Jean Eudes Dazard

Design principle of gene expression used by human stem cells: implication for pluripotency

Human embryonic stem cells (ESC) are undifferentiated and are endowed with the capacities of self‐renewal and pluripotential differentiation. Adult stem cells renew their own tissue, but whether they can transdifferentiate to other tissues is still controversial. To understand the genetic program that underlies the pluripotency of stem cells, we compared the transcription profile of ESC with that of progenitor/stem cells of human hematopoietic and keratinocytic origins, along with their mature cells to be viewed as snapshots along tissue differentiation. ESC gene profiles show higher complexity with significantly more highly expressed genes than adult cells. We hypothesize that ESC use a strategy of expressing genes that represent various differentiation pathways and selection of only a few for continuous expression upon differentiation to a particular target. Such a strategy may be necessary for the pluripotency of ESC. The progenitors of either hematopoietic or keratinocytic cells also follow the same design principle. Using advanced clustering, we show that many of the ESC expressed genes are turned off in the progenitors/stem cells followed by a further down‐regulation in adult tissues. Concomitantly, genes specific to the target tissue are up‐regulated toward mature cells of skin or blood.

Vascular gene expression and phenotypic correlation during differentiation of human embryonic stem cells.

The study of the cascade of events of induction and sequential gene activation that takes place during human embryonic development is hindered by the unavailability of postimplantation embryos at different stages of development. Spontaneous differentiation of human embryonic stem cells (hESCs) can occur by means of the formation of embryoid bodies (EBs), which resemble certain aspects of early embryos to some extent. Embryonic vascular formation, vasculogenesis, is a sequential process that involves complex regulatory cascades. In this study, changes of gene expression along the development of human EBs for 4 weeks were studied by large‐scale gene screening. Two main clusters were identified—one of down‐regulated genes such as POU5, NANOG, TDGF1/Cripto (TDGF, teratocarcinoma‐derived growth factor‐1), LIN28, CD24, TERF1 (telomeric repeat binding factor‐1), LEFTB (left–right determination, factor B), and a second of up‐regulated genes such as TWIST, WNT5A, WT1, AFP, ALB, NCAM1. Focusing on the vascular system development, genes known to be involved in vasculogenesis and angiogenesis were explored. Up‐regulated genes include vasculogenic growth factors such as VEGFA, VEGFC, FIGF (VEGFD), ANG1, ANG2, TGFβ3, and PDGFB, as well as the related receptors FLT1, FLT4, PDGFRB, TGFβR2, and TGFβR3, other markers such as CD34, VCAM1, PECAM1, VE‐CAD, and transcription factors TAL1, GATA2, and GATA3. The reproducibility of the array data was verified independently and illustrated that many genes known to be involved in vascular development are activated during the differentiation of hESCs in culture. Hence, the analysis of the vascular system can be extended to other differentiation pathways, allocating human EBs as an in vitro model to study early human development. Developmental Dynamics 232:487–497, 2005.

Functional interactions between the LRP6 WNT co-receptor and folate supplementation

Crooked tail (Cd) mice bear a gain-of-function mutation in Lrp6, a co-receptor for canonical WNT signaling, and are a model of neural tube defects (NTDs), preventable with dietary folic acid (FA) supplementation. Whether the FA response reflects a direct influence of FA on LRP6 function was tested with prenatal supplementation in LRP6-deficient embryos. The enriched FA (10 ppm) diet reduced the occurrence of birth defects among all litters compared with the control (2 ppm FA) diet, but did so by increasing early lethality of Lrp6(-/-) embryos while actually increasing NTDs among nulls alive at embryonic days 10-13 (E10-13). Proliferation in cranial neural folds was reduced in homozygous Lrp6(-/-) mutants versus wild-type embryos at E10, and FA supplementation increased proliferation in wild-type but not mutant neuroepithelia. Canonical WNT activity was reduced in LRP6-deficient midbrain-hindbrain at E9.5, demonstrated in vivo by a TCF/LEF-reporter transgene. FA levels in media modulated the canonical WNT response in NIH3T3 cells, suggesting that although FA was required for optimal WNT signaling, even modest FA elevations attenuated LRP5/6-dependent canonical WNT responses. Gene expression analysis in embryos and adults showed striking interactions between targeted Lrp6 deficiency and FA supplementation, especially for mitochondrial function, folate and methionine metabolism, WNT signaling and cytoskeletal regulation that together implicate relevant signaling and metabolic pathways supporting cell proliferation, morphology and differentiation. We propose that FA supplementation rescues Lrp6(Cd/Cd) fetuses by normalizing hyperactive WNT activity, whereas in LRP6-deficient embryos, added FA further attenuates reduced WNT activity, thereby compromising development.

