The aim of our study was to improve the understanding of gene expression profile of cumulus cells (CC) in terms of ovarian stimulation protocol and oocyte maturity. We applied Affymetrix gene expression profiling in CC of oocytes at different maturation stages using either GnRH agonists or GnRH antagonists. Two analyses were performed: the first involved CC of immature metaphase I (MI) and mature metaphase II (MII) oocytes where 359 genes were differentially expressed, and the second involved the two GnRH analogues where no differentially expressed genes were observed at the entire transcriptome level. A further analysis of 359 differentially genes was performed, focusing on anti-Müllerian hormone receptor 2 (AMHR2), follicle stimulating hormone receptor (FSHR), vascular endothelial growth factor C (VEGFC) and serine protease inhibitor E2 (SERPINE2). Among other differentially expressed genes we observed a marked number of new genes connected to cell adhesion and neurotransmitters such as dopamine, glycine and γ-Aminobutyric acid (GABA). No differential expression in CC between the two GnRH analogues supports the findings of clinical studies where no significant difference in live birth rates between both GnRH analogues has been proven.In  In vitro fertilization (IVF) has become one of the most common treatments of infertility. In an IVF cycle, ovarian stimulation is established by gonadotropins in combination with gonadotropin-releasing hormone (GnRH) analogues, i.e. GnRH agonists or GnRH antagonists. GnRH analogues are used to prevent premature luteinizing hormone (LH) surge during ovarian stimulation, which improves oocyte yield and increases pregnancy rate Despite great improvements in assisted reproductive technology the success of IVF still remains relatively low. Most of the oocytes retrieved after ovarian stimulation are capable of fertilization; however, only half of them develop into embryos and only a few implant. Therefore, more than one embryo is usually transferred to increase the pregnancy rate, which leads to multiple pregnancies, and increased fetal and maternal morbidity and mortality Cumulus cells are essential for oocytes development. During folliculogenesis, an intense bidirectional communication exists between oocytes and surrounding CC Therefore, we used transcription profiling to perform two analyses: the first was focused on oocyte maturity and the second on the type of ovarian stimulation protocol used: recombinant gondadotropins in combination with either GnRH agonists or GnRH antagonists. The aim of this study was to improve the understanding of the CC gene expression profile in terms of ovarian stimulation protocol. To our knowledge this is the first assessment of both GnRH analogues at the molecular level in a prospective study.In this prospective, randomized study, 21 patients undergoing classical IVF cycle at the Department of Obstetrics and Gynecology, University Medical Center Ljubljana, were included. The study was approved by the national medical ethics committee and all patients have signed informed consent. Randomization was performed according to JL Fleiss Eleven randomly selected patients were administered GnRH agonist buserelin acetate  from day 22 at a daily dose of 0.6 ml (600 pg) subcutaneously. When the criteria for ovarian desensitization were fulfilled , they were subcutaneously administered 225 IU of gonadotrophin folitropin α . To the remaining 10 patients 225 IU of gonadotrophin follitropin α was subcutaneously administered on day 2. When the dominant follicle measured ≥14 mm in diameter, GnRH antagonist cetrorelix acetate  in a dose of 0.25 mg was administered subcutaneously.When at least three follicles were ≥17 mm and serum oestradiol was ≥0.40 nmol/L per follicle all patients were administered 10,000 IU of human chorionic gonadotrophin (hCG) ; 34–36 h later an ultrasound guided transvaginal oocyte retrieval was performed.Oocytes were removed from the follicular fluid. Immediately after oocyte retrieval, CC of each oocyte were removed by a needle and a glass denudation pipette , washed in PBS, snap frozen in liquid nitrogen and stored at −80°C in vials until RNA isolation.The oocytes were further inseminated  and cultured individually. After 24 hours, oocyte fertilization status was assessed. Fertilized oocytes expressed two pronuclei and two polar bodies, whereas unfertilized oocytes did not. All unfertilized cells were denudated to assess the maturation stage. Immature MI oocytes were round cells which did not extrude polar bodies and did not express germinal vesicles, whereas mature MII oocytes had a clearly visible and developed polar body. Only CC obtained from MI and unfertilized MII oocytes were considered for transcriptome analysis. Fertilized oocytes were further cultured to the blastocyst stage in the Universal IVF Medium followed by BlastAssist System  for five days. On day 5, at most two embryos at the blastocyst or morula stage were transferred into the uterus. Supernumerary blastocysts were cryopreserved. Only CC obtained from MII oocytes developed to blastocyst stage embryo were considered in transcriptome analysis.The difference between the two IVF cycle stimulation protocols using GnRH analogues was studied at three different levels of oocyte quality: metaphase I oocytes (MI), unfertilized metaphase II (MII) oocytes (MII-NF) and MII oocytes developed to blastocyst stage embryo (MII-BL). We performed two analyses: firstly we assessed differences in CC gene expression according to the oocyte stage achieved (MI versus MII), and secondly we assessed the differences in CC gene expression according to the GnRH analogue used. By comparing the CC MI and MII expression profiles we sought for gene markers linked to oocyte maturity.RNA was extracted using TRI reagent  according to slightly modified manufacturer's instruction. Due to small sample volume, glycogen was used as a carrier to increase RNA yield. Briefly, CC from individual cumulus – oocyte complexes were homogenized in 500 µL TRI reagent supplemented with 125 µg of glycogen . After 2 min incubation at room temperature, 100 µL chloroform was added and the sample was vortexed vigorously. RNA was precipitated with isopropanol from the aqueous phase and collected after 15 min centrifugation at 12,000× g and 4°C. RNA pellet was washed 3 times by 75% ethanol, dried and dissolved in 15 µL of RNAse free water. The integrity of the RNA samples was assessed on Agilent 2100 Bioanalyzer to assure high quality of total RNA; the RIN number was more than 7 for each sample.Transcriptome analysis was performed using the GeneChip Human Gene 1.0 ST Arrays . The arrays were hybridized according to manufacturer's recommendations. Briefly, 200 ng of RNA was amplified and converted to cDNA using the WT Expression Kit . The resulting cDNA was fragmented and labeled using the GeneChip WT Terminal Labeling and Controls Kit  and hybridized to the arrays for 16 hours. The arrays were washed using GeneChip Fluidics Station 450 according to manufacturer's recommendations, and scanned on the Affymetrix GeneChip Scanner 3000 7G. The images were analyzed and quality of data checked by Affymetrix GeneChip Expression Console software.http://www.ncbi.nlm.nih.gov/geo) in compliance with MIAME standards, under series accession number GSE34230.Data were processed and analyzed using different R/Bioconductor packages The two-way ANOVA model was used to assess the differential expression of genes using LIMMA package by controlling the false discovery rate (FDR) www.ingenuity.com).Annotation of genes and data representation was managed using ANNAFFY and AFFYCORETOOLS packages. Simultaneously, gene set enrichment analyses were performed using Parametric Gene Set Enrichment Analysis (PGSEA) package in combination with LIMMA package by controlling FDR at significance level α = 0.05. Gene sets related to different KEGG pathways were tested for their enrichment with respect to the contrasts of interest and sorted by their enrichment scores. Gene Ontology analysis was performed using GeneCodis PPIB) and 18s rRNA were added for normalization. Genomic DNA contamination was eliminated by DNAse treatment using DNAse I . cDNA for qPCR assays was prepared from 200 ng DNAsed RNA using SuperScript RT III  in 20 µl final volume. Following cDNA synthesis, RNAse free water was added to increase the sample volume to 30 µl. The measurements were performed using LightCycler 480 System . Normalized mRNA levels were obtained by dividing the averaged, efficiency corrected values for mRNA expression by normalization factor calculated from PPIB and 18s rRNA values and were expressed in arbitrary units Quantitative real time PCR (qPCR) was used to validate 4 differentially expressed genes on a subset (30 out of 46) of samples using TaqMan Gene Expression pre-designed assays . Genes for qPCR validation were selected considering their significance and their biological function in folliculogenesis. Cumulus cells samples are small and do not provide enough RNA to perform microarray analysis and qPCR validation in all samples. In our study 30 of the 46 samples provided enough RNA to perform both analyses. Peptidylprolyl isomerase B  were seen on ultrasound, the patient was considered pregnant, embryo heart rate confirmed a viable pregnancy. The pregnancy rate was calculated as a ratio of the number of pregnancies per cycle. The delivery rate was a ratio of the number of deliveries per cycle; all the babies were live born in this study. The miscarriage rate was 33% (n = 2) in the GnRH antagonist group, and 0% in the GnRH agonist group.Further, we assessed the differential expression of genes with respect to various contrasts of interest. The contrasts between GnRH agonist and GnRH antagonist treatments exposed no differentially expressed genes according to the FDR-adjusted p-values. We did not observe any differentially expressed genes at the level of MI, MII-NF and MII-BL between the two GnRH analogues used.To identify the differences in gene expression in CC related to oocyte maturity, we compared the expression of CC MI, CC MII-NF and CC MII-BL regardless of the GnRH analogue used . One hunAmong 359 differentially expressed genes a top ranked gene network associated with Post-Translational Modification, Cellular Development, Cellular Growth and Proliferation was identified by Ingenuity Pathway Analysis . Among tAMHR2), follicle stimulating hormone receptor (FSHR), vascular endothelial growth factor C (VEGFC) and serine protease inhibitor E2 (SERPINE2) as assessed by microarray analysis and qPCR. All genes matched the direction of expression changes using either of the measurement method. Correlation factor (r) between log2 fold change of both methods for all the 4 genes was 0.98 (p = 0.02).Four of the 359 differentially expressed genes were validated by qPCR. To our knowledge, this is the first prospective study comparing the effects of two different GnRH analogues and maturity stage of the oocyte at the level of gene expression in