Robust Multi-array Average Algorithm Research Articles

Curcumin interferes with sepsis-induced cardiomyocyte apoptosis via TLR1 inhibition

ObjectiveSepsis-induced cardiomyopathy is the leading cause of death in sepsis and is characterized by reversible myocardial depression. However, the specific mechanisms responsible for myocardial injury in sepsis are not known. The present study used bioinformatic analysis to explore the possible mechanisms of sepsis-induced myocardial injury and the therapeutic potential of curcumin. MethodsThe GSE125042 microarray gene expression matrix was obtained from the Gene Expression Omnibus database, which includes 10 septic cardiomyocyte samples from cecum ligation perforation constructs and 10 sham-operated groups cardiomyocyte samples. Background correction and matrix data normalization were performed using the robust multiarray average algorithm. Differentially expressed genes (DEGs) screening was performed using the Limma R package expression matrix, and whole gene analysis was performed using the weighted gene co-expression network analysis R package to construct gene networks and identify modules. Enrichment analysis and gene set enrichment analysis was performed on the genes to be selected. Construct cellular and animal models of myocardial injury in sepsis were assessed and the effects of curcumin on a rat or cardiac myocytes were observed. ResultsA total of 2876 DEGs were screened based on the GSE125042 chip, of which 1424 genes were upregulated and 1452 genes were down regulated. WGCNA analysis of the whole genes was also performed and a total of 20 gene modules were generated. Among them, the selected TLR1 gene was present in the most strongly correlated Brown module. Enrichment analysis of the upregulated DEGs with the Brown module showed that they were significantly enriched in biological processes related to ribosomal protein complex generation, cellular components related to phagocytic vesicles and molecular functions related to Toll-like receptor binding, affecting cardiomyocyte survival as a target for molecular intervention in septic cardiomyopathy. Animal experiments showed that curcumin reduced inflammation levels, improved cardiac function and increased survival in rats with septic myocardial injury. Cellular experiments showed that curcumin increased the survival rate of lipopolysaccharide-treated cardiomyocytes and down regulated TLR1 expression and inhibited NF-κB phosphorylation in cells in a dose-dependent manner. Molecular docking analysis revealed that curcumin interacted with TLR1 by hydrogen bonding and could be stably bound to inhibit the biological function of TLR1. ConclusionOur study shows that curcumin attenuates myocardial injury in sepsis by inhibiting TLR1 expression, which provides a molecular theoretical basis for clinical treatment.

Abstract P6-06-07: Gene expression changes in the epithelium and stroma of invasive ductal carcinoma and ductal carcinoma in situ: A meta-analysis

