Gene Expression Profile of Osteoblast-Like Cells on Calcium Phosphate Biomaterials

2007 ◽  
Vol 330-332 ◽  
pp. 1087-1090 ◽  
Author(s):  
Taro Takemura ◽  
Hong Song Fan ◽  
Toshiyuki Ikoma ◽  
M. Tanaka ◽  
Nobutaka Hanagata
Keyword(s):  
Gene Expression ◽  
Calcium Phosphate ◽  
Porous Materials ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Osteoblast Differentiation ◽  
Expression Patterns ◽  
Gene Expressions ◽  
Microarray Analyses ◽  
Cells Cultured

Gene expression profile of osteoblast-like cells cultured on dense disk materials and porous materials of calcium phosphate ceramics was constructed from DNA microarray analyses. The profile revealed that gene expression patterns of porous materials were significantly different from those of dense disk materials. The porous materials had a capacity to induce expressions of genes involved in osteoblast differentiation, while dense disk materials regulated gene expressions related to osteoclastogenesis.

scholarly journals Effect of agonal and postmortem factors on gene expression profile: quality control in microarray analyses of postmortem human brain

2004 ◽  
Vol 55 (4) ◽  
pp. 346-352 ◽  
Author(s):  
Hiroaki Tomita ◽  
Marquis P Vawter ◽  
David M Walsh ◽  
Simon J Evans ◽  
Prabhakara V Choudary ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
Human Brain ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Postmortem Human Brain ◽  
Microarray Analyses

Novel Stratification Scheme for AML Based on a Large-Scale DNA Microarray Analysis of CD133-Positive Blasts.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2043-2043
Author(s):  
Hiroyuki Mano ◽  
Yoshihiro Yamashita
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Microarray Analysis ◽  
Dna Microarray ◽  
Expression Profile ◽  
Treatment Strategy ◽  
Large Scale ◽  
Dna Microarray Analysis ◽  
Wide Range ◽  
Microarray Analyses

Abstract AML is a clonal disorder of immature hematopoietic blasts and has a variable clinical outcome. Current classification of AML is based predominantly on the cytogenetic abnormalities and morphology of the malignant blasts and is not always helpful for optimization of treatment strategy. It is, for instance, very difficult to predict the prognosis of AML patients with a normal karyotype, who constitute ~50% of the AML population. DNA microarray analysis has the potential to provide a novel stratification scheme for AML patients, which is based on gene expression profile, and might help to predict the prognosis of, and optimize the treatment strategy for, each affected individual. However, leukemic blasts derived from bone marrow (BM) of AML-related disorders, are not homogeneous. The blasts may constitute from 20% to almost 100% of mononuclear cells (MNCs) in the marrow. Furthermore, given that many leukemic blasts possess the ability to differentiate to a certain extent, the marrow of AML patients contains not only the immature blasts (leukemic stem clone) but also differentiated blasts. A simple comparison of BM MNCs among heterogeneous AML patients is thus likely to reveal a large number of changes in gene expression that only reflect differences either in the percentage of blasts or in the differentiation ability of the blasts. To minimize such population-shift effects in microarray analyses, we established a large-scale cell depository “Blast Bank” for the storage of CD133 (AC133)-positive hematopoietic stem cell-like fractions from individuals with a wide range of hematopoietic disorders. In the present study, we have used Affymetrix HGU133 A&B microarrays to measure the expression profiles of ~33,000 genes in the Blast Bank specimens of 99 adults with AML-related disorders: 83 individuals with AML and 16 patients in the RAEB stage of MDS. In contrast to the previous microarray analyses of BM MNCs of AML, unsupervised hierarchical clustering of the subjects based on the expression profile did not separate the patients into FAB subtype-matched subgroups. Comparison of gene expression profile between the long-time and short-time survivors has identified a small number of outcome-related genes. Supervised class prediction, based on these genes, with k-nearest neighbor method or Cox proportional hazard model both succeeded to clearly separate individuals into subgroups with statistically distinct prognoses. Our analysis may pave a way toward the expression profile-based novel stratification scheme for AML.

scholarly journals Blood Genomic Expression Profile for Neuronal Injury

2003 ◽  
Vol 23 (3) ◽  
pp. 310-319 ◽  
Author(s):  
Yang Tang ◽  
Alex C. Nee ◽  
Aigang Lu ◽  
Ruiqiong Ran ◽  
Frank R. Sharp
Keyword(s):  
Gene Expression ◽  
Brain Injury ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Whole Blood ◽  
Neuronal Injury ◽  
Adult Rats ◽  
Mild Brain Injury ◽  
Microarray Analyses ◽  
Genomic Response