CC. Considering oocyte maturity we observed 359 differentially expressed genes between CC MI and CC MII. Using either GnRH agonists or GnRH antagonists, we have not observed differentially expressed genes. Moreover, we have not observed differentially expressed genes at any level of maturity stage of the oocyte  between the two GnRH analogues used. Since only MII oocytes are capable of fertilization, our results support and supplement the clinical studies on GnRH analogues The analysis of baseline characteristics of the GnRH agonist and GnRH antagonist-treated patients showed that experimental groups were comparable, excluding sample-dependent biases in data. In line with previous findings The analyses of gene expression in CC for each stage of oocyte maturity have shown that the most significant change at the transcriptional level occurs during oocyte transition from the MI to the MII stage. This is in accordance with previous studies SFRP4 and CDH3) BMP3) BUB1) PTGES) ITGB3, ADAMTS1) TSPAN7, SPOCK2), and group of genes connected to signal transduction . These pathways have been previously recognised to have a key role in folliculogenesis and oocyte maturation Among top down and up regulated genes comparing CC MI and CC MII there were previously described genes which are members of the Wnt signaling pathway  with tissue plasminogen activator (PLAT) were also among downregulated genes. On the contrary, SPOCK 2, NID2 and ADAMTS1 showed upregulation between CC MI and CC MII oocytes. These genes are responsible for extracellular matrix binding. Extracellular matrix of CC has been found crucial for ovulation, oviduct passage and fertilization, especially through TNFIP6 protein ANK2, ANK3 and PALLD are genes which function in cytoskeleton formation. All these genes were much less expressed in CC MII oocytes compared to CC MI oocytes. ANK2 and ANK3 both contain ANKRD57 protein, where the ANKRD57 gene has already been recognized to be influenced by oocyte maturation in CC In the group of genes for extracellular matrix formation there were GLRA2, GABRA5) and dopamine degrading enzyme gene (MAOB) were down regulated in CC MII compared to CC MI oocytes. So far little is known on the function of glycine and GABA in the folliculogenesis and oocyte maturation. A comparative analysis of amino acids in the follicular fluid of preovulatory follicles and in serum showed the amount of glycine differed the most, and was higher in the follicular fluid Among signal transduction genes other than Wnt signaling and TGFβ there were also top down and up regulated genes connected to neurotransmitters. Receptors for two inhibitory neurotransmitters glycine and γ-Aminobutyric acid GABA  and androgen receptor (AR) AMH expression in GC and CC has been found to be the highest in pre-antral and small antral follicles AR, FSHR and AMHR2 mRNA expression FSHR is highly expressed in CC and is critical for oocyte maturation in vitroin vivo after hCG administration et al. have found that FSHR together with AMH, AMHR2, and AR are overexpressed in GC from stimulated follicles of PCOS women indicating an oocyte maturation defect The Ingenuity Pathway Analysis identified SERPINE2 is significantly altered by oocyte stage. SERPINE2 expression is increased by FSH SERPINE2 than CC MI. Finally, the expression difference of VEGFC has been observed between CC MI and CC MII level, where the latter have a significantly higher expression. Vascular endothelial growth factor (VEGF) is an angiogenic substance synthesized in theca cells and GC In addition, In this study the differences in gene expression have been observed between CC MI and CC MII. This is not only in accordance with previous studies, but also provides some new aspects of oocyte maturation at the molecular level. According to our CC gene expression profiling we may conclude that both treatment protocols of ovarian stimulation with either GnRH agonists or GnRH antagonists are equally effective. Herein we show that the expression of genes in CC of mature MII oocytes does not differ using either GnRH agonists or GnRH antagonists. Oocyte maturity is a prerequisite for oocyte fertilization and high-quality embryos. This goes well along with the findings of more recent meta analyses on GnRH analogues where no significant difference in live birth rates between both GnRH analogues has been proven Figure S1Top ranked gene network associated with Post-Translational Modification, Cellular Development, Cellular Growth and Proliferation as identified by Ingenuity Pathways Analysis. Top ranked gene network was obtained from comparisons of differential expression between CC MII and CC MI. Genes are represented as nodes, and the biological relationship between two nodes is represented as edge (line): a plain line indicates direct interaction, a dashed line indicates indirect interaction; a line without arrowhead indicates binding only, a line finishing with a vertical line indicates inhibition; a line with an arrowhead indicates ‘acts on’. The green colour intensity of the nodes indicates the degree of down-regulation while the red colour intensity of the nodes indicates the degree of up-regulation in the CC MII group. CC MI: cumulus cells of metaphase I oocytes. CC MII: cumulus cells of metaphase II oocytes.(EPS)Click here for additional data file.Table S1359 differentially expressed genes, p value, and their expression (log2 fold change) between CC MII and CC MI. CC MI: cumulus cells of metaphase I oocyte; CC MII: cumulus cells of metaphase II oocyte.(DOCX)Click here for additional data file.Table S2Top enriched KEGG pathways by PGSEA and their expression (log2 fold change) between CC MII and CC MI. Significant (p<0.05) expression changes are shown in bold.(DOCX)Click here for additional data file.Table S3Gene Ontology (GO) analysis performed by GeneCodis upon 359 differentially expressed genes. Items: GO group; Items_Details: description of GO group; Hyp_c: corrected value of hypergeometrical test.(DOCX)Click here for additional data file.


Since its discovery, Chikungunya fever caused by a virus (CHIKV) has ravaged most of Africa and Southeast Asia. Despite there being more than a million reported cases in India alone and the seriousness of the disease in the chronic phase, a clear understanding of the disease pathogenesis and host response remains elusive. Here, we use microarray technology and quantitative PCR method to establish the complete miRNA, snoRNA and mRNA signature of host response upon CHIKV infection in human cell line infection model, HEK293T. The results were further validated in human primary cells . miRNA expression profiling revealed regulation of 152 miRNAs post CHIKV infection. An interesting overlap in miRNA signature was seen majorly with HCV, HPV and HIV1 virus. The microarray data further validated by qRT-PCR revealed induction of miR-744, miR-638, miR-503 and others among the top upregulated miRNAs. Notably, we found induction of snoRNAs belonging to C/D cluster including close paralogs of U3, U44, U76 and U78 snoRNAs. Genes were found to be differentially expressed along 3 major pathways; TGF-β, endocytosis and the cell cycle pathways. qRT-PCR data confirmed strong induction of TGF-β  and endocytosis pathway  genes while downregulation of cell cycle genes (CDC27 and CDC23). Interestingly, use of TGF-β inhibitor, SB-431542, increased CHIKV mediated cell death. Overall, this study aims at providing the first complete transcriptome signature of host response upon CHIKV infection to aid identification of possible biomarkers and therapeutic targets. Togaviridae family and Alphavirus genus Aedes mosquito though a few cases of maternal-fetus transmissions have been reported Chikungunya virus (CHIKV) is an enveloped, positive sense, single stranded RNA virus of the Regulated on Activation, Normal T cell Expressed and Secreted) as biomarkers of Chikungunya severity were conducted but their roles were not investigated in detail Viral infections involve active dynamics between the host cell and the virus particle. Virus utilizes host machinery in various ways to its advantage while host cell activates the innate and adaptive responses to block viral replication and thus the spread Cellular response to infection is a dynamic process that involves complex biological responses of the host. To explain these processes at a global level, we recorded changes in gene expression of both coding genes and microRNAs (miRNAs)  post CHIKV infection at various time points. miRNAs are known to be post transcriptional regulators of gene expression. They act by binding to complementary sequences in the 3′UTR of various transcripts and targeting them for degradation or translational block depending on the degree of complementarity 2 in an incubator. The primary human dermal fibroblasts were a kind gift from Dr. Sourabh Ghosh . The CHIKV virus (African strain) used was obtained from National Institute of Virology, Pune.HEK293T, Vero-76 and primary human dermal fibroblast cells (passage 2) were maintained in DMEM (Cell Clone) media supplemented with 100 U/ml penicillin, 100 ug/ml streptomycin and 10% fetal bovine serum and incubated at 37°C in 5% COForward & reverse primers were designed for the 294 base pairs partial nucleotide sequence of E1 gene. The CHIKV was detected by doing semi-quantitative RT-PCR using primers specific for E1 gene as previously described HEK293T or primary human dermal fibroblast cells were infected with the CHIKV virus at a MOI (multiplicity of infection) of 1. Cell supernatant and total RNA were collected at 0, 12 and 24 hr post infection. CHIKV virus titration was done in Vero and HEK293T cells using MTT Assay and calculated in units of TCID50/ml.S2). The microarray data has been submitted to GEO repository. The accession numbers are GSE49985 (gene expression data) and GSE49984 (microRNA expression data).Total RNA isolation was conducted using TriZol reagent (Invitrogen) in accordance with manufacturer's instructions. Total RNA isolated from HEK293T cells infected with CHIKV (MOI of 1) for 0, 12 and 24 hr was sent to iLife Discoveries for miRNA and gene expression profiling. The miRNA microarray was conducted using Affymetrix Gene Chip miRNA 2.0 platform while the mRNA expression profile was generated using Affymetrix Gene Chip PrimeView platform. A cut off of fold change more than or equal to 1.5 was employed to identify the differentially regulated miRNAs and mRNAs . Subsequently, real-time PCR was performed using specific forward and a common Stem-loop reverse universal primer and Power SYBR Green master mix kit (Ambion).Data S3. The qRT-PCR experiments were performed as three biological replicates with three technical replicates each.Similarly, for the validation of gene expression profile data, isolated RNA was reverse transcribed using Rivertaid First strand cDNA synthesis kit (Fermentas) and the cDNA formed was further amplified using primers specific to selected genes and Power SYBR Green master mix kit (Ambion). GAPDH  was used for the normalization of the