Novel Urinary Protein Biomarkers Predicting the Development of Microalbuminuria and Renal Function Decline in Type 1 Diabetes

OBJECTIVE To define a panel of novel protein biomarkers of renal disease. RESEARCH DESIGN AND METHODS Adults with type 1 diabetes in the Coronary Artery Calcification in Type 1 Diabetes study who were initially free of renal complications (n = 465) were followed for development of micro- or macroalbuminuria (MA) and early renal function decline (ERFD, annual decline in estimated glomerular filtration rate of ≥3.3%). The label-free proteomic discovery phase was conducted in 13 patients who progressed to MA by the 6-year visit and 11 control subjects, and four proteins (Tamm-Horsfall glycoprotein, α-1 acid glycoprotein, clusterin, and progranulin) identified in the discovery phase were measured by enzyme-linked immunosorbent assay in 74 subjects: group A, normal renal function (n = 35); group B, ERFD without MA (n = 15); group C, MA without ERFD (n = 16); and group D, both ERFD and MA (n = 8). RESULTS In the label-free analysis, a model of progression to MA was built using 252 peptides, yielding an area under the curve (AUC) of 84.7 ± 5.3%. In the validation study, ordinal logistic regression was used to predict development of ERFD, MA, or both. A panel including Tamm-Horsfall glycoprotein (odds ratio 2.9, 95% CI 1.3–6.2, P = 0.008), progranulin (1.9, 0.8–4.5, P = 0.16), clusterin (0.6, 0.3–1.1, P = 0.09), and α-1 acid glycoprotein (1.6, 0.7–3.7, P = 0.27) improved the AUC from 0.841 to 0.889. CONCLUSIONS A panel of four novel protein biomarkers predicted early renal damage in type 1 diabetes. These findings require further validation in other populations for prediction of renal complications and treatment monitoring.

Human Biomarker Discovery and Predictive Models for Disease Progression for Idiopathic Pneumonia Syndrome Following Allogeneic Stem Cell Transplantation

Allogeneic hematopoietic stem cell transplantation (SCT) is the only curative therapy for many malignant and nonmalignant conditions. Idiopathic pneumonia syndrome (IPS) is a frequently fatal complication that limits successful outcomes. Preclinical models suggest that IPS represents an immune mediated attack on the lung involving elements of both the adaptive and the innate immune system. However, the etiology of IPS in humans is less well understood. To explore the disease pathway and uncover potential biomarkers of disease, we performed two separate label-free, proteomics experiments defining the plasma protein profiles of allogeneic SCT patients with IPS. Samples obtained from SCT recipients without complications served as controls. The initial discovery study, intended to explore the disease pathway in humans, identified a set of 81 IPS-associated proteins. These data revealed similarities between the known IPS pathways in mice and the condition in humans, in particular in the acute phase response. In addition, pattern recognition pathways were judged to be significant as a function of development of IPS, and from this pathway we chose the lipopolysaccaharide-binding protein (LBP) protein as a candidate molecular diagnostic for IPS, and verified its increase as a function of disease using an ELISA assay. In a separately designed study, we identified protein-based classifiers that could predict, at day 0 of SCT, patients who: 1) progress to IPS and 2) respond to cytokine neutralization therapy. Using cross-validation strategies, we built highly predictive classifier models of both disease progression and therapeutic response. In sum, data generated in this report confirm previous clinical and experimental findings, provide new insights into the pathophysiology of IPS, identify potential molecular classifiers of the condition, and uncover a set of markers potentially of interest for patient stratification as a basis for individualized therapy.

Urinary Protein Profiles in a Rat Model for Diabetic Complications

Diabetes mellitus is estimated to affect ∼24 million people in the United States and more than 150 million people worldwide. There are numerous end organ complications of diabetes, the onset of which can be delayed by early diagnosis and treatment. Although assays for diabetes are well founded, tests for its complications lack sufficient specificity and sensitivity to adequately guide these treatment options. In our study, we employed a streptozotocin-induced rat model of diabetes to determine changes in urinary protein profiles that occur during the initial response to the attendant hyperglycemia (e.g. the first two months) with the goal of developing a reliable and reproducible method of analyzing multiple urine samples as well as providing clues to early markers of disease progression. After filtration and buffer exchange, urinary proteins were digested with a specific protease, and the relative amounts of several thousand peptides were compared across rat urine samples representing various times after administration of drug or sham control. Extensive data analysis, including imputation of missing values and normalization of all data was followed by ANOVA analysis to discover peptides that were significantly changing as a function of time, treatment and interaction of the two variables. The data demonstrated significant differences in protein abundance in urine before observable pathophysiological changes occur in this animal model and as function of the measured variables. These included decreases in relative abundance of major urinary protein precursor and increases in pro-alpha collagen, the expression of which is known to be regulated by circulating levels of insulin and/or glucose. Peptides from these proteins represent potential biomarkers, which can be used to stage urogenital complications from diabetes. The expression changes of a pro-alpha 1 collagen peptide was also confirmed via selected reaction monitoring.

