Epiregulin mRNA expression was significantly elevated in cholesteatoma tissues compared with that in normal retroauricular skin. Staining for epiregulin was more intense in the epithelial cells and subepithelial fibroblasts of cholesteatoma tissues than in retroauricular skin. When PHK16-0b cells were cultured with cholesteatoma fibroblasts, their colony-forming efficiency was 50% higher than when these cells were cultured with normal skin fibroblasts. Also, knockdown of epiregulin mRNA in cholesteatoma fibroblasts led to greater suppression of colony formation than knockdown in skin fibroblasts. Furthermore, the colony-forming efficiency of PHK16-0b cells was significantly reduced after treatment with an epiregulin neutralizing antibody in co-culture with cholesteatoma fibroblasts, but not in co-culture with skin fibroblasts. These results suggest that keratinocyte hyperproliferation in cholesteatoma is promoted through overexpression of epiregulin by subepithelial fibroblasts via epithelial–mesenchymal interactions, which may play a crucial role in the pathogenesis of middle ear cholesteatoma.To investigate whether keratinocytes proliferate in response to epiregulin produced by subepithelial fibroblasts derived from middle ear cholesteatoma. Tissue samples were obtained from patients undergoing tympanoplasty. The quantitative polymerase chain reaction and immunohistochemistry were performed to examine epiregulin expression and localization in cholesteatoma tissues and retroauricular skin tissues. Fibroblasts were cultured from cholesteatoma tissues and from normal retroauricular skin. These fibroblasts were used as feeder cells for culture with a human keratinocyte cell line (PHK16-0b). To investigate the role of epiregulin in colony formation by PHK16-0b cells, epiregulin mRNA expression was knocked down in fibroblasts by using short interfering RNA and epiregulin protein was blocked with a neutralizing antibody.  Middle ear cholesteatoma is caused by the hyperproliferation of keratinocytes due to chronic inflammation and it is one of the hyperproliferative epithelial diseases such as psoriasis and acanthoma. Cholesteatoma is a benign keratinizing hyperproliferative epithelial lesion of the petrous temporal bone that can invade the surrounding bone and middle/inner ear structures. In patients with advanced cholesteatoma, erosion of the ossicles and otic capsule can result in hearing loss, vestibular dysfunction, facial paralysis, and even intracranial complications Although the molecular mechanisms involved in the pathogenesis of cholesteatoma are not yet fully understood, it has been suggested that keratinocyte proliferation and migration are mediated by several autocrine and paracrine growth factors and their receptors LARC, GMCSF, epiregulin, ICAM1, and TGFA mRNA was significantly more up-regulated in MECF than in SF by IL-1α and/or IL-1β stimulation with spontaneous expression of epiregulin and TGFA being substantially enhanced in particular. Such differential gene expression suggests that subepidermal fibroblasts may play a role in the occurrence of hyperkeratosis during the growth of cholesteatoma by releasing molecules involved in inflammation and epidermal cell proliferation.We previously identified many differences in the pattern of gene expression between retroauricular skin fibroblasts (SF) and middle ear cholesteatoma fibroblasts (MECF) Epiregulin is a member of the EGF family that includes EGF, TGF-α, heparin-binding EGF, amphiregulin, betacellulin, neuregulin, and tomoregulin. Among these molecules, epiregulin, heparin-binding EGF, and betacellulin are known to bind to ErbB4 as well as to the epidermal growth factor receptor (EGFR)/ErbB1 Among the various growth factors that could play a role in the development of cholesteatoma, KGF was considered likely to be a key effector However, there have been no reports about the role of epiregulin in cholesteatoma. Accordingly, we performed the present study to investigate whether keratinocytes underwent proliferation when exposed to epiregulin produced by MECF or SF.Middle ear cholesteatoma tissues and normal retroauricular skin samples were obtained from 10 patients undergoing tympanoplasty at Jikei University Hospital . The study was approved by the Jikei university ethics review board prior to commencement of the study, and written informed consent was obtained from all subjects.RNAlater  was used to preserve one part of each specimen of cholesteatoma or retroauricular skin tissue for examination of epiregulin mRNA expression. The other part of each specimen was fixed overnight in 10% buffered formalin at room temperature and embedded in paraffin to provide sections for immunohistochemical analysis of epiregulin, epidermal growth factor receptor (EGFR)/ErbB1, and ErbB4 expression.2 in air and were analyzed after 4 passages.Residual tissue pieces were minced into single cells, washed with phosphate-buffered saline (PBS), and then incubated in DMEM/F12  with 10% fetal calf serum (FCS)  and 100 U/ml penicillin-100 µg/ml streptomycin (Life Technologies) for several weeks. As a result, fibroblasts derived from cholesteatomas (MECF) and fibroblasts derived from normal retroauricular skin (SF) were obtained. These cells were incubated at 37°C in a humidified incubator under 5% CO2. Human epidermal keratinocytes (HEK) derived from normal adult skin were purchased from Kurabo  and were cultured in keratinocyte basal medium .A human keratinocyte cell line (PHK16-0b) was obtained from the Health Science Research Resources Bank , and was maintained in defined keratinocyte serum-free medium  with 10 ng/mL recombinant human epithelial growth factor, 1% penicillin/streptomycin, and the growth supplement supplied by the manufacturer at 37°C under 5% CO5′- ATCCTGGCATGTGCTAGGGT-3′, reverse 5′- GTGCTCCAGAGGTCAGCCAT-3′. The level of target mRNA expression was normalized by the average expression of the housekeeping gene GAPDH.Total RNA was isolated by using an RNeasy Mini Kit that included DNase (Qiagen) and the RNA was transcribed to obtain cDNA using superscript II (Life Technologies). Then quantitative PCR was performed with an ABI/PRISM 7700 Sequence Detector System  and SYBR green PCR master mix (Life Technologies). The primers for epiregulin were as follows: forward Immunohistochemistry was performed with a monoclonal antibody for epiregulin , a monoclonal antibody for EGFR , and a polyclonal antibody for ErbB4 . Serial sections were cut at a thickness of 4 µm and were placed onto glass slides, followed by deparaffinization for 10 minutes at 37°C. An Envision™ kit  was used for detection. The blocking solution was applied for 10 minutes, followed by incubation for 1 hour with the primary antibodies. After incubation with the secondary antibody for 30 minutes, DAB was applied for 5 minutes to detect the reaction products for epiregulin, EGFR, and ErbB4. Finally, the sections were counterstained, dehydrated, and coverslipped.PHK16-0b cells were seeded into a 96-well flat-bottomed tissue culture plate. After 72 h of incubation with recombinant epiregulin  with or without 5 µM of the EGF receptor tyrosine kinase inhibitor AG-1478 , cell viability was evaluated with a Cell Count Reagent SF colorimetric assay . Briefly, 10 µl of Cell Count Reagent SF was added to each well and incubation was done for 2 h at 37°C. Then viable cells were assessed by measuring the OD at 450 nm using a microplate reader . Cells cultured without epiregulin treatment served as the control.MECF or SF were incubated in DMEM/F12 containing 10% FCS and were plated into 12-well culture plates. On the next day, the cells were treated with mitomycin C  for 3 hours, and single PHK16-0b or HEK cells were then seeded at a density of 300/well in defined KSFM. The colony-forming efficiency (CFE) was evaluated on day 21 after the cells were fixed with formalin and stained with 1% rhodamine B .Synthetic Stealth siRNA for human epiregulin (Life Technologies) was used to knockdown epiregulin mRNA expression in the feeder cells. MECF or SF at 50% confluence were transfected for 24 hours with the siRNA oligonucleotide using Lipofectamine RNAiMAX (Life Technologies) according to the manufacturer's instructions. Stealth siRNA Negative Control Duplexes (Life Technologies) was used as the negative control.To block epiregulin protein expression on the cell surface and in culture supernatant during co-culture, an epiregulin neutralizing antibody (Santacruz) or a control antibody was added to the wells for the first 7 days.t-test, and differences were considered significant at p<0.05.Data are presented as the mean ± standard error of the mean (SEM). Statistical significance was determined by using the paired Student’s epiregulin mRNA by quantitative PCR, and found that it was significantly higher in middle ear cholesteatoma tissue than in normal retroauricular skin  and recombinant epiregulin. We found that recombinant human epiregulin (1–100 ng/ml) markedly induced the proliferation of PHK16-0b cells. To examine the role of EGFR in cell proliferation mediated by epiregulin, an EGFR tyrosine kinase inhibitor (AG1478) was added to cells in the proliferation assay. As shown in . HEK cells also formed colonies during co-culture, although the CFE of HEK cells was relatively low compared with that of PHK16-0b cells . HEK cells cultured with MECF also showed a higher CFE than cells cultured with SF and larger colonies were obtained by co-culture with MECF .To investigate the influence of fibroblasts derived from cholesteatomas on the clonal growth of keratinocytes, the colony-forming assay was performed using PHK16-0b cells co-cultured for 21 days with mitomycin C-treated MECF or SF as feeder cells. PHK16-0b cells cultured with MECF feeders showed a colony-forming efficiency (CFE) than cells cultured with SF, and there was a 50% increase of the CFE . HEK cel. HEK celWe previously found that the expression of epiregulin mRNA was higher in MECF than in SF. This time, we investigated whether enhanced CFE was mediated through the overproduction of epiregulin by MECF. Initially, we confirmed the effect of silencing epiregulin by transfection of MECF and SF with epiregulin siRNA. When PHK16-0b cells were co-cultured with MECF that had been transfected with epiregulin siRNA, CFE was lower than after co-culture with MECF that had been transfected with negative siRNA, while transfection of SF had no effect (Middle ear cholesteatoma is a non-neoplastic accumulation of keratinizing stratified squamous epithelium along with desquamated keratin debris that occurs in the tympanic cavity and mastoid region. Hyperproliferation of keratinocytes has a crucial role in the pathogenesis of cholesteatoma, and it is thought that keratinocyte proliferation is mediated by various autocrine and paracrine growth factors and their receptors This study demonstrated the overexpression of epiregulin in epithelial cells and subepithelial fibroblasts of cholesteatoma tissue. Overexpression of epiregulin has already been reported in psoriatic epidermis Epiregulin is an EGF family member that activates EGFR and ErbB4 homodimers and all possible heterodimeric ErbB complexes TSC2In the present study, co-culture with MECF was shown to increase CFE relative to co-culture with SF, but the reason for enhancement of epiregulin expression in MECF is unclear. There have been several reports that fibroblasts from lesions display a different phenotype to those from normal tissues The present study also showed that keratinocyte proliferation was inhibited by an EGFR tyrosine kinase inhibitor. Broad-spectrum anti-ErbB agents, such as lapatinib and canertinib, have been reported to show higher efficacy and wide-ranging antitumor activity In conclusion, our present results suggest that keratinocyte hyperproliferation in middle ear cholesteatoma might be induced through overproduction of epiregulin by subepithelial fibroblasts. Thus, epiregulin is a candidate factor responsible for fibroblast overactivity that influences the epithelium in cholesteatoma.


