Project description:Comparison of circulating monocytes from pre- and postmanopausal females with low or high bone mineral density (BMD). Circulating monocytes are progenitors of osteoclasts, and produce factors important to bone metabolism. Results provide insight into the role of monocytes in osteoporosis. We identify osteoporosis genes by microarray analyses of monocytes in high vs. low hip BMD (bone mineral density) subjects. Microarray analyses of monocytes were performed using Affymetrix HG-133A arrays in 80 Caucasian females, including 40 high (20 pre- and 20 postmanopausal) and 40 low hip BMD (20 pre- and 20 postmanopausal) subjects
Project description:Comparison of circulating monocytes from pre- and postmenopausal females with low or high bone mineral density (BMD). Circulating monocytes are progenitors of osteoclasts, and produce factors important to bone metabolism. Results provide insight into the role of monocytes in osteoporosis. We identify osteoporosis genes by microarray analyses of monocytes in high vs. low hip BMD (bone mineral density) subjects. Microarray analyses of monocytes were performed using Affymetrix 1.0 ST arrays in 73 Caucasian females (age: 47-56) with extremely high (mean ZBMD =1.38, n=42, 16 pre- and 26 postmenopausal subjects) or low hip BMD (mean ZBMD=-1.05, n=31, 15 pre- and 16 postmenopausal subjects). Differential gene expression analysis in high vs. low BMD subjects was conducted in the total cohort as well as pre- and post-menopausal subjects.
Project description:SIRT6 has been implicated in a range of biological processes including inflammation, aging and the control of metabolism. Hence inhibitors or activators of SIRT6 have the potential to be therapeutics for a number of indications. Genome wide expression studies were used to investigate the effect of overexpression of SIRT6 and mutant SIRT6 on a wide range of NFκB dependent gene expression HEK293 cells were transfected with expression vectors encoding wild type SIRT6 or the H133W mutant followed by stimulation with TNFα for one hour.
Project description:Transcriptomic analysis of fresh breast cancer tissue versus normal tissues. The Study comprising 45 Saudi-Arabian subjects was designed to take advantage of transcriptomics to prospectively explore the roles of lifestyle and genetic susceptibility in the occurrence of breast cancer. Total RNA isolated from 45 surgically resected breast cancer tissues and 8 healthy breast tissues (3 from Affymetrix) and purified, labeled, and hybridized to Affymetrix Human Gene 1.0 ST Array.
Project description:Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p< 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p<0.001). The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p<0.001 for all the patient groups). Conclusions: We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature Keywords: Disease prediction Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days. If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing.
Project description:In this study, we analyzed transcriptome gene expression microarray, epigenomic miRNA microarray and methylome sequencing data simultaneously in PBMs from 5 high hip BMD subjects and 5 low hip BMD subjects. Through integrating the transcriptomic and epigenomic data, firstly we identified BMD-related genetic factors, including 9 protein coding genes and 2 miRNAs, of which 3 genes (FAM50A, ZNF473 and TMEM55B) and one miRNA (hsa-mir-4291) showed the consistent association evidence from both gene expression and methylation data, and 3 genes (TMEM55B, RNF40 and ALDOA) were confirmed in the meta-analysis of 7 GWAS samples and GEnetic Factors for OSteoporosis consortium (GEFOS-2) GWAS results. Secondly in network analysis we identified an interaction network module with 12 genes and 11 miRNAs including AKT1, STAT3, STAT5A, FLT3, hsa-mir-141 and hsa-mir-34a which have been associated with BMD in previous studies. This module revealed the crosstalk among miRNAs, mRNAs and DNA methylation and showed four potential regulatory patterns of gene expression to influence the BMD status, including regulation by gene methylation, by miRNA and its methylation, by transcription factors and co-regulation by miRNA and gene methylation. In conclusion, the integration of multiple layers of omics can yield more in-depth results than analysis of individual omics data respectively. Integrative analysis from transcriptomics and epigenomic data improves our ability to identify causal genetic factors, and more importantly uncover functional regulation pattern of multi-omics for osteoporosis etiology. 5 high hip BMD subjects and 5 low hip BMD subjects