Abstract Background: Ductal carcinoma in situ (DCIS) currently accounts for 20% of new breast cancer diagnoses. DCIS is typically treated with surgery and radiation therapy, despite having a much better prognosis than invasive disease. Previous studies have documented features and biomarkers in DCIS that are associated with a higher risk of invasion, and have shown that the tumor microenvironment plays a role in invasive progression of DCIS. In this study we leveraged publicly available gene expression datasets in a meta-analysis to identify biological processes underlying differences in invasive potential. We focused on studies using laser capture microdissection to elucidate these differences within both the epithelial and stromal compartments. Methods: All studies included in this analysis were publicly available datasets downloaded from the Gene Expression Omnibus. Studies were included if they reported gene expression of laser-capture microdissected human breast tissue processed on Affymetrix microarrays and included samples that had the histology of IDC or DCIS. We included 5 studies in our analysis, totaling 83 DCIS epithelial samples, 67 IDC epithelial samples, 26 normal epithelial samples, 22 DCIS stromal samples, 20 IDC stromal samples, and 14 normal stromal samples. The raw datasets were normalized using the Robust Multiarray Average algorithm, and batch effects associated with study and platform were corrected using ComBat. To assess differences in gene expression between DCIS and IDC, we conducted differential gene expression analysis using a t-test and Benjamini-Hochberg correction. Next, we used the DAVID Bioinformatics functional annotation tool to identify enriched pathways in the differentially expressed (DE) gene sets. Finally, hierarchical clustering was used to identify groups of samples within the epithelial and stromal groups based on the DE genes. Results: We found 422 and 118 genes that were differentially expressed (FDR < 0.05) between DCIS and IDC, in epithelium and stroma respectively. Interestingly, 102 of the 118 DE genes in the stroma were downregulated in IDC compared DCIS. Within the epithelial DE genes, pathways related to extracellular matrix reorganization, collagen catabolic processes, and cell-matrix adhesion were upregulated in IDC compared to DCIS. Within the stromal DE genes, cell-adhesion, leukocyte migration, and the Ras/MAPK pathways were downregulated in IDC compared to DCIS. When hierarchically clustered based on DE genes, we identify 6 clusters. Two sets of three clusters associated perfectly with histology (stromal vs epithelium). Within each set of histological clusters, two clusters were comprised entirely or nearly entirely (>97%) of specimens from patients with either IDC or DCIS, while the last cluster (mixed) was comprised of samples from both disease subtypes. Interestingly, only a minority (26%) of stromal samples were classified as mixed, in contrast to a majority of epithelial samples (53%). Conclusions: Our meta-analysis identified DE gene sets that were enriched in processes relating to the tumor microenvironment in both the epithelial and stromal components. Many of the DE genes between DCIS and IDC epithelia were related to collagen production, likely due to the inclusion of fibroblasts in the dissected IDC epithelia. Further analysis will seek to extricate underlying biological changes from differences occurring due to sample dissection and processing. The widespread downregulation of genes in the stroma of IDC supports prior theories that invasion may be related to the loss of a suppressive environment in the stroma. Our results suggest that evaluation of the stromal component of DCIS may be critical in understanding its invasive potential and could therefore guide treatment recommendations. Citation Format: Andre Dempsey, Christina Yau, Olivier Harismendy, Jonah Donnenfield, Amrita Basu, Michael Campbell, Alexander D Borowsky, Gillian Hirst, Laura J Esserman. Gene expression changes in the epithelium and stroma of invasive ductal carcinoma and ductal carcinoma in situ: A meta-analysis [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-06-07.

Transcriptomic Profile of Bone Marrow-Derived Mesenchymal Stromal Cells of Pediatric Pre-B Acute Lymphoblastic Leukemia Patients and Healthy Donors after Interaction with Leukemic Cells