From a total of 28 million reads per sample, we identified 1,514 transcripts that were differentially expressed after infection. Both predicted and novel gene changes were detected, as were gene isoforms, and while many of the genes were expressed by all donors, some were unique. Knock-down of genes not previously known to be associated with WNV resistance identified their critical role in control of viral infection. Our study distinguishes both common gene pathways as well as novel cellular responses. Such analyses will be valuable for translational studies of susceptible and resistant individuals—and for targeting therapeutics—in multiple biological settings. The West Nile virus (WNV) is an emerging infection of biodefense concern and there are no available treatments or vaccines. Here we used a high-throughput method based on a novel gene expression analysis, RNA-Seq, to give a global picture of differential gene expression by primary human macrophages of 10 healthy donors infected  West Nile virus (WNV) is a mosquito-borne enveloped positive-strand RNA virus belonging to the family Flaviviridae, which includes yellow fever and dengue viruses . From 19The ability to profile the transcriptome of eukaryotic cells has led to the identification of key components that undergo alterations during diverse biological processes. For example, the profiling of gene expression in different leukemias has led to the classification of distinct subclasses of disease . Recent in vitro with virulent WNV  for 24 hours [To follow the level of expression of host genes that change during flaviviral infection, samples of primary human macrophages from 10 healthy donors were infected 24 hours . Q-PCR c24 hours , suggest24 hours . vs. the expression level for our 10 biological uninfected and infected sample pairs, we noted both upregulation and downregulation of transcripts that reached greater than 4 fold . The down-regulated transcripts were enriched in functional clusters involved in intracellular organelle and membrane transport functions in the samples infected with WNV. Notably, the sensitivity provided by the model also allows identification of rare differentially expressed transcripts in only a few donors for which determining functional significance will require further investigation. We have further classified our results from RNA-Seq in light of our recent genome-scale RNA interference (RNAi) screen for human genes associated with WNV infection . This hiin vitro WNV challenge of macrophages obtained from healthy donors triggers a strong anti-viral response. Since the enriched genes in these pathways are type I IFN and chemokines, two interferon-stimulated genes and two chemokine receptors were selected for RNAi knockdown. The targets selected were IFI27 , AIM2 (absent in melanoma 2), CCR3 (receptor of chemokines CCL5 and CCL8), and CXCR3 (receptor of chemokine CXCL10), which is essential for control of WNV infection in the central nervous system [p = 0.03–p = 0.008). Each of the genes was silenced efficiently as quantified by Q-PCR using gene specific primers; gene expression was determined after 48 h of infection with WNV and was decreased to 10%–20% of levels in non-targeting siRNA transfected control cells  was generously provided by Dr. John Anderson, Connecticut Agricultural Experiment Station, New Haven, CT, USA  and was 6) were transfected with 2 µM of small interfering RNAs (siRNAs) targeting IFI27, AIM2, CCR3 or CXCR3 , using the nucleofection technology . Cells transfected with non-targeting siRNAs will be used as a control. Transfected cells were incubated for 36 h before infection studies. Adherent monocyte-derived macrophages were dislodged using Trypsin/EDTA solution (0.5 mg/mL trypsin and 0.2 mg/mL EDTA in PBS) for 30 min at room temperature. Macrophages , and cDNA was synthesized using AffinityScript Multi Temperature cDNA Synthesis Kit  according to standard protocols. Amplification was performed in an iCycler  for 60 cycles with an annealing temperature at 60 °C and values were normalized to β-actin. Primers and probes for qPCRs were from Dharmacon or synthesized according to customized sequences as follows: WNV-E  and β-actin  . mRNA was fragmented into 150–300 bp fragments by incubation in RNA Fragmentation Reagent  at 70 °C for 5 minutes. The fragmented mRNA was purified away from the fragmentation buffer by Agencourt RNAClean beads  following manufacturer’s instructions. The purified, fragmented mRNA was converted into double-stranded cDNA using the SuperScript Double-Stranded cDNA Synthesis Kit  by priming with random hexamers. cDNA libraries were prepared for Illumina deep-sequencing as previously described . 36 nucl9/transcript length/library) to pseudocounts (# of reads originated from each transcript isoform). The effective transcript length reported by Cufflinks is used for the conversion. The pseudocounts were further normalized across samples using the trimmed mean of M‑values (TMM) method [Hi-Seq sequence data from 10 donors (mock or WNV infected) was processed to obtain estimates expression level of genes and transcripts. Using the fastq files, all sequencing reads were mapped back to human genome (hg19) using default setting of TopHat (v1.1.4) . Cufflin) method . DiffereFor the functional annotation clustering, the 1,514 differentially expressed transcripts were submitted for analysis using the DAVID web server . The “MeWe have employed RNASeq to provide a comprehensive identification of the critical elements in the host innate immune response to infection with WNV. In contrast to microarray experiments, which use pre-defined oligonucleotide probes for measuring differential expression of genes, RNA-Seq as used in this experiment does not introduce a selection bias in terms of the background or gene population. The abundant gene transcripts identified through RNASeq require high level bioinformatics analysis and our analysis achieves higher statistical power through combination of samples. To evaluate both common gene pathways as well as novel cellular responses will be essential, as we examine differences between susceptible and resistant individuals in the population. In our analysis we have shown both predicted and novel gene changes in response to infection with WNV. Several targets identified here as differentially expressed on infection with WNV are common host factors—such as the IFITM proteins—that similarly mediate cellular resistance to other viruses including influenza A virus and dengue virus . The intIn addition, we have identified a large number of genes not previously associated with WNV responses. One such gene identified here, IFI27, is a mitochondrial protein that sensitizes cells to apoptotic stimuli via mitochondrial membrane destabilization . Its ideTo build a model of the key signaling pathways modulated by WNV infection, we focused on functional annotation of pathways with many differentially regulated genes identified by our analysis, such as viral sensing pathways including TLR and RLR, which recognize specific pathogen associated molecular patterns (PAMPs) and initiate signal transduction pathways . Our mod


Digital differential display (DDD) rapidly creates transcription profiles based on EST comparisons and numerically calculates, as a fraction of the pool of ESTs, the relative sequence abundance of known and novel genes. However, the process of identifying the most likely tissue for a specific disease in which to search for candidate genes from the pool of differentially expressed genes remains difficult. Therefore, we have used ‘Gene Ontology semantic similarity score’ to measure the GO similarity between gene products of lung tissue-specific candidate genes from control  and disease (cancer) sets. This semantic similarity score matrix based on hierarchical clustering represents in the form of a dendrogram. The dendrogram cluster stability was assessed by multiple bootstrapping. Multiple bootstrapping also computes a p-value for each cluster and corrects the bias of the bootstrap probability.The tissue-specific Unigene Sets derived from more than one million expressed sequence tags (ESTs) in the NCBI, GenBank database offers a platform for identifying significantly and differentially expressed tissue-specific genes by Subsequent hierarchical clustering by the multiple bootstrapping method (α = 0.95) identified seven clusters. The comparative, as well as subtractive, approach revealed a set of 38 biomarkers comprising four distinct lung cancer signature biomarker clusters (panel 1–4). Further gene enrichment analysis of the four panels revealed that each panel represents a set of lung cancer linked metastasis diagnostic biomarkers (panel 1), chemotherapy/drug resistance biomarkers (panel 2), hypoxia regulated biomarkers (panel 3) and lung extra cellular matrix biomarkers (panel 4).Expression analysis reveals that hypoxia induced lung cancer related biomarkers (panel 3), HIF and its modulating proteins  are significantly down regulated. All down regulated genes in this panel were highly up regulated in most other types of cancers. These panels of proteins may represent signature biomarkers for lung cancer and will aid in lung cancer diagnosis and disease monitoring as well as in the prediction of responses to therapeutics. The Unigene libraries of interest with varying treatment conditions can be digitally ‘pooled’ and compared to control vs. treatment using Digital Differential Display (DDD). It enables the identification of numerical differences in transcript frequency between the individual or pooled Unigene libraries from the various treatment