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A microarray configuration to quantify expression levels and relative abundance of splice variants.

ABSTRACT: Over the past decade, alternative RNA splicing has raised a great interest appearing to be of high importance in the generation of expression diversity. This regulatory process plays a critical role in the normal development and its impact on the initiation and development of human disorders as well as on the pharmacological properties of drugs is increasingly being recognized. Only few studies describe specific alternative splicing expression profiling. Microarray strategies have been conceived to address alternative splicing events but with very few experimental data related to their abilities to provide true quantification values. We have developed a specific microarray configuration relying on a few, well optimized probes per splice event. Basically, five probes of 24mer are used to fully characterize a splice event. These probes are of two types, exon probes and junction probes, and are either specific to a splice event or not. The performances of such a 'splice array' were validated on synthetic model systems and on complex biological materials. The results indicate that DNA chips based on this design combining exon and junction derived probes enable the detection and, absolute and relative quantification of splice variants. In addition, this strategy is compatible with all the microarrays that use oligonucleotide probes.

SUBMITTER: Fehlbaum P

PROVIDER: S-EPMC1064144 | biostudies-literature | 2005 Mar

REPOSITORIES: biostudies-literature

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A microarray configuration to quantify expression levels and relative abundance of splice variants.

Fehlbaum Pascale P Guihal Caroline C Bracco Laurent L Cochet Olivier O

Nucleic acids research 20050310 5

Over the past decade, alternative RNA splicing has raised a great interest appearing to be of high importance in the generation of expression diversity. This regulatory process plays a critical role in the normal development and its impact on the initiation and development of human disorders as well as on the pharmacological properties of drugs is increasingly being recognized. Only few studies describe specific alternative splicing expression profiling. Microarray strategies have been conceived ...[more]

PMID: 15760843

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Project description:BackgroundIntra-tumoral steroidogenesis and constitutive androgen receptor (AR) activity have been associated with castration-resistant prostate cancer (CRPC). This study aimed to examine if CRPC bone metastases expressed higher levels of steroid-converting enzymes than untreated bone metastases. Steroidogenic enzyme levels were also analyzed in relation to expression of constitutively active AR variants (AR-Vs) and to clinical and pathological variables.Methodology/principal findingsUntreated, hormone-naïve (HN, n?=?9) and CRPC bone metastases samples (n?=?45) were obtained from 54 patients at metastasis surgery. Non-malignant and malignant prostate samples were acquired from 13 prostatectomy specimens. Transcript and protein levels were analyzed by real-time RT-PCR, immunohistochemistry and immunoblotting. No differences in steroidogenic enzyme levels were detected between CRPC and HN bone metastases. Significantly higher levels of SRD5A1, AKR1C2, AKR1C3, and HSD17B10 mRNA were however found in bone metastases than in non-malignant and/or malignant prostate tissue, while the CYP11A1, CYP17A1, HSD3B2, SRD5A2, and HSD17B6 mRNA levels in metastases were significantly lower. A sub-group of metastases expressed very high levels of AKR1C3, which was not due to gene amplification as examined by copy number variation assay. No association was found between AKR1C3 expression and nuclear AR staining, tumor cell proliferation or patient outcome after metastases surgery. With only one exception, high AR-V protein levels were found in bone metastases with low AKR1C3 levels, while metastases with high AKR1C3 levels primarily contained low AR-V levels, indicating distinct mechanisms behind castration-resistance in individual bone metastases.Conclusions/significanceInduced capacity of converting adrenal-gland derived steroids into more potent androgens was indicated in a sub-group of PC bone metastases. This was not associated with CRPC but merely with the advanced stage of metastasis. Sub-groups of bone metastases could be identified according to their expression levels of AKR1C3 and AR-Vs, which might be of relevance for patient response to 2(nd) line androgen-deprivation therapy.

Project description:The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.

Dataset Information

A microarray configuration to quantify expression levels and relative abundance of splice variants.

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A microarray configuration to quantify expression levels and relative abundance of splice variants.

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