Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis

<p>Abstract</p> <p>Background</p> <p>Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.</p> <p>Results</p> <p>By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.</p> <p>Conclusions</p> <p>We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream <it>in silico </it>functional inference analyses based on high content data.</p

Multiple platform assessmant of the EGF dependent transcritpome by microarrays and deep TAG sequencing analysis

Abstract Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer. Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions. Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data

Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis

BACKGROUND: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer. RESULTS: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions. CONCLUSIONS: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data

Candida bloodstream infections in intensive care units: analysis of the extended prevalence of infection in intensive care unit study

Item does not contain fulltextOBJECTIVES: To provide a global, up-to-date picture of the prevalence, treatment, and outcomes of Candida bloodstream infections in intensive care unit patients and compare Candida with bacterial bloodstream infection. DESIGN: A retrospective analysis of the Extended Prevalence of Infection in the ICU Study (EPIC II). Demographic, physiological, infection-related and therapeutic data were collected. Patients were grouped as having Candida, Gram-positive, Gram-negative, and combined Candida/bacterial bloodstream infection. Outcome data were assessed at intensive care unit and hospital discharge. SETTING: EPIC II included 1265 intensive care units in 76 countries. PATIENTS: Patients in participating intensive care units on study day. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Of the 14,414 patients in EPIC II, 99 patients had Candida bloodstream infections for a prevalence of 6.9 per 1000 patients. Sixty-one patients had candidemia alone and 38 patients had combined bloodstream infections. Candida albicans (n = 70) was the predominant species. Primary therapy included monotherapy with fluconazole (n = 39), caspofungin (n = 16), and a polyene-based product (n = 12). Combination therapy was infrequently used (n = 10). Compared with patients with Gram-positive (n = 420) and Gram-negative (n = 264) bloodstream infections, patients with candidemia were more likely to have solid tumors (p < .05) and appeared to have been in an intensive care unit longer (14 days [range, 5-25 days], 8 days [range, 3-20 days], and 10 days [range, 2-23 days], respectively), but this difference was not statistically significant. Severity of illness and organ dysfunction scores were similar between groups. Patients with Candida bloodstream infections, compared with patients with Gram-positive and Gram-negative bloodstream infections, had the greatest crude intensive care unit mortality rates (42.6%, 25.3%, and 29.1%, respectively) and longer intensive care unit lengths of stay (median [interquartile range]) (33 days [18-44], 20 days [9-43], and 21 days [8-46], respectively); however, these differences were not statistically significant. CONCLUSION: Candidemia remains a significant problem in intensive care units patients. In the EPIC II population, Candida albicans was the most common organism and fluconazole remained the predominant antifungal agent used. Candida bloodstream infections are associated with high intensive care unit and hospital mortality rates and resource use

Candida bloodstream infections in intensive care units: analysis of the extended prevalence of infection in intensive care unit study

To provide a global, up-to-date picture of the prevalence, treatment, and outcomes of Candida bloodstream infections in intensive care unit patients and compare Candida with bacterial bloodstream infection. DESIGN: A retrospective analysis of the Extended Prevalence of Infection in the ICU Study (EPIC II). Demographic, physiological, infection-related and therapeutic data were collected. Patients were grouped as having Candida, Gram-positive, Gram-negative, and combined Candida/bacterial bloodstream infection. Outcome data were assessed at intensive care unit and hospital discharge. SETTING: EPIC II included 1265 intensive care units in 76 countries. PATIENTS: Patients in participating intensive care units on study day. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Of the 14,414 patients in EPIC II, 99 patients had Candida bloodstream infections for a prevalence of 6.9 per 1000 patients. Sixty-one patients had candidemia alone and 38 patients had combined bloodstream infections. Candida albicans (n = 70) was the predominant species. Primary therapy included monotherapy with fluconazole (n = 39), caspofungin (n = 16), and a polyene-based product (n = 12). Combination therapy was infrequently used (n = 10). Compared with patients with Gram-positive (n = 420) and Gram-negative (n = 264) bloodstream infections, patients with candidemia were more likely to have solid tumors (p < .05) and appeared to have been in an intensive care unit longer (14 days [range, 5-25 days], 8 days [range, 3-20 days], and 10 days [range, 2-23 days], respectively), but this difference was not statistically significant. Severity of illness and organ dysfunction scores were similar between groups. Patients with Candida bloodstream infections, compared with patients with Gram-positive and Gram-negative bloodstream infections, had the greatest crude intensive care unit mortality rates (42.6%, 25.3%, and 29.1%, respectively) and longer intensive care unit lengths of stay (median [interquartile range]) (33 days [18-44], 20 days [9-43], and 21 days [8-46], respectively); however, these differences were not statistically significant. CONCLUSION: Candidemia remains a significant problem in intensive care units patients. In the EPIC II population, Candida albicans was the most common organism and fluconazole remained the predominant antifungal agent used. Candida bloodstream infections are associated with high intensive care unit and hospital mortality rates and resource use