conditions and multiple cDNA libraries. The frequency of each differentially expressed transcripts and their fold change from the pooled libraries have been calculated using Fisher Exact Test. The prioritisation of DDD identification from differentially expressed candidate genes strictly used relative change in the frequency value and its fold change. Apart from DDD, many web tools are freely available to prioritise candidate genes based on the relative change in gene expression profile [Gene expression analysis in the post genomic era through high throughput genomic studies led to identification of enormous candidate genes related to pathophysiological conditions or altered signal transduction. One such freely available high throughput database is ‘Unigene’ . The query genes, in this study, were normal lung tissue specific genes from the Tissue-Specific Genes Database (TiSGeD). The genes of interest were candidate lung cancer genes from DDD ,14. Surphttp://bioinf.xmu.edu.cn/databases/TiSGeD/index.html). Human adult lung tissue related genes with tissue specificity measure score (SPM) ≥ 0.9 (represents high tissue specificity) were considered. The lung tissue specific “Mouse” and “developmental” genes were omitted.The normal lung tissue specific genes were collected from TiSGeD  and cancerous (Case 2) conditions. In Case 1, the Normal lung tissues (11 tissue libraries) were considered as a ‘Reference’ samples and the remaining normal human tissues (251 tissue libraries) were ‘Query’ samples. In Case 2, the Normal lung tissues (11 tissue libraries) were considered as ‘Reference’ samples and the cancerous human lung tissues (8 tissue libraries) were ‘Query’ samples. These comparisons were designed systematically so as to identify altered Gene expression of varying treatment conditions of ‘Reference’ and ‘Query’ samples. These pair wise comparisons resulted in a relative abundance of ESTs among the contrasting cDNA libraries of digitally ‘pooled’ contracts from Unigene Database.Org.Hs.eg.db package in R-program was used for the computation of Semantic similarity score while the GO-based similarity score was computed based on the three orthogonal gene ontologies generated for Molecular Function (MF), Cellular Component (CC) and Biological process (BP). GOSemSim of R-program was used to calculate semantic similarity between the GO terms and the gene products. In this study, GO terms derived from human annotations were used for calculations. The estimation of between-term similarity was based on the Wang semantic similarity measure . Aggregawhere, two sets of GO terms gi = {gi1, gi2, …………., gim} & gj = {gj1, gj2, ……………, gjn} as query and reference sequence. Method max calculates the maximum semantic similarity score over given pairs of GO terms between these two sets, while average calculates the average semantic similarity score over a given pairs of GO terms. The hierarchical clustering of tissue specific, differentially expressed genes in relation with a normal lung tissue is shown in a Dendrogram. In the colour code of heat map, red represents a low semantic similarity below the median level, whereas, the green represents a high semantic similarity above the median level.The clustering analysis was carried out by the program pvclust . It is aIn order to find the lung tissue specific differentially expressed genes, two Unigene pools (A and B) were constructed  and Lactotransferrin (LTF). In the normal lung (DDD1 data), UCHL1 was down-regulated and LTF was up-regulated Table . This waThe cluster 5 was playing the common functional role of immune response and complement activation. The down-regulated RPSA, RPL9, TMSB4X and TUBA1B in normal lung (DDD1) were significantly up-regulated in lung cancer (DDD2)  and ALDOA (7 fold in our study) were regulated by hypoxia inducible factor (HIF) during lung cancer -31. The However, in this cluster, the AZIN1 and TICAM2 were down-regulated and were lacking direct experimental evidence to support their regulation with HIF or hypoxia during cancer. The following literature analysis suggests their possible regulations either with HIF or hypoxia. The AZIN1 was an inhibitor for the antizyme and both were highly regulated in human cancers and antizyme induced HIF, during increased cellular redox potential -53. The All these results suggest that the cluster 6 represents the panel of either HIF or Hypoxia related lung cancer biomarkers.http://methycancer.genomics.org.cn) revealed that amongst the seven, KIAA1324, NET1, NTN3, RPL10 and TFPI2 were epigenetically regulated through DNA methylation. In the remaining two, SFTPA1 was epigenetically regulated [http://www.genecards.org). All these results show that the cluster 7 represented the panel of epigenetically regulated lung cancer specific extra cellular matrix biomarkers.In the Cluster 7, there were seven lung biomarkers, mostly encoding for lung tissue specific extra cellular matrix proteins. The epigenetic analysis using Methycancer database  . So, these subtractive approaches successfully increase the probability of identifying the lung cancer specific probable candidate biomarkers.UniGene database using the DDD tool provides us a computational approach to study and understand the lung tissue specific gene expression levels in both disease and normal conditions . StudyinThe semantic similarity scores amongst the GO terms and the subsequent hierarchical clustering were calculated using the freely available R-software for lung tissue specific candidate genes from normal and cancer conditions. The analysis of members of individual genes from each cluster revealed the functional significance of each cluster. Out of the seven clusters, our approach identified four functionally important clusters. The four clusters represented metastasis diagnostic markers, chemotherapy/drug resistance related biomarkers, and HIF or Hypoxia induced biomarkers and epigenetically regulated extra cellular matrix biomarkers for lung cancer. This suggests that, especially for lungs tissues, the semantic similarity score amongst GO terms between normal and diseases condition from the same tissue can prioritise biomarkers. But, further study is necessary to extend our hypothesis to other tissues. This subtractive approach integrated with semantic similarity score among GO terms can offer a predictive capability for detecting tissue specific signature biomarkers from the tissue specific differentially expressed data. This approach is also complementary to the network based biomarker prediction approach ,61. Our Our study suggests that amongst the 4 panels, HIF or Hypoxia induced lung cancer biomarkers panel (panel 3) is the most important cluster. Because, in other clusters, most of the identified lung cancer biomarkers follow the same expression pattern (either up or down) in other types cancers like breast, ovarian, cervical etc. However, in our study and literature, the expression pattern of genes down regulated in cluster 6 / panel 3 is distinct from almost all types of other cancers. In panel 3, the expression pattern of the HIF and its modulating proteins are completely different when compared to most of the other types of cancers. For example, in most of the cancerous conditions the HIF level is up-regulated . This upTherefore, it is evident from our study that the HIF down regulation also affect the expression level of the other HIF modulating lung cancer biomarkers. All the down-regulated genes, in this Panel 3 showed their significant up-regulation in most of many types of cancers  and other normal tissues (251 libraries) with their fold change and transcript frequency values of Pool A and B. Table S2. DDD2- The complete list of differentially expressed normal lung tissues (11 libraries) and lung cancer tissues (8 libraries) with their fold change and transcript frequency values of Pool A and B. Table S3. The DDD1complete conversion list of unigene identifier to Entrez gene id. Table S4. The DDD2complete conversion list of unigene identifier to Entrez gene id. Click here for fileTable S1. The complete lists of 76 genes from DDD1 were differentially expressed in DDD2. Table S2. The complete list of 58 genes after removing 18 genes expressed in all types of cancers from the 76 genes. Click here for fileTable S1. Number of unique Unigene identifiers and ≥2 fold present in DDD1 and DDD2 Figure: Three- way Venn diagram of DDD1, DDD2 and genes expressing in all types of cancers. Table S2. The complete list of genes and their symbols in different intersections (A to G) of Venn diagram were given. Click here for fileTable S1. The complete list of genes expressed in all type of cancers . Click here for fileTable S1. The complete list of Adult Human Lung Tissue specific genes from TiSGeD having SPM ≥ 0.9. Table S2. The TiSGeD gene symbol conversion to Enterz ID. Click here for file


