In Silico Analysis and Immunohistochemical Characterization of NaPi2b Protein Expression in Ovarian Carcinoma With Monoclonal Antibody Mx35

Introduction: Ovarian adenocarcinoma is frequently detected at the late stage, when therapy efficacy is limited and death occurs in up to 50% of the cases. A potential novel treatment for this disease is a monoclonal antibody that recognizes phosphate transporter sodium-dependent phosphate transporter protein 2b (NaPi2b). Materials and Methods: To better understand the expression of this protein in different histologic types of ovarian carcinomas, we immunostained 50 tumor samples with anti-NaPi2b monoclonal antibody MX35 and, in parallel, we assessed the expression of the gene encoding NaPi2b (SCL34A2) by in silico analysis of microarray data. Results: Both approaches detected higher expression of NaPi2b (SCL34A2) in ovarian carcinoma than in normal tissue. Moreover, a comprehensive analysis indicates that SCL34A2 is the only gene of the several phosphate transporters genes whose expression differentiates normal from carcinoma samples, suggesting it might exert a major role in ovarian carcinomas. Immunohistochemical and mRNA expression data have also shown that 2 histologic subtypes of ovarian carcinoma express particularly high levels of NaPi2b: serous and clear cell adenocarcinomas. Serous adenocarcinomas are the most frequent, contrasting with clear cell carcinomas, rare, and with worse prognosis. Conclusion: This identification of subgroups of patients expressing NaPi2b may be important in selecting cohorts who most likely should be included in future clinical trials, as a recently generated humanized version of MX35 has been developed.

Homeobox gene expression profile indicates HOXA5 as a candidate prognostic marker in oral squamous cell carcinoma

The search for molecular markers to improve diagnosis, individualize treatment and predict behavior of tumors has been the focus of several studies. This study aimed to analyze homeobox gene expression profile in oral squamous cell carcinoma (OSCC) as well as to investigate whether some of these genes are relevant molecular markers of prognosis and/or tumor aggressiveness. Homeobox gene expression levels were assessed by microarrays and qRT-PCR in OSCC tissues and adjacent non-cancerous matched tissues (margin), as well as in OSCC cell lines. Analysis of microarray data revealed the expression of 147 homeobox genes, including one set of six at least 2-fold up-regulated, and another set of 34 at least 2-fold down-regulated homeobox genes in OSCC. After qRT-PCR assays, the three most up-regulated homeobox genes (HOXA5, HOXD10 and HOXD11) revealed higher and statistically significant expression levels in OSCC samples when compared to margins. Patients presenting lower expression of HOXA5 had poorer prognosis compared to those with higher expression (P=0.03). Additionally, the status of HOXA5, HOXD10 and HOXD11 expression levels in OSCC cell lines also showed a significant up-regulation when compared to normal oral keratinocytes. Results confirm the presence of three significantly upregulated (>4-fold) homeobox genes (HOXA5, HOXD10 and HOXD11) in OSCC that may play a significant role in the pathogenesis of these tumors. Moreover, since lower levels of HOXA5 predict poor prognosis, this gene may be a novel candidate for development of therapeutic strategies in OSCC.

Xylella fastidiosa: An in vivo system to study possible survival strategies within citrus xylem vessels based on global gene expression analysis

Xylella fastidiosa inhabits the plant xylem, a nutrient-poor environment, so that mechanisms to sense and respond to adverse environmental conditions are extremely important for bacterial survival in the plant host. Although the complete genome sequences of different Xylella strains have been determined, little is known about stress responses and gene regulation in these organisms. In this work, a DNA microarray was constructed containing 2,600 ORFs identified in the genome sequencing project of Xylella fastidiosa 9a5c strain, and used to check global gene expression differences in the bacteria when it is infecting a symptomatic and a tolerant citrus tree. Different patterns of expression were found in each variety, suggesting that bacteria are responding differentially according to each plant xylem environment. The global gene expression profile was determined and several genes related to bacterial survival in stressed conditions were found to be differentially expressed between varieties, suggesting the involvement of different strategies for adaptation to the environment. The expression pattern of some genes related to the heat shock response, toxin and detoxification processes, adaptation to atypical conditions, repair systems as well as some regulatory genes are discussed in this paper. DNA microarray proved to be a powerful technique for global transcriptome analyses. This is one of the first studies of Xylella fastidiosa gene expression in vivo which helped to increase insight into stress responses and possible bacterial survival mechanisms in the nutrient-poor environment of xylem vessels.