data. The primer sequences used for amplification of specific miRNAs or mRNAs are given in Data S7). The list was matched with the gene expression profile data and genes displaying inverse correlations were listed separately (Data S8).The probable targets for the differentially regulated miRNAs were selected on the basis of prediction by established target prediction programs. The targets were selected if they were found to be predicted by at least 5 target prediction programs out of 11 available target prediction programs TargetScan, PITA, PicTar, miRanda, mirTarget2, NBmirTar, RNAhybrid, MiTarget, MicroInspector, RNA22, and DIANA, MicroT, using the online software, miRECORDS http://david.abcc.ncifcrf.gov/) available online with PANTHER and KEGG Pathways as the selected databases Pathway and Functional analysis for the differentially regulated genes and the genes showing inverse correlations was done using Database for Annotation, Visualization and Integrated Discovery (DAVID) Gene Ontology tool . Out of a total of 152 differentially regulated miRNAs, 65 were up and 33 were down, 12 hr post infection. Similarly, 99 were up and 46 were downregulated at 24 hr post infection. A total of 59 miRNAs were found to be upregulated while 33 were downregulated commonly at 12 and 24 hr post CHIKV infection . Overall most of the miRNAs show increased fold change difference at 24 hr as compared to the 12 hr sample indicating their progressive increase/decrease with increasing time post infection. Interestingly, upregulated miRNAs constitute 65–70% of the total number of differentially regulated miRNAs at both 12 hr and 24 hr post infection (Data S1). Experimental validation was done using quantitative RT-PCR for seven randomly chosen miRNAs (five upregulated and two downregulated) and we noticed significant induction in the levels of miR-503, -638, -663 and -744 while slight downregulation in miR-3175 and miR-671-5p . A CHIKV specific miRNA signature that includes only those miRNAs that are specific for CHIKV infection in human cells is shown in Data S5.To identify the miRNAs involved in CHIKV infection and pathogenesis, total miRNA expression was profiled in human embryonic kidney cell line, HEK293T, following CHIKV infection. HEK293T, an epithelial cell line, has been previously reported to be highly susceptible to CHIKV Data S6). This suggests that these miRNA clusters are co-regulated in response to CHIKV infection. Also, among the induced miRNAs expressed as clusters, we confirmed upto 33 fold induction in hsa-miR-17 using quantitative RT-PCR. Other members of the clusters were also tested and induction confirmed (Data S6).Several human miRNAs are expressed as a cluster. We thus wanted to investigate the pattern of expression of clustered miRNAs particularly with respect to recent findings showing differential expression pattern of members of many of these clusters.Interestingly, several miRNA clusters were observed among the upregulated miRNAs. These include members from the miR-17-92, let-7e/99b, miR-191/425, miR-106b/miR25, miR-23a/24 and miR-15b/16 clusters  were also found to be differentially regulated. These metabolically stable RNAs are localized in the nucleolus and are known to be involved in rRNA processing and ribosome assembly. These can be categorized into 3 main groups - C/D box, H/ACA box and the RNA component of RNase MRP  Data S2).To study the changes in gene expression following CHIKV infection and to determine putative targets of the differentially regulated miRNAs, we determined the gene expression profile using microarray technology (Affymetrix platform) at 12 hr and 24 hr post infection. Genes showing more than 1.5 fold change (p<0.5) were considered to be differentially expressed. A total of 1217 genes were found to be induced while 988 genes were downregulated at 12 hr while 1187 genes were induced and 1018 were downregulated at 24 hrs post CHIKV infection. Of these, 577 genes were commonly induced at both the time points while 951 were downregulated , endocytosis and the adherens junction pathway were significantly upregulated while the cell cycle, proteasome and lysosome pathways were downregulated   binding Figure 6Data S7). Subsequently, these genes obtained from target prediction were matched with those obtained from gene expression profiling and inverse correlations were identified (ie. miRNA is upregulated and the corresponding gene is downregulated and vice versa). About 2000 inverse correlations were predicted in total . These listed targets were then used to conduct gene ontology and pathway analysis using DAVID software, which interestingly yielded results similar to the results obtained for the differentially expressed gene profile. This suggests that miRNA regulation of gene targets may have a profound effect on overall gene signature.Using online tools for miRNA target prediction- miRecords or TargetScan Custom Interestingly, targets of the downregulated miRNAs were enriched in the TGF-β, Wnt and adherens junction pathway. Among the downregulated targets, cell cycle pathway is prominently downregulated. Other pathways like the regulation of actin cytoskeleton and selenoaminoacid metabolism were also suppressed Figure 7In the past decade, CHIKV has presented itself with worrisome pathogenic effects urging the need to identify the key molecular players and fully dissect the events following CHIKV infection. The results presented here reveal the key transcripts and pathways involved in CHIKV pathogenesis in a human host cell line, HEK293T. To our knowledge this is a first comprehensive report of the host small RNA (miRNA and snoRNA) and mRNA signatures post CHIKV infection. Integration of miRNA target transcripts and mRNA profiles also helped us to identify complex miRNA∶mRNAinteractome networks involved., Data S4). This may represent a general viral or host cell response common to these viral infections.Recent work has highlighted important role of miRNAs in host∶pathogen interactions. Accumulating evidences demonstrate differential regulation of both pathogen and host encoded miRNAs in response to infection that ensues in complex regulation of target genes Interestingly, several members of miRNA clusters were also found to be induced. miR-17-92 cluster when upregulated, has been known to promote cell proliferation in human cells and has been proposed to be used as a propagation mechanism induced by various viruses We found several snoRNAs too as being differentially expressed in response to CHIKV infection. This is particularly exciting in light of recent studies suggesting that several human C/D box snoRNAs may have miRNA like functions We next identified the gene expression signature of CHIKV. Among the pathways seen to be induced post infection, notable were the TGF-β signaling pathway, endocytosis pathway and the adherens junction pathway. Induction of the endocytosis pathway can be explained due to greater uptake of viral particles while the adherens junction pathway could be the immediate host cell response to viral entry. Monitoring levels of genes in the adherens junction pathway for longer time periods may show the disruption of this pathway and breakdown of cell to cell contact, ultimately culminating in cell death. Previous studies showed that CHIKV mobilizes the apoptotic pathway of the host cell for proliferation Similarly, the downregulated genes were largely represented by the cell cycle pathways genes or proteasome and lysosome pathways all indicating growth arrest or progression towards cell death caused by the viral infection. The downregulation of the genes involved in the regulation of the actin cytoskeleton may be induced by the virus to disrupt the cell membrane integrity and ease apoptosis.The functional analysis of the upregulated genes point towards a general induction in the transcriptional machinery of the host cell, as shown by the induction of the genes involved in DNA-Binding and Transcription Regulator Activity. The upregulation of the genes in SMAD binding may be directly related to the induction of the SMAD dependent TGF-β Signaling Pathway.We saw huge induction in the level of the chemokine receptor CXCR4 by both microarray and qRT-PCR post infection. This suggests that CHIKV similar to HIV may use CXCR4 receptor for viral entry in the epithelial cells In summary, our study provides a platform to understand the dynamics of infection of Chikungunya Virus and its potential targets for the identification of therapeutic targets or biomarkers. This will aid in the development of anti-CHIKV drugs and diagnostic tools. Recently, Lanford and collaborators demonstrated that anti-miRNA molecules can be successfully used to treat chronic hepatitis C virus in chimpanzees Data S1miRNA expression profiling data. HEK293T cells were infected with CHIKV and miRNA expression profiling was done at 0, 12 and 24 hr post infection. The excel file sheets specifying miRNAs found to be more than 1.5 fold upregulated or downregulated at 12 and 24 hr post CHIKV infection as compared to 0 hr are shown along with the fold change values. The details of the profiling are given in Methods section.(XLSX)Click here for additional data file.Data S2Gene expression profiling data. HEK293T cells were infected with CHIKV and gene expression profiling was done at 0, 12 and 24 hr post infection. The excel file sheets specifying genes found to be more than 1.5 fold upregulated or downregulated at 12 and 24 hr post CHIKV infection as compared to 0 hr are shown along with the fold change values. The details of the profiling are given in Methods section. Hierarchial clustering of differentially expressed genes  in response to CHIKV infection at 12 and 24 hr as compared to 0 hr control in HEK293T cell line is also shown. A and B refer to the biological replicates.(XLSX)Click here for additional data file.Data S3A list of primers used for detection of specific miRNA and mRNA transcripts.(DOCX)Click here for additional data file.Data S4List of CHIKV regulated miRNAs implicated in other virus pathogenesis or associated pathways based on literature survey.(XLSX)Click here for additional data file.Data S5A list of CHIKV specific differentially regulated host miRNAs.(DOC)Click here for additional data file.Data S6List of differentially regulated miRNA clusters. (A) HEK293T Cells were infected with CHIKV and RNA harvested at 12 and 24 hr post infection. From the microarray data, the miRNA expressed as clusters were identified and listed. (B) Real-time PCR data showing differentially regulated miRNA clusters in response to CHIKV infection. The graphical data points represent mean+S.D of at least three independent experiments. . Error bars denote+SD.(PPTX)Click here for additional data file.Data S7List of predicted targets of differentially regulated miRNAs using miRecords and Targetscan software.(XLSX)Click here for additional data file.Data S8List of predicted targets of differentially regulated miRNAs showing inverse correlations.(DOCX)Click here for additional data file.


