Gene expression profiling of monozygotic twins affected by psoriatic arthritis

Introduction: Psoriatic Arthritis (PsA) is a multifactorial disease, where the relative burden of genetic, epigenetic and environmental factors in clinical course and damage accrual is not yet definitively clarified. In clinical practice, there is a real need for useful candidate biomarkers in PsA diagnosis and disease progression, by exploring its underlying transcrip-tomic and epigenomic mechanisms. This work aims to profile the transcriptome in mono-zygotic (MZ) twins with psoriatic arthritis (PsA) highly concordant for clinical presentation, but discordant for the radiographic outcomes’ severity. Methods: We describe i) the clinical case of two MZ twins; ii) their comparative gene expression profiling (HTA 2.0 Affymetrix) and iii) signal pathways and pathophysiological processes in which differentially expressed genes are involved (in silico analysis by the IPA software, QIAGEN). Results: One hundred sixty-three transcripts and 36 coding genes (28 up and 8 down) were differentially expressed between twins, and in the brother with the most erosive form, the transcriptomic profiling highlights the overexpression of genes known to be involved in immunomodulatory processes and on a broad spectrum of PsA manifestations. Discussion: Twins’ clinical cases are still a gold mine in medical research: twin brothers are ideal experimental models in estimating the relative importance of genetic versus nongenetic components as determinants of complex phenotypes, non-Mendelian and multifactorial diseases as PsA

Economic Evaluation of Potential Applications of Gene Expression Profiling in Clinical Oncology

Histopathological analysis of tumor is currently the main tool used to guide cancer management. Gene expression profiling may provide additional valuable information for both classification and prognostication of individual tumors. A number of gene expression profiling assays have been developed recently to inform therapy decisions in women with early stage breast cancer and help identify the primary tumor site in patients with metastatic cancer of unknown primary. The impact of these assays on health and economic outcomes, if introduced into general practice, has not been determined. I aimed to conduct an economic evaluation of regulatory-approved gene expression profiling assays for breast cancer and cancer of unknown primary for the purpose of determining whether these technologies represent value for money from the perspective of the Canadian health care system. I developed decision-analytic models to project the lifetime clinical and economic consequences of early stage breast cancer and metastatic cancer of unknown primary. I used Manitoba Cancer Registry and Manitoba administrative health databases to model current “real-world” Canadian clinical practices. I applied available data about gene expression profiling assays from secondary sources on these models to predict the impact of these assays on current clinical and economic outcomes. In the base case, gene expression profiling assays in early stage breast cancer and cancer of unknown primary resulted in incremental cost effectiveness ratios of less than $100,000 per quality-adjusted life-year gained. These results were most sensitive to the uncertainty associated with the accuracy of the assay, patient-physician response to gene expression profiling information and patient survival. The potential application of these gene expression profiling assays in clinical oncology appears to be cost-effective in the Canadian healthcare system. Field evaluation of these assays to establish their impact on cancer management and patient survival may have a large societal impact and should be initiated in Canada to ensure their clinical utility and cost-effectiveness. The use of Canadian provincial administrative population data in decision modeling is useful to quantify uncertainty about gene expression profiling assays and guide the use of novel funding models such as conditional funding alongside a field evaluation

Technical Variables in High-Throughput miRNA Expression Profiling: Much Work Remains to Be Done

MicroRNA (miRNA) gene expression profiling has provided important insights into plant and animal biology. However, there has not been ample published work about pitfalls associated with technical parameters in miRNA gene expression profiling. One source of pertinent information about technical variables in gene expression profiling is the separate and more well-established literature regarding mRNA expression profiling. However, many aspects of miRNA biochemistry are unique. For example, the cellular processing and compartmentation of miRNAs, the differential stability of specific miRNAs, and aspects of global miRNA expression regulation require specific consideration. Additional possible sources of systematic bias in miRNA expression studies include the differential impact of pre-analytical variables, substrate specificity of nucleic acid processing enzymes used in labeling and amplification, and issues regarding new miRNA discovery and annotation. We conclude that greater focus on technical parameters is required to bolster the validity, reliability, and cultural credibility of miRNA gene expression profiling studies

Gene expression profiling to study racial differences after heart transplantation.