Copy number variation within human β-defensin gene cluster influences progression to AIDS in the multicenter AIDS cohort study

STUDY BACKGROUND DEFB4/103A encoding β-defensin 2 and 3, respectively, inhibit CXCR4-tropic (X4) viruses in vitro. We determined whether DEFB4/103A Copy Number Variation (CNV) influences time-to-X4 and time-to-AIDS outcomes. METHODS We utilized samples from a previously published Multicenter AIDS Cohort Study (MACS), which provides longitudinal account of viral tropism in relation to the full spectrum of rates of disease progression. Using traditional models for time-to-event analysis, we investigated association between DEFB4/103A CNV and the two outcomes, and interaction between DEFB4/103A CNV and disease progression groups, Fast and Slow. RESULTS Time-to-X4 and time-to-AIDS were weakly correlated. There was a stronger relationship between these two outcomes for the fast progressors. DEFB4/103A CNV was associated with time-to-AIDS, but not time-to-X4. The association between higher DEFB4/103A CNV and time-to-AIDS was more pronounced for the slow progressors. CONCLUSION DEFB4/103A CNV was associated with time-to-AIDS in a disease progression group-specific manner in the MACS cohort. Our findings may contribute to enhancing current understanding of how genetic predisposition influences AIDS progression.

Local sparse bump hunting

The search for structures in real datasets, for example, in the form of bumps, components, classes, or clusters, is important as these often reveal underlying phenomena leading to scientific discoveries. One of these tasks, known as bump hunting, is to locate domains of a multidimensional input space where the target function assumes local maxima without prespecifying their total number. A number of related methods already exist, yet are challenged in the context of high-dimensional data. We introduce a novel supervised and multivariate bump hunting strategy for exploring modes or classes of a target function of many continuous variables. This addresses the issues of correlation, interpretability, and high-dimensionality (p ≫ n case), while making minimal assumptions. The method is based upon a divide and conquer strategy, combining a tree-based method, a dimension reduction technique, and the Patient Rule Induction Method (PRIM). Important to this task, we show how to estimate the PRIM meta-parameters. Using accuracy evaluation procedures such as cross-validation and ROC analysis, we show empirically how the method outperforms a naive PRIM as well as competitive nonparametric supervised and unsupervised methods in the problem of class discovery. The method has practical application especially in the case of noisy high-throughput data. It is applied to a class discovery problem in a colon cancer microarray dataset aimed at identifying tumor subtypes in the metastatic stage. Supplemental Materials are available online.

ROCS : a Reproducibility Index and Confidence Score for Interaction Proteomics Studies

BackgroundAffinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones.MethodsTo address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of Indicator Prey Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Prey Proteins account for measures of protein identifiability as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures.ResultsWe illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions, or in combination with other AP-MS scoring methods to significantly improve inferences.ConclusionsOur method addresses important issues encountered in AP-MS datasets, making ROCS a very promising tool for this purpose, either on its own or in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called “ROCS”, freely available from the CRAN repository http://cran.r-project.org/.

Protein Markers Predict Survival in Glioma Patients

Glioblastoma multiforme (GBM) is a genomically complex and aggressive primary adult brain tumor, with a median survival time of 12–14 months. The heterogeneous nature of this disease has made the identification and validation of prognostic biomarkers difficult. Using reverse phase protein array data from 203 primary untreated GBM patients, we have identified a set of 13 proteins with prognostic significance. Our protein signature predictive of glioblastoma (PROTGLIO) patient survival model was constructed and validated on independent data sets and was shown to significantly predict survival in GBM patients (log-rank test: p = 0.0009). Using a multivariate Cox proportional hazards, we have shown that our PROTGLIO model is distinct from other known GBM prognostic factors (age at diagnosis, extent of surgical resection, postoperative Karnofsky performance score (KPS), treatment with temozolomide (TMZ) chemoradiation, and methylation of the MGMT gene). Tenfold cross-validation repetition of our model generation procedure confirmed validation of PROTGLIO. The model was further validated on an independent set of isocitrate dehydrogenase wild-type (IDHwt) lower grade gliomas (LGG)—a portion of these tumors progress rapidly to GBM. The PROTGLIO model contains proteins, such as Cox-2 and Annexin 1, involved in inflammatory response, pointing to potential therapeutic interventions. The PROTGLIO model is a simple and effective predictor of overall survival in glioblastoma patients, making it potentially useful in clinical practice of glioblastoma multiforme.