A major goal of cancer genome sequencing is to identify mutations or other somatic alterations that can be targeted by selective and specific drugs. dGene is an annotation tool designed to rapidly identify genes belonging to one of ten druggable classes that are frequently targeted in cancer drug development. These classes were comprehensively populated by combining and manually curating data from multiple specialized and general databases. dGene was used by The Cancer Genome Atlas squamous cell lung cancer project, and here we further demonstrate its utility using recently released breast cancer genome sequencing data. dGene is designed to be usable by any cancer researcher without the need for support from a bioinformatics specialist. A full description of dGene and options for its implementation are provided here. Cancer genome sequencing studies are now analyzing 50 to 500 patients per study and are documenting thousands of somatic mutations Druggable Genes), is based on the concept of the druggable genome introduced by Hopkins and Groom in 2002 This tool, which we call dGene  illustrate a straightforward case with well curated sources The dGene filter has recently been used by The Cancer Genome Atlas (TCGA) Squamous Cell Lung Cancer project to analyze somatic mutations found in 178 squamous cell lung cancer cases; details can be found in that publication PIK3CA is mutated in 37/77 samples, but an additional patient (BRC44) had a KPDL567 in-frame deletion in PIK3R1, a regulatory subunit that binds PIK3CA. This deletion occurs at the PIK3R1-PIK3CA binding interface and may alter PI3-kinase signaling TEX14 (names: testis-expressed protein 14 or sugen kinase 307) and INSRR (insulin receptor-related receptor) tyrosine kinases are two relatively novel drug targets. TEX14 has been implicated in multiple myeloma and breast cancer The dGene results provide new information about this cancer genome dataset. MAP3K1 are found in 9/77 patients, and most of these events are loss of function mutations MAP3K1’s presence in the dGene output analysis demonstrates that dGene provides no information as to whether a mutation is gain-of-function, loss-of-function, or functionally silent. Given a list of gene symbols, dGene only acts as a filter. The presence of Titin and two collagen genes (COL28A1 and COL6A3) illustrate how very large genes, which frequently contain druggable components and tend to be frequently mutated, will continue to filter through dGene. The presence of a gene in the dGene output does not guarantee a given mutation’s biological relevance.th or above the 80th percentile were considered , leaving 54,301 events in 16,924 genes (TPTE2 (names: transmembrane phosphoinositide 3-phosphatase and tensin homolog 2 or TPIP) is the most commonly lost PTEN family member, with CN losses observed in 14/46 patients, which is a frequency 3.5-fold higher than the PTEN CN losses (4/46). The literature on TPTE2 is limited and it indicates that TPTE2 can inhibit cell growth and initiate apoptosis, similar to the PTEN tumor suppressor dGene can be applied to any dataset containing a list of gene symbols. To illustrate this we analyzed gene copy number (CN) data from the 46 estrogen receptor positive breast cancers that underwent whole genome sequencing (coded “BRC”) 24 genes . Filteri24 genes . The CN 24 genes . TPTE2  to identify mutations that are both likely druggable and likely functional As with all data-based resources, updating dGene will be of the utmost importance. dGene classes tend to be well studied, as illustrated by the fact that 2108 out of 2257 entries can be found in SwissProt, a manually reviewed collection of protein annotations dGene is designed to be used by cancer researchers and not require support from a bioinformatics specialist. dGene is currently hosted as a web-based tool through the Genome Institute at Washington University (dgidb.genome.wustl.edu). There, users can filter gene lists against dGene , which can be imported into various statistical packages and used or customized as needed. Additional functionality of the website includes annotating dGene entries with specific drug information where available . In summary, dGene provides a rapid filter to identify druggable genes across ten classes from cancer genomic studies, and is currently available for use through a professionally constructed website.Classes were populated with human genes through a process of inclusion from specialized databases and reviews, standardization to the NCBI gene list, and manual curation of genes occurring in a single source. During manual curation, bias was towards inclusion. Genes were left in their respective class if they either showed sequence homology to a known member, or if experimental evidence suggested they had the appropriate functionality. Pseudogenes and genes encoding nonfunctional products were included if they showed homology to an included class member.ftp://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz, accessed on July 3, 2012) facilitated comparisons between sources. The NCBI human gene list represents the total collection of human genes recognized in the NCBI data base as well as current annotations, and is updated on a daily basis. The NCBI gene list provides a standard format for all dGene entries –15 columns, including the NCBI geneID, official symbol, and crucially, a list of synonyms used in the literature. To each entry a 16th column, class, has been appended. Mapping was accomplished by converting protein names to gene names with the David Gene ID Conversion Tool A frequent challenge in consolidating disparate sources was the mixing of incompatible gene and protein identifiers. Mapping to the NCBI human Gene List Click here for additional data file.Table S1(CSV)Click here for additional data file.Table S2(XLS)Click here for additional data file.Table S3(XLS)Click here for additional data file.Table S4(XLS)Click here for additional data file.Table S5(XLS)Click here for additional data file.Table S6(XLS)Click here for additional data file.


Type 2 diabetes mellitus (T2DM) has an intersecting underlying pathology with thyroid dysfunction. The literature is punctuated with evidence indicating a contribution of abnormalities of thyroid hormones to type 2 DM. The most probable mechanism leading to T2DM in thyroid dysfunction could be attributed to perturbed genetic expression of a constellation of genes along with physiological aberrations leading to impaired glucose utilization and disposal in muscles, overproduction of hepatic glucose output, and enhanced absorption of splanchnic glucose. These factors contribute to insulin resistance. Insulin resistance is also associated with thyroid dysfunction. Hyper- and hypothyroidism have been associated with insulin resistance which has been reported to be the major cause of impaired glucose metabolism in T2DM. The state-of-art evidence suggests a pivotal role of insulin resistance in underlining the relation between T2DM and thyroid dysfunction. A plethora of preclinical, molecular, and clinical studies have evidenced an undeniable role of thyroid malfunctioning as a comorbid disorder of T2DM. It has been investigated that specifically designed thyroid hormone analogues can be looked upon as the potential therapeutic strategies to alleviate diabetes, obesity, and atherosclerosis. These molecules are in final stages of preclinical and clinical evaluation and may pave the way to unveil a distinct class of drugs to treat metabolic disorders. The role of hyperthyroidism in diabetes was investigated in 1927, by Coller and Huggins proving the association of hyperthyroidism and worsening of diabetes. It was shown that surgical removal of parts of thyroid gland had an ameliorative effect on the restoration of glucose tolerance in hyperthyroid patients suffering from coexisting diabetes [There is a deep underlying relation between diabetes mellitus and thyroid dysfunction . A plethUnmanaged pro diabetes, both type 1 and type 2, may induce a “low T3 state” characterized by low serum total and free T3 levels, increase in reverse T3 (rT3) but near normal serum T4 and TSH concentrations . The relT2DM owes its pathological origin to inappropriate secretion of insulin, due to defective islet cell function or beta cell mass. Continuous consumption of calories-rich meals, junk food and sedentary lifestyle have culminated into an epidemic of diabetes projected to afflict around 300 million people across the globe by 2020 . DefectiThyroid dysfunction is a common endocrine disorder with variable prevalence. Wickham survey reveals that a prevalence of thyroid dysfunction in male adults in England was 6.6% . AccordiThe prevalence of thyroid disorder in diabetic population was reported to be 13.4% with higher prevalence (31.4%) in female T2DM patients as compared to (6.9%) in male T2DM patients . ConsideThyroid hormones directly control insulin secretion. In hypothyroidism, there is a reduction in glucose-induced insulin secretion by beta cells, and the response of beta cells to glucose or catecholamine is increased in hyperthyroidism due to increased beta cell mass. Moreover, insulin clearance is increased in thyrotoxicosis , 16.