Effect of human TGF-beta on the gene expression profile of Schistosoma mansoni adult worms

Schistosoma mansoni is responsible for schistosomiasis, a parasitic disease that affects 200 million people worldwide. Molecular mechanisms of host-parasite interaction are complex and involve a crosstalk between host signals and parasite receptors. TGF-beta signaling pathway has been shown to play an important role in S. mansoni development and embryogenesis. In particular human (h) TGF-beta has been shown to bind to a S. mansoni receptor, transduce a signal that regulates the expression of a schistosome target gene. Here we describe 381 parasite genes whose expression levels are affected by in vitro treatment with hTGF-beta. Among these differentially expressed genes we highlight genes related to morphology, development and cell cycle that could be players of cytokine effects on the parasite. We confirm by qPCR the expression changes detected with microarrays for 5 out of 7 selected genes. We also highlight a set of non-coding RNAs transcribed from the same loci of protein-coding genes that are differentially expressed upon hTCF-beta treatment. These datasets offer potential targets to be explored in order to understand the molecular mechanisms behind the possible role of hTGF-beta effects on parasite biology. (C) 2012 Elsevier B.V. All rights reserved.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Transcriptional effects of 1,25 dihydroxyvitamin D3 physiological and supra-physiological concentrations in breast cancer organotypic culture

Background Vitamin D transcriptional effects were linked to tumor growth control, however, the hormone targets were determined in cell cultures exposed to supra physiological concentrations of 1,25(OH)2D3 (50-100nM). Our aim was to evaluate the transcriptional effects of 1,25(OH)2D3 in a more physiological model of breast cancer, consisting of fresh tumor slices exposed to 1,25(OH)2D3 at concentrations that can be attained in vivo. Methods Tumor samples from post-menopausal breast cancer patients were sliced and cultured for 24 hours with or without 1,25(OH)2D3 0.5nM or 100nM. Gene expression was analyzed by microarray (SAM paired analysis, FDR≤0.1) or RT-qPCR (p≤0.05, Friedman/Wilcoxon test). Expression of candidate genes was then evaluated in mammary epithelial/breast cancer lineages and cancer associated fibroblasts (CAFs), exposed or not to 1,25(OH)2D3 0.5nM, using RT-qPCR, western blot or immunocytochemistry. Results 1,25(OH)2D3 0.5nM or 100nM effects were evaluated in five tumor samples by microarray and seven and 136 genes, respectively, were up-regulated. There was an enrichment of genes containing transcription factor binding sites for the vitamin D receptor (VDR) in samples exposed to 1,25(OH)2D3 near physiological concentration. Genes up-modulated by both 1,25(OH)2D3 concentrations were CYP24A1, DPP4, CA2, EFTUD1, TKTL1, KCNK3. Expression of candidate genes was subsequently evaluated in another 16 samples by RT-qPCR and up-regulation of CYP24A1, DPP4 and CA2 by 1,25(OH)2D3 was confirmed. To evaluate whether the transcripitonal targets of 1,25(OH)2D3 0.5nM were restricted to the epithelial or stromal compartments, gene expression was examined in HB4A, C5.4, SKBR3, MDA-MB231, MCF-7 lineages and CAFs, using RT-qPCR. In epithelial cells, there was a clear induction of CYP24A1, CA2, CD14 and IL1RL1. In fibroblasts, in addition to CYP24A1 induction, there was a trend towards up-regulation of CA2, IL1RL1, and DPP4. A higher protein expression of CD14 in epithelial cells and CA2 and DPP4 in CAFs exposed to 1,25(OH)2D3 0.5nM was detected. Conclusions In breast cancer specimens a short period of 1,25(OH)2D3 exposure at near physiological concentration modestly activates the hormone transcriptional pathway. Induction of CYP24A1, CA2, DPP4, IL1RL1 expression appears to reflect 1,25(OH)2D3 effects in epithelial as well as stromal cells, however, induction of CD14 expression is likely restricted to the epithelial compartment.

Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.

Ruolo di interleuchina-33 sull'espressione di geni profibrotici e sull'ipertrofia dei miofibroblasti nella fibrosi intestinale

Background: Intestinal fibrosis is a serious complication of IBD, with more than a third of Crohn’s disease (CD) patients developing a fibrostenosing phenotype with formation of strictures that will require surgical intervention. Remarkably, SAMP1/YitFc (SAMP) mice, a spontaneous model of CD, develop gut fibrosis; similar to IBD patients, the pathophysiology of SAMP fibrosis is unknown. IL-33 is a member of the IL-1 cytokine family and increased expression is associated with IBD. Emerging evidence suggests its potential role in liver and cutaneous fibrosis, as well as myofibroblast-associated colonic ulcerations . Aim: The aim of this study was to evaluate the role of IL-33 as a potential mediator of profibrotic events leading to intestinal fibrosis and possible stricture formation. Methods: A detailed histologic time course study, with collagen-specific Masson trichrome staining and IHC for ST2 (IL-33 receptor), was performed on SAMP and control AKR (parental strain) mice. qRT-PCR was done on full-thickness ilea for the profibrogenic genes, collagen (coll)-1, coll-3, connective tissue growth factor (CTGF) and insulin-like growth factor 1 (IGF-1). Exogenous IL-33 (33 μg/kg, i.p.) or vehicle was administered daily for 7d to SAMP and AKR mice (N=6/exp group), and ileal tissues evaluated as above. Finally, microarray analysis was performed on full-thickness ilea from SAMP and AKR mice, and IL-33 stimulated subepithelial myofibroblasts (SEMFs). Results: SAMP mice displayed ileal skip lesions with randomly distributed strictures, preceded by typical pre-stricture dilations of the ileum. Ileal wall was visibly thickened with hypertrophy of the serosa, muscularis mucosa, muscularis propria, within which intense collagen deposition was observed, and inflammatory infiltrates in segments showing strictures. Interestingly, intense ST2 staining was present within the inflamed lamina propria of SAMP, notably localized to SEMFs. Fibrosis was first observed at 20 wks, and reached its peak by 50 wks of age. mRNA expression of coll-1 (4.74±0.69-fold; P=0.001), coll-3 (4.92±1.05-fold; P=0.01), IGF1 (12.9±3.45; P=0.006), and CTGF (3.29±0.69; P=0.004) was dramatically elevated in SAMP vs. AKR ilea. IL-33 treatment of AKR mice induced a marked increase in muscle fiber/myofibroblast cellularity and hypertrophy of the muscularis propria (4.13±0.74-fold; P<0.0001), and mRNA expression of coll-1 (5.16±0.89-fold; P=0.0009), coll-3 (1.97±0.14-fold; P=0.01), IGF-1 (9.32±2.27-fold; P=0.004), and CTGF (1.43±0.31-fold; P=0.006) vs. vehicle controls. Microarray data from SAMP ilea and IL-33-treated SEMFs confirmed these trends, displaying a global increase in profibrogenic gene expression. Conclusion: These data suggest an important role for IL-33 in intestinal fibrosis, and may represent a potential target for the treatment of IBD-associated fibrosis and stricture formation.