Ovarian carcinomas consist of at least five distinct diseases: high-grade serous, low-grade serous, clear cell, endometrioid, and mucinous. Biomarker and molecular characterization may represent a more biologically relevant basis for grouping and treating this family of tumors, rather than site of origin. Molecular characteristics have become the new standard for clinical pathology, however development of tailored type-specific therapies is hampered by a failure of basic research to recognize that model systems used to study these diseases must also be stratified. Unrelated model systems do offer value for study of biochemical processes but specific cellular context needs to be applied to assess relevant therapeutic strategies.We have focused on the identification of clear cell carcinoma cell line models. A panel of 32 “ovarian cancer” cell lines has been classified into histotypes using a combination of mutation profiles, IHC mutation-surrogates, and a validated immunohistochemical model. All cell lines were identity verified using STR analysis.ARID1A and PIK3CA) and an overall molecular/immuno-profile typical of primary tumors. Mutations in TP53 were present in the majority of high-grade serous cell lines. Advanced genomic analysis of bona-fide clear cell carcinoma cell lines also support copy number changes in typical biomarkers such at MET and HNF1B and a lack of any recurrent expressed re-arrangements.Many described ovarian clear cell lines have characteristic mutations (including ARID1A and TP53 and a general immuno-profile serve well for establishing histotype of ovarian cancer cell We describe specific biomarkers and molecular features to re-classify generic “ovarian carcinoma” cell lines into type specific categories. Our data supports the use of prototype clear cell lines, such as TOV21G and JHOC-5, and questions the use of SKOV3 and A2780 as models of high-grade serous carcinoma.Conclusions: As with primary ovarian tumors, mutation status of cancer genes like  Ovarian cancer is a diverse set of diseases and amongst the most clinically significant, epithelial ovarian cancers (EOC), at least five distinct entities exist BRCA1/BRCA2 mutations also appear largely restricted to this histology Clinical responses and epidemiological differences are also apparent between histotypes. High-grade serous cancers show the best initial response rates to the current standard chemotherapy regime of platinum and taxanes Regardless of origin or histological similarities and differences, biomarker and genomic studies have been successfully used to distinguish each histotype and may represent a far more biologically relevant basis for classifying and subsequently treating EOCs. Although this concept is well-accepted, and gaining traction on becoming a new clinical standard, ambiguous cell line models perpetuated through molecular biology bench research hamper the development of tailored type-specific therapies. Those using bench experiment model systems must recognize that, like primary cancers, the models used to study these diseases must also be stratified. Although biochemical studies can generate useful information from using a variety of unrelated model systems, disease specific studies need to apply cellular context. The vast majority of research employing functional studies on “ovarian cancer” cell lines does not properly ascertain the background of their model systems. Resulting conclusions may be difficult to interpret and the value of potential therapeutic targets may be questionable as is the true relevance to a particular disease.Cell line studies of ovarian cancer have been severely hampered due to the lack of proper annotation of “ovarian” carcinoma cell lines. Once in culture, cells no longer have easily identifiable morphological traits to aid in histological classification. Additionally, human error, mislabeling and the generic feature of “epithelial-like” cell lines have also led to mix ups of cell lines and contamination which has resulted in un-interpretable data 2. See in vitro culture of primary patient material obtained through the OVCARE Tumor bank. All patients with tissue deposited in the OVCARE tumor bank provided written consent for experimental studies including sequencing, IHC characterization, and derivation of long-term cell lines from tissue samples. The OVCARE tumor bank study was approved under University of British Columbia and British Columbia Cancer Agency Research Ethics Board H05-60119 protocol.Cells were maintained in a humidified incubator at 37C with 5% COAll cell lines were subjected to identity testing using STR genotyping  at the College of American Pathologist's (CAP) accredited Centre For Translational and Applied Genomics (CTAG) as per manufacturer directives. Only lines with profiles matching public repository records, reported STR Cell lines were scraped from culture plates, washed 2× with PBS and pelleted. Cell pellets were re-suspended in ∼500 μl 10% Neutral Buffered Formalin (NBF) and allowed to fix overnight. Cells were pelleted again and re-suspended in a Histo-gel (Thermo-Fisher) plug prior to embedding in paraffin. A tissue microarray (TMA) was constructed as previously described ACTB, SDHA, RPL19, POLR1B, PGK1) as per manufacturers directives. In the case of TFF3 mRNA levels we considered any sample with detectable transcripts to be positive and substituted a score of “1” in place of TFF3 IHC when using COSP. The detection threshold (DT) for mRNA was considered to be the maximum count from spike-in negative control probes  plus 2 standard deviations. Statistical tests were calculated using GraphPad Prism v6.0c software.RNA was extracted from cell lines using Qiazol-miRNeasy kit (Qiagen) protocol and from primary tumors, 12 randomly selected from each histotype, using the miRNeasy FFPE kit (Qiagen). All RNA transcript levels were measured using the NanoString nCounter system ARID1A was done through a combination of custom hybrid capture and transcriptome sequencing on an Illumina GAII next generation sequencing (NGS) system as described previously Genomic DNA was extracted using standard methods . Regions encompassing mutations of known significance (Cancer hotspots) were Sanger sequenced using M13-tagged primers. Sequencing of DNA copy number was inferred from Affymetrix SNP 6.0 genome-wide microarrays. Arrays were run as per manufacturers directives and copy number ratio generated from an unpaired reference. Detection of copy number changed regions was done using a segmentation algorithm. All analysis and visualization was executed with Partek Genomics Suite 6.6, raw data is available from NCBI GEO [Accession GSE48351].TFF3 mRNA in primary samples appeared similar to that reported by IHC TFF3 mRNA for IHC and scored any cell line with detectable mRNA as “1” in our COSP algorithm . Given the strong negative association of mutation status and detectable protein expression BRAF, KRAS, NRAS, ERBB2, EGFR, CTNNB1, PIK3CA, PPP2R1A and DICER1. All coding exons of TP53 were verified in all cell lines. ARID1A mutations were tested using a custom NGS gene hybrid capture strategy ARID1A data from COSMIC and CCLE in addition to IHC as an ARID1A mutation-testing surrogate  and a mutation of unknown significance in TP53 (p.Y126C (het)), though p53 expression by IHC appeared normal. The 2008 cell line had undetectable ARID1A, suggesting loss of function, and also carried two TP53 mutations (c.572_574 delCTC (het)/c.673-1 G>T ) and corresponding p53 IHC overexpression. These atypical combinations of mutation could plausibly be explained by a propensity to accumulate mutations in cell lines with DNA mismatch repair (MMR) deficiencies, as has been reported for IGROV1, SKOV3, and A2780 We took note that a number of cell lines often used as high-grade serous models or generically as “ovarian carcinoma” had both As our primary objective was to describe CCC cell lines we generated copy number profiles of bona-fide CCC cell lines using Affymetrix SNP6.0 microarrays. Consistent with previous reports using primary tumor samples, CCC lines showed a moderate degree of copy number abnormalities, suggesting a genome that has undergone some degree of genomic instability.HNF1B (5/7) and MET (4/7), including one with high-level amplification (JHOC-5), similar to previous reports for CCC tumors ERBB2, in all cases the amplicon segment also encompassed the nearby CCC biomarker HNF1B, and none were positive for HER2 protein expression by IHC (not shown). Copy number loss around TP53 was observed only in a single CCC cell line  and, as noted above, all CCC lines appeared to have a normal-like expression pattern for p53 (IHC score 1).A limited number of literature reports have highlighted genes with mutations, overexpression and/or amplification amongst primary CCC, some with a relationship to survival or advanced disease As with other ovarian carcinoma types, recurrent translocations amongst CCC have not been described, though only a minimal number of studies have been undertaken ARID1A mutations were prevalent in these cell lines. Although three CCC lines did not have identifiable ARID1A mutations, only JHOC-5 cells appeared to have both wild-type sequence and detectable protein expression. The number of ARID1A “normal” CCC lines is lower than might be expected given the frequency of ARID1A mutations (and negative IHC) observed in primary CCC ARID1A null CCC lines to adapt to in vitro culture. However, given the small sample size it may well be a chance occurrence and does not appear to be significant. Other detected mutations  are all consistent with varying frequencies in CCC. TP53 mutations are notably absent in all of our validated CCC cell lines, as a de-facto defining characteristic, and only a single CCC line had heterozygous copy number loss though still retained normal-like p53 IHC.As our initial goal was identification of bonafide CCC cell lines, we are pleased to report that the majority of reported CCC lines were representative of the primary tumors' molecular and pathological phenotype. Our immuno-classification scheme, COSP, predicted most to be CCC and our own mutation data, as well as that from COSMIC and CCLE, suggested loss of function ARID1A and PIK3CA, are common to both types, overall supporting a related origin and similar route to transformation TP53 mutations, atypical for true CCC. IHC was negative for ARID1A, supporting a non-serous origin. We favored an assignment of ENOCa base largely on the TP53 mutation though note that this cell line is quite atypical as it may carry loss of function changes for ARID1A, mutation of TP53, and is positive for the CCC biomarker HNF1B. Arguably errors in cell line histotype reports may be explained simply by historically poor reproducibility in cell type assignment, though it is not unforeseeable that the biological relationship between CCC and ENOCa could be influencing these phenotypes. Given the high degree of overlap between the mutational characteristics of CCC and ENOCa, our panel was not able to further segregate or clarify this apparent confusion. SKOV3 is another unique