β oxidation [Increased glucose output from liver is the pivotal reason for the induction of hyperinsulinaemia, induction of glucose intolerance, and development of peripheral insulin resistance . Glucosexidation , 19. ThiReduced glucose absorption from gastrointestinal tract accompanied by prolonged peripheral glucose accumulation, gluconeogenesis, diminished hepatic glucose output and reduced disposal of glucose are hallmarks of hypothyroidism . In overβ-cell mass [The pathological features of T2DM include increased intestinal glucose absorption, reduced insulin secretion, and change in the ell mass . Furtherell mass , increasell mass , increasell mass , enhanceell mass . These fell mass . Hence, ell mass .Insulin resistance in the muscles and liver is a characteristic feature of T2DM. An undisturbed glucose homeostasis and intact insulin secretary response and unperturbed sensitivity of the tissues to insulin are essential to maintain normal blood glucose levels –31.Glucose disposal is mediated by the conjoint effect of insulin and hyperglycemia to modulate three basic phenomenon. Firstly, diminution of endogeneous (hepatic) glucose production. Secondarily, enhanced uptake of glucose (hepatic and splanchnic). Thirdly, upregulation of glucose by peripheral tissues . Glucose uptake into muscles is modulated by glycolysis and glycogen synthesis. Hepatic insulin resistance is characterized by glucose overproduction inspite of fasting hyperinsulinemia, and enhanced rate of hepatic glucose output was the pivotal modulator of increased fasting plasma glucose (FPG) concentration in T2DM subjects . In insuThe term harmonious quartet is used to address the core pathology of insulin resistance . Deregulβ cell function are inversely correlated with thyroid stimulating hormone which may be explained by insulin-antagonistic effects of thyroid hormones along with an increase in TSH. The higher serum TSH usually corresponds to lower thyroid hormones via negative feedback mechanism. As TSH increased, thyroid hormones decreased and insulin antagonistic effects are weakened. These observations demonstrate that insulin imbalance is closely associated with thyroid dysfunction and the phenomenon id mediated via β cell dysfunction [Insulin resistance and function .Hyperthyroidism has been associated with insulin resistance which has been linked with elevated glucose turnover, increased intestinal glucose absorption, elevated hepatic glucose output, increased free fatty acid concentrations, increased fasting and or postprandial insulin an proinsulin levels, and increased peripheral glucose transport accompanied by glucose utilization , 34. T2DIn hyperthyroidism, endogenous glucose production is elevated and reduces hepatic insulin sensitivity in humans  due to gThere is marked increase in the skeletal glucose utilization in hyperthyroid state . IncreasHaluzik et al. summarized that rate of local lipolysis in the abdominal subcutaneous adipose tissue was a result of modulation of norepinephrine (NE) levels and adrenergic postreceptor signaling by thyroid hormones . Other sin vitro and preclinical studies [Insulin resistance has been shown to be caused in hypothyroidism in various  studies –53 where studies , 54–56. Subclinical hypothyroidism has been reported to be associated with insulin resistance , 57, 58.β2 adrenergic receptor, inhibitory G protein (Gi), phosphoenolpyruvate kinase (PEPCK) [Various genes have been identified which are identified with gluconeogenesis, glycogen metabolism, and insulin signaling. These include glucose 6 phosphate, protein kinase B (Akt2),  (PEPCK) , pyruvat (PEPCK) , 64, mal (PEPCK) , and car (PEPCK) . A raiseTranscription of various enzymes involved in lipid metabolism has been reported to increase in hyperinsulinemic or insulin-resistant mice , 67. Traβ2 adrenergic receptors [The various genes which influence the interaction of thyroid hormone and skeletal muscles include GLUT1, GLUT4 , β2 adreceptors , phosphoeceptors , PPAR gaeceptors , and miteceptors . Amongsteceptors . Mitochondrial uncoupling protein 3 (UCP 3) is a recently identified gene and has been unveiled to be associated with glucose metabolism and decreased fatty acid oxidation . It has α1, TRβ1, TRβ2, and TRβ3. These are four major T3 binding isoforms [α1 is hypothesized to regulate the metabolic effects of thyroid hormone. TRβ1 and TRβ2 are related with maintenance of hypothalamic-pituitary-thyroid axis and keeping the euthyroid state [Autoimmune causes are reported to be responsible for the genetic dysfunction in the diabetic patient suffering from thyroid related disorders. However, these findings advocate an immense clinical evidence to support association between T1DM (Type 1 diabetes mellitus) and autoimmune thyroid dysfunction (AITD) , 76. Arrisoforms . TRα1 isid state . It has been investigated that 3,5,3-triiodothyronine is derived from T4. It can be activated via removing an iodine atom from the phenolic ring by the iodothyronine deiodinases type 1 (D1) and type 2 (D2). Type 3 deiodinase (D3) inactivates thyroid hormone by removing an iodine atom from the tyrosyl ring. The deiodinases are expressed in various tissues, and their expression levels vary enormously during development and are regulated by thyroid hormone status. Type deiodinase (D1) is expressed in liver, while type 2 deiodinase (D2) is expressed in adipose tissue and skeletal muscle. They are involved in regulation of bioavailability of T3 and hence, the response to insulin. Elevated concentrations of T3 are associated novel missense variant (Thr92Ala). This phenomenon is closely associated with insulin resistance. It is also associated with a surge in glucose turnover accompanied by an upregulation of insulin-mediated glucose disposal in skeletal muscle and adipose tissue. This phenomenon mediated via positive regulation of insulin sensitive GLUT-4 transcription , 79 showA separate meta-analysis indicated that intracellular tri-iodothyronine (T3) is responsible for aberrations in insulin sensitivity . It has Investigations using skeletal muscles in hypothyroid and euthyroid humans have revealed a discernable influence on the downregulated expression of glucose transporter 5 (GLUT 5) but not GLUT 4 , 82. GluThyrotoxicosis leads to enhanced lipid peroxidation whereas hypothyroidism causes diminished glucose oxidation. LDL clearance leads to lowered cholesterol and triglyceride levels. TH instigates upregulation of catecholamine action leading to lipolysis on adipocytes and enhancement of circulating FA. Elevated supply of FA counteracts TH-mediated elevated hepatic long-chain FA oxidative process. Elevated circulating FA levels and availability of gluconeogenic substrates from peripheral reserves reciprocates increased gluconeogenesis in T3-treated animals. It has been reported that T3 enhances fasting plasma glucose and free FA levels. Activation of peripheral substrates explains precipitation of hyperglycemia in thyrotoxicosis . ParadoxCappelli et al. evaluated the thyroid hormone profile by studying the interaction between metformin and circulating thyroid function parameters in patients who were started on metformin . A pilotIn vitro studies support the use of metformin in other thyroid diseases other than hypothyroidism. Metformin has inhibited the cell proliferation and growth-stimulatory effect of insulin on thyroid carcinoma cell lines. Same study showed the stimulation of apoptosis and enhancement in the action of chemotherapeutic agents (doxorubicin and cisplatin) by metformin [etformin . Other retformin .Thyroid hormones have profound influence on various physiological processes ranging from metabolism of lipid, protein, and carbohydrate. The literature is punctuated with reports claiming antiatherogenic and lipolytic influences of thyroid hormones. However, their deleterious effects on bone, muscles, and heart are major hurdles . Thyroidβ only and not TRα in liver and white adipose tissue [Development of potent thyroid hormone analogues that selectively elude the harmful effects of thyroid hormone, and at the same time, produce desirable therapeutic effects has been the cynosure of scientific research –94. The e tissue .Various studies have been undertaken to understand the role, importance, and need of determination of thyroid dysfunction in the patients of T2DM. It has been unequivocally apparent that testing for thyroid dysfunction in T2DM patients is necessary and should be carried out annually . GuideliThe “American Thyroid Association” guidelines for T2DM patients require frequent testing for thyroid dysfunction. They recommend testing from 35 years of age, and every 5 years thereafter in adults. High-risk patients may require more frequent testing. The American Association of Clinical Endocrinologists, Thyroid Disease Clinical Practice Guidelines (2002) recommends thyroid palpation and TSH in diagnosis, especially if goitre or other autoimmune disease presents in association with T2DM. Regular screening for thyroid abnormalities in all diabetic patients will allow early treatment of subclinical thyroid dysfunction. A sensitive serum TSH assay is the screening test of choice. It has also been proposed that in T2DM patients, a TSH assay should be performed at diagnosis and then repeated at least every 5 years.In internal medicine, it is repeatedly proven that the association between thyroid dysfunction and diabetes mellitus is evident. Thyroid dysfunction chiefly comprises hypothyroidism and hyperthyroidism although the entity belongs to the same organ but with vast difference in pathophysiology as well as clinical picture. The interface between thyroid malfunction owing to diabetes is a matter of investigation. The literature suggests that polyendocrinal multidysfunction leads to stimulation of a cascade of reactions which are actually antihomeostatic in nature. For instance, hypoadrenalism as well as hypopituitarism exhibits strong linkage with hypothyroidism and consequently diabetes mellitus. Recent findings have evidenced the intricate bond between subclinical hypothyroidism and diabetes mellitus that deceptively contribute to the major complications such as retinopathy and neuropathy. Cardiovascular events and micro- or macro-angiopathies are the counterreflection of resurgence of heavily disturbed lipid metabolism due to thyroid dyscrasias. It is also evident from the existing literature that insulin resistance bears an indispensable role in connecting T2DM and thyroid dysfunction. Novel molecules have shown the path for the development of suitable thyroid hormone receptor analogues to treat metabolic diseases. It is important to diagnose thyroid dysfunction in T2DM patients, and this practice should be inculcated in clinical settings with immediate effect to nourish further understanding of thyroid dysfunction and T2DM.