Background: Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by the abnormal expansion of clonal lymphoblasts in the bone marrow (BM). The behavior of malignant lymphoblasts is not only dependent on their genomic abnormalities but also, on their complex and reciprocal relationships with their local niche. BM microenvironment is a dynamic network of growth factors, cytokines, and stromal cells, providing a permissive environment for leukemogenesis and progression. Despite some evidence about the implication of the bidirectional interaction between mesenchymal stromal cells (MSCs) and leukemic cell on growth and survival, it is still largely unknown. Although an increasing number of studies are reporting on the expression of specific genes in leukemic cells-interacting MSCs, gene expression profiling in cocultured MSCs (with respect to mono culture conditions) has not been done on a transcriptome-wide basis. Taking all this into consideration, we have established cocultures between BM-derived MSCs and the leukemic mononuclear cells and performed gene expression profile (GEP) studies on the MSC population to determine those deregulated genes due to the coculture condition with respect to MSCs in mono culture. Methods: Primary MSCs from BM samples of healthy donors (n=4) and pediatric pre-B ALL patients (n=4) were isolated and expanded and mononuclear cells were, also, isolated from patients peripheral blood. At passage 2, healthy/patient (h/p) MSCs were cocultured with the leukemic cells for 72 hours and subsequently subjected to GEP analyses. RNA isolated from h/pMSCs and biotinylated amplified RNA was synthesized. For the hybridization HG-U133 Plus, 2.0 GeneChip oligonucleotide arrays (Affymetrix) was used. Raw microarray data were processed into expression values through the Affy package in R. RMA (robust multi-array average) algorithm was used for preprocessing. The linear regression model package LIMMA (Linear Models for Microarray Data) was used to identify significant differential expression of genes (DEGs) compared to h/pMSCs after coculture with MSCs from the same origin in mono culture. Minimum 1.5 fold change and p-value < 0.001 were defined as the threshold. Functional enrichment, gene network and pathway analysis, were performed on selected sets of genes after differential expression analyses using two bioinformatics tools: DAVID Bioinformatics Resources 6.8 and WebGestalt 2019. Results: We identified 293 DEGs when analyzing pMSC samples vs. hMSC in coculture condition and 853 DEGs of pMSC vs. hMSC analyze in mono culture condition and only 15 DEGs common in both group. According to the result of functional enrichment analysis of pMSC vs. hMSC in coculture condition, 29 gene ontology (GO) term biological process including (top five) apoptotic process, axon guidance, regulation of JNK cascade, cell-cell adhesion and protein phosphorylation and 4 KEGG pathways of DEGs including axon guidance, FoxO signalling pathway, Wnt signalling pathway and signalling pathways regulating pluripotency of stem cells were determined. Comparing pMSC vs. hMSC in mono culture condition, 292 GO term biological process including extracellular matrix organization, skeletal system development, angiogenesis and signal transduction and 43 KEGG pathways of DEGs including pathways in cancer, TGFß signalling pathway, ECM-receptor interaction, p53 signalling pathway and PI3-Akt signalling pathway were determined.Five genes with upregulated expression after coculture (COL18A1, EFNA5, MAP3K8, CASP1, IGF1R) and after mono culture (MMP1, CXCL1, IL1B, PCDH10, CXCL6) were identified. Conclusions: Deregulated genes especially after coculture condition in axon guidance and ephrin receptor-mediated signalling pathway of which the importance is not yet understood in cancer microenvironment including EPHB1, ephrinA5, ephrinB3, p21 activated kinase 2 (PAK2), and ROBO3 showed highly differential expression pattern between pMSCs and hMSCs. This is the first report indicating the importance of axon guidance genes as a result of interaction between MSCs and leukemic cells. The elucidation of deregulated mechanisms and pathways provides new molecular insights to the contribution of these cells to ALL pathophysiology and understanding cross talk between these cells may lead to found new therapeutic targets. Disclosures No relevant conflicts of interest to declare.

Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis.

Plant root symbiosis with Arbuscular mycorrhizal (AM) fungi improves uptake of water and mineral nutrients, improving plant development under stressful conditions. Unraveling the unified transcriptomic signature of a successful colonization provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonization and its regulating transcription factors in roots of Medicago truncatula. Expression profiles of roots in response to AM species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalization steps: Robust Multi-array Average algorithm, Z-standardization, and quartiling normalization. Then, expression profile of 33685 genes in 18 root samples of Medicago as numerical features, as well as study ID and Arbuscular mycorrhiza type as categorical features, were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, Chi Squared, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated in response to AM (Z-value difference > 0.5). Feature weighting models also documented that this signature is independent from study (batch) effect. The AM inoculation signature obtained was able to differentiate efficiently between AM inoculated and non-inoculated samples. The AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes identified in the signature. We found high correspondence between the AM colonization signature obtained in this study and independent RNA-seq experiments on AM colonization, validating the repeatability of the colonization signature. Promoter analysis of upregulated genes in the transcriptomic signature led to the key regulators of AM colonization, including the essential transcription factors for endosymbiosis establishment and development such as NF-YA factors. The approach developed in this study offers three distinct novel features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalization steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for the transcriptomic signature which contributes to robustness of the signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings.

The Overexpression of CD80 and ISG15 Are Associated with the Progression and Metastasis of Breast Cancer by a Meta-Analysis Integrating Three Microarray Datasets.