Effects of metal-induced oxidative stress on endothelial cells in vitro

Aseptic loosening of metal implants is mainly attributed to the formation of metal degradation products. These include particulate debris and corrosion products, such as metal ions (anodic half-reaction) and ROS (cathodic half-reaction). While numerous clinical studies describe various adverse effects of metal degradation products, detailed knowledge of metal-induced cellular reactions, which might be important for possible therapeutic intervention, is not comprehensive. Since endothelial cells are involved in inflammation and angiogenesis, two processes which are critical for wound healing and integration of metal implants, the effects of different metal alloys and their degradation products on these cells were investigated. Endothelial cells on Ti6Al4V alloy showed signs of oxidative stress, which was similar to the response of endothelial cells to cathodic partial reaction of corrosion induced directly on Ti6Al4V surfaces. Furthermore, oxidative stress on Ti6Al4V alloy reduced the pro-inflammatory stimulation of endothelial cells by TNF-α and LPS. Oxidative stress and other stress-related responses were observed in endothelial cells in contact with Co28Cr6Mo alloy. Importantly, these features could be reduced by coating Co28Cr6Mo with a TiO2 layer, thus favouring the use of such surface modification in the development of medical devices for orthopaedic surgery. The reaction of endothelial cells to Co28Cr6Mo alloy was partially similar to the effects exerted by Co2+, which is known to be released from metal implants. Co2+ also induced ROS formation and DNA damage in endothelial cells. This correlated with p53 and p21 up-regulation, indicating the possibility of cell cycle arrest. Since CoCl2 is used as an hypoxia-mimicking agent, HIF-1α-dependence of cellular responses to Co2+ was studied in comparison to anoxia-induced effects. Although important HIF-1α-dependent genes were identified, a more detailed analysis of microarray data will be required to provide additional information about the mechanisms of Co2+ action. All these reactions of endothelial cells to metal degradation products might play their role in the complex processes taking place in the body following metal device implantation. In the worst case this can lead to aseptic loosening of the implant and requirement for revision surgery. Knowledge of molecular mechanisms of metal-induced responses will hopefully provide the possibility to interfere with undesirable processes at the implant/tissue interface, thus extending the life-time of the implant and the overall success of metal implant applications.

Meta-analyses qualify metzincins and related genes as acute rejection markers in renal transplant patients

Definition of acute renal allograft rejection (AR) markers remains clinically relevant. Features of T-cell-mediated AR are tubulointerstitial and vascular inflammation associated with excessive extracellular matrix (ECM) remodeling, regulated by metzincins, including matrix metalloproteases (MMP). Our study focused on expression of metzincins (METS), and metzincins and related genes (MARGS) in renal allograft biopsies using four independent microarray data sets. Our own cases included normal histology (N, n = 20), borderline changes (BL, n = 4), AR (n = 10) and AR + IF/TA (n = 7). MARGS enriched in all data sets were further examined on mRNA and/or protein level in additional patients. METS and MARGS differentiated AR from BL, AR + IF/TA and N in a principal component analysis. Their expression changes correlated to Banff t- and i-scores. Two AR classifiers, based on METS (including MMP7, TIMP1), or on MARGS were established in our own and validated in the three additional data sets. Thirteen MARGS were significantly enriched in AR patients of all data sets comprising MMP7, -9, TIMP1, -2, thrombospondin2 (THBS2) and fibrillin1. RT-PCR using microdissected glomeruli/tubuli confirmed MMP7, -9 and THBS2 microarray results; immunohistochemistry showed augmentation of MMP2, -9 and TIMP1 in AR. TIMP1 and THBS2 were enriched in AR patient serum. Therefore, differentially expressed METS and MARGS especially TIMP1, MMP7/-9 represent potential molecular AR markers.