Mayer–Rokitansky–Kuster–Hauser (MRKH) syndrome is characterized by uterine and vaginal canal aplasia in normal karyotype human females and is a syndrome with poorly defined etiology. Reproductive toxicity of phthalate esters (PEs) occurs in rat offspring exposed in utero, a phenomenon that is better studied in male offspring than females. The current study reports female reproductive tract malformations in the Sprague–Dawley rat similar to those characteristic of MRKH syndrome, following in utero exposure to a mixture of 5 PEs. We determined that females are ~2-fold less sensitive to the effects of the 5-PE mixture than males for reproductive tract malformations. We were not fully successful in defining the critical exposure period for females; however, incidence of malformations was 88% following dosing from GD8 to 19 versus 22% and 0% for GD8–13 and GD14–19, respectively. Overall, this study provides valuable information regarding female vulnerability to in utero phthalate exposure and further characterizes a potential model for the human MRKH syndrome. The widely used plasticizer compounds, phthalate esters, are recognized as reproductive toxicants in the male rat, with fetal exposure during the sexual differentiation period resulting in reproductive tract malformations reminiscent of human testicular dysgenesis syndrome . NumerouWe previously reported the ability to use individual phthalate potencies to predict the effects of a mixture of five phthalates on fetal male testicular androgens following in utero exposure . The dosPreviously, we found that several female rat offspring of dams exposed from weaning, through puberty, mating and pregnancy to 250 and 500 mg DBP/kg/d  displayed uterine malformations and reduced fecundity . MylchreThe Mayer–Rokitansky–Kuster–Hauser (MRKH) syndrome in humans can be quite variable but is generally characterized by aplasia of the uterus and upper 2/3 of the vagina in a female with normal secondary sexual development and a 46, XX karyotype. The incidence of MRKH syndrome is estimated at 1/4000 to 1/5000 live female births . AlthougPseudomonas and every 4 months for a suite of chemicals including pesticides and heavy metals. The current study was conducted under protocols approved by the National Health and Environmental Effects Research Laboratory Institutional Animal Care and Use Committee in an Association for Assessment and Accreditation of Laboratory Animal Care-approved animal facility.Adult female Sprague–Dawley (SD) rats  were mated by the supplier and shipped on gestation day 2 (GD2), with day of sperm plug positive = GD1. Animals were housed individually in 20 cm × 25 cm × 47 cm clear polycarbonate cages with laboratory-grade heat-treated pine shavings  with a 12:12 light/dark photoperiod (lights off at 19:00 h) at 20–24 °C. Pregnant and lactating females were fed Purina Rat Chow 5008 ad libitum. Animals were provided access to filtered (5 μm filter) municipal drinking water  ad libitum. Water was tested monthly for BBP; CAS 85-68-7, Cat# 308501, lot# 08523JQ, purity 98%); dibutyl phthalate ; di(2-ethylhexyl) phthalate ; diisobutyl phthalate , and dipentyl phthalate  were purchased from Sigma–Aldrich . The doses were delivered in 2.5 μl corn oil per gram body weight.Laboratory-grade corn oil , and butyl benzyl phthalate (n = 6 dams/group) on GD8 in a manner that provided similar mean (±SE) body weight per treatment group prior to dosing. Sample sizes were based on power calculations performed on previous data demonstrating that 5–6 litters per dosage group was appropriate for detecting significant differences in incidence of malformations. Dams were dosed via oral gavage on GD8 through postnatal day (PND) 3 with 0, 65, 130, 260, 520, and 780 mg total phthalates/kg/d. The doses consisted of a mixture of five phthalates, including BBP, DBP, DEHP, DiBP and DPeP in ratio of 3:3:3:3:1, respectively. This ratio was formulated such that each phthalate contributed equally to the reduction in testicular testosterone production in fetal male rats exposed from GD8 to GD18 (Pregnant rat dams were assigned to one of seven treatment groups ( to GD18 .n = 5 dams per treatment group): GD8–19 (continuous), GD8–13 (early) or GD14–19 (late), repeating the second highest dosage level of the mixture from the above study which induced a high incidence of female reproductive tract malformations, but a lower level of perinatal mortality. A control group was dosed with corn oil from GD8–19 (n = 5 dams).Dams were dosed by oral gavage as above with the mixture at 520 mg total phthalate/kg/d for one of 3 dosing periods , the body weight and anogenital distance (AGD) of the pups  were measured as per Hotchkiss . Male ofn = 101 females) were necropsied when they reached PND 77 (11 weeks of age). The remaining females (n = 65) were necropsied beginning at PND350 (50 weeks old). In the critical period assessment study, all female offspring were necropsied at PND 120 (17 weeks of age). Animals in both studies were examined for external  and internal reproductive malformations . Ovaries were excised and weighed. The animals were also examined for abnormalities of the kidney , blood in the bladder and anophthalmia, as reported previously, following DEHP in utero exposure : no detected vagina orifice posterior to the urinary aperture.Uterine malformations: unicornis , one or both of the uterine horns is absent or partially missing.Hydrometrocolpos: accumulation of fluid in a uterine horn resulting in abnormal swelling.Hydronephrosis of the kidney(s): one or both kidneys contains signs of swelling or dilation of the kidney collection system, appears as agenesis of the renal medulla and can be mild (20–40% of medulla) or severe (>40% of medulla missing).Anophthalmia: absence of one or both eyes.The various malformations were defined as follows:p < 0.05. AGD data were analyzed with and without body weight or the cube root of body weight as a covariate. Results were reported as AGD with no correction for body weight since there was no difference in outcome between the analyses with and without body weight as a covariate in Study 1 or 2 and neither covariate improved the error variance in the analysis.Data were analyzed by one-way analysis of variance (ANOVA) using the general linear measures procedures from the Statistical Analysis Systems . Post hoc comparisons were made using the Least Squared Means procedure on SAS. Data were analyzed as litter means, with the exception of the malformation data which were analyzed both as individual means by Fishers exact test  and litter means using ANOVA. Data were considered significant at The combination dose of five phthalates significantly reduced maternal body weight gain at the highest dose administered  and 780 mg/kg/d . The incidence of uterine malformations was also increased with phthalate mixture doses of 520 mg/kg/d  and 780 mg/kg/d . The uterine abnormalities observed were: partial or complete agenesis of the uterine horns, and hydrometrocolpos. Most females with uterine malformations had only one uterine horn missing. The ovaries were present in all females and appeared grossly normal, regardless of treatment group. There were no reproductive malformations observed in female offspring of dams administered doses below 520 mg total phthalate/kg/d.Administration of the phthalate mixture at dose of 520 and 780 mg total phthalate/kg/d significantly increased the incidence of reproductive tract malformations in the female offspring, including absence of a vaginal opening and uterine horn malformations . The absp < 0.05) and occurred in 2/16 females (1/4 litters) of the 520 mg total phthalate/kg/d dose group (NS). In addition, one female in the 520 mg total phthalate/kg/d dose group was born without a tail . Neither body weight at necropsy or incidence of non-reproductive malformations was significantly affected by treatment. Unilateral hydronephrosis was observed in 4/16 females (2/4 litters) in the 520 mg total phthalate/kg/d and another female from this dose group lacked a right kidney; all females with kidney aberrations at the 520 mg total phthalate dose also had reproductive malformations. Bilateral hydronephrosis was observed in one female each of the control and 8.3 mg total phthalate/kg/d dose groups; both of these two females had normal vaginal openings.The incidence of anophthalmia in one or both eyes was significantly increased in the 780 mg total phthalate/kg/d dose group  and bilateral (p < 0.0001) and unilateral (p < 0.05) uterine agenesis were significantly increased in the continuous dosing period group only (GD8–19). One female in the GD8–19 had had partial agenesis of the right uterine horn with the remainder fluid-filled  and GD8–13 (22%), but not GD14–19 0%; . The incd-filled  and one d-filled . One femd-filled .One female in the GD8–19 group had anophthalmia in both eyes and one female in the GD8–13 group had anophthalmia in one eye . HydroneAlthough less studied than in males, phthalate esters are also reproductive toxicants in the fetal and pregnant female rat. In the current study, we report high incidences of reproductive malformations  in the adult female rat following in utero exposure to a mixture of five PEs. We did not fully identify the critical exposure period for inducing these malformations in the female. We do provide evidence to support the hypothesis that the female window is earlier in gestation than the male window which is encompassed by GD14–19 since a low incidence of female reproductive tract malformations were induced with GD8–13 exposure but not GD14–18. In contrast to the female reproductive effects and the fetal mortality, the effects on the perinatal male offspring in study 2 were restricted to those exposed during GD14–19 (reduced AGD and retained nipples).This is the first report demonstrating a high incidence of female reproductive malformations following gestational exposure to a mixture of phthalates. Previous studies focusing on single phthalate exposures report relatively lower incidences of female effects. A combination of in utero and lactational exposure to DBP (250 mg/kg/d) caused reduced fecundity and induced a low incidence of uterine malformations  in female rats, while their male siblings exhibited a low incidence of hypospadias, undescended testes and lowered epididymal sperm counts . Low freFemale rats exposed to phthalate esters from puberty through pregnancy and lactation also show altered reproductive physiology, but no gross reproductive malformations were reported. Female Long Evans (LE) rats orally dosed with DBP (250–1000 mg/kg/d from weaning through pregnancy/lactation) cycled and mated normally , however several females exposed to 500 mg DBP/kg/d aborted their litters around mid-pregnancy, and treatment with 1000 mg/kg/d DBP resulted in complete infertility. In a follow up study, female LE rats treated with DBP  had significantly decreased litter size associated with reduced serum progesterone levels and ovarian progesterone production on GD13 . In addiOn the contrary, the results of the current study lead us to hypothesize that the five phthalate mixture administered during gestational development in the current study is likely operating through earlier gene-related events. If the fetal ovaries were “masculinized” by the phthalate mixture and produced androgens then we would expect to see agenesis of the lower vagina and nipples with no effect on the uterus. However we did not see agenesis of the nipples at 14 days of age. If the fetal ovaries were fully “masculinized” and secreted both anti-Mullerian hormone (AMH) and testosterone then this would cause agenesis of the lower vagina, the utero-cervix complex, and the nipples. Nevertheless, we detected a combination of uterine and vaginal malformations without nipple agenesis. In addition, we observed that both “ovaries” in one treated female from the second experiment were located above the kidneys. Histological confirmation of these tissues as ovarian tissue is in progress. The reproductive malformations seen in the females exposed in utero to the mixture of phthalates (uterus and vagina) do not fully conform to those expected from in utero exposure to excessive androgen and AMH levels . In that case, it appears that masculinization of fetal ovarian hormone production by the phthalates is not likely the primary or sole mechanism responsible for the malformations detected in the current study.Identification of the most critically vulnerable period of development to PEs in the fetal female rat would facilitate investigation of the gene changes associated with the detected malformations. In males, GD16–18 is the period where differentiation to the male phenotype is most sensitive to phthalate disruption . Indeed,This report highlights the striking similarity of the “female phthalate syndrome” to MRKH syndrome in women, including uterine and vaginal agenesis and renal defects. To date, investigations into the etiology of MRKH syndrome have been unsuccessful in identifying the genetic components of the disease . NeverthUntil now, the major focus of phthalate toxicity has been the male anti-androgenic and reproductive toxicity properties, particularly in fetal-exposed offspring. The results presented in this study draw attention to the need to concurrently evaluate female reproductive toxicity of PEs. Not only is this important from the standpoint of phthalate human risk assessment, but as discussed here, further characterization of this model of MRKH syndrome may potentially make great strides toward elucidating the genetic origins of the disease, which may have a resounding impact on the relatively high frequency of afflicted women.