Breast cancer is a common cancer and could result in a substantial mortality. The study aimed to screen gene signatures associated with the development and metastasis of breast cancer and explore their regulation mechanisms. Three datasets of GSE10797, GSE8977 and GSE3744 were downloaded from GEO (Gene Expression Omnibus) database, containing 55 breast cancer samples and 27 normal samples. After data preprocessing using limma software and RMA (robust multi-array average) algorithm, DEGs (differentially expressed genes) between breast tumor and normal tissues in three individual experiments were identified using MADAM package. Function and pathway enrichment analyses were performed for the DEGs. Transcription factors and TAGs (tumor associated genes) among the DEGs were recognized and the PPI (protein-protein-interaction) network for the DEGs was constructed using Cytoscape software. The mRNA expression was analyzed via real-time quantitative PCR and protein expression was measured by western blotting. Totally, 100 DEGs were identified, including 33 up-regulated genes and 67 down-regulated genes. Among them, up-regulated DEGs such as CD80 was enriched in toll-like receptor (TLR) interaction pathway and the TAG, ISG15 was related to RIG-I-like receptor signaling pathway, while CXCL10 was involved in both of the two pathways. Whereas, the down-regulated DEG, CXCL12 was significantly associated with axon guidance pathway. Additionally, these DEGs were also pivotal nodes in the PPI network with high degrees. Besides, CXCL10 and CD80 were both interacted with IFNG. The mRNA expression of ISG15 was obviously enhanced in human breast cancer cells MCF-7, while no significant difference of CXCL10 mRNA level was found between MCF10A and MCF-7 cells. Moreover, the proteins expression levels of CD80 and ISG15 were significantly increased in MCF-7, MDA-MB-468 and MDA-MB-231 breast cancer cells than in normal MCF10A cells. CD80 might be responsible for the breast cancer's progression and metastasis via regulating innate immune system. In addition, ISG15 is identified as a crucial gene signature associated with breast cancer development and metastasis via RIG-I-like receptor signaling pathway.

Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer.

Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in general and in different breast cancer stages, as well as to explore their functions in tumor development. For integrated analysis, a total of 5 gene expression profiling datasets for 3 different stages of breast cancer (stages I–III) were downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information. Pre-processing of these datasets was performed using the Robust Multi-array Average algorithm and global renormalization was performed for all studies. Differentially expressed genes between breast cancer patients and controls were estimated using the empirical Bayes algorithm. The Database for Annotation, Visualization and Integrated Discovery web server was used for analyzing the enrichment of the differentially expressed genes in Gene Ontology terms of the category biological process and in Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, breast cancer target genes were downloaded from the Thomson Reuters Integrity Database. We merged these target genes with the genes in breast cancer datasets. Analysis of anti-breast cancer gene networks was performed using the Genome-scale Integrated Analysis of Gene Networks in Tissues web server. The results demonstrated that the normal functions of the cell cycle, cell migration and cell adhesion were altered in all stages of breast cancer. Furthermore, 12 anti-breast cancer genes were identified to be dysregulated in at least one of the three stages. Among all of these genes, ribonucleotide reductase regulatory subunit M2 (RRM2) exhibited the highest degree of interaction with other interacting genes. Analysis of the network interactions revealed that the transcription factor of RRM2 is crucial for cancer development. Other genes, including mucin 1, progesterone receptor and cyclin-dependent kinase 5 regulatory subunit associated protein 3, also exhibited a high degree of interaction with the associated genes. In conclusion, several key anti-breast cancer genes identified in the present study are mainly associated with the regulation of the cell cycle, cell migration, cell adhesion and other cancer-associated cell functions, particularly RRM2.

Donor Hepatic Steatosis Induce Exacerbated Ischemia-Reperfusion Injury Through Activation of Innate Immune Response Molecular Pathways.