A subset of metzincins and related genes constitutes a marker of human solid organ fibrosis

Metzincins and functionally related genes play important roles in extracellular matrix remodeling both in healthy and fibrotic conditions. We recently presented a transcriptomic classifier consisting of 19 metzincins and related genes (MARGS) discriminating biopsies from renal transplant patients with or without interstitial fibrosis/tubular atrophy (IF/TA) by virtue of gene expression measurement (Roedder et al., Am J Transplant 9:517-526, 2009). Here we demonstrate that the same algorithm has diagnostic value in non-transplant solid organ fibrosis. We used publically available microarray datasets of 325 human heart, liver, lung, kidney cortex, and pancreas microarray samples (265 with fibrosis, 60 healthy controls). Expression of nine commonly differentially expressed genes was confirmed by TaqMan low-density arrays (Applied Biosystems, USA) in 50 independent archival tissue specimens with matched histological diagnoses to microarray patients. In separate and in combined, integrated microarray data analyses of five datasets with 325 samples, the previously published MARGS classifier for renal post-transplant IF/TA had a mean AUC of 87% and 82%, respectively. These data demonstrate that the MARGS gene panel classifier not only discriminates IF/TA from normal renal transplant tissue, but also classifies solid organ fibrotic conditions of human pancreas, liver, heart, kidney, and lung tissue samples with high specificity and accuracy, suggesting that the MARGS classifier is a cross-platform, cross-organ classifier of fibrotic conditions of different etiologies when compared to normal tissue.

An integrated nano-scale approach to profile miRNAs in limited clinical samples

Profiling miRNA expression in cells that directly contribute to human disease pathogenesis is likely to aid the discovery of novel drug targets and biomarkers. However, tissue heterogeneity and the limited amount of human diseased tissue available for research purposes present fundamental difficulties that often constrain the scope and potential of such studies. We established a flow cytometry-based method for isolating pure populations of pathogenic T cells from bronchial biopsy samples of asthma patients, and optimized a high-throughput nano-scale qRT-PCR method capable of accurately measuring 96 miRNAs in as little as 100 cells. Comparison of circulating and airway T cells from healthy and asthmatic subjects revealed asthma-associated and tissue-specific miRNA expression patterns. These results establish the feasibility and utility of investigating miRNA expression in small populations of cells involved in asthma pathogenesis, and set a precedent for application of our nano-scale approach in other human diseases. The microarray data from this study (Figure 7) has been submitted to the NCBI Gene Expression Omnibus (GEO; http://ncbi.nlm.nih.gov/geo) under accession no. GSE31030.

Comparison of the gene expression profiles from normal and Fgfrl1 deficient mouse kidneys reveals downstream targets of Fgfrl1 signaling

Fgfrl1 (fibroblast growth factor receptor-like 1) is a transmembrane receptor that is essential for the development of the metanephric kidney. It is expressed in all nascent nephrogenic structures and in the ureteric bud. Fgfrl1 null mice fail to develop the metanephric kidneys. Mutant kidney rudiments show a dramatic reduction of ureteric branching and a lack of mesenchymal-to-epithelial transition. Here, we compared the expression profiles of wildtype and Fgfrl1 mutant kidneys to identify genes that act downstream of Fgfrl1 signaling during the early steps of nephron formation. We detected 56 differentially expressed transcripts with 2-fold or greater reduction, among them many genes involved in Fgf, Wnt, Bmp, Notch, and Six/Eya/Dach signaling. We validated the microarray data by qPCR and whole-mount in situ hybridization and showed the expression pattern of candidate genes in normal kidneys. Some of these genes might play an important role during early nephron formation. Our study should help to define the minimal set of genes that is required to form a functional nephron.

Multiple Testing With an Empirical Alternative Hypothesis

An optimal multiple testing procedure is identified for linear hypotheses under the general linear model, maximizing the expected number of false null hypotheses rejected at any significance level. The optimal procedure depends on the unknown data-generating distribution, but can be consistently estimated. Drawing information together across many hypotheses, the estimated optimal procedure provides an empirical alternative hypothesis by adapting to underlying patterns of departure from the null. Proposed multiple testing procedures based on the empirical alternative are evaluated through simulations and an application to gene expression microarray data. Compared to a standard multiple testing procedure, it is not unusual for use of an empirical alternative hypothesis to increase by 50% or more the number of true positives identified at a given significance level.