This study determined whether stroke and other types of insults produced a gene expression profile in blood that correlated with the presence of neuronal injury. Adult rats were subjected to ischemic stroke, intracerebral hemorrhage, status epilepticus, and insulin-induced hypoglycemia and compared with untouched, sham surgery, and hypoxia animals that had no brain injury. One day later, microarray analyses showed that 117 genes were upregulated and 80 genes were downregulated in mononuclear blood cells of the “injury” (n = 12) compared with the “no injury” (n = 9) animals. A second experiment examined the whole blood genomic response of adult rats after global ischemia and kainate seizures. Animals with no brain injury were compared with those with brain injury documented by TUNEL and PANT staining. One day later, microarray analyses showed that 37 genes were upregulated and 67 genes were downregulated in whole blood of the injury (n = 4) animals compared with the no-injury (n = 4) animals. Quantitative reverse transcription–polymerase chain reaction confirmed that the vesicular monoamine transporter-2 increased 2.3- and 1.6-fold in animals with severe and mild brain injury, respectively, compared with no-injury animals. Vascular tyrosine phosphatase-1 increased 2.0-fold after severe injury compared with no injury. The data support the hypothesis that there is a peripheral blood genomic response to neuronal injury, and that this blood response is associated with a specific blood mRNA gene expression profile that can be used as a marker of the neuronal damage.

Gene Expression Profile of Osteoblast-Like Cells on Calcium Phosphate Biomaterials

2007 ◽  
pp. 1087-1090
Author(s):  
Taro Takemura ◽  
Hong Song Fan ◽  
Toshiyuki Ikoma ◽  
Junzo Tanaka ◽  
Nobutaka Hanagata
Keyword(s):  
Gene Expression ◽  
Calcium Phosphate ◽  
Gene Expression Profile ◽  
Expression Profile

Ischemic but not pharmacological preconditioning elicits a gene expression profile similar to unprotected myocardium

2004 ◽  
Vol 20 (1) ◽  
pp. 117-130 ◽  
Author(s):  
Rafaela da Silva ◽  
Eliana Lucchinetti ◽  
Thomas Pasch ◽  
Marcus C. Schaub ◽  
Michael Zaugg
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Ischemia Reperfusion ◽  
Expression Patterns ◽  
Principal Component ◽  
Gene Clusters ◽  
Stable Gene ◽  
Transcriptional Responses ◽  
Rat Hearts

Pharmacological (PPC) and ischemic preconditioning (IschPC) provide comparable protection against ischemia in the heart. However, the genomic phenotype may depend on the type of preconditioning. Isolated perfused rat hearts were used to evaluate transcriptional responses to PPC and IschPC in the presence (mediator/effector response) or absence (trigger response) of 40 min of test ischemia using oligonucleotide microarrays. IschPC was induced by 3 cycles of 5 min of ischemia, and PPC by 15 min of 2.1 vol% isoflurane. Unsupervised analysis methods were used to identify gene expression patterns. PPC and IschPC were accompanied by marked alterations in gene expression. PPC and IschPC shared only ∼25% of significantly up- and downregulated genes after triggering. The two types of preconditioning induced a more uniform genomic response after ischemia/reperfusion. Numerous genes separated preconditioned from unprotected ischemic hearts. Three stable gene clusters were identified in the trigger response to preconditioning, while eight stable clusters related to cytoprotection, inflammation, remodeling, and long interspersed nucleotide elements (LINEs) were delineated after prolonged ischemia. A single stable sample cluster emerged from cluster analysis for both IschPC and unprotected myocardium, indicating a close molecular relationship between these two treatments. Principal component analysis revealed differences between PPC vs. IschPC, and trigger vs. mediator/effector responses in transcripts predominantly related to biosynthesis and apoptosis. IschPC and PPC similarly but distinctly reprogram the genetic response to ischemic injury. IschPC elicits a postischemic gene expression profile closer to unprotected myocardium than PPC, which may be therefore more advantageous as therapeutic strategy in cardioprotection.