case as it's immuno-phenotype most closely resembles HGSC, yet it caries a truncating mutation for ARID1A, a mutation that has not been observed in HGSC despite widespread testing PIK3CA mutation. Finally, the TOV112D cell line also presents as an exception with a moderately strong prediction of HGSC immuno-phenotype. In spite of this finding we suggest this line is representative of TP53 mutant ENOCa, based on the presence of an ENOCa characteristic CTNNB1 mutation, pathological review of the primary tumor material in the originating laboratory and expression profiling experiments supporting this conclusion Both CCC and ENOCa appear to arise in a background of endometriosis. Atypical endometriosis adjacent to, or contiguous with, either histotype is not unusual for either CCC or ENOCa NRAS mutant LGSC tumor and the first validated LGSC derived cell line carrying this mutation. The COSMIC database suggests the cell lines LK-1  and TYK-nu  also carry activating NRAS mutations, however we were unable to source these cell lines to confirm/reject their histological identity. In the cases of LGSC cell lines derived in-house, mutations of TP53 were not observed, consistent with IHC based literature reports suggesting this is a major molecular discriminator between HGSC and LGSC Unfortunately our COSP tool is unable to differentiate LGSC. Based on expert re-review of primary material we are aware of two cell lines derived from LGSC primary tumors. We therefore confidently favor this classification for VOA1056_CL and VOA1312_CL despite predictions of HGSC or ENOCa obtained from COSP. The VOA1056_CL line carries a Ras-pathway mutation as might be expected of an LGSC tumor, however this is an NRAS Q61R activating mutation. Activating NRAS mutations were recently described in LGSC at the 2012 AACR annual meeting TP53 homozygous deletion, overexpression by IHC, and KRAS G12V mutation.Finally only a single cell line in our collection was reported to be of mucinous carcinoma origin. The mutation profile of this cell line is consistent with this diagnosis, including a 127-bp Cell line records for epithelial ovarian carcinoma have recently come into question with a number contaminated and redundant cell lines acknowledged in a recent study In the spectrum of ovarian carcinomas, recent evidence strongly supports diagnosis and treatment of the five major histotypes of carcinomas as distinct diseases. Cancer cell lines provide an important intermediate tool for clinically relevant translational science, allowing genomic manipulation and cell biology studies beyond what can be reasonably achieved in clinical trials or animal models of cancer. In order to develop appropriate treatments, translational researchers need to use model systems appropriate to each ovarian carcinoma type. Unfortunately, historical records of ovarian cancer cell lines have rarely included information on histological origin Figure S1127bp homozygous (or hemizygous) deletion affectingTP53exon 4 in the MCAS mucinous carcinoma cell line. This mutation was apparent by Sanger sequencing though not annotated in the CCLE database. Coding bases are annotated in upper case.(PDF)Click here for additional data file.Table S1Cell Lines & Sources.(PDF)Click here for additional data file.Table S2Antibodies and Dilutions.(PDF)Click here for additional data file.Table S3deFuse predicted expressed re-arrangements from transcriptome sequencing data.(XLS)Click here for additional data file.Table S4Mutations Found In Ovarian Carcinoma Cell Lines.(PDF)Click here for additional data file.Table S5Mismatch Repair IHC.(PDF)Click here for additional data file.


Global gene expression profiling was performed on flow-sorted rds and wild-type rod photoreceptors immediately prior and subsequent to times at which OSs are normally elaborated. Dysregulated genes were identified via microarray hybridization, and selected candidates were validated using quantitative PCR analyses. Both the array and qPCR data revealed that gene expression changes were generally modest and dispersed amongst a variety of known functional networks. Although genes showing major (>5-fold) differential expression were identified in a few instances, nearly all displayed transient temporal profiles, returning to WT levels by postnatal day (P) 21. These observations suggest that major defects in photoreceptor cell structure may induce early homeostatic responses, which function in a protective manner to promote cell viability. We identified a single key gene, Egr1, that was dysregulated in a sustained fashion in rds rod photoreceptors and retina. Egr1 upregulation was associated with microglial activation and migration into the outer retina at times subsequent to the major peak of photoreceptor cell death. Interestingly, this response was accompanied by neurotrophic factor upregulation. We hypothesize that activation of Egr1 and neurotrophic factors may represent a protective immune mechanism which contributes to the characteristically slow retinal degeneration of the rds mouse model.Inherited defects in retinal photoreceptor structure impair visual transduction, disrupt relationship with the retinal pigment epithelium (RPE), and compromise cell viability. A variety of progressive retinal degenerative diseases can result, and knowledge of disease etiology remains incomplete. To investigate pathogenic mechanisms in such instances, we have characterized rod photoreceptor and retinal gene expression changes in response to a defined insult to photoreceptor structure, using the  Human vision begins with rod and cone photoreceptors, light-sensitive ciliated sensory neurons situated in the neural retina. These fragile cells are susceptible to a variety of insults, which can impede their function and viability and cause retinal degeneration and vision loss. Vertebrate animal models, and in particular mice, have been a valuable resource to identify molecules essential for normal photoreceptor physiology. Although a wide variety of naturally occurring and engineered mouse models have been investigated for retinal degeneration retinal degeneration slow (rds) mouse  has been the focus of numerous investigations of photoreceptor and retinal structure, function and viability rds  gene to produce a null allele The rds model to identify genes associated with retinal degeneration rds rod photoreceptors to lack of peripherin/rds and absence of OSs. Our findings suggest that a combination of homeostatic mechanisms may contribute to the protracted time course of retinal degeneration in the rds retina.A previous study applied a whole-retina microarray approach to the rds mouse retina, since this animal model possesses a well-defined monogenic defect in photoreceptor structure, and mutations in rds produce a broad spectrum of human retinal disease rds (−/−) photoreceptors differentiate and establish otherwise normal morphology, they fail to elaborate OSs, do not establish a normal relationship with the RPE, and thereafter degenerate over a protracted time course To investigate how inherited defects in photoreceptor structure can affect cell viability, we adopted a flow-sorting method for identifying transcriptome changes in rod photoreceptors rds (−/−) mice were genetically labeled by transfer of a Nrl-eGFP transgene from an established line rds (−/−) phenotype by examination of retinal cryosections. We observed that retinas from P14 WT, Nrl-eGFP, and rds:Nrl-eGFP animals each expressed eGFP, as indicated by robust green fluorescence in their outer nuclear layers  to analyze the data generated by expression profiling to determine affected cellular functional pathways. IPA uses a curated database with structured ontology to extract biological meaning from gene expression data. The analysis identified functional pathways in which differentially regulated genes were overrepresented  using Taqman assays to perform quantitative PCR (qPCR). These experiments incorporated three biological replicates to provide high-confidence data, and were conducted on intact retinas (vs. isolated rod photoreceptors) to eliminate the possibility that we would identify gene expression changes induced by cell isolation procedures. More than seventy genes with defined functions were chosen for analysis at each of four time points . We used three selection criteria and included genes that, 1) exhibited high-fold changes  as assayed by microarray, 2) were potential network partners for the high-fold changers, or 3) were known to have roles in inherited retinal degenerations. Together, they fell into five major functional pathways, including: transcription factors, phototransduction pathway, lipid metabolism, cellular organization, and cell adhesion .Dctn1, Dfnb31, Dnm1, Impdh1, Rpgr, Rpgrip1,) and the other four participate in lipid homeostasis . In only two instances (Mef2C and Egr1), were sustained changes in gene expression observed. Mef2c encodes a transcription factor expressed in a variety of tissues Nrl levels  action has been of substantial interest in a wide variety of biological contexts, and its expression has been documented in the vertebrate retina These pathways likely represent alterations in cellular homeostasis that result from a failure of P/rds expression and OS formation. Interestingly, we found a number of characteristics that were common across all the pathways. Firstly, gene expression changes were generally modest; most (46%) were less than two-fold. Secondly, higher-fold changers were dispersed across the five pathways. Finally, changes in expression were only rarely sustained with time. Interestingly, many genes exhibited a distinct pattern of temporal gene expression changes with transient differential expression observed at P14, in conjunction with little or no change at either of the other three time points studied. l levels . Egr1 . CD68 upregulation initiated slightly later than that of Egr1, likely a reflection of microglial activation at stages subsequent to the time OS are normally elaborated.rds retina – prior to the stage at which OSs are normally elaborated  labeling of filamentous (resting) and ameboid (activated/migrating) microglia. We were particularly interested in documenting whether cells redistributed from INL to ONL, since this criterion reflects an active immune response aborated . RoughlyT retina . These ards at P17 CNTF, GDNF, their receptors CNTFR and GFRA1, and TGF-β were upregulated at least 2-fold in the developing rds retina. In contrast, factors associated with microglial activation and sharply upregulated prior to photoreceptor apoptosis IL6, IL10, and TNF-α, were undetectable in the rds retina (not shown). Taken together, the results suggest that microglial activation may contribute a protective role for photoreceptor cell viability in the rds model.Although traditionally viewed as neurotoxic agents, activated microglia have more recently been proposed to play a variety of roles for neurons A wide variety of inherited defects in retinal proteins adversely affect photoreceptor structure, disrupt the photoreceptor-RPE relationship, and result in progressive