK cell purity was checked by flow cytometry using anti-CD3-FITC, anti-CD45-PerCP, and anti-CD56-APC antibodies (BD Bioscience, San Jose, CA). Isolated NK cells and NK cells activated for 40 hours by IL-2 (Proleukin, Chiron) 600 Units/ml were incubated at various effector to target rations with chromium-51 labeled K562 cells, an NK target, for 4 hours at 37°C and cytotoxicity was assessed by the amount of radiolabeled chromium released. Cytotoxicity was calculated as follows: percentage of target cell lysis = 100 × (counts per minute (cpm) of experimental release - cpm of spontaneous release) / (cpm of maximum release - cpm of spontaneous release).The primary endpoints of this study were safety and feasibility, with secondary endpoints of clinical and immunologic response rates. The incidence, type and severity of adverse events were recorded during the study treatment through 30 days following the last dose of DEX. Descriptive statistics were used to present the data. Adverse events were coded using MEDDRA version 5.0. Survival and time to progression were measured from the date of the first injection to the date of documented disease progression or death. For patients who progressed, the time to disease progression was determined by the interval from the first injection+ 1 day to the last evaluation of disease staging. For patients who did not progress or die during the two year follow up period, the time of disease progression and survival was determined by the interval between the first dose of DEX and the date of last evaluation of disease staging + 1 day and concatenated with the '+' sign.Thirteen patients,  median age 62 years (range 44–72 years) with unresectable pretreated Stage III or IV NSCLC were enrolled. The median time from original diagnosis to study entry was 9.9 months (range 2–61 months) and the median Karnofsky score of the patient population was 80% (range 80–100). DEX therapy was administered to 9/13  patients. Of the 9 dosed patients, 5 patients had Stage IV and 4 patients had Stage IIIB disease. Six patients had stable disease and 3 patients had progressive disease at study entry. Two patients had squamous cell carcinoma, 4 patients had adenocarcinoma, 2 patients had large cell carcinoma, and in 1 case the histological type was not reported. All patients had received prior chemotherapy (median number of cycles: 6.5, range: 3–30), 6/9 patients had received prior radiotherapy and 4/9 patients had prior surgery for cancer treatment. Four patients did not receive DEX for the following reasons: manufacturing failure in 2 cases (DU39, DU83), one of whom had received chemotherapy 13 days prior to leukapheresis and one of whom (DU39) also had rapid disease progression at the time of leukapheresis; delay in shipment in one instance (DU14); and, rapid disease progression prior to planed dosing with DEX in one case (DU44). The characteristics of all dosed patients are listed in Table 2 (see separate file for Table 2).14 (range 4.1 × 1012 to 9.1 × 1014). This quantity of dexosomes in our advanced NSCLC patients is similar in quantity to that generated from healthy donors .The dose of DEX that was selected corresponded to the maximum dose that could be achieved from healthy donors. We confirmed that this dose could be generated in all but two patients with NSCLC. The mean dexosome generation consisted of a total class II number of 3.14 × 10The DEX immunotherapy was generally well tolerated without evidence of serious toxicity. The most frequently reported adverse events causally related to the use of DEX were mild (Grade 1–2) in severity and included: Injection site reactions (erythema, contusion, induration and edema) in 8 patients; flu like syndrome (1 patient); and, peripheral edema and pain in the arm (1 patient). There were no significant organ or laboratory toxicities attributable to the vaccine. No autoimmune reactions were observed.All 9 dosed patients underwent DTH testing with individual tumor-associated peptides prior to and following all doses of DEX. There was no DTH response to the specific peptide antigens prior to DEX therapy. Three patients (DU06, DU24 and DU49) had a positive response of at least 5 mm erythema or induration in the longest dimension 48 hours after skin testing with one of the MAGE peptides. Specifically, DU06 had 5 mm induration and erythema with MAGE-A4(230–239), DU24 had 6 mm induration and erythema with MAGE-A10(254–262) and DU49 had 5 mm induration and erythema with MAGE-A3(112–120), respectively.The peptide-specific immune response to MAGE and CMV was analyzed using ELISPOT in 5 of 9 dosed patients (DU24, DU49, DU50, DU63, DU81). One patient (DU49) exhibited detectable increases in T cell precursor frequency to MAGE-A10(254–262) following in-vitro stimulation (an increase of 12 MAGE-A10-specific cells/20,000 responders). Assays for DU50 and DU63 could not be completed because of poor viability. Robust responses to anti-CD3 and to the control peptide CMV pp65 were observed in DU24, DU81, and DU49, but no MAGE-specific responses were detected.Since most patients did not exhibit a significant increase in antigen-specific T cell activity, we hypothesized that regulatory influences such as CD4+CD25+ regulatory T cell populations might inhibit augmentation of the T cell response. In 2/3 patients who had analyzable specimens available, an increase in CD4+CD25+ T cells as a percentage of CD4+ T cells was observed following completion of DEX therapy when compared with baseline values . The small number of samples available for this analysis precludes any conclusions but does suggest that CD4+CD25+ T cell analyses should accompany future studies of DEX immunotherapy.During the study, new data from Escudier et al (manuscript submitted) suggested that the immunologic activity of DEX might be due to activation of NK cells. We therefore explored the hypothesis that NK cells may be activated following DEX therapy. This was not planned as part of the initial analysis and therefore specimens of PBMC were limiting in all but 4 patients (DU05, DU08, DU24, DU50). Although there was no consistent change in NK percentage before and after immunization . The possible role of negative regulatory mechanisms was suggested by the presence of elevated levels of CD4+CD25+ regulatory T cells following immunization in some patients.We observed increases in systemic immune responses against MAGE by DTH reactivity in 3/9 patients who had no reactivity to the MAGE peptides prior to immunization and activation of NK cells, but found minimal increases in antigen-specific T cell activity in An intriguing immunologic observation was the increase in NK activity following immunization in 2/4 patients analyzed. Although DEX are intended to activate antigen-specific, MHC-restricted T cell responses, it is possible that cytokines released in response to DEX therapy could cause activation of NK cells or that DEX could directly activate NK cells. DEX therapy may stimulate both innate and adaptive arms of the immune response and thereby provide a rationale for maximizing the anti-tumor effect of this approach, even in cases where tumors have lost Class I antigens, a common finding as cancers become more advanced . Indeed,Despite the small sample size and the fact that 3/9 dosed patients had disease progression at the time of initiation of DEX treatment, we observed prolonged disease stabilization in some patients. Large clinical trials in patients with advanced NSCLC have generally reported median time to progression of 3–5 months in patients with advanced NSCLC treated with systemic chemotherapy regimens -30. The DEX therapy was well. Immune activation and stability of disease was observed in some immunized patients with advanced NSCLC.Michael Morse received funding from NIH 5R21CA89957-02. Additionally, portions of this study were funded by Anosys, Inc.Nancy Valente, Revati Shreeniwas, Mary Ann Sutton, Alain Delcayre, Di-Hwei Hsu, and Jean BernardLe Pecq held stock and were employees in Anosys,H. Kim Lyerly was a consultant for Anosys, Inc.MAM was the principal investigator of the study and oversaw all aspects including protocol development, patient management, data collection and analysis, and manuscript preparation.JG enrolled patients to the study and managed their care and participated in data analysis.Takuya Osada performed the NK assays and analyzed the data.SK enrolled patients to the study and managed their care.AH performed in vitro immunologic assays and analyzed the data.TMC oversaw the immunologic analyses performed at Duke University and analyzed the data.NV, RS, and MAS oversaw development of the protocol, data collection and analysis, and manuscript preparation.AD developed and oversaw the MAGE screening for patient eligibility.D-H H oversaw portions of the immunologic analysis and data analysis.J-B L provided scientific direction regarding generation of the dexosomes, protocol development, and data analysis and manuscript preparationHKL provided consultation on immunologic assay development All authors read and approved the final manuscript.Table 2 (DOC) presents the remainder of clinical and immunological data from all patientsClick here for file