Many computational methods have been used to predict novel non-coding RNAs (ncRNAs), but none, to our knowledge, have explicitly investigated the impact of integrating existing cDNA-based Expressed Sequence Tag (EST) data that flank structural RNA predictions. To determine whether flanking EST data can assist in microRNA (miRNA) prediction, we identified genomic sites encoding putative miRNAs by combining functional RNA predictions with flanking ESTs data in a model consistent with miRNAs undergoing cleavage during maturation. In both human and mouse genomes, we observed that the inclusion of flanking ESTs adjacent to and not overlapping predicted miRNAs significantly improved the performance of various methods of miRNA prediction, including direct high-throughput sequencing of small RNA libraries. We analyzed the expression of hundreds of miRNAs predicted to be expressed during myogenic differentiation using a customized microarray and identified several known and predicted myogenic miRNA hairpins. Our results indicate that integrating ESTs flanking structural RNA predictions improves the quality of cleaved miRNA predictions and suggest that this strategy can be used to predict other non-coding RNAs undergoing cleavage during maturation. Over the past decade, microRNAs (miRNAs) have emerged to be important and powerful factors in most biological processes. miRNAs are small (∼21 nt) species of non-coding RNA (ncRNA) molecules that regulate cognate mRNAs exhibiting sequence complementarity. miRNAs are first transcribed as long primary transcripts (pri-miRNAs) containing hairpin-like structures which are excised by DGCR8/Drosha dependent endonuclease cleavage to form pre-miRNAs Drosophila, and the zebrafish Increases in abundance of three major muscle-specific miRNAs  have been observed during muscle cell differentiation in the mouse, Drosophila larvae development These miRNAs are necessary during myogenesis. Completely malformed musculature results when mir-1 is absent during In summary, there is abundant evidence indicating that miRNAs play an important role in specifying the myogenic lineage and it is likely that several undiscovered miRNAs participate in this process. To identify additional miRNAs expressed specifically during myogenic differentiation, we developed and tested a novel strategy of high-throughput tissue-specific miRNA prediction.Methods employed to predict miRNAs have followed a general theme to identify genomic sites with the ability to produce simple, unbranched RNA hairpin structures and to identify putative RNA hairpins with properties making them distinct from spontaneously arising hairpins One of the most abundant but underappreciated reservoirs of data to facilitate this work is Expressed Sequence Tag (EST) data previously generated to characterize the transcriptome with over 60 million EST sequences made publicly available in the NCBI dbEST miRNA precursor molecules are polyadenylated To this end, we developed a methodology that combines techniques from ncRNA prediction with EST sequences. Our novel reinterpretation of preexisting public EST data identifies traces of miRNAs and provides expression predictions from tissue-specific EST libraries. This method constitutes a de facto novel contribution to techniques of predicting tissue-specific ncRNAs.We used mm8, mm9 and hg18 releases of the mouse and human genomes. Four-way conservation data used in RNAz included human (hg18), rat (rn4), dog (canFam2) and mouse . Where required, miRNA, EST annotations, and multiZ annotations were obtained from the appropriate releases of the UCSC genome annotation datasets ncRNA predictions were generated using three methods: RNALfold n, where n equals the number of ESTs mapped to the miRNA associated sequence window. Scores for all miRNAs were aggregated, binned in 10 nt windows, and plotted. Significance scores for peaks observed in each genome assembly were determined by bootstrapping. Briefly, for each miRNA window, window coordinates were shuffled to random locations on the same chromosome and EST termini were identified in ±1 kb genomic windows. This procedure was replicated 10,000 times. EST accumulation was scored as described above, and the scores for each genome assembly and test (upstream/downstream of miRNA) were aggregated. The number of times a replicate generated a central peak in excess of the observations was counted and used to determine a P-value.To identify and visualize relationships between miRNAs and ESTs, 3′ or 5′ coordinates of miRNA annotations on human and mouse genomes  were identified and 2 kb sequence windows centered on this location were retrieved. To compute a normalized score of EST occupancy in this region, coordinates of EST termini mapping within each sequence window were identified and a score for each site was incremented by 1/To screen candidate miRNA hairpins, we filtered hairpin structures based on their normalized ΔG value and the distance to a downstream EST terminus, as true miRNAs were observed to have lower than average normalized free energy (NFE) than the entire set of genomic hairpins computed with RNALfold. We thus examined the effect of filtering predicted structures based on precision/recall values computed using NFEs between 0 and −1 kcal/mol and EST proximities between 0 and 200 nt See . We retaEnrichment statistics for EST supported miRNA predictions were determined by shuffling candidate miRNA annotations to random locations within the same chromosome and strand. Candidate miRNAs were then predicted by intersecting EST data according to the methods described above.Precision and recall of miRNA predictions were computed using as positives a set of known miRNAs or the subset of those supported by transcript data if such data were being used in prediction. MiRNA predictions were considered supported if they had EST annotations within 14 nt for those predicted computationally with RNALfold or RNAz, or within 51 nt for those predicted using high-throughput sequencing. For high-throughput sequencing data, a known miRNA was considered as transcript-supported if there was at least one Illumina sequencing fragment mapped within the known miRNA structure. For miRNA predictions generated by RNALfold and RNAz, a known miRNA was considered as supported if at least 75% of the nucleotides in the predicted hairpin and miRNA structure overlapped.13267 miRNA predictions were generated using RNA hairpin predictions in the mouse genome (mm8). Supporting dbEST annotations  were integrated with RNA structures as explained above to add source tissue predictions. A summary of miRNA predictions generated for mouse (mm9) is in To detect expression of putative miRNAs, a custom oligonucleotide tiling microarray with 60mer probes (Agilent) was designed to tile over selected known and predicted miRNA hairpins with probes overlapping by 20 nt. Control probe sequences including positive and negative controls for known miRNAs, other ncRNAs, randomized negative control sequences, and sequences designed against known protein coding genes were designed . In addimiRNA predictions were calculated using the TargetScan protocol 2. Prior to RNA isolation ES cells were trypsinized and preplated for 1 hr on 0.1% gelatin plates to remove the majority of MEFs. Mouse C2C12 cells and HEK293T cells were maintained in DMEM high glucose medium supplemented with 10% fetal bovine serum (Hyclone) and 1× Pen/Strep at 37°C and 5% CO2. For differentiation, C2C12 cells were grown to confluence and subsequently cultured in DMEM high glucose with 2% horse serum (Hyclone) for 3 days prior to RNA isolation. R1, C2C12, and HEK293T cells are available from ATCC as ATCC Numbers SCRC-1011, CRL-1772, and CRL-11268, respectively.The mouse ES cell line J1 was maintained on a layer of irradiated MEFs (DR4) in DMEM high glucose medium (Invitrogen) supplemented with 15% fetal bovine serum (Hyclone), 1× non essential amino acids, 1× sodium pyruvate, 1× Glutamax, β-Mercaptoethanol, 1× Pen/Strep (Invitrogen), and 1000U ESGRO (chemicon) at 37°C and 5% COTo validate putative miRNAs by Northern blotting, total RNA was isolated using the mirVana kit according to the manufacturer's instructions (Ambion). 5 ug of total RNA was resolved with 12.5% urea-polyacrylamide gels and electroblotted onto Hybond NX membranes (GE). 60mer 5′ biotinilated oligonucleotides complementary to short hairpin RNA sequences were used as probes . MembranTo determine whether ESTs could be used as a general data source in novel miRNA prediction, we examined whether a trend of association exists between the EST termini around known miRNAs in human and mouse. We observed a distinct clustering of EST termini in sequence regions aligned with the 3′ ends of known miRNA in the mouse and human genomes, respectively . By compOne important issue in genome wide screens is ascertaining the degree of background signal present. To estimate the extent of this, the ncRNA-EST analysis was repeated numerous times using randomly shuffled miRNA positions. The results in To investigate whether improvments in miRNA predictions can be attained with this strategy, we considered whether potentially structured RNA regions demonstrating supporting EST evidence would benefit miRNA prediction pipelines, considering three different methods to identify miRNA candidates: simple hairpins generated by RNALfold −80) when using the nucleotides contained within the duplexed region excluding those in the single stranded tails and terminal RNA loop  cells was isolated and hybridized to the microarrays in duplicate, for a total of eight hybridizations. Correlations between replicates are shown in After data normalization and analysis, 148 probe sets with >2 fold enrichment in muscle RNA versus ES derived RNA were identified, including muscle control probe sets designed against known muscle miRNAs mir-206, mir-133a/b , as wellSeveral known miRNAs that were captured by our prediction method were also detected as differentially expressed in myoblasts compared to ES cells. Several putative miRNA probe sets correspond to known miRNAs mir-24-2, mir-351, mir 26a-2, mir-92-2 and mir-7-2. According to these probes, miRNAs mir-24-2, mir-351, and mir-26a-2 were potentially upregulated in differentiating myoblasts, while mir-92-2 and mir-7-2 loci were downregulated . IndividCtdsp2 ; it appears that a shorter Ctdsp2 variant would be produced if this miRNA were excised from the longer transcript. Ctdsp2 is able to induce mesodermal lineage commitment in a Xenopus system To better understand the context by which these microarray signals could have arisen, the genomic contexts of these candidates were examined. Of highest relevance to the observed expression in myoblasts, the miRNA hairpin mapping to mir-26a-2 was found to be intronic to a long splice variant of Zswim4, an uncharacterized zinc finger containing protein.mir-351 is potentially part of a miRNA cluster with mir-503 and mir-322 lying within 2 kb upstream, with numerous ESTs mapped in this region supporting this possibility. However, no contiguous regions of expression appear to span the genomic region defined by these three miRNAs. mir-24-2 is the downstream most member of miRNA cluster containing mir-27a and mir-23a. This cluster is downstream from Dmd) transcript.To identify potential targets for the