retinal degeneration and loss of visual function. Similar pathologies may also apply to more complex diseases for which multifactorial genetic contributions are present, such as age-related macular degeneration. Substantial progress has been made cataloging the physiological consequences of defined defects and modeling how such primary insults adversely impact retinal health and vision rds model represents a spontaneous null mutation resulting from a viral insertion event into chromosome 17rds model, during the period immediately prior and subsequent to the normal time of OS elaboration, and employed a flow-sorting approach to highlight responses of rod photoreceptor cells. Ingenuity Pathways Analysis was applied to screen the transcriptome data, and identified significant changes in five functional pathways including: genetic disorders, small molecule biochemistry, cellular assembly and organization, cellular development, and lipid metabolism. We further analyzed the dataset manually, seeking to identify concerted alterations in expression of genes associated with photoreceptor function, viability, or degeneration. In general however, gene dysregulation was manifested as a broad pattern of transient and typically low-amplitude gene expression changes across numerous functional pathways.The well-studied murine rds retina were generally modest, transient, and dispersed over numerous functional classes. This timespan includes the initiation (P14) and peak (P17) of apoptotic photoreceptor cell death rds can be rationalized in several ways. Firstly, the one-hit model of Clarke, et al suggests that inherited defects can affect disparate aspects of cellular physiology to bring photoreceptors closer to their viability threshold Real-time qPCR validation experiments emphasized that gene expression changes between P6 and P21 in the Dctn1, Impdh1), lipid homeostasis , and photoreceptor-associated transcription . Dctn1 encodes a subunit of the macromolecular complex dynactin, and participates in microtubule-based transport in neurons Impdh1 has been associated with instances of inherited retinal degeneration (RD10), possibly by regulating rhodopsin expression Abcg1 and Plch2 are important for cholesterol and lipid metabolism and their upregulation may represent a response to altered demands for lipid production Mef2c is recently shown to be regulated by NRL and is likely important for photoreceptor development and maintenance rds model was observed only for Egr1, a ubiquitous zinc-finger transcription factor that participates in a broad range of biological processes, including: cellular proliferation and development Egr1 expression in retinal microglia has been more thoroughly characterized, where it appears to participate in the activation process The few instances of large-scale gene dysregulation validated in the present study were (with one exception) transient, and clustered into three known pathways: genes associated with cellular organization  and rds (+/+) mice carrying the Nrl-eGFP transgene.Procedures utilizing animals were in accordance to the guidelines stipulated by Oakland University Animal Care and Use Committee (IACUC #10061) as well as the ARVO Statement for Use of Animals in Ophthalmic and Vision Research. Animals were maintained at 22°C, with a 12 hour fixed dark/light schedule and had unrestricted access to food and water. Nrl-eGFP mice + cells were collected using a BD Biosciences FACSVantage SE cell sorter, essentially as described 6 GFP+ events per retina) using a RNAqueous-4PCR kit . RNA samples were assayed spectrophotometrically for nucleotide concentration via absorbance at 260 nm, and for quality using an Agilent 2100 Bioanalyzer and RNA 6000 Nano LabChip. Samples showing discrete rRNA bands at predicted mobilities were processed for microarray expression analysis, using an Ovation Biotin System v1.0 amplification kit , according to manufacturer's protocol. Amplified cDNAs from were hybridized to Mouse Genome 430 2.0 Array GeneChips (Affymetrix Inc.) and detected, essentially as described Mice at indicated ages  were sacrificed by decapitation 2–3 hours subsequent to light onset. Retinas from 4–5 independent biological replicates were immediately dissected from enucleated eyes, and dissociated with trypsin (1 mg/ml in PBS) for 5 min at 37°C. Reactions were quenched with SBTI (1 mg/ml), and GFPMicroarray based gene expression profiling was performed as described previously 2 and the retina were dissected out under a stereomicroscope, snap-frozen, and immediately stored at −80°C, until further use. Total RNA was prepared using a Absolutely RNA miniprep kit  as recommended by the manufacturer. Concentration was determined spectrophotometrically and quality of the preparation was determined using denaturing agarose gel electrophoresis. A Superscript first strand synthesis system for RT-PCR kit  was utilized to prepare cDNA using 5.0 µg of total RNA.Age and genotype appropriate mice were euthanized by asphyxiation with COReal-time PCR experiments were performed using Taqman reagents from Applied Biosystems, as per manufacturer's instructions. cDNA for a given sample was diluted 100-fold and 3 µl of the dilution was amplified in 20 µl reaction mixtures using a Stratagene MxPro thermocycler. Two technical replicates and three biological replicates were used for each reaction. Relative expression levels were determined by normalizing to the GAPDH cDNA present in individual samples and comparing the normalized value to a standard curve. The standard curve was made using three technical replicates of a dilution series of a mixture of equal volumes of wild type samples for all time points.2 under normal room illumination. The superior hemisphere of each eye was marked, and the eyes were enucleated and placed into 4% paraformaldehyde in 0.1 M sodium phosphate buffer, pH 7.4. The cornea and the lens were removed after an initial fixation of 30 min, and the eyecups were fixed further for 2 hrs. Subsequently the eye cups were cryoprotected in 20% sucrose/0.1 M sodium phosphate buffer overnight and frozen in a solution composed of 2∶1 ratio of 20% sucrose in PBS and optimal cutting temperature compound Mice were sacrificed by asphyxiation with COTable S1Gene expression profiles of rds:Nrl-eGFP rod photoreceptors purified from mouse retina at P6, P14 and P21, as determined by Affymetrix GeneChip hybridizations. Fold-changes are reported as the log reciprocal transformation of the expression ratio. 1603 genes with F.C of ≥2.0 and p-value ≤0.05 were dysregulated at one or more time points.(XLS)Click here for additional data file.


Orb-web and cob-web weaving spiders spin dragline silk fibers that are among the strongest materials known. Draglines are primarily composed of MaSp1 and MaSp2, two spidroins (spider fibrous proteins) expressed in the major ampullate (MA) silk glands. Prior genetic studies of dragline silk have focused mostly on determining the sequence of these spidroins, leaving other genetic aspects of silk synthesis largely uncharacterized.Latrodectus hesperus. We sequenced millions of 3′-anchored “tags” of cDNAs derived either from MA glands or control tissue  mRNAs, then associated the tags with genes by compiling a reference database from our newly constructed normalized L. hesperus cDNA library and published L. hesperus sequences. We were able to determine transcript abundance and alternative polyadenylation of each of three loci encoding MaSp1. The ratio of MaSp1:MaSp2 transcripts varied between individuals, but on average was similar to the estimated ratio of MaSp1:MaSp2 in dragline fibers. We also identified transcription of TuSp1 in MA glands, another spidroin family member that encodes the primary component of egg-sac silk, synthesized in tubuliform glands. In addition to the spidroin paralogs, we identified 30 genes that are more abundantly represented in MA glands than cephalothoraxes and represent new candidates for involvement in spider silk synthesis.Here, we used deep sequencing to profile gene expression patterns in the Western black widow, Modulating expression rates of MaSp1 variants as well as MaSp2 and TuSp1 could lead to differences in mechanical properties of dragline fibers. Many of the newly identified candidate genes likely encode secreted proteins, suggesting they could be incorporated into dragline fibers or assist in protein processing and fiber assembly. Our results demonstrate previously unrecognized transcript complexity in spider silk glands. Spiders (Araneae) use silk throughout their lives for functions such as prey capture, sperm webs, egg cases, safety draglines, and dispersal . Orb-webSpider silks are composed primarily of large structural proteins termed spidroins (spider fibrous proteins) that are encoded by members of a single gene family. Silk types differ from each other in their constituent spidroins ,9. For iMaSp1 and MaSp2 sequences from multiple species (e.g.)b-weaver . This apb-weaver .MaSp1 and MaSp2[AcSp1[Here, we use massively parallel signature sequencing (MPSS) to profile expression patterns in MA glands and cephalothoraxes (fused head-body) of the Western black widow. We targeted this species for several reasons. Black widows synthesize dragline silks that are among the strongest measured ,43. As dand MaSp2 and the Sp2[AcSp1. Our aimWe generated more than 20 million tags, which are 3′-anchored 20 base cDNA fragments, from mRNA transcripts found in the MA glands and cephalothoraxes of adult female Western black widows using MPSS . The only sequences available that included untranscribed regions were for the complete gene sequences of DpnII recognition sequence, 5′-GATC-3′. We determined tag identity by first predicting all possible tag sequences from the coding or sense strand and the complementary or antisense strand (GATC and 16 following bases) in the reference genes. Predicted tags were then matched to the sequenced tags from our four libraries. Of the 515 sequences in our reference database (when MaSp1 and MaSp2 are represented by one cDNA each), 409 contained a predicted tag and 200 were represented by at least one observed tag.The MPSS method we used targeted the 3′-most occurrence of the An exact test implemented in edgeR  identifiApproximately 46% of the reference genes represented by a sequenced tag were represented by both sense and antisense tags, 45% were represented by only a sense tag, and 9% were represented by only an antisense tag . An example of these alternative transcripts is described for MaSp1 below. Because the origin of multiple tags per gene is not always clear, we estimated transcript abundance two ways. First, under the assumption that all tags that matched a gene represent transcription of some kind, we summed the average cpm of all tags from the sense strand, or separately, from the antisense strand  and 11 predicted antisense tags (7 unique) that matched an observed tag. Importantly, all of the tags predicted in the coding region of the full-length gene were observed as well as two downstream of the coding region , consistent with selecting for the 3′-most