Caenorhabditis elegans is a little less lonely than the rest of us—it is a self-fertile hermaphrodite, which as a larva makes and stores sperm before switching to egg production for the remainder of its lifespan.  A sister species, C. briggsae, is also hermaphroditic, but phylogenetic evidence suggests the last common ancestor of the two species had a female/male mode of reproduction. This raises the question of how the sex determination mechanisms, which must have evolved independently, differ between the two species. In this issue, Sudhir Nayak, Johnathan Goree, and Tim Schedl show that a crucial difference lies in the activities of two genes.The nematode C. elegans, the early period of sperm production is controlled by multiple proteins, two of which are the focus of this study, the RNA-binding protein GLD-1 (encoded by the gene gld-1) and the F-box-containing protein FOG-2 (encoded by the gene fog-2). Together, they repress translation of a gene, tra-2, by binding to its messenger RNA. This allows another gene, fem-3, to transiently masculinize the larval germline to produce sperm.In C. elegans and C. briggsae, Schedl and colleagues found they share 30 out of 31 sex determination genes, but not fog-2. More surprisingly, they found that the role of gld-1 in sex determination is opposite in the two species. When C. elegans is deprived of gld-1, would-be hermaphrodites produce only oocytes. But when C. briggsae is deprived of gld-1, would-be hermaphrodites produce only sperm. Thus, the authors conclude, the control of hermaphrodite spermatogenesis is fundamentally different in the two species.Comparing the genomes of C. elegans genome, the authors showed that fog-2 arose from a gene duplication event after the C. elegans–C. briggsae split, which occurred approximately 100 million years ago. Since then, its final exon, which codes for the C-terminal end of the protein, has undergone rapid evolution. The authors also show that this is the “business end” of the protein for its interaction with GLD-1, suggesting that the divergence of C. elegans and C. briggsae sex determination pathways resulted, in part, from FOG-2's new interaction with GLD-1.By further examining the fog-2 is in C. elegans is still unclear. The authors speculate that it may recruit additional factors onto the gld-1/tra-2 mRNA complex, increasing efficiency of translation repression. Much remains to be discovered about C. briggsae sex determination as well. The authors suggest that additional genetic differences promoting self-fertility are likely to have accumulated since the two species diverged, which may act to strengthen the male–female germline switching signal. Investigation of this possibility may shed more light on how hermaphroditism operates in these two species, and how a developmental pathway controlling sex determination can evolve.Exactly what the role of


Arabidopsis.A novel approach for modeling gene-regulatory networks, based on graphical Gaussian modeling, is used to create a network for the isoprenoid biosynthesis pathway in  Arabidopsis thaliana, we detect modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways. Genes of downstream pathways also fit well into the network. We evaluate our approach in a simulation study and using the yeast galactose network.We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in  The analysis of genetic regulatory networks has received a major impetus from the huge amounts of data made available by high-throughput technologies such as DNA microarrays. The genome-wide, massively parallel monitoring of gene activity will increase the understanding of the molecular basis of disease and facilitate the identification of therapeutic targets.To fully uncover regulatory structures, different analysis tools for transcriptomic and other high-throughput data will have to be used in an integrative or iterative fashion. In simple eukaryotes or prokaryotes, gene-expression data has been combined with two-hybrid data  and phenIn higher organisms, however, little is known about regulatory control mechanisms. As a first step in reverse engineering of genetic regulatory networks, structural relationships between genes can be explored on the basis of their expression profiles. Here, we focus on graphical models ,6 as a pGraphical models are powerful for a small number of genes. As the number of genes increases, however, reliable estimates of conditional dependencies require many more observations than are usually available from gene-expression profiling. Furthermore, because the number of models grows super-exponentially with the number of genes, only a small subset of models can be tested . Most imSome of these problems may be circumvented by restricting the number of possible models or edges ,13 or byAs an alternative approach to modeling genetic networks with many genes, we propose not to condition on all genes at a time. Instead, we apply graphical modeling to small subnetworks of three genes to explore the dependence between two of the genes conditional on the third. These subnetworks are then combined for making inferences on the complete network. This modified graphical modeling approach makes it possible to include many genes in the network while studying dependence patterns in a more complex and exhaustive way than with only pairwise correlation-based relationships.Saccharomyces cerevisiae.For an independent validation of our method, we compare our modified graphical Gaussian modeling (GGM) approach with conventional graphical modeling in a simulation study. We show at the end of the Results section that our approach outperforms the standard method in simulation settings with many genes and few observations. For a further evaluation with real data, we apply our approach to the galactose-utilization data from  to detecArabidopsis thaliana . The grIsoprenoids serve numerous biochemical functions in plants: for example, as components of membranes (sterols), as photosynthetic pigments (carotenoids and chlorophylls) and as hormones (gibberellins). Isoprenoids are synthesized through condensation of the five-carbon intermediates isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). In higher plants, two distinct pathways for the formation of IPP and DMAPP exist, one in the cytosol and the other in the chloroplast. The cytosolic pathway, often described as the mevalonate or MVA pathway, starts from acetyl-CoA to form IPP via several steps, including the intermediate mevalonate (MVA). In contrast, the plastidial  pathway involves condensation of pyruvate and glyceraldehyde 3-phosphate via several intermediates to form IPP and DMAPP. Whereas the MVA pathway is responsible for the synthesis of sterols, sesquiterpenes and the side chain of ubiquinone, the MEP pathway is used for the synthesis of isoprenes, carotenoids and the side chains of chlorophyll and plastoquinone. Although both pathways operate independently under normal conditions, interaction between them has been repeatedly reported ,17.Reduced flux through the MVA pathway after treatment with lovastatin can be partially compensated for by the MEP pathway. However, inhibition of the MEP pathway in seedlings leads to reduced levels in carotenoids and chlorophylls, indicating a predominantly unidirectional transport of isoprenoid intermediates from the chloroplast to the cytosol ,18, althTo gain more insight into the cross-talk between both pathways at the transcriptional level, gene-expression patterns were monitored under various experimental conditions using 118 GeneChip (Affymetrix) microarrays . To construct the genetic regulatory network, we focused on 40 genes, 16 of which were assigned to the cytosolic pathway, 19 to the plastidal pathway and five encode proteins located in the mitochondrion. These 40 genes comprise not only genes of known function but also genes whose encoded proteins displayed considerable homology to proteins of known function. For reference, we adopt the notation from  .The genetic-interaction network among these genes was first constructed using GGM with backward selection under the Bayesian information criterion (BIC) . This waet al. [This phenomenon had already been observed in a simulation study by Friedman et al.  and may i and j when their pairwise correlation is not the effect of a third gene. Each edge has therefore a clear interpretation.As described in more detail in Materials and methods, our approach aims at modeling dependencies between two genes by taking the effect of other genes separately into account. In the hope of identifying direct co-regulation between genes, an edge is drawn between two genes θij, which describe the probability for an edge between i and j in a latent random graph. One main benefit of the second version over full graphical models is that one can easily test on a large scale how well additional genes can be incorporated into the network. This allows the selection of additional candidate genes for the network in a fast and efficient way.We have developed two versions of our method: a frequentist approach in which each edge is tested for presence or absence; and a likelihood approach with parameters A. thaliana and by attaching 795 additional genes from 56 other metabolic pathways to it. Figure We have applied and tested our modified GGM approaches by constructing a regulatory network of the 