Project description:Background: Even though much progress has been made in the understanding of the molecular nature of glioma, the survival rates of patients affected of this tumour have not changed significantly during these years. Thus, a deeper understanding of this malignancy is still needed in order to predict its outcome and improve patient treatment. Here, we report that VAV1, a GDP/GTP exchange factor for Rho/Rac proteins with oncogenic potential that is involved in the regulation of cytoskeletal dynamics and cell migration. Methodology/Principal Findings: VAV1 is overexpressed in 32 patients diagnosed with high-grade glioma. Such overexpression is linked to the parallel upregulation of a number of genes coding for proteins also involved in cell invasion- and migration-related processes. Unexpectedly, immunohistochemical experiments revealed that VAV1 is not expressed in glioma cells. Instead, VAV1 is found in non-tumoral astrocyte-like cells that are located either peritumoraly or perivascularly, suggesting that its expression is linked to synergistic signalling cross-talk between cancer and infiltrating cells. Conclusions/Significance: Interestingly, we show that the pattern of expression of VAV1 is a good prognostic factor to unveil populations of high-grade glioma patients with different survival and progression free survival rates. 1. Oligonucleotide microarray analyses Total RNAs were extracted using the Triazol reagent (Life Technologies, Gaithersburg, MD, USA) and purified with the RNeasy Mini kit (Qiagen, Valencia, CA, USA). The integrity of RNA samples obtained was assessed using the 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA). Double-stranded cDNAs and biotinylated cRNAs were synthesized using a T7-polyT primer and the BioArray RNA labelling Kit (Enzo Farningdale, NY, USA), respectively. Labelled RNAs were then fragmented and hybridised to HU-133A oligonucleotide arrays (Affymetrix, Santa Clara, CA, USA) according to standard Affymetrix protocols. After hybridization and washes, arrays were scanned using the Gene Array Scanner (Affymetrix), and the expression value for each probe set calculated using the MAS 5.0 software (Affymetrix). All examples had a scaling factor lower than threefold and 3’/5’ of GAPDH probe set <2.5. Gene levels were transformed to base two logarithms. A median normalization approach was applied. Only genes with al least three “present” calls across all samples were selected. All these steps were done at the Genomics and Proteomics Unit of the “Centro de Investigación del Cáncer, Salamanca”. 2. Microarray data analyses To visualize clusters of genes with similar expression patterns, we used a hierarchical clustering method (Cluster and TreeView software) based on the average-linkage method with the centred correlation metric . A multidimensional scaling method (BRB Arrays Tools version 3.0) was also utilized by using Euclidean distance criteria . Supervised learning was used to identify genes with statistically significant changes in expression among different classes by using the Significant Analysis of Microarrays (SAM) algorithm . All data were permuted over 100 cycles by using the two-class (unpaired) and multi-class response format. Significant genes were selected based on the lowest false discovery ratio (between 0.6 and 0.9). In addition, nonparametric tests such as Wilcoxon rank sum test and Kruskal-Wallis test to compare more than two unpaired group were also used (SPSS 18, SPSS Inc). 3. Functional annotation of microarray data Probe sets showing significant expression change were functionally annotated and grouped according to biological function criteria using GeneOntology biological process descriptions. The functional analysis to identify the most relevant biological mechanism, pathways and functional categories in gene dada sets was generated using the Ingenuity Pathway software (Ingenuity Systems, Mountain View, CA, USA) available in the web (www.ingenuity.com) . A functional network was considered significant when it fulfilled the following criteria: i) to have a minimal score of 15; ii) to have a minimum of 20 direct functional interactions among the network members. 