occurrence of the DpnII recognition sequence.Alignment of all MaSp1 locus, there are other indices that are diagnostic for particular loci  downstream of an observed tag in each of these loci  were predicted from the sense strand representing 100 unique tag sequences (29 from the MaSp2 coding region). In the full-length gene, 33 tag locations were observed (11 unique tag sequences). Four of these observations were well outside the coding region and appeared at low frequencies in all tissues (cpm < 6.3), leaving seven unique observed sense tags within the MaSp2 coding region . All other observed MaSp2 tags were much less abundant (mean cpm in MA glands = 2-68). There was only one additional unique sense tag observed from other MaSp2 sequences. This tag is in the N-terminal encoding region of a partial MaSp2 cDNA (mean cpm in MA glands = 4.39). A tag that only differed by one base pair from this tag was predicted from the corresponding coding region in the full-length gene, but was not observed . TuSp1 was surprisingly abundant, ranking 10th most represented gene of the ones that are more abundant in MA glands than cephalothoraxes, directly following MaSp2  than the other (2 cpm). The difference in expression levels between the two individuals could be explained by individual differences in readiness to make egg-cases. The number of transcripts encoding TuSp1 and other egg-case proteins increase in tubuliform silk glands as the individual nears egg-case production [TuSp1 had large, mature eggs, suggesting that TuSp1 transcription increases in MA glands at the same time as increasing in tubuliform glands. It is possible TuSp1 becomes incorporated in MA fibers at the time of egg-case production, which could contribute to variation in mechanical properties. Alternatively, translational control of TuSp1 prevents protein synthesis in MA glands despite the presence of TuSp1 transcripts. We also cannot exclude the possibility that our detection of TuSp1 in MA glands is due to leakage from mRNAs transcribed in the tubuliform glands.2 Figure . Howeveroduction . The indIn addition to spidroin encoding genes, we identified 30  other genes represented by tags that were significantly more abundant in MA glands than cephalothoraxes . In fact, the 3′-most sense tag of this gene was not significantly different between the two tissues. Thus, the higher abundance in MA glands probably reflects the very high rates of translation to produce dragline spidroins, MaSp1 and MaSp2. In contrast, tags representing fasciclin and lectin-encoding genes were consistently highly abundant in MA glands but either absent or extremely low in cephalothoraxes (log fold change = 9.7-14.5). In various insect species, fasciclins are cell adhesion glycoproteins that play a role in neuronal development . Lectinrosophila,67. Howerosophila,68. TherThirteen of the 20 genes overrepresented in MA glands that were annotated by sequence similarity are predicted to encode proteins that can be grouped into three functional classes Figure . These iEscherichia coli[One of the genes predicted to encode a protein with transferase activity is homologous to a phosphoserine aminotransferase, which is part of the cascade for serine biosynthesis as shown in chia coli. Anotherchia coli. This pachia coli. Becausechia coli, oxidatiMaSp1 and MaSp2 were among the most highly expressed genes in MA glands. Surprisingly, the most abundant transcript in MA glands was one with unknown function. Also unexpected, was the discovery of high levels of TuSp1 expression and an additional six genes that were more abundantly represented in MA glands than MaSp2  or different (intraspecific) spiders.Through cDNA tag profiling, we describe previously unrecognized gene expression complexity in MA glands of the Western black widow. As expected, 2 Figure . In addiLatrodectus. It is possible that these genes resulted from black widow specific evolutionary events. However, due to the paucity of genomic resources for spiders we cannot exclude the possibility that these genes have homologs in other spiders. Identifying homologs expressed in silk glands of other spider species would strengthen the argument that these genes are involved in silk synthesis. Of the genes predicted to encode proteins with functional homologs some may simply reflect the high rates of translation and protein storage in silk glands such as translation factors, amino transferases, and protease inhibitors. Others are likely transcription factors important for regulating gland-specific spidroin expression. Gamma-glutamyl-transferases may be important regulators of pH changes in the MA gland that are necessary for fiber assembly. An overriding theme for many of the genes overrepresented in MA glands is that they are members of larger gene families. Neofunctionalization of gene copies expressed in silk glands may be even more important for silk synthesis than the previously recognized diversity of the spidroin gene family. As such, silk glands represent a model system for understanding the evolution of tissue specific functions. Furthermore, our increased understanding of gene regulation in spider silk glands will ultimately lead to improved recombinant silk production.We identified 34 unique gene sequences represented by tags that were significantly more abundant in MA glands than cephalothoraxes. Besides spidroin genes, we found 30 new candidate genes for dragline silk synthesis. Approximately 1/3 of these genes have no known homolog outside of Latrodectus hesperus adult females caught live in Riverside, California (USA). Total RNA was isolated from each tissue type for both individuals (four separate isolations) by homogenizing tissue in TRIzol®  and further purifying RNA with the RNeasy Mini Kit . Any remaining genomic DNA was removed by incubating RNA with DNase from the Ambion® TURBO DNA-free™ kit (Invitrogen).Major ampullate (MA) glands and cephalothoraxes were dissected from two DpnII kit . In brief, we extracted single stranded poly(A) mRNA using Sera-mag magnetic oligo(dT) beads. Single stranded DNA complementary to the mRNA (cDNA) was synthesized using SuperScript® III Reverse Transcriptase (Invitrogen), primed from the oligo(dT) magnetic beads. Still attached to the magnetic beads, second strand cDNA was synthesized by incubation with RNaseH and DNA Polymerase I. The double stranded cDNA was digested with DpnII, which recognizes 5′-GATC-3′. The digested cDNA was washed and only the portion of cDNA still attached to the magnetic bead was retained. An adapter was ligated to the site of DpnII digestion, introducing the recognition sequence for MmeI. The adapter-cDNA ligation was digested with MmeI, which cuts 20 bp downstream from the recognition sequence and 16 bp downstream of the DpnII site, GATC. A second adapter was then ligated to the MmeI cut site, and the adapter-ligated cDNA construct was enriched by PCR and purified  vector between EcoRI (5′) and XhoI (3′) and used to transform bacterial cells (Escherichia coli strain DH10B from Invitrogen). Over 18,000 recombinant clones were arrayed into 48 384-well plates. Clones were sequenced using M13R or T3 to obtain the 5′ sequence of cDNAs  followed by purification with the RNeasy Mini Kit (Qiagen). mRNA isolation, cDNA synthesis, normalization, library construction, and arraying were performed by BIO S&T . In brief, cDNA was synthesized from 5 μg of mRNA using the SMART™ cDNA library construction kit . Abundant cDNAs were reduced in frequency by subtractive hybridization. This method hybridizes a sample of biotyntilated single stranded cDNA to excess single stranded cDNA copies in the library. Double stranded cDNA is then removed using streptavidin beads. This allows for more efficient library sequencing. Following normalization, cDNAs were amplified, subjected to and XhoI ′ and useL. hesperus nuclear protein-coding sequences were downloaded from the NCBI nucleotide database in June 2012 and combined with the ESTs. We generated a non-redundant set of sequences using CAP3 [-6) comparisons to NCBI’s non-redundant protein database (nr). We predicted signal peptides from translated genes using SignalP 4.1 [All 368 published ing CAP3 , which ring CAP3  . First, we assumed that all sequences from NCBI’s nucleotide database had been correctly annotated. Second, our normalized cDNAs were positionally cloned so that sequencing with M13R or T3 resulted in characterizing the 5′ end of coding sequences. Clones sequenced from the 3′ end of the cDNA were reverse complemented prior to searching for tag sequences. Third, we inspected contigs assembled by CAP3 for retention of directionality. If a contig was reverse complemented relative to the raw sequences, we reverse complemented the contig. Finally, we checked that the coding frames predicted by BLASTX were all positive.We batch imported our FASTA file of reference gene sequences into an R data frame and identified the tag positions by searching for “GATC”  in at least two libraries. Tag Sequence gives the 16 bases that were sequenced. GATC was added to the 5′ end of these sequences for matching tags to genes. Abundance of tags in each library is given as counts per million (CPM) for two individuals (1 or 2) from cephalothoraxes (ceph) or major ampullate glands (MA). Log Fold Change (log FC) is between MA and cephalothoraxes. Log CPM is the log of the average CPM among all four libraries. P-values and the estimated False Discovery Rate (FDR) are associated with an exact test implemented in edgeR for differential expression between MA and cephalothoraxes.Click here for fileSummary information for genes in our reference database that matched an observed tag. geneID gives the GenBank accession number or the CAP3 contig number. (Accession numbers for ESTs generated in this study will be made available upon acceptance of manuscript). Tag sequence is the 16 bases that were sequenced. GATC was added to the 5′ end of these sequences for matching tags to genes. Gene descriptor gives the top BLASTX hit for contigs and newly generated ESTs or the gene descriptor from GenBank. Tag index describes placement of tag along gene, with 0 representing the 3′-most tag and smaller numbers moving progressively toward the 5′ end. Position is the base number of the sequence at which the G of GATC begins. Gene length is the number of bases in the sequence and FDR is the False Discovery Rate from the exact test for differential expression between major ampullate glands and cephalothoraxes (see Additional file Click here for file