40 genes in the isoprenoid pathways in DXR, MCT, CMK and MECPS are nearly fully connected  or the probability parameters θ (latent random graph approach). Figures θ-values as a function of the absolute pairwise correlation.In contrast to the conventional graphical model, we could now identify the connection between θ-values for all gene pairs in the network with and without attaching these additional genes . After addition, if for a gene pair i, j, θij dropped by more than 0.3, it was assumed that the dependence between i and j could be 'explained' by some of the additional genes.Following construction of the isoprenoid genetic network, 795 additional genes from 56 metabolic pathways were incorporated. Among these were genes from pathways downstream of the two isoprenoid biosynthesis pathways, such as phytosterol biosynthesis, mono- and diterpene metabolism, porphyrin/chlorophyll metabolism, carotenoid biosynthesis, plastoquinone biosynthesis for example. Using the second version of our method, that is, the latent random graph approach, we compared s Figure  and 4c. k, GGMs with genes i, j and k were formed. A gene k was considered to explain the dependency between i and j when an edge between i and j was not supported in the GGM, that is, when the null hypothesis ρij|k = 0 was accepted in the corresponding likelihood ratio test. k was then taken to 'attach well' to the gene pair i, j.To find these genes out of all additionally tested candidates i, j whose parameter θij dropped by more than 0.3, we obtained a list of well-attaching genes. Genes appearing significantly frequently in these lists of well-attaching genes were assumed to connect well to the complete genetic network. We tested for significance by randomization: For each gene pair i, j, a randomized list of well-attaching genes was formed with the same size as the original gene list. To explore which pathways attach significantly well to the MVA and MEP pathways, the portion of genes from each of the 56 pathways was summed over all gene pairs i, j. These sums were then compared for the originally attached genes and the sums of randomly attached genes in 100 datasets.Thus, for each gene pair Table 13C] glucose, which revealed that sterols were formed via the MVA pathway, while plastidic isoprenoids (β-carotene, lutein, phytol and plastoquinone-9) were synthesized using intermediates from the MEP pathway [13C]- and [2,3,4,5-13C4]1-deoxy-D-xylulose into β-carotene, lutein and phytol indicated that the carotenoid and chlorophyll biosynthesis pathways proceed from intermediates obtained via the MEP pathway [On a metabolic level, our results are substantiated by earlier labeling experiments using [1- pathway . Moreove pathway .In contrast, a close connection between the MVA and the MEP pathways could not be detected. This suggests that cross-talk on the transcriptional level may be restricted to single genes in both pathways.DXR, MCT, CMK, GGPPS11, GGPPS12, AACT1, HMGR1 and FPPS1, supporting the hypothesis that AACT1 and HMGR1 are involved in communication between the MEP and MVA pathways.In a further analysis step, we examined which gene pairs the four identified pathways (plastoquinone, carotenoid, chlorophyll, and phytosterols) attached to. Genes from the plastoquinone pathway were predominantly linked to the genes DXPS2, HDS, HDR, GGPPS11, DPPS2 and PPDS2, whereas the chlorophyll biosynthesis appears to be related to DXPS2, DXPS3, DXR, CMK, MCT, HDS, HDR, GGPPS11 and GGPPS12. Genes from the phytosterol pathway attach to FPPS1, HMGS, DPPS2, PPDS1 and PPDS2.Genes from the carotenoid pathway attached to σij|, where σij denotes the pairwise correlation between genes i and j.Incorporating 795 additional genes into the isoprenoid genetic network would not have been feasible with standard GGMs as the graphical model would have had to be newly fitted for each additional gene. Also, hierarchical clustering would not have been an appropriate tool for detecting the similarities in the correlation patterns between the two isoprenoid metabolisms and their downstream pathways. Figure The positions of the MVA pathway genes (labeled 'm') and the non-mevalonate pathway genes (labeled 'n'), respectively, are shown to the right of the figure. The symbol + represents the positions of genes from the downstream pathways identified in Table For an independent comparison between the modified and the conventional GGM approaches, we simulated gene-expression data with 40 genes and 100 observations. This simulation framework corresponds to the data for isoprenoid biosynthesis and is thought to be only exemplary at this point. An extensive simulation study is currently underway and will be presented elsewhere.k edges decays as a power law ∝ kγ-. For metabolic and protein networks, γ is usually estimated to range between 2 and 3, which would result in very sparse networks with fewer edges than nodes in our simulation settings. To allow for denser networks, we generated 100 graphs each for γ = 0.5, 1.5 and 2.5. With 40 nodes, these graphs then comprised 88.3, 49.7 and 30.5 edges on average. For each edge, the conditional dependence of the corresponding gene pairs was modeled with a latent random variable in a structural equation model as described in [N(0,Σ).Following recent findings on the topology of metabolic and protein networks ,30, we sribed in . FurtherThe performance of the graphical modeling approaches was monitored using the rate of true and false positives in receiver operator characteristics (ROC) curves  had GAL4p-binding sites. These genes were also identified in [After incorporating all yeast genes into our network of the nine galactose genes, 13 genes were found to attach significantly well. Among these, ified in . This reified in , we did Analysis of gene expression patterns, for example cluster analysis, often focuses on coexpression and pairwise correlation between genes. Graphical models are based on a more sophisticated measure of conditional dependence among genes. However, with this measure, modeling is restricted to a small number of genes. With a larger set of genes, it is rather difficult to interpret the model and to generate hypotheses on the regulation of genetic networks.In our approaches, in the search for significant co-regulation between two genes all other genes in the model are also taken into account. However, the effect of these genes is examined separately, one gene at a time. Because of this simplification, modeling can include a larger number of genes. Also, each edge has a clear interpretation, representing a pair of significantly correlated genes whose dependence cannot be explained by a third gene in the model. Our frequentist method has a resemblance to the first two steps in the SGS and PC algorithms . By restBy using a Gaussian model, we can only reveal linear dependencies between genes. For handling nonlinearities, gene-expression profiles should be discretized and analyzed in a multinomial framework. In principle, it should be straightforward to adopt our approach to a multinomial model. Because we focused on linear dependencies, we have not addressed this problem so far.A. thaliana, we constructed a genetic network and identified candidate genes for cross-talk between both pathways. Interestingly, both positive and negative correlations were found between the identified candidate genes and the corresponding pathways. AACT1 and HMGR1, key genes of the MVA pathway, were found to be negatively correlated to the module of connected genes in the MEP pathway. This suggests that in the experimental conditions tested, AACT1 and HMGR1 may respond differently (than the MEP pathway genes) to environmental conditions, or that they possess a different organ-specific expression profile. In either case, expression within both groups seems to be mutually exclusive. On the other hand, a positive correlation was identified between IPPI1 and members of the MVA pathway, suggesting that this enzyme controls the steady-state levels of IPP and DMAPP in the plastid when a high level of transfer of intermediates between plastid and cytosol takes place.For the isoprenoid biosynthesis pathways in Although we have considered only metabolic genes in this analysis, the method can be extended to identify genes encoding other types of proteins belonging to the same transcription module. In fact, transcription factors and other regulator proteins, as well as structural proteins such as transporters, are often found in the same expression module . Our resSimilarly, the expression of genes in the phytosterol pathway appears to be influenced by genes from the MVA pathway. For the downstream regulation of plastoquinone biosynthesis, however, genes from both pathways seem to be involved. This finding is in agreement with the dual localization of enzymes from the plastoquinone pathway in either the plastid or the cytosol. The regulation of this pathway may therefore depend on processes happening on the metabolic and regulatory level in both compartments.A. thaliana can only be made on the basis of additional knowledge and biological experiments. At this stage, the use of domain knowledge has provided some means of network validation. As genes from the respective downstream pathways were significantly more