Severe liver steatosis is a known risk factor for increased ischemia-reperfusion injury (IRI) and poor outcomes after liver transplantation (LT). This study aimed to identify steatosis-related molecular mechanisms associated with IRI exacerbation after LT. Paired graft biopsies (n = 60) were collected before implantation (L1) and 90 minutes after reperfusion (L2). The LT recipients (n = 30) were classified by graft macrosteatosis: without steatosis (WS) of 5% or less (n = 13) and with steatosis (S) of 25% or greater (n = 17). Plasma samples were collected at L1, L2, and 1 day after LT (postoperative [POD]1) for cytokines evaluation. Tissue RNA was isolated for gene expression microarrays. Probeset summaries were obtained using robust multiarray average algorithm. Pairwise comparisons were fit using 2-sample t test. P values 0.01 or less were significant (false discovery rate <5%). Molecular pathway analyses were conducted using Ingenuity Pathway Analysis tool. Significantly differentially expressed genes were identified for WS and S grafts after reperfusion. Comprehensive comparison analysis of molecular profiles revealed significant association of S grafts molecular profile with innate immune response activation, macrophage production of nitric oxide and reactive oxygen species, IL-6, IL-8, IL-10 signaling activation, recruitment of granulocytes, and accumulation of myeloid cells. Postreperfusion histological patterns of S grafts revealed neutrophilic infiltration surrounding fat accumulation. Circulating proinflammatory cytokines after reperfusion and 24 hours after LT concurred with intragraft-deregulated molecular pathways. All tested cytokines were significantly increased in plasma of S grafts recipients after reperfusion when compared with WS group at same time. Increases of graft steatosis exacerbate IRI by exacerbation of innate immune response after LT. Preemptive strategies should consider it for safety usage of steatotic livers.

A Meta-Analysis of Gene Expression Studies in Severe Sepsis and Septic Shock

Introduction: Sepsis represents a complex inflammatory response to infection. Gene expression studies based on microarrays have shown that this response can affect more than 80% of cellular functions and pathways, in what has been termed a “genomic storm”. For several years, sepsis was regarded as a pro-inflammatory condition, and this concept resulted in several experimental treatment strategies aimed to block inflammation. However, systematic failure of these therapies and recent evidence demonstrating that anti-inflammatory pathways are also activated during sepsis illustrate the complexity and our incomplete knowledge about the pathogenesis of this condition. In the last decade, microarray-based gene expression studies have been used in attempts to improve our understanding about sepsis. Raw data from most of these studies are now collected in public archives, thus offering a unique opportunity to combine the information from different studies by meta-analysis. It has been shown that by analyzing data from multiple experiments, biases and artifacts between datasets can be cancelled out, potentially allowing true relationships to stand out. Accordingly, an increasing number of bioinformatics protocols and guidelines about meta-analysis of gene expression studies have been published in the last years. In the context of sepsis, several high-quality microarray-based gene expression studies are available. However, no systematic meta-analysis of these studies has been performed. In order to identify genes and pathways robustly associated with the pathogenesis of sepsis, we performed a meta-analysis of gene expression studies in human severe sepsis and septic shock.Material and methods: Microarray data were identified by searching two public databases (Gene Expression Omnibus and Array-Express) using the following search criteria: (“sepsis or “septic shock”) AND (“peripheral blood” or “leukocytes”) AND (“homo sapiens”). Inclusion criteria were: studies in humans with severe sepsis or septic shock; RNA obtained from peripheral blood leukocytes; availability of raw data; and matched healthy controls from the same study. To improve consistency, only studies using similar platforms were compared. We used the R/BioConductor environment to preprocess the datasets using the Robust Multi-array Average algorithm (RMA) implemented in the ‘oligo’ package and to perform meta-analysis through the ‘RankProd’ package implementation. This is a non-parametric statistical method that utilizes ranks of the log-ratio statistics for all genes across different studies to generate a list of differentially expressed (DE) genes between two conditions, and considered superior to alternative methodologies. For this study, we selected genes with fold-change of expression above 2 and false discovery rate below 0.01, calculated based on 10,000 permutations. Gene set analysis was initially performed using WebGestalt and confirmed in similar tools (KEGG, Pathway Commons, WikiPathways). Only pathways identified by more than one tool were considered.Results: Forty-five studies were identified, of which five fulfilled inclusion criteria. Our meta-analysis included data from 259 patients and 132 controls. Out of 22,216 probesets, we observed 352 as candidates for DE, 215 of which were up-regulated and 137 down-regulated. Top 5 up-regulated genes were CD177, MMP8, HP, ARG1 and ANXA3. Top 5 down-regulated genes were FCER1A, YME1L1, TRDV3, LRRN3 and MYBL1. The gene ontology term associated with the set of DE genes in both analysis with higher statistical significance was “immune response” (adjP=2.85e-27), and the most significant pathways identified were “Hematopoietic cell lineage” (adjP=8.69e-13), “TCR signaling pathway” (adjP=3.04e-10) and “immune system” (adjP=1.08e-19).Discussion and conclusion: The combined analysis of data generated by high-throughput experiments is an attractive and validated strategy to improve the sensitivity and specificity of genome-wide expression data. This meta-analysis provides a comprehensive list of genes, pathways and expression signatures associated with severe sepsis and septic shock, confirming several results from individual studies. In addition, our meta-analysis potentially provides new biological insights about sepsis, by listing a comprehensive list of new candidate genes with robust associations with this condition. DisclosuresNo relevant conflicts of interest to declare.