Microarray analyses of the effects of NF-κB or PI3K pathway inhibitors on the LPS-induced gene expression profile in RAW264.7 cells

2009 ◽  
Vol 21 (7) ◽  
pp. 1109-1122 ◽  
Author(s):  
Sofia Dos Santos Mendes ◽  
Aurélie Candi ◽  
Martine Vansteenbrugge ◽  
Marie-Rose Pignon ◽  
Hidde Bult ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Pi3k Pathway ◽  
Raw264.7 Cells ◽  
Microarray Analyses

Gene-expression signatures as prognostic for relapse in stage I testicular germ cell tumors (TGCT).

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 493-493
Author(s):  
Jeremy Howard Lewin ◽  
Philippe L. Bedard ◽  
Robert James Hamilton ◽  
Peter W. M. Chung ◽  
Malcolm J. Moore ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Hierarchical Clustering ◽  
Expression Profile ◽  
Expression Patterns ◽  
Rna Extraction ◽  
Germ Cell Tumors ◽  
Gene Expression Pattern ◽  
Stage I ◽  
Testicular Germ Cell

493 Background: Genomic signatures may compliment pathological features in identifying appropriate patients who may benefit from adjuvant therapy in Stage I (SI) TGCT. This study aimed to identify a gene expression pattern to differentiate between relapsed (R) and non-relapsed (NR) SI TGCT. Methods: Patients with SI non-seminoma (NS) and seminoma (S) were identified from an institutional database from 2000 to 2012. All patients were managed with active surveillance. NR-NS and NR-S patients were defined as having no evidence of relapse after 2 and 3 years of surveillance respectively. Following pathology review, RNA extraction and gene expression analysis was performed on archived paraffin embedded tumor and normal testicular tissue using Illumina Whole Genome DASL Human HT-12 V4 BeadChip. Hierarchical clustering analysis, ANOVA and t-tests were used to evaluate candidate genes and expression patterns that could differentiate NR and R samples. Results: 57 patients (12 R-NS, 15 R-S, 15 NR-NS, 15 NR-S) were identified with median relapse time of 5.6 (2.5-18.1) and 19.3 (4.7-65.3) months in NS and S cohorts respectively. 3 additional normal testis samples were included. Poor prognostic factors were more frequent in R versus NR cases (NS: vascular invasion [5/12 vs 0/15]; S: median size [4cm vs 2.8cm]). Unsupervised hierarchical clustering of 22822 probes randomly separated S from NS, indicating no batch effect. One-way ANOVA revealed 4525 significantly varying probes (p < 0.05) however, no statistically significant gene expression profile differentiated the 4 cohorts. A discriminative gene expression profile between R and NR cases was discovered when combining NS and S samples using 10 (p = 0.03) and 30 (p = 0.03) probe signatures with a 10 fold cross-validation. However, this profile was not observed in the S and NS cohorts individually. Conclusions: A discriminating signature for R and NR was identified for SI testis tumors, but not separately for NS and S. Biological relevance of these signatures is to be determined. Further studies are required to corroborate this profile in NS and S. If validated, these expression patterns could help identify patients beyond standard pathological risk algorithms for optimal management.

scholarly journals Gene Expression Profiling ofClostridium botulinumunder Heat Shock Stress

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Wan-dong Liang ◽  
Yun-tian Bi ◽  
Hao-yan Wang ◽  
Sheng Dong ◽  
Ke-shen Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heat Shock ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Environmental Changes ◽  
Expression Patterns ◽  
Trna Synthetase ◽  
Ph Change ◽  
Heat Shock Stress ◽  
Shock Stress

During growth,C. botulinumis always exposed to different environmental changes, such as temperature increase, nutrient deprivation, and pH change; however, its corresponding global transcriptional profile is uncharacterized. This study is the first description of the genome-wide gene expression profile ofC. botulinumin response to heat shock stress. Under heat stress (temperature shift from 37°C to 45°C over a period of 15 min), 176C. botulinumATCC 3502 genes were differentially expressed. The response included overexpression of heat shock protein genes (dnaKoperon,groESL,hsp20,andhtpG) and downregulation of aminoacyl-tRNA synthetase genes (valS,queA,tyrR, andgatAB) and ribosomal and cell division protein genes (ftsZandftsH). In parallel, several transcriptional regulators (marR,merR, andompRfamilies) were induced, suggesting their involvement in reshuffling of the gene expression profile. In addition, many ABC transporters (oligopeptide transport system), energy production and conversion related genes (glpAandhupL), cell wall and membrane biogenesis related genes (fabZ,fabF, andfabG), flagella-associated genes (flhA,flhM,flhJ,flhS, andmotAB), and hypothetical genes also showed changed expression patterns, indicating that they may play important roles in survival under high temperatures.

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