4. Quantitative reverse transcription-PCR Total RNA was quantified in a RNA 6000 Nano Chip (Agilent Technologies) and quantitative PCR performed using the QuantiTect SYBR Green RT–PCR kit (Qiagen). To quantify VAV1 mRNA levels, we used two different sets of probes: PAIR A (5’-AAC AAC GGG AGG TTC ACC CT-3’ and 5’-GGT CCC TCA TGG CAT CCA-3’) and PAIR B (5’-AGC CAT TGG ACC CTT TCT ACG-3’ and 5’-GCC ATG GAC ATA GGG CTT CA-3’). Amplifications were performed using the iCycler apparatus (Bio-Rad Laboratories, California, USA). Analyses of data were done using the iCycler iQ Optical System Software, version 3.0a (Bio-Rad Laboratories). Primers to GAPDH were used as intersample normalizing controls. Variations in expression of VAV1 mRNA were represented as the mean value of the fold change respect the VAV1 expression levels detected in sample #19209 with both pairs of oligonucleotide primers. 5. Immunohistochemical analyses The VAV1 antibody was generated in rabbits using a synthetic peptide and purified by affinity chromatography in Bustelo’s laboratory. This antibody recognizes VAV1 proteins from humans and mice but it does not recognize other VAV family members (unpublished data). For immunostaining, tissue sections were washed thrice with Xylene and once with 100% ethanol, rehydrated by sequential changes in 80%, 70%, and 50% ethanol and a final incubation in phosphate-buffered saline (PBS). Each rehydrating step involved 3 min incubations with the indicated solutions. Endogenous peroxidases were quenched by the addition of a 3% H2O2 solution in methanol for 30 min at room temperature (RT). Tissue sections were subsequently washed twice with PBS. Antigen retrieval was performed by incubation in 1 mM EDTA for 30 min at 37°C. The slides were washed twice in PBS and blocked in blocking buffer (Zymed, CA, USA) for 30 min at RT. Specimens were then incubated with the primary antibody (1:250 dilution) in blocking buffer. After an 1 hr incubation at 37°C, slides were washed three times in PBS, incubated with a biotinylated secondary antibody for 30 min at 37°C, washed thrice in PBS, incubated with horseradish peroxidase-streptavidin for 30 min at 37°C, washed three times in PBS, and developed using the AEC substrate (Zymed). Slides were then washed twice in water, counterstained with hematoxilin (Zymed), washed again in water, and mounted with GVA (Zymed). Samples were analyzed by light microscopy and images acquired suing an Axiophot imaging system (Zeiss, Munich, Germany). 6. Fluorescence in situ hybridization analyses FISH experiments were carried out in 40 cases of glioblastoma multiforme (grade IV) positive for VAV1 expression. For this purpose, we performed dual-colour FISH analyses with locus-specific probes for centromere 7 (Abbott Molecualr, Des Plaines) exactly as previously described. Polysomies were defined when more than 10% of the nuclei surveyed contained three or more CEP signals (chromosome-specific FISH probes that hybridize to highly repetitive human satellite DNA sequences, usually located near centromeres). 7. Immunohistochemistry and fluorescence in situ hybridization (FISH) in paraffin-embedded tumours Four um sections were cut from routinely processed paraffin blocks and mounted onto glass slides with a charged coating. Sections were dewaxed in Xylene and then rehydrated using increasing concentrations of alcohol before being rinsed briefly in water. Slides were heated 2 min in 1 mM EDTA (pH 9.0) in a microwavable pressure cooker. After antigen retrieval, slides were incubated 1 h at RT in a moist chamber with a primary antibody diluted in PBS supplemented with 10% foetal calf serum. Slides were incubated for 1 h with fluorochrome-conjugated antibodies to the appropriate IgG isotypes in a moist chamber in the dark. Finally, slides were washed thrice in PBS containing 0.5% Tween 20 three before FISH analysis. 8. Degenerate oligonucleotide primed-polymerase chain reaction (DOP-PCR) analyses After the staining of tissue sections with VAV1 antibodies (see above), the regions of the tumour were identified, microdissected, and collected using the PALM® microscope system (P.A.L.M. Microlaser Technologies, Munich, Germany). The genomic DNA was extracted as indicated by Isola et al  with modifications to small DNA amounts. Those included the resuspension of the microdissected sections in extraction buffer followed by a digestion with proteinase K (0.6 mg/ml). All samples were resuspended in 10 ul of 10 mM Tris-HCl (pH 7.4) and 0.1 mM EDTA. DOP-PCR amplification was performed in two steps. For the first, low-stringency step, 1 ul of sample was added to 4 ul of buffer A (2.5 ul of 600 uM dNTPs (Roche, Pleasanton, CA), 0.5 ul of 10 uM DOP primer 5’-CCGACTCGAGNNNNNNNATGTGG-3’, where N= A, C, G, or T)  and 1 ul of 5x Sequenase Reaction Buffer (Amersham, Cleveland, OH). Reactions were performed using 5 cycles of 30ºC for 5 min, 37ºC for 2 min, and 96ºC for 2 min, adding 0.65 units of Sequenase in each 30ºC step. The first phase product was then subjected to the second step usin