often attached to the isoprenoid network than were candidate genes from other pathways, we are quite confident that our method can grasp the modularity in the dependence structure within groups of genes and also between groups of genes. Such modularity would have been difficult to detect by standard graphical modeling or clustering.We have shown in a simulation study that for gene-expression data with many genes and few observations, the modified GGM approaches have performed better in recovering conditional dependence structures than conventional GGM. However, a final evaluation of our inferred network for the isoprenoid biosynthesis pathways in q be the number of genes in the network, and n be the number of observations for each gene. The vector of log-scaled gene-expression values, Y = (Y1,...,Yq) is assumed to follow a multivariate normal distribution N(μ,Σ) with mean μ = (μ1,...,μq) and covariance matrix Σ. The partial correlation coefficients ρij|rest, which measure the correlation between genes i and j conditional on all other genes in the model are calculated asLet ωij, 1, j = 1,...,q are the elements of the precision matrix Ω = Σ-1.where ρij|rest can be estimated and tested against the null hypothesis ρij|rest = 0 [i and j is drawn if the null hypothesis is rejected. Since the estimation of the partial correlation coefficients involves matrix inversion, estimators are very sensitive to the rank of the matrix. If the model comprises many genes, estimates are only reliable for a large number of observations.Using likelihood methods, each partial correlation coefficients rest = 0 . An edgeCommonly, the modeling of the graph is carried out in a stepwise backward manner starting from the full model from which edges are removed consecutively. The process stops when no further improvement can be achieved by removal of an additional edge. The final model is usually evaluated by bootstrapping to exclude spurious edges in the model.i, j be a pair of genes. The sample Pearson's correlation coefficient σij is the commonly used measure for coexpression. For examining possible effects of other genes k on σij, we consider GGMs for all triples of genes i, j, k with k ≠ i, j. For each k, the partial correlation coefficient ρij|k is computed and compared to σij. If the expression level of k is independent of i and j, the partial correlation coefficient would not differ from σij. If on the other hand, the correlation between i and j is caused by k since k co-regulates both genes, one would expect ρij|k to be close to 0. Here, we use the terminology, that k 'explains' the correlation between i and j.Let ρij|k values in a biologically and statistically meaningful way, we define an edge between i and j if ρij|k ≠ 0 for all remaining genes k. In particular, if there is at least one k with ρij|k = 0, no edge between i and j is drawn since the correlation between i and j may be the effect of k. Our approach can be implemented as a frequentist approach in which each edge is tested for presence or absence or alternatively, as a likelihood approach with parameters θij, which describe the probability for an edge between i and j in a latent random graph.In order to combine the different i, j and all remaining genes k, p-values ρij|k are obtained from the likelihood ratio test of the null hypothesis ρij|k = 0. In order to combine the different p-values ρij|k, we simply test whether a third gene k exists that 'explains' the correlation between i and j. For this purpose, we apply the following procedure:For the gene pair i, j form the maximum p-value(1) For each pair pij,max = max{pij|k, k ≠ i, j}.pij,max according to standard multiple testing procedures such as FDR [(2) Adjust each h as FDR .pij,max value is smaller than 0.05, draw an edge between the genes i and j; otherwise omit it.(3) If the adjusted q(q - 1))/2 in the model. Implicitly, multiple testing over all genes k is also involved in step 1. However, because the maximum over all pij|k is considered, a multiple testing correction is not necessary.The correction for multiple testing in step 2 is carried out with respect to the possible number of edges  be a sample of n observations. For estimating θ, we maximize the log-likelihood L(θ) = logPθ(y) via the EM-algorithm [The frequentist approach has the disadvantage that a connection between two genes lgorithm .θt be a current estimate of θ. Further, let g be the unobserved graph encoded as an adjacency matrix with gij ∈ {0,1} depending on whether there is an edge between genes i and j or not. In the E-step of the EM-algorithm, the conditional expectation of the complete data log-likelihood is determined with respect to the conditional distribution p(g|y,θt),Let By assuming independence between edges, Equation (1) becomesand further, after replacingand summing out Equation 2 we findP(gij = 1|y,θt) and P(gij = 0|y,θt) at the right side of Equation (3) are approximated by the statistical evidence of edge i, j in GGMs with genes i, j and k. As we only want to estimate the effect of k on the correlation between i and j, we distinguish only the two cases whether k is a common neighbor of i and j, for example, gik = 1 and gjk = 1 or not. When k is a common neighbor, we test ρij|k ≠ 0 versus ρij|k = 0. When k is not a common neighbor of i and j, we test σij ≠ 0 versus σij = 0 for the pairwise correlation coefficients instead. Thus, we obtain and  are p-values of the corresponding likelihood ratio tests. After replacing Equation (4) in Equation (3), the M-step of the EM-algorithm, that is the maximization of Eθ(logPθ(g)|y,θt) with respect to θ, leads to an iterative updating scheme θ t → θt+1 withwhere θ as followsIn summary, we determine the probability parameters i, j, compute P(ρij|k ≠ 0) and P(σij ≠ 0) for all genes k ≠ i, j.(1) For gene pairs θ0, apply iteratively Equation (5) until the error |θt+1 - θt| drops below a prespecified value, for example 10-6.(2) Starting with i, j in step 1 of the analysis, the partial correlation coefficients ρij|k are not only computed and tested for genes k in the model but also for the additional candidate genes. However, the iteration in step 2 is not extended to these candidate genes. In other words, θij is only iteratively updated in Equation (5) if both genes i, j are in the original model. For candidate genes k, θik and θjk are kept fixed at a prespecified value, for example 1, and are not re-estimated in the EM-iteration process.Our latent random graph approach also enables us to fit a large number of additional genes into a constructed genetic network. In this case, for a gene pair θ. If these candidates have an effect on the correlation between i and j, θij will decrease. Thus, by comparing the original network with the network inferred from allowing for additional genes in step 1, we can determine which candidate genes lower the θ-values and, accordingly, fit well into the network.This outline introduces a second level into the modeling process. At the first level, the network between the original genes is constructed. At the second level, we test how additional candidate genes influence the parameters Additional data is available with the online version of this paper. Additional data files The gene expression values of the isoprenoid genesClick here for additional data fileThe gene expression values of the 795 genes from other pathwaysClick here for additional data fileA more detailed description of the microarray data Click here for additional data fileThe correlation pattern of the 40 isoprenoid genes.Click here for additional data file


Carbon dioxide fixation bioprocess in reactors necessitates recycling of D-ribulose1,5-bisphosphate (RuBP) for continuous operation. A radically new close loop of RuBP regenerating reactor design has been proposed that will harbor enzyme-complexes instead of purified enzymes. These reactors will need binders enabling selective capture and release of sugar and intermediate metabolites enabling specific conversions during regeneration. In the current manuscript we describe properties of proteins that will act as potential binders in RuBP regeneration reactors.We demonstrate specific binding of 3-phosphoglycerate (3PGA) and 3-phosphoglyceraldehyde (3PGAL) from sugar mixtures by inactive mutant of yeast enzymes phosphoglycerate mutase and enolase. The reversibility in binding with respect to pH and EDTA has also been shown. No chemical conversion of incubated sugars or sugar intermediate metabolites were found by the inactive enzymatic proteins. The dissociation constants for sugar metabolites are in the micromolar range, both proteins showed lower dissociation constant (Kd) for 3-phosphoglycerate (655–796 μM) compared to 3-phosphoglyceraldehyde (822–966 μM) indicating higher affinity for 3PGA. The proteins did not show binding to glucose, sucrose or fructose within the sensitivity limits of detection. Phosphoglycerate mutase showed slightly lower stability on repeated use than enolase mutants.in-situ removal of sugar intermediate metabolites for forward driving of specific reactions in enzyme-complex reactors.The sugar and their intermediate metabolite binders may have a useful role in RuBP regeneration reactors. The reversibility of binding with respect to changes in physicochemical factors