miRNAs expressed at higher levels in differentiating myoblasts, the TargetScan method of identifying miRNA targets was used. Potential target transcripts, which mature sequences for mir-24-2, mir-26a, and mir-351 might be repressing, were predicted and rather than considering targets individually, scores were combined multiplicatively to identify potential transcripts that might be synergistically repressed by all three , a methoin vivo expression of this longer Dmd transcript is proportionally low. In contrast, Dystrophin's shorter 3′UTR is targeted only by mature mir-26a (Data not shown). As different Dystrophin splice variants are expressed in different tissues, with the 3′ region of the transcript undergoing alternative splicing to produce tissue-specific transcripts in brain, cardiac and muscle fibers Dystrophin is transcribed from a large genomic region (∼3 megabases), contains 79 exons, and undergoes numerous alternative splicing patterns Several putative ncRNA hairpins with strong expression scores were selected to determine whether the expression and molecular size of the RNAs were consistent with miRNA presence. Northern blotting was used to visualize the size of RNA species responsible for microarray signals, using the respective microarray probe sequences. Several of the selected probe sets indeed detected expression of small RNAs in the ∼20–30 nt size range, suggesting that mature miRNAs were being generated . MicroarWe showed that addition of EST data in ncRNA prediction protocols can significantly improve prediction rates, using miRNAs as a representative class of ncRNAs. In doing this, we show that taking into account predicted behaviours of one type of transcript, inferences can be made as to what might be observed in data produced for entirely disparate purposes. In this study, we combined the features of EST generation (priming poly-A transcript ends) with knowledge of the RNA cleavage activity during miRNA biogenesis, to develop an approach that uses EST records and information about predicted RNA structures to predict miRNAs as a class of hairpin derived ncRNAs. Our results show that future development of non-coding gene prediction strategies should combine secondary sources of biological data with primary sources of information such as sequence composition, motifs, and predicted RNA secondary structures when knowledge of RNA transcript behaviour facilitiates the development of an integrative model. We emphasize that our method is not an alternative miRNA prediction method but rather contributes an analytical step that can improve the quality of existing miRNA prediction strategies. For this reason, we explicitly showed that integrating non-overlapping EST and miRNA annotations enable improving miRNA prediction methods.We furthermore expect that including EST data in ncRNA prediction strategies is a generalizable approach to other models of ncRNAs undergoing cleavage during their production or function. We can illustrate this latter point with the Clec2d 3′UTR, which has been reported to contain a self-cleaving hammerhead ribozyme It must be noted that adding EST evidence, as with the addition of any data, imposes a constraint on the analysis such that some miRNA predictions are called as false negatives. This trade-off must be considered. However, the significant increases of precision and recall for miRNA predictions that pass the integration step suggest that follow up experiments using these first tier candidates can experience lower false discovery rates. Nonetheless, those interested can still investigate second tier candidates but now with the additional knowledge that false discovery rates in these predictions will be lower due to the lack of EST evidence to support them.On a practical level, we also illustrated how pre-existing data can produce compelling improvements in the performance of current high-throughput sequencing technologies. The increased precision and recall rates demonstrated in A second practical contribution of ESTs towards ncRNA prediction is in supplying probable tissues of expression in which validation of ncRNAs can be pursued, for example when a specific tissue is of interest. Integrating data this way can also simplify experimental designs by minimizing sample size requirements, limiting resources consumed in pursuing specific questions.Next-generation sequencing is emerging as a successor to EST library sequencing with RNA-seq becoming commonly available. However, as far as we know, no database of next-generation sequence transcript data exists that is comparable to dbEST. The wide distribution of conditions sampled in dbEST and the standardized format of annotations facilitate the discovery of transcripts that possibly occur in very particular conditions such as the ones we have presented here. The analysis of deep-sequencing data in an EST-like fashion still involves curation of primary sequence reads, assembly of short reads into contigs, and finally alignment to genomic positions, each step potentially requiring manual tuning. Should a database of deep-sequencing transcript data similar to dbEST be generated in the future, we have no doubt that our method will be successfully applied to it.Ezh2) during myogenesis Ezh2 is also active in an analogous system – epidermal differentiation – where it has recently been shown to contribute towards control of differentiation rates throughout multistage epidermal shedding Using a custom miRNA microarray allowed us to verify the performance of our method by capturing known miRNAs expressed in myoblast samples. We observed that three known miRNAs, mir24-2, mir-351, and mir-26a-2 were upregulated in myoblasts and showed robust expression. Expressed at 141 times the level observed in the embryonic stem cell sample, mir-24-2 was the most strongly overexpressed mature miRNA, and has previously been captured during myogenic differentiation Finally, mir-351 was upregulated in myoblasts at levels 108-fold over ES. In the mouse, mir-351 lies in a region of EST activity which includes mir-503, mir-322, and an uncharacterized transcript, which appears to undergo splicing (dbEST identifier AK021262). Taken together, these elements may represent a multipart transcript, and interestingly four additional miRNA coding sites  reside nearby, which do not have any evidence of being transcribed based on known dbEST sequence records. The function of mir-351 and this region is unknown, but mir-351 has been observed in SAGE tags derived from embryonic heart Plod2, encoding a collagen cross-linking enzyme with two isoforms exhibiting tissue specific expression in adult, with strongest protein expression observed in muscle tissues Plod2 is an attractive target as the integrity of muscle tissue is strongly dependent on extracellular matrix components, and particular collagen subtypes are associated with myoblasts in different stages of proliferation Plod2 is that both isoforms are transcribed in muscle yet only the longer isoform undergoes translation Plod2 isoforms in muscle and their differential translation, combined with potential targeting by three known miRNAs with expression in muscle, exploring miRNA mediated control of Plod2 expression may be promising.We were interested in identifying predicted co-repression of target transcripts by all three mature miRNAs observed in C2C12s , reasoning that they might act in concert to exert effects on transcripts comparable to much more highly expressed miRNAs that are solitary actors such as mir-206. Considering combined miRNA repression scores, the best target identified was Dmd), encoding a myotubular structural protein critically linked to the manifestation of Duchenne Muscular Dystrophy The second best target identified was Dystrophin  and miRNA-EST distance were calibrated to filter the set of hairpins generated from the genomewide scan of RNALfold. Precision/Recall curves shown for each miRNA-EST distance (d) cutoff, with individual points corresponding to NFE cutoffs at each value of d . Circled point corresponds to d = 14 and NFE = −0.44 kcal/mol. NFE values range from 0 to −1 kcal/mol and miRNA to EST distances range from 0 nt to 200 nt.(TIF)Click here for additional data file.Figure S2Determination of mature miRNA placement within pre-miRNA windows. (A) 553 mature miRNA sequences were mapped within their parent pre-miRNA hairpin annotations and distances between termini were measured. d1 and d2 correspond to the shorter and longer distances, respectively. (B) Distributions of the values. The mean of the d1 and d2 are shown .(TIF)Click here for additional data file.Figure S3Distributions of EST peaks near miRNAs are similar across genome versions. Distributions of miRNA-ESTs are highly similar across different versions of the mouse genome (mm8 and mm9). EST occupancy surrounding miRNAs was calculated as described in (TIF)Click here for additional data file.Figure S4Calibration of real-time RNALfold filter parameters. Precision and recall values were calculated by applying RNALfold on genomic DNA sequences centered on known miRNAs and filtering resultant RNA structures. Filtering parameters used were: hairpin size (Degree of blue shading); hairpin NFE (Degree of red shading); and RNALfold window size . Parameters used in this study and corresponding precision and recall are shown with a white X. For computing precision/recall, miRNA-hairpin annotations had a minimum overlap of 80%.(TIF)Click here for additional data file.Figure S5Distributions of Normalized Free Energies in sets of miRNAs and set of genome-wide predictions. Three distributions are shown: The red, black, and blue distributions correspond to values derived by normalizing the total minimum free energy (MFE) by the number of nucleotides in the hairpin sections as highlighted in the inset figure.(TIF)Click here for additional data file.Figure S6Control miRNAs are expressed correctly on microarray. Heatmap showing correct expression of control miRNA probe sets for muscle (mir-133a and mir-206) and ES cell control (mir-293 containing cluster). Probe sets were composed of multiple probes tiling over each pre-miRNA hairpin sequence, and only probes overlapping mature miRNA sequences yielded expression signals.(TIF)Click here for additional data file.Table S1Overview of predicted miRNA hairpins in different tissues. 13952 unique putative miRNA hairpins were annotated in the mouse genome (mm9), based on predictions generated from RNA structures with supporting EST annotations. Counts above do not correspond to this value due to some miRNA predictions having ESTs derived from multiple sources, and sources with under 10 predictions being excluded due to space limitations.(XLS)Click here for additional data file.Table S2Table of RNA control sequences included on the miRNA microarray. Individual miRNAs with specific roles included where possible. In cases of clustered miRNAs where one member of a cluster was identified with a specific function (e.g. ES miRNA), additional members of the cluster were tiled as well.(XLS)Click here for additional data file.Table S3Probe sequences used in Northern blotting.(XLS)Click here for additional data file.Table S4Pearson correlation between replicates of miRNA tiling microarray. All samples were performed in duplicate.(XLS)Click here for additional data file.