PBMCs express a transcriptome signature predictor of oxygen uptake responsiveness to endurance exercise training in men.

Peripheral blood cells are an accessible environment in which to visualize exercise-induced alterations in global gene expression patterns. We aimed to identify a peripheral blood mononuclear cell (PBMC) signature represented by alterations in gene expression, in response to a standardized endurance exercise training protocol. In addition, we searched for molecular classifiers of the variability in oxygen uptake (V̇o2). Healthy untrained policemen recruits (n = 13, 25 ± 3 yr) were selected. Peak V̇o2 (measured by cardiopulmonary exercise testing) and total RNA from PBMCs were obtained before and after 18 wk of running endurance training (3 times/wk, 60 min). Total RNA was used for whole genome expression analysis using Affymetrix GeneChip Human Gene 1.0 ST. Data were normalized by the robust multiarray average algorithm. Principal component analysis was used to perform correlations between baseline gene expression and V̇o2peak. A set of 211 transcripts was differentially expressed (ANOVA, P < 0.05 and fold change > 1.3). Functional enrichment analysis revealed that transcripts were mainly related to immune function, cell cycle processes, development, and growth. Baseline expression of 98 and 53 transcripts was associated with the absolute and relative V̇o2peak response, respectively, with a strong correlation (r > 0.75, P < 0.01), and this panel was able to classify the 13 individuals according to their potential to improve oxygen uptake. A subset of 10 transcripts represented these signatures to a similar extent. PBMCs reveal a transcriptional signature responsive to endurance training. Additionally, a baseline transcriptional signature was associated with changes in V̇o2peak. Results might illustrate the possibility of obtaining molecular classifiers of endurance capacity changes through a minimally invasive blood sampling procedure.

Inhibition of serine palmitoyltransferase delays the onset of radiation-induced pulmonary fibrosis through the negative regulation of sphingosine kinase-1 expression

The enforcement of sphingosine-1-phosphate (S1P) signaling network protects from radiation-induced pneumonitis. We now demonstrate that, in contrast to early postirradiation period, late postirradiation sphingosine kinase-1 (SphK1) and sphingoid base-1-phosphates are associated with radiation-induced pulmonary fibrosis (RIF). Using the mouse model, we demonstrate that RIF is characterized by a marked upregulation of S1P and dihydrosphingosine-1-phosphate (DHS1P) levels in the lung tissue and in circulation accompanied by increased lung SphK1 expression and activity. Inhibition of sphingolipid de novo biosynthesis by targeting serine palmitoyltransferase (SPT) with myriocin reduced radiation-induced pulmonary inflammation and delayed the onset of RIF as evidenced by increased animal lifespan and decreased expression of markers of fibrogenesis, such as collagen and α-smooth muscle actin (α-SMA), in the lung. Long-term inhibition of SPT also decreased radiation-induced SphK activity in the lung and the levels of S1P-DHS1P in the lung tissue and in circulation. In vitro, inhibition or silencing of serine palmitoyltransferase attenuated transforming growth factor-β1 (TGF-β)-induced upregulation of α-SMA through the negative regulation of SphK1 expression in normal human lung fibroblasts. These data demonstrate a novel role for SPT in regulating TGF-β signaling and fibrogenesis that is linked to the regulation of SphK1 expression and S1P-DHS1P formation.