and stability when subjected to repeated changes in these conditions are expected to make the mutant proteins candidates for  Sustained increase of atmospheric CO2 has already initiated a chain of events with unintended ecological consequences [One of the potential use of sugar and sugar intermediate binding proteins and binders of intermediate sugar metabolites derived from microbes is in new and expanding area of environmental biotechnology particularly in carbon dioxide fixation bioprocess reactors ,2. Accelequences -7. The requences -7.2 at emission source/site into concatenated carbon compounds [2 fixation is converted into 3-phosphoglycerate [2  in 11 steps in a series of bioreactors [Biotechnological process with recombinant catalytic proteins offer contained handling of carbon dioxide and could be one method of abatement of carbon dioxide pollution ,9. Recenompounds . Such a ompounds . After aompounds  where aclycerate ,9. We hareactors .2 emitters leaving only the stationary source of emission to be controlled using this technology [For starting up the process, however, a different scheme was used to generate RuBP from D-glucose rather than from 3-PGA . The linchnology ,9. Thesechnology ,2. In thThe four reactors harboring enzymatic complexes/mixtures  replace Sugar and their intermediate metabolites binding proteins derived from microbial and other sources despite being used for various applications ,16 have The inactive PGDM mutant ,18 was rPaper chromatography was used for initial qualitative detection of sugars or sugar metabolites Figure  subseque2+ ions was very effective for release of ligands from enolase as well as from PGDM  produce a number of binding and metabolizing proteins for sugar and other intermediate metabolic products of sugar metabolism pathways. Complex conversion necessitated using binding entities to enhance reactor performance as well as obtaining converted compounds in purified state.2 fixation bioprocess, three modular reactors enables continuous fixation. The first module is Rubisco reactor where CO2 is fixed onto RuBP and converted into 3PGA, the second involves regeneration of ATP which acts as a cofactor for subsequent process and the third being RuBP regeneration [2, which is constructed before first module [In the biocatalytic COneration ,9. Recent module . Howevert module ,9. In ma2 cycling seems to have less effect on protein stability with respect to binding and release and could be more suitable for use in larger reactors. While these criteria are suitable for use of the proteins for in-situ separation of sugar metabolites in reactors, but high micromolar dissociation constants (655–966 μM) suggest the moderate or even low binding strength. Finding inactive enzymes with high binding affinity or engineering them for this purpose will improve their utility.In the present report we demonstrate binding without chemical modification of 3PGA and 3PGAL by inactive yeast mutant enzymatic proteins PGDM and enolase. The binding seems to be specific as the proteins were not found to bind other sugars (glucose, fructose or sucrose in mixtures that were subjected to incubation). The binding is reversible with respect to pH and EDTA and proteins retain activity even after repeated use. The MgCl2, with three changes in buffer solution. Enolase mutants were subjected to ion exchange chromatography for further purification on Acta prime protein purification system (Amersham Pharmacia Biotech, CA) using Q Sepharose column at a flow rate of 0.5 ml /minute at 4°C. Protein was eluted using a NaCl gradient (0.1 M to 0.3 M), about 95 fractions containing 8.6 ml each were collected. The proteins eluted in about conductivity equivalent of 0.1–0.15 M NaCl (fractions 5 to 9). This purified protein was temporarily stored at 4°C and used for subsequent experiments. Proteins were estimated using Bradford's method [Purified yeast PGDM and recombinant cultures for inactive yeast enolase mutants (S39A and H159A) were obtained from Dr. J. Nairn and Prof. J. M. Brewer as research gift respectively. The enolase mutants were purified following suitable modifications of published protocols ,21. Bries method  using BS3 is dissolved. The dried chromatogram was then dipped through the silver reagent and allowed to air dry for 10 min and subsequently dipped into ethanolic sodium hydroxide and again allowed to air dry. After the spots are visible, the paper was soaked in dilute (5 mg/l) sodium thiosulfate for 1 minute and rinsed in tap water. The last step dissolves the dark background and allows obtaining a permanent record. For staining with alphanapthol, paper chromatogram was dipped in the following solution: 1 per cent w/v alpha-napthol, 10 per cent v/v orthophosphoric acid and distilled water to make up the volume. The air-dried paper or TLC plate was heated for a few minutes at 85°C in a water-jacketed oven for color development.Paper and thin layer chromatography was performed to demonstrate that the purified mutant proteins lacked any sugar modification capacity. Sugar mixtures in appropriate concentration were incubated with Proteins (PGDM or enolase mutants S39A and H159A) for up to 16 hours. At the end of incubation (every 2 hours), the mixtures were centrifuges and the aliquots from experimental mixtures were spotted onto the filter paper chromatograms  or on TLC plates. The TLC analyses were performed on Plastic backed 20 cm × 20 cm Silica Gel 60 F254 plates with 0.2 mm layer thickness (Merck). After spotting with an applicator the samples were air-dried and placed in a TLC tank (27 cm × 24 cm × 7 cm) containing the solvent system. For both chromatography, the spots were air-dried and the chromatograms were dipped in the solvent system [60% v/v n-propanol/30% v/v conc.ammonia/10% v/v distilled water] and allowed to run for 5 hours. The chromatograms were removed from the solvent system and subjected to staining. Three different staining techniques were used to detect sugars, ammonium molybdate, silver nitrate and alpha-naphthol staining ,24. For The dissociation constants for protein-sugar binding was estimated by measurements of area in chromatograms. For this purpose covalently immobilized protein A sepharose beads (Pharmacia Biotech, CA) was used. The proteins were immobilized on protein A using Amino link kit . The known concentration of protein was incubated at room temperature (25°C) with varying concentration of sugar in the range of 1 μM to 1 mM in a 100 μl fixed volume. At the end of incubation (10 min), the mixture was centrifuged at 10000 × g and an aliquot of supernatant was spotted and chromatogram (TLC) was developed. A similar mixture but with BSA coupled beads served as control. Area calibration using varying concentration of sugar with a fixed aliquot spot volume was recorded under identical conditions. From the measurement of area in control and experimental set the free sugar was calculated such bound sugar is control minus sugar left in experimental set. The experimental data was used to draw a Scatchard type plot from where dissociation constant was calculated, represented by P as free protein, L as ligand and PL as the ligand-bound-protein, the dissociation constant is defined as Kd = [Pfree] [Lfree]/ [PL]. The dissociation constant Kd values for PGDM and S39A enolase for 3PGA and 3PGAL were calculated using experimental data using MS excel program.DB and DB carried out purification of enolase mutants, MK and SM carried out the binding assays. MTS, SC, AG and VG participated in design of the study and performing analyses, MTS also helped to draft the manuscript. SKB conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


Translational Research (TR) provides a set of tools and communication context for scientists and clinicians to optimize the drug discovery and development process. In the proceedings of a Princeton conference on this timely topic, the strengths and needs of this developing field were debated. Outcomes and key points from these discussions are summarized in this article which covers the topics of defining what we mean by translational research , ways in which to engender the TR mindset and embed it in organizations such as the pharmaceutical industry in order to optimize the impact of available technologies (including imaging methods), the scientific basis and under-pinnings of TR including genomics knowledge, information sharing, as well as examples of application to drug discovery and development. Importantly, it should be noted that collaborations and communications between the stakeholders in this field, namely academia, industry and regulatory authorities, must be strengthened in order for the promise of TR to be delivered as better therapies to patients. Successful drug development requires satisfying a matrix of domains from relevance to the disease and the drug-ability of the target through feasibility and convenience of drug delivery, demonstration of favorable benefit-risk profile in order to achieve to a drug label that reflects physician and patient acceptance. Herein lies a key role for TR in helping to navigate this journey.There are many challenges facing pharmaceutical companies in the post-genome era not least of which is declining productivity and innovation. Not