BackgroundThe basis for increased mortality after heart transplantation in African Americans and other non-Caucasian racial groups is poorly defined. We hypothesized that increased risk of adverse events is driven by biologic factors. To test this hypothesis in the Invasive Monitoring Attenuation through Gene Expression (IMAGE) study, we determined whether the event rate of the primary outcome of acute rejection, graft dysfunction, death, or retransplantation varied by race as a function of calcineurin inhibitor (CNI) levels and gene expression profile (GEP) scores.MethodsWe determined the event rate of the primary outcome, comparing racial groups, stratified by time after transplant. Logistic regression was used to compute the relative risk across racial groups, and linear modeling was used to measure the dependence of CNI levels and GEP score on race.ResultsIn 580 patients monitored for a median of 19 months, the incidence of the primary end point was 18.3% in African Americans, 22.2% in other non-Caucasians, and 8.5% in Caucasians (p < 0.001). There were small but significant correlations of race and tacrolimus trough levels to the GEP score. Tacrolimus levels were similar among the races. Of patients receiving tacrolimus, other non-Caucasians had higher GEP scores than the other racial groups. African American recipients demonstrated a unique decrease in expression of the FLT3 gene in response to higher tacrolimus levels.ConclusionsAfrican Americans and other non-Caucasian heart transplant recipients were 2.5-times to 3-times more likely than Caucasians to experience outcome events in the Invasive Monitoring Attenuation through Gene Expression study. The increased risk of adverse outcomes may be partly due to the biology of the alloimmune response, which is less effectively inhibited at similar tacrolimus levels in minority racial groups

Gene Expression Profiling in Cancer

The contribution of modern-day genetics in designing efficient gene expression profiles for cancer is immense. The progress of technology and science in recent years provides the opportunity for discovery and application of new techniques for treating various diseases that affect humanity. Methods for finding and analyzing the profile of gene expression of infected cells give scientists the ability to develop more targeted and effective treatments, especially for diseases such as cancer. The development of gene expression profiling is one of the most important achievements in cancer genetics in our time. It is essentially the driving force behind personalized and precision medicine. This book highlights recent developments, applications, and breakthroughs in the field of gene expression profiling in cancer

Gene Expression Profiling and Outcome Prediction in Non-Hodgkin Lymphoma

AbstractGene expression profiling with microarrays has provided new insights into the molecular biology of tumors can that underlie differences in responses to therapy and patient outcomes. In diffuse large B-cell lymphoma, gene expression profiling has revealed at least 2 diseases that are strikingly different in their response to chemotherapy and the inhibition of critical oncogenic pathways. In follicular lymphoma, gene expression profiling showed that the host immune response to tumors is an important determinant of outcome and can strongly predict survival at the time of diagnosis. The application of immunologic therapies that modify the host immune response could have a major effect on survival in patients with follicular lymphoma. Thus, the application of gene expression profiling in non-Hodgkin lymphoma provides important prognostic information at the time of diagnosis and can be translated into therapeutic options that improve patient outcomes

Time-course gene expression profiling of high glucose-induced endothelial cell apoptosis

[[abstract]]Diabetes is a debilitating metabolic disorder resulting from hyperglycemia and glucose intolerance. High glucose-induced endothelial dysfunction is an important contributor to vascular disease in diabetes. In this study, we profiled the global gene expression changes in human umbilical vein endothelial cells treated with high glucose at the 0-, 24-, and 48-hour intervals. Differentially expressed genes were examined through bioinformatics analysis for potential mechanisms of regulation. Our analysis uncovered novel regulatory interactions that may provide insights into the molecular transition from normal cellular activities to apoptosis under high glucose, enhancing our understanding of the mechanisms underlying vascular complications in diabetic patients.[[sponsorship]]中原大學[[conferencetype]]國際[[conferencedate]]20140411~20140414[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]桃園縣, 臺

Exon and junction microarrays detect widespread mouse strain- and sex-bias expression differences

Background: Studies have shown that genetic and sex differences strongly influence gene expression in mice. Given the diversity and complexity of transcripts produced by alternative splicing, we sought to use microarrays to establish the extent of variation found in mouse strains and genders. Here, we surveyed the effect of strain and sex on liver gene and exon expression using male and female mice from three different inbred strains. Results: 71 liver RNA samples from three mouse strains - DBA/2J, C57BL/6J and C3H/HeJ - were profiled using a custom-designed microarray monitoring exon and exon-junction expression of 1,020 genes representing 9,406 exons. Gene expression was calculated via two different methods, using the 3'-most exon probe ("3' gene expression profiling") and using all probes associated with the gene ("whole-transcript gene expression profiling"), while exon expression was determined using exon probes and flanking junction probes that spanned across the neighboring exons ("exon expression profiling"). Widespread strain and sex influences were detected using a two-way Analysis of Variance (ANOVA) regardless of the profiling method used. However, over 90% of the genes identified in 3' gene expression profiling or whole transcript profiling were identified in exon profiling, along with 75% and 38% more genes, respectively, showing evidence of differential isoform expression. Overall, 55% and 32% of genes, respectively, exhibited strain- and sex-bias differential gene or exon expression. Conclusion: Exon expression profiling identifies significantly more variation than both 3' gene expression profiling and whole-transcript gene expression profiling. A large percentage of genes that are not differentially expressed at the gene level demonstrate exon expression variation suggesting an influence of strain and sex on alternative splicing and a need to profile expression changes at sub-gene resolution