Anthropogenic activities have increased the rate of biological extinction many-fold. Recent empirical studies suggest that projected extinction may lead to extensive loss to the Tree of Life, much more than if extinction were random. One suggested cause is that extinction risk is heritable , such that entire higher groups will be lost. We show here with simulation that phylogenetically clustered extinction risks are necessary but not sufficient for the extensive loss of phylogenetic diversity (PD) compared to random extinction. We simulated Yule trees and evolved extinction risks at various levels of heritability (measured using Pagel's  Phylogenetic trees estimate the evolutionary relationships among species inferred from empirical data. The edge lengths of these trees represent accrued change or temporal accounts of diversification events Nee and May We return to this simple question here, and try to quantify loss of PD due to phylogenetically clustered extinction risks on model trees. We build upon Heard and Mooers Yule trees We then simulated a set of continuous traits along each tree under the Brownian Motion (BM) model of change, another common model for continuous trait evolution Above, we produced continuous traits on our trees with a range of phylogenetic clustering from j, while i, while jth taxa in a reference phylogeny.For a given single tree, we used the following two transformations: In order to answer our main question, we compared E(PD) on the modeled p(ext) values with E(PD) obtained from the identical tree with shuffled p(ext) values, representing a useful extension of the uniform p(ext) FOB model reported by Nee and May We observed no noticeable effect in any of our results with an increase in the size of Yule trees except a minor decrease in the confidence limits on our estimates compare . We therVarying the mean extinction risk and lambda produced different distributions of p(ext) across the tips of our trees. At no phylogenetic signal tinction . At , weribution . At highribution .Percentage difference in projected PD or%ΔE(PD) as:We defined the term %ΔE(PD)where Throughout our test scenarios, we observed consistent patterns in the relationship between the amount of phylogenetic clustering and the percentage difference in projected PD; we presented these patterns for 64-tip Yule trees in on model .Further, we also observed the same qualitative pattern in%ΔE(PD) as a function of phylogenetic signal across all three scenarios of different mean extinction risk compare . For a pdecline in the values of%ΔE(PD) with increasing phylogenetic signal , the distribution of%ΔE(PD) as a function of phylogenetic signal was similar to the one produced with randomly generated Yule trees (with 64 or 128 tips), with a maximum value of ∼14% at our highest chosen mean extinction risk . Howeverc signal . This leWe also tested our question on the extreme model of diversification. The extreme case, i.e., coalescent trees, produced the highest  values of%ΔE(PD) as a function of phylogenetic signal . Under tWe highlight two main findings. We interpret our results to mean that phylogenetically clustered extinction risks alone are not sufficient to explain appreciable extra losses of E(PD) [%ΔE(PD)] due to projected extinction. In addition, the overall amount of this additional loss does not substantially scale with tree size, tree balance and extreme cases of temporal changes in the rates of diversification, respectively, though mean p(ext) has a strong relative effect. Rather, we require at least a combination of these factors with uncommonly excessive values to drive a substantial loss of PD.Nee and May Under random extinction scenarios such as those modeled by Nee and May However, contrary to previous studies ustering . Since tThe extra loss of PD [%ΔE(PD)] also changes with different values of mean extinction risk (Equation 4). Logically, an increase in mean extinction risk will decrease both the projected PD due to random extinction Our results also indicate that there is a non-significant increase in the percentage difference in projected PD beyond the perfect Brownian Motion model of change at low mean extinction risk (mean p(ext) = 0.25; see We find an interesting pattern in the percentage difference in projected PD on maximally unbalanced trees. Overall, we see minimal loss. If mean p(ext) is high we actually lose more PD under random than under clustered extinction. Our unbalanced trees had very short internal edges, and a few very long pendant edges. Thus, when risks are clustered on an unbalanced tree, taxa with longer pendant edges are less likely to be pruned than those with shorter edges, decreasing the percentage difference in projected PD.Alternatively, a balanced tree of the same size produces a loss similar to a randomly generated Yule tree. Here, every edge is of the same length and there is the maximum amount of topological redundancy. We therefore expect to lose more PD from lineages that share similar extinction risks.We find that the amount of PD lost on coalescent trees is the most sensitive to particular values of our chosen parameters. For instance, we do find a similar quantitative pattern to Yule trees in the percentage difference in projected PD in coalescent trees at very low and moderate levels of In our study, we were unable to incorporate the exact shape of the distribution of extinction probabilities suggested by recent assessments To conclude, we propose that other non-random processes in addition to phylogenetic clustering of species' extinction risks must explain the appreciable loss of PD projected on real trees. Inferred trees do have very isolated small clades, and the highest extinction risk may be found in such small isolated clades Figure S1Disparity through time (DTT) plots for trait values at various phylogenetic signals. The x-axis indicates the relative time elapsed or age of clade with 0 representing its origin and 1 representing its current age. Solid line indicates the observed disparity values, whereas the dashed line represents the mean of 100 simulated disparity values expected under the Brownian Motion model. A) through G) represent various DTT plots at increasing phylogenetic signal simulated on one example tree. High relative disparity is indicative of more variation in trait values within subclades than between subclades, which are found near the tips of a phylogeny with the trait displaying no phylogenetic signal. In contrast, low relative disparity is indicative of less variation in trait values within subclades than between subclades, which are found near the tips of a phylogeny with the trait displaying an excessive phylogenetic signal.(TIFF)Click here for additional data file.Figure S2Traitgrams and the two measures of phylogenetic signal. The three traitgrams show the evolution of a continuous trait evolving under different phylogenetic signals. The x-axis represents a continuous scale of species' trait values and node depths represent the phylogenetic edge lengths. Traitgram in A) represents the evolution of a trait with no phylogenetic signal (B) and C) represent the evolution of a trait under the perfect BM model and when evolution exceeds the perfect BM model ((TIFF)Click here for additional data file.Figure S3Quantifying percentage difference in projected PD with phylogenetic clustering in 128-tip Yule trees. A) mean p(ext)  = 0.25, B) mean p(ext)  = 0.5, and C) mean p(ext)  = 0.75. Data points in percentage denote the amount of additional loss of projected PD (relative to random extinction) with increasing phylogenetic clustering. Dashed line indicates that loss under random extinction. Error bars around points represent the 95% confidence interval with a sample size of 1000 trees. Note differences in vertical axes.(TIFF)Click here for additional data file.Figure S4The additional loss of PD as function of phylogenetic clustering in 64-tip balanced Yule trees. A) mean p(ext)  = 0.25, B) mean p(ext)  = 0.5, and C) mean p(ext)  = 0.75. Data points in percentage denote the amount of additional loss of projected PD (relative to random extinction) with increasing phylogenetic clustering. Dashed line indicates that loss under random extinction. Error bars around points represent the 95% confidence interval with a sample size of 1000 trees. Note differences in vertical axes.(TIFF)Click here for additional data file.Figure S5The additional loss of PD as function of phylogenetic clustering in 64-tip unbalanced Yule trees. A) mean p(ext)  = 0.25, B) mean p(ext)  = 0.5, and C) mean p(ext)  = 0.75. Data points in percentage denote the amount of additional loss of projected PD (relative to random extinction) with increasing phylogenetic clustering. Dashed line indicates that loss under random extinction. Error bars around points represent the 95% confidence interval with a sample size of 1000 trees. Note differences in vertical axes.(TIFF)Click here for additional data file.Figure S6Percentage difference in projected PD as a function of phylogenetic clustering in 64-tip coalescent trees. A) mean p(ext)  = 0.25, B) mean p(ext)  = 0.5, and C) mean p(ext)  = 0.75. Data points in percentage denote the amount of additional loss of projected PD (relative to random extinction) with increasing phylogenetic clustering. Dashed line indicates that loss under random extinction. Error bars around points represent the 95% confidence interval with a sample size of 1000 trees. Note differences in vertical axes.(TIFF)Click here for additional data file.


Allergy is a common disorder in the western world with a prevalence of 15% to 20%. The combination of ocular symptoms in patients with allergic rhinitis occurs frequently. Nasal mucus for the nasal epithelium and tear fluid for the eye are the first defence barrier against various pathogens including aeroallergens. Little is known about the nasal mucus and particularly about the tear fluid proteome. The aim of the study was to analyse both body fluids on a proteome level and detect possible impact of its proteins in the pathophysiology in allergic rhinoconjunctivitis.Fifty-eight patients  were included in this study. Allergy status was confirmed through symptom history and skin prick test (SPT). Patients sensitized to house dust mite or animals were excluded. Nasal mucus was collected with a special suction device, tear fluid was collected with a glass capillary. Specimens then were sent for LC MS/MS mass spectrometry.In total 86 different proteins could be identified in tear fluid . 74 proteins could be identified with a peptide count of ≥2. Considering a mean spectral count (SC) of ≥4 in either group 18 different proteins could be identified. Calculating a means SC ration between allergic (A) and healthy (H) (A/H) 6 proteins were elevated in allergics (A/H >1) two of which were significantly elevated (lactoperoxidase and prolactin inducible protein). Twelve proteins (A/H<1) were elevated in healthy controls four of which were significantly elevated .With a SC of ≥4 and a peptide count of ≥1, 56 different proteins were found in tear fluid and nasal mucus, 13 of which were found in both fluids, 5 exclusively in tear fluid and 38 exclusively in nasal mucus.Tear fluid proteome is significantly different between allergics and healthy controls, significantly elevated proteins in allergics reflect exposure to exogenic noxa trough peroxidise activity and pathological condition of tissue. In healthy controls proteins reflect normal lacrimal gland secretory function and immunemodulation through steroid binding protein activity. A larger number of proteins are found in nasal mucus only (38 vs. 5), the functional differences need to be further determined.