surprisingly, there is agreement between Industry, Academia and Regulatory communities that the drug discovery and development process needs to change in order to meet the future needs of patients with effective and desirable drugs. A key part of the strategic solution is to leverage the application of TR principles and practices, which if implemented will go a long way towards addressing the challenge posed by FDA's Critical Path Initiative   that hosted a small group of clinical and basic science researchers and individuals from the pharmaceutical industry. Among the topics discussed were how to define "Translational Research", how to expedite the transfer of pre-clinical findings to influence development plans, how to select biomarkers to ensure support for decisions, how new strategies can be effectively translated to practical tactics, and what team players and collaborations are necessary to conduct successful TR. Success factors identified include: Identification and validation of novel drug targets, development of robust and validated assays to screen drug leads for safety and potential efficacy in humans, and the identification of suitable patients for expedited but informative trials.In order to promote discussion on this topic, the International Quality and Productivity Center (IQPC) organized a in vivo measurements and leverage preclinical models that more accurately predict drug effects in humans, TR itself can be defined in many ways. At its core however, is the thesis that information gathered in animal studies can be translated into clinical relevance and vice versa, thus providing a conceptual basis for developing better drugs. It could in fact be argued that the designation of a special term or definition for TR might be unnecessary or even misleading. Historically the term was assigned to create awareness and advocacy for the general public, clinicians and scientific communities, and especially for the government and other private sponsors [While the goal of TR is to implement sponsors  in this Whatever the precise definition TR, it should serve as a forum to find a "common language" for clinicians and scientist in navigating the complexities of basic scientific approaches, data analysis and information processing. It clearly implies the need for an intensive training for scientists and clinicians in multiple disciplines to acquire expertise and experience to conduct TR. For the purposes of this symposium, the scope of translational research was defined as the application of scientific tools and methods to drug discovery and development. This can be achieved by integrating information concerning a) exposure (pharmacokinetics), b) biological activity (pharmacodynamics including safety profiles) delineating differences between species and leading to the validation of target and mechanism biomarkers, and c) outcomes leading to an understanding of efficacy and safety between species and ultimately to the qualification or linkage of biomarkers to clinical outcome (for a fuller discussion on Biomarkers and Surrogate Endpoints see definitions in ). Thus TTherefore, in taking a pragmatic or operational rather than a definitional approach, a key to a successful translation of non-human research to human clinical trials lies in the choice of biomarkers. While biological pathways tend to be homologous across species and more so than pharmacokinetic parameters such as absorption and clearance, animal models themselves have a poor record of predicting human disease outcome. Nonetheless, biomarkers are the key for prediction of biological activity if not drug efficacy in humans. At least three types of biomarkers can be identified: (1) target biomarkers measuring the interactions between a drug and its target; (2) mechanism biomarkers measuring their downstream biological effects and (3) outcome biomarkers that reflect efficacy and safety. A second dimension can also be ascribed to biomarkers to help drug developers assign risk assessment to such approaches. This sub-classification links desired utility to points on a risk continuum; e.g. low, medium and high, in which 'low' describes a biomarker applied solely to animal models for example for selecting compounds for progression into humans, whereas 'medium' is association with utility for some aspects of early clinical profiling of efficacy and safety including across species correlation, and 'high' is associated with reproducibility and qualification as an outcome or even regulatory tool in humans.Additionally, TR itself undergoes an evolution from pathfinding (hypothesis generating) to discovery research, to development, and finally to application. Each of these operational phases is amenable to being evaluated or supported by biomarkers, either for the definition of objectives, proof of principle or in assessing risk and feasibility. Consequently the right choice of biomarkers can help drive decision-making and lower the costs and cycle-time for progression of a new drug from the bench into the clinic. In summary, whatever the definition or classification ultimately used, in practical terms translational tools should be developed and applied on a "fit for purpose" basis with prior assessment and agreement of attendant risks.Traditional Research and Development  paradigms have accentuated the boundaries between the territories of discovery and development worlds and have not been conducive to bridging key transition points. This is unfortunate since the development world tends to lag behind advances made in discovery, a point recognized by FDA in launching the Critical Path Initiative . In brieWhile advances have been made on streamlining forward progression of R&D through organizational linkages, what has not happened to the same degree is a bi-directional flow of information, namely flow of information from the clinic  back into the hands of the discovery scientist. The consequence of this is that the biological models used to qualify drug candidates may fail to be predictive of subsequent drug responses in the clinical setting. Thus a practical outcome of TR is to improve the overall probability for technical success (POS) in drug development.in silico modeling, but also the need to empower key scientists and clinicians with the task of enhancing the prediction and iteration learning cycle. Since there are different organizational solutions for embedding the TR mindset within an organization, a key element is to provide TR expertise to drug development teams. Furthermore, innovation and productivity values are critically linked through information exchange. Rapid iteration (e.g., learn-confirm cycles) and transfer of knowledge gained from prototype development experience will enable more rapid compound redesign against the highly desired target and be reflected as enhanced innovation. On the productivity side, the tools outlined in the Critical Path Initiative [Consequently the next paradigm for R&D optimization depends not only on leveraging emerging technologies such as pathway mapping and itiative , once efThe journey however starts at understanding the scientific foundations of physiology and pathophysiology, thus providing a rational linkage between the gene, its expressed product, disease expression and ultimately outcome. The discipline of biomarker identification and development as mentioned previously encompasses these principles and is a core tool in the TR scientist's armamentarium.a priori agreed performance characteristics, such that there is agreement on the utility of the marker.Biomarkers (which are not necessarily Surrogate endpoints and few are in fact) are key tools for escorting the drug candidate from the bench to the bedside and back. That is they can be both animal "diagnostic" as well as human "diagnostic" tools. A key implementation tool is therefore to identify early on which biomarkers may be of value and to study these in the relevant animal models, that is, specifically include them in preclinical screening paradigms, as well as identify their role (e.g., go / no go decision factors) in the clinical development plan. Biomarkers, which include imaging techniques as well as protein and genetic markers, may fulfill several roles in R&D from compound screening and selection through dose justification, decision-making and risk mitigation, however the key is to overtly link them to the discovery and development plans with There are many good examples of the value or non-value of preclinical models in predicting subsequent human response and safety. The journey from preclinical experience to the clinic is a well-worn one (e.g., Xenograft testing for oncology), albeit without the degree of overall predictiveness we would desire. On the other hand there is a marked paucity of examples in which clinical experience or observation was translated back into a legitimate drug target and discovery effort (e.g. Viagra). Thus, a major opportunity lies in both developing more sensitive and specific animal models of disease (e.g. knock in/out) as well as fully leveraging novel clinical observations. At the same time it is the ultimate validation in the clinic that counts, and rapid feedback of that information will allow the conditional probabilities and learning cycle to be enhanced. By