Abstract 3953: Senescence induced of smooth muscel protein 22-alpha (SM22) and Radiation patients change human-link genes expression level

Abstract P53 and RB protein is famous for its capacity to suppress tumor senescence pathway member. Senescence is a leading cause of cancer-related death, considerable uncertainty still remains about their effectiveness. However, each marker in an increased risk that experience genomic data with a cancer-specific mutations. So, senescence marker that techniques can't develop patient-specific treatment for particular about experimental study-conditions and result-analysis. This study was undertaken to evaluate the sm22 (smooth muscel protein 22-alpha) protein relation of senescence marker and changes condition of the radiation at human-link senescence marker genes. We were determined the expression of senescence-associated β-galactosidase and western-blot assay. It was performed in colon cancers that were 17 patients each of normal and adenoma, 16 patients each of normal and adenocarcinoma. To conformed enzyme efficiency is β-galactosidase, senescence pathway using of p16, p21 and p53. On the other hand, sm22 use of senescence marker in study. we have decided to micro-array analysis that 3 patients each of normal and adenoma, 2 patients each of normal and adenocarcinoma, 2 patients each of normal and irradiation-adenocarcinoma (2 weeks after) available miRNA sequences on the Agilent Human whole genome 8x60K array chip. Gene expression levels were calculated with Feature Extraction v10.7.3.1 (Agilent technologies, CA) Relative signal intensities for each gene were generated using the Robust Multi-Array Average algorithm. Senescence induced p16 expression in dependent β-galactosidase assay results, sm22 protein suppressed cellular senescence levels as well as p16 protein. The expression of p16 and sm22 was analyzed that not involved each normals, adenomas and adenocarcinomas tissue by western blot analysis. In addition, it found that these results were similar to those of micro array. The three groups (adenoma, adeno-carcinoma and irradiation-adeno-carcinoma) can be compared in terms of total 2-fold changed gene (least 200% of controls for up-regulated and lower than 50% of controls for down-regulated genes). We found this integrative data analysis tool to the LCN2, LYPD5 and LOC100132051 genes as well as CDKN2 (p16) and TAGLN2 (sm22) gene expression. Irradiation groups was much less than the adenoma (45.3 %) and adenocarcinoma (71.1 %) 2-fold changed gene expression. The LCN2, LYPD5 and LOC100132051 genes lower than adeno-carcinoma group level. Our observations suggest that shared gene and protein expression profiles may contribute to sm22 and p16 of senescence marker, irradiation induces cellular senescence of colon cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3953. doi:1538-7445.AM2012-3953

Peripheral Blood Monocyte-expressed ANXA2 Gene is Involved in Pathogenesis of Osteoporosis in Humans

Low bone mineral density (BMD) is a risk factor of osteoporosis and has strong genetic determination. Genes influencing BMD and fundamental mechanisms leading to osteoporosis have yet to be fully determined. Peripheral blood monocytes (PBM) are potential osteoclast precursors, which could access to bone resorption surfaces and differentiate into osteoclasts to resorb bone. Herein, we attempted to identify osteoporosis susceptibility gene(s) and characterize their function(s), through an initial proteomics discovery study on PBM in vivo, and multiscale validation studies in vivo and in vitro. Utilizing the quantitative proteomics methodology LC-nano-ESI-MS(E), we discovered that a novel protein, i.e. ANXA2, was up-regulated twofold in PBM in vivo in Caucasians with extremely low BMD (cases) versus those with extremely high BMD (controls) (n = 28, p < 0.05). ANXA2 gene up-regulation in low BMD subjects was replicated at the mRNA level in PBM in vivo in a second and independent case-control sample (n = 80, p < 0.05). At the DNA level, we found that SNPs in the ANXA2 gene were associated with BMD variation in a 3(rd) and independent case-control sample (n = 44, p < 0.05), as well as in a random population sample (n = 997, p < 0.05). The above integrative evidence strongly supports the concept that ANXA2 is involved in the pathogenesis of osteoporosis in humans. Through a follow-up cellular functional study, we found that ANXA2 protein significantly promoted monocyte migration across an endothelial barrier in vitro (p < 0.001). Thus, elevated ANXA2 protein expression level, as detected in low BMD subjects, probably stimulates more PBM migration through the blood vessel walls to bone resorption surfaces in vivo, where they differentiate into higher number of osteoclasts and resorb bone at higher rates, thereby decreasing BMD. In conclusion, this study identified a novel osteoporosis susceptibility gene ANXA2, and suggested a novel pathophysiological mechanism, mediated by ANXA2, for osteoporosis in humans.