enabling these principles through organizational and cultural change, the impact of TR will be determined by direct impact on high-quality mid-phase transitions as well as reduced cycle-times and resource burdens.The era of genome-scale biology has seen an increase in, and production of, vast amounts of biological data together with an extensive increase in biology-oriented databases. To make the best use of biological databases and the knowledge they contain, different kinds of information from different sources must be integrated in ways that make sense to biologists. A major component of the integration effort is the development and use of annotation standards such as ontologies. Ontologies offer a conceptualization of domains of knowledge and facilitate both communication between researchers and the use of domain knowledge by computers for multiple purposes. Therefore, the Gene Ontology (GO) project was founded in 1998, in an attempt to provide consistent descriptors for gene products, in different databases; and to standardize classifications for sequences and sequence features. Since then, the GO Consortium has grown to include many databases, including several of the world's major repositories for plant, animal and microbial genomes . DespiteIn the past, biological processes and the underlying genes, proteins, other molecules and environmental factors, have been studied separately more than on an integrated basis. The challenge, however, for future research on human disease is to understand not only the mechanistic basis, but also the underlying dynamics of gene product expression. Thus, biological research should emphasize the analysis of pattern of gene expression over individual measurements.Molecular Function describes activities, such as catalytic or binding activities, at the molecular level, e.g. kinase activity. (2) Biological Process describes biological goals accomplished by one or more ordered assemblies of molecular functions, e.g. 'cell death' can have both subtypes, such as 'apoptosis', and subprocesses, such as 'apoptotic chromosome condensation'. (3) Cellular Component describes locations, at the levels of subcellular structures and macromolecular complexes, e.g. 'nuclear inner membrane' with the synonym 'inner envelope' [GO has been developed to predict behavior of entire biological systems, being assigned to three aspects: (1) nvelope' .The powerful use of comparative gene expression analysis in human disease was exemplified with a recent study on gene expression profiles of gastric cancer patients and their correlation to survival. Leung et al.  have sho provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences.In summary, the application of mathematical models and computer simulations to analyze gene expression profiles and to compare complex data sets of various origins may provide new insight into the pathogenesis of cancer progression and metastasis. The Gene Ontology (GO) project Cancer vaccines are promising therapeutics designed to elicit immune responses against antigens expressed by tumor cells. However, vaccines that have worked well in preclinical models have not translated into consistent responses in the clinic. Since vaccines are comprised of multiple components, multiple immunological endpoints are used to identify the least effective vaccine components in cancer patients. Post-clinical research strategies are subsequently designed with a focus on improving the least effective vaccine components.To improve the performance of cancer vaccines in the clinics, which are traditionally judged by clinical endpoints, novel endpoints and biomarkers are needed to assist in understanding why cancer vaccines are not working. From clinic to bench, a systematic strategy is needed for pre-clinical optimization that addresses vaccine limitations identified in the clinics; and from bench to clinic, performance criteria need to be established for a follow-up clinical study. After gathering the therapeutic options, testing has to be prioritized on the basis of: a) already available data; b) availability of the therapeutic modality; c) models and assays available internally; d) turnaround time; and e) on the patent landscape.Prioritization and rapid evaluation of novel therapeutics will decrease the turnaround time and facilitate decision-making. However, several tools are needed to make this a reality. For example, complex therapeutic strategies require biomarker or even surrogate endpoints from clinical trials to direct development of second-generation therapeutics. The rapid qualification and choice of surrogate endpoints should be based on knowledge gathered by an "early-stage therapeutic opportunities database". This comprehensive database should include data on therapeutic targets, models, assays and published results and indeed the plethora of new therapeutic strategies in preclinical stages can only be managed by accessing informative databases. Moreover, pre-clinical compound optimization can be facilitated by establishing quantitative endpoints of short duration and lastly go / no go decision points must be established for surrogate endpoints and clinical responses in animal models.However, several current issues of scientific basis also have to be addressed, such as the importance of clinical surrogate endpoints, the relevance of animal models, lack of concordances between assays, and the lack of concordance between surrogate endpoints and the clinical response, in order to improve cancer vaccine development strategies., a non-profit organization conducting more than 100 clinical trials and treating 7000 cancer patients yearly.A core principle of TR revolves around validation of targets, biomarkers and treatment modalities in humans. These activities and drug development itself cannot be undertaken without patients or clinical data. How TR can be integrated in a multi-center, multi-cultural organization involving patient accrual from more than 38 different countries worldwide, for the research and treatment of cancer can be exemplified by EORTC Advancement in basic science and immunology and an overwhelming revolution of biotechnology have changed the targets and endpoints in cancer trials from the mere assessment of cytotoxicity to defined mechanisms for potential anti-tumor effect. That is in the era of "targeted therapies" molecular therapeutics are now being designed to target "strategic" checkpoints that underlie the malignant phenotype. The challenges to be met are: 1) dealing with new compounds affecting novel molecular targets, 2) innovation in design and analysis of clinical trials, 3) cooperation between translational researchers and network of clinical investigators and 4) informed patients. The major concerns in conducting clinical trials are rising costs coupled with efficacy rates as low as 5% in cancer patients, making signal to noise detection not only difficult but expensive. and storage of tissue is de-centralized at the institute where it is collected. To assure equal quality of tissue, which in outcome of scientific experiments can be compared, standardization of the collection and storage methods is fundamental. Therefore, protocols for storage, retrieval and tracking of tissues will be standardized and implemented in all participating laboratories.The need for research on tumor tissue requires the set-up of tumor banks and the associated administrative burden often discourages young oncologists. EORTC established a tumor bank comprising real tissue samples but including a "virtual review" by pathologists. This ensures the availability of a well-categorized and prognostically evaluated collection of primary tumors and allows an online-searchable bank for researchers to access. Indeed the tumor bank harbors paraffin-embedded tumors, as well as frozen tumor tissues Access to the tumor bank allows screening of many available tumor samples for the expression of molecular targets and will help to unravel novel biomarkers for diagnosis and treatment. Such access will allow us to overcome the missed opportunities due to lack of tissue collection in clinical trials, which could have allowed better pre-screening of potentially responsive patients based on expression of certain biomarkers e.g., expression of bcl-2, and the treatment of target positive patients may have ensured a better clinical outcome in this target class.The challenge of testing promising new modalities for the cure of disease that had shown efficacy in experimental models lies in a lack of understanding of the underlying mechanism, heterogeneity of human genetic backgrounds and a lack of suitable controls in human studies. Strategies have been developed at the NIH for the global monitoring of patients by studying, with high-throughput technology, the systemic effects of treatments as well as their effect within the target organ. For this bedside to bench effort, a systematic sampling of human tissues of local (site of immunogen application), systemic (circulation) and peripheral (tumor site) origin needs to be standardized to ensure high quality of samples avoiding degradation of protein, RNA and DNA. This TR approach allows experimental studies in human samples during or after therapy through amplification of transcripts for analysis of minimal sample tissue, and the application of monitoring techniques for genetic profiling. Further, proteomic-based approaches allow following the kinetics of the mechanism of actions of therapeutics.Studying the effects of treatment in a bedside to bench approach provides markers for the characterization of disease process and/or testing hypotheses generated by experimental models. Therefore, the nature of research in the clinical setting can realistically be described as 'hypothesis generating", rather than 'hypothesis driven', through a discovery-driven approach. Analysis of the genetic background can reveal polymorphism of genes involved in immune reactions, such as cytokines and their receptors, which might influence the outcome of immunological interventions in different patient populations .Analysis of disease heterogeneity can be approached by transcriptional analysis, through linear amplification of RNA and subsequent analysis by cDNA array and transcriptome array, and/or functional protein analysis, through protein characterization by proteomics ,7. NumerDespite the many obstacles in monitoring therapeutic effect in early phase clinical trials and the lack of hypothesis, the scientific significance of these trials should be reviewed assuming that the new treatment will not be beneficial. Desirable outcomes include learning about the disease process, the primary goal of the therapy and the reasons for its failure. Another concern should be if we have taken advantage of the patient population accrued at least to learn something, although independent of treatment, about the disease process itself. Clinical trials should therefore be designed, within ethical constructs, to look at questions beyond the ones related solely to treatment. This can be achieved through (1) establishment of libraries of relevant clinical samples for immediate or future studies, (2) prospective collection of data into a consistent format, and (3) tight link between clinical and scientific data.Developing better therapies for chronic inflammatory diseases also exemplifies the use of the latest technological advances in TR such as proteomics, transcriptomics and cellomics, for identification or application of biomarkers. Chronic inflammation frequently precedes the development of cancer in adults, such as lung , esophagSeveral factors, such as: 1) the nuclear protein HMGB1, 2) the S100 family of molecules; 3) purine metabolites, ATP, AMP and uric acid, and 4) heat shock proteins have emerged as relevant mediators or "endogenous damage or danger signals" to recruit inflammatory cells, to promote wound healing and associated stromagenesis, angiogenesis; and ultimately to modulate immune functions .Current attempts for cancer therapy focused on vaccination to antigenic targets or application of cytokines have resulted in measurable anti-tumor reactivity in the blood; however, these therapies have mostly failed to show a correlation with tumor outcome or progression. Therefore, to more completely understand and identify factors assessing tumor death could inform and drive the development of more effective biological therapies for cancer patients. Sample acquisition in the blood includes serum/protein collection for Seldi-Tof mass spectrometry; and the collection of cells for microarray, proteomics, and high contents screening via cellomics. Protein chip Seldi-Tof MS has been already successfully used to discriminate serum expression profiles in various cancer types -13. The Beside proper study design, the models chosen to perform data classification and to estimate classification errors are highly critical for the complex data analysis. The identification of diagnostic markers for cancer, or markers to identify responders vs. non-responders to therapy requires systematical analysis of healthy vs. diseased, then of benign inflammatory disease vs. malignant cancer. Thus, methods to perform statistical analysis (e.g. permutation, randomization) are powerful, intuitive and provide an objective position from which to assess results. To handle these complex data analysis problems, the University of Pittsburgh has formed the Pittsburgh Supercomputing Center (PSC) headed by Dr. Arthur W. Wetzel, in a joint effort with Carnegie Mellon University and Westinghouse Electric Company, and is to date the most powerful open-resource computer available.There are many examples of the value of weaving molecular imaging into Investigational New Drug Development. At the same time, the scale of the initial investments required vs. perceived benefits may not gain the necessary support of decision makers for application into development programs. There is a clear need to educate on the power and limitations of nuclear imaging techniques within the context of enhancing new drug development. Within this context, a primary goal for TR is to emphasize the cultural and operational shifts required of various stakeholders including academia, in order to better partner with industry.The term imaging covers a range of available techniques, including discovery autoradiography, small animal imaging (PET and MRI), traditional anatomical imaging (Ultrasound, MRI, CT), functional imaging (MRI, PET, SPECT) and many new tracers are available as are techniques with increased sensitivity to enable micro-doing studies (AMS) . NuclearFirstly demonstrating drug penetration into the tissue of interest and co-localization or binding with the intended target through receptor occupancy (e.g., labeled ligand displacement), including describing dose vs. target occupancy curves remains a key objective an done used frequently in early clinical research. A second objective involves the quantification of a compound's pharmacokinetic (PK) profile using radio-labeled compound, an analysis that can be performed on a region of interest basis e.g. to assess time on target as well as potential therapeutic benefits vs. side effects. Additionally, imaging can be used to quantify pharmacodynamic (PD) effects of drug action and their relationship to administered dose. In combination, PK/PD information thus derived can be used to select a dose with which to test the clinical hypothesis or help quantify the therapeutic index. From a TR perspective all these techniques can be applied in the discovery and preclinical phases to facilitate compound selection and optimization as well as in the clinical phases.A key question emerges in applying these technologies: "How best to get it done" and the debate of internal imaging centers vs. external networks and academic relationships quickly emerges. On balance, it is clear that there is not one ideal solution here rather in general a collaborative approach between industry and academia is recommended. As a consumer of medical imaging, industry is a critical player in driving innovation and the paradigm shift towards more frequent yet appropriate utilization. However, a partnership approach ultimately generates better value and cost-effectiveness for the Imaging discipline as a whole.TR is an approach to foster communication between the scientific community and clinical practitioners. To maximize the value this can bring requires that public and governmental education has to be improved in order to leverage understanding and advocacy. There are many benefits to be accrued from this, not least of which being for the patient that is waiting for meaningful therapeutic advances. New drugs have to be developed fast and show effect on the right target at the earliest possible stage of development in order for industry to become more innovative and productive and medicines to be less expensive.Amongst other specific aspects required, are the strengthening of educational opportunities for physician scientists to help prepare them to conduct effective TR. At the same time, discovery science should be conducted by scientists who have been trained in relevant disciplines including cell biology and pharmacology as well as molecular biology. This in turn requires grant support for TR-related projects. Specifically, young scientific investigators should have more access to grants from governmental bodies and foundations in order to conduct research on clinical samples. This funding is largely in the hands of government leadership. Other points for disseminated education include the availability of a plethora of tools available to conduct and advance TR and development opportunities that include high quality clinical sample collection.Dr. Marincola founded the Journal of Translational Medicine, an Open Access, peer-reviewed online journal, so that more therapeutic insights may be derived from new scientific ideas – and vice versa .Lastly, since TR is information intensive, considerable efforts are required to provide accessible databases and share knowledge. To help ameliorate this gap and provide access to information derived from human experimentation and to optimize the communication between clinicians and scientist, In conclusion, TR represents a team effort, since no single constituency can be fluent in all aspects, and thus a concerted effort is needed amongst translational researchers to convince stakeholders and legislators of the need to support TR efforts, and thus maximize its potential.