Pre-processing and differential expression analysis of Agilent microRNA arrays using the AgiMicroRna Bioconductor library

BackgroundThe main research tool for identifying microRNAs involved in specific cellular processes is gene expression profiling using microarray technology. Agilent is one of the major producers of microRNA arrays, and microarray data are commonly analyzed by using R and the functions and packages collected in the Bioconductor project. However, an analytical package that integrates the specific characteristics of microRNA Agilent arrays has been lacking.ResultsThis report presents the new bioinformatic tool AgiMicroRNA for the pre-processing and differential expression analysis of Agilent microRNA array data. The software is implemented in the open-source statistical scripting language R and is integrated in the Bioconductor project (http://www.bioconductor.org) under the GPL license. For the pre-processing of the microRNA signal, AgiMicroRNA incorporates the robust multiarray average algorithm, a method that produces a summary measure of the microRNA expression using a linear model that takes into account the probe affinity effect. To obtain a normalized microRNA signal useful for the statistical analysis, AgiMicroRna offers the possibility of employing either the processed signal estimated by the robust multiarray average algorithm or the processed signal produced by the Agilent image analysis software. The AgiMicroRNA package also incorporates different graphical utilities to assess the quality of the data. AgiMicroRna uses the linear model features implemented in the limma package to assess the differential expression between different experimental conditions and provides links to the miRBase for those microRNAs that have been declared as significant in the statistical analysis.ConclusionsAgiMicroRna is a rational collection of Bioconductor functions that have been wrapped into specific functions in order to ease and systematize the pre-processing and statistical analysis of Agilent microRNA data. The development of this package contributes to the Bioconductor project filling the gap in microRNA array data analysis.

Processing of Agilent microRNA array data

BackgroundThe Agilent microRNA microarray platform interrogates each microRNA with several copies of distinct oligonucleotide probes and integrates the results into a total gene signal (TGS), using a proprietary algorithm that makes use of the background subtracted signal. The TGS can be normalized between arrays, and the Agilent recommendation is either not to normalize or to normalize to the 75th percentile signal intensity. The robust multiarray average algorithm (RMA) is an alternative method, originally developed to obtain a summary measure of mRNA Affymetrix gene expression arrays by using a linear model that takes into account the probe affinity effect. The RMA method has been shown to improve the accuracy and precision of expression measurements relative to other competing methods. There is also evidence that it might be preferable to use non-corrected signals for the processing of microRNA data, rather than background-corrected signals. In this study we assess the use of the RMA method to obtain a summarized microRNA signal for the Agilent arrays.FindingsWe have adapted the RMA method to obtain a processed signal for the Agilent arrays and have compared the RMA summarized signal to the TGS generated with the image analysis software provided by the vendor. We also compared the use of the RMA algorithm with uncorrected and background-corrected signals, and compared quantile normalization with the normalization method recommended by the vendor. The pre-processing methods were compared in terms of their ability to reduce the variability (increase precision) of the signals between biological replicates. Application of the RMA method to non-background corrected signals produced more precise signals than either the RMA-background-corrected signal or the quantile-normalized Agilent TGS. The Agilent TGS normalized to the 75% percentile showed more variation than the other measures.ConclusionsUsed without background correction, a summarized signal that takes into account the probe effect might provide a more precise estimate of microRNA expression. The variability of quantile normalization was lower compared with the normalization method recommended by the vendor.